Abstract:
  Off-policy evaluation (OPE) is the task of estimating the expected reward of
a given policy based on offline data previously collected under different
policies. Therefore, OPE is a key step in applying reinforcement learning to
real-world domains such as medical treatment, where interactive data collection
is expensive or even unsafe. As the observed data tends to be noisy and
limited, it is essential to provide rigorous uncertainty quantification, not
just a point estimation, when applying OPE to make high stakes decisions. This
work considers the problem of constructing non-asymptotic confidence intervals
in infinite-horizon off-policy evaluation, which remains a challenging open
question. We develop a practical algorithm through a primal-dual
optimization-based approach, which leverages the kernel Bellman loss (KBL) of
Feng et al.(2019) and a new martingale concentration inequality of KBL
applicable to time-dependent data with unknown mixing conditions. Our algorithm
makes minimum assumptions on the data and the function class of the Q-function,
and works for the behavior-agnostic settings where the data is collected under
a mix of arbitrary unknown behavior policies. We present empirical results that
clearly demonstrate the advantages of our approach over existing methods.


Abstract:
  Solar activity in all its varied manifestations is driven by the magnetic
field. Particularly important for many purposes are two global quantities, the
Sun's total and open magnetic flux, which can be computed from sunspot number
records using models. Such sunspot-driven models, however, do not take into
account the presence of magnetic flux during grand minima, such as the Maunder
minimum. Here we present a major update of a widely used simple model, which
now takes into account the observation that the distribution of all magnetic
features on the Sun follows a single power law. The exponent of the power law
changes over the solar cycle. This allows for the emergence of small-scale
magnetic flux even when no sunspots are present for multiple decades and leads
to non-zero total and open magnetic flux also in the deepest grand minima, such
as the Maunder minimum, thus overcoming a major shortcoming of the earlier
models. The results of the updated model compare well with the available
observations and reconstructions of the solar total and open magnetic flux.
This opens up the possibility of improved reconstructions of sunspot number
from time series of cosmogenic isotope production rate.


Abstract:
  Generalization has been a long-standing challenge for reinforcement learning
(RL). Visual RL, in particular, can be easily distracted by irrelevant factors
in high-dimensional observation space. In this work, we consider robust policy
learning which targets zero-shot generalization to unseen visual environments
with large distributional shift. We propose SECANT, a novel self-expert cloning
technique that leverages image augmentation in two stages to decouple robust
representation learning from policy optimization. Specifically, an expert
policy is first trained by RL from scratch with weak augmentations. A student
network then learns to mimic the expert policy by supervised learning with
strong augmentations, making its representation more robust against visual
variations compared to the expert. Extensive experiments demonstrate that
SECANT significantly advances the state of the art in zero-shot generalization
across 4 challenging domains. Our average reward improvements over prior SOTAs
are: DeepMind Control (+26.5%), robotic manipulation (+337.8%), vision-based
autonomous driving (+47.7%), and indoor object navigation (+15.8%). Code
release and video are available at https://linxifan.github.io/secant-site/.


Abstract:
  The federated learning (FL) framework trains a machine learning model using
decentralized data stored at edge client devices by periodically aggregating
locally trained models. Popular optimization algorithms of FL use vanilla
(stochastic) gradient descent for both local updates at clients and global
updates at the aggregating server. Recently, adaptive optimization methods such
as AdaGrad have been studied for server updates. However, the effect of using
adaptive optimization methods for local updates at clients is not yet
understood. We show in both theory and practice that while local adaptive
methods can accelerate convergence, they can cause a non-vanishing solution
bias, where the final converged solution may be different from the stationary
point of the global objective function. We propose correction techniques to
overcome this inconsistency and complement the local adaptive methods for FL.
Extensive experiments on realistic federated training tasks show that the
proposed algorithms can achieve faster convergence and higher test accuracy
than the baselines without local adaptivity.


Abstract:
  This study investigates the effects of porosity on flying creatures such as
dragonflies, moths, hummingbirds, etc. wing and shows that pores can affect
wing performance. These studies were performed by 3D porous flapping wing flow
analyses on Comsol Multiphysics. In this study, we analyzed different numbers
of the porous wing at different angles of inclination in order to see the
effect of pores on lift and drag forces. To compare the results 9 different
analyses were performed. In these analyses, airflow velocity was taken as 5
m/s, angle of attack as 5 degrees, frequency as 25 Hz, and flapping angle as 30
degrees. By keeping these values constant, the number of pores was changed to
36, 48, and 60, and the pore angles of inclination to 60, 70, and 80 degrees.
Analyses were carried out by giving laminar flow to this wing designed in the
Comsol Multiphysics program. The importance of pores was investigated by
comparing the results of these analyses.


Abstract:
  We study the problem of distributed zero-order optimization for a class of
strongly convex functions. They are formed by the average of local objectives,
associated to different nodes in a prescribed network of connections. We
propose a distributed zero-order projected gradient descent algorithm to solve
this problem. Exchange of information within the network is permitted only
between neighbouring nodes. A key feature of the algorithm is that it can query
only function values, subject to a general noise model, that does not require
zero mean or independent errors. We derive upper bounds for the average
cumulative regret and optimization error of the algorithm which highlight the
role played by a network connectivity parameter, the number of variables, the
noise level, the strong convexity parameter of the global objective and certain
smoothness properties of the local objectives. When the bound is specified to
the standard undistributed setting, we obtain an improvement over the
state-of-the-art bounds, due to the novel gradient estimation procedure
proposed here. We also comment on lower bounds and observe that the dependency
over certain function parameters in the bound is nearly optimal.


Abstract:
  Calibrating a Reynolds-averaged Navier-Stokes (RANS) model against data leads
to an improvement. Determining {\it a priori} if such an improvement
generalizes to flows outside the calibration data is an outstanding challenge.
This work attempts to address this challenge via global epistemic Uncertainty
Quantification (UQ). Unlike the available epistemic UQ methods that are local
and tell us a model's uncertainty at one specific flow condition, the global
epistemic UQ method presented in this work tells us also whether a perturbation
of the original model would generalize. Specifically, the global epistemic UQ
method evaluates a potential improvement in terms of its "effectiveness" and
"inconsistency". Any improvement can be put in one of the following four
quadrants: first, high effectiveness, low inconsistency; second, high
effectiveness, high inconsistency; third, low effectiveness, low inconsistency;
and fourth, low effectiveness, high inconsistency. An improvement would
generalize if and only if it is in the high effectiveness and low inconsistency
quadrant. To demonstrate the concept, we apply the global epistemic UQ to full
Reynolds stress modeling of a stratified shear layer. The global epistemic UQ
results point to a model coefficient in the pressure-strain correlation closure
(among others) as effective and consistent for predicting the quantity of
interest of shear layer's growth. We calibrate the model coefficient such that
our RANS matches direct numerical simulation data at one flow condition. We
show that the calibrated model generalizes to several other test flow
conditions. On the other hand, when calibrating a high inconsistency term, we
get a model that works at only the calibrated condition.


Abstract:
  This paper describes the SPAPL system for the INTERSPEECH 2021 Challenge:
Shared Task on Automatic Speech Recognition for Non-Native Children's Speech in
German. ~ 5 hours of transcribed data and ~ 60 hours of untranscribed data are
provided to develop a German ASR system for children. For the training of the
transcribed data, we propose a non-speech state discriminative loss (NSDL) to
mitigate the influence of long-duration non-speech segments within speech
utterances. In order to explore the use of the untranscribed data, various
approaches are implemented and combined together to incrementally improve the
system performance. First, bidirectional autoregressive predictive coding
(Bi-APC) is used to learn initial parameters for acoustic modelling using the
provided untranscribed data. Second, incremental semi-supervised learning is
further used to iteratively generate pseudo-transcribed data. Third, different
data augmentation schemes are used at different training stages to increase the
variability and size of the training data. Finally, a recurrent neural network
language model (RNNLM) is used for rescoring. Our system achieves a word error
rate (WER) of 39.68% on the evaluation data, an approximately 12% relative
improvement over the official baseline (45.21%).


