Abstract:
  In this paper, a computational resources-aware parameter adaptation method
for visual-inertial navigation systems is proposed with the goal of enabling
the improved deployment of such algorithms on computationally constrained
systems. Such a capacity can prove critical when employed on ultra-lightweight
systems or alongside mission critical computationally expensive processes. To
achieve this objective, the algorithm proposes selected changes in the vision
front-end and optimization back-end of visual-inertial odometry algorithms,
both prior to execution and in real-time based on an online profiling of
available resources. The method also utilizes information from the motion
dynamics experienced by the system to manipulate parameters online. The general
policy is demonstrated on three established algorithms, namely S-MSCKF,
VINS-Mono and OKVIS and has been verified experimentally on the EuRoC dataset.
The proposed approach achieved comparable performance at a fraction of the
original computational cost.


Abstract:
  In this work we computationally study the effect of microstructure on
saturation magnetostriction of Terfenol-D (Tb$_{0.27}$Dy$_{0.73}$Fe$_{2}$) by
means of Finite Element Method. The model is based on the equilibrium
magnetoelastic strain tensor at magnetic saturation, and shows that the crystal
orientation might play a more significant role on saturation magnetostriction
than the morphology of the grains. We also calculate the dependence of
saturation magnetostriction on the dispersion angle of the distribution of
grains in the oriented growth crystal directions $<011>$ and $<111>$, finding
that not highly oriented grain distributions reduce saturation magnetostriction
significantly. This result evinces the importance of high-quality control of
grain orientation in the synthesis of grain-aligned polycrystalline Terfenol-D,
and provides a quantitative estimation for the range of acceptable values for
the dispersion angle of the distribution of the oriented grains.


Abstract:
  Virtual reality (VR) is promising to fundamentally transform a broad spectrum
of industry sectors and the way humans interact with virtual content. However,
despite unprecedented progress, current networking and computing
infrastructures are incompetent to unlock VR's full potential. In this paper,
we consider delivering the wireless multi-tile VR video service over a mobile
edge computing (MEC) network. The primary goal is to minimize the system
latency/energy consumption and to arrive at a tradeoff thereof. To this end, we
first cast the time-varying view popularity as a model-free Markov chain to
effectively capture its dynamic characteristics. After jointly assessing the
caching and computing capacities on both the MEC server and the VR playback
device, a hybrid policy is then implemented to coordinate the dynamic caching
replacement and the deterministic offloading, so as to fully utilize the system
resources. The underlying multi-objective problem is reformulated as a
partially observable Markov decision process, and a deep deterministic policy
gradient algorithm is proposed to iteratively learn its solution, where a long
short-term memory neural network is embedded to continuously predict the
dynamics of the unobservable popularity. Simulation results demonstrate the
superiority of the proposed scheme in achieving a trade-off between the energy
efficiency and the latency reduction over the baseline methods.


Abstract:
  The rigidity and flexibility of homologous psychrophilic(P), mesophilic(M)
and thermophilic(T) proteins have been investigated at the global and local
levels in terms of packing factor and atomic fluctuations obtained from
B-factors. For comparison of atomic fluctuations, correction of errors by
considering errors in B-factors from all sources in a consolidated manner and
conversion of the fluctuations to the same temperature have been suggested and
validated. Results indicate no differences in the global values like average
packing factor among the three classes of protein homologs but at local levels
there are differences. Comparison of homologous proteins triplets show that the
average atomic fluctuations at a given temperature obey the order P>M>T.
Packing factors and the atomic fluctuations are anti-correlated suggesting that
altering the rigidity of the active site might be a potential strategy to make
tailor made psychrophilic or thermophilic proteins from their mesophilic
homologs.


Abstract:
  We report Brownian Dynamics simulation results for the relative permittivity
of electrorheological (ER) fluids in an applied electric field. The relative
permittivity of an ER fluid can be calculated from the Clausius-Mosotti (CM)
equation in the small applied field limit. When a strong field is applied,
however, the ER spheres are organized into chains and assemblies of chains in
which case the ER spheres are polarized not only by the external field but by
each other. This manifests itself in an enhanced dielectric response, e.g., in
an increase in the relative permittivity. The correction to the relative
permittivity and the time dependence of this correction is simulated on the
basis of a model in which the ER particles are represented as polarizable
spheres. In this model, the spheres are also polarized by each other in
addition to the applied field. Our results are qualitatively similar to those
obtained by Horv\'ath and Szalai experimentally (\textit{Phys. Rev. E},
\textbf{86}, 061403, 2012). We report characteristic time constants obtained
from bi-exponential fits that can be associated with formation of pairs and
short chains as well as with aggregation of chains. The electric field
dependence of the induced dielectric increment reveals the same qualitative
behavior that experiments did: three regions with different slopes
corresponding to different aggregation processes are identified.


Abstract:
  Meta-learning algorithms leverage regularities that are present on a set of
tasks to speed up and improve the performance of a subsidiary learning process.
Recent work on deep neural networks has shown that prior gradient-based
learning of meta-parameters can greatly improve the efficiency of subsequent
learning. Here, we present a biologically plausible meta-learning algorithm
based on equilibrium propagation. Instead of explicitly differentiating the
learning process, our contrastive meta-learning rule estimates meta-parameter
gradients by executing the subsidiary process more than once. This avoids
reversing the learning dynamics in time and computing second-order derivatives.
In spite of this, and unlike previous first-order methods, our rule recovers an
arbitrarily accurate meta-parameter update given enough compute. We establish
theoretical bounds on its performance and present experiments on a set of
standard benchmarks and neural network architectures.


Abstract:
  In this work, we study the behavior of standard models for community
detection under spectral manipulations. Through various ablation experiments,
we evaluate the impact of bandpass filtering on the performance of a GCN: we
empirically show that most of the necessary and used information for nodes
classification is contained in the low-frequency domain, and thus contrary to
images, high frequencies are less crucial to community detection. In
particular, it is sometimes possible to obtain accuracies at a state-of-the-art
level with simple classifiers that rely only on a few low frequencies.


Abstract:
  Disturbance observer is an inner-loop output-feedback controller whose role
is to reject external disturbances and to make the outer-loop baseline
controller robust against plant's uncertainties. Therefore, the closed-loop
system with the DOB approximates the nominal closed-loop by the baseline
controller and the nominal plant model with no disturbances. This article
presents how the disturbance observer works under what conditions, and how one
can design a disturbance observer to guarantee robust stability and to recover
the nominal performance not only in the steady-state but also for the transient
response under large uncertainty and disturbance.


Abstract:
  We approach the task of network congestion control in datacenters using
Reinforcement Learning (RL). Successful congestion control algorithms can
dramatically improve latency and overall network throughput. Until today, no
such learning-based algorithms have shown practical potential in this domain.
Evidently, the most popular recent deployments rely on rule-based heuristics
that are tested on a predetermined set of benchmarks. Consequently, these
heuristics do not generalize well to newly-seen scenarios. Contrarily, we
devise an RL-based algorithm with the aim of generalizing to different
configurations of real-world datacenter networks. We overcome challenges such
as partial-observability, non-stationarity, and multi-objectiveness. We further
propose a policy gradient algorithm that leverages the analytical structure of
the reward function to approximate its derivative and improve stability. We
show that this scheme outperforms alternative popular RL approaches, and
generalizes to scenarios that were not seen during training. Our experiments,
conducted on a realistic simulator that emulates communication networks'
behavior, exhibit improved performance concurrently on the multiple considered
metrics compared to the popular algorithms deployed today in real datacenters.
Our algorithm is being productized to replace heuristics in some of the largest
datacenters in the world.


Abstract:
  BACKGROUND: Meniere's Disease (MD) is a condition of the inner ear with
symptoms affecting both vestibular and hearing functions. Some patients with MD
experience vestibular drop attacks (VDAs), which are violent falls caused by
spurious vestibular signals from the utricle and/or saccule. Recent surgical
work has shown that patients who experience VDAs also show distrupted utricular
otolithic membranes. OBJECTIVE: The objective of this study is to determine if
otolithic membrane damage alone is sufficient to induce spurious vestibular
signals, thus potentially eliciting VDAs and the vestibular dysfunction seen in
patients with MD. METHODS: We use a previously developed numerical model to
describe the nonlinear dynamics of an array of active, elastically coupled hair
cells. We then reduce the coupling strength of a selected region of the
membrane to model the effects of tissue damage. RESULTS: As we reduce the
coupling strength, we observe large and abrupt spikes in hair bundle position.
As bundle displacements from the equilibrium position have been shown to lead
to depolarization of the hair-cell soma and hence trigger neural activity, this
spontaneous activity could elicit false detection of a vestibular signal.
CONCLUSIONS: The results of this numerical model suggest that otolithic
membrane damage alone may be sufficient to induce VDAs and the vestibular
dysfunction seen in patients with MD. Future experimental work is needed to
confirm these results in vitro.


Abstract:
  In this paper, the authors consider an initial boundary value problem for the
heat flow of equation of surfaces with constant mean curvatures, which was
investigated in [On the heat flow of equation of surfaces of constant mean
curvatures, Manuscripta Mathematica, 2011, 134: 259-271] by Huang et al., where
global well-posedness and finite time blow-up of regular solutions were
obtained. Their results are complemented in this paper in the sense that some
new conditions on the initial data are provided for the solutions to develop
finite time singularity.


Abstract:
  We study the linear stability of entire radial solutions
$u(re^{i\theta})=f(r)e^{i\theta}$, with positive increasing profile $f(r)$, to
the anisotropic Ginzburg-Landau equation \[ -\Delta u -\delta
(\partial_x+i\partial_y)^2\bar u =(1-|u|^2)u,\quad -1<\delta <1, \] which
arises in various liquid crystal models. In the isotropic case $\delta=0$,
Mironescu showed that such solution is nondegenerately stable. We prove
stability of this radial solution in the range $\delta\in (\delta_1,0]$ for
some $-1<\delta_1<0$, and instability outside this range. In strong contrast
with the isotropic case, stability with respect to higher Fourier modes is
\emph{not} a direct consequence of stability with respect to lower Fourier
modes. In particular, in the case where $\delta\approx -1$, lower modes are
stable and yet higher modes are unstable.


