Abstract:
  Learning with graphs has attracted significant attention recently. Existing
representation learning methods on graphs have achieved state-of-the-art
performance on various graph-related tasks such as node classification, link
prediction, etc. However, we observe that these methods could leak serious
private information. For instance, one can accurately infer the links (or node
identity) in a graph from a node classifier (or link predictor) trained on the
learnt node representations by existing methods. To address the issue, we
propose a privacy-preserving representation learning framework on graphs from
the \emph{mutual information} perspective. Specifically, our framework includes
a primary learning task and a privacy protection task, and we consider node
classification and link prediction as the two tasks of interest. Our goal is to
learn node representations such that they can be used to achieve high
performance for the primary learning task, while obtaining performance for the
privacy protection task close to random guessing. We formally formulate our
goal via mutual information objectives. However, it is intractable to compute
mutual information in practice. Then, we derive tractable variational bounds
for the mutual information terms, where each bound can be parameterized via a
neural network. Next, we train these parameterized neural networks to
approximate the true mutual information and learn privacy-preserving node
representations. We finally evaluate our framework on various graph datasets.


Abstract:
  We propose a novel framework for controllable pathological image synthesis
for data augmentation. Inspired by CycleGAN, we perform cycle-consistent
image-to-image translation between two domains: healthy and pathological.
Guided by a semantic mask, an adversarially trained generator synthesizes
pathology on a healthy image in the specified location. We demonstrate our
approach on an institutional dataset of cerebral microbleeds in traumatic brain
injury patients. We utilize synthetic images generated with our method for data
augmentation in cerebral microbleeds detection. Enriching the training dataset
with synthetic images exhibits the potential to increase detection performance
for cerebral microbleeds in traumatic brain injury patients.


Abstract:
  We consider a particle undergoing Brownian motion in Euclidean space of any
dimension, forced by a Gaussian random velocity field that is white in time and
smooth in space. We show that conditional on the velocity field, the quenched
density of the particle after a long time can be approximated pointwise by the
product of a deterministic Gaussian density and a spacetime-stationary random
field $U$. If the velocity field is additionally assumed to be incompressible,
then $U\equiv 1$ almost surely and we obtain a local central limit theorem.


Abstract:
  We use the Holder regularity analysis to study the symmetry breaking and
recovery due to a parametric potential generated via the strictly isospectral
factorization method. The initial potential is two-dimensional and periodic in
the two Cartesian directions, with the symmetry group $P_{4mm}$. The resulting
parametric isospectral potential display a P_m symmetry for values of the
parameter moderately close to the singular value gamma_s. However, at large
values of the parameter, visually around gamma=gamma_s+110, the original
symmetry is recovered. For a much higher precision value of the parameter for
this symmetry recovery, we show that the multifractal spectrum of the
parametric potential can be conveniently used. In the latter case, we obtain
gamma=gamma_s+201.085 for three decimal digits precision.


Abstract:
  In this note, we explicitly compute the vacuum expectation value of the
commutator of scalar fields in a d-dimensional conformal field theory on the
cylinder. We find from explicit calculations that we need smearing not only in
space but also in time to have finite commutators except for those of free
scalar operators. Thus the equal time commutators of the scalar fields are not
well-defined for a non-free conformal field theory, even if which is defined
from the Lagrangian. We also have the commutator for a conformal field theory
on Minkowski space, instead of the cylinder, by taking the small distance
limit. For the conformal field theory on Minkowski space, the above statements
are also applied.


Abstract:
  We investigate contributions to the anomalous magnetic moments of charged
leptons in the neutrino-extended Standard Model Effective Field Theory
($\nu$SMEFT). We discuss how $\nu$SMEFT operators can contribute to a lepton's
magnetic moment at one- and two-loop order. We show that only one operator can
account for existing electronic and muonic discrepancies, assuming new physics
appears above $1$ TeV. In particular, we find that a right-handed charged
current in combination with minimal sterile-active mixing can explain the
discrepancy for sterile neutrino masses of $\mathcal O(100)$ GeV while avoiding
direct and indirect constraints. We discuss how searches for sterile neutrino
production at the (HL-)LHC, measurements of $h\rightarrow \mu^+ \mu^-$ and
searches for $h\rightarrow e^+ e^-$, neutrinoless double beta decay
experiments, and improved unitarity tests of the CKM matrix can further probe
the relevant parameter space.


Abstract:
  Joint design of control and communication in Wireless Networked Control
Systems (WNCS) is a promising approach for future wireless industrial
applications. In this context, Age of Information (AoI) has been increasingly
utilized as a metric that is more representative than latency in the context of
systems with a sense-compute-actuate cycle. Nevertheless, AoI is commonly
defined for a single communication direction, Downlink or Uplink, which does
not capture the closed-loop dynamics. In this paper, we extend the concept of
AoI by defining a new metric, Age of Loop (AoL), relevant for WNCS closed-loop
systems. The AoL is defined as the time elapsed since the piece of information
causing the latest action or state (depending on the selected time origin) was
generated. We then use the proposed metric to learn the WNCS latency and
freshness bounds and we apply such learning methodology to minimize the long
term WNCS cost with the least amount of bandwidth. We show that, using the AoL,
we can learn the control system requirement and use this information to
optimize network resources.


Abstract:
  Pre-trained language models have achieved human-level performance on many
Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these
models truly understand language or answer questions by exploiting statistical
biases in datasets. Here, we demonstrate a simple yet effective method to
attack MRC models and reveal the statistical biases in these models. We apply
the method to the RACE dataset, for which the answer to each MRC question is
selected from 4 options. It is found that several pre-trained language models,
including BERT, ALBERT, and RoBERTa, show consistent preference to some
options, even when these options are irrelevant to the question. When
interfered by these irrelevant options, the performance of MRC models can be
reduced from human-level performance to the chance-level performance. Human
readers, however, are not clearly affected by these irrelevant options.
Finally, we propose an augmented training method that can greatly reduce
models' statistical biases.


Abstract:
  We study the problem of entity-relation extraction in the presence of
symbolic domain knowledge. Such knowledge takes the form of an ontology
defining relations and their permissible arguments. Previous approaches set out
to integrate such knowledge in their learning approaches either through
self-training, or through approximations that lose the precise meaning of the
logical expressions. By contrast, our approach employs semantic loss which
captures the precise meaning of a logical sentence through maintaining a
probability distribution over all possible states, and guiding the model to
solutions which minimize any constraint violations. With a focus on low-data
regimes, we show that semantic loss outperforms the baselines by a wide margin.


Abstract:
  Non-intrusive load monitoring (NILM) is a technique that uses a single sensor
to measure the total power consumption of a building. Using an energy
disaggregation method, the consumption of individual appliances can be
estimated from the aggregate measurement. Recent disaggregation algorithms have
significantly improved the performance of NILM systems. However, the
generalization capability of these methods to different houses as well as the
disaggregation of multi-state appliances are still major challenges. In this
paper we address these issues and propose an energy disaggregation approach
based on the variational autoencoders framework. The probabilistic encoder
makes this approach an efficient model for encoding information relevant to the
reconstruction of the target appliance consumption. In particular, the proposed
model accurately generates more complex load profiles, thus improving the power
signal reconstruction of multi-state appliances. Moreover, its regularized
latent space improves the generalization capabilities of the model across
different houses. The proposed model is compared to state-of-the-art NILM
approaches on the UK-DALE and REFIT datasets, and yields competitive results.
The mean absolute error reduces by 18% on average across all appliances
compared to the state-of-the-art. The F1-Score increases by more than 11%,
showing improvements for the detection of the target appliance in the aggregate
measurement.


Abstract:
  The attempt to include the Pauli principle in the Monte Carlo method by
acting also on the free flight step and not only at the end of each collision
is investigated. The charge transport in suspended monolayer graphene is
considered as test case. The results are compared with those obtained in the
standard Ensemble Monte Carlo technique and in the new Direct Simulation Monte
Carlo algorithm which is able to correctly handle with Pauli's principle. The
physical aspects of the investigated approach are analyzed as well.


Abstract:
  Inspired by recent work on extended image volumes that lays the ground for
randomized probing of extremely large seismic wavefield matrices, we present a
memory frugal and computationally efficient inversion methodology that uses
techniques from randomized linear algebra. By means of a carefully selected
realistic synthetic example, we demonstrate that we are capable of achieving
competitive inversion results at a fraction of the memory cost of conventional
full-waveform inversion with limited computational overhead. By exchanging
memory for negligible computational overhead, we open with the presented
technology the door towards the use of low-memory accelerators such as GPUs.


Abstract:
  This paper describes the performance of the team cs60075_team2 at SemEval
2021 Task 1 - Lexical Complexity Prediction. The main contribution of this
paper is to fine-tune transformer-based language models pre-trained on several
text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the
corpora from which the CompLex Dataset was extracted, and others being from
other specific domains such as Finance, Law, etc. We perform ablation studies
on selecting the transformer models and how their individual complexity scores
are aggregated to get the resulting complexity scores. Our method achieves a
best Pearson Correlation of $0.784$ in sub-task 1 (single word) and $0.836$ in
sub-task 2 (multiple word expressions).


Abstract:
  Type-2 fuzzy differential equations (T2FDEs) of order 1 are already known and
the solution method of type-2 fuzzy initial value problems (T2FIVPs) for them
was given by M. Mazandarani and M. Najariyan \cite{MN} in 2014. We give the
solution method of second-order T2FIVPs in this paper. Furthermore, we would
like to propose new notations for type-2 fuzzy theory where symbols tend to be
complicated and misleading. In particular, the Hukuhara differential symbols
introduced experimentally in this paper will give us clearler meanings and
expressions.


