Abstract:
  Goal-conditioned reinforcement learning endows an agent with a large variety
of skills, but it often struggles to solve tasks that require more temporally
extended reasoning. In this work, we propose to incorporate imagined subgoals
into policy learning to facilitate learning of complex tasks. Imagined subgoals
are predicted by a separate high-level policy, which is trained simultaneously
with the policy and its critic. This high-level policy predicts intermediate
states halfway to the goal using the value function as a reachability metric.
We don't require the policy to reach these subgoals explicitly. Instead, we use
them to define a prior policy, and incorporate this prior into a KL-constrained
policy iteration scheme to speed up and regularize learning. Imagined subgoals
are used during policy learning, but not during test time, where we only apply
the learned policy. We evaluate our approach on complex robotic navigation and
manipulation tasks and show that it outperforms existing methods by a large
margin.


Abstract:
  State-of-the-art training algorithms for deep learning models are based on
stochastic gradient descent (SGD). Recently, many variations have been
explored: perturbing parameters for better accuracy (such as in Extragradient),
limiting SGD updates to a subset of parameters for increased efficiency (such
as meProp) or a combination of both (such as Dropout). However, the convergence
of these methods is often not studied in theory. We propose a unified
theoretical framework to study such SGD variants -- encompassing the
aforementioned algorithms and additionally a broad variety of methods used for
communication efficient training or model compression. Our insights can be used
as a guide to improve the efficiency of such methods and facilitate
generalization to new applications. As an example, we tackle the task of
jointly training networks, a version of which (limited to sub-networks) is used
to create Slimmable Networks. By training a low-rank Transformer jointly with a
standard one we obtain superior performance than when it is trained separately.


Abstract:
  During the evolution of coupled nonlinear oscillators on a lattice, with
dynamics dictated by the discrete nonlinear Schr\"odinger equation (DNLSE
systems), two quantities are conserved: system energy (Hamiltonian) and system
density (number of particles). If the number of system oscillators is large
enough, a significant portion of the array can be considered to be an "open
system", in intimate energy and density contact with a "bath" - the rest of the
array. Thus, as indicated in previous works, the grand canonical formulation
can be exploited in order to determine equilibrium statistical properties of
thermalized DNLSE systems. In this work, given the values of the two conserved
quantities, we have calculated the necessary values of the two Lagrange
parameters (typically designated $\beta,\mu$) associated with the grand
canonical partition function in two different ways. One is numerical and the
other is analytic, based on a published approximate entropy expression. In
addition we have accessed a purposely-derived approximate PDF expression of
site-densities. Applying these mathematical tools we have generated maps of
temperatures, chemical potentials, and field correlations for DNLSE systems
over the entire thermalization zone of the DNLSE phase diagram, subjected to
all system-nonlinearity levels. The end result is a rather complete picture,
characterizing equilibrated large DNLSE systems.


Abstract:
  We present an overview of a proposal in relativistic proton-proton ($pp$)
collisions emphasizing the thermal or kinetic freeze-out stage in the framework
of the Tsallis distribution. In this paper we take into account the chemical
potential present in the Tsallis distribution by following a two step
procedure. In the first step we used the redundancy present in the variables
such as the system temperature, $T$, volume, $V$, Tsallis exponent, $q$,
chemical potential, $\mu$, and performed all fits by effectively setting to
zero the chemical potential. In the second step the value $q$ is kept fixed at
the value determined in the first step. This way the complete set of variables
$T, q, V$ and $\mu$ can be determined. The final results show a weak energy
dependence in $pp$ collisions at the centre-of-mass energy $\sqrt{s}= 6$ GeV to
13 TeV. The chemical potential $\mu$ at kinetic freeze-out shows an increase
with beam energy. This simplifies the description of the thermal freeze-out
stage in $pp$ collisions as the values of $T$ and of the freeze-out radius $R$
vary only mildly over a wide range of beam energies.


Abstract:
  We prove a general Mosco convergence theorem for bounded Euclidean domains
satisfying a set of mild geometric hypotheses. For bounded domains, this notion
implies norm-resolvent convergence for the Dirichlet Laplacian which in turn
ensures spectral convergence. A key element of the proof is the development of
a novel, explicit Poincar\'e-type inequality. These results allow us to
construct a universal algorithm capable of computing the eigenvalues of the
Dirichlet Laplacian on a wide class of rough domains. Many domains with fractal
boundaries, such as the Koch snowflake and certain filled Julia sets, are
included among this class. Conversely, we construct a counter example showing
that there does not exist a universal algorithm of the same type capable of
computing the eigenvalues of the Dirichlet Laplacian on an arbitrary bounded
domain.


Abstract:
  Two dimensional layered van der Waals (vdW) magnets have demonstrated their
potential to study both fundamental and applied physics due to their remarkable
electronic properties. However, the connection of vdW magnets to spintronics as
well as quantum information science is not clear. In particular, it remains
elusive whether there are novel magnetic phenomena only belonging to vdW
magnets, but absent in the widely studied crystalline magnets. Here we consider
the quantum correlations of magnons in a layered vdW magnet and identify an
entanglement channel of magnons across the magnetic layers, which can be
effectively tuned and even deterministically switched on and off by both
magnetic and electric means. This is a unique feature of vdW magnets in which
the underlying physics is well understood in terms of the competing roles of
exchange and anisotropy fields that contribute to the magnon excitation.
Furthermore, we show that such a tunable entanglement channel can mediate the
electrically controllable entanglement of two distant qubits, which also
provides a protocol to indirectly measure the entanglement of magnons. Our
findings provide a novel avenue to electrically manipulate the qubits and
further open up new opportunities to utilize vdW magnets for quantum
information science.


Abstract:
  A detailed study of the BL Lacertae PKS 0903-57 has been done for the first
time with 12 years of Fermi Large Area Telescope data. We have identified two
bright gamma-ray flares in 2018 and 2020. Many sub-structures were observed
during multiple time binning of these flares. We have performed detailed
temporal and spectral study on all the sub-structures separately. A single-zone
emission model is used for time-dependent leptonic modeling of the
multi-wavelength spectral energy distributions. Our estimated values of
variability time scale, magnetic field in the emission region, jet power
obtained from leptonic modeling of PKS 0903-57 are presented in this work.
Currently, we have a minimal number of observations in X-rays and other bands.
Hence, more simultaneous multi-wavelength monitoring of this source is required
to have a better understanding of the physical processes happening in the jet
of the blazar PKS 0903-57.


Abstract:
  We theoretically studied the quantum Cram\'{e}r-Rao bound of an actively
correlated Mach-Zehnder interferometer (ACMZI), where the quantum Fisher
information obtained by the phase-averaging method can give the proper
phase-sensing limit without any external phase reference. We numerically
calculate the phase sensitivities with the method of homodyne detection and
intensity detection in the presence of losses. Under lossless and very low loss
conditions, the ACMZI is operated in a balanced case to beat the standard
quantum limit (SQL). As the loss increases, the reduction in sensitivity
increases. However within a certain range, we can adjust the gain parameters of
the beam recombination process to reduce the reduction in sensitivity and
realize the sensitivity can continue to beat the SQL in an unbalanced
situation. Our scheme provides an optimization method of phase estimation in
the presence of losses.


Abstract:
  The infinite Atlas model describes a countable system of competing Brownian
particles where the lowest particle gets a unit upward drift and the rest
evolve as standard Brownian motions. The stochastic process of gaps between the
particles in the infinite Atlas model does not have a unique stationary
distribution and in fact for every $a \ge 0$, $\pi_a :=
\bigotimes_{i=1}^{\infty} \operatorname{Exp}(2 + ia)$ is a stationary measure
for the gap process. We say that an initial distribution of gaps is in the weak
domain of attraction of the stationary measure $\pi_a$ if the time averaged
laws of the stochastic process of the gaps, when initialized using that
distribution, converge to $\pi_a$ weakly in the large time limit. We provide
general sufficient conditions on the initial gap distribution of the Atlas
particles for it to lie in the weak domain of attraction of $\pi_a$ for each
$a\ge 0$. The cases $a=0$ and $a>0$ are qualitatively different as is seen from
the analysis and the sufficient conditions that we provide. Proofs are based on
the analysis of synchronous couplings, namely, couplings of the ranked particle
systems started from different initial configurations, but driven using the
same set of Brownian motions.


Abstract:
  While the large paramagnetic response measured in certain ensembles of
metallic nanoparticles has been assigned to orbital effects of conduction
electrons, the spin-orbit coupling has been pointed out as a possible origin of
the anomalously large diamagnetic response observed in other cases. Such a
relativistic effect, arising from the inhomogeneous electrostatic potential
seen by the conduction electrons, might originate from the host ionic lattice,
impurities, or the self-consistent confining potential. Here we theoretically
investigate the effect of the spin-orbit coupling arising from the confining
potential, quantifying its contribution to the zero-field magnetic
susceptibility and gauging it against the ones generated by other
weakly-relativistic corrections. Two ideal geometries are considered in detail,
the sphere and the half-sphere, focusing on the expected increased role of the
spin-orbit coupling upon a symmetry reduction, and the application of these
results to actual metallic nanoparticles is discussed. The matrix elements of
the different weakly-relativistic corrections are obtained and incorporated in
a perturbative treatment of the magnetic field, leading to tractable
semi-analytical and semiclassical expressions for the case of the sphere, while
a numerical treatment becomes necessary for the half-sphere. The correction to
the zero-field susceptibility arising from the spin-orbit coupling in a single
sphere is quite small, and it is dominated by the weakly-relativistic kinetic
energy correction, which in turn remains considerably smaller than the typical
values of the nonrelativistic zero-field susceptibility. Moreover, the
spin-orbit contribution to the average response for ensembles of nanoparticles
with a large size dispersion is shown to vanish. The symmetry reduction in
going from the single sphere to the half-sphere does not translate into a
significant (...)


Abstract:
  In this paper we consider new geometric flow equations, called D-flow, which
describe the variation of space-time geometries under the change of the number
of dimensions. The D-flow is originating from the non-trivial dependence of the
volume of space-time manifolds on the number of space-time dimensions and it is
driven by certain curvature invariants. We will work out specific examples of
D-flow equations and their solutions for the case of D-dimensional spheres and
Freund-Rubin Compactification. The discussion of the paper is motivated from
recent swampland considerations, where the number $D$ of space-time dimensions
is treated as a new swampland parameter.