Abstract:
  In this article we describe, a numerical simulation has been performed for a
channel flow with sudden expansion. Different geometric expansion ratios are
considered in the current study such as 1:2, 1:3, and 1:4. The bifurcation
phenomenon has been investigated for the baseline case and the critical
Reynolds number is controlled using a passive modification in the channel. The
geometry of sudden expansion channel is modified by introducing a
two-dimensional plate along the center line of the channel at the expansion
plane. This is one of the passive control techniques, which has been explored
in the present study. The computations indicate that the critical Reynolds
number of the symmetry-breaking bifurcation increases as the length of the
plate increases. An empirical relation has been developed amongst the critical
Reynolds number, length of control plates and expansion ratio (h/H). The
critical Reynolds number predicted by the empirical relation for different
plate lengths are in good agreement with computational results.


Abstract:
  In previous work, we study the Gan-Gross-Prasad problem for unipotent
representations of finite classical groups. In this paper, we deduce the
Gan-Gross-Prasad problem for arbitrary representations from the unipotent
representations by Lusztig correspondence.


Abstract:
  Solar modules are currently characterized at standard test conditions (STC),
defined at 1000W/m2 and 25 {\deg}C. However, solar modules in actual outdoor
operating conditions typically operate at lower illumination and higher
temperature than STC, which significantly affects their performance ratio
(average harvesting efficiency over efficiency in STC). Silicon heterojunction
(SHJ) technology displays both good temperature coefficient and good
low-illumination performances, leading to outstanding performance ratios. We
investigate here SHJ solar cells that use a-SiCx(n) layer as front doped layer
with different carbon contents under different climates conditions. Adding
carbon increases transparency but also resistive losses at room temperature
(compared with carbon-free layers), leading to a significant decrease in
efficiency at STC. We demonstrate that despite this difference at STC, the
difference in energy harvesting efficiency is much smaller in all investigated
climates. Furthermore, we show that a relative gain of 0.4 to 0.8 percent in
harvesting efficiency is possible by adding a certain content of carbon in the
front (n) layer, compared with carbon-free cells optimized for STC.


Abstract:
  This article develops a new theoretical basis for decomposing signals that
are formed by the linear superposition of a finite number of modes. Each mode
depends nonlinearly upon several parameters; we seek both these parameters and
the weights within the superposition. The particular focus of this article is
upon solving the decomposition problem when the number of modes is not known in
advance. A sheaf-theoretic formalism is introduced that describes all modal
decomposition problems, and it is shown that minimizing the local consistency
radius within this sheaf is guaranteed to solve them. Since the modes may or
may not be well-separated, a greedy algorithm that identifies the most distinct
modes first may not work reliably. This article introduces a novel mathematical
formalism, aggregation sheaves, and shows how they characterize the behavior of
greedy algorithms that attempt to solve modal decomposition problems.


Abstract:
  Electronic Health Records (EHRs) are commonly used to investigate
relationships between patient health information and outcomes. Deep learning
methods are emerging as powerful tools to learn such relationships, given the
characteristic high dimension and large sample size of EHR datasets. The
Physionet 2012 Challenge involves an EHR dataset pertaining to 12,000 ICU
patients, where researchers investigated the relationships between clinical
measurements, and in-hospital mortality. However, the prevalence and complexity
of missing data in the Physionet data present significant challenges for the
application of deep learning methods, such as Variational Autoencoders (VAEs).
Although a rich literature exists regarding the treatment of missing data in
traditional statistical models, it is unclear how this extends to deep learning
architectures. To address these issues, we propose a novel extension of VAEs
called Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not
At Random (MNAR) patterns in the Physionet data. Our proposed method models the
missingness mechanism using an embedded neural network, eliminating the need to
specify the exact form of the missingness mechanism a priori. We show that the
use of our method leads to more realistic imputed values relative to the
state-of-the-art, as well as significant differences in fitted downstream
models for mortality.


Abstract:
  A central goal of explainable artificial intelligence (XAI) is to improve the
trust relationship in human-AI interaction. One assumption underlying research
in transparent AI systems is that explanations help to better assess
predictions of machine learning (ML) models, for instance by enabling humans to
identify wrong predictions more efficiently. Recent empirical evidence however
shows that explanations can have the opposite effect: When presenting
explanations of ML predictions humans often tend to trust ML predictions even
when these are wrong. Experimental evidence suggests that this effect can be
attributed to how intuitive, or human, an AI or explanation appears. This
effect challenges the very goal of XAI and implies that responsible usage of
transparent AI methods has to consider the ability of humans to distinguish
machine generated from human explanations. Here we propose a quantitative
metric for XAI methods based on Turing's imitation game, a Turing Test for
Transparency. A human interrogator is asked to judge whether an explanation was
generated by a human or by an XAI method. Explanations of XAI methods that can
not be detected by humans above chance performance in this binary
classification task are passing the test. Detecting such explanations is a
requirement for assessing and calibrating the trust relationship in human-AI
interaction. We present experimental results on a crowd-sourced text
classification task demonstrating that even for basic ML models and XAI
approaches most participants were not able to differentiate human from machine
generated explanations. We discuss ethical and practical implications of our
results for applications of transparent ML.


Abstract:
  Deep neural networks (DNN) have achieved remarkable success in computer
vision (CV). However, training and inference of DNN models are both memory and
computation intensive, incurring significant overhead in terms of energy
consumption and silicon area. In particular, inference is much more
cost-sensitive than training because training can be done offline with powerful
platforms, while inference may have to be done on battery powered devices with
constrained form factors, especially for mobile or edge vision applications. In
order to accelerate DNN inference, model quantization was proposed. However
previous works only focus on the quantization rate without considering the
efficiency of operations. In this paper, we propose Dendrite-Tree based Neural
Network (DTNN) for energy-efficient inference with table lookup operations
enabled by activation quantization. In DTNN both costly weight access and
arithmetic computations are eliminated for inference. We conducted experiments
on various kinds of DNN models such as LeNet-5, MobileNet, VGG, and ResNet with
different datasets, including MNIST, Cifar10/Cifar100, SVHN, and ImageNet. DTNN
achieved significant energy saving (19.4X and 64.9X improvement on ResNet-18
and VGG-11 with ImageNet, respectively) with negligible loss of accuracy. To
further validate the effectiveness of DTNN and compare with state-of-the-art
low energy implementation for edge vision, we design and implement DTNN based
MLP image classifiers using off-the-shelf FPGAs. The results show that DTNN on
the FPGA, with higher accuracy, could achieve orders of magnitude better energy
consumption and latency compared with the state-of-the-art low energy
approaches reported that use ASIC chips.


Abstract:
  Domain walls in magnets, vortex lattices in superconductors, contact lines at
depinning, and many other systems can be modelled as an elastic system subject
to quenched disorder. Its field theory possesses a well-controlled perturbative
expansion around its upper critical dimension. Contrary to standard field
theory, the renormalization group flow involves a function, the disorder
correlator $\Delta(w)$, therefore termed the functional renormalization group
(FRG). $\Delta(w)$ is a physical observable, the auto-correlation function of
the centre of mass of the elastic manifold. In this review, we give a
pedagogical introduction into its phenomenology and techniques. This allows us
to treat both equilibrium (statics), and depinning (dynamics). Building on
these techniques, avalanche observables are accessible: distributions of size,
duration, and velocity, as well as the spatial and temporal shape. Various
equivalences between disordered elastic manifolds, and sandpile models exist:
an elastic string driven at a point and the Oslo model; disordered elastic
manifolds and Manna sandpiles; charge density waves and Abelian sandpiles or
loop-erased random walks. Each of these mappings requires specific techniques,
which we develop, including modelling of discrete stochastic systems via
coarse-grained stochastic equations of motion, super-symmetry techniques, and
cellular automata. Stronger than quadratic nearest-neighbour interactions lead
to directed percolation, and non-linear surface growth with additional KPZ
terms. On the other hand, KPZ without disorder can be mapped back to disordered
elastic manifolds, either on the directed polymer for its steady state, or a
single particle for its decay. Other topics covered are the relation between
functional RG and replica symmetry breaking, and random field magnets. Emphasis
is given to numerical and experimental tests of the theory.