Abstract:
  The nano-particle systems under theoretically and experimentally
investigation because of the potential applications in the nano-technology such
as drug delivery, ferrofluids mechanics, magnetic data storage, sensors,
magnetic resonance imaging, and cancer therapy. Recently, it is reported that
interacting nano-particles behave as spin-glasses and experimentally show that
the relaxation of these systems obeys stretched exponential i.e., KWW
relaxation. Therefore, in this study, considering the interacting nano-particle
systems we model the relaxation and investigate frequency and temperature
behaviour depends on slow relaxation by using a simple operator formalism. We
show that relaxation deviates from Debye and obeys to KWW in the presence of
the memory effects in the system. Furthermore, we obtain the frequency and
temperature behaviour depend on KWW relaxation. We conclude that the obtained
results are consistent with experimental results and the simple model,
presented here, is very useful and pedagogical to discuss the slow relaxation
of the interacting nano-particles.


Abstract:
  In this paper, we study forward problem and inverse problem for the
fractional magnetic Schrodinger equation with nonlinear electric potential. We
first investigate the maximum principle for the linearized equation and apply
it to show that the problem is well-posed under suitable assumptions on the
exterior data. Moreover, we explore uniqueness of recovery of both magnetic and
electric potentials.


Abstract:
  Spin-momentum locking of evanescent waves describes the relationship between
the propagation constant of an evanescent mode and the polarization of its
electromagnetic field, giving rise to applications in light nano-routing and
polarimetry among many others. The use of complex numbers in physics is a
powerful representation in areas such as quantum mechanics or electromagnetism;
it is well known that a lossy waveguide can be modelled with the addition of an
imaginary part to the propagation constant. Here we explore how these losses
are entangled with the polarization of the associated evanescent tails for the
waveguide, revealing a well-defined mapping between waveguide losses and the
Poincar\'e sphere of polarizations, in what could be understood as a
"polarization-loss locking" of evanescent waves. We analyse the implications
for near-field directional coupling of sources to waveguides, as optimized
dipoles must take into account the losses for a perfectly unidirectional
excitation. We also reveal the potential advantage of calculating the angular
spectrum of a source defined in a complex, rather than the traditionally purely
real, transverse wavevector space formalism.


Abstract:
  We introduce the problem of weakly supervised Multi-Object Tracking and
Segmentation, i.e. joint weakly supervised instance segmentation and
multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking
advantage of multi-task learning, i.e. classification and tracking tasks guide
the training of the unsupervised instance segmentation. For that purpose, we
extract weak foreground localization information, provided by Grad-CAM
heatmaps, to generate a partial ground truth to learn from. Additionally, RGB
image level information is employed to refine the mask prediction at the edges
of the objects. We evaluate our method on KITTI MOTS, the most representative
benchmark for this task, reducing the performance gap on the MOTSP metric
between the fully supervised and weakly supervised approach to just 12% and
12.7% for cars and pedestrians, respectively.


Abstract:
  In the present paper we introduce a semi-quasi-Einstein manifold from a semi
symmetric metric connection. Among others, the popular Schwarzschild and
Kottler spacetimes are shown to possess this structure. Certain curvature
conditions are studied in such a manifold with a Killing generator.


Abstract:
  The excited state population of single solid-state emitters is subjected to
energy fluctuations around the equilibrium driven by the bath and relaxation
through the emission of phonons or photons. Simultaneous measurement of the
associated spectral dynamics requires a technique with a high spectral and
temporal resolution with an additionally high temporal dynamic range. We
propose a pulsed excitation-laser analog of Photon-Correlation Fourier
Spectroscopy (PCFS), which extracts the lineshape and spectral diffusion
dynamics along the emission lifetime trajectory of the emitter, effectively
discriminating spectral dynamics from relaxation and bath fluctuations. This
lifetime-resolved PCFS correlates photon-pairs at the output arm of a Michelson
interferometer in both their time-delay between laser-excitation and
photon-detection and the time-delay between two photons. We propose the utility
of the technique for systems with changing relative contributions to the
emission from multiple states, for example, quantum emitters exhibiting
phonon-mediated exchange between different fine-structure states.


Abstract:
  The aim of this paper is to introduce Bell polynomials and numbers of the
second kind and poly-Bell polynomials and numbers of the second kind, and to
derive their explicit expressions, recurrence relations and some identities
involving those polynomials and numbers. We also consider degenerate versions
of those polynomials and numbers, namely degenerate Bell polynomials and
numbers of the second kind and degenerate poly-Bell polynomials and numbers of
the second kind, and deduce their similar results.


Abstract:
  Recently published ALMA observations suggest the presence of 20 ppb PH$_3$ in
the upper clouds of Venus. This is an unexpected result, as PH$_3$ does not
have a readily apparent source and should be rapidly photochemically destroyed
according to our current understanding of Venus atmospheric chemistry. While
the reported PH$_3$ spectral line at 266.94 GHz is nearly co-located with an
SO$_2$ spectral line, the non-detection of stronger SO$_2$ lines in the
wideband ALMA data is used to rule out SO$_2$ as the origin of the feature. We
present a reassessment of wideband and narrowband datasets derived from these
ALMA observations. The ALMA observations are re-reduced following both the
initial and revised calibration procedures discussed by the authors of the
original study. We also investigate the phenomenon of apparent spectral line
dilution over varying spatial scales resulting from the ALMA antenna
configuration. A 266.94 GHz spectral feature is apparent in the narrowband data
using the initial calibration procedures, but this same feature can not be
identified following calibration revisions. The feature is also not reproduced
in the wideband data. While the SO$_2$ spectral line is not observed at 257.54
GHz in the ALMA wideband data, our dilution simulations suggest that SO$_2$
abundances greater than the previously suggested 10 ppb limit would also not be
detected by ALMA. Additional millimeter, sub-millimeter, and infrared
observations of Venus should be undertaken to further investigate the
possibility of PH$_3$ in the Venus atmosphere.


Abstract:
  We demonstrate that dislocations in two-dimensional non-Hermitian systems can
give rise to density accumulation or depletion through the localization of an
extensive number of states. These effects are shown by numerical simulations in
a prototype lattice model and expose a different face of non-Hermitian skin
effect, by disentangling it from the need for boundaries. We identify a
topological invariant responsible for the dislocation skin effect, which takes
the form of a ${\mathbb Z}_2$ Hopf index that depends on the Burgers vector
characterizing the dislocations. Remarkably, we find that this effect and its
corresponding signature for defects in Hermitian systems falls outside of the
known topological classification based on bulk-defect correspondence.


Abstract:
  In real-time strategy (RTS) game artificial intelligence research, various
multi-agent deep reinforcement learning (MADRL) algorithms are widely and
actively used nowadays. Most of the research is based on StarCraft II
environment because it is the most well-known RTS games in world-wide. In our
proposed MADRL-based algorithm, distributed MADRL is fundamentally used that is
called QMIX. In addition to QMIX-based distributed computation, we consider
state categorization which can reduce computational complexity significantly.
Furthermore, self-attention mechanisms are used for identifying the
relationship among agents in the form of graphs. Based on these approaches, we
propose a categorized state graph attention policy (CSGA-policy). As observed
in the performance evaluation of our proposed CSGA-policy with the most
well-known StarCraft II simulation environment, our proposed algorithm works
well in various settings, as expected.


Abstract:
  Language-queried video actor segmentation aims to predict the pixel-level
mask of the actor which performs the actions described by a natural language
query in the target frames. Existing methods adopt 3D CNNs over the video clip
as a general encoder to extract a mixed spatio-temporal feature for the target
frame. Though 3D convolutions are amenable to recognizing which actor is
performing the queried actions, it also inevitably introduces misaligned
spatial information from adjacent frames, which confuses features of the target
frame and yields inaccurate segmentation. Therefore, we propose a collaborative
spatial-temporal encoder-decoder framework which contains a 3D temporal encoder
over the video clip to recognize the queried actions, and a 2D spatial encoder
over the target frame to accurately segment the queried actors. In the decoder,
a Language-Guided Feature Selection (LGFS) module is proposed to flexibly
integrate spatial and temporal features from the two encoders. We also propose
a Cross-Modal Adaptive Modulation (CMAM) module to dynamically recombine
spatial- and temporal-relevant linguistic features for multimodal feature
interaction in each stage of the two encoders. Our method achieves new
state-of-the-art performance on two popular benchmarks with less computational
overhead than previous approaches.


Abstract:
  Animals are known to make efficient probabilistic inferences based on
uncertain and noisy information from the outside world. Although it is known
that generic neural networks can perform near-optimal point estimation by
probabilistic population codes which has been proposed as a neural basis for
encoding of probability distribution, the mechanisms of sampling-based
inference has not been clarified. In this study, we trained two types of
artificial neural networks: feedforward neural networks (FFNNs) and recurrent
neural networks (RNNs) to perform sampling based probabilistic inference. Then,
we analyzed and compared the mechanisms of sampling in the RNN with those in
the FFNN. As a result, it was found that sampling in RNN is performed by a
mechanism that efficiently utilizes the properties of dynamical systems, unlike
FFNN. It was also found that sampling in RNNs acts as an inductive bias,
enabling more accurate estimation than in MAP estimation. These results will
provide important implications for the discussion of the relationship between
dynamical systems and information processing in neural networks.


Abstract:
  Weakly supervised question answering usually has only the final answers as
supervision signals while the correct solutions to derive the answers are not
provided. This setting gives rise to the spurious solution problem: there may
exist many spurious solutions that coincidentally derive the correct answer,
but training on such solutions can hurt model performance (e.g., producing
wrong solutions or answers). For example, for discrete reasoning tasks as on
DROP, there may exist many equations to derive a numeric answer, and typically
only one of them is correct. Previous learning methods mostly filter out
spurious solutions with heuristics or using model confidence, but do not
explicitly exploit the semantic correlations between a question and its
solution. In this paper, to alleviate the spurious solution problem, we propose
to explicitly exploit such semantic correlations by maximizing the mutual
information between question-answer pairs and predicted solutions. Extensive
experiments on four question answering datasets show that our method
significantly outperforms previous learning methods in terms of task
performance and is more effective in training models to produce correct
solutions.