Abstract:
  This paper introduces neural architecture search (NAS) for the automatic
discovery of end-to-end keyword spotting (KWS) models in limited resource
environments. We employ a differentiable NAS approach to optimize the structure
of convolutional neural networks (CNNs) operating on raw audio waveforms. After
a suitable KWS model is found with NAS, we conduct quantization of weights and
activations to reduce the memory footprint. We conduct extensive experiments on
the Google speech commands dataset. In particular, we compare our end-to-end
approach to mel-frequency cepstral coefficient (MFCC) based systems. For
quantization, we compare fixed bit-width quantization and trained bit-width
quantization. Using NAS only, we were able to obtain a highly efficient model
with an accuracy of 95.55% using 75.7k parameters and 13.6M operations. Using
trained bit-width quantization, the same model achieves a test accuracy of
93.76% while using on average only 2.91 bits per activation and 2.51 bits per
weight.


Abstract:
  We consider the problem of explaining the predictions of graph neural
networks (GNNs), which otherwise are considered as black boxes. Existing
methods invariably focus on explaining the importance of graph nodes or edges
but ignore the substructures of graphs, which are more intuitive and
human-intelligible. In this work, we propose a novel method, known as
SubgraphX, to explain GNNs by identifying important subgraphs. Given a trained
GNN model and an input graph, our SubgraphX explains its predictions by
efficiently exploring different subgraphs with Monte Carlo tree search. To make
the tree search more effective, we propose to use Shapley values as a measure
of subgraph importance, which can also capture the interactions among different
subgraphs. To expedite computations, we propose efficient approximation schemes
to compute Shapley values for graph data. Our work represents the first attempt
to explain GNNs via identifying subgraphs explicitly and directly. Experimental
results show that our SubgraphX achieves significantly improved explanations,
while keeping computations at a reasonable level.


Abstract:
  We employ model predictive control for a multi-period portfolio optimization
problem. In addition to the mean-variance objective, we construct a portfolio
whose allocation is given by model predictive control with a risk-parity
objective, and provide a successive convex program algorithm that provides 30
times faster and robust solutions in the experiments. Computational results on
the multi-asset universe show that multi-period models perform better than
their single period counterparts in out-of-sample period, 2006-2020. The
out-of-sample risk-adjusted performance of both mean-variance and risk-parity
formulations beat the fix-mix benchmark, and achieve Sharpe ratio of 0.64 and
0.97, respectively.


Abstract:
  Anomaly detection with weakly supervised video-level labels is typically
formulated as a multiple instance learning (MIL) problem, in which we aim to
identify snippets containing abnormal events, with each video represented as a
bag of video snippets. Although current methods show effective detection
performance, their recognition of the positive instances, i.e., rare abnormal
snippets in the abnormal videos, is largely biased by the dominant negative
instances, especially when the abnormal events are subtle anomalies that
exhibit only small differences compared with normal events. This issue is
exacerbated in many methods that ignore important video temporal dependencies.
To address this issue, we introduce a novel and theoretically sound method,
named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature
magnitude learning function to effectively recognise the positive instances,
substantially improving the robustness of the MIL approach to the negative
instances from abnormal videos. RTFM also adapts dilated convolutions and
self-attention mechanisms to capture long- and short-range temporal
dependencies to learn the feature magnitude more faithfully. Extensive
experiments show that the RTFM-enabled MIL model (i) outperforms several
state-of-the-art methods by a large margin on four benchmark data sets
(ShanghaiTech, UCF-Crime, XD-Violence and UCSD-Peds) and (ii) achieves
significantly improved subtle anomaly discriminability and sample efficiency.
Code is available at https://github.com/tianyu0207/RTFM.


Abstract:
  The attenuation of ultrasound waves in photoacoustic and thermoacoustic
imaging presents an important drawback in the applicability of these
modalities. This issue has been addressed previously in the applied and
theoretical literature, and some advances have been made on the topic. In
particular, stability inequalities have been proposed for the inverse problem
of initial source recovery with partial observations under the assumption of
unique determination of the initial pressure. The main goal of this work is to
fill this gap, this is, we prove the uniqueness property for the inverse
problem and establish the associated stability estimates as well. The problem
of reconstructing the initial condition of acoustic waves in the complete-data
setting is revisited and a new Neumann series reconstruction formula is
obtained for the case of partial observations in a semi-bounded geometry. A
numerical simulation is also included to test the method.


Abstract:
  The production of the hypermagnetic gyrotropy is investigated under the
assumption that the gauge coupling smoothly evolves during a quasi-de Sitter
phase and then flattens out in the radiation epoch by always remaining
perturbative. In the plane defined by the strength of the anomalous
interactions and by the rate of evolution of the gauge coupling the actual
weight of the pseudoscalar interactions turns out to be always rather modest if
major deviations from the homogeneity are to be avoided during the inflationary
phase. Even if the gauge power spectra are related by duality only in the
absence of anomalous contributions, an approximate duality symmetry constrains
the late-time form of the hypermagnetic power spectra. Since the hypermagnetic
gyrotropy associated with the modes reentering prior to the phase transition
must be released into fermions later on, the portions of the parameter space
where the obtained baryon asymmetry is close to the observed value are the most
relevant for the present ends. For the same range of parameters the magnetic
power spectra associated with the modes reentering after symmetry breaking may
even be of the order of a few hundredths of a nG over typical length scales
comparable with the Mpc prior to the collapse of the protogalaxy.


Abstract:
  The asymptotic law for the expected nodal volume of random non-Gaussian
monochromatic band-limited functions is determined in vast generality. Our
methods combine microlocal analytic techniques and modern probability theory. A
particularly challenging obstacle needed to overcome is the possible
concentration of nodal volume on a small proportion of the manifold, requiring
solutions in both disciplines. As for the fine aspects of the distribution of
nodal volume, such as its variance, it is expected that the non-Gaussian
monochromatic functions behave qualitatively differently compared to their
Gaussian counterpart, with some conjectures been put forward.


Abstract:
  In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training
strategy to tackle pronoun resolution in a fully unsupervised setting. Firstly,
We evaluate our pre-trained model on various pronoun resolution datasets
without any finetuning. Our method outperforms all previous unsupervised
methods on all datasets by large margins. Secondly, we proceed to a few-shot
setting where we finetune our pre-trained model on WinoGrande-S and XS
separately. Our method outperforms RoBERTa-large baseline with large margins,
meanwhile, achieving a higher AUC score after further finetuning on the
remaining three official splits of WinoGrande.


Abstract:
  The abundance of data has given machine learning considerable momentum in
natural sciences and engineering. However, the modeling of simulated physical
processes remains difficult. A key problem is the correct handling of geometric
boundaries. While triangularized geometric boundaries are very common in
engineering applications, they are notoriously difficult to model by machine
learning approaches due to their heterogeneity with respect to size and
orientation. In this work, we introduce Boundary Graph Neural Networks (BGNNs),
which dynamically modify graph structures to address boundary conditions.
Boundary graph structures are constructed via modifying edges, augmenting node
features, and dynamically inserting virtual nodes. The new BGNNs are tested on
complex 3D granular flow processes of hoppers and rotating drums which are
standard components of industrial machinery. Using precise simulations that are
obtained by an expensive and complex discrete element method, BGNNs are
evaluated in terms of computational efficiency as well as prediction accuracy
of particle flows and mixing entropies. Even if complex boundaries are present,
BGNNs are able to accurately reproduce 3D granular flows within simulation
uncertainties over hundreds of thousands of simulation timesteps, and most
notably particles completely stay within the geometric objects without using
handcrafted conditions or restrictions.


Abstract:
  We resolve the min-max complexity of distributed stochastic convex
optimization (up to a log factor) in the intermittent communication setting,
where $M$ machines work in parallel over the course of $R$ rounds of
communication to optimize the objective, and during each round of
communication, each machine may sequentially compute $K$ stochastic gradient
estimates. We present a novel lower bound with a matching upper bound that
establishes an optimal algorithm.


Abstract:
  We observe a $n$-sample, the distribution of which is assumed to belong, or
at least to be close enough, to a given mixture model. We propose an estimator
of this distribution that belongs to our model and possesses some robustness
properties with respect to a possible misspecification of it. We establish a
non-asymptotic deviation bound for the Hellinger distance between the target
distribution and its estimator when the model consists of a mixture of
densities that belong to VC-subgraph classes. Under suitable assumptions and
when the mixture model is well-specified, we derive risk bounds for the
parameters of the mixture. Finally, we design a statistical procedure that
allows us to select from the data the number of components as well as suitable
models for each of the densities that are involved in the mixture. These models
are chosen among a collection of candidate ones and we show that our selection
rule combined with our estimation strategy result in an estimator which
satisfies an oracle-type inequality.


Abstract:
  We define certain arithmetic derivatives on $\mathbb{Z}$ that respect the
Leibniz rule, are additive for a chosen equation $a+b=c$, and satisfy a
suitable non-degeneracy condition. Using Geometry of Numbers, we
unconditionally show their existence with controlled size. We prove that any
power-saving improvement on our size bounds would give a version of the $abc$
Conjecture. In fact, we show that the existence of sufficiently small
arithmetic derivatives in our sense is equivalent to the $abc$ Conjecture. Our
results give an explicit manifestation of an analogy suggested by Vojta in the
eighties, relating Geometry of Numbers in arithmetic to derivatives in function
fields and Nevanlinna theory. In addition, our construction formalizes the
widespread intuition that the $abc$ Conjecture should be related to arithmetic
derivatives of some sort.


Abstract:
  Python libraries are widely used for machine learning and scientific
computing tasks today. APIs in Python libraries are deprecated due to feature
enhancements and bug fixes in the same way as in other languages. These
deprecated APIs are discouraged from being used in further software
development. Manually detecting and replacing deprecated APIs is a tedious and
time-consuming task due to the large number of API calls used in the projects.
Moreover, the lack of proper documentation for these deprecated APIs makes the
task challenging. To address this challenge, we propose an algorithm and a tool
APIScanner that automatically detects deprecated APIs in Python libraries. This
algorithm parses the source code of the libraries using abstract syntax tree
(ASTs) and identifies the deprecated APIs via decorator, hard-coded warning or
comments. APIScanner is a Visual Studio Code Extension that highlights and
warns the developer on the use of deprecated API elements while writing the
source code. The tool can help developers to avoid using deprecated API
elements without the execution of code. We tested our algorithm and tool on six
popular Python libraries, which detected 838 of 871 deprecated API elements.
Demo of APIScanner: https://youtu.be/1hy_ugf-iek. Documentation, tool, and
source code can be found here: https://rishitha957.github.io/APIScanner.