Abstract:
  We conducted an online study with $n = 235$ Signal users on their
understanding and usage of PINs in Signal. In our study, we observe a split in
PIN management and composition strategies between users who can explain the
purpose of the Signal PINs (56%; enthusiasts) and users who cannot (44%; casual
users). Encouraging adoption of PINs by Signal appears quite successful: only
14% opted-out of setting a PIN entirely. Among those who did set a PIN, most
enthusiasts had long, complex alphanumeric PINs generated by and saved in a
password manager. Meanwhile more casual Signal users mostly relied on short
numeric-only PINs. Our results suggest that better communication about the
purpose of the Signal PIN could help more casual users understand the features
PINs enable (such as that it is not simply a personal identification number).
This communication could encourage a stronger security posture.


Abstract:
  In this work we investigate novel spherical core-shell nanoparticles with
band inversion. The core and the embedding medium are normal semiconductors
while the shell material is assumed to be a topological insulator. The envelope
functions are found to satisfy a Dirac-like equation that can be solved in a
closed form. The core-shell nanoparticle supports midgap bound states located
at both interfaces due to band inversion. These states are robust since they
are topologically protected. The energy spectrum presents mirror symmetry due
to the chiral symmetry of the Dirac-like Hamiltonian. As a major result, we
show that the thickness of the shell acts as an additional parameter for the
fine tuning of the energy levels, which paves the way for electronics and
optoelectronics applications.


Abstract:
  The importance of parameter selection in supervised learning is well known.
However, due to the many parameter combinations, an incomplete or an
insufficient procedure is often applied. This situation may cause misleading or
confusing conclusions. In this opinion paper, through an intriguing example we
point out that the seriousness goes beyond what is generally recognized. In the
topic of multi-label classification for medical code prediction, one
influential paper conducted a proper parameter selection on a set, but when
moving to a subset of frequently occurring labels, the authors used the same
parameters without a separate tuning. The set of frequent labels became a
popular benchmark in subsequent studies, which kept pushing the state of the
art. However, we discovered that most of the results in these studies cannot
surpass the approach in the original paper if a parameter tuning had been
conducted at the time. Thus it is unclear how much progress the subsequent
developments have actually brought. The lesson clearly indicates that without
enough attention on parameter selection, the research progress in our field can
be uncertain or even illusive.


Abstract:
  We study the aggregation of two risks when the marginal distributions are
known and the dependence structure is unknown, under the additional constraint
that one risk is smaller than or equal to the other. Risk aggregation problems
with the order constraint are closely related to the recently introduced notion
of the directional lower (DL) coupling. The largest aggregate risk in concave
order (thus, the smallest aggregate risk in convex order) is attained by the DL
coupling. These results are further generalized to calculate the best-case and
worst-case values of tail risk measures. In particular, we obtain analytical
formulas for bounds on Value-at-Risk. Our numerical results suggest that the
new bounds on risk measures with the extra order constraint can greatly improve
those with full dependence uncertainty.


Abstract:
  Bibliometric studies are a rare undertaking in the field of English language
teaching, especially at a journal level. To celebrate the 30th anniversary of
TEFLIN Journal, this study exhibits a bibliometric portrait of its publication,
indexation, and citation from 1990 to 2019. Two pieces of free software were
adopted to conduct the descriptive and network analyses of bibliographic data
from Microsoft Academic. The publication and citation metrics have risen during
its lifetime. The bibliographic network identifies the most productive authors,
institutions, and countries along with the co-authorship pattern, type of
top-cited articles, and top-used keywords. The articles relatedness is also
sighted in terms of the citation frequency and number of shared references.
Even though the analyses were complicated by some missing articles and improper
indexation, this study could still take a full-length bibliometric portrait of
the journal during its 30-year journey between the commitment to competence and
the quest for higher impact.


Abstract:
  Recent single-image super-resolution (SISR) networks, which can adapt their
network parameters to specific input images, have shown promising results by
exploiting the information available within the input data as well as large
external datasets. However, the extension of these self-supervised SISR
approaches to video handling has yet to be studied. Thus, we present a new
learning algorithm that allows conventional video super-resolution (VSR)
networks to adapt their parameters to test video frames without using the
ground-truth datasets. By utilizing many self-similar patches across space and
time, we improve the performance of fully pre-trained VSR networks and produce
temporally consistent video frames. Moreover, we present a test-time knowledge
distillation technique that accelerates the adaptation speed with less hardware
resources. In our experiments, we demonstrate that our novel learning algorithm
can fine-tune state-of-the-art VSR networks and substantially elevate
performance on numerous benchmark datasets.


Abstract:
  The variety of services and functionality offered by various cloud service
providers (CSP) have exploded lately. Utilizing such services has created
numerous opportunities for enterprises infrastructure to become cloud-based
and, in turn, assisted the enterprises to easily and flexibly offer services to
their customers. The practice of renting out access to servers to clients for
computing and storage purposes is known as Infrastructure as a Service (IaaS).
The popularity of IaaS has led to serious and critical concerns with respect to
the cyber security and privacy. In particular, malware is often leveraged by
malicious entities against cloud services to compromise sensitive data or to
obstruct their functionality. In response to this growing menace, malware
detection for cloud environments has become a widely researched topic with
numerous methods being proposed and deployed. In this paper, we present online
malware detection based on process level performance metrics, and analyze the
effectiveness of different baseline machine learning models including, Support
Vector Classifier (SVC), Random Forest Classifier (RFC), KNearest Neighbor
(KNN), Gradient Boosted Classifier (GBC), Gaussian Naive Bayes (GNB) and
Convolutional Neural Networks (CNN). Our analysis conclude that neural network
models can most accurately detect the impact malware have on the process level
features of virtual machines in the cloud, and therefore are best suited to
detect them. Our models were trained, validated, and tested by using a dataset
of 40,680 malicious and benign samples. The dataset was complied by running
different families of malware (collected from VirusTotal) in a live cloud
environment and collecting the process level features.


Abstract:
  Protostellar jets have a fundamental role at the earliest evolution of
protostars of all masses. In the case of low-mass (<8 Msun) protostars, strong
observational evidence exists that the launching and collimation is due to the
X- and/or disk-wind mechanisms. In these models, it is the protostar/disk
system that creates all the necessary conditions to launch and collimate the
jets near the protostar via strong magnetic fields. The origin of jets from
more massive protostars has been investigated much less, in part because of the
difficulty of resolving the collimation zone in these more distant objects.
Here we present the highest angular resolution observations of a jet powered by
a massive protostar, the Cep A HW2 radio jet. We imaged the radio emission at
projected distances of only ~20 au from the protostar, resolving the innermost
100 au of a massive protostellar jet for the first time. The morphology of the
radio jet emission in this massive object is very different than what is
usually observed in jets from low-mass protostars. We found that the outflowing
material in HW2 has two components: a wide-angle wind launched from the
protostar/disk system, and a highly collimated jet starting at 20-30 au from
the protostar. We discuss two possible scenarios: an extension of the classical
disk-wind to a massive protostar, or external collimation of a wide-angle wind.
These results have important consequences for our understanding of how stars of
different masses are formed.


Abstract:
  We establish quantitative bounds on the $U^k[N]$ Gowers norms of the M\"obius
function $\mu$ and the von Mangoldt function $\Lambda$ for all $k$, with error
terms of shape $O((\log\log N)^{-c})$. As a consequence, we obtain quantitative
bounds for the number of solutions to any linear system of equations of finite
complexity in the primes, with the same shape of error terms. We also obtain
the first quantitative bounds on the size of sets containing no $k$-term
arithmetic progressions with shifted prime difference.


