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Title: Super-Isolated Elliptic Curves and Abelian Surfaces in Cryptography, Abstract: We call a simple abelian variety over $\mathbb{F}_p$ super-isolated if its ($\mathbb{F}_p$-rational) isogeny class contains no other varieties. The motivation for considering these varieties comes from concerns about isogeny based attacks on the discrete log problem. We heuristically estimate that the number of super-isolated elliptic curves over $\mathbb{F}_p$ with prime order and $p \leq N$, is roughly $\tilde{\Theta}(\sqrt{N})$. In contrast, we prove that there are only 2 super-isolated surfaces of cryptographic size and near-prime order.

Title: Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC, Abstract: Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet constituent momenta to a fully connected network. This latter approach lends itself well to the use of Recurrent Neural Networks. In this work the applicability of architectures incorporating Long Short-Term Memory (LSTM) networks is explored. Several network architectures, methods of ordering of jet constituents, and input pre-processing are studied. The best performing LSTM network achieves a background rejection of 100 for 50% signal efficiency. This represents more than a factor of two improvement over a fully connected Deep Neural Network (DNN) trained on similar types of inputs.

Title: On Hoffman's conjectural identity, Abstract: In this paper, we shall prove the equality \[ \zeta(3,\{2\}^{n},1,2)=\zeta(\{2\}^{n+3})+2\zeta(3,3,\{2\}^{n}) \] conjectured by Hoffman using certain identities among iterated integrals on $\mathbb{P}^{1}\setminus\{0,1,\infty,z\}$.

Title: Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise, Abstract: In this paper, we propose a unified view of gradient-based algorithms for stochastic convex composite optimization. By extending the concept of estimate sequence introduced by Nesterov, we interpret a large class of stochastic optimization methods as procedures that iteratively minimize a surrogate of the objective. This point of view covers stochastic gradient descent (SGD), the variance-reduction approaches SAGA, SVRG, MISO, their proximal variants, and has several advantages: (i) we provide a simple generic proof of convergence for all of the aforementioned methods; (ii) we naturally obtain new algorithms with the same guarantees; (iii) we derive generic strategies to make these algorithms robust to stochastic noise, which is useful when data is corrupted by small random perturbations. Finally, we show that this viewpoint is useful to obtain accelerated algorithms.

Title: Passive Compliance Control of Aerial Manipulators, Abstract: This paper presents a passive compliance control for aerial manipulators to achieve stable environmental interactions. The main challenge is the absence of actuation along body-planar directions of the aerial vehicle which might be required during the interaction to preserve passivity. The controller proposed in this paper guarantees passivity of the manipulator through a proper choice of end-effector coordinates, and that of vehicle fuselage is guaranteed by exploiting time domain passivity technique. Simulation studies validate the proposed approach.

Title: The phase retrieval problem for solutions of the Helmholtz equation, Abstract: In this paper we consider the phase retrieval problem for Herglotz functions, that is, solutions of the Helmholtz equation $\Delta u+\lambda^2u=0$ on domains $\Omega\subset\mathbb{R}^d$, $d\geq2$. In dimension $d=2$, if $u,v$ are two such solutions then $|u|=|v|$ implies that either $u=cv$ or $u=c\bar v$ for some $c\in\mathbb{C}$ with $|c|=1$. In dimension $d\geq3$, the same conclusion holds under some restriction on $u$ and $v$: either they are real valued or zonal functions or have non vanishing mean.

Title: Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition, Abstract: Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achieve input-output mappings with a given data set. In this paper, we propose the non-negative task decomposition method, which applies non-negative matrix factorization to a trained layered neural network. This enables us to decompose the inference mechanism of a trained layered neural network into multiple principal tasks of input-output mapping, and reveal the roles of hidden units in terms of their contribution to each principal task.

Title: Deconfined quantum critical points: symmetries and dualities, Abstract: The deconfined quantum critical point (QCP), separating the Néel and valence bond solid phases in a 2D antiferromagnet, was proposed as an example of $2+1$D criticality fundamentally different from standard Landau-Ginzburg-Wilson-Fisher {criticality}. In this work we present multiple equivalent descriptions of deconfined QCPs, and use these to address the possibility of enlarged emergent symmetries in the low energy limit. The easy-plane deconfined QCP, besides its previously discussed self-duality, is dual to $N_f = 2$ fermionic quantum electrodynamics (QED), which has its own self-duality and hence may have an O(4)$\times Z_2^T$ symmetry. We propose several dualities for the deconfined QCP with ${\mathrm{SU}(2)}$ spin symmetry which together make natural the emergence of a previously suggested $SO(5)$ symmetry rotating the Néel and VBS orders. These emergent symmetries are implemented anomalously. The associated infra-red theories can also be viewed as surface descriptions of 3+1D topological paramagnets, giving further insight into the dualities. We describe a number of numerical tests of these dualities. We also discuss the possibility of "pseudocritical" behavior for deconfined critical points, and the meaning of the dualities and emergent symmetries in such a scenario.

Title: Link Before You Share: Managing Privacy Policies through Blockchain, Abstract: With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. Users consent to providers to gather and share their Personally Identifiable Information (PII). We have developed a novel framework to automatically track details about how a users' PII data is stored, used and shared by the provider. We have integrated our Data Privacy ontology with the properties of blockchain, to develop an automated access control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by Big Data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.

Title: Convergence Results for Neural Networks via Electrodynamics, Abstract: We study whether a depth two neural network can learn another depth two network using gradient descent. Assuming a linear output node, we show that the question of whether gradient descent converges to the target function is equivalent to the following question in electrodynamics: Given $k$ fixed protons in $\mathbb{R}^d,$ and $k$ electrons, each moving due to the attractive force from the protons and repulsive force from the remaining electrons, whether at equilibrium all the electrons will be matched up with the protons, up to a permutation. Under the standard electrical force, this follows from the classic Earnshaw's theorem. In our setting, the force is determined by the activation function and the input distribution. Building on this equivalence, we prove the existence of an activation function such that gradient descent learns at least one of the hidden nodes in the target network. Iterating, we show that gradient descent can be used to learn the entire network one node at a time.

Title: Nonparametric relative error estimation of the regression function for censored data, Abstract: Let $ (T_i)_i$ be a sequence of independent identically distributed (i.i.d.) random variables (r.v.) of interest distributed as $ T$ and $(X_i)_i$ be a corresponding vector of covariates taking values on $ \mathbb{R}^d$. In censorship models the r.v. $T$ is subject to random censoring by another r.v. $C$. In this paper we built a new kernel estimator based on the so-called synthetic data of the mean squared relative error for the regression function. We establish the uniform almost sure convergence with rate over a compact set and its asymptotic normality. The asymptotic variance is explicitly given and as product we give a confidence bands. A simulation study has been conducted to comfort our theoretical results

Title: Towards Probabilistic Formal Modeling of Robotic Cell Injection Systems, Abstract: Cell injection is a technique in the domain of biological cell micro-manipulation for the delivery of small volumes of samples into the suspended or adherent cells. It has been widely applied in various areas, such as gene injection, in-vitro fertilization (IVF), intracytoplasmic sperm injection (ISCI) and drug development. However, the existing manual and semi-automated cell injection systems require lengthy training and suffer from high probability of contamination and low success rate. In the recently introduced fully automated cell injection systems, the injection force plays a vital role in the success of the process since even a tiny excessive force can destroy the membrane or tissue of the biological cell. Traditionally, the force control algorithms are analyzed using simulation, which is inherently non-exhaustive and incomplete in terms of detecting system failures. Moreover, the uncertainties in the system are generally ignored in the analysis. To overcome these limitations, we present a formal analysis methodology based on probabilistic model checking to analyze a robotic cell injection system utilizing the impedance force control algorithm. The proposed methodology, developed using the PRISM model checker, allowed to find a discrepancy in the algorithm, which was not found by any of the previous analysis using the traditional methods.

Title: Microservices: Granularity vs. Performance, Abstract: Microservice Architectures (MA) have the potential to increase the agility of software development. In an era where businesses require software applications to evolve to support software emerging requirements, particularly for Internet of Things (IoT) applications, we examine the issue of microservice granularity and explore its effect upon application latency. Two approaches to microservice deployment are simulated; the first with microservices in a single container, and the second with microservices partitioned across separate containers. We observed a neglibible increase in service latency for the multiple container deployment over a single container.

Title: Synthesis of Highly Anisotropic Semiconducting GaTe Nanomaterials and Emerging Properties Enabled by Epitaxy, Abstract: Pseudo-one dimensional (pseudo-1D) materials are a new-class of materials where atoms are arranged in chain like structures in two-dimensions (2D). Examples include recently discovered black phosphorus, ReS2 and ReSe2 from transition metal dichalcogenides, TiS3 and ZrS3 from transition metal trichalcogenides and most recently GaTe. The presence of structural anisotropy impacts their physical properties and leads to direction dependent light-matter interactions, dichroic optical responses, high mobility channels, and anisotropic thermal conduction. Despite the numerous reports on the vapor phase growth of isotropic TMDCs and post transition metal chalcogenides such as MoS2 and GaSe, the synthesis of pseudo-1D materials is particularly difficult due to the anisotropy in interfacial energy, which stabilizes dendritic growth rather than single crystalline growth with well-defined orientation. The growth of monoclinic GaTe has been demonstrated on flexible mica substrates with superior photodetecting performance. In this work, we demonstrate that pseudo-1D monoclinic GaTe layers can be synthesized on a variety of other substrates including GaAs (111), Si (111) and c-cut sapphire by physical vapor transport techniques. High resolution transmission electron microscopy (HRTEM) measurements, together with angle resolved micro-PL and micro-Raman techniques, provide for the very first time atomic scale resolution experiments on pseudo-1D structures in monoclinic GaTe and anisotropic properties. Interestingly, GaTe nanomaterials grown on sapphire exhibit highly efficient and narrow localized emission peaks below the band gap energy, which are found to be related to select types of line and point defects as evidenced by PL and Raman mapping scans. It makes the samples grown on sapphire more prominent than those grown on GaAs and Si, which demonstrate more regular properties.

