Abstract:
  Structural flexibility and/or dynamic interactions with other molecules is a
critical aspect of protein function. CryoEM provides direct visualization of
individual macromolecules sampling different conformational and compositional
states. While numerous methods are available for computational classification
of discrete states, characterization of continuous conformational changes or
large numbers of discrete state without human supervision remains challenging.
Here we present e2gmm, a machine learning algorithm to determine a
conformational landscape for proteins or complexes using a 3-D Gaussian mixture
model mapped onto 2-D particle images in known orientations. Using a deep
neural network architecture, e2gmm can automatically resolve the structural
heterogeneity within the protein complex and map particles onto a small latent
space describing conformational and compositional changes. This system presents
a more intuitive and flexible representation than other manifold methods
currently in use. We demonstrate this method on both simulated data as well as
three biological systems, to explore compositional and conformational changes
at a range of scales. The software is distributed as part of EMAN2.


Abstract:
  Many modern time-series datasets contain large numbers of output response
variables sampled for prolonged periods of time. For example, in neuroscience,
the activities of 100s-1000's of neurons are recorded during behaviors and in
response to sensory stimuli. Multi-output Gaussian process models leverage the
nonparametric nature of Gaussian processes to capture structure across multiple
outputs. However, this class of models typically assumes that the correlations
between the output response variables are invariant in the input space.
Stochastic linear mixing models (SLMM) assume the mixture coefficients depend
on input, making them more flexible and effective to capture complex output
dependence. However, currently, the inference for SLMMs is intractable for
large datasets, making them inapplicable to several modern time-series
problems. In this paper, we propose a new regression framework, the orthogonal
stochastic linear mixing model (OSLMM) that introduces an orthogonal constraint
amongst the mixing coefficients. This constraint reduces the computational
burden of inference while retaining the capability to handle complex output
dependence. We provide Markov chain Monte Carlo inference procedures for both
SLMM and OSLMM and demonstrate superior model scalability and reduced
prediction error of OSLMM compared with state-of-the-art methods on several
real-world applications. In neurophysiology recordings, we use the inferred
latent functions for compact visualization of population responses to auditory
stimuli, and demonstrate superior results compared to a competing method
(GPFA). Together, these results demonstrate that OSLMM will be useful for the
analysis of diverse, large-scale time-series datasets.


Abstract:
  Compared to abundant classical statistics-based literature, to date, very
little Bayesian literature exists on Procrustes shape analysis in Geometric
Morphometrics, probably because of being a relatively new branch of statistical
research and because of inherent computational difficulty associated with
Bayesian analysis. Moreover, we may obtain a plethora of novel inferences from
Bayesian Procrustes analysis of shape parameter distributions. In this paper,
we propose to regard the posterior of Procrustes shape variance as
morphological variability indicators. Here we propose novel Bayesian
methodologies for Procrustes shape analysis based on landmark data's isotropic
variance assumption and propose a Bayesian statistical test for model
validation of new species discovery using morphological variation reflected in
the posterior distribution of landmark-variance of objects studied under
Geometric Morphometrics. We will consider Gaussian distribution-based and
heavy-tailed t distribution-based models for Procrustes analysis.
  To date, we are not aware of any direct R package for Bayesian Procrustes
analysis for landmark-based Geometric Morphometrics. Hence, we introduce a
novel, simple R package \textbf{BPviGM1} ("Bayesian Procrustes Variance-based
inferences in Geometric Morphometrics 1"), which essentially contains the R
code implementations of the computations for proposed models and methodologies,
such as R function for Markov Chain Monte Carlo (MCMC) run for drawing samples
from posterior of parameters of concern and R function for the proposed
Bayesian test of model validation based on significance morphological
variation. As an application, we can quantitatively show that primate male-face
may be genetically viable to more shape-variation than the same for females.


Abstract:
  Since 2012, in a case-study in Bucaramanga-Colombia, 179 pedestrians died in
car accidents, and another 2873 pedestrians were injured. Each day, at least
one passerby is involved in a tragedy. Knowing the causes to decrease accidents
is crucial, and using system-dynamics to reproduce the collisions' events is
critical to prevent further accidents. This work implements simulations to save
lives by reducing the city's accidental rate and suggesting new safety policies
to implement. Simulation's inputs are video recordings in some areas of the
city. Deep Learning analysis of the images results in the segmentation of the
different objects in the scene, and an interaction model identifies the primary
reasons which prevail in the pedestrians or vehicles' behaviours. The first and
most efficient safety policy to implement-validated by our simulations-would be
to build speed bumps in specific places before the crossings reducing the
accident rate by 80%.


Abstract:
  We explore the decays of $B\to V_1V_2$ ($V_{1,2}= (\rho, \omega,K^*, \phi)$
and $B= (B^0, B^+,B_s)$) with transverse polarizations. We explicitly evaluate
the eigenstates of T-odd scalar operators involving spins for the first time,
which offer physical insight among the T violating observables. Based on the
helicity suppression of tree operators for transverse polarizations in the
standard model (SM), we deduce that $\Delta \phi _p= \Delta \phi_\parallel -
\Delta \phi_\perp=0$ with $\Delta\phi_{\perp,\parallel}$ the weak phases of the
transverse amplitudes. In contrast, the experiments show that $\Delta \phi _p
(B^0 \to K^{*0} \omega)= -0.84\pm 0.54$, which could be a signal of new
physics. There is also a discrepancy between our result in the SM and the
experimental data for the transverse polarized branching ratio in $B^0 \to
K^{*0} \omega$. In addition, by counting the helicity flips, we argue that
$\sin(\phi_\parallel - \phi_\perp ) \approx 0$, which meets well with most of
the experiments except for the ones with $B^0\to K^{*0}\omega$, where
$\phi_{\parallel,\perp}$ are the strong phases in the transverse amplitudes.


Abstract:
  In view of the great contribution of neutrino-electron scattering to the deep
understanding of electroweak interactions, we focus in this paper on the study
of elastic scattering of a muon neutrino by an electron
$(e^{-}\nu_{\mu}\rightarrow e^{-}\nu_{\mu})$ in the presence of a circularly
polarized electromagnetic field. We perform our theoretical calculation within
the framework of Fermi theory using the exact wave functions of charged
particles in an electromagnetic field. The expression of the differential cross
section (DCS) for this process is obtained analytically in the absence and
presence of the laser field. The effect of the field strength and frequency on
the exchange of photons as well as on the DCS is presented and analyzed.
Massive neutrino effects are also included and discussed. This study, added to
the previous ones, will significantly enrich our knowledge in fundamental
physics.


Abstract:
  Image-to-image (I2I) translation has matured in recent years and is able to
generate high-quality realistic images. However, despite current success, it
still faces important challenges when applied to small domains. Existing
methods use transfer learning for I2I translation, but they still require the
learning of millions of parameters from scratch. This drawback severely limits
its application on small domains. In this paper, we propose a new transfer
learning for I2I translation (TransferI2I). We decouple our learning process
into the image generation step and the I2I translation step. In the first step
we propose two novel techniques: source-target initialization and
self-initialization of the adaptor layer. The former finetunes the pretrained
generative model (e.g., StyleGAN) on source and target data. The latter allows
to initialize all non-pretrained network parameters without the need of any
data. These techniques provide a better initialization for the I2I translation
step. In addition, we introduce an auxiliary GAN that further facilitates the
training of deep I2I systems even from small datasets. In extensive experiments
on three datasets, (Animal faces, Birds, and Foods), we show that we outperform
existing methods and that mFID improves on several datasets with over 25
points.


Abstract:
  We consider various integrable two-parameter deformations of the $AdS_3
\times S^3 \times T^4$ superstring with quantum group symmetry. Working on the
string worldsheet in light-cone gauge and to quadratic order in fermions, we
obtain their common massive tree-level two-body S matrix, which matches the
expansion of the conjectured exact $q$-deformed S matrix. We then analyze the
behavior of the exact S matrix under mirror transformation -- a double Wick
rotation on the worldsheet -- and find that it satisfies a mirror duality
relation analogous to the distinguished $q$-deformed $AdS_5 \times S^5$ S
matrix in the one parameter deformation limit. Finally, we show that the
fermionic $q$-deformed $AdS_5 \times S^5$ S matrix also satisfies such a
relation.


Abstract:
  In data-driven SHM, the signals recorded from systems in operation can be
noisy and incomplete. Data corresponding to each of the operational,
environmental, and damage states are rarely available a priori; furthermore,
labelling to describe the measurements is often unavailable. In consequence,
the algorithms used to implement SHM should be robust and adaptive, while
accommodating for missing information in the training-data -- such that new
information can be included if it becomes available. By reviewing novel
techniques for statistical learning (introduced in previous work), it is argued
that probabilistic algorithms offer a natural solution to the modelling of SHM
data in practice. In three case-studies, probabilistic methods are adapted for
applications to SHM signals -- including semi-supervised learning, active
learning, and multi-task learning.


Abstract:
  Stability and dynamical behavior of binary Bose-Einstein condensed mixtures
trapped on the surface of a rigid spherical shell are investigated in the
mean-field level, exploring the miscibility with and without vortex charges,
considering repulsive and attractive interactions. In order to compute the
critical points for the stability, we follow the Bogoliubov-de Gennes method
for the analysis of perturbed solutions, with the constraint that initially the
stationary states are in a complete miscible configuration. For the perturbed
equal density mixture, of a homogeneous uniform gas and when hidden vorticity
is verified, with the species having opposite azimuthal circulation, we
consider small perturbation analysis for each unstable mode, providing a
complete diagram with the intra- and inter-species interaction role on the
stability of the miscible system. Finally, beyond small perturbation analysis,
we explore the dynamics of some repulsive and attractive inter-species states
by full numerical solutions of the time-dependent Gross-Pitaevskii equation.


Abstract:
  We present optical and near-infrared (NIR, $YJH$-band) observations of 42
Type Ia supernovae (SNe Ia) discovered by the untargeted intermediate Palomar
Transient Factory (iPTF) survey. This new data-set covers a broad range of
redshifts and host galaxy stellar masses, compared to previous SN Ia efforts in
the NIR. We construct a sample, using also literature data at optical and NIR
wavelengths, to examine claimed correlations between the host stellar masses
and the Hubble diagram residuals. The SN magnitudes are corrected for host
galaxy extinction using either a global total-to-selective extinction ratio,
$R_V$=2.0 for all SNe, or a best-fit $R_V$ for each SN individually. Unlike
previous studies which were based on a narrower range in host stellar mass, we
do not find evidence for a "mass-step", between the color- and
stretch-corrected peak $J$ and $H$ magnitudes for galaxies below and above
$\log(M_{*}/M_{\odot}) = 10$. However, the mass-step remains significant
($3\sigma$) at optical wavelengths ($g,r,i$) when using a global $R_V$, but
vanishes when each SN is corrected using their individual best-fit $R_V$. Our
study confirms the benefits of the NIR SN Ia distance estimates, as these are
largely exempted from the empirical corrections dominating the systematic
uncertainties in the optical.