Abstract:
  Understanding the nature of charge carriers in doped Mott insulators holds
the key to unravelling puzzling properties of strongly correlated electron
systems, including cuprate superconductors. Several theoretical models
suggested that dopants can be understood as bound states of partons, the
analogues of quarks in high-energy physics. However, direct signatures of
spinon-chargon bound states are lacking, both in experiment and theory. Here we
numerically identify long-lived rotational resonances at low doping, which
directly reveal the microscopic structure of spinon-chargon bound states.
Similar to Regge trajectories reflecting the quark structure of mesons, we
establish a linear dependence of the rotational energy on the super-exchange
coupling. Rotational excitations are strongly suppressed in standard
angle-resolved photo-emission (ARPES) spectra, but we propose a multi-photon
rotational extension of ARPES where they have strong spectral weight. Our
findings suggest that multi-photon spectroscopy experiments should provide new
insights into emergent universal features of strongly correlated electron
systems.


Abstract:
  A manual for computations in direct Dark Matter detection phenomenology.
Featuring self-contained sections on non-relativistic expansion, elastic and
inelastic scattering kinematics, Dark Matter velocity distribution, hadronic
matrix elements, nuclear form factors, cross sections, rate spectra and
parameter-space constraints, as well as a handy two-page summary and Q&A
section for a quick reference. A pedagogical, yet general and model independent
guide, with examples from standard and non-standard particle Dark Matter
models.


Abstract:
  Identifying prostate cancer patients that are harboring aggressive forms of
prostate cancer remains a significant clinical challenge. To shed light on this
problem, we develop an approach based on multispectral deep-ultraviolet (UV)
microscopy that provides novel quantitative insight into the aggressiveness and
grade of this disease. First, we find that UV spectral signatures from
endogenous molecules give rise to a phenotypical continuum that differentiates
critical structures of thin tissue sections with subcellular spatial
resolution, including nuclei, cytoplasm, stroma, basal cells, nerves, and
inflammation. Further, we show that this phenotypical continuum can be applied
as a surrogate biomarker of prostate cancer malignancy, where patients with the
most aggressive tumors show a ubiquitous glandular phenotypical shift. Lastly,
we adapt a two-part Cycle-consistent Generative Adversarial Network to
translate the label-free deep-UV images into virtual hematoxylin and eosin
(H&E) stained images. Agreement between the virtual H&E images and the gold
standard H&E-stained tissue sections is evaluated by a panel of pathologists
who find that the two modalities are in excellent agreement. This work has
significant implications towards improving our ability to objectively quantify
prostate cancer grade and aggressiveness, thus improving the management and
clinical outcomes of prostate cancer patients. This same approach can also be
applied broadly in other tumor types to achieve low-cost, stain-free,
quantitative histopathological analysis.


Abstract:
  The concept of quantum coherence and its possible use as a resource are
currently the subject of active researches. Uncertainty and complementarity
relations for quantum coherence allow one to study its changes with respect to
other characteristics of the process of interest. Protocols of quantum
information processing often use measurements that have a special inner
structure. Quantum designs are considered as tools with a perspective of
fruitful applications in quantum information processing. We obtain uncertainty
and certainty relations for coherence averaged with respect to a set of
design-structured POVMs of rank one. To characterize the degree of coherence,
the relative entropy of coherence is utilized. The derived relations are
exemplified with quantum designs in the two-dimensional Hilbert space.


Abstract:
  We present a novel model for capturing the behavior of an agent exhibiting
sunk-cost bias in a stochastic environment. Agents exhibiting sunk-cost bias
take into account the effort they have already spent on an endeavor when they
evaluate whether to continue or abandon it. We model planning tasks in which an
agent with this type of bias tries to reach a designated goal. Our model
structures this problem as a type of Markov decision process: loosely speaking,
the agent traverses a directed acyclic graph with probabilistic transitions,
paying costs for its actions as it tries to reach a target node containing a
specified reward. The agent's sunk cost bias is modeled by a cost that it
incurs for abandoning the traversal: if the agent decides to stop traversing
the graph, it incurs a cost of $\lambda \cdot C_{sunk}$, where ${\lambda \geq
0}$ is a parameter that captures the extent of the bias and $C_{sunk}$ is the
sum of costs already invested.
  We analyze the behavior of two types of agents: naive agents that are unaware
of their bias, and sophisticated agents that are aware of it. Since optimal
(bias-free) behavior in this problem can involve abandoning the traversal
before reaching the goal, the bias exhibited by these types of agents can
result in sub-optimal behavior by shifting their decisions about abandonment.
We show that in contrast to optimal agents, it is computationally hard to
compute the optimal policy for a sophisticated agent. Our main results quantify
the loss exhibited by these two types of agents with respect to an optimal
agent. We present both general and topology-specific bounds.


Abstract:
  Optical quantum circuits can be optimized using gradient descent methods, as
the gates in a circuit can be parametrized by continuous parameters. However,
the parameter space as seen by the cost function is not Euclidean, which means
that the Euclidean gradient does not generally point in the direction of
steepest ascent. In order to retrieve the steepest ascent direction, in this
work we implement Natural Gradient descent in the optical quantum circuit
setting, which takes the local metric tensor into account. In particular, we
adapt the Natural Gradient approach to a complex-valued parameter space. We
then compare the Natural Gradient approach to vanilla gradient descent and to
Adam over two state preparation tasks: a single-photon source and a
Gottesman-Kitaev-Preskill state source. We observe that the NG approach has a
faster convergence (due in part to the possibility of using larger learning
rates) and a significantly smoother decay of the cost function throughout the
optimization.


Abstract:
  This paper deals with linear stochastic partial differential equations with
variable coefficients driven by L\'{e}vy white noise. We first derive an
existence theorem for integral transforms of L\'{e}vy white noise and prove the
existence of generalized and mild solutions of second order elliptic partial
differential equations. Furthermore, we discuss the generalized electric
Schr\"odinger operator for different potential functions $V$.


Abstract:
  We modularly build increasingly larger fragments of the ZX-calculus by
modularly adding new generators and relations, at each point, giving some
concrete semantics in terms of some category of spans. This is performed using
Lack's technique of composing props via distributive laws, as well as the
technique of pushout cubes of Zanasi. We do this for the fragment of the
ZX-calculus with only the black $\pi$-phase (and no Hadamard gate) as well as
well as the fragment which additionally has the and gate as a generator (which
is equivalent to the natural number H-box fragment of the ZH-calculus). In the
former case, we show that this is equivalent to the full subcategory of spans
of (possibly empty) free, finite dimensional affine $\mathbb F_2$-vector
spaces, where the objects are the non-empty affine vector spaces. In the latter
case, we show that this is equivalent to the full subcategory of spans of
finite sets where the objects are powers of the two element set. Because these
fragments of the ZX-calculus have semantics in terms of full subcategories of
categories of spans, they can not be presented by distributive laws over
groupoids. Instead, we first construct their subcategories of partial
isomorphisms via distributive laws over all isomorphims with subobjects
adjoined. After which, the full subcategory of spans are obtained by freely
adjoining units and counits the the semi-Frobenius structures given by the
diagonal and codiagonal maps.


Abstract:
  Measurements of the surface velocity and surface texture of a freely
propagating shear jamming front in a dense suspension are compared. The
velocity fields are captured with particle image velocimetry (PIV), while the
surface texture is captured in a separated experiment by observing a direct
reflection on the suspension surface with high-speed cameras. A method for
quantifying the surface features and their orientation is presented based on
the fast Fourier transform of localised windows. The region that exhibits
strong surface features corresponds to the the solid-like jammed region
identified via the PIV measurements. Moreover, the surface features within the
jammed region are predominantly oriented in the same direction as the
eigenvectors of the strain tensor. Thus, from images of the free surface, our
analysis is able to show that the surface texture contains information on the
principle strain directions and the propagation of the jamming front.