Title: Projection Free Rank-Drop Steps, Abstract: The Frank-Wolfe (FW) algorithm has been widely used in solving nuclear norm constrained problems, since it does not require projections. However, FW often yields high rank intermediate iterates, which can be very expensive in time and space costs for large problems. To address this issue, we propose a rank-drop method for nuclear norm constrained problems. The goal is to generate descent steps that lead to rank decreases, maintaining low-rank solutions throughout the algorithm. Moreover, the optimization problems are constrained to ensure that the rank-drop step is also feasible and can be readily incorporated into a projection-free minimization method, e.g., Frank-Wolfe. We demonstrate that by incorporating rank-drop steps into the Frank-Wolfe algorithm, the rank of the solution is greatly reduced compared to the original Frank-Wolfe or its common variants.

Title: Doubly autoparallel structure on the probability simplex, Abstract: On the probability simplex, we can consider the standard information geometric structure with the e- and m-affine connections mutually dual with respect to the Fisher metric. The geometry naturally defines submanifolds simultaneously autoparallel for the both affine connections, which we call {\em doubly autoparallel submanifolds}. In this note we discuss their several interesting common properties. Further, we algebraically characterize doubly autoparallel submanifolds on the probability simplex and give their classification.

Title: Maximum a Posteriori Joint State Path and Parameter Estimation in Stochastic Differential Equations, Abstract: A wide variety of phenomena of engineering and scientific interest are of a continuous-time nature and can be modeled by stochastic differential equations (SDEs), which represent the evolution of the uncertainty in the states of a system. For systems of this class, some parameters of the SDE might be unknown and the measured data often includes noise, so state and parameter estimators are needed to perform inference and further analysis using the system state path. The distributions of SDEs which are nonlinear or subject to non-Gaussian measurement noise do not admit tractable analytic expressions, so state and parameter estimators for these systems are often approximations based on heuristics, such as the extended and unscented Kalman smoothers, or the prediction error method using nonlinear Kalman filters. However, the Onsager Machlup functional can be used to obtain fictitious densities for the parameters and state-paths of SDEs with analytic expressions. In this thesis, we provide a unified theoretical framework for maximum a posteriori (MAP) estimation of general random variables, possibly infinite-dimensional, and show how the Onsager--Machlup functional can be used to construct the joint MAP state-path and parameter estimator for SDEs. We also prove that the minimum energy estimator, which is often thought to be the MAP state-path estimator, actually gives the state paths associated to the MAP noise paths. Furthermore, we prove that the discretized MAP state-path and parameter estimators, which have emerged recently as powerful alternatives to nonlinear Kalman smoothers, converge hypographically as the discretization step vanishes. Their hypographical limit, however, is the MAP estimator for SDEs when the trapezoidal discretization is used and the minimum energy estimator when the Euler discretization is used, associating different interpretations to each discretized estimate.

Title: Spreading of an infectious disease between different locations, Abstract: The endogenous adaptation of agents, that may adjust their local contact network in response to the risk of being infected, can have the perverse effect of increasing the overall systemic infectiveness of a disease. We study a dynamical model over two geographically distinct but interacting locations, to better understand theoretically the mechanism at play. Moreover, we provide empirical motivation from the Italian National Bovine Database, for the period 2006-2013.

Title: Iterative Machine Teaching, Abstract: In this paper, we consider the problem of machine teaching, the inverse problem of machine learning. Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner. We show that the teaching complexity in the iterative case is very different from that in the batch case. Instead of constructing a minimal training set for learners, our iterative machine teaching focuses on achieving fast convergence in the learner model. Depending on the level of information the teacher has from the learner model, we design teaching algorithms which can provably reduce the number of teaching examples and achieve faster convergence than learning without teachers. We also validate our theoretical findings with extensive experiments on different data distribution and real image datasets.

Title: Optimization by gradient boosting, Abstract: Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees---by solving an infinite-dimensional convex optimization problem. We provide in the present paper a thorough analysis of two widespread versions of gradient boosting, and introduce a general framework for studying these algorithms from the point of view of functional optimization. We prove their convergence as the number of iterations tends to infinity and highlight the importance of having a strongly convex risk functional to minimize. We also present a reasonable statistical context ensuring consistency properties of the boosting predictors as the sample size grows. In our approach, the optimization procedures are run forever (that is, without resorting to an early stopping strategy), and statistical regularization is basically achieved via an appropriate $L^2$ penalization of the loss and strong convexity arguments.

Title: From Curves to Tropical Jacobians and Back, Abstract: Given a curve defined over an algebraically closed field which is complete with respect to a nontrivial valuation, we study its tropical Jacobian. This is done by first tropicalizing the curve, and then computing the Jacobian of the resulting weighted metric graph. In general, it is not known how to find the abstract tropicalization of a curve defined by polynomial equations, since an embedded tropicalization may not be faithful, and there is no known algorithm for carrying out semistable reduction in practice. We solve this problem in the case of hyperelliptic curves by studying admissible covers. We also describe how to take a weighted metric graph and compute its period matrix, which gives its tropical Jacobian and tropical theta divisor. Lastly, we describe the present status of reversing this process, namely how to compute a curve which has a given matrix as its period matrix.

Title: Asymptotics and Optimal Bandwidth Selection for Nonparametric Estimation of Density Level Sets, Abstract: Bandwidth selection is crucial in the kernel estimation of density level sets. Risk based on the symmetric difference between the estimated and true level sets is usually used to measure their proximity. In this paper we provide an asymptotic $L^p$ approximation to this risk, where $p$ is characterized by the weight function in the risk. In particular the excess risk corresponds to an $L^2$ type of risk, and is adopted in an optimal bandwidth selection rule for nonparametric level set estimation of $d$-dimensional density functions ($d\geq 1$).

Title: A Graph Analytics Framework for Ranking Authors, Papers and Venues, Abstract: A lot of scientific works are published in different areas of science, technology, engineering and mathematics. It is not easy, even for experts, to judge the quality of authors, papers and venues (conferences and journals). An objective measure to assign scores to these entities and to rank them is very useful. Although, several metrics and indexes have been proposed earlier, they suffer from various problems. In this paper, we propose a graph-based analytics framework to assign scores and to rank authors, papers and venues. Our algorithm considers only the link structures of the underlying graphs. It does not take into account other aspects, such as the associated texts and the reputation of these entities. In the limit of large number of iterations, the solution of the iterative equations gives the unique entity scores. This framework can be easily extended to other interdependent networks.

Title: Gradient Sensing via Cell Communication, Abstract: The chemotactic dynamics of cells and organisms that have no specialized gradient sensing organelles is not well understood. In fact, chemotaxis of this sort of organism is especially challenging to explain when the external chemical gradient is so small as to make variations of concentrations minute over the length of each of the organisms. Experimental evidence lends support to the conjecture that chemotactic behavior of chains of cells can be achieved via cell-to-cell communication. This is the chemotactic basis for the Local Excitation, Global Inhibition (LEGI) model. A generalization of the model for the communication component of the LEGI model is proposed. Doing so permits us to study in detail how gradient sensing changes as a function of the structure of the communication term. The key findings of this study are, an accounting of how gradient sensing is affected by the competition of communication and diffusive processes; the determination of the scale dependence of the model outcomes; the sensitivity of communication to parameters in the model. Together with an essential analysis of the dynamics of the model, these findings can prove useful in suggesting experiments aimed at determining the viability of a communication mechanism in chemotactic dynamics of chains and networks of cells exposed to a chemical concentration gradient.

Title: Nichols Algebras and Quantum Principal Bundles, Abstract: A general procedure for constructing Yetter-Drinfeld modules from quantum principal bundles is introduced. As an application a Yetter-Drinfeld structure is put on the cotangent space of the Heckenberger-Kolb calculi of the quantum Grassmannians. For the special case of quantum projective space the associated braiding is shown to be non-diagonal and of Hecke type. Moreover, its Nichols algebra is shown to be finite-dimensional and equal to the anti-holomorphic part of the total differential calculus.

Title: Inference of signals with unknown correlation structure from nonlinear measurements, Abstract: We present a method to reconstruct autocorrelated signals together with their autocorrelation structure from nonlinear, noisy measurements for arbitrary monotonous nonlinear instrument response. In the presented formulation the algorithm provides a significant speedup compared to prior implementations, allowing for a wider range of application. The nonlinearity can be used to model instrument characteristics or to enforce properties on the underlying signal, such as positivity. Uncertainties on any posterior quantities can be provided due to independent samples from an approximate posterior distribution. We demonstrate the methods applicability via simulated and real measurements, using different measurement instruments, nonlinearities and dimensionality.