Abstract:
  We present a guide to compute the phase-boundaries of classical systems using
a dynamic Clausius-Clapeyron integration (dCCI) method within the LAMMPS
(Large-scale Atomic/Molecular Massively Parallel Simulator) code. The advantage
of the dCCI method is because it provides coexistence curves spanning a wide
range of thermodynamic states using relatively short single non-equilibrium
simulations. We describe the state-of-the-art of non-equilibrium free-energy
methods that allow us to compute the Gibbs free-energy in a wide interval of
pressure and/or temperature. We present the dCCI method in details, discuss its
implementation in the LAMMPS package and make available source code, scripts,
as well as auxiliary files. As an illustrative example, we determine the phase
diagram of silicon in a range of pressures covering from 0 to 15 GPa and
temperatures as low as 400 K up to the liquid phase, in order to obtain the
phase boundaries and triple point between diamond, liquid and beta-Sn phases.


Abstract:
  Classification, recommendation, and ranking problems often involve competing
goals with additional constraints (e.g., to satisfy fairness or diversity
criteria). Such optimization problems are quite challenging, often involving
non-convex functions along with considerations of user preferences in balancing
trade-offs. Pareto solutions represent optimal frontiers for jointly optimizing
multiple competing objectives. A major obstacle for frequently used
linear-scalarization strategies is that the resulting optimization problem
might not always converge to a global optimum. Furthermore, such methods only
return one solution point per run. A Pareto solution set is a subset of all
such global optima over multiple runs for different trade-off choices.
Therefore, a Pareto front can only be guaranteed with multiple runs of the
linear-scalarization problem, where all runs converge to their respective
global optima. Consequently, extracting a Pareto front for practical problems
is computationally intractable with substantial computational overheads,
limited scalability, and reduced accuracy. We propose a robust, low cost,
two-stage, hybrid neural Pareto optimization approach that is accurate and
scales (compute space and time) with data dimensions, as well as number of
functions and constraints. The first stage (neural network) efficiently
extracts a weak Pareto front, using Fritz-John conditions as the discriminator,
with no assumptions of convexity on the objectives or constraints. The second
stage (efficient Pareto filter) extracts the strong Pareto optimal subset given
the weak front from stage 1. Fritz-John conditions provide us with theoretical
bounds on approximation error between the true and network extracted weak
Pareto front. Numerical experiments demonstrates the accuracy and efficiency on
a canonical set of benchmark problems and a fairness optimization task from
prior works.


Abstract:
  We propose a model for a thermally driven microswimmer in which three spheres
are connected by two springs with odd elasticity. We demonstrate that the
presence of odd elasticity leads to the directional locomotion of the
stochastic microswimmer.


Abstract:
  We consider a class (convex set) of quantum states containing all finite rank
states and infinite rank states with the sufficient rate of decreasing of
eigenvalues (in particular, all Gaussian states). Quantum states from this
class are characterized by the property (called the FA-property) that allows to
obtain several results concerning finite-dimensional approximation of basic
entropic and information characteristics of quantum systems and channels.
  We obtain a simple sufficient condition of the FA-property. We show that this
property implies finiteness of the von Neumann entropy, but leave unsolved the
question concerning the converse implication.
  We obtain uniform approximation results for characteristics depending on a
pair (channel, input state) and for characteristics depending on a pair
(channel, input ensemble). We establish the uniform continuity of the above
characteristics as functions of a channel w.r.t. the strong convergence
provided that the FA-property holds either for the input state or for the
average state of input ensemble.


Abstract:
  Session-based recommendation is an important task for e-commerce services,
where a large number of users browse anonymously or may have very distinct
interests for different sessions. In this paper we present one of the winning
solutions for the Recommendation task of the SIGIR 2021 Workshop on E-commerce
Data Challenge. Our solution was inspired by NLP techniques and consists of an
ensemble of two Transformer architectures - Transformer-XL and XLNet - trained
with autoregressive and autoencoding approaches. To leverage most of the rich
dataset made available for the competition, we describe how we prepared
multi-model features by combining tabular events with textual and image
vectors. We also present a model prediction analysis to better understand the
effectiveness of our architectures for the session-based recommendation.


Abstract:
  Generation of maps from satellite images is conventionally done by a range of
tools. Maps became an important part of life whose conversion from satellite
images may be a bit expensive but Generative models can pander to this
challenge. These models aims at finding the patterns between the input and
output image. Image to image translation is employed to convert satellite image
to corresponding map. Different techniques for image to image translations like
Generative adversarial network, Conditional adversarial networks and
Co-Variational Auto encoders are used to generate the corresponding
human-readable maps for that region, which takes a satellite image at a given
zoom level as its input. We are training our model on Conditional Generative
Adversarial Network which comprises of Generator model which which generates
fake images while the discriminator tries to classify the image as real or fake
and both these models are trained synchronously in adversarial manner where
both try to fool each other and result in enhancing model performance.


Abstract:
  There have been numerous studies on mining temporal specifications from
execution traces. These approaches learn finite-state automata (FSA) from
execution traces when running tests. To learn accurate specifications of a
software system, many tests are required. Existing approaches generalize from a
limited number of traces or use simple test generation strategies.
Unfortunately, these strategies may not exercise uncommon usage patterns of a
software system. To address this problem, we propose a new approach,
adversarial specification mining, and develop a prototype, DICE (Diversity
through Counter-Examples). DICE has two components: DICE-Tester and DICE-Miner.
After mining Linear Temporal Logic specifications from an input test suite,
DICE-Tester adversarially guides test generation, searching for counterexamples
to these specifications to invalidate spurious properties. These
counterexamples represent gaps in the diversity of the input test suite. This
process produces execution traces of usage patterns that were unrepresented in
the input test suite. Next, we propose a new specification inference algorithm,
DICE-Miner, to infer FSAs using the traces, guided by the temporal
specifications. We find that the inferred specifications are of higher quality
than those produced by existing state-of-the-art specification miners. Finally,
we use the FSAs in a fuzzer for servers of stateful protocols, increasing its
coverage.


Abstract:
  RISC-V is a promising open-source architecture primarily targeted for
embedded systems. Programs compiled using the RISC-V toolchain can run
bare-metal on the system, and, as such, can be vulnerable to several memory
corruption vulnerabilities. In this work, we present HeapSafe, a lightweight
hardware assisted heap-buffer protection scheme to mitigate heap overflow and
use-after-free vulnerabilities in a RISC-V SoC. The proposed scheme tags
pointers associated with heap buffers with metadata indices and enforces tag
propagation for commonly used pointer operations. The HeapSafe hardware is
decoupled from the core and is designed as a configurable coprocessor and is
responsible for validating the heap buffer accesses. Benchmark results show a
1.5X performance overhead and 1.59% area overhead, while being 22% faster than
a software protection. We further implemented a HeapSafe-nb, an asynchronous
validation design, which improves performance by 27% over the synchronous
HeapSafe.


Abstract:
  How can we explain the predictions of a machine learning model? When the data
is structured as a multivariate time series, this question induces additional
difficulties such as the necessity for the explanation to embody the time
dependency and the large number of inputs. To address these challenges, we
propose dynamic masks (Dynamask). This method produces instance-wise importance
scores for each feature at each time step by fitting a perturbation mask to the
input sequence. In order to incorporate the time dependency of the data,
Dynamask studies the effects of dynamic perturbation operators. In order to
tackle the large number of inputs, we propose a scheme to make the feature
selection parsimonious (to select no more feature than necessary) and legible
(a notion that we detail by making a parallel with information theory). With
synthetic and real-world data, we demonstrate that the dynamic underpinning of
Dynamask, together with its parsimony, offer a neat improvement in the
identification of feature importance over time. The modularity of Dynamask
makes it ideal as a plug-in to increase the transparency of a wide range of
machine learning models in areas such as medicine and finance, where time
series are abundant.


Abstract:
  Let X be a minimal complex surface of general type such that its image via
the canonical map is a surface; we denote by d the degree of the canonical map.
  In this expository work, first of all we recall the known possibilities for
the canonical image and for d when the canonical map is not birational, which
are quite a few, and then we consider the question of producing concrete
examples for all of them.
  We present the two main methods of construction of such examples and we give
several instances of their application. We end the paper outlining the state of
the art on this topic and raising several questions.


Abstract:
  The performance of a reinforcement learning (RL) system depends on the
computational architecture used to approximate a value function. Deep learning
methods provide both optimization techniques and architectures for
approximating nonlinear functions from noisy, high-dimensional observations.
However, prevailing optimization techniques are not designed for
strictly-incremental online updates. Nor are standard architectures designed
for observations with an a priori unknown structure: for example, light sensors
randomly dispersed in space. This paper proposes an online RL prediction
algorithm with an adaptive architecture that efficiently finds useful nonlinear
features. The algorithm is evaluated in a spatial domain with high-dimensional,
stochastic observations. The algorithm outperforms non-adaptive baseline
architectures and approaches the performance of an architecture given
side-channel information. These results are a step towards scalable RL
algorithms for more general problems, where the observation structure is not
available.


Abstract:
  The Gaussian Orthogonal Ensemble (GOE) of random matrices has been widely
employed to describe diverse phenomena in strongly coupled quantum systems. An
important prediction is that the decay rates of the GOE eigenstates fluctuate
according to the distribution for one degree of freedom, as derived by Brink
and by Porter and Thomas. However, we find that the coupling to the decay
channels can change the effective number of degrees of freedom from $\nu = 1$
to $\nu = 2$. Our conclusions are based on a configuration-interaction
Hamiltonian originally constructed to test the validity of transition-state
theory, also known as Rice-Ramsperger-Kassel-Marcus (RRKM) theory in chemistry.
The internal Hamiltonian consists of two sets of GOE reservoirs connected by an
internal channel. We find that the effective number of degrees of freedom $\nu$
can vary from one to two depending on the control parameter $\rho \Gamma$,
where $\rho$ is the level density in the first reservoir and $\Gamma$ is the
level decay width. The $\nu = 2$ distribution is a well-known property of the
Gaussian Unitary ensemble (GUE); our model demonstrates that the GUE
fluctuations can be present under much milder conditions. Our treatment of the
model permits an analytic derivation for $\rho\Gamma \gtrsim 1$.


Abstract:
  Remarkable performance from Transformer networks in Natural Language
Processing promote the development of these models in dealing with computer
vision tasks such as image recognition and segmentation. In this paper, we
introduce a novel framework, called Multi-level Multi-scale Point Transformer
(MLMSPT) that works directly on the irregular point clouds for representation
learning. Specifically, a point pyramid transformer is investigated to model
features with diverse resolutions or scales we defined, followed by a
multi-level transformer module to aggregate contextual information from
different levels of each scale and enhance their interactions. While a
multi-scale transformer module is designed to capture the dependencies among
representations across different scales. Extensive evaluation on public
benchmark datasets demonstrate the effectiveness and the competitive
performance of our methods on 3D shape classification, part segmentation and
semantic segmentation tasks.