Abstract:
  The heterogeneous computing paradigm represents a real programming challenge
due to the proliferation of devices with different hardware characteristics.
Recently Intel introduced oneAPI, a new programming environment that allows
code developed in DPC++ to be run on different devices such as CPUs, GPUs,
FPGAs, among others. This paper presents our first experiences in porting two
CUDA applications to DPC++ using the oneAPI dpct tool. From the experimental
work, it was possible to verify that dpct does not achieve 100% of the
migration task; however, it performs most of the work, reporting the programmer
of possible pending adaptations. Additionally, it was possible to verify the
functional portability of the DPC++ code obtained, having successfully executed
it on different CPU and GPU architectures.


Abstract:
  We prove that an infinite field interpretable in a $p$-adically closed field
$K$ is definably isomorphic to a finite extension of $K$. The result remains
true in any $P$-minimal field where definable functions are generically
differentiable.


Abstract:
  We present a Flexible Ansatz for N-body Configuration Interaction (FANCI)
that includes any multideterminant wavefunction. This ansatz is a
generalization of the Configuration Interaction (CI) wavefunction, where the
coefficients are replaced by a specified function of certain parameters. By
making an appropriate choice for this function, we can reproduce popular
wavefunction structures like CI, Coupled-Cluster, Tensor Network States, and
geminal-product wavefunctions. The universality of this framework suggests a
programming structure that allows for the easy construction and optimization of
arbitrary wavefunctions. Here, we will discuss the structures of the FANCI
framework and its implications for wavefunction properties, particularly
accuracy, cost, and size-consistency. We demonstrate the flexibility of this
framework by reconstructing popular wavefunction ans\"{a}tze and modifying them
to construct novel wavefunction forms. FANCI provides a powerful framework for
exploring, developing, and testing new wavefunction forms.


Abstract:
  In this paper, we address the problem of estimating a covariance matrix of a
multivariate Gaussian distribution, relative to a Stein loss function, from a
decision theoretic point of view. We investigate the case where the covariance
matrix is invertible and the case when it is non--invertible in a unified
approach.


Abstract:
  A physical theory is presented for polarized light from an aspect of
polarization singularity.This is carried out by analyzing the evolution
equation of the Stokes parameters that is derived from the nonlinear
Schrodinger type equation. The problem is explored from two aspects:The first
is concerning the single mode Stokes parameter that is described by the
propagation distance $ z $. The trajectory of the polarization singularities is
simply given by the points satisfying a rule: $ S_3 (z) = +(-)S_0 $ and $ 0 $
($ S_3 $ represents the third component of the Stokes parameters), which form a
tube like surface centered with the C-line surrounded by L-surfaces in the
presence of the linear as well as the nonlinear birefringence. It is pointed
out that the optical activity (or chirality) plays an "obstacle" to reveal the
polarization singularity. As the other aspect, we consider the field theoretic
aspect of the Stokes parameters, for which a special solution, called an
optical skyrmion, is constructed to explicate the polarization singularity.The
skyrmion forms a {tube}, and the dynamical content of which is briefly
discussed. On the basis of the work presented here further analysis would be
expected to reveal hidden aspects of polarization optics.


Abstract:
  The KATRIN experiment has recently reported a new upper limit to the mass of
the electron neutrino of $m<0.8 eV$ (90\%CL), and a best value of $m^2= 0.26
\pm 0.34 eV^2/c^4$ based on a fit to the observed tritium beta decay spectrum.
Here another interpretation of their results is discussed.


Abstract:
  Let $\xi_1$, $\xi_2,\ldots$ be i.i.d. random variables of zero mean and
finite variance and $\eta_1$, $\eta_2,\ldots$ positive i.i.d. random variables
whose distribution belongs to the domain of attraction of an $\alpha$-stable
distribution, $\alpha\in (0,1)$. The two collections are assumed independent.
We consider a Markov chain with jumps of two types. If the present position of
the Markov chain is positive, then the jump $\xi_k$ occurs; if the present
position of the Markov chain is nonpositive, then its next position is
$\eta_j$. We prove a functional limit theorem for this Markov chain under
Donsker's scaling. The weak limit is a nonnegative process $(X(t))_{t\geq 0}$
satisfying a stochastic equation ${\rm d}X(t)={\rm d}W(t)+ {\rm
d}U_\alpha(L_X^{(0)}(t))$, where $W$ is a Brownian motion, $U_\alpha$ is an
$\alpha$-stable subordinator which is independent of $W$, and $L_X^{(0)}$ is a
local time of $X$ at $0$. Also, we explain that $X$ is a Feller Brownian motion
with a `jump-type' exit from $0$.


Abstract:
  In this paper, we propose the use of two Passive Optical Network (PON)-based
network architectures to connect free-space Optical Wireless Communication
(OWC) Access Points (APs) within a room with multiple users. We optimize
through a Mixed Linear Integer Programming (MILP) model the assignment of
mobile OWC users to more than one AP to improve the resilience of the fronthaul
network, i.e the OWC system and the wired network linked to APs, and study the
impact of users distribution and channel characteristics.


Abstract:
  In this study, we present both data mining and information visualization
techniques to identify accident-prone areas, most accident-prone time, day, and
month. Also, we surveyed among volunteers to understand which visualization
techniques help non-expert users to understand the findings better. Findings of
this study suggest that most accidents occur in the dusk (i.e., between 6 to 7
pm), and on Fridays. Results also suggest that most accidents occurred in
October, which is a popular month for tourism. These findings are consistent
with social information and can help policymakers, residents, tourists, and
other law enforcement agencies. This study can be extended to draw broader
implications.


Abstract:
  A \textit{functional $k$-batch} code of dimension $s$ consists of $n$ servers
storing linear combinations of $s$ linearly independent information bits. Any
multiset request of size $k$ of linear combinations (or requests) of the
information bits can be recovered by $k$ disjoint subsets of the servers. The
goal under this paradigm is to find the minimum number of servers for given
values of $s$ and $k$. A recent conjecture states that for any $k=2^{s-1}$
requests the optimal solution requires $2^s-1$ servers. This conjecture is
verified for $s\leq 5$ but previous work could only show that codes with
$n=2^s-1$ servers can support a solution for $k=2^{s-2} + 2^{s-4} +
\left\lfloor \frac{ 2^{s/2}}{\sqrt{24}} \right\rfloor$ requests. This paper
reduces this gap and shows the existence of codes for $k=\lfloor
\frac{5}{6}2^{s-1} \rfloor - s$ requests with the same number of servers.
Another construction in the paper provides a code with $n=2^{s+1}-2$ servers
and $k=2^{s}$ requests, which is an optimal result.These constructions are
mainly based on Hadamard codes and equivalently provide constructions for
\textit{parallel Random I/O (RIO)} codes.


Abstract:
  Given a singular modulus $j_0$ and a set of primes $S$, we study the problem
of finding effective bounds for the cardinality of the set of singular moduli
$j$ such that $j-j_0$ is an $S$-unit. For every $j_0 \neq 0$, we provide a way
of finding these bounds for infinitely many sets $S$. The same is true if
$j_0=0$ and we assume the Generalized Riemann Hypothesis. Certain numerical
experiments will also lead to the formulation of a "uniformity conjecture" for
singular $S$-units.


Abstract:
  Mixing halides in metal halide perovskites (MHPs) is an effective approach to
adjust MHPs bandgap for applications in tandem solar cells. However,
mixed-halide (MH-) MHPs undergo light-induced-phase-segregation (LIPS) under
continuous illumination. Therefore, understanding the mechanism of LIPS is
necessary for developing stable MH-MHPs. In this work, we investigated LIPS in
layered (L) MHPs and discovered a critical role of spacer cations in LIPS.
Through probing chemical changes of LIPS, we unveil
light-induced-iodide-repulsion and the formation of Br-rich-phase in
illuminated regions during LIPS. This discovery also gives insight into LIPS
process in three dimensional (3D) MHPs. By further investigating LIPS in 3D
MHPs, we reveal that LIPS induces not only the formation of Br-rich and I-rich
domains but also an overall change of halide distribution along the film
thickness direction, which can affect the electronic energy alignment and
consequently MHPs devices performance. Moreover, LIPS is more significant in
the bulk due to larger population of photogenerated charge carriers. Overall,
this study reveals the chemical mechanism of LIPS in MHPs and its potential
effect on device performance, offering insight into understanding LIPS
mechanism and improving the stability of MHPs.


Abstract:
  How to effectively incorporate cross-utterance information cues into a neural
language model (LM) has emerged as one of the intriguing issues for automatic
speech recognition (ASR). Existing research efforts on improving
contextualization of an LM typically regard previous utterances as a sequence
of additional input and may fail to capture complex global structural
dependencies among these utterances. In view of this, we in this paper seek to
represent the historical context information of an utterance as
graph-structured data so as to distill cross-utterances, global word
interaction relationships. To this end, we apply a graph convolutional network
(GCN) on the resulting graph to obtain the corresponding GCN embeddings of
historical words. GCN has recently found its versatile applications on
social-network analysis, text summarization, and among others due mainly to its
ability of effectively capturing rich relational information among elements.
However, GCN remains largely underexplored in the context of ASR, especially
for dealing with conversational speech. In addition, we frame ASR N-best
reranking as a prediction problem, leveraging bidirectional encoder
representations from transformers (BERT) as the vehicle to not only seize the
local intrinsic word regularity patterns inherent in a candidate hypothesis but
also incorporate the cross-utterance, historical word interaction cues
distilled by GCN for promoting performance. Extensive experiments conducted on
the AMI benchmark dataset seem to confirm the pragmatic utility of our methods,
in relation to some current top-of-the-line methods.