Title: Optical and structural study of the pressure-induced phase transition of CdWO$_4$, Abstract: The optical absorption of CdWO$_4$ is reported at high pressures up to 23 GPa. The onset of a phase transition was detected at 19.5 GPa, in good agreement with a previous Raman spectroscopy study. The crystal structure of the high-pressure phase of CdWO$_4$ was solved at 22 GPa employing single-crystal synchrotron x-ray diffraction. The symmetry changes from space group $P$2/$c$ in the low-pressure wolframite phase to $P2_1/c$ in the high-pressure post-wolframite phase accompanied by a doubling of the unit-cell volume. The octahedral oxygen coordination of the tungsten and cadmium ions is increased to [7]-fold and [6+1]-fold, respectively, at the phase transition. The compressibility of the low-pressure phase of CdWO$_4$ has been reevaluated with powder x-ray diffraction up to 15 GPa finding a bulk modulus of $B_0$ = 123 GPa. The direct band gap of the low-pressure phase increases with compression up to 16.9 GPa at 12 meV/GPa. At this point an indirect band gap crosses the direct band gap and decreases at -2 meV/GPa up to 19.5 GPa where the phase transition starts. At the phase transition the band gap collapses by 0.7 eV and another direct band gap decreases at -50 meV/GPa up to the maximum measured pressure. The structural stability of the post-wolframite structure is confirmed by \textit{ab initio} calculations finding the post-wolframite-type phase to be more stable than the wolframite at 18 GPa. Lattice dynamic calculations based on space group $P2_1/c$ explain well the Raman-active modes previously measured in the high-pressure post-wolframite phase. The pressure-induced band gap crossing in the wolframite phase as well as the pressure dependence of the direct band gap in the high-pressure phase are further discussed with respect to the calculations.

Title: Learning Context-Sensitive Convolutional Filters for Text Processing, Abstract: Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a small meta network to learn context-sensitive convolutional filters for text processing. The role of meta network is to abstract the contextual information of a sentence or document into a set of input-aware filters. We further generalize this framework to model sentence pairs, where a bidirectional filter generation mechanism is introduced to encapsulate co-dependent sentence representations. In our benchmarks on four different tasks, including ontology classification, sentiment analysis, answer sentence selection, and paraphrase identification, our proposed model, a modified CNN with context-sensitive filters, consistently outperforms the standard CNN and attention-based CNN baselines. By visualizing the learned context-sensitive filters, we further validate and rationalize the effectiveness of proposed framework.

Title: Approximate Ranking from Pairwise Comparisons, Abstract: A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes according to their scores, which includes identification of the top-k items as the most prominent special case. The score of a given item is defined as the probability that it beats a randomly chosen other item. Finding an exact ranking typically requires a prohibitively large number of comparisons, but in practice, approximate rankings are often adequate. Accordingly, we study the problem of finding approximate rankings from pairwise comparisons. We analyze an active ranking algorithm that counts the number of comparisons won, and decides whether to stop or which pair of items to compare next, based on confidence intervals computed from the data collected in previous steps. We show that this algorithm succeeds in recovering approximate rankings using a number of comparisons that is close to optimal up to logarithmic factors. We also present numerical results, showing that in practice, approximation can drastically reduce the number of comparisons required to estimate a ranking.

Title: On Recoverable and Two-Stage Robust Selection Problems with Budgeted Uncertainty, Abstract: In this paper the problem of selecting $p$ out of $n$ available items is discussed, such that their total cost is minimized. We assume that costs are not known exactly, but stem from a set of possible outcomes. Robust recoverable and two-stage models of this selection problem are analyzed. In the two-stage problem, up to $p$ items is chosen in the first stage, and the solution is completed once the scenario becomes revealed in the second stage. In the recoverable problem, a set of $p$ items is selected in the first stage, and can be modified by exchanging up to $k$ items in the second stage, after a scenario reveals. We assume that uncertain costs are modeled through bounded uncertainty sets, i.e., the interval uncertainty sets with an additional linear (budget) constraint, in their discrete and continuous variants. Polynomial algorithms for recoverable and two-stage selection problems with continuous bounded uncertainty, and compact mixed integer formulations in the case of discrete bounded uncertainty are constructed.

Title: The homotopy Lie algebra of symplectomorphism groups of 3-fold blow-ups of $(S^2 \times S^2, σ_{std} \oplus σ_{std}) $, Abstract: We consider the 3-point blow-up of the manifold $ (S^2 \times S^2, \sigma \oplus \sigma)$ where $\sigma$ is the standard symplectic form which gives area 1 to the sphere $S^2$, and study its group of symplectomorphisms $\rm{Symp} ( S^2 \times S^2 \#\, 3\overline{\mathbb C\mathbb P}\,\!^2, \omega)$. So far, the monotone case was studied by J. Evans and he proved that this group is contractible. Moreover, J. Li, T. J. Li and W. Wu showed that the group Symp$_{h}(S^2 \times S^2 \#\, 3\overline{ \mathbb C\mathbb P}\,\!^2,\omega) $ of symplectomorphisms that act trivially on homology is always connected and recently they also computed its fundamental group. We describe, in full detail, the rational homotopy Lie algebra of this group. We show that some particular circle actions contained in the symplectomorphism group generate its full topology. More precisely, they give the generators of the homotopy graded Lie algebra of $\rm{Symp} (S^2 \times S^2 \#\, 3\overline{ \mathbb C\mathbb P}\,\!^2, \omega)$. Our study depends on Karshon's classification of Hamiltonian circle actions and the inflation technique introduced by Lalonde-McDuff. As an application, we deduce the rank of the homotopy groups of $\rm{Symp}({\mathbb C\mathbb P}^2 \#\, 5\overline{\mathbb C\mathbb P}\,\!^2, \tilde \omega)$, in the case of small blow-ups.

Title: Linguistic Matrix Theory, Abstract: Recent research in computational linguistics has developed algorithms which associate matrices with adjectives and verbs, based on the distribution of words in a corpus of text. These matrices are linear operators on a vector space of context words. They are used to construct the meaning of composite expressions from that of the elementary constituents, forming part of a compositional distributional approach to semantics. We propose a Matrix Theory approach to this data, based on permutation symmetry along with Gaussian weights and their perturbations. A simple Gaussian model is tested against word matrices created from a large corpus of text. We characterize the cubic and quartic departures from the model, which we propose, alongside the Gaussian parameters, as signatures for comparison of linguistic corpora. We propose that perturbed Gaussian models with permutation symmetry provide a promising framework for characterizing the nature of universality in the statistical properties of word matrices. The matrix theory framework developed here exploits the view of statistics as zero dimensional perturbative quantum field theory. It perceives language as a physical system realizing a universality class of matrix statistics characterized by permutation symmetry.

Title: Fast Spectral Clustering Using Autoencoders and Landmarks, Abstract: In this paper, we introduce an algorithm for performing spectral clustering efficiently. Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition. In this work, we first build the adjacency matrix of the corresponding graph of the dataset. To build this matrix, we only consider a limited number of points, called landmarks, and compute the similarity of all data points with the landmarks. Then, we present a definition of the Laplacian matrix of the graph that enable us to perform eigen decomposition efficiently, using a deep autoencoder. The overall complexity of the algorithm for eigen decomposition is $O(np)$, where $n$ is the number of data points and $p$ is the number of landmarks. At last, we evaluate the performance of the algorithm in different experiments.

Title: Gate-controlled magnonic-assisted switching of magnetization in ferroelectric/ferromagnetic junctions, Abstract: Interfacing a ferromagnet with a polarized ferroelectric gate generates a non-uniform, interfacial spin density coupled to the ferroelectric polarization allowing so for an electric field control of effective transversal field to magnetization. Here we study the dynamic magnetization switching behavior of such a multilayer system based on the Landau-Lifshitz-Baryakhtar equation, demonstrating that interfacial magnetoelectric coupling is utilizable as a highly localized and efficient tool for manipulating magnetism.

Title: MUTAN: Multimodal Tucker Fusion for Visual Question Answering, Abstract: Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks. They help to learn high level associations between question meaning and visual concepts in the image, but they suffer from huge dimensionality issues. We introduce MUTAN, a multimodal tensor-based Tucker decomposition to efficiently parametrize bilinear interactions between visual and textual representations. Additionally to the Tucker framework, we design a low-rank matrix-based decomposition to explicitly constrain the interaction rank. With MUTAN, we control the complexity of the merging scheme while keeping nice interpretable fusion relations. We show how our MUTAN model generalizes some of the latest VQA architectures, providing state-of-the-art results.

Title: Viscosity solutions and the minimal surface system, Abstract: We give a definition of viscosity solution for the minimal surface system and prove a version of Allard regularity theorem in this setting.