Abstract:
  Likelihood-free methods are useful for parameter estimation of complex models
with intractable likelihood functions for which it is easy to simulate data.
Such models are prevalent in many disciplines including genetics, biology,
ecology and cosmology. Likelihood-free methods avoid explicit likelihood
evaluation by finding parameter values of the model that generate data close to
the observed data. The general consensus has been that it is most efficient to
compare datasets on the basis of a low dimensional informative summary
statistic, incurring information loss in favour of reduced dimensionality. More
recently, researchers have explored various approaches for efficiently
comparing empirical distributions in the likelihood-free context in an effort
to avoid data summarisation. This article provides a review of these full data
distance based approaches, and conducts the first comprehensive comparison of
such methods, both qualitatively and empirically. We also conduct a substantive
empirical comparison with summary statistic based likelihood-free methods. The
discussion and results offer guidance to practitioners considering a
likelihood-free approach. Whilst we find the best approach to be problem
dependent, we also find that the full data distance based approaches are
promising and warrant further development. We discuss some opportunities for
future research in this space.


Abstract:
  Millions of repetitive code snippets are submitted to code repositories every
day. To search from these large codebases using simple natural language queries
would allow programmers to ideate, prototype, and develop easier and faster.
Although the existing methods have shown good performance in searching codes
when the natural language description contains keywords from the code, they are
still far behind in searching codes based on the semantic meaning of the
natural language query and semantic structure of the code. In recent years,
both natural language and programming language research communities have
created techniques to embed them in vector spaces. In this work, we leverage
the efficacy of these embedding models using a simple, lightweight 2-layer
neural network in the task of semantic code search. We show that our model
learns the inherent relationship between the embedding spaces and further
probes into the scope of improvement by empirically analyzing the embedding
methods. In this analysis, we show that the quality of the code embedding model
is the bottleneck for our model's performance, and discuss future directions of
study in this area.


Abstract:
  Let $Mat_{\mathbb{C}}(K,N)$ be the space of $K\times N$ complex matrices. Let
$\mathbf{B}_t$ be Brownian motion on $Mat_{\mathbb{C}}(K,N)$ starting from the
zero matrix and $\mathbf{M}\in Mat_{\mathbb{C}}(K,N)$. We prove that, with
$K\ge N$, the $N$ eigenvalues of
$\left(\mathbf{B}_t+t\mathbf{M}\right)^*\left(\mathbf{B}_t+t\mathbf{M}\right)$
form a Markov process with an explicit transition kernel. This generalizes a
classical result of Rogers and Pitman for multidimensional Brownian motion with
drift which corresponds to $N=1$. We then give two more descriptions for this
Markov process. First, as independent squared Bessel diffusion processes in the
wide sense, introduced by Watanabe and studied by Pitman and Yor, conditioned
to never intersect. Second, as the distribution of the top row of interacting
squared Bessel type diffusions in some interlacting array. The last two
descriptions also extend to a general class of one-dimensional diffusions.


Abstract:
  In this article we study the connected components of moduli spaces of p-adic
shtukas and local Shimura varieties. Given local shtuka datum (G, b, mu) with G
an unramified reductive group over Qp and (b, mu) HN-irreducible, we determine
the locally profinite set of geometric connected components
pi0(Sht(G,b,[mu],infty) x Cp) and describe explicitly the continuous right
action of the group G(Qp)x Jb(Qp)x WE on this space. This confirms and
generalizes conjecture 4.26 of [30] in the unramified case. This work builds on
and generalizes M. Chens Theorem B of [4].


Abstract:
  The traditional process of building interactive machine learning systems can
be viewed as a teacher-learner interaction scenario where the machine-learners
are trained by one or more human-teachers. In this work, we explore the idea of
using a conversational interface to investigate the interaction between
human-teachers and interactive machine-learners. Specifically, we examine
whether teachable AI agents can reliably learn from human-teachers through
conversational interactions, and how this learning compare with traditional
supervised learning algorithms. Results validate the concept of teachable
conversational agents and highlight the factors relevant for the development of
machine learning systems that intend to learn from conversational interactions.


Abstract:
  This thesis pretends to be another step in the development of numerical
research in disordered systems. Specifically, we will focus on spin glasses
which have demonstrated to be a fertile field from both, experimental and
theoretical approaches. Throughout this thesis, we will discuss a variety of
interesting phenomenons and we will also open new avenues to previously
unexplored effects in the context of spin glasses. However, without a doubt,
the leitmotiv conducting this thesis is the role of numerical simulations as a
valuable tool to explore spin-glass physics.
  This thesis is organized into five different parts. The first part,
containing the Chapter 1, is focused on introducing the spin glasses to the
reader.
  The second part, containing the Chapter 2, is dedicated to discussing the
metastate.
  The third part, shaped by the Chapter 3 and Chapter 4, is devoted to studying
the off-equilibrium dynamics in spin glasses. Specifically, Chapter 3 is
focused on discussing the growth of the coherence length in spin glasses, a key
quantity that characterizes the off-equilibrium evolution of those systems. In
Chapter 4 we will discuss an interesting phenomenon: the Mpemba effect.
  The fourth part, containing the Chapter 5, Chapter 6 and Chapter 7 is devoted
to study the Temperature Chaos phenomenon in spin glasses. In Chapter 5 we
introduce the main historical results on Temperature Chaos, from its origins to
the last steps. In Chapter 6 we study equilibrated spin glasses and we
characterize the Temperature Chaos from a static and a dynamical point of view.
In Chapter 7 we tackle the problem of characterizing Temperature Chaos in
off-equilibrium dynamics.
  The fifth and last part of the main body of the thesis corresponds to the
conclusions.


Abstract:
  We report on the first measurements of branching fractions, CP-violating
charge asymmetries, and longitudinal polarization fractions in charmless $B$
decays at the Belle II experiment. We use a sample of electron-positron
collisions collected in 2019 and 2020 at the $\Upsilon(4S)$ resonance that
corresponds to 34.6 fb$^{-1}$ of integrated luminosity. The results are
compatible with the known values, which indicates a good understanding of
detector performance.


Abstract:
  Given a global field $F$ with absolute Galois group $G_F$, we define a
category $SMod_F$ whose objects are finite $G_F$-modules decorated with local
conditions. We define this category so that `taking the Selmer group' defines a
functor $Sel$ from $SMod_F$ to $Ab$. After defining a duality functor $\vee$ on
$SMod_F$, we show that every short exact sequence $0 \to M_1 \to M \to M_2 \to
0$ in $SMod_F$ gives rise to a natural bilinear pairing $$Sel (M_2) \times Sel
(M_1^{\vee}) \to \mathbb{Q}/\mathbb{Z}$$ whose left and right kernels are the
images of $Sel (M)$ and $Sel (M^{\vee})$, respectively. This construction
generalizes the Cassels-Tate pairing defined on Shafarevich-Tate groups of
abelian varieties over global fields. Our main results generalize the fact that
the classical pairing is antisymmetric for principally polarized abelian
varieties but, by work of Poonen and Stoll, not necessarily alternating.
  We can express many previously-defined class field theoretic pairings as
instances of this construction, giving the study of these pairings access to
our general results for $SMod_F$. As one application, we reprove and extend
recent results about the class groups of number fields containing many roots of
unity.


Abstract:
  Designing efficient exploration is central to Reinforcement Learning due to
the fundamental problem posed by the exploration-exploitation dilemma. Bayesian
exploration strategies like Thompson Sampling resolve this trade-off in a
principled way by modeling and updating the distribution of the parameters of
the action-value function, the outcome model of the environment. However, this
technique becomes infeasible for complex environments due to the computational
intractability of maintaining probability distributions over parameters of
outcome models of corresponding complexity. Moreover, the approximation
techniques introduced to mitigate this issue typically result in poor
exploration-exploitation trade-offs, as observed in the case of deep neural
network models with approximate posterior methods that have been shown to
underperform in the deep bandit scenario. In this paper we introduce Sample
Average Uncertainty (SAU), a simple and efficient uncertainty measure for
contextual bandits. While Bayesian approaches like Thompson Sampling estimate
outcomes uncertainty indirectly by first quantifying the variability over the
parameters of the outcome model, SAU is a frequentist approach that directly
estimates the uncertainty of the outcomes based on the value predictions.
Importantly, we show theoretically that the uncertainty measure estimated by
SAU asymptotically matches the uncertainty provided by Thompson Sampling, as
well as its regret bounds. Because of its simplicity SAU can be seamlessly
applied to deep contextual bandits as a very scalable drop-in replacement for
epsilon-greedy exploration. We confirm empirically our theory by showing that
SAU-based exploration outperforms current state-of-the-art deep Bayesian bandit
methods on several real-world datasets at modest computation cost. Code is
available at \url{https://github.com/ibm/sau-explore}.


Abstract:
  Personalized federated learning is tasked with training machine learning
models for multiple clients, each with its own data distribution. The goal is
to train personalized models in a collaborative way while accounting for data
disparities across clients and reducing communication costs. We propose a novel
approach to this problem using hypernetworks, termed pFedHN for personalized
Federated HyperNetworks. In this approach, a central hypernetwork model is
trained to generate a set of models, one model for each client. This
architecture provides effective parameter sharing across clients, while
maintaining the capacity to generate unique and diverse personal models.
Furthermore, since hypernetwork parameters are never transmitted, this approach
decouples the communication cost from the trainable model size. We test pFedHN
empirically in several personalized federated learning challenges and find that
it outperforms previous methods. Finally, since hypernetworks share information
across clients we show that pFedHN can generalize better to new clients whose
distributions differ from any client observed during training.