Abstract:
  Simulation-based optimization using agent-based models is typically carried
out under the assumption that the gradient describing the sensitivity of the
simulation output to the input cannot be evaluated directly. To still apply
gradient-based optimization methods, which efficiently steer the optimization
towards a local optimum, gradient estimation methods can be employed. However,
many simulation runs are needed to obtain accurate estimates if the input
dimension is large. Automatic differentiation (AD) is a family of techniques to
compute gradients of general programs directly. Here, we explore the use of AD
in the context of time-driven agent-based simulations. By substituting common
discrete model elements such as conditional branching with smooth
approximations, we obtain gradient information across discontinuities in the
model logic. On the example of microscopic traffic models and an epidemics
model, we study the fidelity and overhead of the differentiable models, as well
as the convergence speed and solution quality achieved by gradient-based
optimization compared to gradient-free methods. In traffic signal timing
optimization problems with high input dimension, the gradient-based methods
exhibit substantially superior performance. Finally, we demonstrate that the
approach enables gradient-based training of neural network-controlled
simulation entities embedded in the model logic.


Abstract:
  Since Transformer has found widespread use in NLP, the potential of
Transformer in CV has been realized and has inspired many new approaches.
However, the computation required for replacing word tokens with image patches
for Transformer after the tokenization of the image is vast(e.g., ViT), which
bottlenecks model training and inference. In this paper, we propose a new
attention mechanism in Transformer termed Cross Attention, which alternates
attention inner the image patch instead of the whole image to capture local
information and apply attention between image patches which are divided from
single-channel feature maps capture global information. Both operations have
less computation than standard self-attention in Transformer. By alternately
applying attention inner patch and between patches, we implement cross
attention to maintain the performance with lower computational cost and build a
hierarchical network called Cross Attention Transformer(CAT) for other vision
tasks. Our base model achieves state-of-the-arts on ImageNet-1K, and improves
the performance of other methods on COCO and ADE20K, illustrating that our
network has the potential to serve as general backbones. The code and models
are available at \url{https://github.com/linhezheng19/CAT}.


Abstract:
  Sequential whole-body 18F-Fluorodeoxyglucose (FDG) positron emission
tomography (PET) scans are regarded as the imaging modality of choice for the
assessment of treatment response in the lymphomas because they detect treatment
response when there may not be changes on anatomical imaging. Any computerized
analysis of lymphomas in whole-body PET requires automatic segmentation of the
studies so that sites of disease can be quantitatively monitored over time.
State-of-the-art PET image segmentation methods are based on convolutional
neural networks (CNNs) given their ability to leverage annotated datasets to
derive high-level features about the disease process. Such methods, however,
focus on PET images from a single time-point and discard information from other
scans or are targeted towards specific organs and cannot cater for the multiple
structures in whole-body PET images. In this study, we propose a
spatio-temporal 'dual-stream' neural network (ST-DSNN) to segment sequential
whole-body PET scans. Our ST-DSNN learns and accumulates image features from
the PET images done over time. The accumulated image features are used to
enhance the organs / structures that are consistent over time to allow easier
identification of sites of active lymphoma. Our results show that our method
outperforms the state-of-the-art PET image segmentation methods.


Abstract:
  Building on work by Chabauty from 1941, Coleman proved in 1985 an explicit
bound for the number of rational points of a curve $C$ of genus $g\ge 2$
defined over a number field $F$, with Jacobian of rank at most $g-1$. Namely,
in the case $F=\mathbb{Q}$, if $p>2g$ is a prime of good reduction, then the
number of rational points of $C$ is at most the number of $\mathbb{F}_p$-points
plus a contribution coming from the canonical class of $C$. We prove a result
analogous to Coleman's bound in the case of a hyperbolic surface $X$ over a
number field, embedded in an abelian variety $A$ of rank at most one, under
suitable conditions on the reduction type at the auxiliary prime. This provides
the first extension of Coleman's explicit bound beyond the case of curves. The
main innovation in our approach is a new method to study the intersection of a
$p$-adic analytic subgroup with a subvariety of $A$ by means of overdetermined
systems of differential equations in positive characteristic.


Abstract:
  Automatic Identification System (AIS) data represents a rich source of
information about maritime traffic and offers a great potential for data
analytics and predictive modeling solutions, which can help optimizing logistic
chains and to reduce environmental impacts. In this work, we address the main
limitations of the validity of AIS navigational data fields, by proposing a
machine learning-based data-driven methodology to detect and (to the possible
extent) also correct erroneous data. Additionally, we propose a metric that can
be used by vessel operators and ports to express numerically their business and
environmental efficiency through time and spatial dimensions, enabled with the
obtained validated AIS data. We also demonstrate Port Area Vessel Movements
(PARES) tool, which demonstrates the proposed solutions.


Abstract:
  Transformers have offered a new methodology of designing neural networks for
visual recognition. Compared to convolutional networks, Transformers enjoy the
ability of referring to global features at each stage, yet the attention module
brings higher computational overhead that obstructs the application of
Transformers to process high-resolution visual data. This paper aims to
alleviate the conflict between efficiency and flexibility, for which we propose
a specialized token for each region that serves as a messenger (MSG). Hence, by
manipulating these MSG tokens, one can flexibly exchange visual information
across regions and the computational complexity is reduced. We then integrate
the MSG token into a multi-scale architecture named MSG-Transformer. In
standard image classification and object detection, MSG-Transformer achieves
competitive performance and the inference on both GPU and CPU is accelerated.
Code is available at https://github.com/hustvl/MSG-Transformer.


Abstract:
  Terahertz (THz) technology is promising in several applications such as
imaging, spectroscopy and communications. Among several methods in the
generation and detection of THz waves, a THz time domain system (TDS) that is
developed using photoconductive antennas (PCA) as emitter and detector presents
several advantages such as simple alignment, low cost, high performance etc. In
this work, we report the design, fabrication and characterization of a 2-D PCA
array that is capable of detecting both the amplitude and phase of the THz
pulse. The PCA array is fabricated using LT-GaAs and has 8 channels with 64
pixels (8x8). The infrared probe beam is steered and focused towards each pixel
of the PCA array using a spatial light modulator (SLM). The measured
photocurrent (amplitude and phase) from each channel is recorded separately and
the frequencies up to 1.4 THz can be detected. Furthermore, the parameters such
as directional time delay of the THz pulse, crosstalk between the channels
etc., were characterized. Finally, we show that the proposed 2D PCA array
design is flexible and can be used for accelerated THz spectral image
acquisition.


Abstract:
  Through lockdowns and other severe changes to daily life, almost everyone is
affected by the COVID-19 pandemic. Scientists and medical doctors are - among
others - mainly interested in researching, monitoring, and improving physical
and mental health of the general population. Mobile health apps (mHealth), and
apps conducting ecological momentary assessments (EMA) respectively, can help
in this context. However, developing such mobile applications poses many
challenges like costly software development efforts, strict privacy rules,
compliance with ethical guidelines, local laws, and regulations. In this paper,
we present TrackYourHealth (TYH), a highly configurable, generic, and modular
mobile data collection and EMA platform, which enabled us to develop and
release two mobile multi-platform applications related to COVID-19 in just a
few weeks. We present TYH and highlight specific challenges researchers and
developers of similar apps may also face, especially when developing apps
related to the medical field.


Abstract:
  In this paper we show that a third order PDE system that is a general form of
a CR-geometry PDE system has at most a ten-dimensional Lie symmetry algebra. We
also show that this estimate is precise.


Abstract:
  Radio Active Galactic Nuclei (RAGNs) are mainly found in dense structures
(i.e., clusters/groups) at redshifts of z$<$2 and are commonly used to detect
protoclusters at higher redshift. Here, we attempt to study the host and
environmental properties of two relatively faint ($\mathrm L_\mathrm{1.4GHz}
\sim10^{25}$ W Hz$^{-1}$) RAGNs in a known protocluster at z=3.3 in the PCl
J0227-0421 field, detected using the latest radio observation obtained as part
of the Observations of Redshift Evolution in Large-Scale Environments (ORELSE)
Survey. Using new spectroscopic observations obtained from Keck/MOSFIRE as part
of the Charting Cluster Construction with the VIMOS Ultra-Deep Survey (VUDS)
and ORELSE (C3VO) survey and previous spectroscopic data obtained as part of
the VIMOS-VLT Deep Survey (VVDS) and VUDS, we revise the three-dimensional
overdensity field around this protocluster. The protocluster is embedded in a
large scale overdensity protostructure. This protostructure has an estimated
total mass of $\sim$2.6$\times10^{15} M_\odot$ and contains several overdensity
peaks. Both RAGNs are hosted by very bright and massive galaxies, while their
hosts show extreme differences color, indicating that they have different ages
and are in different evolutionary stages. Furthermore, we find that they are
not in the most locally dense parts of the protostructure, but are fairly close
to the centers of their parent overdensity peaks. We propose a scenario where
merging might already have happened in both cases, which lowered the local
density of their surrounding area and boosted their stellar mass. This work is
the first time that two RAGNs at low luminosity have been found and studied
within a high redshift protostructure.


Abstract:
  Many social events and policies generate spillover effects in both time and
space. Their occurrence influences not only the outcomes of interest in the
future, but also these outcomes in nearby areas. In this paper, we propose a
design-based approach to estimate the direct and indirect/spillover treatment
effects of any event or policy under the assumption of sequential ignorability,
when both temporal and spatial interference are allowed to present. The
proposed estimators are shown to be consistent and asymptotically Normal if the
degree of interference dependence does not grow too fast relative to the sample
size. The conventional difference-in-differences (DID) or two-way fixed effects
model, nevertheless, leads to biased estimates in this scenario. We apply the
method to examine the impact of Hong Kong's Umbrella Movement on the result of
the ensuing election and how an institutional reform affects real estate
assessment in New York State.