Abstract:
  Deep Neural Networks (DNNs) have achieved remarkable performance on a variety
of applications but are extremely vulnerable to adversarial perturbation. To
address this issue, various defense methods have been proposed to enhance model
robustness. Unfortunately, the most representative and promising methods, such
as adversarial training and its variants, usually degrade model accuracy on
benign samples, limiting practical utility. This indicates that it is difficult
to extract both robust and accurate features using a single network under
certain conditions, such as limited training data, resulting in a trade-off
between accuracy and robustness. To tackle this problem, we propose an
Adversarial Feature Stacking (AFS) model that can jointly take advantage of
features with varied levels of robustness and accuracy, thus significantly
alleviating the aforementioned trade-off. Specifically, we adopt multiple
networks adversarially trained with different perturbation budgets to extract
either more robust features or more accurate features. These features are then
fused by a learnable merger to give final predictions. We evaluate the AFS
model on CIFAR-10 and CIFAR-100 datasets with strong adaptive attack methods,
which significantly advances the state-of-the-art in terms of the trade-off.
Without extra training data, the AFS model achieves a benign accuracy
improvement of 6% on CIFAR-10 and 9% on CIFAR-100 with comparable or even
stronger robustness than the state-of-the-art adversarial training methods.
This work demonstrates the feasibility to obtain both accurate and robust
models under the circumstances of limited training data.


Abstract:
  The concept of balancedly splittable orthogonal designs is introduced along
with a recursive construction. As an application, equiangular tight frames over
the real, complex, and quaternions meeting the Delsarte-Goethals-Seidel upper
bound is obtained.


Abstract:
  In this paper, we introduce a noisy framework for SFTs, allowing some amount
of forbidden patterns to appear. Using the Besicovitch distance, which permits
a global comparison of configurations, we then study the closeness of noisy
measures to non-noisy ones as the amount of noise goes to 0. Our first main
result is the full classification of the (in)stability in the one-dimensional
case. Our second main result is a stability property under Bernoulli noise for
higher-dimensional periodic SFTs, which we finally extend to an aperiodic
example through a variant of the Robinson tiling.


Abstract:
  We study Cayley graphs of abelian groups from the perspective of quantum
symmetries. We develop a general strategy for determining the quantum
automorphism groups of such graphs. Applying this procedure, we find the
quantum symmetries of the halved cube graph, the folded cube graph and the
Hamming graphs.


Abstract:
  Gravitational waves are a radically new way to peer into the darkest depths
of the cosmos. Pulsars can be used to make direct detections of gravitational
waves through precision timing. When a gravitational wave passes between a
pulsar and the Earth, it stretches and squeezes the intermediate space-time,
leading to deviations of the measured pulse arrival times away from model
expectations. Combining the data from many Galactic pulsars can corroborate
such a signal, and enhance its detection significance. This technique is known
as a Pulsar Timing Array (PTA). Here I provide an overview of PTAs as a
precision gravitational-wave detection instrument, then review the types of
signal and noise processes that we encounter in typical pulsar data analysis. I
take a pragmatic approach, illustrating how searches are performed in real
life, and where possible directing the reader to codes or techniques that they
can explore for themselves. The goal is to provide theoretical background and
practical recipes for data exploration that allow the reader to join in the
exciting hunt for very low frequency gravitational waves.


Abstract:
  We study variation in policing outcomes attributable to differential policing
practices in New York City (NYC) using geographic regression discontinuity
designs. By focusing on small geographic windows near police precinct
boundaries we can estimate local average treatment effects of precincts on
arrest rates. The standard geographic regression discontinuity design relies on
continuity assumptions of the potential outcome surface or a local
randomization assumption within a window around the boundary. While these
assumptions are often thought to be more realistic than other assumptions used
to infer causality from observational data, they can easily be violated in
realistic applications. We develop a novel and robust approach to testing
whether there are differences in policing outcomes that are caused by
differences in police precincts across NYC. In particular, our test is robust
to violations of the assumptions traditionally made in geographic regression
discontinuity designs and is valid under much weaker assumptions. We use a
unique form of resampling to identify new geographic boundaries that are known
to have no treatment effect, which provides a valid estimate of our estimator's
null distribution even under violations of standard assumptions. We find that
this procedure gives substantially different results in the analysis of NYC
arrest rates than those that rely on standard assumptions, thereby providing
more robust estimates of the nature of the effect of police precincts on arrest
rates in NYC.


Abstract:
  The emergence of Big Data in recent years has resulted in a growing need for
efficient data processing solutions. While infrastructures with sufficient
compute power are available, the I/O bottleneck remains. The Linux page cache
is an efficient approach to reduce I/O overheads, but few experimental studies
of its interactions with Big Data applications exist, partly due to limitations
of real-world experiments. Simulation is a popular approach to address these
issues, however, existing simulation frameworks do not simulate page caching
fully, or even at all. As a result, simulation-based performance studies of
data-intensive applications lead to inaccurate results.
  In this paper, we propose an I/O simulation model that includes the key
features of the Linux page cache. We have implemented this model as part of the
WRENCH workflow simulation framework, which itself builds on the popular
SimGrid distributed systems simulation framework. Our model and its
implementation enable the simulation of both single-threaded and multithreaded
applications, and of both writeback and writethrough caches for local or
network-based filesystems. We evaluate the accuracy of our model in different
conditions, including sequential and concurrent applications, as well as local
and remote I/Os. We find that our page cache model reduces the simulation error
by up to an order of magnitude when compared to state-of-the-art, cacheless
simulations.


Abstract:
  Song et al. [Phys. Rev. C 102, 065208 (2020)] presented results for the
$\Lambda_c N$ interaction based on an extrapolation of lattice simulations by
the HAL QCD Collaboration at unphysical quark masses to the physical point via
covariant chiral effective field theory. We point out that their predictions
for the $^3D_1$ partial wave disagree with available lattice results. We
discuss the origin of that disagreement and present a comparison with
predictions from conventional (non-relativistic) chiral effective field theory.


Abstract:
  A $(0,1)$-matrix has the consecutive-ones property (C1P) if its columns can
be permuted to make the $1$'s in each row appear consecutively. This property
was characterised in terms of forbidden submatrices by Tucker in 1972. Several
graph classes were characterised by means of this property, including interval
graphs and strongly chordal digraphs. In this work, we define and characterise
2-nested matrices, which are $(0,1)$-matrices with a variant of the C1P and for
which there is also certain assignment of one of two colors to each block of
consecutive $1$'s in each row. The characterization of 2-nested matrices in the
present work is of key importance to characterise split graphs that are also
circle by minimal forbidden induced subgraphs.


Abstract:
  In this article, we define the $m$-prismatic site and the $m$-$q$-crystalline
site, which is a higher level analogue of the prismatic site and the
$q$-crystalline site respectively. We prove a certain equivalence between the
category of crystals on the $m$-prismatic site (resp. the $m$-$q$-crystalline
site) and that on the prismatic site (resp. the $q$-crystalline site), which
can be regarded as the prismatic (resp. the $q$-crystalline) analogue of the
Frobenius descent due to Berthelot and the Cartier transform due to
Ogus-Vologodsky, Oyama and Xu. We also prove the equivalence between the
category of crystals on the $m$-prismatic site and that on the
$(m-1)$-$q$-crystalline site.


Abstract:
  Gadolinium-based contrast agents (GBCAs) have been widely used to better
visualize disease in brain magnetic resonance imaging (MRI). However,
gadolinium deposition within the brain and body has raised safety concerns
about the use of GBCAs. Therefore, the development of novel approaches that can
decrease or even eliminate GBCA exposure while providing similar contrast
information would be of significant use clinically. For brain tumor patients,
standard-of-care includes repeated MRI with gadolinium-based contrast for
disease monitoring, increasing the risk of gadolinium deposition. In this work,
we present a deep learning based approach for contrast-enhanced T1 synthesis on
brain tumor patients. A 3D high-resolution fully convolutional network (FCN),
which maintains high resolution information through processing and aggregates
multi-scale information in parallel, is designed to map pre-contrast MRI
sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast
MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are
utilized as inputs and the post-contrast T1 sequences are utilized as target
output. To alleviate the data imbalance problem between normal tissues and the
tumor regions, we introduce a local loss to improve the contribution of the
tumor regions, which leads to better enhancement results on tumors. Extensive
quantitative and visual assessments are performed, with our proposed model
achieving a PSNR of 28.24dB in the brain and 21.2dB in tumor regions. Our
results suggests the potential of substituting GBCAs with synthetic contrast
images generated via deep learning.


Abstract:
  In this paper, we investigate the Lie algebra structures of weight one
subspaces of $C_2$-cofinite vertex operator superalgebras. We also show that
for any positive integer $k$, vertex operator superalgebras
$L_{sl(1|n+1)}(k,0)$ and $L_{osp(2|2n)}(k,0)$ have infinitely many irreducible
admissible modules. As a consequence, we give a proof of the fact that
$L_{\mathfrak g}(k,0)$ is $C_2$-cofinite if and only if $\mathfrak g$ is either
a simple Lie algebra, or $\mathfrak g=osp(1|2n)$, and $k$ is a nonnegative
integer. As an application, we show that $L_{G(3)}(1,0)$ is a vertex operator
superalgebra such that the category of $L_{G(3)}(1,0)$-modules is semisimple
but $L_{G(3)}(1,0)$ is not $C_2$-cofinite.