Abstract:
  Networks have advanced the study of nonlocality beyond Bell's theorem. Here,
we introduce the concept of full network nonlocality, which describes
correlations that necessitate all links in a network to distribute nonlocal
resources. Showcasing that this notion is stronger than standard network
nonlocality, we prove that the most well-known network Bell test does not
witness full network nonlocality. In contrast, we demonstrate that its
generalisation to star networks is capable of detecting full network
nonlocality in quantum theory. More generally, we point out that established
methods for analysing local and theory-independent correlations in networks can
be combined in order to systematically deduce sufficient conditions for full
network nonlocality in any network and input/output scenario. We demonstrate
the usefulness of these methods by constructing polynomial witnesses of full
network nonlocality for the bilocal scenario. Then, we show that these
inequalities can be violated via quantum Elegant Joint Measurements.


Abstract:
  We investigate the renormalization group theory of generalized multi-vertex
sine-Gordon model by employing the dimensional regularization method and also
the Wilson renormalization group method. The vertex interaction is given by
$\cos(k_j\cdot \phi)$ where $k_j$ ($j=1,2,\cdots,M$) are momentum vectors and
$\phi$ is an $N$-component scalar field. The beta functions are calculated for
the sine-Gordon model with multi cosine interactions. The second-order
correction in the renormalization procedure is given by the two-point
scattering amplitude for tachyon scattering. We show that new vertex
interaction with momentum vector $k_{\ell}$ is generated from two vertex
interactions with vectors $k_i$ and $k_j$ when $k_i$ and $k_j$ meet the
condition $k_{\ell}=k_i\pm k_j$ called the triangle condition. Further
condition $k_i\cdot k_j=\pm 1/2$ is required within the dimensional
regularization method. The renormalization group equations form a set of closed
equations when $\{k_j\}$ form an equilateral triangle for $N=2$ or a regular
tetrahedron for $N=3$. The Wilsonian renormalization group method gives
qualitatively the same result for beta functions.


Abstract:
  A triangle with rational sides and rational area is called a rational
triangle. In this paper we consider three problems of finding pairs of rational
triangles which have a common circumradius as well as either a common perimeter
or a common inradius or a common area. While several similar problems
concerning pairs of rational triangles have been considered in the existing
literature, these three problems have not been studied till now. For each of
these problems, we give a parametric solution and we also indicate how
additional parametric solutions of these problems may be obtained.


Abstract:
  A Kahler-type form is a symplectic form compatible with an integrable complex
structure. Let M be a either a torus or a K3-surface equipped with a
Kahler-type form. We show that the homology class of any Maslov-zero Lagrangian
torus in M has to be non-zero and primitive. This extends previous results of
Abouzaid-Smith (for tori) and Sheridan-Smith (for K3-surfaces) who proved it
for particular Kahler-type forms on M. In the K3 case our proof uses dynamical
properties of the action of the diffeomorphism group of M on the space of the
Kahler-type forms. These properties are obtained using Shah's arithmetic
version of Ratner's orbit closure theorem.


Abstract:
  Curiosity is a general method for augmenting an environment reward with an
intrinsic reward, which encourages exploration and is especially useful in
sparse reward settings. As curiosity is calculated using next state prediction
error, the type of state encoding used has a large impact on performance.
Random features and inverse-dynamics features are generally preferred over VAEs
based on previous results from Atari and other mostly 2D environments. However,
unlike VAEs, they may not encode sufficient information for optimal behaviour,
which becomes increasingly important as environments become more complex. In
this paper, we use the sparse reward 3D physics environment Animal-AI, to
demonstrate how a fixed $\beta$-VAE encoding can be used effectively with
curiosity. We combine this with curriculum learning to solve the previously
unsolved exploration intensive detour tasks while achieving 22\% gain in sample
efficiency on the training curriculum against the next best encoding. We also
corroborate the results on Atari Breakout, with our custom encoding
outperforming random features and inverse-dynamics features.


Abstract:
  Iron sand is one of the abundant natural resources in Indonesia, especially
on the south coast of Cidaun; West Java which is the basic material for
building and metal industry. Iron mineral content is generally metal oxide such
as magnetite, hematite and silica/quartz. Sand with iron content used in this
study is derived from beach sand Desa Kertajadi, Kecamatan Cidaun, Kabupaten
Cianjur, Jawa Barat. Then mass of 2 kg sand was separated using a magnetic
separator in order to obtain magnetic and nonmagnetic mineral content. After
nine rounds of separation takes two different types of samples that are no
separation sand (TS) sample and concentrate in the third separation (S3)
sample. The sample is then examined by X-Ray Diffraction (XRD) measurement and
analyzed quantitatively using MAUD software to determine the content of Fe3O4
(magnetite) by using the Rietveld refinement method from XRD data. As the
analysis result, the magnetite content contained in iron sand is counted
quantitatively for each different sample. For iron sand samples (TS) yielding a
24.27 percent of magnetite and a third concentrate separation sample (S3)
yields 61.98 percent.


Abstract:
  In this paper, by introducing the degenerate Fubini-type polynomials, we give
several relations with the help of the Fa\`a di Bruno formula and some
properties of Bell polynomials, and generating function methods. Also, we
derive some new explicit formulas and recurrence relations for Fubini-type
polynomials and numbers. Associating the degenerate Fubini-type polynomials
newly defined here with degenerate Apostol-Bernoulli polynomials and degenerate
Apostol-Euler polynomials of order {\alpha} enables us to present additional
relations for some degenerate special polynomials and numbers.


Abstract:
  Citation context analysis (CCA) is an important task in natural language
processing that studies how and why scholars discuss each others' work. Despite
decades of study, traditional frameworks for CCA have largely relied on
overly-simplistic assumptions of how authors cite, which ignore several
important phenomena. For instance, scholarly papers often contain rich
discussions of cited work that span multiple sentences and express multiple
intents concurrently. Yet, CCA is typically approached as a single-sentence,
single-label classification task, and thus existing datasets fail to capture
this interesting discourse. In our work, we address this research gap by
proposing a novel framework for CCA as a document-level context extraction and
labeling task. We release MultiCite, a new dataset of 12,653 citation contexts
from over 1,200 computational linguistics papers. Not only is it the largest
collection of expert-annotated citation contexts to-date, MultiCite contains
multi-sentence, multi-label citation contexts within full paper texts. Finally,
we demonstrate how our dataset, while still usable for training classic CCA
models, also supports the development of new types of models for CCA beyond
fixed-width text classification. We release our code and dataset at
https://github.com/allenai/multicite.


Abstract:
  Despite advances in the programmable logic capabilities of modern trigger
systems, a significant bottleneck remains in the amount of data to be
transported from the detector to off-detector logic where trigger decisions are
made. We demonstrate that a neural network autoencoder model can be implemented
in a radiation tolerant ASIC to perform lossy data compression alleviating the
data transmission problem while preserving critical information of the detector
energy profile. For our application, we consider the high-granularity
calorimeter from the CMS experiment at the CERN Large Hadron Collider. The
advantage of the machine learning approach is in the flexibility and
configurability of the algorithm. By changing the neural network weights, a
unique data compression algorithm can be deployed for each sensor in different
detector regions, and changing detector or collider conditions. To meet area,
performance, and power constraints, we perform a quantization-aware training to
create an optimized neural network hardware implementation. The design is
achieved through the use of high-level synthesis tools and the hls4ml
framework, and was processed through synthesis and physical layout flows based
on a LP CMOS 65 nm technology node. The flow anticipates 200 Mrad of ionizing
radiation to select gates, and reports a total area of 3.6 mm^2 and consumes 95
mW of power. The simulated energy consumption per inference is 2.4 nJ. This is
the first radiation tolerant on-detector ASIC implementation of a neural
network that has been designed for particle physics applications.


Abstract:
  A crucial aspect in reliable machine learning is to design a deployable
system in generalizing new related but unobserved environments. Domain
generalization aims to alleviate such a prediction gap between the observed and
unseen environments. Previous approaches commonly incorporated learning
invariant representation for achieving good empirical performance. In this
paper, we reveal that merely learning invariant representation is vulnerable to
the unseen environment. To this end, we derive novel theoretical analysis to
control the unseen test environment error in the representation learning, which
highlights the importance of controlling the smoothness of representation. In
practice, our analysis further inspires an efficient regularization method to
improve the robustness in domain generalization. Our regularization is
orthogonal to and can be straightforwardly adopted in existing domain
generalization algorithms for invariant representation learning. Empirical
results show that our algorithm outperforms the base versions in various
dataset and invariance criteria.


Abstract:
  The aim of this sequel to arXiv:1812.02935 is to set up the cornerstones of
Koszul duality and Koszulity in the context of a large class of operadic
categories. In particular, we will prove that operads, in the generalized sense
of Batanin-Markl, governing important operad- and/or PROP-like structures such
as the classical operads, their variants such as cyclic, modular or wheeled
operads, and also diverse versions of PROPs such as properads, dioperads,
1/2PROPs, and still more exotic objects such as permutads and pre-permutads are
binary quadratic, and describe their Koszul duals. We then prove, using the
results of arXiv:2002.06640, that operads governing the most common structures
are Koszul.


Abstract:
  The article reveals the main theoretical approaches to the analysis and study
of the phenomenon of corruption. Special attention is paid to the consideration
of the index approach to the analysis of corruption.


Abstract:
  Advances in the realm of Generative Adversarial Networks (GANs) have led to
architectures capable of producing amazingly realistic images such as
StyleGAN2, which, when trained on the FFHQ dataset, generates images of human
faces from random vectors in a lower-dimensional latent space. Unfortunately,
this space is entangled - translating a latent vector along its axes does not
correspond to a meaningful transformation in the output space (e.g., smiling
mouth, squinting eyes). The model behaves as a black box, providing neither
control over its output nor insight into the structures it has learned from the
data. We present a method to explore the manifolds of changes of spatially
localized regions of the face. Our method discovers smoothly varying sequences
of latent vectors along these manifolds suitable for creating animations.
Unlike existing disentanglement methods that either require labelled data or
explicitly alter internal model parameters, our method is an optimization-based
approach guided by a custom loss function and manually defined region of
change. Our code is open-sourced, which can be found, along with supplementary
results, on our project page: https://github.com/bmolab/masked-gan-manifold


Abstract:
  Single crystalline samples of the van der Waals antiferromagnet CrPS4 were
studied by measurements of specific heat and comprehensive anisotropic
temperature- and magnetic field-dependent magnetization. In addition,
measurements of the heat capacity and magnetization were performed under
pressures of up to ~21 kbar and ~14 kbar respectively. At ambient pressure, two
magnetic transitions are observed, second order from a paramagnetic to an
antiferromagnetic state at TN ~ 37 K, and a first-order spin reorientation
transition at T* ~ 34 K. Anisotropic H - T phase diagrams were constructed
using the M(T,H) data. As pressure is increased, TN is weakly suppressed with
dTN/dP ~ -0.1 K/kbar. T*, on the other hand, is suppressed quite rapidly, with
dT*/dP ~ -2 K/kbar, extrapolating to a possible quantum phase transition at Pc
~ 15 kbar.