Abstract:
  Cell migration plays essential roles in development, wound healing, diseases,
and in the maintenance of a complex body. Experiments in collective cell
migration generally measure quantities such as cell displacement and velocity.
The observed short-time diffusion regime for mean square displacement in
single-cell migration experiments on flat surfaces calls into question the
definition of cell velocity and the measurement protocol. Theoretical results
in stochastic modeling for single-cell migration have shown that this fast
diffusive regime is explained by a white noise acting on displacement on the
direction perpendicular to the migrating cell polarization axis (not on
velocity). The prediction is that only the component of velocity parallel to
the polarization axis is a well-defined quantity, with a robust measurement
protocol. Here, we ask whether we can find a definition of a migrating-cell
polarization that is able to predict the cell's subsequent displacement, based
on measurements of its shape. Supported by experimental evidence that cell
nucleus lags behind the cell center of mass in a migrating cell, we propose a
robust parametrization for cell migration where the distance between cell
nucleus and the cell's center of mass defines cell shape polarization. We
tested the proposed methods by applying to a simulation model for
three-dimensional cells performed in the CompuCell3D environment, previously
shown to reproduce biological cells kinematics migrating on a flat surface.


Abstract:
  Multi-armed bandits (MAB) provide a principled online learning approach to
attain the balance between exploration and exploitation. Due to the superior
performance and low feedback learning without the learning to act in multiple
situations, Multi-armed Bandits drawing widespread attention in applications
ranging such as recommender systems. Likewise, within the recommender system,
collaborative filtering (CF) is arguably the earliest and most influential
method in the recommender system. Crucially, new users and an ever-changing
pool of recommended items are the challenges that recommender systems need to
address. For collaborative filtering, the classical method is training the
model offline, then perform the online testing, but this approach can no longer
handle the dynamic changes in user preferences which is the so-called cold
start. So how to effectively recommend items to users in the absence of
effective information? To address the aforementioned problems, a multi-armed
bandit based collaborative filtering recommender system has been proposed,
named BanditMF. BanditMF is designed to address two challenges in the
multi-armed bandits algorithm and collaborative filtering: (1) how to solve the
cold start problem for collaborative filtering under the condition of scarcity
of valid information, (2) how to solve the sub-optimal problem of bandit
algorithms in strong social relations domains caused by independently
estimating unknown parameters associated with each user and ignoring
correlations between users.


Abstract:
  The Bernays-Sch\"onfinkel first-order logic fragment over simple linear real
arithmetic constraints BS(SLR) is known to be decidable. We prove that BS(SLR)
clause sets with both universally and existentially quantified verification
conditions (conjectures) can be translated into BS(SLR) clause sets over a
finite set of first-order constants. For the Horn case, we provide a Datalog
hammer preserving validity and satisfiability. A toolchain from the BS(LRA)
prover SPASS-SPL to the Datalog reasoner VLog establishes an effective way of
deciding verification conditions in the Horn fragment. This is exemplified by
the verification of supervisor code for a lane change assistant in a car and of
an electronic control unit for a supercharged combustion engine.


Abstract:
  We study the existence and non-existence of classical solutions for
inequalities of type $$ \pm \Delta^m u \geq \big(\Psi(|x|)*u^p\big)u^q
\quad\mbox{ in } {\mathbb R}^N (N\geq 1). $$ Here, $\Delta^m$ $(m\geq 1)$ is
the polyharmonic operator, $p, q>0$ and $*$ denotes the convolution operator,
where $\Psi>0$ is a continuous non-increasing function. We devise new methods
to deduce that solutions of the above inequalities satisfy the
poly-superharmonic property. This further allows us to obtain various Liouville
type results. Our study is also extended to the case of systems of simultaneous
inequalities.


Abstract:
  Over the last decades, the incidence of skin cancer, melanoma and
non-melanoma, has increased at a continuous rate. In particular for melanoma,
the deadliest type of skin cancer, early detection is important to increase
patient prognosis. Recently, deep neural networks (DNNs) have become viable to
deal with skin cancer detection. In this work, we present a smartphone-based
application to assist on skin cancer detection. This application is based on a
Convolutional Neural Network(CNN) trained on clinical images and patients
demographics, both collected from smartphones. Also, as skin cancer datasets
are imbalanced, we present an approach, based on the mutation operator of
Differential Evolution (DE) algorithm, to balance data. In this sense, beyond
provides a flexible tool to assist doctors on skin cancer screening phase, the
method obtains promising results with a balanced accuracy of 85% and a recall
of 96%.


Abstract:
  The detection of gravitational waves emitted by binary black holes raises
questions regarding the origin of binaries. There are several models in the
literature involving binary evolution in both the field and clusters. We
compare the predictions of these models with observations and establish the
reliability of this comparison. We use the likelihood calculation to compare
the models in the space spanned by the observed chirp mass and the luminosity
distance of the source. We rank the models by their ability to explain all
current gravitational wave detections. It is shown that the most probable
models correspond to binary evolution with low metallicity. Several variants of
such evolution have similar likelihoods. The globular cluster model, considered
here, is disfavoured. We present the usefulness of the method in distinguishing
between models when new observations become available. We calculate the number
of observations required to distinguish between each pair of models. We find
that the number varies from ten to several thousand for some pairs of models,
yet almost two-thirds of pairs are distinguishable with at most 100
observations.


Abstract:
  Python is a popular programming language known for its flexibility,
usability, readability, and focus on developer productivity. The quantum
software community has adopted Python on a number of large-scale efforts due to
these characteristics, as well as the remote nature of near-term quantum
processors. The use of Python has enabled quick prototyping for quantum code
that directly benefits pertinent research and development efforts in quantum
scientific computing. However, this rapid prototyping ability comes at the cost
of future performant integration for tightly-coupled CPU-QPU architectures with
fast-feedback. Here we present a language extension to Python that enables
heterogeneous quantum-classical computing via a robust C++ infrastructure for
quantum just-in-time (QJIT) compilation. Our work builds off the QCOR C++
language extension and compiler infrastructure to enable a single-source,
quantum hardware-agnostic approach to quantum-classical computing that retains
the performance required for tightly coupled CPU-QPU compute models. We detail
this Pythonic extension, its programming model and underlying software
architecture, and provide a robust set of examples to demonstrate the utility
of our approach.


Abstract:
  Control of molecular orientation is emerging as crucial for the
characterization of the stereodynamics of kinetics processes beyond structural
stereochemistry. The special role played in chiral discrimination phenomena has
been particularly emphasized by the authors after their extensive probes of
experimental control of molecular alignment and orientation. In this work, the
role of the orientation has been demonstrated for the first time in
first-principles molecular dynamics simulations: stationary points
characterized on potential energy surfaces have been calculated for the study
of chemical reactions occurring between the bisulfide anion HS- and oriented
prototypical chiral molecules CHFXY (where X = CH3 or CN and Y = Cl or I). The
important reaction channels are those corresponding to bimolecular nucleophilic
substitution (SN2) and to bimolecular elimination (E2): their relative role has
been assessed and alternative pathways due to the mirror forms of the oriented
chiral molecule are revealed by the different reactivity of the two enantiomers
of CHFCNI in SN2 reaction.


Abstract:
  A family of Lempel-Ziv factorizations is a well-studied string structure. The
LZ-End factorization is a member of the family that achieved faster extraction
of any substrings (Kreft & Navarro, TCS 2013). One of the interests for LZ-End
factorizations is the possible difference between the size of LZ-End and LZ77
factorizations. They also showed families of strings where the approximation
ratio of the number of LZ-End phrases to the number of LZ77 phrases
asymptotically approaches 2. However, the alphabet size of these strings is
unbounded. In this paper, we analyze the LZ-End factorization of the
period-doubling sequence. We also show that the approximation ratio for the
period-doubling sequence asymptotically approaches 2 for the binary alphabet.


Abstract:
  We establish an extension of the Hopf-Tsuji-Sullivan dichotomy to any Zariski
dense discrete subgroup of a semisimple real algebraic group $G$. We then apply
this dichotomy to Anosov subgroups of $G$, which surprisingly presents a
different phenomenon depending on the rank of the ambient group $G$.