Abstract:
  Gas phase Elemental abundances in Molecular CloudS (GEMS) is an IRAM 30m
Large Program designed to estimate the S, C, N, and O depletions and gas
ionization degree, X(e-), in a set of star-forming filaments of Taurus, Perseus
and Orion. Our immediate goal is to build up a complete database of molecular
abundances that can serve as an observational basis for estimating X(e-) and
the C, O, N, and S depletions through chemical modeling. We observed and
derived the abundances of 14 species (13CO, C18O, HCO+, H13CO+, HC18O+, HCN,
H13CN, HNC, HCS+, CS, SO, 34SO, H2S, and OCS) in 244 positions, covering the AV
3 to 100 mag, n(H2) a few 10$^{3}$ to 10$^6$ cm$^{-3}$, and Tk 10 to 30 K
ranges in these clouds, avoiding protostars, HII regions, and outflows. A
statistical analysis is carried out to identify general trends between
different species and with physical parameters. Relations between molecules
reveal strong linear correlations which define three different families: (1)
13CO and C18O; (2) H13CO+, HC18O+, H13CN, and HNC; and (3) the S-bearing
molecules. The abundances of the CO isotopologs increase with the gas kinetic
temperature until TK 15 K. For higher temperatures, the abundance remains
constant with a scatter of a factor of 3. The abundances of H13CO+, HC18O+,
H13CN, and HNC are well correlated with each other, and all of them decrease
with molecular hydrogen density, following the law n(H2)$^{-0.8\pm0.2}$. The
abundances of S-bearing species also decrease with n(H2) at a rate of
(S-bearing/H)gas n(H2)$^{-0.6\pm0.1}$. The abundances of molecules belonging to
groups 2 and 3 do not present any clear trend with gas temperature. At scales
of molecular clouds, the C18O abundance is the quantity that better correlates
with the cloud mass. We discuss the utility of the 13CO/C18O, HCO+/H13CO+, and
H13CO+/H13CN abundance ratios as chemical diagnostics of star formation in
external galaxies.


Abstract:
  We give an abstract approach to approximations with a wide range of
regularity classes $X$ in spaces of pseudocontinuable functions
$K^p_\vartheta$, where $\vartheta$ is an inner function and $p>0$. More
precisely, we demonstrate a general principle, attributed to A. B. Aleksandrov,
which asserts that if a certain linear manifold $X$ is dense in the space of
pseudocontinuable functions $K^{p_0}_\vartheta$, for some $p_0>0$, then $X$ is
in fact dense in $K^p_{\vartheta}$, for all $p>0$. %This allows for
generalizations of the recent result on density by functions with smooth
boundary extensions. Moreover, for a rich class of Banach spaces of analytic
functions $X$, we describe the precise mechanism that determines when $X$ is
dense in a certain space of pseudocontinuable functions. As a consequence, we
obtain an extension of Aleksandrov's density theorem to the class of analytic
functions with uniformly convergent Taylor series.


Abstract:
  Memory-compute disaggregation promises transparent elasticity, high
utilization and balanced usage for resources in data centers by physically
separating memory and compute into network-attached resource "blades". However,
existing designs achieve performance at the cost of resource elasticity,
restricting memory sharing to a single compute blade to avoid costly memory
coherence traffic over the network.
  In this work, we show that emerging programmable network switches can enable
an efficient shared memory abstraction for disaggregated architectures by
placing memory management logic in the network fabric. We find that
centralizing memory management in the network permits bandwidth and
latency-efficient realization of in-network cache coherence protocols, while
programmable switch ASICs support other memory management logic at line-rate.
We realize these insights into MIND, an in-network memory management unit for
rack-scale memory disaggregation. MIND enables transparent resource elasticity
while matching the performance of prior memory disaggregation proposals for
real-world workloads.


Abstract:
  Action segmentation refers to inferring boundaries of semantically consistent
visual concepts in videos and is an important requirement for many video
understanding tasks. For this and other video understanding tasks, supervised
approaches have achieved encouraging performance but require a high volume of
detailed frame-level annotations. We present a fully automatic and unsupervised
approach for segmenting actions in a video that does not require any training.
Our proposal is an effective temporally-weighted hierarchical clustering
algorithm that can group semantically consistent frames of the video. Our main
finding is that representing a video with a 1-nearest neighbor graph by taking
into account the time progression is sufficient to form semantically and
temporally consistent clusters of frames where each cluster may represent some
action in the video. Additionally, we establish strong unsupervised baselines
for action segmentation and show significant performance improvements over
published unsupervised methods on five challenging action segmentation
datasets. Our code is available at
https://github.com/ssarfraz/FINCH-Clustering/tree/master/TW-FINCH


Abstract:
  We study the problem of out-of-distribution dynamics (OODD) detection, which
involves detecting when the dynamics of a temporal process change compared to
the training-distribution dynamics. This is relevant to applications in
control, reinforcement learning (RL), and multi-variate time-series, where
changes to test time dynamics can impact the performance of learning
controllers/predictors in unknown ways. This problem is particularly important
in the context of deep RL, where learned controllers often overfit to the
training environment. Currently, however, there is a lack of established OODD
benchmarks for the types of environments commonly used in RL research. Our
first contribution is to design a set of OODD benchmarks derived from common RL
environments with varying types and intensities of OODD. Our second
contribution is to design a strong OODD baseline approach based on recurrent
implicit quantile networks (RIQNs), which monitors autoregressive prediction
errors for OODD detection. Our final contribution is to evaluate the RIQN
approach on the benchmarks to provide baseline results for future comparison.


Abstract:
  Boundary layer turbulence in coastal regions differs from that in deep ocean
because of bottom interactions. In this paper, we focus on the merging of
surface and bottom boundary layers in a finite-depth coastal ocean by
numerically solving the wave-averaged equations using a large eddy simulation
method. The ocean fluid is driven by combined effects of wind stress, surface
wave, and a steady current in the presence of stable vertical stratification.
The resulting flow consists of two overlapping boundary layers, i.e. surface
and bottom boundary layers, separated by an interior stratification. The
overlapping boundary layers evolve through three phases, i.e. a rapid
deepening, an oscillatory equilibrium and a prompt merger, separated by two
transitions. Before the merger, internal waves are observed in the stratified
layer, and they are excited mainly by Langmuir turbulence in the surface
boundary layer. These waves induce a clear modulation on the bottom-generated
turbulence, facilitating the interaction between the surface and bottom
boundary layers. After the merger, the Langmuir circulations originally
confined to the surface layer are found to grow in size and extend down to the
sea bottom (even though the surface waves do not feel the bottom), reminiscent
of the well-organized Langmuir supercells. These full-depth Langmuir
circulations promote the vertical mixing and enhance the bottom shear, leading
to a significant enhancement of turbulence levels in the vertical column.


Abstract:
  Deep neural networks have become a pervasive tool in science and engineering.
However, modern deep neural networks' growing energy requirements now
increasingly limit their scaling and broader use. We propose a radical
alternative for implementing deep neural network models: Physical Neural
Networks. We introduce a hybrid physical-digital algorithm called Physics-Aware
Training to efficiently train sequences of controllable physical systems to act
as deep neural networks. This method automatically trains the functionality of
any sequence of real physical systems, directly, using backpropagation, the
same technique used for modern deep neural networks. To illustrate their
generality, we demonstrate physical neural networks with three diverse physical
systems-optical, mechanical, and electrical. Physical neural networks may
facilitate unconventional machine learning hardware that is orders of magnitude
faster and more energy efficient than conventional electronic processors.


Abstract:
  Correlated band theory implemented as a combination of density functional
theory with exact diagonalization [DFT+U(ED)] of the Anderson impurity term
with Coulomb repulsion $U$ in the open 14-orbital $5f$ shell is applied to
UTe$_2$. The small gap for $U$=0, evidence of the half-filled $j=\frac{5}{2}$
subshell of $5f^3$ uranium, is converted for $U$=3 eV to a flat band semimetal
with small heavy-carrier Fermi surfaces that will make properties sensitive to
pressure, magnetic field, and off-stoichiometry, as observed experimentally.
The predicted Kondo temperature around 100 K matches the experimental values
from resistivity. The electric field gradients for the two Te sites are
calculated by DFT+U(ED) to differ by a factor of seven, indicating a strong
site distinction, while the anisotropy factor $\eta=0.18$ is similar for all
three sites. The calculated uranium moment $<M^2>^{1/2}$ of 3.5$\mu_B$ is
roughly consistent with the published experimental Curie-Weiss values of
2.8$\mu_B$ and 3.3$\mu_B$ (which are field-direction dependent), and the
calculated separate spin and orbital moments are remarkably similar to Hund's
rule values for an $f^3$ ion. The $U$=3 eV spectral density is compared with
angle-integrated and angle-resolved photoemission spectra, with agreement that
there is strong $5f$ character at, and for several hundred meV below, the Fermi
energy. Our results support the picture that the underlying ground state of
UTe$_2$ is that of a half-filled $j=\frac{5}{2}$ subshell with two half-filled
$m_j=\pm\frac{1}{2}$ orbitals forming a narrow gap by hybridization, then
driven to a conducting state by configuration mixing (spin-charge
fluctuations). UTe$_2$ displays similarities to UPt$_3$ with its $5f$ dominated
Fermi surfaces rather than a strongly localized Kondo lattice system.


Abstract:
  The matrix functions can be defined by Cauchy's integral formula and can be
approximated by the linear combination of inverses of shifted matrices using a
quadrature formula. In this paper, we show a concrete construction of a
framework to implement the linear combination of the inverses on quantum
computers and propose a quantum algorithm for matrix functions based on the
framework. Compared with the previous study [S. Takahira, A. Ohashi, T. Sogabe,
and T.S. Usuda, Quant. Inf. Comput., 20, 1&2, 14--36, (Feb. 2020)] that
proposed a quantum algorithm to compute a quantum state for the matrix function
based on the circular contour centered at the origin, the quantum algorithm in
the present paper can be applied to a more general contour. Moreover, the
algorithm is described by the block-encoding framework. Similarly to the
previous study, the algorithm can be applied even if the input matrix is not a
Hermitian or normal matrix.


Abstract:
  The AWAKE experiment relies on the self-modulation instability of a long
proton bunch to effectively drive wakefields and accelerate an electron bunch
to GeV-level energies. During the first experimental run (2016-2018) the
instability was made phase reproducible by means of a seeding process: a short
laser pulse co-propagates within the proton bunch in a rubidium vapor. Thus,
the fast creation of plasma and the onset of beam-plasma interaction within the
bunch drives seed wakefields. However, this seeding method leaves the front of
the bunch not modulated. The bunch front could self-modulate in a second,
preformed plasma and drive wakefields that would interfere with those driven by
the (already self-modulated) back of the bunch and with the acceleration
process. We present studies of the seeded the self-modulation (SSM) of a long
proton bunch using a short electron bunch. The short seed bunch is placed ahead
of the proton bunch leading to self-modulation of the entire bunch. Numerical
simulations show that this method have other advantages when compared to the
ionization front method. We discuss the requirements for the electron bunch
parameters (charge, emittance, transverse size at the focal point, length), to
effectively seed the self-modulation process. We also present preliminary
experimental studies on the electron bunch seed wakefields generation.