Abstract:
  Organic-inorganic layered perovskites are two-dimensional quantum wells with
layers of lead-halide octahedra stacked between organic ligand barriers. The
combination of their dielectric confinement and ionic sublattice results in
excitonic excitations with substantial binding energies that are strongly
coupled to the surrounding soft, polar lattice. However, the ligand environment
in layered perovskites can significantly alter their optical properties due to
the complex dynamic disorder of soft perovskite lattice. Here, we observe the
dynamic disorder through phonon dephasing lifetimes initiated by ultrafast
photoexcitation employing high-resolution resonant impulsive stimulated Raman
spectroscopy of a variety of ligand substitutions. We demonstrate that
vibrational relaxation in layered perovskite formed from flexible alkyl-amines
as organic barriers is fast and relatively independent of the lattice
temperature. Relaxation in aromatic amine based layered perovskite is slower,
though still fast relative to pure inorganic lead bromide lattices, with a rate
that is temperature dependent. Using molecular dynamics simulations, we explain
the fast rates of relaxation by quantifying the large anharmonic coupling of
the optical modes with the ligand layers and rationalize the temperature
independence due to their amorphous packing. This work provides a molecular and
time-domain depiction of the relaxation of nascent optical excitations and
opens opportunities to understand how they couple to the complex layered
perovskite lattice, elucidating design principles for optoelectronic devices.


Abstract:
  The aim of this paper is to investigate the cardiorespiratory synchronization
in athletes subjected to extreme physical stress combined with a cognitive
stress tasks. ECG and respiration were measured in 14 athletes before and after
the Ironmen competition. Stroop test was applied between the measurements
before and after the Ironmen competition to induce cognitive stress.
Synchrogram and empirical mode decomposition analysis were used for the first
time to investigate the effects of physical stress, induced by the Ironmen
competition, on the phase synchronization of the cardiac and respiratory
systems of Ironmen athletes before and after the competition. A cognitive
stress task (Stroop test) was performed both pre- and post-Ironman event in
order to prevent the athletes from cognitively controlling their breathing
rates. Our analysis showed that cardiorespiratory synchronization increased
post-Ironman race compared to pre-Ironman. The results suggest that the amount
of stress the athletes are recovering from post-competition is greater than the
effects of the Stroop test. This indicates that the recovery phase after the
competition is more important for restoring and maintaining homeostasis, which
could be another reason for stronger synchronization.


Abstract:
  Many panel data have the latent subgroup effect on individuals, and it is
important to correctly identify these groups since the efficiency of resulting
estimators can be improved significantly by pooling the information of
individuals within each group. However, the currently assumed parametric and
semiparametric relationship between the response and predictors may be
misspecified, which leads to a wrong grouping result, and the nonparametric
approach hence can be considered to avoid such mistakes. Moreover, the response
may depend on predictors in different ways at various quantile levels, and the
corresponding grouping structure may also vary. To tackle these problems, this
paper proposes a nonparametric quantile regression method for homogeneity
pursuit, and a pairwise fused penalty is used to automatically select the
number of groups. The asymptotic properties are established, and an ADMM
algorithm is also developed. The finite sample performance is evaluated by
simulation experiments, and the usefulness of the proposed methodology is
further illustrated by an empirical example.


Abstract:
  Severe geomagnetic disturbances increase the magnitude of the electric field
on the Earth's surface (E-field) and drive geomagnetically-induced currents
(GICs) along the transmission lines in electric grids. These additional
currents can pose severe risks, such as current distortions, transformer
saturation and increased reactive power losses, each of which can lead to
system unreliability. Several mitigation actions (e.g., changing grid topology)
exist that can reduce the harmful GIC effects on the grids. Making such
decisions can be challenging, however, because the magnitude and direction of
the E-field are uncertain and non-stationary. In this paper, we model uncertain
E-fields using the distributionally robust optimization (DRO) approach that
determines optimal transmission grid operations such that the worst-case
expectation of the system cost is minimized. We also capture the effect of GICs
on the nonlinear AC power flow equations. For solution approaches, we develop
an accelerated column-and-constraint generation (CCG) algorithm by exploiting a
special structure of the support set of uncertain parameters representing the
E-field. Extensive numerical experiments based on "epri-21" and "uiuc-150"
systems, designed for GMD studies, demonstrate (i) the computational
performance of the accelerated CCG algorithm, and (ii) the performance of
distributionally robust grid operations that satisfy nonlinear, nonconvex AC
power flow equations and GIC constraints.


Abstract:
  We use 78 reverberation-measured Mg II time-lag quasars (QSOs) in the
redshift range $0.0033 \leq z \leq 1.89$ to constrain cosmological parameters
in six different cosmological models. The basis of our method is the use of the
radius-luminosity or $R-L$ relation to standardize these 78 Mg II QSOs. In each
cosmological model we simultaneously determine $R-L$ relation and cosmological
model parameters, thus avoiding the circularity problem. We find that the $R-L$
relation parameter values are independent of the cosmological model used in the
analysis thus establishing that current Mg II QSOs are standardizable candles.
Cosmological constraints obtained using these QSOs are significantly weaker
than, but consistent with, those obtained from a joint analysis of baryon
acoustic oscillation (BAO) observations and Hubble parameter [$H(z)$]
measurements. So, we also analyse these QSOs in conjunction with the BAO +
$H(z)$ data and find cosmological constraints consistent with the standard
spatially-flat $\Lambda$CDM model as well as with mild dark energy dynamics and
a little spatial curvature. A larger sample of higher-quality
reverberation-measured QSOs should have a smaller intrinsic dispersion and so
should provide tighter constraints on cosmological parameters.


Abstract:
  Threat actors can be persistent, motivated and agile, and leverage a
diversified and extensive set of tactics and techniques to attain their goals.
In response to that, defenders establish threat intelligence programs to stay
threat-informed and lower risk. Actionable threat intelligence is integrated
into security information and event management systems (SIEM) or is accessed
via more dedicated tools like threat intelligence platforms. A threat
intelligence platform gives access to contextual threat information by
aggregating, processing, correlating, and analyzing real-time data and
information from multiple sources, and in many cases, it provides centralized
analysis and reporting of an organization's security events. Sysmon logs is a
data source that has received considerable attention for endpoint visibility.
Approaches for threat detection using Sysmon have been proposed, mainly
focusing on search engine technologies like NoSQL database systems. This paper
demonstrates one of the many use cases of Sysmon and cyber threat intelligence.
In particular, we present a threat assessment system that relies on a cyber
threat intelligence ontology to automatically classify executed software into
different threat levels by analyzing Sysmon log streams. The presented system
and approach augments cyber defensive capabilities through situational
awareness, prediction, and automated courses of action.


Abstract:
  Stellar Intensity Interferometry is a technique based on the measurement of
the second order spatial correlation of the light emitted from a star. The
physical information provided by these measurements is the angular size and
structure of the emitting source. A worldwide effort is presently under way to
implement stellar intensity interferometry on telescopes separated by long
baselines and on future arrays of Cherenkov telescopes. We describe an
experiment of this type, realized at the Asiago Observatory (Italy), in which
we performed for the first time measurements of the correlation counting photon
coincidences in post-processing by means of a single photon software correlator
and exploiting entirely the quantum properties of the light emitted from a
star. We successfully detected the temporal correlation of Vega at zero
baseline and performed a measurement of the correlation on a projected baseline
of $\sim$2 km. The average discrete degree of coherence at zero baseline for
Vega is $< g^{(2)} > \, = 1.0034 \pm 0.0008$, providing a detection with a
signal-to-noise ratio $S/N \gtrsim 4$. No correlation is detected over the km
baseline. The measurements are consistent with the expected degree of spatial
coherence for a source with the 3.3 mas angular diameter of Vega. The
experience gained with the Asiago experiment will serve for future
implementations of stellar intensity interferometry on long-baseline arrays of
Cherenkov telescopes.


Abstract:
  In recent years, mobile clients' computing ability and storage capacity have
greatly improved, efficiently dealing with some applications locally. Federated
learning is a promising distributed machine learning solution that uses local
computing and local data to train the Artificial Intelligence (AI) model.
Combining local computing and federated learning can train a powerful AI model
under the premise of ensuring local data privacy while making full use of
mobile clients' resources. However, the heterogeneity of local data, that is,
Non-independent and identical distribution (Non-IID) and imbalance of local
data size, may bring a bottleneck hindering the application of federated
learning in mobile edge computing (MEC) system. Inspired by this, we propose a
cluster-based clients selection method that can generate a federated virtual
dataset that satisfies the global distribution to offset the impact of data
heterogeneity and proved that the proposed scheme could converge to an
approximate optimal solution. Based on the clustering method, we propose an
auction-based clients selection scheme within each cluster that fully considers
the system's energy heterogeneity and gives the Nash equilibrium solution of
the proposed scheme for balance the energy consumption and improving the
convergence rate. The simulation results show that our proposed selection
methods and auction-based federated learning can achieve better performance
with the Convolutional Neural Network model (CNN) under different data
distributions.


Abstract:
  Despite the modern level of development of computational chemistry methods
and techno-logical progress, fast and accurate determination of solvation free
energy remains a huge problem for physical chemists. In this paper, we describe
two computational schemes that can potentially solve this problem. We consider
systems of poorly soluble drug compounds in supercritical carbon dioxide.
Considering that the biggest contribution among all inter-molecular
interactions is made by van der Waals interactions, we model solute and solvent
particles as coarse-grained ones interacting via the effective Lennard-Jones
potential. The first proposed approach is based on the classical density
functional theory and the second one relies on molecular dynamics simulation of
the Lennard-Jones fluid. Sacrificing the precision of the molecular structure
description while capturing the phase behavior of the fluid with sufficient
accuracy, we propose computationally advantageous paths to obtaining the
solvation free energy values with the accuracy satisfactory for engineering
applications. The agreement reached between the results of such coarse-graining
models and the experimental data indicates that the use of the all-atom
molecular dynamic simulations for the studied systems seems to be excessive.


Abstract:
  This paper illustrates two algorithms designed in Forneron & Ng (2020): the
resampled Newton-Raphson (rNR) and resampled quasi-Newton (rqN) algorithms
which speed-up estimation and bootstrap inference for structural models. An
empirical application to BLP shows that computation time decreases from nearly
5 hours with the standard bootstrap to just over 1 hour with rNR, and only 15
minutes using rqN. A first Monte-Carlo exercise illustrates the accuracy of the
method for estimation and inference in a probit IV regression. A second
exercise additionally illustrates statistical efficiency gains relative to
standard estimation for simulation-based estimation using a dynamic panel
regression example.