Title: Ray-tracing semiclassical low frequency acoustic modeling with local and extended reaction boundaries, Abstract: The recently introduced acoustic ray-tracing semiclassical (RTS) method is validated for a set of practically relevant boundary conditions. RTS is a frequency domain geometrical method which directly reproduces the acoustic Green's function. As previously demonstrated for a rectangular room and weakly absorbing boundaries with a real and frequency-independent impedance, RTS is capable of modeling also the lowest modes of such a room, which makes it a useful method for low frequency sound field modeling in enclosures. In practice, rooms are furnished with diverse types of materials and acoustic elements, resulting in a frequency-dependent, phase-modifying absorption/reflection. In a realistic setting, we test the RTS method with two additional boundary conditions: a local reaction boundary simulating a resonating membrane absorber and an extended reaction boundary representing a porous layer backed by a rigid boundary described within the Delany-Bazley-Miki model, as well as a combination thereof. The RTS-modeled spatially dependent pressure response and octave band decay curves with the corresponding reverberation times are compared to those obtained by the finite element method.

Title: Hierarchical State Abstractions for Decision-Making Problems with Computational Constraints, Abstract: In this semi-tutorial paper, we first review the information-theoretic approach to account for the computational costs incurred during the search for optimal actions in a sequential decision-making problem. The traditional (MDP) framework ignores computational limitations while searching for optimal policies, essentially assuming that the acting agent is perfectly rational and aims for exact optimality. Using the free-energy, a variational principle is introduced that accounts not only for the value of a policy alone, but also considers the cost of finding this optimal policy. The solution of the variational equations arising from this formulation can be obtained using familiar Bellman-like value iterations from dynamic programming (DP) and the Blahut-Arimoto (BA) algorithm from rate distortion theory. Finally, we demonstrate the utility of the approach for generating hierarchies of state abstractions that can be used to best exploit the available computational resources. A numerical example showcases these concepts for a path-planning problem in a grid world environment.

Title: The content correlation of multiple streaming edges, Abstract: We study how to detect clusters in a graph defined by a stream of edges, without storing the entire graph. We extend the approach to dynamic graphs defined by the most recent edges of the stream and to several streams. The {\em content correlation }of two streams $\rho(t)$ is the Jaccard similarity of their clusters in the windows before time $t$. We propose a simple and efficient method to approximate this correlation online and show that for dynamic random graphs which follow a power law degree distribution, we can guarantee a good approximation. As an application, we follow Twitter streams and compute their content correlations online. We then propose a {\em search by correlation} where answers to sets of keywords are entirely based on the small correlations of the streams. Answers are ordered by the correlations, and explanations can be traced with the stored clusters.

Title: Precision Prediction for the Cosmological Density Distribution, Abstract: The distribution of matter in the universe is, to first order, lognormal. Improving this approximation requires characterization of the third moment (skewness) of the log density field. Thus, using Millennium Simulation phenomenology and building on previous work, we present analytic fits for the mean, variance, and skewness of the log density field $A$. We further show that a Generalized Extreme Value (GEV) distribution accurately models $A$; we submit that this GEV behavior is the result of strong intrapixel correlations, without which the smoothed distribution would tend (by the Central Limit Theorem) toward a Gaussian. Our GEV model yields cumulative distribution functions accurate to within 1.7 per cent for near-concordance cosmologies, over a range of redshifts and smoothing scales.

Title: Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions, Abstract: Accurate noise modelling is important for training of deep learning reconstruction algorithms. While noise models are well known for traditional imaging techniques, the noise distribution of a novel sensor may be difficult to determine a priori. Therefore, we propose learning arbitrary noise distributions. To do so, this paper proposes a fully connected neural network model to map samples from a uniform distribution to samples of any explicitly known probability density function. During the training, the Jensen-Shannon divergence between the distribution of the model's output and the target distribution is minimized. We experimentally demonstrate that our model converges towards the desired state. It provides an alternative to existing sampling methods such as inversion sampling, rejection sampling, Gaussian mixture models and Markov-Chain-Monte-Carlo. Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.

Title: Computing the Lusztig--Vogan Bijection, Abstract: Let $G$ be a connected complex reductive algebraic group with Lie algebra $\mathfrak{g}$. The Lusztig--Vogan bijection relates two bases for the bounded derived category of $G$-equivariant coherent sheaves on the nilpotent cone $\mathcal{N}$ of $\mathfrak{g}$. One basis is indexed by $\Lambda^+$, the set of dominant weights of $G$, and the other by $\Omega$, the set of pairs $(\mathcal{O}, \mathcal{E})$ consisting of a nilpotent orbit $\mathcal{O} \subset \mathcal{N}$ and an irreducible $G$-equivariant vector bundle $\mathcal{E} \rightarrow \mathcal{O}$. The existence of the Lusztig--Vogan bijection $\gamma \colon \Omega \rightarrow \Lambda^+$ was proven by Bezrukavnikov, and an algorithm computing $\gamma$ in type $A$ was given by Achar. Herein we present a combinatorial description of $\gamma$ in type $A$ that subsumes and dramatically simplifies Achar's algorithm.

Title: Divide and Conquer: Variable Set Separation in Hybrid Systems Reachability Analysis, Abstract: In this paper we propose an improvement for flowpipe-construction-based reachability analysis techniques for hybrid systems. Such methods apply iterative successor computations to pave the reachable region of the state space by state sets in an over-approximative manner. As the computational costs steeply increase with the dimension, in this work we analyse the possibilities for improving scalability by dividing the search space in sub-spaces and execute reachability computations in the sub-spaces instead of the global space. We formalise such an algorithm and provide experimental evaluations to compare the efficiency as well as the precision of our sub-space search to the original search in the global space.

Title: Hierarchy of Information Scrambling, Thermalization, and Hydrodynamic Flow in Graphene, Abstract: We determine the information scrambling rate $\lambda_{L}$ due to electron-electron Coulomb interaction in graphene. $\lambda_{L}$ characterizes the growth of chaos and has been argued to give information about the thermalization and hydrodynamic transport coefficients of a many-body system. We demonstrate that $\lambda_{L}$ behaves for strong coupling similar to transport and energy relaxation rates. A weak coupling analysis, however, reveals that scrambling is related to dephasing or single particle relaxation. Furthermore, $\lambda_{L}$ is found to be parametrically larger than the collision rate relevant for hydrodynamic processes, such as electrical conduction or viscous flow, and the rate of energy relaxation, relevant for thermalization. Thus, while scrambling is obviously necessary for thermalization and quantum transport, it does generically not set the time scale for these processes. In addition we derive a quantum kinetic theory for information scrambling that resembles the celebrated Boltzmann equation and offers a physically transparent insight into quantum chaos in many-body systems.

Title: Neural Collaborative Autoencoder, Abstract: In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing models. First, these models cannot work on both explicit and implicit feedback, since the network structures are specially designed for one particular case. Second, due to the difficulty on training deep neural networks, existing explicit models do not fully exploit the expressive potential of deep learning. Third, neural network models are easier to overfit on the implicit setting than shallow models. To tackle these issues, we present a generic recommender framework called Neural Collaborative Autoencoder (NCAE) to perform collaborative filtering, which works well for both explicit feedback and implicit feedback. NCAE can effectively capture the subtle hidden relationships between interactions via a non-linear matrix factorization process. To optimize the deep architecture of NCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, to prevent overfitting on the implicit setting, we propose an error reweighting module and a sparsity-aware data-augmentation strategy. Extensive experiments on three real-world datasets demonstrate that NCAE can significantly advance the state-of-the-art.

Title: Pentavalent symmetric graphs of order four times an odd square-free integer, Abstract: A graph is said to be symmetric if its automorphism group is transitive on its arcs. Guo et al. (Electronic J. Combin. 18, \#P233, 2011) and Pan et al. (Electronic J. Combin. 20, \#P36, 2013) determined all pentavalent symmetric graphs of order $4pq$. In this paper, we shall generalize this result by determining all connected pentavalent symmetric graphs of order four times an odd square-free integer. It is shown in this paper that, for each of such graphs $\it\Gamma$, either the full automorphism group ${\sf Aut}\it\Gamma$ is isomorphic to ${\sf PSL}(2,p)$, ${\sf PGL}(2,p)$, ${\sf PSL}(2,p){\times}\mathbb{Z}_2$ or ${\sf PGL}(2,p){\times}\mathbb{Z}_2$, or $\it\Gamma$ is isomorphic to one of 8 graphs.

Title: Implementing implicit OpenMP data sharing on GPUs, Abstract: OpenMP is a shared memory programming model which supports the offloading of target regions to accelerators such as NVIDIA GPUs. The implementation in Clang/LLVM aims to deliver a generic GPU compilation toolchain that supports both the native CUDA C/C++ and the OpenMP device offloading models. There are situations where the semantics of OpenMP and those of CUDA diverge. One such example is the policy for implicitly handling local variables. In CUDA, local variables are implicitly mapped to thread local memory and thus become private to a CUDA thread. In OpenMP, due to semantics that allow the nesting of regions executed by different numbers of threads, variables need to be implicitly \emph{shared} among the threads of a contention group. In this paper we introduce a re-design of the OpenMP device data sharing infrastructure that is responsible for the implicit sharing of local variables in the Clang/LLVM toolchain. We introduce a new data sharing infrastructure that lowers implicitly shared variables to the shared memory of the GPU. We measure the amount of shared memory used by our scheme in cases that involve scalar variables and statically allocated arrays. The evaluation is carried out by offloading to K40 and P100 NVIDIA GPUs. For scalar variables the pressure on shared memory is relatively low, under 26\% of shared memory utilization for the K40, and does not negatively impact occupancy. The limiting occupancy factor in that case is register pressure. The data sharing scheme offers the users a simple memory model for controlling the implicit allocation of device shared memory.