Abstract:
  We consider the problem of providing users of deep Reinforcement Learning
(RL) based systems with a better understanding of when their output can be
trusted. We offer an explainable artificial intelligence (XAI) framework that
provides a three-fold explanation: a graphical depiction of the systems
generalization and performance in the current game state, how well the agent
would play in semantically similar environments, and a narrative explanation of
what the graphical information implies. We created a user-interface for our XAI
framework and evaluated its efficacy via a human-user experiment. The results
demonstrate a statistically significant increase in user trust and acceptance
of the AI system with explanation, versus the AI system without explanation.


Abstract:
  Understanding the effects of atomic structure modification in hematite
photoanodes is essential for the rational design of high-efficiency
functionalizations. Recently it was found that interface modification with
Sn/Sb segregates considerably increases hematite photocatalytic efficiency.
However, the understanding of the different electronic effects of these
modifications at the atomic level is still lacking. This letter describes the
segregation effects of two different dopants-Sn and Sb-on both the solid-solid
(grain-boundaries) and solid-liquid interfaces (surfaces) of hematite. Within
an ab-initio approach, we quantitatively extract the potential barrier
reduction on polycrystalline interfaces due to the dopant, which causes an
increase in the inter-grain electron transport. Concomitantly, the dopants'
segregation on hematite surfaces results in a decrease of the oxygen vacancy
formation energy. Such vacancies lead to the experimentally observed rise of
the flat-band potential. The comprehension of the electronic effects of dopants
on both types of interfaces explains the experimental peak efficiency of
interface-modified hematite with dopant segregates, also enabling the control
and design of interfaces for different higher-efficiency applications.


Abstract:
  Due to the use of huge number of sensors and the increasing use of
communication networks, cyber-physical systems (CPS) are becoming vulnerable to
cyber-attacks. The ever-increasing complexity of CPS bring up the need for
data-driven machine learning applications to fill in the need of model creation
to describe the system behavior. In this paper, a novel stability condition
predictor based on cascaded feedforward neural network is proposed. The
proposed method aims to identify anomaly due to cyber or physical disturbances
as an early sign of instability. The proposed neural network utilizes cascaded
connections in order to increase accuracy of the prediction. The conjugate
gradient backpropagation and Polak-Ribi\`ere formula are utilized for training
process. This method also can predict the critical generators to mitigate the
effect of the cascading failure and consequent blackout in the system.
Simulations results on the IEEE 39-bus system indicate the superiority of the
proposed method in terms of accuracy, speed, and robustness.


Abstract:
  Computer simulations generate microscopic trajectories of complex systems at
a single thermodynamic state point. We recently introduced a Maximum Caliber
(MaxCal) approach for dynamical reweighting. Our approach mapped these
trajectories to a Markovian description on the configurational coordinates, and
reweighted path probabilities as a function of external forces. Trajectory
probabilities can be dynamically reweighted both from and to equilibrium or
non-equilibrium steady states. As the system's dimensionality increases, an
exhaustive description of the microtrajectories becomes prohibitive--even with
a Markovian assumption. Instead we reduce the dimensionality of the
configurational space to collective variables (CVs). Going from configurational
to CV space, we define local entropy productions derived from configurationally
averaged mean forces. The entropy production is shown to be a suitable
constraint on MaxCal for non-equilibrium steady states expressed as a function
of CVs. We test the reweighting procedure on two systems: a particle subject to
a two-dimensional potential and a coarse-grained peptide. Our CV-based MaxCal
approach expands dynamical reweighting to larger systems, for both static and
dynamical properties, and across a large range of driving forces.


Abstract:
  Motivation: Traditional image attribution methods struggle to satisfactorily
explain predictions of neural networks. Prediction explanation is important,
especially in medical imaging, for avoiding the unintended consequences of
deploying AI systems when false positive predictions can impact patient care.
Thus, there is a pressing need to develop improved models for model
explainability and introspection. Specific problem: A new approach is to
transform input images to increase or decrease features which cause the
prediction. However, current approaches are difficult to implement as they are
monolithic or rely on GANs. These hurdles prevent wide adoption. Our approach:
Given an arbitrary classifier, we propose a simple autoencoder and gradient
update (Latent Shift) that can transform the latent representation of a
specific input image to exaggerate or curtail the features used for prediction.
We use this method to study chest X-ray classifiers and evaluate their
performance. We conduct a reader study with two radiologists assessing 240
chest X-ray predictions to identify which ones are false positives (half are)
using traditional attribution maps or our proposed method. Results: We found
low overlap with ground truth pathology masks for models with reasonably high
accuracy. However, the results from our reader study indicate that these models
are generally looking at the correct features. We also found that the Latent
Shift explanation allows a user to have more confidence in true positive
predictions compared to traditional approaches (0.15$\pm$0.95 in a 5 point
scale with p=0.01) with only a small increase in false positive predictions
(0.04$\pm$1.06 with p=0.57).
  Accompanying webpage: https://mlmed.org/gifsplanation
  Source code: https://github.com/mlmed/gifsplanation


Abstract:
  Tropical geometry with the max-plus algebra has been applied to statistical
learning models over tree spaces because geometry with the tropical metric over
tree spaces has some nice properties such as convexity in terms of the tropical
metric. One of the challenges in applications of tropical geometry to tree
spaces is the difficulty interpreting outcomes of statistical models with the
tropical metric. This paper focuses on combinatorics of tree topologies along a
tropical line segment, an intrinsic geodesic with the tropical metric, between
two phylogenetic trees over the tree space and we show some properties of a
tropical line segment between two trees. Specifically we show that a
probability of a tropical line segment of two randomly chosen trees going
through the origin (the star tree) is zero if the number of leave is greater
than four, and we also show that if two given trees differ only one nearest
neighbor interchange (NNI) move, then the tree topology of a tree in the
tropical line segment between them is the same tree topology of one of these
given two trees with possible zero branch lengths.


Abstract:
  The ORBIT code has been widely used for beam dynamics simulations including
injection and acceleration in high-intensity hadron synchrotrons. When the
ORBIT's 1D longitudinal tracking was employed for the acceleration process in
CSNS/RCS, the longitudinal emittance in eV-s was found decreasing substantially
during acceleration, though the adiabatic condition is still met during this
process. This is against the Liouville theorem that predicts the preservation
of the emittance during acceleration. The recent machine study in the
accelerator and the simulations with a self-made code demonstrate that the
longitudinal emittance is almost invariant, which further indicates that the
ORBIT longitudinal tracking might be incorrect. A detailed check-over in the
ORBIT code source finds that the longitudinal finite difference equation used
in the code is erroneous when applied to an acceleration process. The new code
format PyORBIT has the same problem. After the small secondary factor is
included in the code, ORBIT can produce results keeping the longitudinal
emittance invariant. This paper presents some details about the study.


Abstract:
  The Everett interpretation of quantum mechanics divides naturally into two
parts: first, the interpretation of the structure of the quantum state, in
terms of branching, and second, the interpretation of this branching structure
in terms of probability. This is the first of two reviews of the Everett
interpretation, and focuses on structure, with particular attention to the role
of decoherence theory. Written in terms of the quantum histories formalism,
decoherence theory just is the theory of branching structure, in Everett's
sense.


Abstract:
  The literature on awareness modeling includes both syntax-free and
syntax-based frameworks. Heifetz, Meier \& Schipper (HMS) propose a lattice
model of awareness that is syntax-free. While their lattice approach is elegant
and intuitive, it precludes the simple option of relying on formal language to
induce lattices, and does not explicitly distinguish uncertainty from
unawareness. Contra this, the most prominent syntax-based solution, the
Fagin-Halpern (FH) model, accounts for this distinction and offers a simple
representation of awareness, but lacks the intuitiveness of the lattice
structure. Here, we combine these two approaches by providing a lattice of
Kripke models, induced by atom subset inclusion, in which uncertainty and
unawareness are separate. We show our model equivalent to both HMS and FH
models by defining transformations between them which preserve satisfaction of
formulas of a language for explicit knowledge, and obtain completeness through
our and HMS' results. Lastly, we prove that the Kripke lattice model can be
shown equivalent to the FH model (when awareness is propositionally determined)
also with respect to the language of the Logic of General Awareness, for which
the FH model where originally proposed.


Abstract:
  Transfer learning, also referred as knowledge transfer, aims at reusing
knowledge from a source dataset to a similar target one. While many empirical
studies illustrate the benefits of transfer learning, few theoretical results
are established especially for regression problems. In this paper a theoretical
framework for the problem of parameter transfer for the linear model is
proposed. It is shown that the quality of transfer for a new input vector $x$
depends on its representation in an eigenbasis involving the parameters of the
problem. Furthermore a statistical test is constructed to predict whether a
fine-tuned model has a lower prediction quadratic risk than the base target
model for an unobserved sample. Efficiency of the test is illustrated on
synthetic data as well as real electricity consumption data.


Abstract:
  New mass-produced, wide-field, small-aperture telescopes have the potential
to revolutionize ground-based astronomy by greatly reducing the cost of
collecting area. In this paper, we introduce a new class of large telescope
based on these advances: an all-sky, arcsecond-resolution, 1000-telescope array
which builds a simultaneously high-cadence and deep survey by observing the
entire sky all night. As a concrete example, we describe the Argus Array, a
5m-class telescope with an all-sky field of view and the ability to reach
extremely high cadences using low-noise CMOS detectors. Each 55 GPix Argus
exposure covers 20% of the entire sky to g=19.6 each minute and g=21.9 each
hour; a high-speed mode will allow sub-second survey cadences for short times.
Deep coadds will reach g=23.6 every five nights over 47% of the sky; a
larger-aperture array telescope, with an \'etendue close to the Rubin
Observatory, could reach g=24.3 in five nights. These arrays can build
two-color, million-epoch movies of the sky, enabling sensitive and rapid
searches for high-speed transients, fast-radio-burst counterparts,
gravitational-wave counterparts, exoplanet microlensing events, occultations by
distant solar system bodies, and myriad other phenomena. An array of O(1,000)
telescopes, however, would be one of the most complex astronomical instruments
yet built. Standard arrays with hundreds of tracking mounts entail thousands of
moving parts and exposed optics, and maintenance costs would rapidly outpace
the mass-produced-hardware cost savings compared to a monolithic large
telescope. We discuss how to greatly reduce operations costs by placing all
optics in a thermally controlled, sealed dome with a single moving part.
Coupled with careful software scope control and use of existing pipelines, we
show that the Argus Array could become the deepest and fastest Northern sky
survey, with total costs below $20M.