Abstract:
  Warm Jupiters -- defined here as planets larger than 6 Earth radii with
orbital periods of 8--200 days -- are a key missing piece in our understanding
of how planetary systems form and evolve. It is currently debated whether Warm
Jupiters form in situ, undergo disk or high eccentricity tidal migration, or
have a mixture of origin channels. These different classes of origin channels
lead to different expectations for Warm Jupiters' properties, which are
currently difficult to evaluate due to the small sample size. We take advantage
of the \TESS survey and systematically search for Warm Jupiter candidates
around main-sequence host stars brighter than the \TESS-band magnitude of 12 in
the Full-Frame Images in Year 1 of the \TESS Prime Mission data. We introduce a
catalog of 55 Warm Jupiter candidates, including 19 candidates that were not
originally released as \TESS Objects of Interest (TOIs) by the \TESS team. We
fit their \TESS light curves, characterize their eccentricities and
transit-timing variations (TTVs), and prioritize a list for ground-based
follow-up and \TESS Extended Mission observations. Using hierarchical Bayesian
modeling, we find the preliminary eccentricity distributions of our
Warm-Jupiter-candidate catalog using a Beta distribution, a Rayleigh
distribution, and a two-component Gaussian distribution as the functional forms
of the eccentricity distribution. Additional follow-up observations will be
required to clean the sample of false positives for a full statistical study,
derive the orbital solutions to break the eccentricity degeneracy, and provide
mass measurements.


Abstract:
  A hallmark of topological superconductivity is the non-Abelian statistics of
Majorana bound states (MBS), its chargeless zero-energy emergent
quasiparticles. The resulting fractionalization of a single electron, stored
nonlocally as a two spatially separated MBS, provides a powerful platform for
implementing fault-tolerant topological quantum computing. However, despite
intensive efforts, experimental support for MBS remains indirect and does not
probe their non-Abelian statistics. Here we propose how to overcome this
obstacle in mini-gate controlled planar Josephson junctions (JJ) and
demonstrate non-Abelian statistics through MBS fusion, detected by charge
sensing using a quantum point contact. The feasibility of preparing,
manipulating, and fusing MBS in two-dimensional (2D) systems is supported in
our experiments which demonstrate the control of superconducting properties
with five mini gates in InAs/Al-based JJs. While we focus on this
well-established platform, where the topological superconductivity was already
experimentally detected, our proposal to identify elusive non-Abelian
statistics motivates also further MBS studies in other gate-controlled 2D
systems.


Abstract:
  This tutorial aims at giving an introductory treatment of the circuit
analysis of superconducting qubits, i.e., two-level systems in superconducting
circuits. It also touches upon couplings between such qubits and how microwave
driving and these couplings can be used for single- and two-qubit gates, as
well as how to include noise when calculating the dynamics of the system. We
also discuss higher-dimensional superconducting qudits. The tutorial is
intended for new researchers with limited or no experience with the field but
should be accessible to anyone with a bachelor's degree in physics. The
tutorial introduces the basic methods used in quantum circuit analysis,
starting from a circuit diagram and ending with a quantized Hamiltonian, that
may be truncated to the lowest levels. We provide examples of all the basic
techniques throughout the discussion, while in the last part of the tutorial we
discuss several of the most commonly used circuits for quantum-information
applications. This includes both worked examples of single qubits and examples
of how to analyze the coupling methods that allow multiqubit operations. In
several detailed appendices, we provide the interested reader with an
introduction to more advanced techniques for handling larger circuit designs.


Abstract:
  We give a complete description of the third-moment (skewness) characteristics
of both linear and nonlinear momentum trading strategies, the latter being
understood as transformations of a normalised moving-average filter (EMA). We
explain in detail why the skewness is generally positive and has a term
structure.
  This paper is a synthesis of two papers published by the author in RISK in
2012, with some updates and comments.


Abstract:
  We propose an agent-based network formation model under uncertainty with the
objective of relaxing the common assumption of complete information, calling
attention to the role beliefs may play in segregation. We demonstrate that our
model is capable of generating a set of networks that encompasses those of a
complete information model. Further, we show that by allowing agents to be
biased toward each other based on observable attributes, our model is able to
generate homophilous equilibria with preferences that are indifferent to these
attributes. We accompany our theoretical results with a simulation-based
investigation of the relationship between beliefs and segregation and show that
biased beliefs are an important driver of segregation under incomplete
information.


Abstract:
  Gapped bilayer graphene can support the presence of intragap states due to
kink gate potentials applied to the graphene layers. Electrons in these states
display valley-momentum locking, which makes them attractive for topological
valleytronics. Here, we show that kink-antikink local potentials enable
modulated scattering of topological currents. We find that the kink-antikink
coupling leads to anomalous steps in the junction conductance. Further, when
the constriction detaches from the propagating modes, forming a loop, the
conductance reveals the system energy spectrum. Remarkably, these kink-antikink
devices can also work as valley filters with tiny magnetic fields by tuning a
central gate.


Abstract:
  Rigidity regulates the integrity and function of many physical and biological
systems. This is the first of two papers on the origin of rigidity, wherein we
propose that "energetic rigidity," in which all non-trivial deformations raise
the energy of a structure, is a more useful notion of rigidity in practice than
two more commonly used rigidity tests: Maxwell-Calladine constraint counting
(first-order rigidity) and second-order rigidity. We find that constraint
counting robustly predicts energetic rigidity only when the system has no
states of self stress. When the system has states of self stress, we show that
second-order rigidity can imply energetic rigidity in systems that are not
considered rigid based on constraint counting, and is even more reliable than
shear modulus. We also show that there may be systems for which neither first
nor second-order rigidity imply energetic rigidity. The formalism of energetic
rigidity unifies our understanding of mechanical stability and also suggests
new avenues for material design.


Abstract:
  We present protocols for quantum key distribution in a prepare-and-measure
setup with an asymmetric level of trust. While the device of the sender (Alice)
is partially characterized, the receiver's (Bob's) device is treated as a
black-box. The security of the protocols is based on the assumption that
Alice's prepared states have limited overlaps, but no explicit bound on the
Hilbert space dimension is required. The protocols are immune to attacks on the
receiver's device, such as blinding attacks. The users can establish a secret
key while continuously monitoring the correct functioning of their devices
through observed statistics. We report a proof-of-principle demonstration,
involving mostly off-the-shelf equipment, as well as a high-efficiency
superconducting nanowire detector. A positive key rate is demonstrated over a
4.8 km low-loss optical fiber with finite-key analysis. The prospects of
implementing these protocols over longer distances is discussed.


Abstract:
  For robotics systems to be used in high risk, real-world situations, they
have to be quickly deployable and robust to environmental changes,
under-performing hardware, and mission subtask failures. Robots are often
designed to consider a single sequence of mission events, with complex
algorithms lowering individual subtask failure rates under some critical
constraints. Our approach is to leverage common techniques in vision and
control and encode robustness into mission structure through outcome monitoring
and recovery strategies, aided by a system infrastructure that allows for quick
mission deployments under tight time constraints and no central communication.
We also detail lessons in rapid field robotics development and testing. Systems
were developed and evaluated through real-robot experiments at an outdoor test
site in Pittsburgh, Pennsylvania, USA, as well as in the 2020 Mohamed Bin Zayed
International Robotics Challenge. All competition trials were completed in
fully autonomous mode without RTK-GPS. Our system led to 4th place in Challenge
2 and 7th place in the Grand Challenge, and achievements like popping five
balloons (Challenge 1), successfully picking and placing a block (Challenge 2),
and dispensing the most water autonomously with a UAV of all teams onto an
outdoor, real fire (Challenge 3).


Abstract:
  We consider bijective maps $\phi$ on the full operator algebra
$\mathcal{B}(\mathcal{H})$ of an infinite dimensional Hilbert space with the
property that, for every $A,B,X\in \mathcal{B}(\mathcal{H})$, $X$ is the
Douglas solution of the equation $A=BX$ if and only if $Y=\phi(X)$ is the
Douglas solution of the equation $\phi(A)=\phi(B)Y$. We prove that those maps
are implemented by a unitary or anti-unitary map $U$, i.e., $\phi(A)=UAU^*$.


Abstract:
  (Multi-type) branching processes are a natural and well-studied model for
generating random infinite trees. Branching processes feature both
nondeterministic and probabilistic branching, generalizing both transition
systems and Markov chains (but not generally Markov decision processes). We
study the complexity of model checking branching processes against linear-time
omega-regular specifications: is it the case almost surely that every branch of
a tree randomly generated by the branching process satisfies the omega-regular
specification? The main result is that for LTL specifications this problem is
in PSPACE, subsuming classical results for transition systems and Markov
chains, respectively. The underlying general model-checking algorithm is based
on the automata-theoretic approach, using unambiguous B\"uchi automata.


Abstract:
  Axons in white matter have been shown to have varying geometries within a
bundle using ex vivo imaging techniques, but what does this mean for diffusion
MRI (dMRI) based spherical deconvolution (SD)? SD attempts to estimate the
fibre orientation distribution function (fODF) by assuming a single dMRI fibre
response function (FRF) for all white matter populations and deconvolving this
FRF from the dMRI signal at each voxel to estimate the fODF. Variable fibre
geometry within a bundle however suggests the FRF might not be constant even
within a single voxel. We test what impact realistic fibre geometry has on SD
by simulating the dMRI signal in a range of realistic white matter numerical
phantoms, including synthetic phantoms and real axons segmented from electron
microscopy. We demonstrate that variable fibre geometry leads to a variable FRF
across axons and that in general no single FRF is effective to recover the
underlying fibre orientation distribution function (fODF). This finding
suggests that assuming a single FRF can lead to misestimation of the fODF,
causing further downstream errors in techniques such as tractography.