Abstract:
  Cryptocurrencies (CCs) become more interesting for institutional investors'
strategic asset allocation and will be a fixed component of professional
portfolios in future. This asset class differs from established assets
especially in terms of the severe manifestation of statistical parameters. The
question arises whether CCs with similar statistical key figures exist. On this
basis, a core market incorporating CCs with comparable properties enables the
implementation of a tracking error approach. A prerequisite for this is the
segmentation of the CC market into a core and a satellite, the latter
comprising the accumulation of the residual CCs remaining in the complement.
Using a concrete example, we segment the CC market into these components, based
on modern methods from image / pattern recognition.


Abstract:
  Let $\Gamma$ be a finitely generated group and let $(X,\mu_X)$ be an ergodic
standard Borel probability $\Gamma$-space. Suppose that $G$ is the connected
component of the identity of the isometry group of a Hermitian symmetric space.
Given a Zariski dense measurable cocycle $\sigma:\Gamma\times X\rightarrow G$,
we define the notion of parametrized K\"{a}hler class and we show that it
completely determines the cocycle up to cohomology.


Abstract:
  We consider the algorithmic question of choosing a subset of candidates of a
given size $k$ from a set of $m$ candidates, with knowledge of voters' ordinal
rankings over all candidates. We consider the well-known and classic scoring
rule for achieving diverse representation: the Chamberlin-Courant (CC) or
$1$-Borda rule, where the score of a committee is the average over the voters,
of the rank of the best candidate in the committee for that voter; and its
generalization to the average of the top $s$ best candidates, called the
$s$-Borda rule.
  Our first result is an improved analysis of the natural and well-studied
greedy heuristic. We show that greedy achieves a $\left(1 -
\frac{2}{k+1}\right)$-approximation to the maximization (or satisfaction)
version of CC rule, and a $\left(1 - \frac{2s}{k+1}\right)$-approximation to
the $s$-Borda score. Our result improves on the best known approximation
algorithm for this problem. We show that these bounds are almost tight.
  For the dissatisfaction (or minimization) version of the problem, we show
that the score of $\frac{m+1}{k+1}$ can be viewed as an optimal benchmark for
the CC rule, as it is essentially the best achievable score of any
polynomial-time algorithm even when the optimal score is a polynomial factor
smaller (under standard computational complexity assumptions). We show that
another well-studied algorithm for this problem, called the Banzhaf rule,
attains this benchmark.
  We finally show that for the $s$-Borda rule, when the optimal value is small,
these algorithms can be improved by a factor of $\tilde \Omega(\sqrt{s})$ via
LP rounding. Our upper and lower bounds are a significant improvement over
previous results, and taken together, not only enable us to perform a finer
comparison of greedy algorithms for these problems, but also provide analytic
justification for using such algorithms in practice.


Abstract:
  New methods for computing parametric local $b$-functions are introduced for
$\mu$-constant deformations of semi-weighted homogeneous singularities. The
keys of the methods are comprehensive Gr\"obner systems in
Poincar\'e-Birkhoff-Witt algebra and holonomic ${\mathcal D}$-modules. It is
shown that the use of semi-weighted homogeneity reduces the computational
complexity of $b$-functions associated with $\mu$-constant deformations. In the
case of inner modality 2, local $b$-functions associated with $\mu$-constant
deformations are obtained by the resulting method and given the list of
parametric local $b$-functions.


Abstract:
  Neural Networks are sensitive to various corruptions that usually occur in
real-world applications such as blurs, noises, low-lighting conditions, etc. To
estimate the robustness of neural networks to these common corruptions, we
generally use a group of modeled corruptions gathered into a benchmark.
Unfortunately, no objective criterion exists to determine whether a benchmark
is representative of a large diversity of independent corruptions. In this
paper, we propose a metric called corruption overlapping score, which can be
used to reveal flaws in corruption benchmarks. Two corruptions overlap when the
robustnesses of neural networks to these corruptions are correlated. We argue
that taking into account overlappings between corruptions can help to improve
existing benchmarks or build better ones.


Abstract:
  This paper presents a matching network to establish point correspondence
between images. We propose a Multi-Arm Network (MAN) to learn region overlap
and depth, which can greatly improve the keypoint matching robustness while
bringing little computational cost during the inference stage. Another design
that makes this framework different from many existing learning based pipelines
that require re-training when a different keypoint detector is adopted, our
network can directly work with different keypoint detectors without such a
time-consuming re-training process. Comprehensive experiments conducted on
outdoor and indoor datasets demonstrated that our proposed MAN outperforms
state-of-the-art methods. Code will be made publicly available.


Abstract:
  Wireless Capsule Endoscopy (WCE) helps physicians examine the
gastrointestinal (GI) tract noninvasively. There are few studies that address
pathological assessment of endoscopy images in multiclass classification and
most of them are based on binary anomaly detection or aim to detect a specific
type of anomaly. Multiclass anomaly detection is challenging, especially when
the dataset is poorly sampled or imbalanced. Many available datasets in
endoscopy field, such as KID2, suffer from an imbalance issue, which makes it
difficult to train a high-performance model. Additionally, increasing the
number of classes makes classification more difficult. We proposed a multiclass
classification algorithm that is extensible to any number of classes and can
handle an imbalance issue. The proposed method uses multiple autoencoders where
each one is trained on one class to extract features with the most
discrimination from other classes. The loss function of autoencoders is set
based on reconstruction, compactness, distance from other classes, and
Kullback-Leibler (KL) divergence. The extracted features are clustered and then
classified using an ensemble of support vector data descriptors. A total of
1,778 normal, 227 inflammation, 303 vascular, and 44 polyp images from the KID2
dataset are used for evaluation. The entire algorithm ran 5 times and achieved
F1-score of 96.3 +- 0.2% and 85.0 +- 0.4% on the test set for binary and
multiclass anomaly detection, respectively. The impact of each step of the
algorithm was investigated by various ablation studies and the results were
compared with published works. The suggested approach is a competitive option
for detecting multiclass anomalies in the GI field.


Abstract:
  The recent return of the US to the Paris Climate Accord, massive increase in
solar panel production and energy storage solutions has resulted in pressure on
supply for solar cell materials and recycling of panels installed in the 90's
and beginning of the 2000's which have reached their end of life. In this work
we focus on recycling silicon wafers and dies by stripping previous structures
from the die using potent acids after which its base material is characterized
and binned. We demonstrate the process for silicon p-type substrates where
n-type doping is attained by using a simple solution of phosphoric acid, which
is diffused into the substrate using a furnace thus creating a PN junction. In
case the substrate is n-type it could be replaced by boric acid. This is
followed by deposition of a conductive antireflective coating, bus bars and
rear wafer metal coating. The initial demonstrated laboratory results indicate
the feasibility of recycling wafers using simple low cost standard industrial
methods.


Abstract:
  Conversion Rate (\emph{CVR}) prediction in modern industrial e-commerce
platforms is becoming increasingly important, which directly contributes to the
final revenue. In order to address the well-known sample selection bias
(\emph{SSB}) and data sparsity (\emph{DS}) issues encountered during CVR
modeling, the abundant labeled macro behaviors ($i.e.$, user's interactions
with items) are used. Nonetheless, we observe that several purchase-related
micro behaviors ($i.e.$, user's interactions with specific components on the
item detail page) can supplement fine-grained cues for \emph{CVR} prediction.
Motivated by this observation, we propose a novel \emph{CVR} prediction method
by Hierarchically Modeling both Micro and Macro behaviors ($HM^3$).
Specifically, we first construct a complete user sequential behavior graph to
hierarchically represent micro behaviors and macro behaviors as one-hop and
two-hop post-click nodes. Then, we embody $HM^3$ as a multi-head deep neural
network, which predicts six probability variables corresponding to explicit
sub-paths in the graph. They are further combined into the prediction targets
of four auxiliary tasks as well as the final $CVR$ according to the conditional
probability rule defined on the graph. By employing multi-task learning and
leveraging the abundant supervisory labels from micro and macro behaviors,
$HM^3$ can be trained end-to-end and address the \emph{SSB} and \emph{DS}
issues. Extensive experiments on both offline and online settings demonstrate
the superiority of the proposed $HM^3$ over representative state-of-the-art
methods.


Abstract:
  The basis generation in reduced order modeling usually requires multiple
high-fidelity large-scale simulations that could take a huge computational
cost. In order to accelerate these numerical simulations, we introduce a
FOM/ROM hybrid approach in this paper. It is developed based on an a posteriori
error estimation for the output approximation of the dynamical system. By
controlling the estimated error, the method dynamically switches between the
full-order model and the reduced-oder model generated on the fly. Therefore, it
reduces the computational cost of a high-fidelity simulation while achieving a
prescribed accuracy level. Numerical tests on the non-parametric and parametric
PDEs illustrate the efficacy of the proposed approach.


Abstract:
  We address the use of selfie ocular images captured with smartphones to
estimate age and gender. Partial face occlusion has become an issue due to the
mandatory use of face masks. Also, the use of mobile devices has exploded, with
the pandemic further accelerating the migration to digital services. However,
state-of-the-art solutions in related tasks such as identity or expression
recognition employ large Convolutional Neural Networks, whose use in mobile
devices is infeasible due to hardware limitations and size restrictions of
downloadable applications. To counteract this, we adapt two existing
lightweight CNNs proposed in the context of the ImageNet Challenge, and two
additional architectures proposed for mobile face recognition. Since datasets
for soft-biometrics prediction using selfie images are limited, we counteract
over-fitting by using networks pre-trained on ImageNet. Furthermore, some
networks are further pre-trained for face recognition, for which very large
training databases are available. Since both tasks employ similar input data,
we hypothesize that such strategy can be beneficial for soft-biometrics
estimation. A comprehensive study of the effects of different pre-training over
the employed architectures is carried out, showing that, in most cases, a
better accuracy is obtained after the networks have been fine-tuned for face
recognition.