Abstract:
  The advent of the Internet and a large number of digital technologies has
brought with it many different challenges. A large amount of data is found on
the web, which in most cases is unstructured and unorganized, and this
contributes to the fact that the use and manipulation of this data is quite a
difficult process. Due to this fact, the usage of different machine and deep
learning techniques for Text Classification has gained its importance, which
improved this discipline and made it more interesting for scientists and
researchers for further study. This paper aims to classify the pedagogical
content using two different models, the K-Nearest Neighbor (KNN) from the
conventional models and the Long short-term memory (LSTM) recurrent neural
network from the deep learning models. The result indicates that the accuracy
of classifying the pedagogical content reaches 92.52 % using KNN model and
87.71 % using LSTM model.


Abstract:
  In this work, we investigate if the wav2vec 2.0 self-supervised pretraining
helps mitigate the overfitting issues with connectionist temporal
classification (CTC) training to reduce its performance gap with flat-start
lattice-free MMI (E2E-LFMMI) for automatic speech recognition with limited
training data. Towards that objective, we use the pretrained wav2vec 2.0 BASE
model and fine-tune it on three different datasets including out-of-domain
(Switchboard) and cross-lingual (Babel) scenarios. Our results show that for
supervised adaptation of the wav2vec 2.0 model, both E2E-LFMMI and CTC achieve
similar results; significantly outperforming the baselines trained only with
supervised data. Fine-tuning the wav2vec 2.0 model with E2E-LFMMI and CTC we
obtain the following relative WER improvements over the supervised baseline
trained with E2E-LFMMI. We get relative improvements of 40% and 44% on the
clean-set and 64% and 58% on the test set of Librispeech (100h) respectively.
On Switchboard (300h) we obtain relative improvements of 33% and 35%
respectively. Finally, for Babel languages, we obtain relative improvements of
26% and 23% on Swahili (38h) and 18% and 17% on Tagalog (84h) respectively.


Abstract:
  Scattered polynomials over a finite field $\mathbb{F}_{q^n}$ have been
introduced by Sheekey in 2016, and a central open problem regards the
classification of those that are exceptional. So far, only two families of
exceptional scattered polynomials are known. Very recently, Longobardi and
Zanella weakened the property of being scattered by introducing the notion of
L-$q^t$-partially scattered and R-$q^t$-partially scattered polynomials, for
$t$ a divisor of $n$. Indeed, a polynomial is scattered if and only if it is
both L-$q^t$-partially scattered and R-$q^t$-partially scattered. In this
paper, by using techniques from algebraic geometry over finite fields and
function fields theory, we show that the property which is is the hardest to be
preserved is the L-$q^t$-partially scattered one. On the one hand, we are able
to extend the classification results of exceptional scattered polynomials to
exceptional L-$q^t$-partially scattered polynomials. On the other hand, the
R-$q^t$-partially scattered property seems more stable. We present a large
family of R-$q^t$-partially scattered polynomials, containing examples of
exceptional R-$q^t$-partially scattered polynomials, which turn out to be
connected with linear sets of so-called pseudoregulus type. In order to detect
new examples of polynomials which are R-$q^t$-partially scattered, we introduce
two different notions of equivalence preserving this property and concerning
natural actions of the groups ${\rm \Gamma L}(2,q^n)$ and ${\rm \Gamma
L}(2n/t,q^t)$. In particular, our family contains many examples of inequivalent
polynomials, and geometric arguments are used to determine the equivalence
classes under the action of ${\rm \Gamma L}(2n/t,q^t)$.


Abstract:
  This paper aims to solve a distributed learning problem under Byzantine
attacks. In the underlying distributed system, a number of unknown but
malicious workers (termed as Byzantine workers) can send arbitrary messages to
the master and bias the learning process, due to data corruptions, computation
errors or malicious attacks. Prior work has considered a total variation (TV)
norm-penalized approximation formulation to handle the Byzantine attacks, where
the TV norm penalty forces the regular workers' local variables to be close,
and meanwhile, tolerates the outliers sent by the Byzantine workers. To solve
the TV norm-penalized approximation formulation, we propose a Byzantine-robust
stochastic alternating direction method of multipliers (ADMM) that fully
utilizes the separable problem structure. Theoretically, we prove that the
proposed method converges to a bounded neighborhood of the optimal solution at
a rate of O(1/k) under mild assumptions, where k is the number of iterations
and the size of neighborhood is determined by the number of Byzantine workers.
Numerical experiments on the MNIST and COVERTYPE datasets demonstrate the
effectiveness of the proposed method to various Byzantine attacks.


Abstract:
  The propagators for the Faddeev-Popov (FP) ghosts in Yang-Mills theory and
perturbative gravity in the covariant gauge are infrared (IR) divergent in de
Sitter spacetime. An IR cutoff in the momentum space to regularize these
divergences breaks the de Sitter invariance. These IR divergences are due to
the spatially constant modes in the Yang-Mills case and the modes proportional
to the Killing vectors in the case of perturbative gravity. It has been
proposed that these IR divergences can be removed, with the de Sitter
invariance preserved, by first regularizing them with an additional mass term
for the FP ghosts and then taking the massless limit. In the Yang-Mills case,
this procedure has been shown to correspond to requiring that the physical
states, and the vacuum state in particular, be annihilated by some conserved
charges in the Landau gauge. In this paper we show that there are similar
conserved charges in perturbative gravity in the covariant Landau gauge in de
Sitter spacetime and that the IR-regularization procedure described above also
correspond to requiring that the vacuum state be annihilated by these charges
with a natural definition of the interacting vacuum state.


Abstract:
  In this paper, the asymptotic behavior of the solutions of a monotone problem
posed in a locally periodic oscillating domain is studied. Nonlinear monotone
boundary conditions are imposed on the oscillating part of the boundary whereas
the Dirichlet condition is considered on the smooth separate part. Using the
unfolding method, under natural hypothesis on the regularity of the domain, we
prove the weak $L^2$-convergence of the zero-extended solutions of the
nonlinear problem and their flows to the solutions of a limit distributional
problem.


Abstract:
  We compute Witt groups of maximal isotropic Grassmannians, aka. spinor
varieties. They are examples of type D homogenuous varieties. Our method relies
on the Blow-up setup of Balmer-Calm\`es, and we investigate the connecting
homomorphism via the projective bundle formula of Walter-Nenashev, the
projection formula of Calm\`es-Hornbostel and the excess intersection formula
of Fasel. The computation in the Type D case can be presented by so called
"even shifted young diagrams".


Abstract:
  Electromagnetic waves in vacuum and most materials have transverse
polarization. Longitudinal electromagnetic waves with electric field parallel
to wave vector are very rare and appear under special conditions in a limited
class of media, for example in plasma. In this work, we study the dispersion
properties of an easy-to-manufacture metamaterial consisting of two
three-dimensional cubic lattices of connected metallic wires inserted one into
another, also known as an interlaced wire medium. It is shown that the
metamaterial supports longitudinal waves at extremely wide frequency band from
very low frequencies up to the Bragg resonances of the structure. The waves
feature unprecedentedly short wavelengths comparable to the period of the
material. The revealed effects highlight spatially dispersive response of
interlaced wire medium and provide a route toward generating electromagnetic
fields with strong spatial variation.


Abstract:
  The mobile communication system has transformed to be the fundamental
infrastructure to support digital demands from all industry sectors, and 6G is
envisioned to go far beyond the communication-only purpose. There is coming to
a consensus that 6G will treat Artificial Intelligence (AI) as the cornerstone
and has a potential capability to provide "intelligence inclusion", which
implies to enable the access of AI services at anytime and anywhere by anyone.
Apparently, the intelligent inclusion vision produces far-reaching influence on
the corresponding network architecture design in 6G and deserves a clean-slate
rethink. In this article, we propose an end-to-end system architecture design
scope for 6G, and talk about the necessity to incorporate an independent data
plane and a novel intelligent plane with particular emphasis on end-to-end AI
workflow orchestration, management and operation. We also highlight the
advantages to provision converged connectivity and computing services at the
network function plane. Benefiting from these approaches, we believe that 6G
will turn to an "everything as a service" (XaaS) platform with significantly
enhanced business merits.


Abstract:
  When multiple parties that deal with private data aim for a collaborative
prediction task such as medical image classification, they are often
constrained by data protection regulations and lack of trust among
collaborating parties. If done in a privacy-preserving manner, predictive
analytics can benefit from the collective prediction capability of multiple
parties holding complementary datasets on the same machine learning task. This
paper presents PRICURE, a system that combines complementary strengths of
secure multi-party computation (SMPC) and differential privacy (DP) to enable
privacy-preserving collaborative prediction among multiple model owners. SMPC
enables secret-sharing of private models and client inputs with non-colluding
secure servers to compute predictions without leaking model parameters and
inputs. DP masks true prediction results via noisy aggregation so as to deter a
semi-honest client who may mount membership inference attacks. We evaluate
PRICURE on neural networks across four datasets including benchmark medical
image classification datasets. Our results suggest PRICURE guarantees privacy
for tens of model owners and clients with acceptable accuracy loss. We also
show that DP reduces membership inference attack exposure without hurting
accuracy.


Abstract:
  For a knot $K$, the doubly slice genus $g_{ds}(K)$ is the minimal $g$ such
that $K$ divides a closed, orientable, and unknotted surface of genus $g$
embedded in $S^4$. In this paper, we identify the doubly slice genera of 2909
of the 2977 prime knots which have a crossing number of 12 or fewer.


