Abstract:
  Deep neural networks have achieved remarkable performance on a range of
classification tasks, with softmax cross-entropy (CE) loss emerging as the
de-facto objective function. The CE loss encourages features of a class to have
a higher projection score on the true class-vector compared to the negative
classes. However, this is a relative constraint and does not explicitly force
different class features to be well-separated. Motivated by the observation
that ground-truth class representations in CE loss are orthogonal (one-hot
encoded vectors), we develop a novel loss function termed `Orthogonal
Projection Loss' (OPL) which imposes orthogonality in the feature space. OPL
augments the properties of CE loss and directly enforces inter-class separation
alongside intra-class clustering in the feature space through orthogonality
constraints on the mini-batch level. As compared to other alternatives of CE,
OPL offers unique advantages e.g., no additional learnable parameters, does not
require careful negative mining and is not sensitive to the batch size. Given
the plug-and-play nature of OPL, we evaluate it on a diverse range of tasks
including image recognition (CIFAR-100), large-scale classification (ImageNet),
domain generalization (PACS) and few-shot learning (miniImageNet, CIFAR-FS,
tiered-ImageNet and Meta-dataset) and demonstrate its effectiveness across the
board. Furthermore, OPL offers better robustness against practical nuisances
such as adversarial attacks and label noise. Code is available at:
https://github.com/kahnchana/opl.


Abstract:
  Computer vision is playing an increasingly important role in automated
malware detection with to the rise of the image-based binary representation.
These binary images are fast to generate, require no feature engineering, and
are resilient to popular obfuscation methods. Significant research has been
conducted in this area, however, it has been restricted to small-scale or
private datasets that only a few industry labs and research teams have access
to. This lack of availability hinders examination of existing work, development
of new research, and dissemination of ideas. We introduce MalNet, the largest
publicly available cybersecurity image database, offering 133x more images and
27x more classes than the only other public binary-image database. MalNet
contains over 1.2 million images across a hierarchy of 47 types and 696
families. We provide extensive analysis of MalNet, discussing its properties
and provenance. The scale and diversity of MalNet unlocks new and exciting
cybersecurity opportunities to the computer vision community--enabling
discoveries and research directions that were previously not possible. The
database is publicly available at www.mal-net.org.


Abstract:
  The dark matter (DM) can consist of the primordial black holes (PBHs) in
addition to the conventional weakly interacting massive particles (WIMPs). The
Poisson fluctuations of the PBH number density produce the isocurvature
perturbations which can dominate the matter power spectrum at small scales and
enhance the early structure formation. We study how the WIMP annihilation from
those early formed structures can affect the CMB (in particular the E-mode
polarization anisotropies and $y$-type spectral distortions) and global 21cm
signals. Our studies would be of particular interest for the light (sub-GeV)
WIMP scenarios which have been less explored compared with the mixed DM
scenarios consisting of PBHs and heavy ($\gtrsim 1$ GeV) WIMPs. For instance,
for the self-annihilating DM mass $m_{\chi}=1$ MeV and the thermally averaged
annihilation cross section $\langle \sigma v \rangle \sim 10^{-30} \rm cm^3/s$,
the latest Planck CMB data requires the PBH fraction with respect to the whole
DM to be at most ${\cal O}(10^{-3})$ for the sub-solar mass PBHs and an even
tighter bound (by a factor $\sim 5$) can be obtained from the global 21-cm
measurements.


Abstract:
  We propose and demonstrate a high performance four-wavelength erbium-doped
fiber laser (EDFL), enabled by a figure-8 compound-ring-cavity (F8-CRC) filter
for single-longitudinal-mode (SLM) selection and a polarization-managed
four-channel filter (PM-FCF) for defining four lasing wavelengths. We introduce
a novel methodology utilizing signal-flow graph combined with Mason's rule to
analyze a CRC filter in general and apply it to obtain the important design
parameters for the F8-CRC filter used in this paper. By combining the functions
of the F8-CRC filter and the PM-FCF filter assisted by the enhanced
polarization hole-burning and polarization dependent loss, we achieve the EDFL
with fifteen lasing states, including four single-, six dual-, four tri- and
one quad-wavelength lasing operations. In particular, all the four
single-wavelength operations are in stable SLM oscillation, typically with a
linewidth of <600 Hz, a RIN of <=-154.58 dB/Hz@>=3 MHz and an output power
fluctuation of <=+/-3.45%. In addition, all the six dual-wavelength operations
have very similar performances, with the performance parameters close to those
of the single-wavelength lasing operations. Finally, we achieve the wavelength
spacing tuning of the dual-wavelength operations for the photonic generation of
tunable microwave signals, and successfully obtain a signal at 23.10 GHz as a
demonstration.


Abstract:
  The multifractal superconducting state originates from the interplay of
Anderson localization and interaction effects. In this article we overview the
recent theory of the superconductivity enhancement by multifractality and
extend it to describe the spectral properties of superconductors on the scales
of the order of the superconducting gap. Specifically, using the approach based
on renormalization group within the nonlinear sigma model, we develop the
theory of a multifractal superconducting state in thin films. We derive a
modified Usadel equation that incorporates the interplay of disorder and
interactions at energy scales larger than the spectral gap and study the effect
of such an interplay on the low-energy physics. We determine the spectral gap
at zero temperature which occurs to be proportional to the multifracally
enhanced superconducting transition temperature. The modified Usadel equation
results in the disorder-averaged density of states that, near the spectral gap,
resembles the one obtained in the model of a spatially random superconducting
order parameter. We reveal strong mesoscopic fluctuations of the local density
of states in the superconducting state. Such strong mesoscopic fluctuations
imply that the interval of energies in which the superconducting gap
establishes is parametrically large in systems with multifractally-enhanced
superconductivity.


Abstract:
  In this paper, we present a task space-based local motion planner that
incorporates collision avoidance and constraints on end-effector motion during
the execution of a task. Our key technical contribution is the development of a
novel kinematic state evolution model of the robot where the collision
avoidance is encoded as a complementarity constraint. We show that the
kinematic state evolution with collision avoidance can be represented as a
Linear Complementarity Problem (LCP). Using the LCP model along with Screw
Linear Interpolation (ScLERP) in SE(3), we show that it may be possible to
compute a path between two given task space poses by directly moving from the
start to the goal pose, even if there are potential collisions with obstacles.
The scalability of the planner is demonstrated with experiments using a
physical robot. We present simulation and experimental results with both
collision avoidance and task constraints to show the efficacy of our approach.


Abstract:
  A new belief space planning algorithm, called covariance steering Belief
RoadMap (CS-BRM), is introduced, which is a multi-query algorithm for motion
planning of dynamical systems under simultaneous motion and observation
uncertainties. CS-BRM extends the probabilistic roadmap (PRM) approach to
belief spaces and is based on the recently developed theory of covariance
steering (CS) that enables guaranteed satisfaction of terminal belief
constraints in finite-time. The nodes in the CS-BRM are sampled in belief space
and represent distributions of the system states. A covariance steering
controller steers the system from one BRM node to another, thus acting as an
edge controller of the corresponding belief graph that ensures belief
constraint satisfaction. After the edge controller is computed, a specific edge
cost is assigned to that edge. The CS-BRM algorithm allows the sampling of
non-stationary belief nodes, and thus is able to explore the velocity space and
find efficient motion plans. The performance of CS-BRM is evaluated and
compared to a previous belief space planning method, demonstrating the benefits
of the proposed approach.


Abstract:
  How much do individuals contribute to team output? I propose an econometric
framework to quantify individual contributions when only the output of their
teams is observed. The identification strategy relies on following individuals
who work in different teams over time. I consider two production technologies.
For a production function that is additive in worker inputs, I propose a
regression estimator and show how to obtain unbiased estimates of variance
components that measure the contributions of heterogeneity and sorting. To
estimate nonlinear models with complementarity, I propose a mixture approach
under the assumption that individual types are discrete, and rely on a
mean-field variational approximation for estimation. To illustrate the methods,
I estimate the impact of economists on their research output, and the
contributions of inventors to the quality of their patents.


Abstract:
  We study Thompson sampling (TS) in online decision-making problems where the
uncertain environment is sampled from a mixture distribution. This is relevant
to multi-task settings, where a learning agent is faced with different classes
of problems. We incorporate this structure in a natural way by initializing TS
with a mixture prior -- dubbed MixTS -- and develop a novel, general technique
for analyzing the regret of TS with such priors. We apply this technique to
derive Bayes regret bounds for MixTS in both linear bandits and tabular Markov
decision processes (MDPs). Our regret bounds reflect the structure of the
problem and depend on the number of components and confidence width of each
component of the prior. Finally, we demonstrate the empirical effectiveness of
MixTS in both synthetic and real-world experiments.


Abstract:
  Synthetic data became already an essential component of machine
learning-based perception in the field of autonomous driving. Yet it still
cannot replace real data completely due to the sim2real domain shift. In this
work, we propose a method that leverages the advantages of the augmentation
process and adversarial training to synthesize realistic data for the
pedestrian recognition task. Our approach utilizes an attention mechanism
driven by an adversarial loss to learn domain discrepancies and improve
sim2real adaptation. Our experiments confirm that the proposed adaptation
method is robust to such discrepancies and reveals both visual realism and
semantic consistency. Furthermore, we evaluate our data generation pipeline on
the task of pedestrian recognition and demonstrate that generated data resemble
properties of the real domain.


Abstract:
  Stars within the innermost part of the Nuclear Star Cluster can reach orbital
velocities up to a few percent of the light speed. As analyzed by Rafikov
(2020), Doppler boosting of stellar light may be of relevance at the pericenter
of stellar orbits, especially with the upcoming high-precision photometry in
the near- and mid-infrared bands. Here we analyze the previously neglected
effect of infrared spectral index of monitored objects on the Doppler-boosted
continuum emission in a narrow band. In contrast to main-sequences stars, the
detected compact infrared-excess dust-enshrouded objects have an enhanced
Doppler-boosting effect by as much as an order of magnitude, with the
variability amplitude of the order of ten percent for the most eccentric
orbits. In a similar way, pulsars dominated by non-thermal synchrotron emission
are also expected to exhibit a stronger Doppler-boosted signal by a factor of
at least four in comparison with canonical S stars. In case the stellar orbit
is robustly determined, the relative flux variation can thus provide hints
about the nature of the objects. For extended dust-enshrouded objects, such as
G1, that are variable due to tidal, ellipsoidal, bow-shock, and irradiation
effects, the subtraction of the expected Doppler-boosting variations will help
to better comprehend their internal physics. In addition, the relative flux
variability due to higher-order relativistic effects is also modified for
different negative spectral indices in a way that it can obtain both positive
and negative values with the relative variability of the order of one percent.