Abstract:
  Biological systems need to react to stimuli over a broad spectrum of
timescales. If and how this ability can emerge without external fine-tuning is
a puzzle. We consider here this problem in discrete Markovian systems, where we
can leverage results from random matrix theory. Indeed, generic large
transition matrices are governed by universal results, which predict the
absence of long timescales unless fine-tuned. We consider an ensemble of
transition matrices and motivate a temperature-like variable that controls the
dynamic range of matrix elements, which we show plays a crucial role in the
applicability of the large matrix limit: as the dynamic range increases, a
phase transition occurs whereby the random matrix theory result is avoided, and
long relaxation times ensue, in the entire `ordered' phase. We furthermore show
that this phase transition is accompanied by a drop in the entropy rate and a
peak in complexity, as measured by predictive information (Bialek, Nemenman,
Tishby Neural Computation 13(21) 2001). Extending the Markov model to a Hidden
Markov model (HMM), we show that observable sequences inherit properties of the
hidden sequences, allowing HMMs to be understood in terms of more accessible
Markov models. We then apply our findings to fMRI data from 820 human subjects
scanned at wakeful rest. We show that the data can be quantitatively understood
in terms of the random model, and that brain activity lies close to the phase
transition when engaged in unconstrained, task-free cognition -- supporting the
brain criticality hypothesis in this context.


Abstract:
  Under any Multiclass Classification (MCC) setting defined by a collection of
labeled point-cloud specified by a feature-set, we extract only stochastic
partial orderings from all possible triplets of point-cloud without explicitly
measuring the three cloud-to-cloud distances. We demonstrate that such a
collective of partial ordering can efficiently compute a label embedding tree
geometry on the Label-space. This tree in turn gives rise to a predictive
graph, or a network with precisely weighted linkages. Such two multiscale
geometries are taken as the coarse scale information content of MCC. They
indeed jointly shed lights on explainable knowledge on why and how labeling
comes about and facilitates error-free prediction with potential multiple
candidate labels supported by data. For revealing within-label heterogeneity,
we further undergo labeling naturally found clusters within each point-cloud,
and likewise derive multiscale geometry as its fine-scale information content
contained in data. This fine-scale endeavor shows that our computational
proposal is indeed scalable to a MCC setting having a large label-space.
Overall the computed multiscale collective of data-driven patterns and
knowledge will serve as a basis for constructing visible and explainable
subject matter intelligence regarding the system of interest.


Abstract:
  In the article we discuss the notion of the generalized invariant manifold
introduced in our previous study. In the literature the method of the
differential constraints is well known as a tool for constructing particular
solutions for the nonlinear partial differential equations. Its essence is in
adding to the nonlinear PDE, a much simpler, as a rule ordinary, differential
equation, compatible with the given one. Then any solution of the ODE is a
particular solution of the PDE as well. However the main problem is to find
this compatible ODE. Our generalization is that we look for an ordinary
differential equation that is compatible not with the nonlinear partial
differential equation itself, but with its linearization. Such a generalized
invariant manifold is effectively sought. Moreover, it allows one to construct
such important attributes of integrability theory as Lax pairs and recursion
operators for integrable nonlinear equations. In this paper, we show that they
provide a way to construct particular solutions to the equation as well.


Abstract:
  The ability to control the spin textures in semiconductors is a fundamental
step toward novel spintronic devices, while seeking desirable materials
exhibiting persistent spin texture (PST) remains a key challenge. The PST is
the property of materials preserving a unidirectional spin orientation in the
momentum space, which has been predicted to support an extraordinarily long
spin lifetime of carriers. Herein, by using first-principles density functional
theory calculations, we report the emergence of the PST in the two-dimensional
(2D) germanium monochalcogenides (GeMC). By considering two stable formations
of the 2D GeMC, namely the pure GeX and Janus Ge2XY monolayers (X, Y = S, Se,
and Te), we observed the PST around the valence band maximum where the spin
orientation is enforced by the lower point group symmetry of the crystal. In
the case of the pure GeX monolayers, we found that the PST is characterized by
fully out-of-plane spin orientation protected by C2v point group, while the
canted PST in the y-z plane is observed in the case of the Janus Ge2XY
monolayers due to the lowering symmetry into Cs point group. More importantly,
we find large spin-orbit coupling (SOC) parameter in which the PST sustains,
which could be effectively tuned by in-plane strain. The large SOC parameter
observed in the present systems leads to the small wavelength of the spatially
periodic mode of the spin polarization, which is promising for short spin
channel in the spin Hall transistor devices.


Abstract:
  There is a need to address the urban-rural disparities in healthcare
regarding equal access and quality of care. Due to higher rates of chronic
disease, reduced access to providers, and a continuous decline in rural
hospitals, it is imperative that Appalachian cancer patients adopt the use of
health information technology (HIT). The NCCN Distress Thermometer and Problem
List (DT) is under-utilized, not patient-centered, does not consider provider
needs, and is outdated in the current digital landscape. Digitizing patient
distress screening poses advantages, such as allowing for more frequent
screenings, removing geographical barriers, and rural patient autonomy. In this
paper, we discuss how knowledge gained from patient-centered design led to the
underpinnings of developing a rural remote patient monitoring app that provides
delightful and insightful experiences to users.


Abstract:
  Functional Electrical Stimulation (FES) can restore motion to a paralysed
person's muscles. Yet, control stimulating many muscles to restore the
practical function of entire limbs is an unsolved problem. Current
neurostimulation engineering still relies on 20th Century control approaches
and correspondingly shows only modest results that require daily tinkering to
operate at all. Here, we present our state of the art Deep Reinforcement
Learning (RL) developed for real time adaptive neurostimulation of paralysed
legs for FES cycling. Core to our approach is the integration of a personalised
neuromechanical component into our reinforcement learning framework that allows
us to train the model efficiently without demanding extended training sessions
with the patient and working out of the box. Our neuromechanical component
includes merges musculoskeletal models of muscle and or tendon function and a
multistate model of muscle fatigue, to render the neurostimulation responsive
to a paraplegic's cyclist instantaneous muscle capacity. Our RL approach
outperforms PID and Fuzzy Logic controllers in accuracy and performance.
Crucially, our system learned to stimulate a cyclist's legs from ramping up
speed at the start to maintaining a high cadence in steady state racing as the
muscles fatigue. A part of our RL neurostimulation system has been successfully
deployed at the Cybathlon 2020 bionic Olympics in the FES discipline with our
paraplegic cyclist winning the Silver medal among 9 competing teams.


Abstract:
  When a stable ordered phase and a metastable disordered phase are separated
by a flat interface, the metastable state changes to the stable state through
the propagation of the interface. For cases in which latent heat is generated,
the interface displacement during some time interval is proportional to the
square root of the time interval when the extent of supercooling is less than a
certain value. We demonstrate this behavior by deriving a perturbative solution
for a propagating interface in the phase field model. We calculate the
leading-order contribution explicitly, and find that the interface temperature
deviates from the equilibrium transition temperature in proportion to the
interface velocity.


Abstract:
  We study the algorithmic stability of Nesterov's accelerated gradient method.
For convex quadratic objectives, Chen et al. (2018) proved that the uniform
stability of the method grows quadratically with the number of optimization
steps, and conjectured that the same is true for the general convex and smooth
case. We disprove this conjecture and show, for two notions of algorithmic
stability (including uniform stability), that the stability of Nesterov's
accelerated method in fact deteriorates exponentially fast with the number of
gradient steps. This stands in sharp contrast to the bounds in the quadratic
case, but also to known results for non-accelerated gradient methods where
stability typically grows linearly with the number of steps.


Abstract:
  A prominent class of model FQH ground states is those realized as correlation
function of $\mathbb{Z}_k^{(r)}$-algebras. In this paper, we study the
interplay between these algebras and their corresponding wavefunctions. In the
hopes of realizing these wavefunctions as a unique densest zero energy state,
we propose a generalization for the projection Hamiltonians
$\mathscr{H}_{k+1}^{2r-1}$. Finally, using techniques from invariants of binary
forms, an ansatz for computation of correlations $\langle
\psi(z_1)\cdots\psi(z_{2k})\rangle \prod_{i<j}(z_i-z_j)^{2r/k}$ is devised. We
provide some evidence that, at least when $r=2$, our proposed Hamiltonian
realizes $\mathbb{Z}_k^{(2)}$-wavefunctions as a \emph{unique} ground state.


Abstract:
  We introduce new message passing algorithms for inference with graphical
models. The standard min-sum and sum-product belief propagation algorithms are
guaranteed to converge when the graph is tree-structured, but may not converge
and can be sensitive to the initialization when the graph contains cycles. This
paper describes modifications to the standard belief propagation algorithms
that are guaranteed to converge to a unique solution regardless of the topology
of the graph.


Abstract:
  Stochastic spreading models defined on complex network topologies are used to
mimic the diffusion of diseases, information, and opinions in real-world
systems. Existing theoretical approaches to the characterization of the models
in terms of microscopic configurations rely on some approximation of
independence among dynamical variables, thus introducing a systematic bias in
the prediction of the ground-truth dynamics. Here, we develop a combinatorial
framework based on the approximation that spreading may occur only along the
shortest paths connecting pairs of nodes. The approximation overestimates
dynamical correlations among node states and leads to biased predictions.
Systematic bias is, however, pointing in the opposite direction of existing
approximations. We show that the combination of the two biased approaches
generates predictions of the ground-truth dynamics that are more accurate than
the ones given by the two approximations if used in isolation. We further take
advantage of the combinatorial approximation to characterize theoretical
properties of some inference problems, and show that the reconstruction of
microscopic configurations is very sensitive to both the place where and the
time when partial knowledge of the system is acquired.


Abstract:
  The Randomized Incremental Construction (RIC) of search DAGs for point
location in planar subdivisions, nearest-neighbor search in 2D points, and
extreme point search in 3D convex hulls, are well known to take ${\cal O}(n
\log n)$ expected time for structures of ${\cal O}(n)$ expected size. Moreover,
searching takes w.h.p. ${\cal O}(\log n)$ comparisons in the first and w.h.p.
${\cal O}(\log^2 n)$ comparisons in the latter two DAGs. However, the expected
depth of the DAGs and high probability bounds for their size are unknown.
  Using a novel analysis technique, we show that the three DAGs have w.h.p. i)
a size of ${\cal O}(n)$, ii) a depth of ${\cal O}(\log n)$, and iii) a
construction time of ${\cal O}(n \log n)$. One application of these new and
improved results are \emph{remarkably simple} Las Vegas verifiers to obtain
search DAGs with optimal worst-case bounds. This positively answers the
conjectured logarithmic search cost in the DAG of Delaunay triangulations
[Guibas et al.; ICALP 1990] and a conjecture on the depth of the DAG of
Trapezoidal subdivisions [Hemmer et al.; ESA 2012]. It also shows that
history-based RIC circumvents a lower bound on runtime tail estimates of
conflict-graph RICs [Sen; STACS 2019].