Abstract:
  In this paper, we propose LGG, a neurologically inspired graph neural
network, to learn local-global-graph representations from
Electroencephalography (EEG) for a Brain-Computer Interface (BCI). A temporal
convolutional layer with multi-scale 1D convolutional kernels and kernel-level
attention fusion is proposed to learn the temporal dynamics of EEG. Inspired by
neurological knowledge of cognitive processes in the brain, we propose local
and global graph-filtering layers to learn the brain activities within and
between different functional areas of the brain to model the complex relations
among them during the cognitive processes. Under the robust nested
cross-validation settings, the proposed method is evaluated on the publicly
available dataset DEAP, and the classification performance is compared with
state-of-the-art methods, such as FBFgMDM, FBTSC, Unsupervised learning,
DeepConvNet, ShallowConvNet, EEGNet, and TSception. The results show that the
proposed method outperforms all these state-of-the-art methods, and the
improvements are statistically significant (p<0.05) in most cases. The source
code can be found at: https://github.com/yi-ding-cs/LGG


Abstract:
  The cosmological constant $\Lambda$ is usually interpreted as Dark Energy
(DE) or modified gravity (MG). Here we propose instead that $\Lambda$
corresponds to a boundary term in the action of classical General Relativity.
The action is zero for a perfect fluid solution and this fixes $\Lambda$ to the
average density $\rho$ and pressure $p$ inside a primordial causal boundary:
$\Lambda = 4\pi G <\rho+3p>$. This explains both why the observed value of
$\Lambda$ is related to the matter density today and also why other
contributions to $\Lambda$, such as DE or MG, do not produce cosmic expansion.
Cosmic acceleration results from the repulsive boundary force that occurs when
the expansion reaches the causal horizon. This universe is similar to the
$\Lambda$CDM universe, except on the largest observable scales, where we expect
departures from homogeneity/isotropy, such as CMB anomalies and variations in
cosmological parameters indicated by recent observations.


Abstract:
  We develop photoelectron interferometry based on laser-assisted extreme
ultraviolet ionization for randomly oriented chiral molecules. As in the well
established 'reconstruction of attosecond beating by interference of two-photon
transitions', an infrared or visible laser pulse promotes interferences between
components of the photoelectron wave packet ionized by a comb of XUV photons,
applied here to a sample of chiral molecules. We show that the magnitude of the
resulting chiral signal is simply controlled by the time delay between the XUV
and laser pulses, the choice of the laser frequency determines the
photoelectron energy at which the chiral signal is probed, and comparison of
different polarization configurations in the two-photon process allows for
disentangling the contributions of bound and continuum states to the chiral
response. Our proposal provides a simple, experimentally feasible, robust and
versatile tool for the control of photoelectron circular dichroism.


Abstract:
  Intelligent reflecting surface (IRS) has recently emerged as a new solution
to enhance wireless communication performance via passive signal reflection. In
this letter, we unlock the potential of IRS controller in relaying information
and propose a novel IRS-assisted communication system with both IRS passive
reflection and active relaying. Specifically, we jointly optimize the time
allocations for decode-and-forward (DF) relaying by the IRS controller and the
IRS passive beamforming to maximize the achievable rate of the proposed system.
We also compare the rate performance of the proposed system with the
conventional IRS without relaying, and reveal the conditions for one to
outperform the other. Simulation results demonstrate that the proposed new
design can significantly improve the coverage/rate performance of IRS-assisted
systems.


Abstract:
  A salami is a connected, locally finite, weighted graph with non-negative
Ollivier Ricci curvature and at least two ends of infinite volume. We show that
every salami has exactly two ends and no vertices with positive curvature. We
moreover show that every salami is recurrent and admits harmonic functions with
constant gradient. The proofs are based on extremal Lipschitz extensions, a
variational principle and the study of harmonic functions. Assuming a lower
bound on the edge weight, we prove that salamis are quasi-isometric to the
line, that the space of all harmonic functions has finite dimension, and that
the space of subexponentially growing harmonic functions is two-dimensional.
Moreover, we give a Cheng-Yau gradient estimate for harmonic functions on
balls.


Abstract:
  Simultaneous, post-hoc inference is desirable in large-scale hypotheses
testing as it allows for exploration of data while deciding on criteria for
proclaiming discoveries. It was recently proved that all admissible post-hoc
inference methods for the number of true discoveries must be based on closed
testing. In this paper we investigate tractable and efficient closed testing
with local tests of different properties, such as monotonicty, symmetry and
separability, meaning that the test thresholds a monotonic or symmetric
function or a function of sums of test scores for the individual hypotheses.
This class includes well-known global null tests by Fisher, Stouffer and
Ruschendorf, as well as newly proposed ones based on harmonic means and Cauchy
combinations. Under monotonicity, we propose a new linear time statistic
("coma") that quantifies the cost of multiplicity adjustments. If the tests are
also symmetric and separable, we develop several fast (mostly linear-time)
algorithms for post-hoc inference, making closed testing tractable. Paired with
recent advances in global null tests based on generalized means, our work
immediately instantiates a series of simultaneous inference methods that can
handle many complex dependence structures and signal compositions. We provide
guidance on choosing from these methods via theoretical investigation of the
conservativeness and sensitivity for different local tests, as well as
simulations that find analogous behavior for local tests and full closed
testing. One result of independent interest is the following: if
$P_1,\dots,P_d$ are $p$-values from a multivariate Gaussian with arbitrary
covariance, then their arithmetic average P satisfies $Pr(P \leq t) \leq t$ for
$t \leq \frac{1}{2d}$.


Abstract:
  In this paper, a method to evaluate the flexibility of aggregates of domestic
electric water heaters is proposed and applied to the Italian case. Flexibility
is defined as the capability of the aggregate to vary its power demand for a
given time interval. The evaluation method consists of a Monte Carlo analysis,
that uses the thermal model of electric water heaters and a proper elaboration
of the external inputs, such as ambient and cold water temperatures, and hot
water demand. The case of large aggregates defined along the Italian territory
has been studied showing the dependence of flexibility on seasons and on time.


Abstract:
  In this paper, we investigate representations of links that are either
centrally symmetric in $\mathbb{R}^3$ or antipodally symmetric in
$\mathbb{S}^3$. By using the notions of antipodally self-dual and antipodally
symmetric maps, introduced and studied by the authors, we are able to present
sufficient combinatorial conditions for a link $L$ to admit such
representations. The latter naturally arises sufficient conditions for $L$ to
be amphichiral.
  We also introduce another (closely related) method yielding again to
sufficient conditions for $L$ to be amphichiral. We finally prove that a link
$L$, associated to a map $G$, is amphichiral if the self-dual pairing of $G$ is
not one of 6 specific ones among the classification of the 24 self-dual pairing
$Cor(G) \rhd Aut(G)$.


Abstract:
  This study first reconstructs three deep learning powered stock trading
models and their associated strategies that are representative of distinct
approaches to the problem and established upon different aspects of the many
theories evolved around deep learning. It then seeks to compare the performance
of these strategies from different perspectives through trading simulations ran
on three scenarios when the benchmarks are kept at historical low points for
extended periods of time. The results show that in extremely adverse market
climates, investment portfolios managed by deep learning powered algorithms are
able to avert accumulated losses by generating return sequences that shift the
constantly negative CSI 300 benchmark return upward. Among the three, the LSTM
model's strategy yields the best performance when the benchmark sustains
continued loss.


Abstract:
  We present a new approach to study (1+1)-dimensional Dirac equation in the
background of an effective mass $M$ by exploiting the possibility of a
position-dependent fermi velocity $v_f$. We explore the resulting structure of
the coupled equations and arrive at an interesting constraint of $M$ turning
out to be the inverse square of $v_f$. We address several solutions of the
effective potential that include the free particle, shifted harmonic
oscillator, Morse potential, and CPRS potential.


Abstract:
  This paper provides a view of Maxwell's equations from the perspective of
complex variables. The study is made through complex differential forms and the
Hodge star operator in $\mathbb{C}^2$ with respect to the Euclidean and the
Minkowski metrics. It shows that holomorphic functions give rise to nontrivial
solutions, and the inner product between the electric and the magnetic fields
is considered in this case. Further, it obtains a simple necessary and
sufficient condition regarding harmonic solutions to the equations. In the end,
the paper gives an interpretation of the Lorenz gauge condition in terms of the
codifferential operator.


Abstract:
  The demand for higher resolution displays drives the demand for smaller
pixels. Displays show a trend of doubling the pixel number every 4 years and
doubling the pixel per inch (PPI) every 6 years. As the prospective candidate
for next-generation display technology, microLED (micro Light Emitting Diode)
will suffer from sidewall current leakage and poor extraction efficiency as its
lateral size reduces. Using Finite Element Analysis (FEA) method and
Finite-Difference Time-Domain (FDTD) method, we find that reducing the
thickness of the LED can reduce the current leaking to the sidewalls and reduce
the total internal reflection simultaneously. A promising solution to this
problem is by using atomically thin 2D materials to make LEDs. However,
monolayer inorganic 2D materials that can provide red, green and blue emission
are still lacking. Based on the blue light-emitting material fluorographene
(CF), partially fluorinated graphene (CFx) is synthesized in this work to emit
red and green colors from 683 nm to 555 nm (limited by the instrument). This
work also demonstrates lithographically defined regions with different colors,
paving the way for the scaling of microLED.


Abstract:
  We compute the entanglement negativity for various pure and mixed state
configurations in a bath coupled to an evaporating two dimensional non-extremal
Jackiw-Teitelboim (JT) black hole obtained through the partial dimensional
reduction of a three dimensional BTZ black hole. Our results exactly reproduce
the analogues of the Page curve for the entanglement negativity which were
recently determined through diagrammatic technique developed in the context of
random matrix theory.