Abstract:
  Some years after the appearance of the so-called non-diffracting beams, there
was the development of methods capable of structuring them spatially, being the
so called Frozen Waves method the first and, perhaps, the most efficient one.
That method allowed modelling the longitudinal intensity pattern of
non-diffracting beams, being, however, little efficient in controlling their
transverse spatial pattern, granting only the possibility of choosing their
transverse dimensions, which remain invariant throughout the propagation. In
this work, we have extended the Frozen Wave method in such a way to control, in
addition to the longitudinal pattern, the transverse beam structure along the
propagation. The new transversally and longitudinally structured beams can have
potential applications in areas such as photonics, optical manipulation,
optical atom guidance, lithography, etc..


Abstract:
  In this paper, we focus on the solution of a hard single machine scheduling
problem by new heuristic algorithms embedding techniques from machine learning
field and scheduling theory. These heuristics transform an instance of the hard
problem into an instance of a simpler one solved to optimality. The obtained
schedule is then transposed to the original problem. Computational experiments
show that they are competitive with state-of-the-art heuristics, notably on
large instances.


Abstract:
  In several industrial applications, such as crystallization, pollution
control, and flow assurance, an accurate understanding of the aqueous
electrolyte solutions is crucial. Electrolyte equilibrium calculation
contributes with the design and optimization of processes by providing
important information, such as species concentration, solution pH and potential
for solid formation. In this work, a pure Python library distributed under
BSD-3 license was developed for the calculation of aqueous electrolyte
equilibrium. The package takes as inputs the feed components of a given
solution, and it automatically identifies its composing ions and the chemical
reactions involved to calculate equilibrium conditions. Moreover, there is no
established electrolyte activity coefficient model for a broad range of
operational conditions. Hence, in this package, built-in activity coefficient
models are structured in a modular approach, so that the non-ideality
calculation can be performed by a user provided function, which allows further
research in the topic. The package can be used by researchers to readily
identify the equilibrium reactions and possible solid phases in a user friendly
language.


Abstract:
  Medical entity retrieval is an integral component for understanding and
communicating information across various health systems. Current approaches
tend to work well on specific medical domains but generalize poorly to unseen
sub-specialties. This is of increasing concern under a public health crisis as
new medical conditions and drug treatments come to light frequently. Zero-shot
retrieval is challenging due to the high degree of ambiguity and variability in
medical corpora, making it difficult to build an accurate similarity measure
between mentions and concepts. Medical knowledge graphs (KG), however, contain
rich semantics including large numbers of synonyms as well as its curated
graphical structures. To take advantage of this valuable information, we
propose a suite of learning tasks designed for training efficient zero-shot
entity retrieval models. Without requiring any human annotation, our knowledge
graph enriched architecture significantly outperforms common zero-shot
benchmarks including BM25 and Clinical BERT with 7% to 30% higher recall across
multiple major medical ontologies, such as UMLS, SNOMED, and ICD-10.


Abstract:
  We study the local well-posedness of the nonlinear Schr{\''o}dinger equation
associated to the Grushin operator with random initial data. To the best of our
knowledge, no well-posedness result is known in the Sobolev spaces $H^k$ when
$k \leq \frac{3}{2}$. In this article, we prove that there exists a large
family of initial data such that, with respect to a suitable randomization in
$H^k$, $k \in (1,\frac{3}{2}]$, almost-sure local well-posedness holds. The
proof relies on bilinear and trilinear estimates.


Abstract:
  Given the omnipresence of non-covalent interactions (NCIs), their accurate
simulations are of crucial importance across various scientific disciplines.
Here we construct accurate models for the description of NCIs by an
interpolation along the M{\o}ller-Plesset adiabatic connection (MP AC). Our
interpolation approximates the correlation energy, by recovering MP2 at small
coupling strengths and the correct large-coupling strength expansion of the MP
AC, recently shown to be a functional of the Hartree-Fock density. Our models
are size consistent for fragments with non-degenerate ground states, have the
same cost as double hybrids and require no dispersion corrections to capture
NCIs accurately. These interpolations greatly reduce large MP2 errors for
typical $\pi$-stacking complexes (e.g., benzene-pyridine dimers) and for the L7
dataset. They are also competitive with state-of-the-art dispersion enhanced
functionals and can even significantly outperform them for a variety of
datasets, such as CT7 and L7.


Abstract:
  Real-time traffic prediction models play a pivotal role in smart mobility
systems and have been widely used in route guidance, emerging mobility
services, and advanced traffic management systems. With the availability of
massive traffic data, neural network-based deep learning methods, especially
the graph convolutional networks (GCN) have demonstrated outstanding
performance in mining spatio-temporal information and achieving high prediction
accuracy. Recent studies reveal the vulnerability of GCN under adversarial
attacks, while there is a lack of studies to understand the vulnerability
issues of the GCN-based traffic prediction models. Given this, this paper
proposes a new task -- diffusion attack, to study the robustness of GCN-based
traffic prediction models. The diffusion attack aims to select and attack a
small set of nodes to degrade the performance of the entire prediction model.
To conduct the diffusion attack, we propose a novel attack algorithm, which
consists of two major components: 1) approximating the gradient of the
black-box prediction model with Simultaneous Perturbation Stochastic
Approximation (SPSA); 2) adapting the knapsack greedy algorithm to select the
attack nodes. The proposed algorithm is examined with three GCN-based traffic
prediction models: St-Gcn, T-Gcn, and A3t-Gcn on two cities. The proposed
algorithm demonstrates high efficiency in the adversarial attack tasks under
various scenarios, and it can still generate adversarial samples under the drop
regularization such as DropOut, DropNode, and DropEdge. The research outcomes
could help to improve the robustness of the GCN-based traffic prediction models
and better protect the smart mobility systems. Our code is available at
https://github.com/LYZ98/Adversarial-Diffusion-Attacks-on-Graph-based-Traffic-Prediction-Models


Abstract:
  Dipolar Bose-Einstein condensates represent a powerful platform for the
exploration of quantum many-body phenomena arising from long-range
interactions. A series of recent experiments has demonstrated the formation of
supersolid states of matter. Subsequent theoretical works have shown that
quantum fluctuations can affect the underlying phase transition and may lead to
the emergence of supersolids with various lattice structures in dipolar
condensates. In this work we explore the signatures of such different
geometries in confined finite condensates. In addition to previously found
triangular lattices, our analysis reveals a rich spectrum of states, from
honeycomb patterns and ring structures to striped supersolids. By optimizing
relevant parameters we show that transitions between distinct supersolids
should be observable in current experiments.


Abstract:
  In this paper, we propose to combine the fifth order Hermite weighted
essentially non-oscillatory (HWENO) scheme and fast sweeping method (FSM) for
the solution of the steady-state $S_{N}$ transport equation in the finite
volume framework. It is well-known that the $S_{N}$ transport equation
asymptotically converges to a macroscopic diffusion equation in the limit of
optically thick systems with small absorption and sources. Numerical methods
which can preserve the asymptotic limit are referred to as asymptotic
preserving methods. In the one-dimensional case, we provide the analysis to
demonstrate the asymptotic preserving property of the high order finite volume
HWENO method, by showing that its cell-edge and cell-average fluxes possess the
thick diffusion limit. Numerical results in both one- and two- dimensions are
presented to validate its asymptotic preserving property. A hybrid strategy to
compute the nonlinear weights in the HWENO reconstruction is introduced to save
computational cost. Extensive one- and two-dimensional numerical experiments
are performed to verify the accuracy, asymptotic preserving property and
positivity of the proposed HWENO FSM.


Abstract:
  Many neural networks for graphs are based on the graph convolution operator,
proposed more than a decade ago. Since then, many alternative definitions have
been proposed, that tend to add complexity (and non-linearity) to the model. In
this paper, we follow the opposite direction by proposing simple graph
convolution operators, that can be implemented in single-layer graph
convolutional networks. We show that our convolution operators are more
theoretically grounded than many proposals in literature, and exhibit
state-of-the-art predictive performance on the considered benchmark datasets.


Abstract:
  Neural network pruning is a popular technique used to reduce the inference
costs of modern, potentially overparameterized, networks. Starting from a
pre-trained network, the process is as follows: remove redundant parameters,
retrain, and repeat while maintaining the same test accuracy. The result is a
model that is a fraction of the size of the original with comparable predictive
performance (test accuracy). Here, we reassess and evaluate whether the use of
test accuracy alone in the terminating condition is sufficient to ensure that
the resulting model performs well across a wide spectrum of "harder" metrics
such as generalization to out-of-distribution data and resilience to noise.
Across evaluations on varying architectures and data sets, we find that pruned
networks effectively approximate the unpruned model, however, the prune ratio
at which pruned networks achieve commensurate performance varies significantly
across tasks. These results call into question the extent of \emph{genuine}
overparameterization in deep learning and raise concerns about the
practicability of deploying pruned networks, specifically in the context of
safety-critical systems, unless they are widely evaluated beyond test accuracy
to reliably predict their performance. Our code is available at
https://github.com/lucaslie/torchprune.


Abstract:
  Depth map records distance between the viewpoint and objects in the scene,
which plays a critical role in many real-world applications. However, depth map
captured by consumer-grade RGB-D cameras suffers from low spatial resolution.
Guided depth map super-resolution (DSR) is a popular approach to address this
problem, which attempts to restore a high-resolution (HR) depth map from the
input low-resolution (LR) depth and its coupled HR RGB image that serves as the
guidance.
  The most challenging problems for guided DSR are how to correctly select
consistent structures and propagate them, and properly handle inconsistent
ones. In this paper, we propose a novel attention-based hierarchical
multi-modal fusion (AHMF) network for guided DSR. Specifically, to effectively
extract and combine relevant information from LR depth and HR guidance, we
propose a multi-modal attention based fusion (MMAF) strategy for hierarchical
convolutional layers, including a feature enhance block to select valuable
features and a feature recalibration block to unify the similarity metrics of
modalities with different appearance characteristics. Furthermore, we propose a
bi-directional hierarchical feature collaboration (BHFC) module to fully
leverage low-level spatial information and high-level structure information
among multi-scale features. Experimental results show that our approach
outperforms state-of-the-art methods in terms of reconstruction accuracy,
running speed and memory efficiency.