Title: Real-Time Impulse Noise Removal from MR Images for Radiosurgery Applications, Abstract: In the recent years image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Medical applications have been so much affected by these techniques which some of them are embedded in medical instruments such as MRI, CT and other medical devices. Among these techniques, medical image enhancement algorithms play an essential role in removal of the noise which can be produced by medical instruments and during image transfer. It has been proved that impulse noise is a major type of noise, which is produced during medical operations, such as MRI, CT, and angiography, by their image capturing devices. An embeddable hardware module which is able to denoise medical images before and during surgical operations could be very helpful. In this paper an accurate algorithm is proposed for real-time removal of impulse noise in medical images. All image blocks are divided into three categories of edge, smooth, and disordered areas. A different reconstruction method is applied to each category of blocks for the purpose of noise removal. The proposed method is tested on MR images. Simulation results show acceptable denoising accuracy for various levels of noise. Also an FPAG implementation of our denoising algorithm shows acceptable hardware resource utilization. Hence, the algorithm is suitable for embedding in medical hardware instruments such as radiosurgery devices.

Title: Randomized CP Tensor Decomposition, Abstract: The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular dimensionality-reduction method for multiway data. Dimensionality reduction is often sought since many high-dimensional tensors have low intrinsic rank relative to the dimension of the ambient measurement space. However, the emergence of `big data' poses significant computational challenges for computing this fundamental tensor decomposition. Leveraging modern randomized algorithms, we demonstrate that the coherent structure can be learned from a smaller representation of the tensor in a fraction of the time. Moreover, the high-dimensional signal can be faithfully approximated from the compressed measurements. Thus, this simple but powerful algorithm enables one to compute the approximate CP decomposition even for massive tensors. The approximation error can thereby be controlled via oversampling and the computation of power iterations. In addition to theoretical results, several empirical results demonstrate the performance of the proposed algorithm.

Title: Adversarial Attacks on Node Embeddings, Abstract: The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods there is currently no study of their robustness to adversarial attacks. We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks. We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. We further show that our attacks are transferable -- they generalize to many models -- and are successful even when the attacker has restricted actions.

Title: Dimensionality-strain phase diagram of strontium iridates, Abstract: The competition between spin-orbit coupling, bandwidth ($W$) and electron-electron interaction ($U$) makes iridates highly susceptible to small external perturbations, which can trigger the onset of novel types of electronic and magnetic states. Here we employ {\em first principles} calculations based on density functional theory and on the constrained random phase approximation to study how dimensionality and strain affect the strength of $U$ and $W$ in (SrIrO$_3$)$_m$/(SrTiO$_3$) superlattices. The result is a phase diagram explaining two different types of controllable magnetic and electronic transitions, spin-flop and insulator-to-metal, connected with the disruption of the $J_{eff}=1/2$ state which cannnot be understood within a simplified local picture.

Title: SGDLibrary: A MATLAB library for stochastic gradient descent algorithms, Abstract: We consider the problem of finding the minimizer of a function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ of the finite-sum form $\min f(w) = 1/n\sum_{i}^n f_i(w)$. This problem has been studied intensively in recent years in the field of machine learning (ML). One promising approach for large-scale data is to use a stochastic optimization algorithm to solve the problem. SGDLibrary is a readable, flexible and extensible pure-MATLAB library of a collection of stochastic optimization algorithms. The purpose of the library is to provide researchers and implementers a comprehensive evaluation environment for the use of these algorithms on various ML problems.

Title: Optimization, fast and slow: optimally switching between local and Bayesian optimization, Abstract: We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects between multiple alternative acquisition functions and traditional local optimization at each step. This is combined with a novel stopping condition based on expected regret. This pairing allows us to obtain the best characteristics of both local and Bayesian optimization, making efficient use of function evaluations while yielding superior convergence to the global minimum on a selection of optimization problems, and also halting optimization once a principled and intuitive stopping condition has been fulfilled.

Title: Optimization of exposure time division for wide field observations, Abstract: The optical observations of wide fields of view encounter the problem of selection of best exposure time. As there are usually plenty of objects observed simultaneously, the quality of photometry of the brightest ones is always better than of the dimmer ones. Frequently all of them are equally interesting for the astronomers and thus it is desired to have all of them measured with the highest possible accuracy. In this paper we present a novel optimization algorithm dedicated for the division of exposure time into sub-exposures, which allows to perform photometry with more balanced noise budget. Thanks to the proposed technique, the photometric precision of dimmer objects is increased at the expense of the measurement fidelity of the brightest ones. We tested the method on real observations using two telescope setups demonstrating its usefulness and good agreement with the theoretical expectations. The main application of our approach is a wide range of sky surveys, including the ones performed by the space telescopes. The method can be applied for planning virtually any photometric observations, in which the objects of interest show a wide range of magnitudes.

Title: DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem, Abstract: This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network predicts whether or not they should be adjacent in the correct assembly of the puzzle, using nothing but the pixels of each piece. The proposed metric exhibits an extremely high precision even though no manual feature extraction is performed. When incorporated into an existing puzzle solver, the solution's accuracy increases significantly, achieving thereby a new state-of-the-art standard.

Title: Attack Analysis for Distributed Control Systems: An Internal Model Principle Approach, Abstract: Although adverse effects of attacks have been acknowledged in many cyber-physical systems, there is no system-theoretic comprehension of how a compromised agent can leverage communication capabilities to maximize the damage in distributed multi-agent systems. A rigorous analysis of cyber-physical attacks enables us to increase the system awareness against attacks and design more resilient control protocols. To this end, we will take the role of the attacker to identify the worst effects of attacks on root nodes and non-root nodes in a distributed control system. More specifically, we show that a stealthy attack on root nodes can mislead the entire network to a wrong understanding of the situation and even destabilize the synchronization process. This will be called the internal model principle for the attacker and will intensify the urgency of designing novel control protocols to mitigate these types of attacks.

Title: See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS, Abstract: The Intelligent Transportation System (ITS) targets to a coordinated traffic system by applying the advanced wireless communication technologies for road traffic scheduling. Towards an accurate road traffic control, the short-term traffic forecasting to predict the road traffic at the particular site in a short period is often useful and important. In existing works, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a popular approach. The scheme however encounters two challenges: 1) the analysis on related data is insufficient whereas some important features of data may be neglected; and 2) with data presenting different features, it is unlikely to have one predictive model that can fit all situations. To tackle above issues, in this work, we develop a hybrid model to improve accuracy of SARIMA. In specific, we first explore the autocorrelation and distribution features existed in traffic flow to revise structure of the time series model. Based on the Gaussian distribution of traffic flow, a hybrid model with a Bayesian learning algorithm is developed which can effectively expand the application scenarios of SARIMA. We show the efficiency and accuracy of our proposal using both analysis and experimental studies. Using the real-world trace data, we show that the proposed predicting approach can achieve satisfactory performance in practice.

Title: Single-Crystal N-polar GaN p-n Diodes by Plasma-Assisted Molecular Beam Epitaxy, Abstract: N-polar GaN p-n diodes are realized on single-crystal N-polar GaN bulk wafers by plasma-assisted molecular beam epitaxy growth. The current-voltage characteristics show high-quality rectification and electroluminescence characteristics with a high on/off current ratio and interband photon emission. The measured electroluminescence spectrum is dominated by strong near-band edge emission, while deep level luminescence is greatly suppressed. A very low dislocation density leads to a high reverse breakdown electric field. The low leakage current N-polar diodes open up several potential applications in polarization-engineered photonic and electronic devices.

Title: Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks, Abstract: Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.

Title: Fast Rigid 3D Registration Solution: A Simple Method Free of SVD and Eigen-Decomposition, Abstract: A novel solution is obtained to solve the rigid 3D registration problem, motivated by previous eigen-decomposition approaches. Different from existing solvers, the proposed algorithm does not require sophisticated matrix operations e.g. singular value decomposition or eigenvalue decomposition. Instead, the optimal eigenvector of the point cross-covariance matrix can be computed within several iterations. It is also proven that the optimal rotation matrix can be directly computed for cases without need of quaternion. The simple framework provides very easy approach of integer-implementation on embedded platforms. Simulations on noise-corrupted point clouds have verified the robustness and computation speed of the proposed method. The final results indicate that the proposed algorithm is accurate, robust and owns over $60\% \sim 80\%$ less computation time than representatives. It has also been applied to real-world applications for faster relative robotic navigation.

Title: Scale relativistic formulation of non-differentiable mechanics II: The Schroedinger picture, Abstract: This article is the second in a series of two presenting the Scale Relativistic approach to non-differentiability in mechanics and its relation to quantum mechanics. Here, we show Schroedinger's equation to be a reformulation of Newton's fundamental relation of dynamics as generalized to non-differentiable geometries in the first paper \cite{paper1}. It motivates an alternative interpretation of the other axioms of standard quantum mechanics in a coherent picture. This exercise validates the Scale Relativistic approach and, at the same time, it allows to identify macroscopic chaotic systems considered at time scales exceeding their horizon of predictability as candidates in which to search for quantum-like structuring or behavior.