Abstract:
  The first stages of planet formation usually occur when the host star is
still in a (relatively) dense star-forming region, where the effects of the
external environment may be important for understanding the outcome of the
planet formation process. In particular, star-forming regions that contain
massive stars have strong far ultraviolet (FUV) and extreme ultraviolet (EUV)
radiation fields, which can induce mass-loss from protoplanetary discs due to
photoevaporation. In this paper we present a parameter-space study of the
expected FUV and EUV fields in N-body simulations of star-forming regions with
a range of initial conditions. We then use recently published models to
determine the mass-loss due to photoevaporation from protoplanetary discs. In
particular, we focus on the effects of changing the initial degree of spatial
structure and initial virial ratio in the star-forming regions, as well as the
initial stellar density. We find that the FUV fields in star-forming regions
are much higher than in the interstellar medium, even when the regions have
stellar densities as low as in the Galactic field, due to the presence of
intermediate-mass, and massive, stars (>5Msun). These strong radiation fields
lead to the destruction of the gas component in protoplanetary discs within 1
Myr, implying that gas giant planets must either form extremely rapidly (<1
Myr), or that they exclusively form in star-forming regions like Taurus, which
contain no intermediate-mass or massive stars. The latter scenario is in direct
tension with meteoritic evidence from the Solar system that suggests the Sun
and its protoplanetary disc was born in close proximity to massive stars.


Abstract:
  Most of the existing single object trackers track the target in a unitary
local search window, making them particularly vulnerable to challenging factors
such as heavy occlusions and out-of-view movements. Despite the attempts to
further incorporate global search, prevailing mechanisms that cooperate local
and global search are relatively static, thus are still sub-optimal for
improving tracking performance. By further studying the local and global search
results, we raise a question: can we allow more dynamics for cooperating both
results? In this paper, we propose to introduce more dynamics by devising a
dynamic attention-guided multi-trajectory tracking strategy. In particular, we
construct dynamic appearance model that contains multiple target templates,
each of which provides its own attention for locating the target in the new
frame. Guided by different attention, we maintain diversified tracking results
for the target to build multi-trajectory tracking history, allowing more
candidates to represent the true target trajectory. After spanning the whole
sequence, we introduce a multi-trajectory selection network to find the best
trajectory that delivers improved tracking performance. Extensive experimental
results show that our proposed tracking strategy achieves compelling
performance on various large-scale tracking benchmarks. The project page of
this paper can be found at https://sites.google.com/view/mt-track/.


Abstract:
  Young star clusters are dynamically active stellar systems and are a common
birthplace for massive stars. Low-mass star clusters ($\sim{}300-10^3$
M$_\odot$) are more numerous than massive systems and are characterized by a
two-body relaxation time scale of a few Myr: the most massive stars sink to the
cluster core and dynamically interact with each other even before they give
birth to compact objects. Here, we explore the properties of black holes (BHs)
and binary black holes (BBHs) formed in low-mass young star clusters, by means
of a suite of $10^5$ direct $N$-body simulations with a high original binary
fraction (100 % for stars with mass $>5$ M$_\odot$). Most BHs are ejected in
the first $\sim{}20$ Myr by dynamical interactions. Dynamical exchanges are the
main formation channel of BBHs, accounting for $\sim{}40-80$ % of all the
systems. Most BBH mergers in low-mass young star clusters involve primary BHs
with mass $<40$ M$_\odot$ and low mass ratios are extremely more common than in
the field. Comparing our data with those of more massive star clusters
($10^3-3\times{}10^4$ M$_\odot$), we find a strong dependence of the percentage
of exchanged BBHs on the mass of the host star cluster. In contrast, our
results show just a mild correlation between the mass of the host star cluster
and the efficiency of BBH mergers.


Abstract:
  We report a discovery of a new long-period X-ray pulsar SRGA
J204318.2+443815/SRGe J204319.0+443820 in the Be binary system. The source was
found in the second all-sky survey by the Mikhail Pavlinsky telescope on board
the SRG mission. The follow-up observations with XMM-Newton, NICER and NuSTAR
observatories allowed us to discover a strong coherent signal in the source
light curve with the period of $\sim742$ s. The pulsed fraction was found to
depend on the energy increasing from $\sim20$% in soft X-rays to $>50$% at high
energies, as it is typical for X-ray pulsars. The source demonstrate a quite
hard spectrum with an exponential cutoff at high energies and bolometric
luminosity of $L_X \simeq 4\times10^{35}$ erg/s. Dedicated optical and infrared
observations with the RTT-150, NOT, Keck and Palomar telescopes revealed a
number of emission lines (H$_{\alpha}$, HeI, Pashen and Braket series) with the
strongly absorbed continuum. All of above suggests that SRGAJ204318.2+443815/
SRGeJ204319.0+443820 is a new persistent low luminosity X-ray pulsar in a
distant binary system with a Be-star of the B0-B2e class. Thus the SRG
observatory allow us to unveil the hidden population of faint persistent
objects including the population of slowly rotating X-ray pulsars in Be
systems.


Abstract:
  The theme of the influence of Bourbaki's ideas and methodologies on
disciplines that transcend the mathematical field has been frequently addressed
and, in particular, the reflection on the relationship between Bourbaki and
Oulipo has animated a passionate debate within literary criticism. The purpose
of this contribution is to investigate the relations between Le Lionnais,
Queneau and Roubaud - and more generally Oulipo - and Bourbaki. Through a
comparative examination between the two groups, the existence of an undeniable
charisma of the "polycephalic mathematician" on the artistic-literary movement
was highlighted.


Abstract:
  The ${\it\text{state-of-the-art}}$ theoretical description of magnetic
materials relies on solving effective Heisenberg spin problems or their
generalizations to relativistic or multi-spin-interaction cases that explicitly
assume the presence of local magnetic moments in the system. We start with a
general interacting fermionic model that is often obtained in ${\it\text{ab
initio}}$ electronic structure calculations and show that the corresponding
spin problem can be introduced even in the paramagnetic regime, which is
characterized by a zero average value of the magnetization. Further, we derive
a physical criterion for the formation of the local magnetic moment and confirm
that the latter exists already at high temperatures well above the transition
to the ordered magnetic state. The use of path-integral techniques allows us to
disentangle spin and electronic degrees of freedom and to carefully separate
rotational dynamics of the local magnetic moment from Higgs fluctuations of its
absolute value. It also allows us to accurately derive the topological Berry
phase and relate it to a physical bosonic variable that describes dynamics of
the spin degrees of freedom. As the result, we demonstrate that the equation of
motion for the derived spin problem takes a conventional Landau-Lifshitz form
that explicitly accounts for the Gilbert damping due to itinerant nature of the
original electronic model.


Abstract:
  For many IoT domains, Machine Learning and more particularly Deep Learning
brings very efficient solutions to handle complex data and perform challenging
and mostly critical tasks. However, the deployment of models in a large variety
of devices faces several obstacles related to trust and security. The latest is
particularly critical since the demonstrations of severe flaws impacting the
integrity, confidentiality and accessibility of neural network models. However,
the attack surface of such embedded systems cannot be reduced to abstract flaws
but must encompass the physical threats related to the implementation of these
models within hardware platforms (e.g., 32-bit microcontrollers). Among
physical attacks, Fault Injection Analysis (FIA) are known to be very powerful
with a large spectrum of attack vectors. Most importantly, highly focused FIA
techniques such as laser beam injection enable very accurate evaluation of the
vulnerabilities as well as the robustness of embedded systems. Here, we propose
to discuss how laser injection with state-of-the-art equipment, combined with
theoretical evidences from Adversarial Machine Learning, highlights worrying
threats against the integrity of deep learning inference and claims that join
efforts from the theoretical AI and Physical Security communities are a urgent
need.