Abstract:
  Deep neural networks have usually to be compressed and accelerated for their
usage in low-power, e.g. mobile, devices. Recently, massively-parallel hardware
accelerators were developed that offer high throughput and low latency at low
power by utilizing in-memory computation. However, to exploit these benefits
the computational graph of a neural network has to fit into the in-computation
memory of these hardware systems that is usually rather limited in size. In
this study, we introduce a class of network models that have a small memory
footprint in terms of their computational graphs. To this end, the graph is
designed to contain loops by iteratively executing a single network building
block. Furthermore, the trade-off between accuracy and latency of these
so-called iterative neural networks is improved by adding multiple intermediate
outputs during both training and inference. We show state-of-the-art results
for semantic segmentation on the CamVid and Cityscapes datasets that are
especially demanding in terms of computational resources. In ablation studies,
the improvement of network training by intermediate network outputs as well as
the trade-off between weight sharing over iterations and the network size are
investigated.


Abstract:
  We consider an axion cloud around a black hole with background magnetic
fields. We calculate the decay rate of the axion cloud due to the axion-photon
conversion associated with the axion-photon coupling. For simplicity, we
consider the situation where the axion configuration is dominated by a solution
for the eigenvalue equation equivalent to that for the Hydrogen atom, and the
coupling term can be evaluated by a successive perturbation method. For the
monopole background, we find the decay rate of the axion cloud is given by
$\sim q^2\kappa^2(GM)^5\mu^8$, where $\mu$, $M$, $G$, $\kappa$ and $q$ are the
axion mass, black hole mass, gravitational constant, coupling constant of the
axion-photon coupling and monopole charge, respectively. For the uniform
background magnetic field, we obtain the decay rate of the axion cloud $\sim
B_0^2\kappa^2 (GM)^7\mu^6$, where $B_0$ is the magnetic field strength.
Applying our formula to the central black hole in our galaxy, we find that the
value of the decay rate for the case of the uniform magnetic field is
comparable to the growth rate of the superradiant instability with $\kappa\sim
10^{-12}{\rm GeV^{-1}}$, $B_0\sim 10^3{\rm G}$ and $\mu\sim 10^{-18}{\rm eV}$.
The ratio is $10^5$ times larger for the monopole magnetic field with the same
values of the parameters.


Abstract:
  We consider a coupled Patlak-Keller-Segel-Navier-Stokes system in
$\mathbb{R}^2$ that describes the collective motion of cells and fluid flow,
where the cells are attracted by a chemical substance and transported by
ambient fluid velocity, and the fluid flow is forced by the friction induced by
the cells. The main result of the paper is to show the global existence of
free-energy solutions to the 2D Patlak-Keller-Segel-Navier-Stokes system with
critical and subcritical mass.


Abstract:
  3D recovery from multi-stereo and stereo images, as an important application
of the image-based perspective geometry, serves many applications in computer
vision, remote sensing and Geomatics. In this chapter, the authors utilize the
imaging geometry and present approaches that perform 3D reconstruction from
cross-view images that are drastically different in their viewpoints. We
introduce our framework that takes ground-view images and satellite images for
full 3D recovery, which includes necessary methods in satellite and
ground-based point cloud generation from images, 3D data co-registration,
fusion and mesh generation. We demonstrate our proposed framework on a dataset
consisting of twelve satellite images and 150k video frames acquired through a
vehicle-mounted Go-pro camera and demonstrate the reconstruction results. We
have also compared our results with results generated from an intuitive
processing pipeline that involves typical geo-registration and meshing methods.


Abstract:
  The present paper is to deliberate the geometric composition of a perfect
fluid spacetime with torse-forming vector field {\xi} in connection with
conformal Ricci-Yamabe metric and conformal {\eta}-Ricci-Yamabe metric. Here we
have delineated the conditions for conformal Ricci-Yamabe soliton to be
expanding, steady, or shrinking. Later, we have acquired Laplace equation from
conformal {\eta}-Ricci-Yamabe soliton equation when the potential vector field
{\xi} of the soliton is of gradient type. Lastly, we have designated perfect
fluid with Robertson-Walker spacetime and some applications of physics and
gravity.


Abstract:
  AM dependency parsing is a method for neural semantic graph parsing that
exploits the principle of compositionality. While AM dependency parsers have
been shown to be fast and accurate across several graphbanks, they require
explicit annotations of the compositional tree structures for training. In the
past, these were obtained using complex graphbank-specific heuristics written
by experts. Here we show how they can instead be trained directly on the graphs
with a neural latent-variable model, drastically reducing the amount and
complexity of manual heuristics. We demonstrate that our model picks up on
several linguistic phenomena on its own and achieves comparable accuracy to
supervised training, greatly facilitating the use of AM dependency parsing for
new sembanks.


Abstract:
  Cyberattacks are a major issues and it causes organizations great financial,
and reputation harm. However, due to various factors, the current network
intrusion detection systems (NIDS) seem to be insufficent. Predominant NIDS
identifies Cyberattacks through a handcrafted dataset of rules. Although the
recent applications of machine learning and deep learning have alleviated the
enormous effort in NIDS, the security of network data has always been a prime
concern. However, to encounter the security problem and enable sharing among
organizations, Federated Learning (FL) scheme is employed. Although the current
FL systems have been successful, a network's data distribution does not always
fit into a single global model as in FL. Thus, in such cases, having a single
global model in FL is no feasible. In this paper, we propose a
Segmented-Federated Learning (Segmented-FL) learning scheme for a more
efficient NIDS. The Segmented-FL approach employs periodic local model
evaluation based on which the segmentation occurs. We aim to bring similar
network environments to the same group. Further, the Segmented-FL system is
coupled with a weighted aggregation of local model parameters based on the
number of data samples a worker possesses to further augment the performance.
The improved performance by our system as compared to the FL and centralized
systems on standard dataset further validates our system and makes a strong
case for extending our technique across various tasks. The solution finds its
application in organizations that want to collaboratively learn on diverse
network environments and protect the privacy of individual datasets.


Abstract:
  The resonance frequency of membranes depends on the gas pressure due to the
squeeze-film effect, induced by the compression of a thin gas film that is
trapped underneath the resonator by the high frequency motion. This effect is
particularly large in low-mass graphene membranes, which makes them promising
candidates for pressure sensing applications. Here, we study the squeeze-film
effect in single layer graphene resonators and find that their resonance
frequency is lower than expected from models assuming ideal compression. To
understand this deviation, we perform Boltzmann and continuum finite-element
simulations, and propose an improved model that includes the effects of gas
leakage and can account for the observed pressure dependence of the resonance
frequency. Thus, this work provides further understanding of the squeeze-film
effect and provides further directions into optimizing the design of
squeeze-film pressure sensors from 2D materials.


Abstract:
  A new method for simulation of a binary homogeneous Markov process using a
quantum computer was proposed. This new method allows using the distinguished
properties of the quantum mechanical systems -- superposition, entanglement and
probability calculations. Implementation of an algorithm based on this method
requires the creation of a new quantum logic gate, which creates entangled
state between two qubits. This is a two-qubit logic gate and it must perform a
predefined rotation over the X-axis for the qubit that acts as a target, where
the rotation accurately represents the transient probabilities for a given
Markov process. This gate fires only when the control qubit is in state |1>. It
is necessary to develop an algorithm, which uses the distribution for the
transient probabilities of the process in a simple and intuitive way and then
transform those into X-axis offsets. The creation of a quantum controlled n-th
root of X gate using only the existing basic quantum logic gates at the
available cloud platforms is possible, although the hardware devices are still
too noisy, which results in a significant measurement error increase. The IBM's
Yorktown 'bow-tie' back-end performs quite better than the 5-qubit T-shaped and
the 14-qubit Melbourne quantum processors in terms of quantum fidelity. The
simulation of the binary homogeneous Markov process on a real quantum processor
gives best results on the Vigo and Yorktown (both 5-qubit) back-ends with
Hellinger fidelity of near 0.82. The choice of the right quantum circuit, based
on the available hardware (topology, size, timing properties), would be the
approach for maximizing the fidelity.


Abstract:
  Conventional machine learning techniques are conducted in a centralized
manner. Recently, the massive volume of generated wireless data, the privacy
concerns and the increasing computing capabilities of wireless end-devices have
led to the emergence of a promising decentralized solution, termed as Wireless
Federated Learning (WFL). In this first of the two parts paper, we present the
application of WFL in the sixth generation of wireless networks (6G), which is
envisioned to be an integrated communication and computing platform. After
analyzing the key concepts of WFL, we discuss the core challenges of WFL
imposed by the wireless (or mobile communication) environment. Finally, we shed
light to the future directions of WFL, aiming to compose a constructive
integration of FL into the future wireless networks.


Abstract:
  Sampled environment transitions are a critical input to deep reinforcement
learning (DRL) algorithms. Current DRL benchmarks often allow for the cheap and
easy generation of large amounts of samples such that perceived progress in DRL
does not necessarily correspond to improved sample efficiency. As simulating
real world processes is often prohibitively hard and collecting real world
experience is costly, sample efficiency is an important indicator for
economically relevant applications of DRL. We investigate progress in sample
efficiency on Atari games and continuous control tasks by comparing the number
of samples that a variety of algorithms need to reach a given performance level
according to training curves in the corresponding publications. We find
exponential progress in sample efficiency with estimated doubling times of
around 10 to 18 months on Atari, 5 to 24 months on state-based continuous
control and of around 4 to 9 months on pixel-based continuous control depending
on the specific task and performance level.