Abstract:
  This paper proposes various new analysis techniques for Bayes networks in
which conditional probability tables (CPTs) may contain symbolic variables. The
key idea is to exploit scalable and powerful techniques for synthesis problems
in parametric Markov chains. Our techniques are applicable to arbitrarily many,
possibly dependent parameters that may occur in various CPTs. This lifts the
severe restrictions on parameters, e.g., by restricting the number of
parametrized CPTs to one or two, or by avoiding parameter dependencies between
several CPTs, in existing works for parametric Bayes networks (pBNs). We
describe how our techniques can be used for various pBN synthesis problems
studied in the literature such as computing sensitivity functions (and values),
simple and difference parameter tuning, ratio parameter tuning, and minimal
change tuning. Experiments on several benchmarks show that our prototypical
tool built on top of the probabilistic model checker Storm can handle several
hundreds of parameters.


Abstract:
  Particle methods can provide detailed descriptions of sea ice dynamics that
explicitly model fracture and discontinuities in the ice, which are difficult
to capture with traditional continuum approaches. We use the ParticLS software
library to develop a discrete element method (DEM) model for sea ice dynamics
at regional scales and smaller (<100 km). We model the sea ice as a collection
of discrete rigid particles that are initially bonded together using a cohesive
beam model that approximates the response of an Euler-Bernoulli beam located
between particle centroids. Ice fracture and lead formation are determined
based on the value of a non-local stress state around each particle and a
Mohr-Coulomb fracture model. Therefore, large ice floes are modeled as
continuous objects made up of many bonded particles that can interact with each
other, deform, and fracture. We generate realistic particle configurations by
discretizing the ice in MODIS satellite imagery into polygonal floes that fill
the ice shape and extent that occurred in nature. The model is tested on ice
advecting through an idealized channel and through Nares Strait. The results
indicate that the bonded DEM model is capable of capturing the behavior of sea
ice over a wide range of spatial scales, as well as the dynamic sea ice
patterns through constrictions (arching, lead formation).


Abstract:
  Renewable energy forecasting is attaining greater importance due to its
constant increase in contribution to the electrical power grids. Solar energy
is one of the most significant contributors to renewable energy and is
dependent on solar irradiation. For the effective management of electrical
power grids, forecasting models that predict solar irradiation, with high
accuracy, are needed. In the current study, Machine Learning techniques such as
Linear Regression, Extreme Gradient Boosting and Genetic Algorithm Optimization
are used to forecast solar irradiation. The data used for training and
validation is recorded from across three different geographical stations in the
United States that are part of the SURFRAD network. A Global Horizontal Index
(GHI) is predicted for the models built and compared. Genetic Algorithm
Optimization is applied to XGB to further improve the accuracy of solar
irradiation prediction.


Abstract:
  Angle disorder is an intrinsic feature of twisted bilayer graphene and other
moir\'e materials. Here, we discuss electron transport in twisted bilayer
graphene in the presence of angle disorder. We compute the local density of
states and the Landauer-B\"uttiker transmission through an angle disorder
barrier of width comparable to the moir\'e period, using a decimation technique
based on a real space description. We find that barriers which separate regions
where the width of the bands differ by 50% or more lead to a minor suppression
of the transmission, and that the transmission is close to one for normal
incidence, which is reminiscent of Klein tunneling. These results suggest that
transport in twisted bilayer graphene is weakly affected by twist angle
disorder.


Abstract:
  We consider the setting of vector valued non-linear dynamical systems
$X_{t+1} = \phi(A^* X_t) + \eta_t$, where $\eta_t$ is unbiased noise and $\phi
: \mathbb{R} \to \mathbb{R}$ is a known link function that satisfies certain
{\em expansivity property}. The goal is to learn $A^*$ from a single trajectory
$X_1,\cdots,X_T$ of {\em dependent or correlated} samples. While the problem is
well-studied in the linear case, where $\phi$ is identity, with optimal error
rates even for non-mixing systems, existing results in the non-linear case hold
only for mixing systems. In this work, we improve existing results for learning
nonlinear systems in a number of ways: a) we provide the first offline
algorithm that can learn non-linear dynamical systems without the mixing
assumption, b) we significantly improve upon the sample complexity of existing
results for mixing systems, c) in the much harder one-pass, streaming setting
we study a SGD with Reverse Experience Replay ($\mathsf{SGD-RER}$) method, and
demonstrate that for mixing systems, it achieves the same sample complexity as
our offline algorithm, d) we justify the expansivity assumption by showing that
for the popular ReLU link function -- a non-expansive but easy to learn link
function with i.i.d. samples -- any method would require exponentially many
samples (with respect to dimension of $X_t$) from the dynamical system. We
validate our results via. simulations and demonstrate that a naive application
of SGD can be highly sub-optimal. Indeed, our work demonstrates that for
correlated data, specialized methods designed for the dependency structure in
data can significantly outperform standard SGD based methods.


Abstract:
  This paper presents a novel unmanned aerial vehicle (UAV) aided mobile edge
computing (MEC) architecture for vehicular networks. It is considered that the
vehicles should complete latency critical computation intensive tasks either
locally with on-board computation units or by offloading part of their tasks to
road side units (RSUs) with collocated MEC servers. In this direction, a
hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an
aerial relay and further offload the computation tasks to a ground RSU (GRSU).
In order to significantly reduce the delay during data offloading and
downloading, this architecture relies on the benefits of massive multiple input
multiple output (MIMO). Therefore, it is considered that the vehicles, the
ARSU, and the GRSU employ large scale antennas. A three dimensional (3D)
geometrical representation of the MEC enabled network is introduced and an
optimization method is proposed that minimizes the weighted total energy
consumption (WTEC) of the vehicles and ARSU subject to transmit power
allocation, task allocation, and timeslot scheduling. The numerical results
verify the theoretical derivations, emphasize on the effectiveness of the
massive MIMO transmission, and provide useful engineering insights.


Abstract:
  In this paper, the non-BPS amplitudes ($Z < 2m$) are considered. Utilizing
on-shell methods, the three point amplitudes of two equal-mass particles and
one massless particle were constructed, where the two massive particles are
non-BPS states. We verify the result by matching the $Z = 0$ and BPS limit with
$N = 2$ supersymmetry. As an application we derive the non-BPS coupling for $N
= 4$ super-Maxwell and supergravity.


Abstract:
  Testing 3D hydrodynamic models of stellar atmospheres is feasible by
retrieving spectral line shapes across stellar disks, using differential
spectroscopy during exoplanet transits. From synthetic data at hyper-high
spectral resolution, characteristic patterns for FeI and FeII lines were
identified in Paper IV from 3D models spanning T=3964-6726K (spectral types
approx. K8V-F3V). The observability of patterns among lines of different
strength, excitation potential and ionization level are now examined, as
observed at ordinary spectral resolutions and in the presence of noise. Time
variability in 3D atmospheres induces changes in spectral-line parameters, some
of which are correlated. An adequate calibration could identify proxies for the
jitter in apparent radial velocity to enable adjustments to actual stellar
radial motion. We also examined the center-to-limb temporal variability.
Recovery of spatially resolved line profiles with fitted widths and depths is
shown for various noise levels. Signals during exoplanet transit are simulated.
In addition to Rossiter-McLaughlin type signatures in apparent radial velocity,
analogous effects are shown for line depths and widths. From exoplanet
transits, overall stellar line parameters of width, depth and wavelength
position can be retrieved already with moderate efforts, but a very good
signal-to-noise ratio is required to reveal the more subtle signatures between
subgroups of spectral lines, where finer details of atmospheric structure are
encoded. In a solar model, temporal variability shows correlations between
jittering in apparent radial velocity and fluctuations in line depth. Since
both fluctuations in line depth and jittering in wavelength can be measured
from the ground, searches for low-mass exoplanets should explore these to
adjust apparent radial velocities to actual stellar motion.


Abstract:
  We report on the first measurement of the direct $CP$-violating asymmetry
($\mathcal{A}$) in the charmless decay $B^0 \to K^0\pi^0$ at Belle II and an
updated measurement of its branching fraction ($\mathcal{B}$). We use a sample
of electron-positron collisions collected in 2019 and 2020 at the
$\Upsilon(4S)$ resonance and corresponding to $62.8$ $\text{fb}^{-1}$ of
integrated luminosity. We reconstruct and select about $50$ $B^0 \to K_S^0
\pi^0$ candidates, and we measure $\mathcal{A}_{{K^0\pi^0}} =
-0.40_{-0.44}^{+0.46} (\text{stat}) \pm 0.04 (\text{syst})$ and
$\mathcal{B}(B^0 \to K^0 \pi^0) = [8.5_{-1.6}^{+1.7} (\text{stat}) \pm 1.2
(\text{syst})] \times 10^{-6}$. This is the first measurement of $CP$ violation
in $B^0 \to K^0\pi^0$ decays reported by Belle II. The results agree with
previous determinations and show a detector performance comparable with the
best Belle results.


Abstract:
  We present a basis set correction scheme for the coupled-cluster singles and
doubles (CCSD) method. The scheme is based on employing frozen natural orbitals
(FNOs) and diagrammatically decomposed contributions to the electronic
correlation energy that dominate the basis set incompleteness error (BSIE). As
recently discussed in [https://doi.org/10.1103/PhysRevLett.123.156401], the
BSIE of the CCSD correlation energy is dominated by the second-order
M{\o}ller-Plesset (MP2) perturbation energy and the particle-particle ladder
term. Here, we derive a simple approximation to the BSIE of the
particle-particle ladder term that effectively corresponds to a rescaled
pair-specific MP2 BSIE, where the scaling factor depends on the spatially
averaged correlation hole depth of the coupled-cluster and first-order pair
wavefunctions. The evaluation of the derived expressions is simple to implement
in any existing code. We demonstrate the effectiveness of the method for the
uniform electron gas. Furthermore, we apply the method to coupled-cluster
theory calculations of atoms and molecules using FNOs. Employing the proposed
correction and an increasing number of FNOs per occupied orbital, we
demonstrate for a test set that rapidly convergent closed and open-shell
reaction energies, atomization energies, electron affinities, and ionization
potentials can be obtained. Moreover, we show that a similarly excellent
trade-off between required virtual orbital basis set size and remaining BSIEs
can be achieved for the perturbative triples contribution to the CCSD(T) energy
employing FNOs and the (T*) approximation.