Abstract:
  In this paper, we present two new approaches for computing exact nonnegative
matrix factorizations (NMFs). Exact NMF can be defined as follows: given an
input nonnegative matrix $V \in \mathbb{R}_+^{F \times N}$ and a factorization
rank $K$, compute, if possible, two nonnegative matrices, $W \in
\mathbb{R}_+^{F \times K}$ and $H \in \mathbb{R}_+^{K \times N}$, such that
$V=WH$. The two proposed approaches to tackle exact NMF, which is NP-hard in
general, rely on the same two steps. First, we reformulate exact NMF as
minimizing a concave function over a product of convex cones; one approach is
based on the exponential cone, and the other on the second-order cone. Second,
we solve these reformulations iteratively: at each step, we minimize exactly,
over the feasible set, a majorization of the objective functions obtained via
linearization at the current iterate. Hence these subproblems are convex conic
programs and can be solved efficiently using dedicated algorithms. We show that
our approaches, which we call successive conic convex approximations, are able
to compute exact NMFs, and compete favorably with the state of the art when
applied to several classes of nonnegative matrices; namely, randomly generated,
infinite rigid and slack matrices.


Abstract:
  Robots are moving towards applications in less structured environments, but
their model-based controllers are challenged by the tasks' complexity and
intrinsic environmental unpredictability. Studying biological motor control can
provide insights into overcoming these limitations due to the high dexterity
and stability observable in humans and animals. This work presents a
geometrical solution to the postural optimisation of 7-DoF limbs-like
mechanisms, which are robust to singularities and computationally efficient.
The theoretical formulation identified two separate decoupled optimisation
strategies. The shoulder and elbow strategy align the plane of motion with the
expected plane of motion and guarantee the reachability of the end-posture. The
wrist strategy ensures the end-effector orientation, which is essential to
retain manipulability when nearing a singular configuration. The numerical
results confirmed the theoretical observations and allowed us to identify the
effect of different grasp strategies on system manipulability. The geometrical
method was numerically tested in thousands of configurations proving to be both
robust and accurate. The tested scenarios include left and right arm postures,
singular configurations, and walking scenarios. The proposed geometrical
approach can find application in developing efficient and robust interaction
controllers that could be applied in computational neuroscience and robotics.


Abstract:
  In this research paper we modify a classical SIR model to better adapt to the
dynamics of COVID-19, that is we propose the heterogeneous SQAIRD model where
COVID-19 spreads over a population of economic agents, namely: the elderly,
adults and young people. We then compute and simulate an optimal control
problem faced by a Government, where its objective is to minimize the costs
generated by the pandemics using as control a compulsory quarantine measure
(that is, a lockdown). We first analyze the problem from a theoretical
perspective, claiming that different lockdown policies (total lockdown, no
lockdown or partial lockdown) may justified by different cost (concave or
convex) structures of the economies. We then focus on a particular cost
structure (convex costs) and we simulate a targeted optimal policy vs. a
uniform optimal policy, by dividing the whole population in three demographic
groups (young, adults and old). We also simulate the dynamic of the pandemic
with no policy implemented. Simulations highlighted the fact that: a) a policy
of lockdown is always better than the \emph{laissez faire} policy, because it
limits the costs that the pandemic generates in an uncontrolled situation; b) a
targeted policy based on age of the individuals outperforms a uniform policy in
terms of costs that it generates, being a targeted policy less costly and
equally effective in the control of the pandemic.


Abstract:
  We study Hardy space $H^1_L(X)$ related to a self-adjoint operator $L$
defined on Euclidean domain $X \subseteq \mathbb{R}^d$. Under certain
assumptions on the heat semigroup $\exp(-tL)$ we prove characterization of
$H^1_L(X)$ by the Riesz transforms related to $L$. As an application, we prove
the Riesz transform characterization for multidimensional Bessel and Laguerre
operators.


Abstract:
  By using four layered graphene/gallium nitride (GaN) Schottky diodes with an
undoped GaN spacer, we demonstrate highly effective gating of graphene at low
bias rendering this type of structure very promising for potential
applications. An observed Raman G band position shift larger than 8.5 cm-1
corresponds to an increase in carrier concentration of about 1.2x10^13 cm-2.
The presence of a distinct G band splitting together with a narrow symmetric 2D
band indicates turbostratic layer stacking and suggests the presence of a high
potential gradient near the Schottky junction even at zero bias. The subbands
characterized by the highest Raman energies correspond to the largest
concentration of electrons. An analysis based on electroreflectance
measurements and a modified Richardson equation confirmed that graphene on
n-GaN separated by an undoped GaN spacer behaves like a capacitor at reverse
bias. At least 60% of G subband position shifts occur at forward bias, which is
related to a rapid reduction of electric field near the Schottky junction.
Raman micromapping shows a high uniformity of gating efficiency on the surface.
Therefore, our studies demonstrate the usefulness of few layer turbostratic
graphene deposited on GaN for tracing electron-phonon coupling in graphene.
Multilayer graphene also provides uniform and stable electric contacts.
Moreover, the observed bias sensitive G band splitting can be used as an
indicator of charge transfer in sensor applications in the low bias regime.


Abstract:
  In many statistical problems, incorporating priors can significantly improve
performance. However, the use of prior knowledge in differentially private
query release has remained underexplored, despite such priors commonly being
available in the form of public datasets, such as previous US Census releases.
With the goal of releasing statistics about a private dataset, we present
PMW^Pub, which -- unlike existing baselines -- leverages public data drawn from
a related distribution as prior information. We provide a theoretical analysis
and an empirical evaluation on the American Community Survey (ACS) and ADULT
datasets, which shows that our method outperforms state-of-the-art methods.
Furthermore, PMW^Pub scales well to high-dimensional data domains, where
running many existing methods would be computationally infeasible.


Abstract:
  We report a self-consistent quasinormal mode theory for nanometer scale
electromagnetism where the possible nonlocal and quantum effects are treated
through quantum surface responses. With Feibelman's frequency-dependent
\textit{d} parameters to describe the quantum surface responses, we formulate
the source-free Maxwell's equations into a generalized linear eigenvalue
problem to define the quasinormal modes. We then construct an orthonormal
relation for the modes and consequently unlock the powerful toolbox of modal
analysis. The orthonormal relation is validated by the reconstruction of the
full numerical results through modal contributions. Significant changes in the
landscape of the modes are observed due to the incorporation of the quantum
surface responses for a number of nanostructures. Our semi-analytical modal
analysis enables transparent physical interpretation of the spontaneous
emission enhancement of a dipolar emitter as well as the near-field and
far-field responses of planewave excitations in the nanostructures.


Abstract:
  We present a novel class of high-order space-time finite element schemes for
the Poisson-Nernst-Planck (PNP) equations. We prove that our schemes are mass
conservative, positivity preserving, and unconditionally energy stable for any
order of approximation. To the best of our knowledge, this is the first class
of (arbitrarily) high-order accurate schemes for the PNP equations that
simultaneously achieve all these three properties.
  This is accomplished via (1) using finite elements to directly approximate
the so-called entropy variable instead of the density variable, and (2) using a
discontinuous Galerkin (DG) discretization in time. The entropy variable
formulation, which was originally developed by Metti et al. [17] under the name
of a log-density formulation, guarantees both positivity of densities and a
continuous-in-time energy stability result. The DG in time discretization
further ensures an unconditional energy stability in the fully discrete level
for any approximation order, where the lowest order case is exactly the
backward Euler discretization and in this case we recover the method of Metti
et al. [17].


Abstract:
  This note is concerned with the presentation of new delay-dependent
dissipativity-based convex conditions (expressed in the form of linear matrix
inequalities) for the design of static output feedback (SOF) stabilizing gains
for open-loop unstable discrete-time systems with input time-varying delays. A
modified definition of QSR-dissipativity combined with the use of
Lyapunov-Krasovskii functionals as storage functions and the application of
Finsler's Lemma lead to the gathering of non-interactive design conditions. We
show that, differently from most works dealing with controller design for
time-delayed systems, the developed conditions present very small conservatism
compared to stability analysis conditions derived with the same strategy. Due
to being a particular case of SOF with an identity output matrix, static state
feedback (SSF) gains can also trivially be computed from the conditions.