Abstract:
  We present the Australian Square Kilometre Array Pathfinder (ASKAP)
localization and follow-up observations of the host galaxy of the repeating
fast radio burst (FRB) source, FRB20201124A, the fifth such extragalactic
repeating FRB with an identified host. From spectroscopic observations using
the 6.5-m MMT Observatory, we derive a redshift of $z=0.0979 \pm 0.0001$, a
star formation rate inferred from H$\alpha$ emission of SFR(H$\alpha$) $\approx
2.1 M_{\odot}$ yr$^{-1}$, and a gas-phase metallicity of 12+log(O/H)$\approx
9.0$. By jointly modeling the 12-filter optical-mid-infrared (MIR) photometry
and spectroscopy of the host, we infer a median stellar mass of $\approx 2
\times 10^{10} M_{\odot}$, internal dust extinction of $A_V\approx 1-1.5$ mag,
and a mass-weighted stellar population age of $\approx 5-6$ Gyr. Connecting
these data to the radio and X-ray observations, we cannot reconcile the
broad-band behavior with strong AGN activity and instead attribute the dominant
source of persistent radio emission to star formation, likely originating from
the circumnuclear region of the host. The modeling also indicates a hot dust
component contributing to the MIR luminosity at a level of $\approx 10-30\%$.
We model the host galaxy's star formation and mass assembly histories, finding
that the host assembled $>90\%$ of its mass by 1 Gyr ago and exhibited a fairly
constant SFR for most of its existence, with no clear evidence of past
star-burst activity.


Abstract:
  The concept of negativity is adapted in order to explore the quantum and
thermal entanglement of the mixed spin-(1/2,$S$) Heisenberg dimers in presence
of an external magnetic field. The mutual interplay between the spin size $S$,
XXZ exchange and uniaxial single-ion anisotropy is thoroughly examined with a
goal to tune the degree and thermal stability of the pairwise entanglement. It
turns out that the antiferromagnetic spin-(1/2,$S$) Heisenberg dimers exhibit
higher degree of entanglement and higher threshold temperature in comparison
with their ferromagnetic counterparts when assuming the same set of model
parameters. The increasing spin magnitude $S$ accompanied with an easy-plane
uniaxial single-ion anisotropy can enhance not only the thermal stability but
simultaneously the degree of entanglement. It is additionally shown that the
further enhancement of a bipartite entanglement can be achieved in the mixed
spin-(1/2,$S$) Heisenberg dimers, involving half-odd-integer spins $S$. Under
this condition the thermal negativity saturates at low-enough temperatures in
its maximal value regardless of the magnitude of half-odd-integer spin $S$. The
magnetic field induces consecutive discontinuous phase transitions in the mixed
spin-(1/2,$S$) Heisenberg dimers with $S\!>\!1$, which are manifested in a
surprising oscillating magnetic-field dependence of the negativity observed at
low enough temperature.


Abstract:
  Public blockchains have spurred the growing popularity of decentralized
transactions and smart contracts, but they exhibit limitations on the
transaction throughput, storage, and computation. To avoid transaction
gridlock, public blockchains impose large fees and per-block resource limits,
making it difficult to accommodate the ever-growing transaction demand.
Previous research endeavors to improve the scalability of blockchain through
various technologies, such as side-chaining, sharding, secured off-chain
computation, communication network optimizations, and efficient consensus
protocols. However, these approaches have not attained a widespread adoption
due to their inability in delivering a cloud-like performance, in terms of the
scalability in transaction throughput, storage, and compute capacity. In this
work, we determine that the major obstacle to public blockchain scalability is
their underlying unstructured P2P networks. We further show that a centralized
network can support the deployment of decentralized smart contracts. We propose
a novel approach for achieving scalable decentralization: instead of trying to
make blockchain scalable, we deliver decentralization to already scalable cloud
by using an Ethereum smart contract. We introduce Blockumulus, a framework that
can deploy decentralized cloud smart contract environments using a novel
technique called overlay consensus. Through experiments, we demonstrate that
Blockumulus is scalable in all three dimensions: computation, data storage, and
transaction throughput. Besides eliminating the current code execution and
storage restrictions, Blockumulus delivers a transaction latency between 2 and
5 seconds under normal load. Moreover, the stress test of our prototype reveals
the ability to execute 20,000 simultaneous transactions under 26 seconds, which
is on par with the average throughput of worldwide credit card transactions.


Abstract:
  The application of high-dimensional quantum systems (qudits) in quantum
computing and communications seems to be a promising avenue due to the
possibility of increasing the amount of information encoded in one physical
carrier. In this work, we propose a method for implementing single-qudit gates
for qudits based on light modes with orbital angular momentum. Method for
logical qudits encoding, which ensures the quasi-cyclicity of operations, is
introduced. Based on the protocol for converting the orbital angular momentum
of light in the Raman quantum memory scheme [Vashukevich E.A. et. al. PRA, 101,
033830 (2020)], we show that the considered gates provide an extremely high
level of fidelity of single-qudit transformations. We also compare quantum
gates' properties for systems of different dimensions and find the optimal
conditions for carrying out transformations in the protocol under
consideration.


Abstract:
  Let $G$ be a ribbon graph. Matthew Baker and Yao Wang proved that the
rotor-routing torsor and the Bernardi torsor for $G$, which are two torsor
structures on the set of spanning trees for the Picard group of $G$, coincide
when $G$ is planar. We prove the conjecture raised by them that the two torsors
disagree when $G$ is non-planar.


Abstract:
  The temperature dependence of AC susceptibility (ACS) has been measured for a
very high-quality plate-like slightly overdoped YBCO single crystal for
different frequencies and AC magnetic field amplitudes. Frequency dependence of
the ACS is weak irrespective of the magnetic field orientation but significant
effects of field orientation with respect to the CuO2 planes and field
magnitude on real and imaginary components of fundamental ACS were observed.
The height of the loss peak saturates as full penetration of magnetic field is
achieved. The peak temperature, Tp, in \c{hi}" shifts to lower temperatures
with increasing magnetic field amplitude for both HIIc and HIIab. The value of
Tp depends on the orientation of the magnetic field with respect to the
crystallographic axes, illustrating the anisotropy in the magnetic flux
dynamics. The superconducting transition width increases weakly with increasing
magnetic field. The Cole-Cole plot [\c{hi}"(\c{hi}')] shows qualitatively and
quantitatively identical features for HIIc and HIIab, independent of the
orientation of the magnetic field with respect to the sample geometry and
shielding current paths. The general features of \c{hi}"(\c{hi}') implies that,
there is no flux creep for the range of frequencies and AC fields employed in
this investigation. The maximum value of the loss peak and its position with
respect to \c{hi}' in the Cole-Cole plot are largely consistent with the Bean
critical state model. Slightly increased peak value in comparison to the
predicted peak value within the Bean critical state model is probably due to a
weak field dependence of Jc. The results obtained here are compared with
various theoretical models and experimental findings. Prominent differences are
noted and discussed in details in this study.


Abstract:
  In this paper, we analyse spectral properties of Seidel matrix (denoted by
$S$) of connected threshold graphs. We compute the characteristic polynomial
and determinant of Seidel matrix of threshold graphs. We derive formulas for
the multiplicity of the eigenvalues $\pm 1$ of $S$. Further we determine
threshold graphs with at most 5 distinct Seidel eigenvalues. Finally we
construct families of Seidel cospectral threshold graphs.


Abstract:
  We describe a theory of logarithmic Chow rings and tautological subrings for
logarithmically smooth algebraic stacks, via a generalisation of the notion of
piecewise-polynomial functions. Using this machinery we prove that the
double-double ramification cycle lies in the tautological subring of the
(classical) Chow ring of the moduli space of curves, and that the logarithmic
double ramification cycle is divisorial (as conjectured by Molcho,
Pandharipande, and Schmitt).


Abstract:
  We present elemental abundances and stellar population ages for 68 massive
quiescent galaxies at $0.59\leq z\leq0.75$ from the LEGA-C survey. The
abundance patterns and ages, derived from full-spectrum modeling, are examined
as a function of stellar mass ($M_*$) and size (i.e., half-light radius;
$R_e$). We find that both [Mg/H] and [Fe/H] do not vary with stellar mass but
are correlated with $M_*/R_e$ for quiescent galaxies with $M_*>10^{10.5}
M_\odot$. Thus, at fixed mass, compact quiescent galaxies are on average more
metal rich. This result reinforces the picture that supernova feedback and
gravitational potential regulate chemical enrichment. [Mg/Fe] does not vary
with $M_*$ or $M_*/R_e$, but there is a marginal positive relation between age
and mass. Our results support low-redshift findings that more massive galaxies
form their stars at earlier times. However, in contrast to low-redshift
studies, star formation timescale does not appear to depend on mass or size. We
also compare the mass-[Fe/H] and mass-[Mg/H] relations to stacks of quiescent
galaxies at $z\sim0$ and find that both relations increase by $\sim0.2$ dex
over the past 7 Gyr. Furthermore, at $z\sim0.7$ we find a clear trend with age,
such that older quiescent galaxies have lower metallicities. Both results can
be explained by a chemical evolution model in which galaxies quench via gas
removal. Future work, in particular with JWST/NIRSpec, will extend this
analysis to higher redshifts, allowing us to fully exploit abundance patterns
to study the formation histories of quiescent galaxies.


Abstract:
  The two-dimensional (2D) logarithmic character of Coulomb interaction between
charges and the resulting logarithmic confinement is a remarkable inherent
property of high dielectric constant (high-$\kappa$) thin films with far
reaching implications. Most and foremost, this is the charge
Berezinskii-Kosterlitz-Thouless transition with the notable manifestation,
low-temperature superinsulating topological phase. Here we show that the range
of the confinement can be tuned by the external gate electrode and unravel a
variety of electrostatic interactions in high-$\kappa$ films. We find that by
reducing the distance from the gate to the film, we decrease the spatial range
of the 2D long-range logarithmic interaction, changing it to predominantly
dipolar or even to exponential one at lateral distances exceeding the dimension
of the film-gate separation. Our findings offer a unique laboratory for the
in-depth study of topological phase transitions and related phenomena that
range from criticality of quantum metal- and superconductor-insulator
transitions to the effects of charge-trapping and Coulomb scalability in memory
nanodevices.