Abstract:
  Let $\Omega \subset \mathbb{R}^{n+1}$, $n\ge 2$, be a 1-sided
non-tangentially accessible domain (i.e., quantitatively open and
path-connected) satisfiying the capacity density condition. Let $L_0
u=-\mathrm{div}(A_0 \nabla u)$, $Lu=-\mathrm{div}(A\nabla u)$ be two real
uniformly elliptic operators in $\Omega$, with $\omega_{L_0}, \omega_L$ the
associated elliptic measures. We establish the equivalence between the
following properties: (i) $\omega_L \in A_{\infty}(\omega_{L_0})$, (ii) $L$ is
$L^p(\omega_{L_0})$-solvable for some $p\in (1,\infty)$, (iii) bounded null
solutions of $L$ satisfy Carleson measure estimates with respect to
$\omega_{L_0}$, (iv) the conical square function is controlled by the
non-tangential maximal function in $L^q(\omega_{L_0})$ for some (or for all)
$q\in (0,\infty)$ for any null solution of $L$, and (v) $L$ is
$\mathrm{BMO}(\omega_{L_0})$-solvable. Moreover, in each of the properties
(ii)-(v) it is enough to consider the class of solutions $u(X)=\omega_L^X(S)$
with arbitrary Borel sets $S\subset\partial\Omega$.
  Also, we characterize the absolute continuity of $\omega_{L_0}$ with respect
to $\omega_L$ in terms of some qualitative local $L^2(\omega_{L_0})$ estimates
for the truncated conical square function for any bounded null solution of $L$.
This is also equivalent to the finiteness $\omega_{L_0}$-a.e. of the truncated
conical square function for any bounded null solution of $L$. As applications,
we show that $\omega_{L_0}\ll\omega_L$ if the disagreement of the coefficients
satisfies some qualitative quadratic estimate in truncated cones for
$\omega_{L_0}$-a.e. vertex. Finally, when $L_0$ is either the transpose of $L$
or its symmetric part, we obtain the corresponding absolute continuity when the
antisymmetric part of the coefficients has some controlled oscillation in
truncated cones for $\omega_{L_0}$-a.e. vertex.


Abstract:
  We propose a model for hierarchical structured data as an extension to the
stochastic temporal convolutional network. The proposed model combines an
autoregressive model with a hierarchical variational autoencoder and
downsampling to achieve superior computational complexity. We evaluate the
proposed model on two different types of sequential data: speech and
handwritten text. The results are promising with the proposed model achieving
state-of-the-art performance.


Abstract:
  We present a determination of the ratio of kaon and pion leptonic decay
constants in isosymmetric QCD (isoQCD), $f_K / f_\pi$, making use of the gauge
ensembles produced by the Extended Twisted Mass Collaboration (ETMC) with $N_f
= 2 + 1 + 1$ flavors of Wilson-clover twisted-mass quarks, including
configurations close to the physical point for all dynamical flavors. The
simulations are carried out at three values of the lattice spacing ranging from
$\sim 0.068$ to $\sim 0.092$ fm with linear lattice size up to $L \sim 5.5$~fm.
The scale is set by the PDG value of the pion decay constant, $f_\pi^{isoQCD} =
130.4~(2)$ MeV, at the isoQCD pion point, $M_\pi^{isoQCD} = 135.0~(2)$ MeV,
obtaining for the gradient-flow (GF) scales the values $w_0 = 0.17383~(63)$ fm,
$\sqrt{t_0} = 0.14436~(61)$ fm and $t_0 / w_0 = 0.11969~(62)$ fm. The data are
analyzed within the framework of SU(2) Chiral Perturbation Theory (ChPT)
without resorting to the use of renormalized quark masses. At the isoQCD kaon
point $M_K^{isoQCD} = 494.2~(4)$ MeV we get $(f_K / f_\pi)^{isoQCD} =
1.1995~(44)$, where the error includes both statistical and systematic
uncertainties. Implications for the Cabibbo-Kobayashi-Maskawa (CKM) matrix
element $|V_{us}|$ and for the first-row CKM unitarity are discussed.


Abstract:
  The Mullineux map is a combinatorial function on partitions which describes
the effect of tensoring a simple module for the symmetric group in
characteristic $p$ with the one-dimensional sign representation. It can also be
interpreted as an isomorphism between crystal graphs for
$\widehat{\mathfrak{sl}}_p$. We give a new combinatorial description of the
Mullineux map by expressing this crystal isomorphism as a composition of
isomorphisms between different crystals. These isomorphisms are defined in
terms of new generalised regularisation maps introduced by Millan Berdasco.
  We then given two applications of our new realisation of the Mullineux map,
by providing purely combinatorial proofs of a conjecture of Lyle relating the
Mullineux map with regularisation, and a theorem of Paget describing the
Mullineux map in RoCK blocks of symmetric groups.


Abstract:
  We present a network formulation for a traffic flow model with nonlocal
velocity in the flux function. The modeling framework includes suitable
coupling conditions at intersections to either ensure maximum flux or
distribution parameters. Based on an upwind type numerical scheme, we prove the
maximum principle and the existence of weak solutions on networks. We also
investigate the limiting behavior of the proposed models when the nonlocal
influence tends to infinity. Numerical examples show the difference between the
proposed coupling conditions and a comparison to the Lighthill-Whitham-Richards
network model.


Abstract:
  Knowledge Graph (KG) completion has been excessively studied with a massive
number of models proposed for the Link Prediction (LP) task. The main
limitation of such models is their insensitivity to time. Indeed, the temporal
aspect of stored facts is often ignored. To this end, more and more works
consider time as a parameter to complete KGs. In this paper, we first
demonstrate that, by simply increasing the number of negative samples, the
recent AttH model can achieve competitive or even better performance than the
state-of-the-art on Temporal KGs (TKGs), albeit its nontemporality. We further
propose Hercules, a time-aware extension of AttH model, which defines the
curvature of a Riemannian manifold as the product of both relation and time.
Our experiments show that both Hercules and AttH achieve competitive or new
state-of-the-art performances on ICEWS04 and ICEWS05-15 datasets. Therefore,
one should raise awareness when learning TKGs representations to identify
whether time truly boosts performances.


Abstract:
  Current work in explainable reinforcement learning generally produces
policies in the form of a decision tree over the state space. Such policies can
be used for formal safety verification, agent behavior prediction, and manual
inspection of important features. However, existing approaches fit a decision
tree after training or use a custom learning procedure which is not compatible
with new learning techniques, such as those which use neural networks. To
address this limitation, we propose a novel Markov Decision Process (MDP) type
for learning decision tree policies: Iterative Bounding MDPs (IBMDPs). An IBMDP
is constructed around a base MDP so each IBMDP policy is guaranteed to
correspond to a decision tree policy for the base MDP when using a
method-agnostic masking procedure. Because of this decision tree equivalence,
any function approximator can be used during training, including a neural
network, while yielding a decision tree policy for the base MDP. We present the
required masking procedure as well as a modified value update step which allows
IBMDPs to be solved using existing algorithms. We apply this procedure to
produce IBMDP variants of recent reinforcement learning methods. We empirically
show the benefits of our approach by solving IBMDPs to produce decision tree
policies for the base MDPs.


Abstract:
  In the near future, robots are expected to work with humans or operate alone
and may replace human workers in various fields such as homes and factories. In
a previous study, we proposed bilateral control-based imitation learning that
enables robots to utilize force information and operate almost simultaneously
with an expert's demonstration. In addition, we recently proposed an
autoregressive neural network model (SM2SM) for bilateral control-based
imitation learning to obtain long-term inferences. In the SM2SM model, both
master and slave states must be input, but the master states are obtained from
the previous outputs of the SM2SM model, resulting in destabilized estimation
under large environmental variations. Hence, a new autoregressive neural
network model (S2SM) is proposed in this study. This model requires only the
slave state as input and its outputs are the next slave and master states,
thereby improving the task success rates. In addition, a new feedback
controller that utilizes the error between the responses and estimates of the
slave is proposed, which shows better reproducibility.


Abstract:
  The primary objective of this paper is to investigate the modified
Leray-alpha equation on the two-dimensional sphere $\mathbb{S}^2$, the square
torus $\mathbb{T}^2$ and the three-torus $\mathbb{T}^3$. In the strategy, we
prove the existence and the uniqueness of the weak solutions and also the
existence of the global attractor for the equation. Then we establish the upper
and lower bounds of the Hausdorff and fractal dimensions of the global
attractor on both $\mathbb{S}^2$ and $\mathbb{T}^2$. Our method is based on the
estimates for the vorticity scalar equations and the stationary solutions
around the invariant manifold that are constructed by using the Kolmogorov
flows. Finally, we will use the results on $\mathbb{T}^2$ to study the lower
bound for attractor's dimensions on the case of $\mathbb{T}^3$.


Abstract:
  We extend recent results on the exact hydrodynamics of a system of diffusive
active particles displaying a motility-induced phase separation to account for
typical fluctuations of the dynamical fields. By calculating correlation
functions exactly in the homogeneous phase, we find that two macroscopic length
scales develop in the system. The first is related to the diffusive length of
the particles and the other to the collective behavior of the particles. The
latter diverges as the critical point is approached. Our results show that the
critical behavior of the model in one dimension belongs to the universality
class of a mean-field Ising model, both for static and dynamic properties, when
the thermodynamic limit is taken in a specified manner. The results are
compared to the critical behavior exhibited by the ABC model. In particular, we
find that in contrast to the ABC model the density large deviation function, at
its Gaussian approximation, does not contain algebraically decaying
interactions but is of a finite, macroscopic, extent which is dictated by the
diverging correlation length.


Abstract:
  In this paper, we consider a closed Riemannian manifold $M^{n+1}$ with
dimension $3\leq n+1\leq 7$, and a compact Lie group $G$ acting as isometries
on $M$ with cohomogeneity at least $3$. Suppose the union of non-principal
orbits $M\setminus M^{reg}$ is a smooth embedded submanifold of $M$ without
boundary and ${\rm dim}(M\setminus M^{reg})\leq n-2 $. Then for any
$c\in\mathbb{R}$, we show the existence of a nontrivial, smooth, closed,
$G$-equivariant almost embedded $G$-invariant hypersurface $\Sigma^n$ of
constant mean curvature $c$.


Abstract:
  A comprehensive study of magnetocrystalline anisotropy of a layered
van-der-Waals ferromagnet VI3 was performed. We measured angular dependences of
the torque and magnetization with respect to the direction of the applied
magnetic field within the "ac" plane perpendicular to and within the basal ab
plane, respectively. A two-fold butterfly-like signal was detected by
magnetization in the perpendicular "ac" plane. This signal symmetry remains
conserved throughout all magnetic regimes as well as through the known
structural transition down to the lowest temperatures. The maximum of the
magnetization signal and the resulting magnetization easy axis is significantly
tilted from the principal c axis by ~40{\deg}. The close relation of the
magnetocrystalline anisotropy to the crystal structure was documented. In
contrast, a two-fold-like angular signal was detected in the paramagnetic
region within the ab plane in the monoclinic phase, which transforms into a
six-fold-like signal below the Curie temperature TC. With further cooling,
another six-fold-like signal with an angular shift of ~30{\deg} grows
approaching TFM. Below TFM, in the triclinic phase, the original six-fold-like
signal vanishes, being replaced by a secondary six-fold-like signal with an
angular shift of ~30{\deg}.