Abstract:
  Recently, there emerges a series of vision Transformers, which show superior
performance with a more compact model size than conventional convolutional
neural networks, thanks to the strong ability of Transformers to model
long-range dependencies. However, the advantages of vision Transformers also
come with a price: Self-attention, the core part of Transformer, has a
quadratic complexity to the input sequence length. This leads to a dramatic
increase of computation and memory cost with the increase of sequence length,
thus introducing difficulties when applying Transformers to the vision tasks
that require dense predictions based on high-resolution feature maps. In this
paper, we propose a new vision Transformer, named Glance-and-Gaze Transformer
(GG-Transformer), to address the aforementioned issues. It is motivated by the
Glance and Gaze behavior of human beings when recognizing objects in natural
scenes, with the ability to efficiently model both long-range dependencies and
local context. In GG-Transformer, the Glance and Gaze behavior is realized by
two parallel branches: The Glance branch is achieved by performing
self-attention on the adaptively-dilated partitions of the input, which leads
to a linear complexity while still enjoying a global receptive field; The Gaze
branch is implemented by a simple depth-wise convolutional layer, which
compensates local image context to the features obtained by the Glance
mechanism. We empirically demonstrate our method achieves consistently superior
performance over previous state-of-the-art Transformers on various vision tasks
and benchmarks. The codes and models will be made available at
https://github.com/yucornetto/GG-Transformer.


Abstract:
  Electronic Health Records (EHRs) provide a wealth of information for machine
learning algorithms to predict the patient outcome from the data including
diagnostic information, vital signals, lab tests, drug administration, and
demographic information. Machine learning models can be built, for example, to
evaluate patients based on their predicted mortality or morbidity and to
predict required resources for efficient resource management in hospitals. In
this paper, we demonstrate that an attacker can manipulate the machine learning
predictions with EHRs easily and selectively at test time by backdoor attacks
with the poisoned training data. Furthermore, the poison we create has
statistically similar features to the original data making it hard to detect,
and can also attack multiple machine learning models without any knowledge of
the models. With less than 5% of the raw EHR data poisoned, we achieve average
attack success rates of 97% on mortality prediction tasks with MIMIC-III
database against Logistic Regression, Multilayer Perceptron, and Long
Short-term Memory models simultaneously.


Abstract:
  A detailed analysis of the stimulated parametric down-conversion (PDC)
process is performed to investigate the effects of the spatiotemporal degrees
of freedom. The analysis provides information that would be useful for
PDC-based metrology applications. Using a Wigner functional approach, we obtain
the parametric down-converted state as the Bogoliubov transformed input state,
in terms of Bogoliubov kernel functions. The result is used to consider the
case for a coherent state seeding stimulated PDC. We also compute the
background which is obtained from spontaneous PDC.


Abstract:
  We present a study of metallicities in a sample of main sequence stars with
spectral types M, K, G and F ($T_{\rm eff}$ $\sim$ 3200 -- 6500K and log $g$
$\sim$ 4.3 -- 5.0 dex) belonging to the solar neighborhood young open cluster
Coma Berenices. Metallicities were determined using the high-resolution
(R=$\lambda$/$\Delta$ $\lambda$ $\sim$ 22,500) NIR spectra ($\lambda$1.51 --
$\lambda$1.69 $\mu$m) of the SDSS-IV APOGEE survey. Membership to the cluster
was confirmed using previous studies in the literature along with APOGEE radial
velocities and Gaia DR2. An LTE analysis using plane-parallel MARCS model
atmospheres and the APOGEE DR16 line list was adopted to compute synthetic
spectra and derive atmospheric parameters ($T_{\rm eff}$ and log $g$) for the M
dwarfs and metallicities for the sample. The derived metallicities are near
solar and are homogeneous at the level of the expected uncertainties, in
particular when considering stars from a given stellar class. The mean
metallicity computed for the sample of G, K, and M dwarfs is
$\langle$[Fe/H]$\rangle$ = +0.04 $\pm$ 0.02 dex; however, the metallicities of
the F-type stars are slightly lower, by about 0.04 dex, when compared to cooler
and less massive members. Models of atomic diffusion can explain this modest
abundance dip for the F dwarfs, indicating that atomic diffusion operates in
Coma Berenices stars. The [Fe/H] dip occurs in nearly the same effective
temperature range as that found in previous analyses of the lithium and
beryllium abundances in Coma Berenices.


Abstract:
  Email is a channel of communication which is considered to be a confidential
medium of communication for exchange of information among individuals and
organisations. The confidentiality consideration about e-mail is no longer the
case as attackers send malicious emails to users to deceive them into
disclosing their private personal information such as username, password, and
bank card details, etc. In search of a solution to combat phishing cybercrime
attacks, different approaches have been developed. However, the traditional
exiting solutions have been limited in assisting email users to identify
phishing emails from legitimate ones. This paper reveals the different email
and website phishing solutions in phishing attack detection. It first provides
a literature analysis of different existing phishing mitigation approaches. It
then provides a discussion on the limitations of the techniques, before
concluding with an exploration into how phishing detection can be improved.


Abstract:
  Mapping new and old buildings are of great significance for understanding
socio-economic development in rural areas. In recent years, deep neural
networks have achieved remarkable building segmentation results in
high-resolution remote sensing images. However, the scarce training data and
the varying geographical environments have posed challenges for scalable
building segmentation. This study proposes a novel framework based on Mask
R-CNN, named HTMask R-CNN, to extract new and old rural buildings even when the
label is scarce. The framework adopts the result of single-object instance
segmentation from the orthodox Mask R-CNN. Further, it classifies the rural
buildings into new and old ones based on a dynamic grayscale threshold inferred
from the result of a two-object instance segmentation task where training data
is scarce. We found that the framework can extract more buildings and achieve a
much higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. We
tested the novel framework's performance with increasing training data and
found that it converged even when the training samples were limited. This
framework's main contribution is to allow scalable segmentation by using
significantly fewer training samples than traditional machine learning
practices. That makes mapping China's new and old rural buildings viable.


Abstract:
  This work presents a quantitative approach to estimate the total time spent
in the vicinity of a bus stop including the deceleration time, the boarding and
alighting time (developed in an earlier study), the acceleration time, and
re-entry time (time required to merge into the adjacent lane). Different
statistical models were used to compute the deceleration, acceleration and
merge times. Typical deceleration and acceleration levels were computed using
kinematic equations that were then used to compute both the deceleration and
acceleration times. The adopted method to estimate both the deceleration time
and the acceleration time was validated utilizing transit data from Blacksburg
Transit (BT) using the mean absolute percentage error (MAPE) and root mean
square error (RMSE). The MAPE and RMSE values were calculated to be 0.3 and
13.3 percent for the deceleration time and 0.42 and 2.72 percent for the
acceleration time, respectively. The re-entry time was estimated to be a
function of the adjacent roadway traffic density using both a multiple linear
regression and Bayesian regression approach. Both methods showed consistency in
estimating the merge-time model coefficients. The proposed models can be
integrated with transit applications to estimate transit vehicle travel times.


Abstract:
  Deep convolutional models often produce inadequate predictions for inputs
foreign to the training distribution. Consequently, the problem of detecting
outlier images has recently been receiving a lot of attention. Unlike most
previous work, we address this problem in the dense prediction context in order
to be able to locate outlier objects in front of in-distribution background.
Our approach is based on two reasonable assumptions. First, we assume that the
inlier dataset is related to some narrow application field (e.g.~road driving).
Second, we assume that there exists a general-purpose dataset which is much
more diverse than the inlier dataset (e.g.~ImageNet-1k). We consider pixels
from the general-purpose dataset as noisy negative training samples since most
(but not all) of them are outliers. We encourage the model to recognize borders
between known and unknown by pasting jittered negative patches over inlier
training images. Our experiments target two dense open-set recognition
benchmarks (WildDash 1 and Fishyscapes) and one dense open-set recognition
dataset (StreetHazard). Extensive performance evaluation indicates competitive
potential of the proposed approach.


Abstract:
  Countermeasures to effectively fight the ever increasing hate speech online
without blocking freedom of speech is of great social interest. Natural
Language Generation (NLG), is uniquely capable of developing scalable
solutions. However, off-the-shelf NLG methods are primarily
sequence-to-sequence neural models and they are limited in that they generate
commonplace, repetitive and safe responses regardless of the hate speech (e.g.,
"Please refrain from using such language.") or irrelevant responses, making
them ineffective for de-escalating hateful conversations. In this paper, we
design a three-module pipeline approach to effectively improve the diversity
and relevance. Our proposed pipeline first generates various counterspeech
candidates by a generative model to promote diversity, then filters the
ungrammatical ones using a BERT model, and finally selects the most relevant
counterspeech response using a novel retrieval-based method. Extensive
Experiments on three representative datasets demonstrate the efficacy of our
approach in generating diverse and relevant counterspeech.


Abstract:
  Graph-based Neural Networks (GNNs) are recent models created for learning
representations of nodes (and graphs), which have achieved promising results
when detecting patterns that occur in large-scale data relating different
entities. Among these patterns, financial fraud stands out for its
socioeconomic relevance and for presenting particular challenges, such as the
extreme imbalance between the positive (fraud) and negative (legitimate
transactions) classes, and the concept drift (i.e., statistical properties of
the data change over time). Since GNNs are based on message propagation, the
representation of a node is strongly impacted by its neighbors and by the
network's hubs, amplifying the imbalance effects. Recent works attempt to adapt
undersampling and oversampling strategies for GNNs in order to mitigate this
effect without, however, accounting for concept drift. In this work, we conduct
experiments to evaluate existing techniques for detecting network fraud,
considering the two previous challenges. For this, we use real data sets,
complemented by synthetic data created from a new methodology introduced here.
Based on this analysis, we propose a series of improvement points that should
be investigated in future research.