Title: Manifold learning with bi-stochastic kernels, Abstract: In this paper we answer the following question: what is the infinitesimal generator of the diffusion process defined by a kernel that is normalized such that it is bi-stochastic with respect to a specified measure? More precisely, under the assumption that data is sampled from a Riemannian manifold we determine how the resulting infinitesimal generator depends on the potentially nonuniform distribution of the sample points, and the specified measure for the bi-stochastic normalization. In a special case, we demonstrate a connection to the heat kernel. We consider both the case where only a single data set is given, and the case where a data set and a reference set are given. The spectral theory of the constructed operators is studied, and Nyström extension formulas for the gradients of the eigenfunctions are computed. Applications to discrete point sets and manifold learning are discussed.

Title: A local search 2.917-approximation algorithm for duo-preservation string mapping, Abstract: We study the {\em maximum duo-preservation string mapping} ({\sc Max-Duo}) problem, which is the complement of the well studied {\em minimum common string partition} ({\sc MCSP}) problem. Both problems have applications in many fields including text compression and bioinformatics. Motivated by an earlier local search algorithm, we present an improved approximation and show that its performance ratio is no greater than ${35}/{12} < 2.917$. This beats the current best $3.25$-approximation for {\sc Max-Duo}. The performance analysis of our algorithm is done through a complex yet interesting amortization. Two lower bounds on the locality gap of our algorithm are also provided.

Title: Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning, Abstract: We propose a general approach to modeling semi-supervised learning (SSL) algorithms. Specifically, we present a declarative language for modeling both traditional supervised classification tasks and many SSL heuristics, including both well-known heuristics such as co-training and novel domain-specific heuristics. In addition to representing individual SSL heuristics, we show that multiple heuristics can be automatically combined using Bayesian optimization methods. We experiment with two classes of tasks, link-based text classification and relation extraction. We show modest improvements on well-studied link-based classification benchmarks, and state-of-the-art results on relation-extraction tasks for two realistic domains.

Title: Fabrication of a centimeter-long cavity on a nanofiber for cavity QED, Abstract: We report the fabrication of a 1.2 cm long cavity directly on a nanofiber using femtosecond laser ablation. The cavity modes with finesse value in the range 200-400 can still maintain the transmission between 40-60%, which can enable "strong-coupling" regime of cavity QED for a single atom trapped 200 nm away from the fiber surface. For such cavity modes, we estimate the one-pass intra-cavity transmission to be 99.53%. Other cavity modes, which can enable high cooperativity in the range 3-10, show transmission over 60-85% and are suitable for fiber-based single photon sources and quantum nonlinear optics in the "Purcell" regime.

Title: Strongly convex stochastic online optimization on a unit simplex with application to the mixing least square regression, Abstract: In this paper we propose a new approach to obtain mixing least square regression estimate by means of stochastic online mirror descent in non-euclidian set-up.

Title: Unifying DAGs and UGs, Abstract: We introduce a new class of graphical models that generalizes Lauritzen-Wermuth-Frydenberg chain graphs by relaxing the semi-directed acyclity constraint so that only directed cycles are forbidden. Moreover, up to two edges are allowed between any pair of nodes. Specifically, we present local, pairwise and global Markov properties for the new graphical models and prove their equivalence. We also present an equivalent factorization property. Finally, we present a causal interpretation of the new models.

Title: Program Completionin the Input Language of GRINGO, Abstract: We argue that turning a logic program into a set of completed definitions can be sometimes thought of as the "reverse engineering" process of generating a set of conditions that could serve as a specification for it. Accordingly, it may be useful to define completion for a large class of ASP programs and to automate the process of generating and simplifying completion formulas. Examining the output produced by this kind of software may help programmers to see more clearly what their program does, and to what degree its behavior conforms with their expectations. As a step toward this goal, we propose here a definition of program completion for a large class of programs in the input language of the ASP grounder GRINGO, and study its properties. This note is under consideration for publication in Theory and Practice of Logic Programming.

Title: Numerical modelling of surface water wave interaction with a moving wall, Abstract: In the present manuscript, we consider the practical problem of wave interaction with a vertical wall. However, the novelty here consists in the fact that the wall can move horizontally due to a system of springs. The water wave evolution is described with the free surface potential flow model. Then, a semi-analytical numerical method is presented. It is based on a mapping technique and a finite difference scheme in the transformed domain. The idea is to pose the equations on a fixed domain. This method is thoroughly tested and validated in our study. By choosing specific values of spring parameters, this system can be used to damp (or in other words to extract the energy of) incident water waves.

Title: DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN, Abstract: Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task. In particular, we propose to oversample infrequent normal samples - normal samples that occur with small probability, e.g., rare normal events. We show that these samples are responsible for false positives in anomaly detection. However, oversampling of infrequent normal samples is challenging for real-world high-dimensional data with multimodal distributions. To address this challenge, we propose to use a GAN variant known as the adversarial autoencoder (AAE) to transform the high-dimensional multimodal data distributions into low-dimensional unimodal latent distributions with well-defined tail probability. Then, we systematically oversample at the `edge' of the latent distributions to increase the density of infrequent normal samples. We show that our oversampling pipeline is a unified one: it is generally applicable to datasets with different complex data distributions. To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection. We validate our method by demonstrating consistent improvements across several real-world datasets.

Title: Conditional Variance Penalties and Domain Shift Robustness, Abstract: When training a deep network for image classification, one can broadly distinguish between two types of latent features of images that will drive the classification. Following the notation of Gong et al. (2016), we can divide latent features into (i) "core" features $X^\text{core}$ whose distribution $X^\text{core}\vert Y$ does not change substantially across domains and (ii) "style" features $X^{\text{style}}$ whose distribution $X^{\text{style}}\vert Y$ can change substantially across domains. These latter orthogonal features would generally include features such as rotation, image quality or brightness but also more complex ones like hair color or posture for images of persons. Guarding against future adversarial domain shifts implies that the influence of the second type of style features in the prediction has to be limited. We assume that the domain itself is not observed and hence a latent variable. We do assume, however, that we can sometimes observe a typically discrete identifier or $\mathrm{ID}$ variable. We know in some applications, for example, that two images show the same person, and $\mathrm{ID}$ then refers to the identity of the person. The method requires only a small fraction of images to have an $\mathrm{ID}$ variable. We group data samples if they share the same class and identifier $(Y,\mathrm{ID})=(y,\mathrm{id})$ and penalize the conditional variance of the prediction if we condition on $(Y,\mathrm{ID})$. Using this approach is shown to protect against shifts in the distribution of the style variables for both regression and classification models. Specifically, the conditional variance penalty CoRe is shown to be equivalent to minimizing the risk under noise interventions in a regression setting and is shown to lead to adversarial risk consistency in a partially linear classification setting.

Title: The First Optical Spectra of Wolf Rayet Stars in M101 Revealed with Gemini/GMOS, Abstract: Deep narrow-band HST imaging of the iconic spiral galaxy M101 has revealed over a thousand new Wolf Rayet (WR) candidates. We report spectrographic confirmation of 10 HeII emission line sources hosting 15 WR stars. We find WR stars present at both sub- and super-solar metalicities with WC stars favouring more metal-rich regions compared to WN stars. We investigate the association of WR stars with HII regions using archival HST imaging and conclude that the majority of WR stars are in or associated with HII regions. Of the 10 emission lines sources, only one appears to be unassociated with a star-forming region. Our spectroscopic survey provides confidence that our narrow-band photometric candidates are in fact bonafide WR stars, which will allow us to characterise the progenitors of any core-collapse supernovae that erupt in the future in M101.

Title: Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification, Abstract: We propose a Label Propagation based algorithm for weakly supervised text classification. We construct a graph where each document is represented by a node and edge weights represent similarities among the documents. Additionally, we discover underlying topics using Latent Dirichlet Allocation (LDA) and enrich the document graph by including the topics in the form of additional nodes. The edge weights between a topic and a text document represent level of "affinity" between them. Our approach does not require document level labelling, instead it expects manual labels only for topic nodes. This significantly minimizes the level of supervision needed as only a few topics are observed to be enough for achieving sufficiently high accuracy. The Label Propagation Algorithm is employed on this enriched graph to propagate labels among the nodes. Our approach combines the advantages of Label Propagation (through document-document similarities) and Topic Modelling (for minimal but smart supervision). We demonstrate the effectiveness of our approach on various datasets and compare with state-of-the-art weakly supervised text classification approaches.

Title: The Robot Routing Problem for Collecting Aggregate Stochastic Rewards, Abstract: We propose a new model for formalizing reward collection problems on graphs with dynamically generated rewards which may appear and disappear based on a stochastic model. The *robot routing problem* is modeled as a graph whose nodes are stochastic processes generating potential rewards over discrete time. The rewards are generated according to the stochastic process, but at each step, an existing reward disappears with a given probability. The edges in the graph encode the (unit-distance) paths between the rewards' locations. On visiting a node, the robot collects the accumulated reward at the node at that time, but traveling between the nodes takes time. The optimization question asks to compute an optimal (or epsilon-optimal) path that maximizes the expected collected rewards. We consider the finite and infinite-horizon robot routing problems. For finite-horizon, the goal is to maximize the total expected reward, while for infinite horizon we consider limit-average objectives. We study the computational and strategy complexity of these problems, establish NP-lower bounds and show that optimal strategies require memory in general. We also provide an algorithm for computing epsilon-optimal infinite paths for arbitrary epsilon > 0.