Abstract:
  Context. The [CII] 158micron far-infrared fine-structure line is one of the
dominant cooling lines of the star-forming interstellar medium (ISM). Hence
[CII] emission originates in and thus can be used to trace a range of ISM
processes. Velocity-resolved large-scale mapping of [CII] in star-forming
regions provides a unique perspective of the kinematics of these regions and
their interactions with the exciting source of radiation.
  Aims. We explore the scientific applications of large-scale mapping of
velocity-resolved [CII] observations. With the [CII] observations, we
investigate the effect of stellar feedback on the ISM. We present the details
of observation, calibration, and data reduction using a heterodyne array
receiver mounted on an airborne observatory.
  Results. A square-degree [CII] map with a spectral resolution of 0.3 km/s is
presented. The scientific potential of this data is summarized with discussion
of mechanical and radiative stellar feedback, filament tracing using [CII],
[CII] opacity effects, [CII] and carbon recombination lines, and [CII]
interaction with the large molecular cloud. The data quality and calibration is
discussed in detail, and new techniques are presented to mitigate the effects
of unavoidable instrument deficiencies (e.g. baseline stability) and thus to
improve the data quality. A comparison with a smaller [CII] map taken with the
Herschel/Heterodyne Instrument for the Far-Infrared (HIFI) spectrometer is
presented.


Abstract:
  This work aims to study the dynamic between research in the industry and
academia in computer vision. The results are demonstrated on a set of top-5
vision conferences that are representative of the field. Since data for such
analysis was not readily available, significant effort was spent on gathering
and processing meta-data from the original publications. First, this study
quantifies the share of industry-sponsored research. Specifically, it shows
that the proportion of papers published by industry-affiliated researchers is
increasing and that more academics join companies or collaborate with them.
Next, the possible impact of industry presence is further explored, namely in
the distribution of research topics and citation patterns. The results indicate
that the distribution of the research topics is similar in industry and
academic papers. However, there is a strong preference towards citing industry
papers. Finally, possible reasons for citation bias, such as code availability
and influence, are investigated.


Abstract:
  Data pre-treatment plays a significant role in improving data quality, thus
allowing extraction of accurate information from raw data. One of the data
pre-treatment techniques commonly used is outliers detection. The so-called
3${\sigma}$ method is a common practice to identify the outliers. As shown in
the manuscript, it does not identify all outliers, resulting in possible
distortion of the overall statistics of the data. This problem can have a
significant impact on further data analysis and can lead to reduction in the
accuracy of predictive models. There is a plethora of various techniques for
outliers detection, however, aside from theoretical work, they all require case
study work. Two types of outliers were considered: short-term (erroneous data,
noise) and long-term outliers (e.g. malfunctioning for longer periods). The
data used were taken from the vacuum distillation unit (VDU) of an Asian
refinery and included 40 physical sensors (temperature, pressure and flow
rate). We used a modified method for 3${\sigma}$ thresholds to identify the
short-term outliers, i.e. ensors data are divided into chunks determined by
change points and 3${\sigma}$ thresholds are calculated within each chunk
representing near-normal distribution. We have shown that piecewise 3${\sigma}$
method offers a better approach to short-term outliers detection than
3${\sigma}$ method applied to the entire time series. Nevertheless, this does
not perform well for long-term outliers (which can represent another state in
the data). In this case, we used principal component analysis (PCA) with
Hotelling's $T^2$ statistics to identify the long-term outliers. The results
obtained with PCA were subject to DBSCAN clustering method. The outliers (which
were visually obvious and correctly detected by the PCA method) were also
correctly identified by DBSCAN which supported the consistency and accuracy of
the PCA method.


Abstract:
  We study geometric set cover problems in dynamic settings, allowing
insertions and deletions of points and objects. We present the first dynamic
data structure that can maintain an $O(1)$-approximation in sublinear update
time for set cover for axis-aligned squares in 2D. More precisely, we obtain
randomized update time $O(n^{2/3+\delta})$ for an arbitrarily small constant
$\delta>0$. Previously, a dynamic geometric set cover data structure with
sublinear update time was known only for unit squares by Agarwal, Chang, Suri,
Xiao, and Xue [SoCG 2020]. If only an approximate size of the solution is
needed, then we can also obtain sublinear amortized update time for disks in 2D
and halfspaces in 3D. As a byproduct, our techniques for dynamic set cover also
yield an optimal randomized $O(n\log n)$-time algorithm for static set cover
for 2D disks and 3D halfspaces, improving our earlier $O(n\log n(\log\log
n)^{O(1)})$ result [SoCG 2020].


Abstract:
  Mutually unbiased measurements are a generalization of mutually unbiased
bases in which the measurement operators need not to be rank one projectors. In
a $d$-dimension space, the purity of measurement elements ranges from $1/d$ for
the measurement operators corresponding to maximally mixed states to $1$ for
the rank one projectors. In this contribution, we provide a class of MUM that
encompasses the full range of purity. Similar to the MUB in which the operators
corresponding to different outcomes of the same measurement commute mutually,
our class of MUM possesses this sense of compatibility within each measurement.
This makes the provided class more similar to the MUB, so that the main
difference between them and MUB is due to the purity of the measurement
operators. The spectra of these MUMs provides a way to construct a class of
$d$-dimensional orthogonal matrices which leave the vector of equal components
invariant. Based on this property, and by using the MUM-based entanglement
witnesses, we investigate the role of purity to detect entanglement of
bipartite states.


Abstract:
  With graphs rapidly growing in size and deeper graph neural networks (GNNs)
emerging, the training and inference of GNNs become increasingly expensive.
Existing network weight pruning algorithms cannot address the main space and
computational bottleneck in GNNs, caused by the size and connectivity of the
graph. To this end, this paper first presents a unified GNN sparsification
(UGS) framework that simultaneously prunes the graph adjacency matrix and the
model weights, for effectively accelerating GNN inference on large-scale
graphs. Leveraging this new tool, we further generalize the recently popular
lottery ticket hypothesis to GNNs for the first time, by defining a graph
lottery ticket (GLT) as a pair of core sub-dataset and sparse sub-network,
which can be jointly identified from the original GNN and the full dense graph
by iteratively applying UGS. Like its counterpart in convolutional neural
networks, GLT can be trained in isolation to match the performance of training
with the full model and graph, and can be drawn from both randomly initialized
and self-supervised pre-trained GNNs. Our proposal has been experimentally
verified across various GNN architectures and diverse tasks, on both
small-scale graph datasets (Cora, Citeseer and PubMed), and large-scale
datasets from the challenging Open Graph Benchmark (OGB). Specifically, for
node classification, our found GLTs achieve the same accuracies with 20%~98%
MACs saving on small graphs and 25%~85% MACs saving on large ones. For link
prediction, GLTs lead to 48%~97% and 70% MACs saving on small and large graph
datasets, respectively, without compromising predictive performance. Codes
available at https://github.com/VITA-Group/Unified-LTH-GNN.


Abstract:
  Our goal in this work is to better understand the relationship between
replica symmetry breaking, shattering, and metastability. To this end, we study
the static and dynamic behaviour of spherical pure $p$-spin glasses above the
replica symmetry breaking temperature $T_{s}$. In this regime, we find that
there are at least two distinct temperatures related to non-trivial behaviour.
First we prove that there is a regime of temperatures in which the spherical
$p$-spin model exhibits a shattering phase. Our results holds in a regime above
but near $T_s$. We then find that metastable states exist up to an even higher
temperature $T_{BBM}$ as predicted by Barrat--Burioni--M\'ezard which is
expected to be higher than the phase boundary for the shattering phase $T_d
<T_{BBM}$. We develop this work by first developing a
Thouless--Anderson--Palmer decomposition which builds on the work of Subag. We
then present a series of questions and conjectures regarding the sharp phase
boundaries for shattering and slow mixing.


Abstract:
  We propose a method for building a squeezed vacuum state laser with zero
diffusion, which results from the introduction of the reservoir engineering
technique into the laser theory. As well as the reservoir engineering, our
squeezed vacuum laser demands the construction of an effective atom-field
interaction. And by building an isomorphism between the cavity field operators
in the effective and the Jaynes-Cummings Hamiltonians, we derive the equations
of our effective laser directly from the conventional laser theory. Our method,
which is less susceptible to errors than reservoir engineering, can be extended
for the construction of other nonclassical state lasers, and our squeezed
vacuum laser can contribute to the newly emerging field of gravitational
interferometry.


Abstract:
  Trust in human-robot interactions (HRI) is measured in two main ways: through
subjective questionnaires and through behavioral tasks. To optimize
measurements of trust through questionnaires, the field of HRI faces two
challenges: the development of standardized measures that apply to a variety of
robots with different capabilities, and the exploration of social and
relational dimensions of trust in robots (e.g., benevolence). In this paper we
look at how different trust questionnaires fare given these challenges that
pull in different directions (being general vs. being exploratory) by studying
whether people think the items in these questionnaires are applicable to
different kinds of robots and interactions. In Study 1 we show that after being
presented with a robot (non-humanoid) and an interaction scenario (fire
evacuation), participants rated multiple questionnaire items such as "This
robot is principled" as "Non-applicable to robots in general" or
"Non-applicable to this robot". In Study 2 we show that the frequency of these
ratings change (indeed, even for items rated as N/A to robots in general) when
a new scenario is presented (game playing with a humanoid robot). Finally,
while overall trust scores remained robust to N/A ratings, our results revealed
potential fallacies in the way these scores are commonly interpreted. We
conclude with recommendations for the development, use and results-reporting of
trust questionnaires for future studies, as well as theoretical implications
for the field of HRI.


Abstract:
  We demonstrate that the general model of a linearly time-dependent crossing
of two energy bands is integrable. Namely, the Hamiltonian of this model has a
quadratically time-dependent commuting operator. We apply this property to
four-state Landau-Zener (LZ) models that have previously been used to describe
the Landau-St\"uckelberg interferometry experiments with an electron shuttling
between two semiconductor quantum dots. The integrability then leads to simple
but nontrivial exact relations for the transition probabilities. In addition,
the integrability leads to a semiclassical theory that provides analytical
approximation for the transition probabilities in these models for all
parameter values. The results predict a dynamic phase transition, and show that
similarly-looking models belong to different topological classes.