Abstract:
  The phase III BNT162b2 mRNA COVID-19 vaccine trial is based on a Bayesian
design and analysis, and the main evidence of vaccine efficacy is presented in
Bayesian statistics. Confusion and mistakes are produced in the presentation of
the Bayesian results. Some key statistics, such as Bayesian credible intervals,
are mislabeled and stated as confidence intervals. Posterior probabilities of
the vaccine efficacy are not reported as the main results. We illustrate the
main differences in the reporting of Bayesian analysis results for a clinical
trial and provide four recommendations. We argue that statistical evidence from
a Bayesian trial, when presented properly, is easier to interpret and directly
addresses the main clinical questions, thereby better supporting regulatory
decision making. We also recommend using abbreviation "BI" to represent
Bayesian credible intervals as a differentiation to "CI" which stands for
confidence interval.


Abstract:
  Frequency analysis of long-term ultra-precise photometry can lead to precise
values of rotation frequencies of rotating stars with ``hump and spike''
features in their periodograms. Using these features, we computed the rotation
frequencies and amplitudes. The corresponding equatorial rotational velocity
($v_{\rm rot}$) and spot size were estimated. On fitting the autocorrelation
functions of the light-curves with the appropriate model, we determined the
starspot decay-time scale. The $v_{\rm rot}$ agrees well with the projected
rotational velocity ($v\,{\rm sin}\,i$) in the literature. Considering a single
circular and black spot we estimate its radius from the amplitude of the
``spike''. No evidence for a significant difference in the average ``spike''
amplitude and spot radius was found for Am/Fm and normal A stars. Indeed, we
derived an average value of $\rm \sim 21\pm2$ and $\sim 19\pm2\,{\rm ppm}$ for
the photometric amplitude and of $\rm 1.01\,\pm\,0.13$ and
$1.16\,\pm\,0.12\,R_{\rm E}$ for the spot radius (where $R_{\rm E}$ is the
Earth radius), respectively. We do find a significant difference for the
average spot decay-time scale, which amounts to $3.6\pm0.2$ and $1.5\pm0.2$
days for Am/Fm and normal A stars, respectively. In general, spots on normal A
stars are similar in size to those on Am/Fm stars, and both are weaker than
previously estimated. The existence of the ``spikes'' in the frequency spectra
may not be strongly dependent on the appearance of starspots on the stellar
surface. In comparison with G, K and M stars, spots in normal A and Am/Fm stars
are weak which may indicate the presence of a weak magnetic field.


Abstract:
  Recently, neural architecture search (NAS) has been exploited to design
feature pyramid networks (FPNs) and achieved promising results for visual
object detection. Encouraged by the success, we propose a novel One-Shot Path
Aggregation Network Architecture Search (OPANAS) algorithm, which significantly
improves both searching efficiency and detection accuracy. Specifically, we
first introduce six heterogeneous information paths to build our search space,
namely top-down, bottom-up, fusing-splitting, scale-equalizing, skip-connect
and none. Second, we propose a novel search space of FPNs, in which each FPN
candidate is represented by a densely-connected directed acyclic graph (each
node is a feature pyramid and each edge is one of the six heterogeneous
information paths). Third, we propose an efficient one-shot search method to
find the optimal path aggregation architecture, that is, we first train a
super-net and then find the optimal candidate with an evolutionary algorithm.
Experimental results demonstrate the efficacy of the proposed OPANAS for object
detection: (1) OPANAS is more efficient than state-of-the-art methods (e.g.,
NAS-FPN and Auto-FPN), at significantly smaller searching cost (e.g., only 4
GPU days on MS-COCO); (2) the optimal architecture found by OPANAS
significantly improves main-stream detectors including RetinaNet, Faster R-CNN
and Cascade R-CNN, by 2.3-3.2 % mAP comparing to their FPN counterparts; and
(3) a new state-of-the-art accuracy-speed trade-off (52.2 % mAP at 7.6 FPS) at
smaller training costs than comparable state-of-the-arts. Code will be released
at https://github.com/VDIGPKU/OPANAS.


Abstract:
  In this work, we introduce the unsupervised Optimum-Path Forest (OPF)
classifier for learning visual dictionaries in the context of Barrett's
esophagus (BE) and automatic adenocarcinoma diagnosis. The proposed approach
was validated in two datasets (MICCAI 2015 and Augsburg) using three different
feature extractors (SIFT, SURF, and the not yet applied to the BE context
A-KAZE), as well as five supervised classifiers, including two variants of the
OPF, Support Vector Machines with Radial Basis Function and Linear kernels, and
a Bayesian classifier. Concerning MICCAI 2015 dataset, the best results were
obtained using unsupervised OPF for dictionary generation using supervised OPF
for classification purposes and using SURF feature extractor with accuracy
nearly to 78% for distinguishing BE patients from adenocarcinoma ones.
Regarding the Augsburg dataset, the most accurate results were also obtained
using both OPF classifiers but with A-KAZE as the feature extractor with
accuracy close to 73%. The combination of feature extraction and
bag-of-visual-words techniques showed results that outperformed others obtained
recently in the literature, as well as we highlight new advances in the related
research area. Reinforcing the significance of this work, to the best of our
knowledge, this is the first one that aimed at addressing computer-aided BE
identification using bag-of-visual-words and OPF classifiers, being this
application of unsupervised technique in the BE feature calculation the major
contribution of this work. It is also proposed a new BE and adenocarcinoma
description using the A-KAZE features, not yet applied in the literature.


Abstract:
  Background and Objectives: Substitution-box (s-box) is one of the essential
components to create confusion and nonlinear properties in cryptography. To
strengthening a cipher against various attacks, including side channel attacks,
these boxes need to have numerous security properties. In this paper, a novel
method to generate s-boxes is introduced aimed at improving the resistance of
s-boxes against side channel attacks. Methods: In the preprocessing phase of
this approach, a suitable initial s-box which has some basic security
properties is generated by adopting a fast algorithm. Then, in the main stage,
using the initial s-box, we generate new s-boxes which not only have the
properties of the initial S-box but also have been significantly improved under
another set of security properties. To do this, new s-boxes are generated using
a genetic algorithm on a particular subset of the linear combination set of
coordinate functions of the initial s-box in the preprocessing stage. Results:
The performed experiments demonstrate that the values of all security
properties of these new s-boxes, especially the measures of transparency order,
signal-to-noise ratio, confusion coefficient, bijection property, fixed point,
and opposite fixed points, have been substantially improved. For example, our
experiments indicate that 70, 220, 2071, 43, and 406 s-boxes are found better
than the initial s-box, respectively, in the dimensions of 4x4 through 8x8
Conclusion: In this article, a new s-box construction method is introduced in
which the properties related to side channel attacks are improved, without
reducing other security properties. Besides, some results obtained from
generated s-boxes in the dimensions of 4x4 through 8x8 demonstrated that the
generated s-boxes are not only improved relative to the initial s-box, but in
some cases, considerably better than some well-known s-boxes.


Abstract:
  In 1943 from September to December Kiyoshi Oka wrote a series of papers
numbered from VII to XI, as the research reports to Teiji Takagi (then,
Professor of Tokyo Imperial University), in which he solved affirmatively the
so-called Levi Problem (Hartogs' Inverse Problem termed by Oka) for unramified
Riemann domains over $\C^n$. This problem which had been left open for more
than thirty years then, was the last one of the Three Big Problems summarized
by Behnke--Thullen 1934.
  The papers were hand-written in Japanese, consist of pp.~108 in total, and
have not been published by themselves. The aim of the present article is to
provide an English translation of the most important, last paper (Part II) with
preparation (Part I). At the end of Part I we will discuss a problem which K.
Oka left and is still open.


Abstract:
  The spin-glass behavior in the ferromagnetic phase of $\mathrm{Tm_2Cu_2In}$
was investigated by dc-, ac-magnetization, and non-equilibrium dynamics
characterizations. Arc-melted polycrystalline compound of $\mathrm{Tm_2Cu_2In}$
crystallizes in $\mathrm{Mo_2FeB_2}$-type tetragonal structure (space group
$P_4/mbm$). The temperature variation dc-magnetization exhibits a ferromagnetic
behavior with Curie temperature $T_C$ = 32.5 K. The zero-field cooled (ZFC) and
field cooled (FC) magnetic curves show a thermomagnetic irreversible behavior
below $T_C$. The field dependence of irreversible temperature follows
Almeida-Thouless line. The frequency and ac-driven field-dependent anomalies in
ac-susceptibility results indicate the existence of spin-glass state in
$\mathrm{Tm_2Cu_2In}$. The time dependence of magnetization further supports
the spin-glass behavior observed in $\mathrm{Tm_2Cu_2In}$. The frequency
dependence of the freezing temperature in the real part of ac-susceptibility
has been analyzed on the basis power-law divergence and Vogel-Fulcher law. The
obtained results revealed that $\mathrm{Tm_2Cu_2In}$ belongs to a ferromagnetic
canonical spin-glass class of compounds.


Abstract:
  The Chern-Simons magnetohydrodynamics (CSMHD) is introduced using a
Maxwell-Chern-Simons (MCS) Lagrangian including an axion-like field $\Theta$.
The MCS equation of motion derived from this Lagrangian consists of a modified
current, including a chiral magnetic (CM) and an anomalous Hall (AH) current,
in addition to the ordinary Ohm current of resistive magnetohydrodynamics
(MHD). The former consists of an axial chemical potential, which is given in
terms of the temporal comoving derivative of $\Theta$, and the latter arises
from the spatial gradient of $\Theta$. As it turns out, the existence of the
axial chemical potential is a nonequilibrium effect that plays no role in the
linear stability analysis, whereas the AH current arises as in the first-order
linear perturbation of the thermal equilibrium. We analyze the linear stability
and causality of the CSMHD in a resistive and chiral medium. We show that the
Alfven modes propagating sufficiently close to the direction of the magnetic
field are unstable but causal. They are also accompanied by a genuine nonhydro
mode. A stable mode in a particular direction can correspond to an unstable
mode propagating in the exact opposite direction. The AH instability is a
manifestation of a breakdown of the parity. A numerical analysis of the phase
velocity confirms these results.