Abstract:
  We present a 5.4$\sigma$ detection of the pairwise kinematic
Sunyaev-Zel'dovich (kSZ) effect using Atacama Cosmology Telescope (ACT) and
$\it{Planck}$ CMB observations in combination with Luminous Red Galaxy samples
from the Sloan Digital Sky Survey (SDSS) DR15 catalog. Results are obtained
using three ACT CMB maps: co-added 150 GHz and 98 GHz maps, combining
observations from 2008-2018 (ACT DR5), which overlap with SDSS DR15 over 3,700
sq. deg., and a component-separated map using night-time only observations from
2014-2015 (ACT DR4), overlapping with SDSS DR15 over 2,089 sq. deg. Comparisons
of the results from these three maps provide consistency checks in relation to
potential frequency-dependent foreground contamination. A total of 343,647
galaxies are used as tracers to identify and locate galaxy groups and clusters
from which the kSZ signal is extracted using aperture photometry. We consider
the impact of various aperture photometry assumptions and covariance estimation
methods on the signal extraction. Theoretical predictions of the pairwise
velocities are used to obtain best-fit, mass-averaged, optical depth estimates
for each of five luminosity-selected tracer samples. A comparison of the
kSZ-derived optical depth measurements obtained here to those derived from the
thermal SZ effect for the same sample is presented in a companion paper.


Abstract:
  Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog
system. Most dialog systems are trained on a pool of annotated OOD data to
achieve this goal. However, collecting the annotated OOD data for a given
domain is an expensive process. To mitigate this issue, previous works have
proposed generative adversarial networks (GAN) based models to generate OOD
data for a given domain automatically. However, these proposed models do not
work directly with the text. They work with the text's latent space instead,
enforcing these models to include components responsible for encoding text into
latent space and decoding it back, such as auto-encoder. These components
increase the model complexity, making it difficult to train. We propose OodGAN,
a sequential generative adversarial network (SeqGAN) based model for OOD data
generation. Our proposed model works directly on the text and hence eliminates
the need to include an auto-encoder. OOD data generated using OodGAN model
outperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative
improvement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR
0.95) (Zheng et al., 2020).


Abstract:
  The aim of this notes is to give a concise introduction to control theory for
systems governed by stochastic partial differential equations. We shall mainly
focus on controllability and optimal control problems for these systems. For
the first one, we present results for the exact controllability of stochastic
transport equations, null and approximate controllability of stochastic
parabolic equations and lack of exact controllability of stochastic hyperbolic
equations. For the second one, we first introduce the stochastic linear
quadratic optimal control problems and then the Pontryagin type maximum
principle for general optimal control problems. It deserves mentioning that, in
order to solve some difficult problems in this field, one has to develop new
tools, say, the stochastic transposition method introduced in our previous
works.


Abstract:
  Language models (LMs) for virtual assistants (VAs) are typically trained on
large amounts of data, resulting in prohibitively large models which require
excessive memory and/or cannot be used to serve user requests in real-time.
Entropy pruning results in smaller models but with significant degradation of
effectiveness in the tail of the user request distribution. We customize
entropy pruning by allowing for a keep list of infrequent n-grams that require
a more relaxed pruning threshold, and propose three methods to construct the
keep list. Each method has its own advantages and disadvantages with respect to
LM size, ASR accuracy and cost of constructing the keep list. Our best LM gives
8% average Word Error Rate (WER) reduction on a targeted test set, but is 3
times larger than the baseline. We also propose discriminative methods to
reduce the size of the LM while retaining the majority of the WER gains
achieved by the largest LM.


Abstract:
  We study the thermalization of a small $XX$ chain coupled to long, gapped
$XXZ$ leads at either side by observing the relaxation dynamics of the whole
system. Using extensive tensor network simulations, we show that such systems,
although not integrable, appear to show either extremely slow thermalization or
even lack thereof since the two can not be distinguished within the accuracy of
our numerics. We show that the persistent oscillations observed in the spin
current in the middle of the $XX$ chain are related to eigenstates of the
entire system located within the gap of the boundary chains. We find from exact
diagonalization that some of these states remain strictly localized within the
$XX$ chain and do not hybridize with the rest of the system. The frequencies of
the persistent oscillations determined by numerical simulations of dynamics
match the energy differences between these states exactly. This has important
implications for open systems, where the strongly interacting leads are often
assumed to thermalize the central system. Our results suggest that if we employ
gapped systems for the leads, this assumption does not hold; this finding is
particularly relevant to any potential future experimental studies of open
quantum systems.


Abstract:
  Background: The novel coronavirus pandemic has affected Brazil's Santa
Catarina State (SC) severely. At the time of writing (24 March 2021), over
764,000 cases and over 9,800 deaths by COVID-19 have been confirmed, hospitals
were fully occupied with local news reporting at least 397 people in the
waiting list for an ICU bed. In an attempt to better inform local policy
making, we applied an existing Bayesian algorithm to model the spread of the
pandemic in the seven geographic macro-regions of the state. Here we propose
changes to extend the model and improve its forecasting capabilities.
  Methods: Our four proposed variations of the original method allow accessing
data of daily reported infections and take into account under-reporting of
cases more explicitly. Two of the proposed versions also attempt to model the
delay in test reporting. We simulated weekly forecasting of deaths from the
period from 31/05/2020 until 31/01/2021. First week data were used as a
cold-start to the algorithm, after which weekly calibrations of the model were
able to converge in fewer iterations. Google Mobility data were used as
covariates to the model, as well as to estimate of the susceptible population
at each simulated run.
  Findings: The changes made the model significantly less reactive and more
rapid in adapting to scenarios after a peak in deaths is observed. Assuming
that the cases are under-reported greatly benefited the model in its stability,
and modelling retroactively-added data (due to the "hot" nature of the data
used) had a negligible impact in performance.
  Interpretation: Although not as reliable as death statistics, case
statistics, when modelled in conjunction with an overestimate parameter,
provide a good alternative for improving the forecasting of models, especially
in long-range predictions and after the peak of an infection wave.


Abstract:
  With the wide use of deep neural networks (DNN), model interpretability has
become a critical concern, since explainable decisions are preferred in
high-stake scenarios. Current interpretation techniques mainly focus on the
feature attribution perspective, which are limited in indicating why and how
particular explanations are related to the prediction. To this end, an
intriguing class of explanations, named counterfactuals, has been developed to
further explore the "what-if" circumstances for interpretation, and enables the
reasoning capability on black-box models. However, generating counterfactuals
for raw data instances (i.e., text and image) is still in the early stage due
to its challenges on high data dimensionality and unsemantic raw features. In
this paper, we design a framework to generate counterfactuals specifically for
raw data instances with the proposed Attribute-Informed Perturbation (AIP). By
utilizing generative models conditioned with different attributes,
counterfactuals with desired labels can be obtained effectively and
efficiently. Instead of directly modifying instances in the data space, we
iteratively optimize the constructed attribute-informed latent space, where
features are more robust and semantic. Experimental results on real-world texts
and images demonstrate the effectiveness, sample quality as well as efficiency
of our designed framework, and show the superiority over other alternatives.
Besides, we also introduce some practical applications based on our framework,
indicating its potential beyond the model interpretability aspect.


Abstract:
  Suzuki's split-step quantum walk on the one-dimensional integer lattice can
be naturally viewed as a chirally symmetric quantum walk. Given the unitary
time-evolution of such a chirally symmetric quantum walk, we can separately
introduce well-defined indices for the eigenvalues $\pm 1.$ The bulk-edge
correspondence for Suzuki's split-step quantum walk is twofold. Firstly, we
show that the multiplicities of the eigenvalues $\pm 1$ coincide with the
absolute values of the associated indices. Note that this can be viewed as the
symmetry protection of bound states, and that the indices we consider are
robust in the sense that these depend only on the asymptotic behaviour of the
parameters of the given model. Secondly, we show that that such bound states
exhibit exponential decay at spatial infinity.


Abstract:
  This paper addresses distributional offline continuous-time reinforcement
learning (DOCTR-L) with stochastic policies for high-dimensional optimal
control. A soft distributional version of the classical Hamilton-Jacobi-Bellman
(HJB) equation is given by a semilinear partial differential equation (PDE).
This `soft HJB equation' can be learned from offline data without assuming that
the latter correspond to a previous optimal or near-optimal policy. A
data-driven solution of the soft HJB equation uses methods of Neural PDEs and
Physics-Informed Neural Networks developed in the field of Scientific Machine
Learning (SciML). The suggested approach, dubbed `SciPhy RL', thus reduces
DOCTR-L to solving neural PDEs from data. Our algorithm called Deep DOCTR-L
converts offline high-dimensional data into an optimal policy in one step by
reducing it to supervised learning, instead of relying on value iteration or
policy iteration methods. The method enables a computable approach to the
quality control of obtained policies in terms of both their expected returns
and uncertainties about their values.


Abstract:
  We study the motion of a charged particle with magnetic moment in external
electromagnetic fields utilizing covariant unification of Gilbertian and
Amperian descriptions of particle magnetic dipole moment. Considering the case
of a current loop, our approach is verified by comparing classical dynamics
with the classical limit of relativistic quantum dynamics. We obtain motion of
a charged particle in the presence of an external linearly polarized EM (laser)
plane wave field incorporating the effect of spin dynamics. For specific
laser-particle initial configurations, we determine that the Stern-Gerlach
force can have a cumulative effect on the trajectory of charged particles.


Abstract:
  Modeling the burning processes of biomass such as wood, grass, and crops is
crucial for the modeling and prediction of wildland and urban fire behavior.
Despite its importance, the burning of solid fuels remains poorly understood,
which can be partly attributed to the unknown chemical kinetics of most solid
fuels. Most available kinetic models were built upon expert knowledge, which
requires chemical insights and years of experience. This work presents a
framework for autonomously discovering biomass pyrolysis kinetic models from
thermogravimetric analyzer (TGA) experimental data using the recently developed
chemical reaction neural networks (CRNN). The approach incorporated the CRNN
model into the framework of neural ordinary differential equations to predict
the residual mass in TGA data. In addition to the flexibility of
neural-network-based models, the learned CRNN model is fully interpretable, by
incorporating the fundamental physics laws, such as the law of mass action and
Arrhenius law, into the neural network structure. The learned CRNN model can
then be translated into the classical forms of biomass chemical kinetic models,
which facilitates the extraction of chemical insights and the integration of
the kinetic model into large-scale fire simulations. We demonstrated the
effectiveness of the framework in predicting the pyrolysis and oxidation of
cellulose. This successful demonstration opens the possibility of rapid and
autonomous chemical kinetic modeling of solid fuels, such as wildfire fuels and
industrial polymers.