Abstract:
  In this paper, we consider the optimal control problem for a class of
evolution inclusions with Volterra type operators, which can be
history-dependent. We establish the existence of a solution to the stated
optimal control problem under some hypothesis on data-in. The motivation for
this work comes from the optimal control problems for variational inequalities
arising in the study of injection molding processes, contact mechanics,
principles of electro-wetting on dielectric, and others.


Abstract:
  Over the past decade, wind energy has gained more attention in the world.
However, owing to its indirectness and volatility properties, wind power
penetration has increased the difficulty and complexity in dispatching and
planning of electric power systems. Therefore, it is needed to make the
high-precision wind power prediction in order to balance the electrical power.
For this purpose, in this study, the prediction performance of linear
regression, k-nearest neighbor regression and decision tree regression
algorithms is compared in detail. k-nearest neighbor regression algorithm
provides lower coefficient of determination values, while decision tree
regression algorithm produces lower mean absolute error values. In addition,
the meteorological parameters of wind speed, wind direction, barometric
pressure and air temperature are evaluated in terms of their importance on the
wind power parameter. The biggest importance factor is achieved by wind speed
parameter. In consequence, many useful assessments are made for wind power
predictions.


Abstract:
  The manifold Helmholtzian (1-Laplacian) operator $\Delta_1$ elegantly
generalizes the Laplace-Beltrami operator to vector fields on a manifold
$\mathcal M$. In this work, we propose the estimation of the manifold
Helmholtzian from point cloud data by a weighted 1-Laplacian $\mathbf{\mathcal
L}_1$. While higher order Laplacians ave been introduced and studied, this work
is the first to present a graph Helmholtzian constructed from a simplicial
complex as an estimator for the continuous operator in a non-parametric
setting. Equipped with the geometric and topological information about
$\mathcal M$, the Helmholtzian is a useful tool for the analysis of flows and
vector fields on $\mathcal M$ via the Helmholtz-Hodge theorem. In addition, the
$\mathbf{\mathcal L}_1$ allows the smoothing, prediction, and feature
extraction of the flows. We demonstrate these possibilities on substantial sets
of synthetic and real point cloud datasets with non-trivial topological
structures; and provide theoretical results on the limit of $\mathbf{\mathcal
L}_1$ to $\Delta_1$.


Abstract:
  It has been well known for a long time that the height function of random
lozenge tilings of large domains follow a law of large number and possible
limits called dimer limit shapes are well understood. For the next order, it is
expected that fluctuations behave like version of a Gaussian Free field, at
least away from some special "frozen" regions. However despite being one of the
main questions in the domain for 20 years, only special cases have been
obtained. In this paper we show that for any specified limit shape with no
frozen region, one can construct a sequence of domains whose height functions
converge to that limit shape and where the height fluctuation converge to a
variant of the Gaussian Free Field.


Abstract:
  The widespread application of deep neural network (DNN) techniques is being
challenged by adversarial examples, the legitimate input added with
imperceptible and well-designed perturbations that can fool DNNs easily in the
DNN testing/deploying stage. Previous adversarial example generation algorithms
for adversarial white-box attacks used Jacobian gradient information to add
perturbations. This information is too imprecise and inexplicit, which will
cause unnecessary perturbations when generating adversarial examples. This
paper aims to address this issue. We first propose to apply a more informative
and distilled gradient information, namely integrated gradient, to generate
adversarial examples. To further make the perturbations more imperceptible, we
propose to employ the restriction combination of $L_0$ and $L_1/L_2$ secondly,
which can restrict the total perturbations and perturbation points
simultaneously. Meanwhile, to address the non-differentiable problem of $L_1$,
we explore a proximal operation of $L_1$ thirdly. Based on these three works,
we propose two Integrated gradient based White-box Adversarial example
generation algorithms (IWA): IFPA and IUA. IFPA is suitable for situations
where there are a determined number of points to be perturbed. IUA is suitable
for situations where no perturbation point number is preset in order to obtain
more adversarial examples. We verify the effectiveness of the proposed
algorithms on both structured and unstructured datasets, and we compare them
with five baseline generation algorithms. The results show that our proposed
algorithms do craft adversarial examples with more imperceptible perturbations
and satisfactory crafting rate. $L_2$ restriction is more suitable for
unstructured dataset and $L_1$ restriction performs better in structured
dataset.


Abstract:
  The Vehicle Routing Problem (VRP) is one of the most intensively studied
combinatorial optimisation problems for which numerous models and algorithms
have been proposed. To tackle the complexities, uncertainties and dynamics
involved in real-world VRP applications, Machine Learning (ML) methods have
been used in combination with analytical approaches to enhance problem
formulations and algorithmic performance across different problem solving
scenarios. However, the relevant papers are scattered in several traditional
research fields with very different, sometimes confusing, terminologies. This
paper presents a first, comprehensive review of hybrid methods that combine
analytical techniques with ML tools in addressing VRP problems. Specifically,
we review the emerging research streams on ML-assisted VRP modelling and
ML-assisted VRP optimisation. We conclude that ML can be beneficial in
enhancing VRP modelling, and improving the performance of algorithms for both
online and offline VRP optimisations. Finally, challenges and future
opportunities of VRP research are discussed.


Abstract:
  Scaling and structural evolutions are contemplated in a new perspective for
turbulent channel flows. The total integrated turbulence kinetic energy remains
constant when normalized by the friction velocity squared, while the total
dissipation increases linearly with respect to the Reynolds number. This serves
as a global constraint on the turbulence structure. Motivated by the flux
balances in the root turbulence variables, we also discover dissipative scaling
for u2 and v2, respectively through its first and second gradients. This
self-similarity allows for profile reconstructions at any Reynolds numbers
based on a common template, through a simple multiplicative operation. Using
these scaled variables in the Lagrangian transport equation derives the
Reynolds shear stress, which in turn computes the mean velocity profile. The
self-similarities along with the transport equations render possible succinct
views of the turbulence dynamics and computability of the full structure in
channel flows.


Abstract:
  In general, most of the substances in nature exist in mixtures, and the
noninvasive identification of mixture composition with high speed and accuracy
remains a difficult task. However, the development of Raman spectroscopy,
machine learning, and deep learning techniques have paved the way for achieving
efficient analytical tools capable of identifying mixture components, making an
apparent breakthrough in the identification of mixtures beyond the traditional
chemical analysis methods. This article summarizes the work of Raman
spectroscopy in identifying the composition of substances as well as provides
detailed reviews on the preprocessing process of Raman spectroscopy, the
analysis methods and applications of artificial intelligence. This review
summarizes the work of Raman spectroscopy in identifying the composition of
substances and reviews the preprocessing process of Raman spectroscopy, the
analysis methods and applications of artificial intelligence. Finally, the
advantages and disadvantages and development prospects of Raman spectroscopy
are discussed in detail.


Abstract:
  Deep Neural Network (DNN) frameworks use distributed training to enable
faster time to convergence and alleviate memory capacity limitations when
training large models and/or using high dimension inputs. With the steady
increase in datasets and model sizes, model/hybrid parallelism is deemed to
have an important role in the future of distributed training of DNNs. We
analyze the compute, communication, and memory requirements of Convolutional
Neural Networks (CNNs) to understand the trade-offs between different
parallelism approaches on performance and scalability. We leverage our
model-driven analysis to be the basis for an oracle utility which can help in
detecting the limitations and bottlenecks of different parallelism approaches
at scale. We evaluate the oracle on six parallelization strategies, with four
CNN models and multiple datasets (2D and 3D), on up to 1024 GPUs. The results
demonstrate that the oracle has an average accuracy of about 86.74% when
compared to empirical results, and as high as 97.57% for data parallelism.


Abstract:
  We present EDIpack, an exact diagonalization package to solve generic quantum
impurity problems. The algorithm includes a generalization of the look-up
method introduced in [Lin, Gubernatis Comput. Phys., 7 (4) (1993), 400] and
enables a massively parallel execution of the matrix-vector linear operations
required by Lanczos and Arnoldi algorithms. We show that a suitable Fock basis
organization is crucial to optimize the inter-processors communication in a
distributed memory setup and to reach sub-linear scaling in sufficiently large
systems. We discuss the algorithm in details indicating how to deal with
multiple orbitals and electron-phonon coupling. Finally, we outline the
download, installation and functioning of the package.


Abstract:
  Using the notions of Topological dynamics, H. Furstenberg defined central
sets and proved the Central Sets Theorem. Later V. Bergelson and N. Hindman
characterized central sets in terms of algebra of the Stone-\v{C}ech
Compactification of discrete semigroup. They found that central sets are the
members of the minimal idempotents of $\beta S$, the Stone-\v{C}ech
Compactification of a semigroup $\left(S,\cdot\right)$. We know that any closed
subsemigroup of $\beta S$ is generated by a filter. We call a set $A$ to be a
$\mathcal{F}$-central set if it is a member of a minimal idempotent of a closed
subsemigroup of $\beta S$, generated by the filter $\mathcal{F}$. In this
article we will characterize the $\mathcal{F}$-central sets dynamically.


Abstract:
  To understand the behavior of large dynamical systems like transportation
networks, one must often rely on measurements transmitted by a set of sensors,
for instance individual vehicles. Such measurements are likely to be incomplete
and imprecise, which makes it hard to recover the underlying signal of
interest.Hoping to quantify this phenomenon, we study the properties of a
partially-observed state-space model. In our setting, the latent state $X$
follows a high-dimensional Vector AutoRegressive process $X_t = \theta X_{t-1}
+ \varepsilon_t$. Meanwhile, the observations $Y$ are given by a
noise-corrupted random sample from the state $Y_t = \Pi_t X_t + \eta_t$.
Several random sampling mechanisms are studied, allowing us to investigate the
effect of spatial and temporal correlations in the distribution of the sampling
matrices $\Pi_t$.We first prove a lower bound on the minimax estimation error
for the transition matrix $\theta$. We then describe a sparse estimator based
on the Dantzig selector and upper bound its non-asymptotic error, showing that
it achieves the optimal convergence rate for most of our sampling mechanisms.
Numerical experiments on simulated time series validate our theoretical
findings, while an application to open railway data highlights the relevance of
this model for public transport traffic analysis.


Abstract:
  Introducing an interpolation method we estimate the spectral gap for Brownian
motion on general domains with sticky-reflecting boundary diffusion associated
to the first nontrivial eigenvalue for the Laplace operator with corresponding
Wentzell-type boundary condition. In the manifold case our proofs involve novel
applications of the celebrated Reilly formula.