Abstract:
  In this paper, we propose a new macro-micro approach to modeling parking. We
first develop a microscopic parking simulation model considering both on- and
off-street parking with limited capacity. In the microscopic model, a parking
search algorithm is proposed to mimic cruising-for-parking based on the
principle of proximity, and a parking-related state tracking algorithm is
proposed to acquire an event-based simulated data set. Some key aspects of
parking modeling are discussed based on the sim-ulated evidence and theoretical
analysis. Results suggest (i) although the low cruising speed reduces the
network performance, it does not significantly alter the macroscopic or network
fundamental diagram (MFD or NFD) unless the cruising vehicles dominate the
traffic stream; (ii) distance to park is not uniquely determined by parking
occupancy because factors such as cruising speed and parking dura-tion also
contribute; and (iii) multiscale parking occupancy-driven intelligent parking
guidance can re-duce distance to park yielding considerable network efficiency
gains. Using the microscopic model, we then extend, calibrate, and validate a
macroscopic parking dynamics model with an NFD representation. The demonstrated
consistency between the macro- and micro-models permits integration of the two
for online parking pricing optimization via model predictive control. Numerical
experiments highlight the effectiveness of the proposed approach as well as one
caveat. That is, when pricing on-street parking, the road network connected to
the alternate off-street parking lots must have sufficient capacity to
ac-commodate the increased parking demand; otherwise, local congestion may
arise that violates the ho-mogeneity assumption underlying the macroscopic
model.


Abstract:
  HD 106906 is a young, binary stellar system, located in the Lower Centaurus
Crux (LCC) group. This system is unique among discovered systems in that it
contains an asymmetrical debris disk, as well as an 11 M$_{Jup}$ planet
companion, at a separation of $\sim$735 AU. Only a handful of other systems are
known to contain both a disk and directly imaged planet, where HD 106906 is the
only one in which the planet has apparently been scattered. The debris disk is
nearly edge on, and extends roughly to $>$500 AU, where previous studies with
HST have shown the outer regions to have high asymmetry. To better understand
the structure and composition of the disk, we have performed a deep
polarimetric study of HD 106906's asymmetrical debris disk using newly obtained
$H$-, $J$-, and $K1$-band polarimetric data from the Gemini Planet Imager
(GPI). An empirical analysis of our data supports a disk that is asymmetrical
in surface brightness and structure, where fitting an inclined ring model to
the disk spine suggests that the disk may be highly eccentric ($e\gtrsim0.16$).
A comparison of the disk flux with the stellar flux in each band suggests a
blue color that also does not significantly vary across the disk. We discuss
these results in terms of possible sources of asymmetry, where we find that
dynamical interaction with the planet companion, HD 106906b, is a likely
candidate.


Abstract:
  Engineering phase change materials (PCM) to realize superior data storage
devices requires a detailed understanding of crystallization kinetics and its
temperature dependence. The temperature dependence of crystallization differs
distinctly between crystallizing from the glassy phase and the undercooled
liquid (UCL). Hence, knowing the phase from which crystallization occurs is
necessary for predicting the switching ability. Here, we measure the glassy
dynamics and crystallization kinetics using calorimetry for heating rates
spanning over six orders of magnitude. Our results show that the prominent PCM
(Ag,In)-doped Sb2Te (AIST) exhibits a change from crystallizing from the glassy
phase to crystallizing from the UCL at a critical heating rate of 5,000 K/s.
Above the glass transition, the activation energy of crystallization changes
drastically enabling rapid crystallization at elevated temperatures.


Abstract:
  Team Semantics is a generalization of Tarskian Semantics that can be used to
add to First Order Logic atoms and connectives expressing dependencies between
the possible values of variables. Some of these extensions are more expressive
than First Order Logic, while others are reducible to it. In this work, I fully
characterize the (relativizable) atoms and families of atoms that do not
increase the expressive power of First Order Logic with Team Semantics when
they and their negations are added to it, separately or jointly (or,
equivalently, when they are added to First Order Logic augmented with a
contradictory negation connective applicable only to literals and dependency
atoms).


Abstract:
  A method to minimize the cost of the structural design of reinforced concrete
structures using Biogeography-Based Optimization, an evolutionary algorithm, is
presented. SAP2000 is used as computational engine, taking into account
modelling aspects such as static soil-structure interaction (SSSI). The
optimization problem is formulated to properly reflect an actual design
problem, limiting e.g. the size of reinforcement bars to commercially available
sections. Strategies to reduce the computational cost of the optimization
procedure are proposed and an extensive parameter tuning was performed. The
resulting tuned optimization algorithm allows to reduce the direct cost of the
construction of a particular structure project with 21% compared to a design
based on traditional criteria. We also evaluate the effect on the cost of the
superstructure when SSSI is takeninto account.


Abstract:
  Machine learning (ML) has been used to develop increasingly accurate link
quality estimators for wireless networks. However, more in-depth questions
regarding the most suitable class of models, most suitable metrics and model
performance on imbalanced datasets remain open. In this paper, we propose a new
tree-based link quality classifier that meets high performance and fairly
classifies the minority class and, at the same time, incurs low training cost.
We compare the tree-based model, to a multilayer perceptron (MLP) non-linear
model and two linear models, namely logistic regression (LR) and SVM, on a
selected imbalanced dataset and evaluate their results using five different
performance metrics. Our study shows that 1) non-linear models perform slightly
better than linear models in general, 2) the proposed non-linear tree-based
model yields the best performance trade-off considering F1, training time and
fairness, 3) single metric aggregated evaluations based only on accuracy can
hide poor, unfair performance especially on minority classes, and 4) it is
possible to improve the performance on minority classes, by over 40% through
feature selection and by over 20% through resampling, therefore leading to
fairer classification results.


Abstract:
  A neural multimodal machine translation (MMT) system is one that aims to
perform better translation by extending conventional text-only translation
models with multimodal information. Many recent studies report improvements
when equipping their models with the multimodal module, despite the controversy
of whether such improvements indeed come from the multimodal part. We revisit
the contribution of multimodal information in MMT by devising two interpretable
MMT models. To our surprise, although our models replicate similar gains as
recently developed multimodal-integrated systems achieved, our models learn to
ignore the multimodal information. Upon further investigation, we discover that
the improvements achieved by the multimodal models over text-only counterparts
are in fact results of the regularization effect. We report empirical findings
that highlight the importance of MMT models' interpretability, and discuss how
our findings will benefit future research.


Abstract:
  We prove that any surjective self-morphism with $\delta_f > 1$ on a
potentially dense smooth projective surface defined over a number field $K$ has
densely many $L$-rational points for a finite extension $L/K$.


Abstract:
  Following Abbatiello et al. [ DCCDS-A (41), 2020], we introduce dissipative
turbulent solutions to a simple model of a mixture of two non interacting
compressible fluids {\tc filling a bounded domain with general non zero
inflow/outflow boundary conditions.} We prove existence of such solutions for
all adiabatic coefficients $\gamma>1$, their compatibility with classical
solutions, the relative energy inequality, and the weak strong uniqueness
principle in this class. The class of dissipative turbulent solutions is so far
the largest class of generalized solutions which still enjoys the weak strong
uniqueness property.


Abstract:
  We study the radiation from charged particles crossing a cholesteric plate in
the shortwave approximation when the wavelength of photons is much smaller than
the pitch of the cholesteric helix whereas the escaping angle of the photon and
the anisotropy of the permittivity tensor can be arbitrary. The radiation of
photons is treated in the framework of quantum electrodynamics with classical
currents. The radiation of the plane-wave photons and the photons with definite
projection of the angular momentum (the twisted photons) produced by charged
particles crossing the cholesteric plate and moving rectilinearly and uniformly
is considered. The explicit expressions for the average number of radiated
photons and their spectra with respect to the energy and the projection of the
angular momentum are obtained in this case. It turns out that in the paraxial
approximation the projection of the orbital angular momentum, $l$, of radiated
twisted photons is related to the harmonic number $n\in \mathbb{Z}$ as
$l=2n+1$, i.e., the given system is a pure source of twisted photons as
expected. It is shown that in the paraxial shortwave regime the main part of
radiated photons is linearly polarized with $l=\pm1$ at the harmonics
$n=\{-1,0\}$. The applicability conditions of the approach developed are
discussed. As the examples, we consider the production of $6.3$ eV twisted
photons from uranium nuclei and the production of X-ray twisted photons from
$120$ MeV electrons.


Abstract:
  Understanding the formation of molecules under conditions relevant to
interstellar chemistry is fundamental to characterize the chemical evolution of
the universe. Using reactive molecular dynamics simulations with model-based or
high-quality potential energy surfaces provides a means to specifically and
quantitatively probe individual reaction channels at a molecular level. The
formation of CO$_2$ from collision of CO($^1 \Sigma$) and O($^1$D) is
characterized on amorphous solid water (ASW) under conditions typical in cold
molecular clouds. Recombination takes place on the sub-nanosecond time scale
and internal energy redistribution leads to stabilization of the product with
CO$_2$ remaining adsorbed on the ASW on extended time scales. Using a
high-level, reproducing kernel-based potential energy surface for CO$_2$,
formation into and stabilization of CO$_2$ and COO is observed.


Abstract:
  Many dynamical systems exhibit abrupt transitions or tipping as the control
parameter is varied. In scenarios where the parameter is varied continuously,
the rate of change of control parameter greatly affects the performance of
early warning signals (EWS) for such critical transitions.We study the impact
of variation of the control parameter with a finite rate on the performance of
\textcolor{black}{EWS for critical transitions} in a thermoacoustic system (a
horizontal Rijke tube) exhibiting subcritical Hopf bifurcation. There is a
growing interest in developing early warning signals for tipping in real
systems. Firstly, we explore the efficacy of early warning signals based on
critical slowing down and fractal characteristics. From this study, lag-1
autocorrelation (AC) and Hurst exponent H are found to be good measures to
predict the transition well-before the tipping point. The warning time,
obtained using AC and $H$, reduces with an increase in the rate of change of
the control parameter following an inverse power law relation. Hence, for very
fast rates, the warning time may be too short to perform any control action.
Furthermore, we report the observation of a hyperexponential scaling relation
between the AC and the variance of fluctuations during such dynamic Hopf
bifurcation. We construct a theoretical model for noisy Hopf bifurcation
wherein the control parameter is continuously varied at different rates to
study the effect of rate of change of parameter on EWS. Similar results,
including the hyperexponential scaling, are observed in the model as well.