Title: Estimating occupation time functionals, Abstract: We study the estimation of integral type functionals $\int_{0}^{t}f(X_{r})dr$ for a function $f$ and a $d$-dimensional càdlàg process $X$ with respect to discrete observations by a Riemann-sum estimator. Based on novel semimartingale approximations in the Fourier domain, central limit theorems are proved for $L^{2}$-Sobolev functions $f$ with fractional smoothness and continuous Itô semimartingales $X$. General $L^{2}(\mathbb{P})$-upper bounds on the error for càdlàg processes are given under weak assumptions. These bounds combine and generalize all previously obtained results in the literature and apply also to non-Markovian processes. Several detailed examples are discussed. As application the approximation of local times for fractional Brownian motion is studied. The optimality of the $L^{2}(\mathbb{P})$-upper bounds is shown by proving the corresponding lower bounds in case of Brownian motion.

Title: Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS, Abstract: The features of collaboration patterns are often considered to be different from discipline to discipline. Meanwhile, collaborating among disciplines is an obvious feature emerged in modern scientific research, which incubates several interdisciplines. The features of collaborations in and among the disciplines of biological, physical and social sciences are analyzed based on 52,803 papers published in a multidisciplinary journal PNAS during 1999 to 2013. From those data, we found similar transitivity and assortativity of collaboration patterns as well as the identical distribution type of collaborators per author and that of papers per author, namely a mixture of generalized Poisson and power-law distributions. In addition, we found that interdisciplinary research is undertaken by a considerable fraction of authors, not just those with many collaborators or those with many papers. This case study provides a window for understanding aspects of multidisciplinary and interdisciplinary collaboration patterns.

Title: Distinction of representations via Bruhat-Tits buildings of p-adic groups, Abstract: Introductory and pedagogical treatmeant of the article : P. Broussous "Distinction of the Steinberg representation", with an appendix by François Courtès, IMRN 2014, no 11, 3140-3157. To appear in Proceedings of Chaire Jean Morlet, Dipendra Prasad, Volker Heiermann Ed. 2017. Contains modified and simplified proofs of loc. cit. This article is written in memory of François Courtès who passed away in september 2016.

Title: Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments, Abstract: Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.

Title: Random active path model of deep neural networks with diluted binary synapses, Abstract: Deep learning has become a powerful and popular tool for a variety of machine learning tasks. However, it is challenging to understand the mechanism of deep learning from a theoretical perspective. In this work, we propose a random active path model to study collective properties of deep neural networks with binary synapses, under the removal perturbation of connections between layers. In the model, the path from input to output is randomly activated, and the corresponding input unit constrains the weights along the path into the form of a $p$-weight interaction glass model. A critical value of the perturbation is observed to separate a spin glass regime from a paramagnetic regime, with the transition being of the first order. The paramagnetic phase is conjectured to have a poor generalization performance.

Title: Super Generalized Central Limit Theorem: Limit distributions for sums of non-identical random variables with power-laws, Abstract: In nature or societies, the power-law is present ubiquitously, and then it is important to investigate the mathematical characteristics of power-laws in the recent era of big data. In this paper we prove the superposition of non-identical stochastic processes with power-laws converges in density to a unique stable distribution. This property can be used to explain the universality of stable laws such that the sums of the logarithmic return of non-identical stock price fluctuations follow stable distributions.

Title: Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning, Abstract: We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household. The RL agent learns to determine which survey question to ask next and when to stop to make a prediction about their likelihood of malaria based on their responses hitherto. The agent incurs a small penalty for each question asked, and a large reward/penalty for making the correct/wrong prediction; it thus has to learn to balance the length of the survey with the accuracy of its final predictions. Our RL agent is a Deep Q-network that learns a policy directly from the responses to the questions, with an action defined for each possible survey question and for each possible prediction class. We focus on Kenya, where malaria is a massive health burden, and train the RL agent on a dataset of 6481 households from the Kenya Malaria Indicator Survey 2015. To investigate the importance of having survey questions be adaptive to responses, we compare our RL agent to a supervised learning (SL) baseline that fixes its set of survey questions a priori. We evaluate on prediction accuracy and on the number of survey questions asked on a holdout set and find that the RL agent is able to predict with 80% accuracy, using only 2.5 questions on average. In addition, the RL agent learns to survey adaptively to responses and is able to match the SL baseline in prediction accuracy while significantly reducing survey length.

Title: A proof of the Muir-Suffridge conjecture for convex maps of the unit ball in $\mathbb C^n$, Abstract: We prove (and improve) the Muir-Suffridge conjecture for holomorphic convex maps. Namely, let $F:\mathbb B^n\to \mathbb C^n$ be a univalent map from the unit ball whose image $D$ is convex. Let $\mathcal S\subset \partial \mathbb B^n$ be the set of points $\xi$ such that $\lim_{z\to \xi}\|F(z)\|=\infty$. Then we prove that $\mathcal S$ is either empty, or contains one or two points and $F$ extends as a homeomorphism $\tilde{F}:\overline{\mathbb B^n}\setminus \mathcal S\to \overline{D}$. Moreover, $\mathcal S=\emptyset$ if $D$ is bounded, $\mathcal S$ has one point if $D$ has one connected component at $\infty$ and $\mathcal S$ has two points if $D$ has two connected components at $\infty$ and, up to composition with an affine map, $F$ is an extension of the strip map in the plane to higher dimension.

Title: Evolution of structure, magnetism and electronic transport in doped pyrochlore iridate Y$_2$Ir$_{2-x}$Ru$_{x}$O$_7$, Abstract: The interplay between spin-orbit coupling (SOC) and electron correlation ($U$) is considered for many exotic phenomena in iridium oxides. We have investigated the evolution of structural, magnetic and electronic properties in pyrochlore iridate Y$_2$Ir$_{2-x}$Ru$_{x}$O$_7$ where the substitution of Ru has been aimed to tune this interplay. The Ru substitution does not introduce any structural phase transition, however, we do observe an evolution of lattice parameters with the doping level $x$. X-ray photoemission spectroscopy (XPS) study indicates Ru adopts charge state of Ru$^{4+}$ and replaces the Ir$^{4+}$ accordingly. Magnetization data reveal both the onset of magnetic irreversibility and the magnetic moment decreases with progressive substitution of Ru. These materials show non-equilibrium low temperature magnetic state as revealed by magnetic relaxation data. Interestingly, we find magnetic relaxation rate increases with substitution of Ru. The electrical resistivity shows an insulating behavior in whole temperature range, however, resistivity decreases with substitution of Ru. Nature of electronic conduction has been found to follow power-law behavior for all the materials.

Title: Cieliebak's Invariance Theorem and contact structures via connected sums, Abstract: We present a strong version of Abouzaid's No-Escape Lemma, which allows varying contact forms on the boundary and which can be used instead of the Maximum Principle. Moreover, we give a clarified proof of Cieliebak's Invariance Theorem for Symplectic homology under subcritical handle attachment. Finally, we introduce the notion of asymptotically finitely generated contact structures, which states essentially that the Symplectic homology in a certain degree of any filling of such contact manifolds is uniformly generated by only finitely many Reeb orbits. This property is then used to show that a large class of manifolds carries infinitely many exactly fillable contact structures.

Title: Gap Acceptance During Lane Changes by Large-Truck Drivers-An Image-Based Analysis, Abstract: This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events were acquired from a large-scale naturalistic driving data set. The kinematic variables from the image-based gap analysis were filtered by the weighted linear least squares in order to extrapolate them at the lane change time. In addition, the time-to-collision and required deceleration were computed, and potential safety threshold values are provided. The resulting range and range rate distributions showed directional discrepancies, i.e., in left lane changes, large trucks are often slower than other vehicles in the target lane, whereas they are usually faster in right lane changes. Video observations have confirmed that major motivations for changing lanes are different depending on the direction of move, i.e., moving to the left (faster) lane occurs due to a slower vehicle ahead or a merging vehicle on the right-hand side, whereas right lane changes are frequently made to return to the original lane after passing.

Title: Diversity-Sensitive Conditional Generative Adversarial Networks, Abstract: We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code. To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes. The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives. Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity. We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction. We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.

Title: An a Priori Exponential Tail Bound for k-Folds Cross-Validation, Abstract: We consider a priori generalization bounds developed in terms of cross-validation estimates and the stability of learners. In particular, we first derive an exponential Efron-Stein type tail inequality for the concentration of a general function of n independent random variables. Next, under some reasonable notion of stability, we use this exponential tail bound to analyze the concentration of the k-fold cross-validation (KFCV) estimate around the true risk of a hypothesis generated by a general learning rule. While the accumulated literature has often attributed this concentration to the bias and variance of the estimator, our bound attributes this concentration to the stability of the learning rule and the number of folds k. This insight raises valid concerns related to the practical use of KFCV and suggests research directions to obtain reliable empirical estimates of the actual risk.

Title: Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning, Abstract: This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using optimal weight initialization learned from first solving a similar easier environment. We show that incremental learning can produce vastly superior results than standard methods by providing a strong baseline method across ten Dex environments. We finally develop a saliency method for qualitative analysis of reinforcement learning, which shows the impact incremental learning has on network attention.