Abstract:
  This study proposes a real-time distributed energy resource (DER)
coordination model that can exploit flexibility from the DERs to solve voltage
and overloading issues using both active and reactive power. The model
considers time-coupling devices including electric vehicles and heat pumps by
deviating as little as possible from their original schedules while
prioritizing DERs with the most urgent demand using dynamic cost terms. The
model does not require a multi-period setting or a multi-period-ahead forecast,
which enables the model to alleviate the computational difficulty and enhance
its applicability for DSOs to manage the grids in real time. A case study using
a Dutch low-voltage grid assuming a 100% penetration scenario of electric
vehicles, heat pumps, and photovoltaics (PVs) in the households validates that
the proposed model can resolve the network issues while not affecting user
comfort.


Abstract:
  We study the statistics and the arithmetic properties of the Robin spectrum
of a rectangle. A number of results are obtained for the multiplicities in the
spectrum, depending on the Diophantine nature of the aspect ratio. In
particular, it is shown that for the square, unlike the case of Neumann
eigenvalues where there are unbounded multiplicities of arithmetic origin,
there are no multiplicities in the Robin spectrum for sufficiently small (but
nonzero) Robin parameter except a systematic symmetry. In addition, uniform
lower and upper bounds are established for the Robin-Neumann gaps in terms of
their limiting mean spacing. Finally, that the pair correlation function of the
Robin spectrum on a Diophantine rectangle is shown to be Poissonian.


Abstract:
  We investigate the behavior of equilibria in an $M/M/1$ feedback queue where
price and time sensitive customers are homogeneous with respect to service
valuation and cost per unit time of waiting. Upon arrival, customers can
observe the number of customers in the system and then decide to join or to
balk. Customers are served in order of arrival. After being served, each
customer either successfully completes the service and departs the system with
probability $q$, or the service fails and the customer immediately joins the
end of the queue to wait to be served again until she successfully completes
it.
  We analyse this decision problem as a noncooperative game among the
customers. We show that there exists a unique symmetric Nash equilibrium
threshold strategy. We then prove that the symmetric Nash equilibrium threshold
strategy is evolutionarily stable. Moreover, if we relax the strategy
restrictions by allowing customers to renege, in the new Nash equilibrium,
customers have a greater incentive to join. However, this does not necessarily
increase the equilibrium expected payoff, and for some parameter values, it
decreases it.


Abstract:
  In this paper we generalize the $j$-invariant criterion for the semistable
reduction type of an elliptic curve to superelliptic curves $X$ given by
$y^{n}=f(x)$. We first define a set of tropical invariants for $f(x)$ using
symmetrized Pl\"{u}cker coordinates and we show that these invariants determine
the tree associated to $f(x)$. We then prove that this tree completely
determines the reduction type of $X$ for $n$ that are not divisible by the
residue characteristic. The conditions on the tropical invariants that
distinguish between the different types are given by half-spaces as in the
elliptic curve case. These half-spaces arise naturally as the moduli spaces of
certain Newton polygon configurations. We give a procedure to write down their
equations and we illustrate this by giving the half-spaces for polynomials
$f(x)$ of degree $d\leq{5}$.


Abstract:
  We use the topology of simplicial complexes to model political structures
following [1]. Simplicial complexes are a natural tool to encode interactions
in the structures since a simplex can be used to represent a subset of
compatible agents. We translate the wedge, cone, and suspension operations into
the language of political structures and show how these constructions
correspond to merging structures and introducing mediators. We introduce the
notions of the viability of an agent and the stability of a political system
and examine their interplay with the simplicial complex topology, casting their
interactions in category-theoretic language whenever possible. We introduce a
refinement of the model by assigning weights to simplices corresponding to the
number of issues the agents agree on. In addition, homology of simplicial
complexes is used to detect non-viabilities, certain cycles of incompatible
agents, and the (non)presence of mediators. Finally, we extend some results
from [1], bringing viability and stability into the language of friendly
delegations and using homology to examine the existence of R-compromises and
D-compromises.


Abstract:
  We prove explicit bounds for the number of sums of consecutive prime squares
below a given magnitude.


Abstract:
  It is argued that cosmic chronometers yield estimates of the spatially
averaged expansion rate even in a universe that is not well described by a
global FLRW model - as long as the Universe is statistically homogeneous and
isotropic with a sufficiently small homogeneity scale. On the other hand,
measurements of the expansion rate based on observations of redshift drift will
not in general yield estimates of the spatially averaged expansion rate - but
it will in the case where the universe is described well by a single FLRW model
on large scales. Therefore, a disagreement between measurements of the
expansion rate based on cosmic chronometers versus redshift drift is an
expected signal of non-negligible cosmic backreaction.


Abstract:
  Orbitally ordered states exhibit unique features which make them a promising
platform for exploring the ultrafast dynamics of long-range order in solids:
Their free energy typically has multiple discrete minima, and electric laser
fields or selectively excited phonons can exert effective forces that may be
used to steer the order parameter through these free energy landscapes.
Moreover, their free energy strongly depends on fluctuations, and in some cases
restoring forces close to a minimum are exclusively of entropic origin
(order-by-disorder mechanisms). This can open pathways to control the dynamics
of the order parameter via non-thermal fluctuations. In this work, we study the
laser-induced non-equilibrium dynamics in a $120^\circ$ compass model, using
time-dependent Ginzburg-Landau theory. We analyze protocols to switch the order
parameter between equivalent configurations, with a focus on the interplay
between the external force due to the driving field, and the non-thermal
entropic forces. In particular, we find that remanent non-thermal fluctuations
after some excitation can stabilize the high-symmetry phase even when the
homogeneous potential has retrieved its low-temperature form, which facilitates
laser-induced switching.


Abstract:
  We disclose the effects of Lifshitz dynamical exponent $z$ on the properties
of holographic paramagnetic-ferromagnetic phase transition in the background of
Lifshitz spacetime. To preserve the conformal invariance in higher dimensions,
we consider the Power-Maxwell (PM) electrodynamics as our gauge field. We
introduce a massive $2$-form coupled to the PM field and perform the numerical
shooting method in the probe limit by assuming the PM and the $2$-form fields
do not back-react on the background geometry. The obtained results indicate
that the critical temperature decreases with increasing the strength of the
power parameter $q$ and dynamical exponent $z$. Besides, the formation of the
magnetic moment in the black hole background is harder in the absence of an
external magnetic field. At low temperatures, and in the absence of an external
magnetic field, our result show the spontaneous magnetization and the
ferromagnetic phase transition. We find that the critical exponent takes the
universal value $\beta= 1/2$ regardless of the parameters $q, z, d$, which is
in agreement with the mean field theory. In the presence of an external
magnetic field, the magnetic susceptibility satisfies the Curie-Weiss law.


Abstract:
  We consider the problem of fair allocation of indivisible items among $n$
agents with additive valuations, when agents have equal entitlements to the
goods, and there are no transfers. Best-of-Both-Worlds (BoBW) fairness
mechanisms aim to give all agents both an ex-ante guarantee (such as getting
the proportional share in expectation) and an ex-post guarantee. Prior BoBW
results have focused on ex-post guarantees that are based on the "up to one
item" paradigm, such as envy-free up to one item (EF1). In this work we attempt
to give every agent a high value ex-post, and specifically, a constant fraction
of his maximin share (MMS). The up to one item paradigm fails to give such a
guarantee, and it is not difficult to present examples in which previous BoBW
mechanisms give agents only a $\frac{1}{n}$ fraction of their MMS.
  Our main result is a deterministic polynomial time algorithm that computes a
distribution over allocations that is ex-ante proportional, and ex-post, every
allocation gives every agent at least his proportional share up to one item,
and more importantly, at least half of his MMS. Moreover, this last ex-post
guarantee holds even with respect to a more demanding notion of a share,
introduced in this paper, that we refer to as the truncated proportional share
(TPS). Our guarantees are nearly best possible, in the sense that one cannot
guarantee agents more than their proportional share ex-ante, and one cannot
guarantee agents more than a $\frac{n}{2n-1}$ fraction of their TPS ex-post.


Abstract:
  For the pristine phosphorene nanoribbons (PNRs) with edge states, there exist
two categories of edge bands near the Fermi energy (EF), i.e., the
shuttle-shaped twofold-degenerate and the near-flat simple degenerate edge
bands. However, the usual experimental measurement may not distinguish the
difference between the two categories of edge bands. Here we study the varying
rule for the edge bands of PNRs under an external electrostatic field. By using
the KWANT code based on the tight-binding approach, we find that the
twofold-degenerate edge bands can be divided into two separated shuttles until
the degeneracy is completely removed and a gap near EFis opened under a
sufficiently strong in-plane electric field. Importantly, each shuttle from the
ribbon upper or lower edge outmost atoms is identified according to the local
density of states. However, under a small off-plane field the shuttle-shaped
bands are easily induced into two near-flat bands contributed from the edge
atoms of the top and bottom sublayers, respectively. The evidence provides the
edge and sublayer degrees of freedom (DOF) for the PNRs with shuttle-shaped
edge bands, of which is obviously different from another category PNRs
intrinsically with near-flat edge bands. This is because that the former
category of ribbons solely have four zigzag-like atomic configurations at the
edges in each unit cell, which also results in that the property is robust
against the point defect in the ribbon center area. As an application,
furthermore, based on this issue we propose a homogenous junction of a
shuttle-edge-band PNR attached by two electric gates. Interestingly, the
transport property of the junction with field manipulation well reflects the
characteristics of the two DOFs. These findings may provide a further
understanding on PNRs and initiate new developments in PNR-based electronics.


Abstract:
  We suggest a novel stochastic approximation algorithm to compute a Symmetric
Nash Equilibrium strategy in a general queueing game with a finite action
space. The algorithm involves a single simulation of the queueing process with
dynamic updating of the strategy at regeneration times. Under mild assumptions
regarding the regenerative structure of the process the algorithm converges to
a symmetric equilibrium strategy almost surely. This yields a powerful tool
that can be used to approximate equilibrium strategies in a broad range of
strategic queueing models in which direct analysis is impracticable.