Abstract:
  Individuals of modern societies share ideas and participate in collective
processes within a pervasive, variable, and mostly hidden ecosystem of content
filtering technologies that determine what information we see online. Despite
the impact of these algorithms on daily life and society, little is known about
their effect on information transfer and opinion formation. It is thus unclear
to what extent algorithmic bias has a harmful influence on collective
decision-making, such as a tendency to polarize debate. Here we introduce a
general theoretical framework to systematically link models of opinion
dynamics, social network structure, and content filtering. We showcase the
flexibility of our framework by exploring a family of binary-state opinion
dynamics models where information exchange lies in a spectrum from pairwise to
group interactions. All models show an opinion polarization regime driven by
algorithmic bias and modular network structure. The role of content filtering
is, however, surprisingly nuanced; for pairwise interactions it leads to
polarization, while for group interactions it promotes coexistence of opinions.
This allows us to pinpoint which social interactions are robust against
algorithmic bias, and which ones are susceptible to bias-enhanced opinion
polarization. Our framework gives theoretical ground for the development of
heuristics to tackle harmful effects of online bias, such as information
bottlenecks, echo chambers, and opinion radicalization.


Abstract:
  The question of how and why the phenomenon of mode connectivity occurs in
training deep neural networks has gained remarkable attention in the research
community. From a theoretical perspective, two possible explanations have been
proposed: (i) the loss function has connected sublevel sets, and (ii) the
solutions found by stochastic gradient descent are dropout stable. While these
explanations provide insights into the phenomenon, their assumptions are not
always satisfied in practice. In particular, the first approach requires the
network to have one layer with order of $N$ neurons ($N$ being the number of
training samples), while the second one requires the loss to be almost
invariant after removing half of the neurons at each layer (up to some
rescaling of the remaining ones). In this work, we improve both conditions by
exploiting the quality of the features at every intermediate layer together
with a milder over-parameterization condition. More specifically, we show that:
(i) under generic assumptions on the features of intermediate layers, it
suffices that the last two hidden layers have order of $\sqrt{N}$ neurons, and
(ii) if subsets of features at each layer are linearly separable, then no
over-parameterization is needed to show the connectivity. Our experiments
confirm that the proposed condition ensures the connectivity of solutions found
by stochastic gradient descent, even in settings where the previous
requirements do not hold.


Abstract:
  We report a dramatic current reduction, or a resistance increase, by a few
orders of magnitude of two common-anode Au/Ti/pGaN Schottky junctions annealed
within a certain annealing condition window (600 - 700 oC, 1 - 4 min). Results
from similar common-anode Schottky junctions made of Au/p-GaN, Al/Ti/p-GaN and
Au/Ti/graphene/p-GaN junctions demonstrated that all the three layers (Au, Ti
and p-GaN) are essential for the increased resistance. Raman characterization
of the p-GaN showed a decrease of the Mg-N bonding, i.e., the deactivation of
Mg, which is consistent with the Hall measurement results. Moreover, this
high-resistance junction structure was employed in p-GaN gate AlGaN/GaN HEMTs.
It was shown to be an effective gate technology that was capable to boost the
gate breakdown voltage from 9.9 V to 13.8 V with a negligible effect on the
threshold voltage or the sub-threshold slope.


Abstract:
  A description of tilting complexes is given for a class of PI algebras whose
prime spectrum is canonically homeomorphic to the prime spectrum of its center.
Some Sklyanin algebras are the kind of algebras considered. As an application,
it is proved that any algebra derived equivalent to such kind of algebra, is
Morita equivalent to it.


Abstract:
  In this paper we propose a system capable of tracking multiple soccer players
in different types of video quality. The main goal, in contrast to most
state-of-art soccer player tracking systems, is the ability of execute
effectively tracking in videos of low-quality. We adapted a state-of-art
Multiple Object Tracking to the task. In order to do that adaptation, we
created a Detection and a Tracking Dataset for 3 different qualities of video.
The results of our system are conclusive of its high performance.


Abstract:
  In this article, we investigate inverse source problems for a wide range of
PDEs of parabolic and hyperbolic types as well as time-fractional evolution
equations by partial interior observation. Restricting the source terms to the
form of separated variables, we establish uniqueness results for simultaneously
determining both temporal and spatial components without non-vanishing
assumptions at $t=0$, which seems novel to the best of our knowledge.
Remarkably, mostly we allow a rather flexible choice of the observation time
not necessarily starting from $t=0$, which fits into various situations in
practice. Our main approach is based on the combination of the Titchmarsh
convolution theorem with unique continuation properties and time-analyticity of
the PDEs under consideration.


Abstract:
  The Crouzeix-Raviart triangular finite elements are $\inf$-$\sup$ stable for
the Stokes equations for any mesh with at least one interior vertex. This
result affirms a {\em conjecture of Crouzeix-Falk} from 1989 for $p=3$. Our
proof applies to {\em any odd degree} $p\ge 3$ and hence Crouzeix-Raviart
triangular finite elements of degree $p$ in two dimensions and the piecewise
polynomials of degree $p-1$ with vanishing integral form a stable Stokes pair
{\em for all positive integers} $p$.


Abstract:
  What topological spaces can be partitioned into copies of the Cantor space
$2^\omega$? An obvious necessary condition is that a space can be partitioned
into copies of $2^\omega$ only if it can be covered with copies of $2^\omega$.
We prove three theorems concerning when this necessary condition is also
sufficient.
  If $X$ is a metrizable space and $|X| \leq \mathfrak{c}^{+\omega}$ (the least
limit cardinal $>\!\mathfrak{c}$), then $X$ can be partitioned into copies of
$2^\omega$ if and only if $X$ can be covered with copies of $2^\omega$. To show
this cardinality bound is sharp, we construct a metrizable space of size
$\mathfrak{c}^{+(\omega+1)}$ that can be covered with copies of $2^\omega$, but
not partitioned into copies of $2^\omega$.
  Similarly, if $X$ is first countable and $|X| \leq \mathfrak{c}$, then $X$
can be partitioned into copies of $2^\omega$ if and only if $X$ can be covered
with copies of $2^\omega$. On the other hand, there is a first countable space
of size $\mathfrak{c}^+$ that can be covered with copies of $2^\omega$, but not
partitioned into copies of $2^\omega$.
  Finally, we show that a completely metrizable space can be partitioned into
copies of $2^\omega$ if and only if it can be covered with copies of $2^\omega$
if and only if it has no isolated points.


Abstract:
  The product of two zero mean correlated normal random variables has received
much attention in the statistics literature and appears in many application
areas. However, many important distributional properties are yet to tabulated.
This paper fills this gap by providing the basic distributional theory for the
product of two zero mean correlated normal random variables.


Abstract:
  Simultaneous object recognition and pose estimation are two key
functionalities for robots to safely interact with humans as well as
environments. Although both object recognition and pose estimation use visual
input, most state-of-the-art tackles them as two separate problems since the
former needs a view-invariant representation while object pose estimation
necessitates a view-dependent description. Nowadays, multi-view Convolutional
Neural Network (MVCNN) approaches show state-of-the-art classification
performance. Although MVCNN object recognition has been widely explored, there
has been very little research on multi-view object pose estimation methods, and
even less on addressing these two problems simultaneously. The pose of virtual
cameras in MVCNN methods is often pre-defined in advance, leading to bound the
application of such approaches. In this paper, we propose an approach capable
of handling object recognition and pose estimation simultaneously. In
particular, we develop a deep object-agnostic entropy estimation model, capable
of predicting the best viewpoints of a given 3D object. The obtained views of
the object are then fed to the network to simultaneously predict the pose and
category label of the target object. Experimental results showed that the views
obtained from such positions are descriptive enough to achieve a good accuracy
score. Code is available online at: https://github.com/tparisotto/more_mvcnn


Abstract:
  Few-shot learning (FSL) aims to classify images under low-data regimes, where
the conventional pooled global feature is likely to lose useful local
characteristics. Recent work has achieved promising performances by using deep
descriptors. They generally take all deep descriptors from neural networks into
consideration while ignoring that some of them are useless in classification
due to their limited receptive field, e.g., task-irrelevant descriptors could
be misleading and multiple aggregative descriptors from background clutter
could even overwhelm the object's presence. In this paper, we argue that a
Mutual Nearest Neighbor (MNN) relation should be established to explicitly
select the query descriptors that are most relevant to each task and discard
less relevant ones from aggregative clutters in FSL. Specifically, we propose
Discriminative Mutual Nearest Neighbor Neural Network (DMN4) for FSL. Extensive
experiments demonstrate that our method outperforms the existing
state-of-the-arts on both fine-grained and generalized datasets.


Abstract:
  In the last few years, the release of BERT, a multilingual transformer based
model, has taken the NLP community by storm. BERT-based models have achieved
state-of-the-art results on various NLP tasks, including dialog tasks. One of
the limitation of BERT is the lack of ability to handle long text sequence. By
default, BERT has a maximum wordpiece token sequence length of 512. Recently,
there has been renewed interest to tackle the BERT limitation to handle long
text sequences with the addition of new self-attention based architectures.
However, there has been little to no research on the impact of this limitation
with respect to dialog tasks. Dialog tasks are inherently different from other
NLP tasks due to: a) the presence of multiple utterances from multiple
speakers, which may be interlinked to each other across different turns and b)
longer length of dialogs. In this work, we empirically evaluate the impact of
dialog length on the performance of BERT model for the Next Response Selection
dialog task on four publicly available and one internal multi-turn dialog
datasets. We observe that there is little impact on performance with long
dialogs and even the simplest approach of truncating input works really well.


Abstract:
  Different categories of visual stimuli activate different responses in the
human brain. These signals can be captured with EEG for utilization in
applications such as Brain-Computer Interface (BCI). However, accurate
classification of single-trial data is challenging due to low signal-to-noise
ratio of EEG. This work introduces an EEG-ConvTranformer network that is based
on multi-headed self-attention. Unlike other transformers, the model
incorporates self-attention to capture inter-region interactions. It further
extends to adjunct convolutional filters with multi-head attention as a single
module to learn temporal patterns. Experimental results demonstrate that
EEG-ConvTransformer achieves improved classification accuracy over the
state-of-the-art techniques across five different visual stimuli classification
tasks. Finally, quantitative analysis of inter-head diversity also shows low
similarity in representational subspaces, emphasizing the implicit diversity of
multi-head attention.