Title: The first order partial differential equations resolved with any derivatives, Abstract: In this paper we discuss the first order partial differential equations resolved with any derivatives. At first, we transform the first order partial differential equation resolved with respect to a time derivative into a system of linear equations. Secondly, we convert it into a system of the first order linear partial differential equations with constant coefficients and nonlinear algebraic equations. Thirdly, we solve them by the Fourier transform and convert them into the equivalent integral equations. At last, we extend to discuss the first order partial differential equations resolved with respect to time derivatives and the general scenario resolved with any derivatives.

Title: Real Time Collision Detection and Identification for Robotic Manipulators, Abstract: The majority of everyday tasks involve interacting with unstructured environments. This implies that, in order for robots to be truly useful they must be able to handle contacts. This paper explores how a particle filter can be used to localize a contact point and estimate the external force. We demonstrate the capability of the particle filter on a simulated 4DoF planar robotic arm, and compare it to a well-established analytical approach.

Title: A multiple timescales approach to bridging spiking- and population-level dynamics, Abstract: A rigorous bridge between spiking-level and macroscopic quantities is an on-going and well-developed story for asynchronously firing neurons, but focus has shifted to include neural populations exhibiting varying synchronous dynamics. Recent literature has used the Ott--Antonsen ansatz (2008) to great effect, allowing a rigorous derivation of an order parameter for large oscillator populations. The ansatz has been successfully applied using several models including networks of Kuramoto oscillators, theta models, and integrate-and-fire neurons, along with many types of network topologies. In the present study, we take a converse approach: given the mean field dynamics of slow synapses, predict the synchronization properties of finite neural populations. The slow synapse assumption is amenable to averaging theory and the method of multiple timescales. Our proposed theory applies to two heterogeneous populations of N excitatory n-dimensional and N inhibitory m-dimensional oscillators with homogeneous synaptic weights. We then demonstrate our theory using two examples. In the first example we take a network of excitatory and inhibitory theta neurons and consider the case with and without heterogeneous inputs. In the second example we use Traub models with calcium for the excitatory neurons and Wang-Buzs{á}ki models for the inhibitory neurons. We accurately predict phase drift and phase locking in each example even when the slow synapses exhibit non-trivial mean-field dynamics.

Title: Sentiment Identification in Code-Mixed Social Media Text, Abstract: Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is a presence of sentiment in the text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach.While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labeling Facebook posts with their associated sentiments.

Title: Microscopic theory of refractive index applied to metamaterials: Effective current response tensor corresponding to standard relation $n^2 = \varepsilon_{\mathrm{eff}} μ_{\mathrm{eff}}$, Abstract: In this article, we first derive the wavevector- and frequency-dependent, microscopic current response tensor which corresponds to the "macroscopic" ansatz $\vec D = \varepsilon_0 \varepsilon_{\mathrm{eff}} \vec E$ and $\vec B = \mu_0 \mu_{\mathrm{eff}} \vec H$ with wavevector- and frequency-independent, "effective" material constants $\varepsilon_{\mathrm{eff}}$ and $\mu_{\mathrm{eff}}$. We then deduce the electromagnetic and optical properties of this effective material model by employing exact, microscopic response relations. In particular, we argue that for recovering the standard relation $n^2 = \varepsilon_{\mathrm{eff}} \mu_{\mathrm{eff}}$ between the refractive index and the effective material constants, it is imperative to start from the microscopic wave equation in terms of the transverse dielectric function, $\varepsilon_{\mathrm{T}}(\vec k, \omega) = 0$. On the phenomenological side, our result is especially relevant for metamaterials research, which draws directly on the standard relation for the refractive index in terms of effective material constants. Since for a wide class of materials the current response tensor can be calculated from first principles and compared to the model expression derived here, this work also paves the way for a systematic search for new metamaterials.

Title: Semidefinite Relaxation-Based Optimization of Multiple-Input Wireless Power Transfer Systems, Abstract: An optimization procedure for multi-transmitter (MISO) wireless power transfer (WPT) systems based on tight semidefinite relaxation (SDR) is presented. This method ensures physical realizability of MISO WPT systems designed via convex optimization -- a robust, semi-analytical and intuitive route to optimizing such systems. To that end, the nonconvex constraints requiring that power is fed into rather than drawn from the system via all transmitter ports are incorporated in a convex semidefinite relaxation, which is efficiently and reliably solvable by dedicated algorithms. A test of the solution then confirms that this modified problem is equivalent (tight relaxation) to the original (nonconvex) one and that the true global optimum has been found. This is a clear advantage over global optimization methods (e.g. genetic algorithms), where convergence to the true global optimum cannot be ensured or tested. Discussions of numerical results yielded by both the closed-form expressions and the refined technique illustrate the importance and practicability of the new method. It, is shown that this technique offers a rigorous optimization framework for a broad range of current and emerging WPT applications.

Title: Counterfactual Reasoning with Disjunctive Knowledge in a Linear Structural Equation Model, Abstract: We consider the problem of estimating counterfactual quantities when prior knowledge is available in the form of disjunctive statements. These include disjunction of conditions (e.g., "the patient is more than 60 years of age") as well as disjuction of antecedants (e.g., "had the patient taken either drug A or drug B"). Focusing on linear structural equation models (SEM) and imperfect control plans, we extend the counterfactual framework of Balke and Pearl (1995) , Chen and Pearl (2015), and Pearl (2009, pp. 389-391) from unconditional to conditional plans, from a univariate treatment to a set of treatments, and from point type knowledge to disjunctive knowledge. Finally, we provide improved matrix representations of the resulting counterfactual parameters, and improved computational methods of their evaluation.

Title: Neighborhood selection with application to social networks, Abstract: The topic of this paper is modeling and analyzing dependence in stochastic social networks. Using a latent variable block model allows the analysis of dependence between blocks via the analysis of a latent graphical model. Our approach to the analysis of the graphical model then is based on the idea underlying the neighborhood selection scheme put forward by Meinshausen and Bühlmann (2006). However, because of the latent nature of our model, estimates have to be used in lieu of the unobserved variables. This leads to a novel analysis of graphical models under uncertainty, in the spirit of Rosenbaum et al. (2010), or Belloni et al. (2017). Lasso-based selectors, and a class of Dantzig-type selectors are studied.

Title: Average-radius list-recovery of random linear codes: it really ties the room together, Abstract: We analyze the list-decodability, and related notions, of random linear codes. This has been studied extensively before: there are many different parameter regimes and many different variants. Previous works have used complementary styles of arguments---which each work in their own parameter regimes but not in others---and moreover have left some gaps in our understanding of the list-decodability of random linear codes. In particular, none of these arguments work well for list-recovery, a generalization of list-decoding that has been useful in a variety of settings. In this work, we present a new approach, which works across parameter regimes and further generalizes to list-recovery. Our main theorem can establish better list-decoding and list-recovery results for low-rate random linear codes over large fields; list-recovery of high-rate random linear codes; and it can recover the rate bounds of Guruswami, Hastad, and Kopparty for constant-rate random linear codes (although with large list sizes).

Title: From Dirac semimetals to topological phases in three dimensions: a coupled wire construction, Abstract: Weyl and Dirac (semi)metals in three dimensions have robust gapless electronic band structures. Their massless single-body energy spectra are protected by symmetries such as lattice translation, (screw) rotation and time reversal. In this manuscript, we discuss many-body interactions in these systems. We focus on strong interactions that preserve symmetries and are outside the single-body mean-field regime. By mapping a Dirac (semi)metal to a model based on a three dimensional array of coupled Dirac wires, we show (1) the Dirac (semi)metal can acquire a many-body excitation energy gap without breaking the relevant symmetries, and (2) interaction can enable an anomalous Weyl (semi)metallic phase that is otherwise forbidden by symmetries in the single-body setting and can only be present holographically on the boundary of a four dimensional weak topological insulator. Both of these topological states support fractional gapped (gapless) bulk (resp. boundary) quasiparticle excitations.

Title: Generalization of Special Functions and its Applications to Multiplicative and Ordinary Fractional Derivatives, Abstract: The goal of this paper is to extend the classical and multiplicative fractional derivatives. For this purpose, it is introduced the new extended modified Bessel function and also given an important relation between this new function I(v,q;x) and the confluent hypergeometric function. Besides, it is used to generalize the hypergeometric, the confluent hypergeometric and the extended beta functions by using the new extended modified Bessel function. Also, the asymptotic formulae and the generating function of the extended modified Bessel function are obtained. The extensions of classical and multiplicative fractional derivatives are defined via extended modified Bessel function and, first time the fractional derivative of rational functions is explicitly given via complex partial fraction decomposition.

Title: Adaptive clustering procedure for continuous gravitational wave searches, Abstract: In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same root cause (be it a signal or a disturbance), based on a predefined cluster volume. In this paper, we present a procedure that adapts the cluster volume to the data itself and checks for consistency of such volume with what is expected from a signal. This significantly improves the noise rejection capabilities at fixed detection threshold, and at fixed computing resources for the follow-up stages, this results in an overall more sensitive search. This new procedure was employed in the first Einstein@Home search on data from the first science run of the advanced LIGO detectors (O1) [11].