Abstract:
  The ability to accurately evaluate the performance of location determination
systems is crucial for many applications. Typically, the performance of such
systems is obtained by comparing ground truth locations with estimated
locations. However, these ground truth locations are usually obtained by
clicking on a map or using other worldwide available technologies like GPS.
This introduces ground truth errors that are due to the marking process, map
distortions, or inherent GPS inaccuracy.
  In this paper, we present a theoretical framework for analyzing the effect of
ground truth errors on the evaluation of localization systems. Based on that,
we design two algorithms for computing the real algorithmic error from the
validation error and marking/map ground truth errors, respectively. We further
establish bounds on different performance metrics.
  Validation of our theoretical assumptions and analysis using real data
collected in a typical environment shows the ability of our theoretical
framework to correct the estimated error of a localization algorithm in the
presence of ground truth errors. Specifically, our marking error algorithm
matches the real error CDF within 4%, and our map error algorithm provides a
more accurate estimate of the median/tail error by 150%/72% when the map is
shifted by 6m.


Abstract:
  We show that a metastable dark matter candidate arises naturally from the
conformal transformation between the Einstein metric, where gravitons are
normalised states, and the Jordan metric dictating the coupling between gravity
and matter. Despite being secluded from the Standard Model by a large scale
above which the Jordan metric shows modifications to the Einstein frame metric,
dark matter couples to the energy momentum tensor of the Higgs field in the
primordial plasma primarily. This allows for the production of dark matter in a
sufficient amount which complies with observations. The seclusion of dark
matter makes it long-lived for masses $\lesssim 1$ MeV, with a lifetime much
above the age of the Universe and the present experimental limits. Such a dark
matter scenario has clear monochromatic signatures generated by the decay of
the dark matter candidate into neutrino and/or $\gamma-$rays.


Abstract:
  We study approximation classes for adaptive time-stepping finite element
methods for time-dependent Partial Differential Equations (PDE). We measure the
approximation error in $L_2([0,T)\times\Omega)$ and consider the approximation
with discontinuous finite elements in time and continuous finite elements in
space, of any degree. As a byproduct we define Besov spaces for vector-valued
functions on an interval and derive some embeddings, as well as Jackson- and
Whitney-type estimates.


Abstract:
  This paper proposes new acoustic feature signatures based on the multiscale
fractal dimension (MFD), which are robust against the diversity of
environmental sounds, for the content-based similarity search. The diversity of
sound sources and acoustic compositions is a typical feature of environmental
sounds. Several acoustic features have been proposed for environmental sounds.
Among them is the widely-used Mel-Frequency Cepstral Coefficients (MFCCs),
which describes frequency-domain features. However, in addition to these
features in the frequency domain, environmental sounds have other important
features in the time domain with various time scales. In our previous paper, we
proposed enhanced multiscale fractal dimension signature (EMFD) for
environmental sounds. This paper extends EMFD by using the kernel density
estimation method, which results in better performance of the similarity search
tasks. Furthermore, it newly proposes another acoustic feature signature based
on MFD, namely very-long-range multiscale fractal dimension signature (MFD-VL).
The MFD-VL signature describes several features of the time-varying envelope
for long periods of time. The MFD-VL signature has stability and robustness
against background noise and small fluctuations in the parameters of sound
sources, which are produced in field recordings. We discuss the effectiveness
of these signatures in the similarity sound search by comparing with acoustic
features proposed in the DCASE 2018 challenges. Due to the unique
descriptiveness of our proposed signatures, we confirmed the signatures are
effective when they are used with other acoustic features.


Abstract:
  While predictive policing has become increasingly common in assisting with
decisions in the criminal justice system, the use of these results is still
controversial. Some software based on deep learning lacks accuracy (e.g., in
F-1), and importantly many decision processes are not transparent, causing
doubt about decision bias, such as perceived racial and age disparities. This
paper addresses bias issues with post-hoc explanations to provide a trustable
prediction of whether a person will receive future criminal charges given one's
previous criminal records by learning temporal behavior patterns over twenty
years. Bi-LSTM relieves the vanishing gradient problem, attentional mechanisms
allow learning and interpretation of feature importance, and complex-valued
networks inspired quantum physics to facilitate a certain level of transparency
in modeling the decision process. Our approach shows a consistent and reliable
prediction precision and recall on a real-life dataset. Our analysis of the
importance of each input feature shows the critical causal impact on
decision-making, suggesting that criminal histories are statistically
significant factors, while identifiers, such as race and age, are not. Finally,
our algorithm indicates that a suspect tends to rather than suddenly increase
crime severity level over time gradually.


Abstract:
  This contribution presents a diagnosis scheme for batteries to detect and
isolate internal faults in the form of small parameter changes. This scheme is
based on an electrochemical reduced-order model of the battery, which allows
the inclusion of physically meaningful faults that might affect the battery
performance. The sensitivity properties of the model are analyzed. The model is
then used to compute residuals based on an unscented Kalman filter. Primary
residuals and a limiting covariance matrix are obtained thanks to the local
approach, allowing for fault detection and isolation by chi-squared statistical
tests. Results show that faults resulting in limited 0.15% capacity and 0.004%
power fade can be effectively detected by the local approach. The algorithm is
also able to correctly isolate faults related with sensitive parameters,
whereas parameters with low sensitivity or linearly correlated are more
difficult to precise.


Abstract:
  Near Ambient Pressure Scanning Photoelectron Microscopy adds to the widely
used photoemission spectroscopy and its chemically selective capability two key
features: (i) the possibility to chemically analyse samples in a more realistic
environmental, gas pressure condition, and (ii) the capability to investigate a
system at the relevant spatial scale. To achieve these goals the approach
developed at the ESCA Microscopy beamline at the Elettra Synchrotron facility
combines the submicron lateral resolution of a Scanning Photoelectron
Microscope with a custom designed Near Ambient Pressure Cell where a gas
pressure up to 0.1 mbar is confined inside it around the sample. In this
manuscript a review of experiments performed with this unique setup will be
presented to illustrate its potentiality in both fundamental and applicative
research such as the oxidation reactivity and gas sensitivity of metal oxides
and semiconductors. In particular the capability to do operando experiment with
this setup opens the possibility to perform investigations with active devices
to properly address the real nature of the studied systems, because it can
yield to more conclusive results when microscopy and spectroscopy are
simultaneously combined in a single technique.


Abstract:
  While most neural generative models generate outputs in a single pass, the
human creative process is usually one of iterative building and refinement.
Recent work has proposed models of editing processes, but these mostly focus on
editing sequential data and/or only model a single editing pass. In this paper,
we present a generic model for incremental editing of structured data (i.e.,
"structural edits"). Particularly, we focus on tree-structured data, taking
abstract syntax trees of computer programs as our canonical example. Our editor
learns to iteratively generate tree edits (e.g., deleting or adding a subtree)
and applies them to the partially edited data, thereby the entire editing
process can be formulated as consecutive, incremental tree transformations. To
show the unique benefits of modeling tree edits directly, we further propose a
novel edit encoder for learning to represent edits, as well as an imitation
learning method that allows the editor to be more robust. We evaluate our
proposed editor on two source code edit datasets, where results show that, with
the proposed edit encoder, our editor significantly improves accuracy over
previous approaches that generate the edited program directly in one pass.
Finally, we demonstrate that training our editor to imitate experts and correct
its mistakes dynamically can further improve its performance.


Abstract:
  Deep learning based methods, especially convolutional neural networks (CNNs)
have been successfully applied in the field of single image super-resolution
(SISR). To obtain better fidelity and visual quality, most of existing networks
are of heavy design with massive computation. However, the computation
resources of modern mobile devices are limited, which cannot easily support the
expensive cost. To this end, this paper explores a novel frequency-aware
dynamic network for dividing the input into multiple parts according to its
coefficients in the discrete cosine transform (DCT) domain. In practice, the
high-frequency part will be processed using expensive operations and the
lower-frequency part is assigned with cheap operations to relieve the
computation burden. Since pixels or image patches belong to low-frequency areas
contain relatively few textural details, this dynamic network will not affect
the quality of resulting super-resolution images. In addition, we embed
predictors into the proposed dynamic network to end-to-end fine-tune the
handcrafted frequency-aware masks. Extensive experiments conducted on benchmark
SISR models and datasets show that the frequency-aware dynamic network can be
employed for various SISR neural architectures to obtain the better tradeoff
between visual quality and computational complexity. For instance, we can
reduce the FLOPs of SR models by approximate 50% while preserving
state-of-the-art SISR performance.


Abstract:
  Sampling-based motion planners rely on incremental densification to discover
progressively shorter paths. After computing feasible path $\xi$ between start
$x_s$ and goal $x_t$, the Informed Set (IS) prunes the configuration space
$\mathcal{C}$ by conservatively eliminating points that cannot yield shorter
paths. Densification via sampling from this Informed Set retains asymptotic
optimality of sampling from the entire configuration space. For path length
$c(\xi)$ and Euclidean heuristic $h$, $IS = \{ x | x \in \mathcal{C}, h(x_s, x)
+ h(x, x_t) \leq c(\xi) \}$.
  Relying on the heuristic can render the IS especially conservative in high
dimensions or complex environments. Furthermore, the IS only shrinks when
shorter paths are discovered. Thus, the computational effort from each
iteration of densification and planning is wasted if it fails to yield a
shorter path, despite improving the cost-to-come for vertices in the search
tree. Our key insight is that even in such a failure, shorter paths to vertices
in the search tree (rather than just the goal) can immediately improve the
planner's sampling strategy. Guided Incremental Local Densification (GuILD)
leverages this information to sample from Local Subsets of the IS. We show that
GuILD significantly outperforms uniform sampling of the Informed Set in
simulated $\mathbb{R}^2$, $SE(2)$ environments and manipulation tasks in
$\mathbb{R}^7$.


Abstract:
  The utility of machine learning in understanding the motor system is
promising a revolution in how to collect, measure, and analyze data. The field
of movement science already elegantly incorporates theory and engineering
principles to guide experimental work, and in this review we discuss the
growing use of machine learning: from pose estimation, kinematic analyses,
dimensionality reduction, and closed-loop feedback, to its use in understanding
neural correlates and untangling sensorimotor systems. We also give our
perspective on new avenues where markerless motion capture combined with
biomechanical modeling and neural networks could be a new platform for
hypothesis-driven research.


Abstract:
  Zero-shot learning deals with the ability to recognize objects without any
visual training sample. To counterbalance this lack of visual data, each class
to recognize is associated with a semantic prototype that reflects the
essential features of the object. The general approach is to learn a mapping
from visual data to semantic prototypes, then use it at inference to classify
visual samples from the class prototypes only. Different settings of this
general configuration can be considered depending on the use case of interest,
in particular whether one only wants to classify objects that have not been
employed to learn the mapping or whether one can use unlabelled visual examples
to learn the mapping. This chapter presents a review of the approaches based on
deep neural networks to tackle the ZSL problem. We highlight findings that had
a large impact on the evolution of this domain and list its current challenges.