Abstract:
  Let $K$ be a complex bi-quadratic field with ring of integers
$\mathcal{O}_{K}$. For $K = \mathbb{Q}(\sqrt{-m}$, $\sqrt{n}$), where $ m
\equiv 3 \pmod 4 $ and $ n \equiv 1 \pmod 4$, we prove that every algebraic
integer can be written as sum of integral squares. Using this, we prove that
for any complex bi-quadratic field $K$, every element of $4\mathcal{O}_K$ can
be written as sum of five integral squares. In addition, we show that the
Pythagoras number of ring of integers of any CM field is at most five.
Moreover, we give two classes of complex bi-quadratic fields for which
$p(\mathcal{O}_{K})= 3$ and $p(4\mathcal{O}_{K})=3 $ respectively. Here,
$p(\mathcal{O}_{K})$ is the Pythagoras number of ring of integers of $K$ and
$p(4\mathcal{O}_{K})$ is the smallest positive integer $t$ such that every
element of $4\mathcal{O}_{K}$ can be written as sum of $t$ integral squares.


Abstract:
  In the past two decades, machine learning potentials (MLP) have reached a
level of maturity that now enables applications to large-scale atomistic
simulations of a wide range of systems in chemistry, physics and materials
science. Different machine learning algorithms have been used with great
success in the construction of these MLPs. In this review, we discuss an
important group of MLPs relying on artificial neural networks to establish a
mapping from the atomic structure to the potential energy. In spite of this
common feature, there are important conceptual differences, which concern the
dimensionality of the systems, the inclusion of long-range electrostatic
interactions, global phenomena like non-local charge transfer, and the type of
descriptor used to represent the atomic structure, which can either be
predefined or learnable. A concise overview is given along with a discussion of
the open challenges in the field.


Abstract:
  We apply the Tangent Method of Colomo and Sportiello to predict the arctic
curves of the Six Vertex model with reflecting (U-turn) boundary and of the
related Twenty Vertex model with suitable domain wall boundary conditions on a
quadrangle, both in their Disordered phase.


Abstract:
  Android malware detection is a significat problem that affects billions of
users using millions of Android applications (apps) in existing markets. This
paper proposes PetaDroid, a framework for accurate Android malware detection
and family clustering on top of static analyses. PetaDroid automatically adapts
to Android malware and benign changes over time with resilience to common
binary obfuscation techniques. The framework employs novel techniques
elaborated on top of natural language processing (NLP) and machine learning
techniques to achieve accurate, adaptive, and resilient Android malware
detection and family clustering. PetaDroid identifies malware using an ensemble
of convolutional neural network (CNN) on proposed Inst2Vec features. The
framework clusters the detected malware samples into malware family groups
utilizing sample feature digests generated using deep neural auto-encoder. For
change adaptation, PetaDroid leverages the detection confidence probability
during deployment to automatically collect extension datasets and periodically
use them to build new malware detection models. Besides, PetaDroid uses
code-fragment randomization during the training to enhance the resiliency to
common obfuscation techniques. We extensively evaluated PetaDroid on multiple
reference datasets. PetaDroid achieved a high detection rate (98-99% f1-score)
under different evaluation settings with high homogeneity in the produced
clusters (96%). We conducted a thorough quantitative comparison with
state-of-the-art solutions MaMaDroid, DroidAPIMiner, MalDozer, in which
PetaDroid outperforms them under all the evaluation settings.


Abstract:
  One of the most significant differences of M5 over previous forecasting
competitions is that it was held on Kaggle, an online platform of data
scientists and machine learning practitioners. Kaggle provides a gathering
place, or virtual community, for web users who are interested in the M5
competition. Users can share code, models, features, loss functions, etc.
through online notebooks and discussion forums. This paper aims to study the
social influence of virtual community on user behaviors in the M5 competition.
We first research the content of the M5 virtual community by topic modeling and
trend analysis. Further, we perform social media analysis to identify the
potential relationship network of the virtual community. We study the roles and
characteristics of some key participants that promote the diffusion of
information within the M5 virtual community. Overall, this study provides
in-depth insights into the mechanism of the virtual community's influence on
the participants and has potential implications for future online competitions.


Abstract:
  Recent interest in exploiting Deep Learning techniques for Noise Suppression,
has led to the creation of Hybrid Denoising Systems that combine classic Signal
Processing with Deep Learning. In this paper, we concentrated our efforts on
extending the RNNoise denoising system (arXiv:1709.08243) with the inclusion of
complementary features during the training phase. We present a comprehensive
explanation of the set-up process of a modified system and present the
comparative results derived from a performance evaluation analysis, using a
reference version of RNNoise as control.


Abstract:
  Contrary to a paradigm of metal-enhanced fluorescence, unusually thick
dielectric coatings can be very favorable to achieve extreme values of averaged
fluorescence enhancement factor $\bar F\gtrsim 3000$ for emitters located on
the surface, or in the interior, of the shell of Au@dielectric spherical
core-shell particles under realistic conditions, even for the emitters with
100% intrinsic quantum yield. Thick dielectric coatings facilitate high-quality
transverse electric (TE) multipole ($\ell=7$) resonances which are shown as the
major cause for the reported extraordinary values of $\bar F$.


Abstract:
  Embedded systems represent a billionaire market and are present in most of
the processors produced in the world.Embedded software interacts with external
peripherals such as sensors and actuators through drivers. The strong
interaction between drivers and external peripherals often hamper embedded
software development, in special the testing task, giving that the physical
environment may not be deterministic and difficult to be recreated during the
tests, the hardware may not be available or its presence may not be desirable
or costly. Aiming at addressing these problems, this paper introduces a
solution to test drivers on microcontrollers, based on a method that uses three
components: a Device Under Test (DUT), a Double device, and a computer. The
computer runs a test orchestration code, while the DUT runs the test target
code, and the Double plays the role of the real external peripherals that
interact with the DUT. The proposed solution was successfully implemented and
validated using different protocols.


Abstract:
  We study the total $\alpha$-powered length of the rooted edges in a random
minimal directed spanning tree - first introduced in \cite{BR04} - on a Poisson
process with intensity $s \ge 1$ on the unit cube $[0,1]^d$ for $d \ge 3$.
While a Dickman limit was proved in \cite{PW04} in the case of $d=2$, in
dimensions three and higher, \cite{BLP06} showed a Gaussian central limit
theorem when $\alpha=1$, with a rate of convergence of the order $(\log
s)^{-(d-2)/4} (\log \log s)^{(d+1)/2}$. In this paper, we extend these results
and prove a central limit theorem in any dimension $d \ge 3$ for any
$\alpha>0$. Moreover, making use of recent results in Stein's method for
region-stabilizing functionals, we provide presumably optimal non-asymptotic
bounds of the order $(\log s)^{-(d-2)/2}$ on the Wasserstein and the Kolmogorov
distances between the distribution of the total $\alpha$-powered length of
rooted edges, suitably normalized, and that of a standard Gaussian random
variable.


Abstract:
  Nodal-line semimetals (NLSMs) contains Dirac/Weyl type band-crossing nodes
extending into shapes of line, loop and chain in the reciprocal space, leading
to novel band topology and transport responses. Robust NLSMs against spin-orbit
coupling typically occur in three-dimensional materials with more symmetry
operations to protect the line nodes of band crossing, while the possibilities
in lower-dimensional materials are rarely discussed. Here we demonstrate robust
NLSM phase in a quasi-one-dimensional nonmagnetic semimetal TaNiTe5. Combining
angle-resolved photoemission spectroscopy measurements and first-principles
calculations, we reveal how reduced dimension can interact with nonsymmorphic
symmetry and result into multiple Dirac-type nodal lines with four-fold
degeneracy. Our findings suggest rich physics and application in
(quasi-)one-dimensional topological materials and call for further
investigation on the interplay between the quantum confinement and nontrivial
band topology.


Abstract:
  The Multi-Phase Transport model (AMPT) is used to study the effects of the
parton-scattering cross-sections ($\sigma_{pp}$) and hadronic re-scattering on
the linear contributions to the flow harmonic $\textit{v}_{4}$, the non-linear
response coefficients, and the correlations between different order flow
symmetry planes in Au+Au collisions at 200~GeV. The model results, which agree
with current experimental measurements, indicate that the higher-order flow
harmonics are sensitive to the $\sigma_{pp}$ variations. However, the
non-linear response coefficients and the correlations between different order
flow symmetry planes are $\sigma_{pp}$ independent. These results suggest that
further detailed experimental measurements which span a broad range of
collision systems and beam energies could serve as an additional constraint for
the theoretical models' calculations.


Abstract:
  This is the problem of the 8$^\mathrm{th}$ International Experimental Physics
Olympiad (EPO). The task of the EPO8 is to determine Plank constant
$\hbar=h/2\pi$ using the given set-up with LED. If you have an idea how to do
it, do it and send us the result; skip the reading of the detailed step by step
instructions with increasing difficulties. We expect the participants to follow
the suggested items -- they are instructive for physics education in
general.Only the reading of historical remarks given in the first section can
be omitted during the Olympiad without loss of generality. Participants should
try solving as much tasks as they can without paying attention to the age
categories: give your best.


Abstract:
  In the paper, we consider a stochastic hybrid Korteweg - de Vries - Burgers
type equation with multiplicative noise in the form of cylindrical Wiener
process. We prove the existence of a martingale solution to the equation
studied. The proof of the existence of the solution is based on two
approximations of the considered problem and the compactness method. First, we
introduce an auxiliary problem corresponding to the equation studied. Then, we
prove the existence of a martingale solution to this problem. Finally, we show
that the solution of the auxiliary problem converges, in some sense, to the
solution of the equation under consideration.