Abstract:
  Novel quantum technologies and devices place unprecedented demands on the
performance of experimental components, while their widespread deployment
beyond the laboratory necessitates increased robustness and fast, affordable
production. We show how the use of additive manufacturing, together with
mathematical optimization techniques and innovative designs, allows the
production of compact, lightweight components with greatly enhanced
performance. We use such components to produce a magneto-optical trap that
captures $\sim 2 \times 10^8$ rubidium atoms, employing for this purpose a
compact and highly stable device for spectroscopy and optical power
distribution, optimized neodymium magnet arrays for magnetic field generation,
and a lightweight, additively manufactured ultra-high vacuum chamber. We show
how the use of additive manufacturing enables substantial weight reduction and
stability enhancement, while also illustrating the transferability of our
approach to experiments and devices across the quantum technology sector and
beyond.


Abstract:
  Twisted \'etale groupoid algebras have been studied recently in the algebraic
setting by several authors in connection with an abstract theory of Cartan
pairs of rings. In this paper, we show that extensions of ample groupoids
correspond in a precise manner to extensions of Boolean inverse semigroups. In
particular, discrete twists over ample groupoids correspond to certain abelian
extensions of Boolean inverse semigroups and we show that they are classified
by Lausch's second cohomology group of an inverse semigroup. The cohomology
group structure corresponds to the Baer sum operation on twists.
  We also define a novel notion of inverse semigroup crossed product,
generalizing skew inverse semigroup rings, and prove that twisted Steinberg
algebras of Hausdorff ample groupoids are instances of inverse semigroup
crossed products. The cocycle defining the crossed product is the same cocycle
that classifies the twist in Lausch cohomology.


Abstract:
  Unconventional superconductivity and magnetism are intertwined on a
microscopic level in a wide class of materials, including high-$T_c$ cuprates,
iron pnictides, and heavy-fermion compounds. A new approach to this most
fundamental and hotly debated subject focuses on the role of interactions
between superconducting electrons and bosonic fluctuations at the interface
between adjacent layers in heterostructures. A recent state-of-the-art
molecular-beam-epitaxy technique has enabled us to fabricate superlattices
consisting of different heavy-fermion compounds with atomic thickness. These
Kondo superlattices provide a unique opportunity to study the mutual
interaction between unconventional superconductivity and magnetic order through
the atomic interface. Here, we design and fabricate hybrid Kondo superlattices
consisting of alternating layers of superconducting CeCoIn$_5$ with $d$-wave
pairing symmetry and nonmagnetic metal YbCoIn$_5$ or antiferromagnetic heavy
fermion metals, such as CeRhIn$_5$ and CeIn$_3$. In these Kondo superlattices,
superconducting heavy electrons are confined within the two-dimensional
CeCoIn$_5$ block layers and interact with the neighboring nonmagnetic or
magnetic layers through the interface. In CeCoIn$_5$/YbCoIn$_5$ superlattices,
the superconductivity is strongly influenced by the local inversion symmetry
breaking at the interface. In CeCoIn$_5$/CeRhIn$_5$ and CeCoIn$_5$/CeIn$_3$
superlattices, the superconducting and antiferromagnetic states coexist in
spatially separated layers, but their mutual coupling via the interface
significantly modifies the superconducting and magnetic properties. The
fabrication of a wide variety of hybrid superlattices paves a new way to study
the relationship between unconventional superconductivity and magnetism in
strongly correlated materials.


Abstract:
  We calculate the plateau moduli of several single-chain slip-link and
slip-spring models for entangled polymers. In these models, the entanglement
effects are phenomenologically modeled by introducing topological constraints
such as slip-links and slip-springs. The average number of segments between two
neighboring slip-links or slip-springs, $N_{0}$, is an input parameter in these
models. To analyze experimental data, the characteristic number of segments in
entangled polymers $N_{e}$ estimated from the plateau modulus is used instead.
Both $N_{0}$ and $N_{e}$ characterize the topological constraints in entangled
polymers, and naively $N_{0}$ is considered to be the same as $N_{e}$. However,
earlier studies showed that $N_{0}$ and $N_{e}$ (or the plateau modulus) should
be considered as independent parameters. In this work, we show that due to the
fluctuations at the short time scale, $N_{e}$ deviates from $N_{0}$. This means
that the relation between $N_{0}$ and the plateau modulus is not simple as
naively expected. The plateau modulus (or $N_{e}$) depends on the
subchain-scale details of the employed model, as well as the average number of
segments $N_{0}$. This is due to the fact that the subchain-scale fluctuation
mechanisms depend on the model rather strongly. We theoretically calculate the
plateau moduli for several single-chain slip-link and slip-spring models. Our
results explicitly show that the relation between $N_{0}$ and $N_{e}$ is
model-dependent. We compare theoretical results with various simulation data in
the literature, and show that our theoretical expressions reasonably explain
the simulation results.


Abstract:
  We study, for the first time, the relations of two strong diffuse bands
(DIBs) at 9633 and 9577~\AA, commonly attributed to C$_{60}^+$, to other
interstellar features seen in optical and UV spectra including H{\sc i}, Ca{\sc
i}, Fe{\sc ii}, Na{\sc i}, Ti{\sc ii}, CN, CH, CH$^+$, and C$_2$ and DIBs 5780,
5797, 6196, 6269, 6284, and 6614. We analyzed 62 lines of sight where the
stellar contamination by Mg{\sc ii} was corrected or found negligible for DIB
9633. Equivalent widths of DIB 9577 were measured in 62 lines of sight. Poor
mutual correlation between the strengths of the above features and the major
diffuse bands (5780 and 5797) as well as with other DIBs (with some exceptions)
were revealed. The considered DIBs are also poorly correlated with the features
of neutral hydrogen, molecular carbon, and those of simple interstellar
radicals. Perhaps this phenomenon can be explained if the diffuse band 9577 is
an unresolved blend of two or more interstellar features. There are indications
that 9633 and 9577 diffuse bands are stronger in $\sigma$-type clouds, i.e.
these features resemble the behavior of reasonably broad DIBs, which are strong
in the lines of sight where the UV flux from the very hot nearby stars plays an
important role.


Abstract:
  Users increasingly face multiple interface features on one hand, and
constraints on available resources (e.g., time, attention) on the other.
Understanding the sensitivity of users' well-being to feature type and resource
constraints, is critical for informed design. Building on microeconomic theory,
and focusing on social information features, users' interface choices were
conceptualized as an exchange of resources (e.g., time), in return for access
to goods (social information features). We studied how sensitive users'
well-being is to features' type, and to their cost level and type. We found
that (1) increased cost of feature use leads to decreased well-being, (2)
users' well-being is a function of features' cost type, and (3) users'
well-being is sensitive to differences in feature type. The approach used here
to quantify user well-being derived from interface features offers a basis for
asynchronous feature comparison.


Abstract:
  In this article, we study the propagation of scalar fields in the
gravitational background of a spherically symmetric, electrically charged,
regular-de Sitter black holes in higher dimensions. We carry out a thorough
investigation of the grey body factors, by deriving analytical expressions for
both minimally and non-minimally coupled scalar fields. Then we study the
profile of grey body factors in terms of particle properties, namely the
angular momentum quantum number and the non-minimal coupling constant, and the
spacetime properties, namely the dimension, the cosmological constant and the
non-linear charge parameter. We then derive the Hawking radiation spectra for a
higher-dimensional electrically charged regular-de Sitter black hole. We find
that the non-minimal coupling may suppress the greybody factor and the
non-linear charge parameter may enhance it, but they both suppress the energy
emission rate of Hawking radiation.


Abstract:
  Accurate ab initio calculations of 3d transition metal monoxide molecules
have attracted extensive attention because of its relevance in physical and
chemical science, as well as theoretical challenges in treating strong electron
correlation. Meanwhile, recent years have witnessed the rapid development of
full configuration interaction quantum Monte Carlo (FCIQMC) method to tackle
electron correlation. In this study, we carry out FCIQMC simulations to ScO,
TiO and VO molecules and obtain accurate descriptions of 13 low-lying
electronic states (ScO $^2\Sigma^+$, $^2\Delta$, $^2\Pi$; TiO $^3\Delta$,
$^1\Delta$, $^1\Sigma^+$, $^3\Pi$, $^3\Phi$; VO $^4\Sigma^-$, $^4\Phi$,
$^4\Pi$, $^2\Gamma$, $^2\Delta$), including states that have significant
multi-configurational character. The FCIQMC results are used to assess the
performance of several other wave function theory and density functional theory
methods. Our study highlights the challenging nature of electronic structure of
transition metal oxides and demonstrates FCIQMC as a promising technique going
forward to treat more complex transition metal oxide molecules and materials.


Abstract:
  We investigate four-dimensional Heterotic solitons, defined as a particular
class of solutions of the equations of motion of Heterotic supergravity on a
four-manifold $M$. Heterotic solitons depend on a parameter $\kappa$ and
consist of a Riemannian metric $g$, a metric connection with skew torsion $H$
on $TM$ and a closed one-form $\varphi$ on $M$ satisfying a differential
system. In the limit $\kappa \to 0$, Heterotic solitons reduce to a class of
generalized Ricci solitons and can be considered as a higher-order curvature
modification of the latter. If the torsion $H$ is equal to the Hodge dual of
$\varphi$, Heterotic solitons consist of either flat tori or closed
Einstein-Weyl structures on manifolds of type $S^1\times S^3$ as introduced by
P. Gauduchon. We prove that the moduli space of such closed Einstein-Weyl
structures is isomorphic to the product of $\mathbb{R}$ with a certain finite
quotient of the Cartan torus of the isometry group of the typical fiber of a
natural fibration $M\to S^1$. We also consider the associated space of
essential infinitesimal deformations, which we prove to be obstructed. More
generally, we characterize several families of Heterotic solitons as
suspensions of certain three-manifolds with prescribed constant principal Ricci
curvatures, amongst which we find hyperbolic manifolds, manifolds covered by
$\widetilde{\mathrm{Sl}}(2,\mathbb{R})$ and E$(1,1)$ or certain Sasakian
three-manifolds. These solutions exhibit a topological dependence in the string
slope parameter $\kappa$ and yield, to the best of our knowledge, the first
examples of Heterotic compactification backgrounds not locally isomorphic to
supersymmetric compactification backgrounds.