Abstract:
  We consider fast high-fidelity quantum control by using a shortcut to
adiabaticity (STA) technique and optimal control theory (OCT). Three specific
examples, including expansion of cold atoms from the harmonic trap, atomic
transport by moving harmonic trap, and spin dynamics in the presence of
dissipation, are explicitly detailed. Using OCT as a qualitative guide, we
demonstrate how STA protocols designed from inverse engineering method, can
approach with very high precision optimal solutions built about physical
constraints, by a proper choice of the interpolation function and with a very
reduced number of adjustable parameters.


Abstract:
  Tactile sensing on human feet is crucial for motion control, however, has not
been explored in robotic counterparts. This work is dedicated to endowing
tactile sensing to legged robot's feet and showing that a single-legged robot
can be stabilized with only tactile sensing signals from its foot. We propose a
robot leg with a novel vision-based tactile sensing foot system and implement a
processing algorithm to extract contact information for feedback control in
stabilizing tasks. A pipeline to convert images of the foot skin into
high-level contact information using a deep learning framework is presented.
The leg was quantitatively evaluated in a stabilization task on a tilting
surface to show that the tactile foot was able to estimate both the surface
tilting angle and the foot poses. Feasibility and effectiveness of the tactile
system were investigated qualitatively in comparison with conventional
single-legged robotic systems using inertia measurement units (IMU).
Experiments demonstrate the capability of vision-based tactile sensors in
assisting legged robots to maintain stability on unknown terrains and the
potential for regulating more complex motions for humanoid robots.


Abstract:
  Ultralight axions and other bosons are dark matter candidates present in many
high energy physics theories beyond the Standard Model. In particular, the
string axiverse postulates the existence of up to $\mathcal{O}(100)$ light
scalar bosons constituting the dark sector. Considering a mixture of axions and
cold dark matter, we obtain upper bounds for the axion relic density $\Omega_a
h^2 < 0.004$ for axions of mass $10^{-31}\;\mathrm{eV}\leq m_a \leq
10^{-26}\;\mathrm{eV}$ at 95% confidence. We also improve existing constraints
by a factor of over 4.5 and 2.1 for axion masses of $10^{-25}$ eV and
$10^{-32}$ eV, respectively. We use the Fourier-space galaxy clustering
statistics from the Baryon Oscillation Spectroscopic Survey (BOSS) and
demonstrate how galaxy surveys break important degeneracies in the axion
parameter space compared to the cosmic microwave background (CMB). We test the
validity of the effective field theory of large-scale structure approach to
mixed ultralight axion dark matter by making our own mock galaxy catalogs and
find an anisotropic ultralight axion signature in the galaxy quadrupole. We
also observe an enhancement of the linear galaxy bias from 1.8 to 2.4 when
allowing for 5% of the dark matter to be composed of a $10^{-28}$ eV axion in
our simulations. Finally, we develop an augmented interpolation scheme allowing
a fast computation of the axion contribution to the linear matter power
spectrum leading to a 70% reduction of the computational cost for the full
Monte Carlo Markov chains analysis.


Abstract:
  Given a singular projective variety in some projective space, we characterize
the smooth curves contracted by the Gauss map in terms of normal bundles. As a
consequence, we show that if the variety is normal, then a contracted line
always has local obstruction for the embedded deformation and each component of
the Hilbert scheme where the line lies is non-reduced everywhere.


Abstract:
  Self-supervised learning algorithms based on instance discrimination train
encoders to be invariant to pre-defined transformations of the same instance.
While most methods treat different views of the same image as positives for a
contrastive loss, we are interested in using positives from other instances in
the dataset. Our method, Nearest-Neighbor Contrastive Learning of visual
Representations (NNCLR), samples the nearest neighbors from the dataset in the
latent space, and treats them as positives. This provides more semantic
variations than pre-defined transformations.
  We find that using the nearest-neighbor as positive in contrastive losses
improves performance significantly on ImageNet classification, from 71.7% to
75.6%, outperforming previous state-of-the-art methods. On semi-supervised
learning benchmarks we improve performance significantly when only 1% ImageNet
labels are available, from 53.8% to 56.5%. On transfer learning benchmarks our
method outperforms state-of-the-art methods (including supervised learning with
ImageNet) on 8 out of 12 downstream datasets. Furthermore, we demonstrate
empirically that our method is less reliant on complex data augmentations. We
see a relative reduction of only 2.1% ImageNet Top-1 accuracy when we train
using only random crops.


Abstract:
  The problem of classifying tuples of nilpotent matrices over a field under
simultaneous conjugation is considered "hopeless". However, for any given
matrix order over a finite field, the number of concerned orbits is always
finite. This paper gives a closed formula for the number of absolutely
indecomposable orbits using the same methodology as Hua [5]; those orbits are
non-splittable over field extensions. As a consequence, those numbers are
always polynomials in the cardinality of the base field with integral
coefficients. It is conjectured that those coefficients are always
non-negative.


Abstract:
  Electronic stopping cross section of tungsten for light ions was
experimentally measured in a wide energy interval (20 to 6000 keV for protons
and 50 to 9000 keV for helium) in backscattering and transmission geometries.
The measurements were carried out in three laboratories (Austria, Germany and
Sweden) using five different set-ups, the stopping data deduced from different
data sets showed excellent agreement amongst each other, with total uncertainty
varying within 1.5 - 3.8\% for protons and 2.2 - 5.5\% for helium, averaged
over the respective energy range of each data set. The final data is compared
to available data and to widely adopted semi-empirical and theoretical
approaches, and found to be in good agreement with most adopted models at
energies around and above the stopping maximum. Most importantly, our results
extend the energy regime towards lower energies, and are thus of high
technological relevance, e.g., in fusion research. At these low energies, our
findings also revealed that tungsten - featured with fully and partially
occupied f- and d-subshells, respectively, can be modeled as an electron gas
for the energy loss process.


Abstract:
  Fluctuations associated with relaxations in far-from-equilibrium regime is of
fundamental interest for a large variety of systems within broad scales. Recent
advances in techniques such as spectroscopy have generated the possibility for
measuring the fluctuations of the mesoscopic systems in connection to the
relaxation processes when driving the underlying quantum systems far from
equilibrium. We present a general nonequilibrium Fluctuation-Dissipation
Theorem (FDT) for quantum Markovian processes where the detailed-balance
condition is violated. Apart from the fluctuations, the relaxation involves
extra correlation that is governed by the quantum curl flux emerged in the
far-from-equilibrium regime. Such a contribution vanishes for the thermal
equilibrium, so that the conventional FDT is recovered. We finally apply the
nonequilibrium FDT to the molecular junctions, elaborating the
detailed-balance-breaking effects on the optical transmission spectrum. Our
results have the advantage of and exceed the scope of the
fluctuation-dissipation relation in the perturbative and near equilibrium
regimes, and is of broad interest for the study of quantum thermodynamics.ation
in the perturbative and near equilibrium regimes, and is of broad interest for
the study of quantum thermodynamics.


Abstract:
  We apply the tools of the dynamical system theory in order to revisit and
uncover the structure of a nongravitational interaction between pressureless
dark matter and dark energy described by a scalar field $\phi$. For a coupling
function $Q = -(\alpha d\rho_m/dt + \beta d\rho_\phi/dt )$, where t is the
cosmic time, we have found that it can be rewritten in the form $Q = 3H (\alpha
\rho_m + \beta (d\phi/dt)^2 )/(1-\alpha +\beta)$, so that its dependence on the
dark matter density and on the kinetic term of the scalar field is linear and
proportional to the Hubble parameter. We analyze the scenarios $\alpha=0$,
$\alpha = \beta$ and $\alpha = -\beta$, separately and in order to describe the
cosmological evolution we have calculated various observables. A notable result
of this work is that, unlike for the noninteracting scalar field with
exponential potential where five critical points appear, in the case studied
here, with the exception of the matter dominated solution, the remaining
singular points are transformed into scaling solutions enriching the phase
space. It is shown that for $\alpha \neq 0$, a separatrix arises modifying
prominently the structure of the phase space. This represents a novel feature
no mentioned before in the literature.


Abstract:
  Understanding why the scale of emergent hadron mass is obvious in the proton
but hidden in the pion may rest on mapping the distribution functions (DFs) of
all partons within the pion and comparing them with those in the proton; and
since glue provides binding in quantum chromodynamics, the glue DF could play a
special role. Producing reliable predictions for the proton's DFs is difficult
because the proton is a three valence-body bound-state problem. As sketched
herein, the situation for the pion, a two valence-body problem, is much better,
with continuum and lattice predictions for the valence-quark and glue DFs in
agreement. This beginning of theory alignment is timely because experimental
facilities now either in operation or planning promise to realise the
longstanding goal of providing pion targets, thereby enabling precision
experimental tests of rigorous theory predictions concerning Nature's most
fundamental Nambu-Goldstone bosons.


Abstract:
  The topic of this tutorial is Least Squares Sparse Principal Components
Analysis (LS SPCA) which is a simple method for computing approximated
Principal Components which are combinations of only a few of the observed
variables. Analogously to Principal Components, these components are
uncorrelated and sequentially best approximate the dataset. The derivation of
LS SPCA is intuitive for anyone familiar with linear regression. Since LS SPCA
is based on a different optimality from other SPCA methods and does not suffer
from their serious drawbacks. I will demonstrate on two datasets how useful and
parsimonious sparse PCs can be computed. An R package for computing LS SPCA is
available for download.


Abstract:
  We present an analysis of hadroproduction of $J/\psi$ and $\psi(2S)$ at
fixed-target energies in the framework of non-relativistic QCD (NRQCD). Using
both pion- and proton-induced data, a new determination of the color-octet
long-distance matrix elements (LDMEs) is obtained. Compared with previous
results, the contributions from the $q \bar{q}$ and color-octet processes are
significantly enhanced, especially at lower energies. A good agreement between
the pion-induced $J/\psi$ production data and NRQCD calculations using the
newly obtained LDMEs is achieved. We find that the pion-induced charmonium
production data are sensitive to the gluon density of pions, and favor pion
PDFs with relatively large gluon contents at large $x$.