Abstract:
  Distributed quantum information processing is essential for building quantum
networks and enabling more extensive quantum computations. In this regime,
several spatially separated parties share a multipartite quantum system, and
the most natural set of operations is Local Operations and Classical
Communication (LOCC). As a pivotal part in quantum information theory and
practice, LOCC has led to many vital protocols such as quantum teleportation.
However, designing practical LOCC protocols is challenging due to LOCC's
intractable structure and limitations set by near-term quantum devices. Here we
introduce LOCCNet, a machine learning framework facilitating protocol design
and optimization for distributed quantum information processing tasks. As
applications, we explore various quantum information tasks such as entanglement
distillation, quantum state discrimination, and quantum channel simulation. We
discover protocols with evident improvements, in particular, for entanglement
distillation with quantum states of interest in quantum information. Our
approach opens up new opportunities for exploring entanglement and its
applications with machine learning, which will potentially sharpen our
understanding of the power and limitations of LOCC. An implementation of
LOCCNet is available in Paddle Quantum, a quantum machine learning Python
package based on PaddlePaddle deep learning platform.


Abstract:
  The $F_\beta$ score is a commonly used measure of classification performance,
which plays crucial roles in classification tasks with imbalanced data sets.
However, the $F_\beta$ score cannot be used as a loss function by
gradient-based learning algorithms for optimizing neural network parameters due
to its non-differentiability. On the other hand, commonly used loss functions
such as the binary cross-entropy (BCE) loss are not directly related to
performance measures such as the $F_\beta$ score, so that neural networks
optimized by using the loss functions may not yield optimal performance
measures. In this study, we investigate a relationship between classification
performance measures and loss functions in terms of the gradients with respect
to the model parameters. Then, we propose a differentiable surrogate loss
function for the optimization of the $F_\beta$ score. We show that the gradient
paths of the proposed surrogate $F_\beta$ loss function approximate the
gradient paths of the large sample limit of the $F_\beta$ score. Through
numerical experiments using ResNets and benchmark image data sets, it is
demonstrated that the proposed surrogate $F_\beta$ loss function is effective
for optimizing $F_\beta$ scores under class imbalances in binary classification
tasks compared with other loss functions.


Abstract:
  Social events provide valuable insights into group social behaviors and
public concerns and therefore have many applications in fields such as product
recommendation and crisis management. The complexity and streaming nature of
social messages make it appealing to address social event detection in an
incremental learning setting, where acquiring, preserving, and extending
knowledge are major concerns. Most existing methods, including those based on
incremental clustering and community detection, learn limited amounts of
knowledge as they ignore the rich semantics and structural information
contained in social data. Moreover, they cannot memorize previously acquired
knowledge. In this paper, we propose a novel Knowledge-Preserving Incremental
Heterogeneous Graph Neural Network (KPGNN) for incremental social event
detection. To acquire more knowledge, KPGNN models complex social messages into
unified social graphs to facilitate data utilization and explores the
expressive power of GNNs for knowledge extraction. To continuously adapt to the
incoming data, KPGNN adopts contrastive loss terms that cope with a changing
number of event classes. It also leverages the inductive learning ability of
GNNs to efficiently detect events and extends its knowledge from previously
unseen data. To deal with large social streams, KPGNN adopts a mini-batch
subgraph sampling strategy for scalable training, and periodically removes
obsolete data to maintain a dynamic embedding space. KPGNN requires no feature
engineering and has few hyperparameters to tune. Extensive experiment results
demonstrate the superiority of KPGNN over various baselines.


Abstract:
  In this article we disprove the conjectures asserting the positivity of the
coefficients of the Ehrhart polynomial of matroid polytopes by De Loera, Haws
and K\"oppe (2007) and of generalized permutohedra by Castillo and Liu (2018).
We prove constructively that for every $n\geq 19$ there exist connected
matroids on $n$ elements that are not Ehrhart positive. Also, we prove that for
every $k\geq 3$ there exist connected matroids of rank $k$ that are not Ehrhart
positive.


Abstract:
  High-resolution cosmological hydrodynamic simulations are currently limited
to relatively small volumes due to their computational expense. However, much
larger volumes are required to probe rare, overdense environments, and measure
clustering statistics of the large scale structure. Typically, zoom simulations
of individual regions are used to study rare environments, and semi-analytic
models and halo occupation models applied to dark matter only (DMO) simulations
are used to study the Universe in the large-volume regime. We propose a new
approach, using a machine learning framework to explore the halo-galaxy
relationship in the periodic EAGLE simulations, and zoom C-EAGLE simulations of
galaxy clusters. We train a tree based machine learning method to predict the
baryonic properties of galaxies based on their host dark matter halo
properties. The trained model successfully reproduces a number of key
distribution functions for an infinitesimal fraction of the computational cost
of a full hydrodynamic simulation. By training on both periodic simulations as
well as zooms of overdense environments, we learn the bias of galaxy evolution
in differing environments. This allows us to apply the trained model to a
larger DMO volume than would be possible if we only trained on a periodic
simulation. We demonstrate this application using the $(800 \; \mathrm{Mpc})^3$
P-Millennium simulation, and present predictions for key baryonic distribution
functions and clustering statistics from the EAGLE model in this large volume.


Abstract:
  We work with continuous-time, continuous-space stochastic dynamical systems
described by stochastic differential equations (SDE). We present a new approach
to compute probabilistic safety regions, namely sets of initial conditions of
the SDE associated to trajectories that are safe with a probability larger than
a given threshold. We introduce a functional that is minimised at the border of
the probabilistic safety region, then solve an optimisation problem using
techniques from Malliavin Calculus that computes such region. Unlike existing
results in the literature, this new approach allows one to compute
probabilistic safety regions without gridding the state space of the SDE.


Abstract:
  Principal component analysis (PCA) is a versatile tool to reduce the
dimensionality which has wide applications in statistics and machine learning
community. It is particularly useful to model data in high-dimensional
scenarios where the number of variables $p$ is comparable to, or much larger
than the sample size $n$. Despite extensive literature on this topic,
researchers have focused on modeling static principal eigenvectors or
subspaces, which are not suitable for stochastic processes that are dynamic in
nature. To characterize the change in the whole course of high-dimensional data
collection, we propose a unified framework to directly estimate dynamic
principal subspaces spanned by leading eigenvectors of covariance matrices. We
formulate an optimization problem by combining the local linear smoothing and
regularization penalty together with the orthogonality constraint, which can be
effectively solved by the proximal gradient method for manifold optimization.
We show that our method is suitable for high-dimensional data observed under
both common and irregular designs. In addition, theoretical properties of the
estimators are investigated under $l_q (0 \leq q \leq 1)$ sparsity. Extensive
experiments demonstrate the effectiveness of the proposed method in both
simulated and real data examples.


Abstract:
  Self-supervised learning of depth map prediction and motion estimation from
monocular video sequences is of vital importance -- since it realizes a broad
range of tasks in robotics and autonomous vehicles. A large number of research
efforts have enhanced the performance by tackling illumination variation,
occlusions, and dynamic objects, to name a few. However, each of those efforts
targets individual goals and endures as separate works. Moreover, most of
previous works have adopted the same CNN architecture, not reaping
architectural benefits. Therefore, the need to investigate the inter-dependency
of the previous methods and the effect of architectural factors remains. To
achieve these objectives, we revisit numerous previously proposed
self-supervised methods for joint learning of depth and motion, perform a
comprehensive empirical study, and unveil multiple crucial insights.
Furthermore, we remarkably enhance the performance as a result of our study --
outperforming previous state-of-the-art performance.


Abstract:
  We study the performance of the hybrid template-machine-learning photometric
redshift (photo-$z$) algorithm Delight, which uses Gaussian processes, on a
subset of the early data release of the Physics of the Accelerating Universe
Survey (PAUS). We calibrate the fluxes of the $40$ PAUS narrow bands with $6$
broadband fluxes ($uBVriz$) in the COSMOS field using three different methods,
including a new method which utilises the correlation between the apparent size
and overall flux of the galaxy. We use a rich set of empirically derived galaxy
spectral templates as guides to train the Gaussian process, and we show that
our results are competitive with other standard photometric redshift
algorithms. Delight achieves a photo-$z$ $68$th percentile error of
$\sigma_{68}=0.0081(1+z)$ without any quality cut for galaxies with
$i_\mathrm{auto}<22.5$ as compared to $0.0089(1+z)$ and $0.0202(1+z)$ for the
BPz and ANNz2 codes, respectively. Delight is also shown to produce more
accurate probability distribution functions for individual redshift estimates
than BPz and ANNz2. Common photo-$z$ outliers of Delight and BCNz2 (previously
applied to PAUS) are found to be primarily caused by outliers in the narrowband
fluxes, with a small number of cases potentially indicating spectroscopic
redshift failures in the reference sample. In the process, we introduce
performance metrics derived from the results of BCNz2 and Delight, allowing us
to achieve a photo-$z$ quality of $\sigma_{68}<0.0035(1+z)$ at a magnitude of
$i_\mathrm{auto}<22.5$ while keeping $50$ per cent objects of the galaxy
sample.


Abstract:
  Convolutional neural networks (CNNs) are highly successful for
super-resolution (SR) but often require sophisticated architectures with heavy
memory cost and computational overhead, significantly restricts their practical
deployments on resource-limited devices. In this paper, we proposed a novel
contrastive self-distillation (CSD) framework to simultaneously compress and
accelerate various off-the-shelf SR models. In particular, a channel-splitting
super-resolution network can first be constructed from a target teacher network
as a compact student network. Then, we propose a novel contrastive loss to
improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer.
Extensive experiments demonstrate that the proposed CSD scheme effectively
compresses and accelerates several standard SR models such as EDSR, RCAN and
CARN. Code is available at https://github.com/Booooooooooo/CSD.


Abstract:
  We report on the onset of anti-resonant behaviour of mass transport systems
driven by time-dependent forces. Anti-resonances arise from the coupling of a
sufficiently high number of space-time modes of the force. The presence of
forces having a wide space-time spectrum, a necessary condition for the
formation of an anti-resonance, is typical of confined systems with uneven and
deformable walls that induce entropic forces dependent on space and time. We
have analyzed, in particular, the case of polymer chains confined in a flexible
channel and shown how they can be sorted and trapped. The presence of
resonance-antiresonance pairs found can be exploited to design protocols able
to engineer optimal transport processes and to manipulate the dynamics of
nano-objects.