Abstract:
  We investigate the impact of the substrate on the structural properties and
the morphology of alkanethiol self-assembled monolayers (SAMs) on gold, using
first principles calculations and atomistic molecular dynamics simulations. We
consider hexadecanethiols on Au(211), Au(221) and Au(311) surfaces which
contain few-atom wide terraces separated by monoatomic steps similar to the
complex Au surfaces used in experiments. The structure of the SAMs is probed
via several structural properties including tilt angles, mean C atom heights
from the surface, precession angles, gauche defects, gyration tensors and
relative shape anisotropy. Comparing these properties to those of the
well-studied SAMs on Au(111), we observe similarities but also striking
differences. A clear order to disorder transition is observed by changing the
substrate: well-ordered SAMs on (111) and (211) surfaces become mixed
ordered-disordered structures on (311) and fully disordered on (221). The
presence of steps on the Au surfaces also results in preferential tilt
orientations with long-range order. Our results show that in addition to the
expected grafting density dependence, the transition from order to disorder
crucially depends on substrate morphology. The onset of ordering behavior is
related to the atomic structure of the surface. The key parameter that affects
long-range order is the energy for changing the dihedral angle between
Au-S-$\mathrm{C^{(1)}-C^{(2)}}$ of the adsorbed alkanethiol.


Abstract:
  We consider a one-dimensional stochastic differential equation driven by a
Wiener process, where the diffusion coefficient depends on an ergodic fast
process. The averaging principle is satisfied: it is well-known that the slow
component converges in distribution to the solution of an averaged equation,
with generator determined by averaging the square of the diffusion coefficient.
  We propose a version of the averaging principle, where the solution is
interpreted as the sum of two terms: one depending on the average of the
diffusion coefficient, the other giving fluctuations around that average. Both
the average and fluctuation terms contribute to the limit, which illustrates
why it is required to average the square of the diffusion coefficient to find
the limit behavior.


Abstract:
  Researchers, editors, educators and publishers need to understand the mix of
research methods used in their field to guide decision making, with a current
concern being that qualitative research is threatened by big data. Although
there have been many studies of the prevalence of different methods within
individual narrow fields, there have been no systematic studies across
academia. In response, this article assesses the prevalence and citation impact
of academic research 1996-2019 that reports one of four common methods to
gather qualitative data: interviews; focus groups; case studies; ethnography.
The results show that, with minor exceptions, the prevalence of qualitative
data has increased, often substantially, since 1996. In addition, all 27 broad
fields (as classified by Scopus) now publish some qualitative research, with
interviewing being by far the most common approach. This suggest that
qualitative methods teaching and should increase, and researchers, editors and
publishers should be increasingly open to the value that qualitative data can
bring.


Abstract:
  Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at
adapting the model trained on a labeled source-domain dataset to a
target-domain dataset without any further annotations. Most successful UDA-ReID
approaches combine clustering-based pseudo-label prediction with representation
learning and perform the two steps in an alternating fashion. However, offline
interaction between these two steps may allow noisy pseudo labels to
substantially hinder the capability of the model. In this paper, we propose a
Group-aware Label Transfer (GLT) algorithm, which enables the online
interaction and mutual promotion of pseudo-label prediction and representation
learning. Specifically, a label transfer algorithm simultaneously uses pseudo
labels to train the data while refining the pseudo labels as an online
clustering algorithm. It treats the online label refinery problem as an optimal
transport problem, which explores the minimum cost for assigning M samples to N
pseudo labels. More importantly, we introduce a group-aware strategy to assign
implicit attribute group IDs to samples. The combination of the online label
refining algorithm and the group-aware strategy can better correct the noisy
pseudo label in an online fashion and narrow down the search space of the
target identity. The effectiveness of the proposed GLT is demonstrated by the
experimental results (Rank-1 accuracy) for Market1501$\to$DukeMTMC (82.0\%) and
DukeMTMC$\to$Market1501 (92.2\%), remarkably closing the gap between
unsupervised and supervised performance on person re-identification.


Abstract:
  In complex Finsler geometry, an open problem is: does there exist a weakly
K\"ahler Finsler metric which is not K\"ahler?
  In this paper, we give an affirmative answer to this open problem. More
precisely, we construct a family of the weakly K\"ahler Finsler metrics which
are non-K\"ahler. The examples belong to the unitary invariant complex Randers
metrics. Furthermore, a uniformization theorem of the unitary invariant complex
Randers metrics with constant holomorphic curvature is proved under the weakly
K\"ahler condition.


Abstract:
  The study of pattern containment and avoidance for linear permutations is a
well-established area of enumerative combinatorics. A cyclic permutation is the
set of all rotations of a linear permutation. Callan initiated the study of
permutation avoidance in cyclic permutations and characterized the avoidance
classes for all single permutations of length 4. We continue this work. In
particular, we establish a cyclic variant of the Erdos-Szekeres Theorem that
any linear permutation of length mn+1 must contain either the increasing
pattern of length m+1 or the decreasing pattern of length n+1. We then derive
results about avoidance of multiple patterns of length 4. We also determine
generating functions for the cyclic descent statistic on these classes.
Finally, we end with various open questions and avenues for future research.


Abstract:
  Self-supervised learning has enabled significant improvements on natural
image benchmarks. However, there is less work in the medical imaging domain in
this area. The optimal models have not yet been determined among the various
options. Moreover, little work has evaluated the current applicability limits
of novel self-supervised methods. In this paper, we evaluate a range of current
contrastive self-supervised methods on out-of-distribution generalization in
order to evaluate their applicability to medical imaging. We show that
self-supervised models are not as robust as expected based on their results in
natural imaging benchmarks and can be outperformed by supervised learning with
dropout. We also show that this behavior can be countered with extensive
augmentation. Our results highlight the need for out-of-distribution
generalization standards and benchmarks to adopt the self-supervised methods in
the medical imaging community.


Abstract:
  We are interested in multilayer graph clustering, which aims at dividing the
graph nodes into categories or communities. To do so, we propose to learn a
clustering-friendly embedding of the graph nodes by solving an optimization
problem that involves a fidelity term to the layers of a given multilayer
graph, and a regularization on the (single-layer) graph induced by the
embedding. The fidelity term uses the contrastive loss to properly aggregate
the observed layers into a representative embedding. The regularization pushes
for a sparse and community-aware graph, and it is based on a measure of graph
sparsification called "effective resistance", coupled with a penalization of
the first few eigenvalues of the representative graph Laplacian matrix to favor
the formation of communities. The proposed optimization problem is nonconvex
but fully differentiable, and thus can be solved via the descent gradient
method. Experiments show that our method leads to a significant improvement
w.r.t. state-of-the-art multilayer graph clustering algorithms.


Abstract:
  In this article, we developed and analyzed a thresholding method in which
soft thresholding estimators are independently expanded by empirical scaling
values. The scaling values have a common hyper-parameter that is an order of
expansion of an ideal scaling value that achieves hard thresholding. We simply
call this estimator a scaled soft thresholding estimator. The scaled soft
thresholding is a general method that includes the soft thresholding and
non-negative garrote as special cases and gives an another derivation of
adaptive LASSO. We then derived the degree of freedom of the scaled soft
thresholding by means of the Stein's unbiased risk estimate and found that it
is decomposed into the degree of freedom of soft thresholding and the reminder
connecting to hard thresholding. In this meaning, the scaled soft thresholding
gives a natural bridge between soft and hard thresholding methods. Since the
degree of freedom represents the degree of over-fitting, this result implies
that there are two sources of over-fitting in the scaled soft thresholding. The
first source originated from soft thresholding is determined by the number of
un-removed coefficients and is a natural measure of the degree of over-fitting.
We analyzed the second source in a particular case of the scaled soft
thresholding by referring a known result for hard thresholding. We then found
that, in a sparse, large sample and non-parametric setting, the second source
is largely determined by coefficient estimates whose true values are zeros and
has an influence on over-fitting when threshold levels are around noise levels
in those coefficient estimates. In a simple numerical example, these
theoretical implications has well explained the behavior of the degree of
freedom. Moreover, based on the results here and some known facts, we explained
the behaviors of risks of soft, hard and scaled soft thresholding methods.


Abstract:
  Wireless networks are a key component of the telecommunications
infrastructure in our society, and wireless services become increasingly
important as the applications of wireless devices have penetrated every aspect
of our lives. Although wireless technologies have significantly advanced in the
past decades, most wireless networks are still vulnerable to radio jamming
attacks due to the openness nature of wireless channels, and the progress in
the design of jamming-resistant wireless networking systems remains limited.
This stagnation can be attributed to the lack of practical physical-layer
wireless technologies that can efficiently decode data packets in the presence
of jamming attacks. This article surveys existing jamming attacks and
anti-jamming strategies in wireless local area networks (WLANs), cellular
networks, cognitive radio networks (CRNs), ZigBee networks, Bluetooth networks,
vehicular networks, LoRa networks, RFID networks, and GPS system, with the
objective of offering a comprehensive knowledge landscape of existing
jamming/anti-jamming strategies and stimulating more research efforts to secure
wireless networks against jamming attacks. Different from prior survey papers,
this article conducts a comprehensive, in-depth review on jamming and
anti-jamming strategies, casting insights on the design of jamming-resilient
wireless networking systems. An outlook on promising antijamming techniques is
offered at the end of this article to delineate important research directions.


Abstract:
  In this work, we study the Schr\"odinger equation
$i\partial_t\psi=-\Delta\psi+\eta(t)\sum_{j=1}^J\delta_{x=a_j(t)}\psi$ on
$L^2((0,1),C)$ where $\eta:[0,T]\longrightarrow R^+$ and
$a_j:[0,T]\longrightarrow (0,1)$, $j=1,...,J$. We show how to permute the
energy associated to different eigenmodes of the Schr\"odinger equation via
suitable choice of the functions $\eta$ and $a_j$. To the purpose, we mime the
control processes introduced in [17] for a very similar equation where the
Dirac potential is replaced by a smooth approximation supported in a
neighborhood of $x=a(t)$. We also propose a Galerkin approximation that we
prove to be convergent and illustrate the control process with some numerical
simulations.