Abstract:
  Molecule generation, which requires generating valid molecules with desired
properties, is a fundamental but challenging task. Recent years have witnessed
the rapid development of atom-level auto-regressive models, which usually
construct graphs following sequential actions of adding atom-level nodes and
edges. However, these atom-level models ignore high-frequency substructures,
which not only capture the regularities of atomic combination in molecules but
are also often related to desired chemical properties, and therefore may be
sub-optimal for generating high-quality molecules. In this paper, we propose a
method to automatically discover such common substructures, which we call graph
pieces, from given molecular graphs. We also present a graph piece variational
autoencoder (GP-VAE) for generating molecular graphs based on graph pieces.
Experiments show that our GP-VAE models not only achieve better performance
than the state-of-the-art baseline for distribution-learning, property
optimization, and constrained property optimization tasks but are also
computationally efficient.


Abstract:
  In this paper we present the new deep images from the VEGAS survey of three
massive ($M_{*} \simeq 10^{12}$~M$_\odot$) galaxies from the MUSE Most Massive
Galaxies (M3G) project, with distances in the range $151\leq D \leq 183$ Mpc:
PGC007748, PGC015524 and PGC049940. The long integration time and the wide
field of view of OmegaCam@VST allowed us to map the light and color
distributions down to $\mu_g\simeq30$~mag/arcsec$^2$ and out to $\sim 2R_e$.
The deep data are crucial to estimate the contribution of the different
galaxy's components, in particular the accreted fraction in the stellar halo.
The available integral-field observations with MUSE cover a limited portion of
each galaxy (out to $\sim 1R_e$), but, from the imaging analysis we find that
they map the kinematics and stellar population beyond the first transition
radius, where the contribution of the accreted component starts to dominate.
The main goal of this work is to correlate the scales of the different
components derived from the image analysis with the kinematics and stellar
population profiles from the MUSE data. Results were used to address the
assembly history of the three galaxies with the help of the theoretical
predictions. Our results suggest that PGC049940 has the lowest accreted mass
fraction of 77%. The higher accreted mass fraction estimated for PGC007748 and
PGC015524 (86% and 89%, respectively), combined with the flat $\lambda_R$
profiles suggest that a great majority of the mass has been acquired through
major mergers, which have also shaped the shallower metallicity profiles
observed at larger radii.


Abstract:
  By using a set of scaling limits, the authors in \cite{ADFL,SS} proposed a
framework of deriving the Hall-MHD equations from the two-fluids Euler-Maxwell
equations for electrons and ions. In this paper, we derive the Hall-MHD
equations from the Navier-Stokes-Maxwell equations with generalized Ohm's law
in a mathematically rigorous way via the spectral analysis and energy methods.


Abstract:
  Deepfakes pose growing challenges to the trust of information on the
Internet. Thus, detecting deepfakes has attracted increasing attentions from
both academia and industry. State-of-the-art deepfake detection methods consist
of two key components, i.e., face extractor and face classifier, which extract
the face region in an image and classify it to be real/fake, respectively.
Existing studies mainly focused on improving the detection performance in
non-adversarial settings, leaving security of deepfake detection in adversarial
settings largely unexplored. In this work, we aim to bridge the gap. In
particular, we perform a systematic measurement study to understand the
security of the state-of-the-art deepfake detection methods in adversarial
settings. We use two large-scale public deepfakes data sources including
FaceForensics++ and Facebook Deepfake Detection Challenge, where the deepfakes
are fake face images; and we train state-of-the-art deepfake detection methods.
These detection methods can achieve 0.94--0.99 accuracies in non-adversarial
settings on these datasets. However, our measurement results uncover multiple
security limitations of the deepfake detection methods in adversarial settings.
First, we find that an attacker can evade a face extractor, i.e., the face
extractor fails to extract the correct face regions, via adding small Gaussian
noise to its deepfake images. Second, we find that a face classifier trained
using deepfakes generated by one method cannot detect deepfakes generated by
another method, i.e., an attacker can evade detection via generating deepfakes
using a new method. Third, we find that an attacker can leverage backdoor
attacks developed by the adversarial machine learning community to evade a face
classifier. Our results highlight that deepfake detection should consider the
adversarial nature of the problem.


Abstract:
  According to first principle Lattice QCD calculations, the transition from
quark-gluon plasma to hadronic matter is a smooth crossover in the region
$\mu_{\rm B}\leq T_{c}$. In this range the ratio, $C_{6}/C_{2}$, of net-baryon
distributions are predicted to be negative. In this paper, we report the first
measurement of the midrapidity net-proton $C_{6}/C_{2}$ from 27, 54.4 and 200
GeV Au+Au collisions at RHIC. The dependence on collision centrality and
kinematic acceptance in ($p_{T}$, $y$) are analyzed. While for 27 and 54.4 GeV
collisions the $C_{6}/C_{2}$ values are close to zero within uncertainties, it
is observed that for 200 GeV collisions, the $C_{6}/C_{2}$ ratio becomes
progressively negative from peripheral to central collisions. Transport model
calculations without critical dynamics predict mostly positive values except
for the most central collisions within uncertainties. These observations seem
to favor a smooth crossover in the high energy nuclear collisions at top RHIC
energy.


Abstract:
  Action recognition and detection in the context of long untrimmed video
sequences has seen an increased attention from the research community. However,
annotation of complex activities is usually time consuming and challenging in
practice. Therefore, recent works started to tackle the problem of unsupervised
learning of sub-actions in complex activities. This paper proposes a novel
approach for unsupervised sub-action learning in complex activities. The
proposed method maps both visual and temporal representations to a latent space
where the sub-actions are learnt discriminatively in an end-to-end fashion. To
this end, we propose to learn sub-actions as latent concepts and a novel
discriminative latent concept learning (DLCL) module aids in learning
sub-actions. The proposed DLCL module lends on the idea of latent concepts to
learn compact representations in the latent embedding space in an unsupervised
way. The result is a set of latent vectors that can be interpreted as cluster
centers in the embedding space. The latent space itself is formed by a joint
visual and temporal embedding capturing the visual similarity and temporal
ordering of the data. Our joint learning with discriminative latent concept
module is novel which eliminates the need for explicit clustering. We validate
our approach on three benchmark datasets and show that the proposed combination
of visual-temporal embedding and discriminative latent concepts allow to learn
robust action representations in an unsupervised setting.


Abstract:
  Accurately predicting the performance of different optimization algorithms
for previously unseen problem instances is crucial for high-performing
algorithm selection and configuration techniques. In the context of numerical
optimization, supervised regression approaches built on top of exploratory
landscape analysis are becoming very popular. From the point of view of Machine
Learning (ML), however, the approaches are often rather naive, using default
regression or classification techniques without proper investigation of the
suitability of the ML tools. With this work, we bring to the attention of our
community the possibility to personalize regression models to specific types of
optimization problems. Instead of aiming for a single model that works well
across a whole set of possibly diverse problems, our personalized regression
approach acknowledges that different models may suite different types of
problems. Going one step further, we also investigate the impact of selecting
not a single regression model per problem, but personalized ensembles. We test
our approach on predicting the performance of numerical optimization heuristics
on the BBOB benchmark collection.


Abstract:
  We introduce kernel thinning, a new procedure for compressing a distribution
$\mathbb{P}$ more effectively than i.i.d.\ sampling or standard thinning. Given
a suitable reproducing kernel $\mathbf{k}$ and $\mathcal{O}(n^2)$ time, kernel
thinning compresses an $n$-point approximation to $\mathbb{P}$ into a
$\sqrt{n}$-point approximation with comparable worst-case integration error
across the associated reproducing kernel Hilbert space. With high probability,
the maximum discrepancy in integration error is
$\mathcal{O}_d(n^{-1/2}\sqrt{\log n})$ for compactly supported $\mathbb{P}$ and
$\mathcal{O}_d(n^{-\frac{1}{2}} (\log n)^{(d+1)/2}\sqrt{\log\log n})$ for
sub-exponential $\mathbb{P}$ on $\mathbb{R}^d$. In contrast, an equal-sized
i.i.d.\ sample from $\mathbb{P}$ suffers $\Omega(n^{-1/4})$ integration error.
Our sub-exponential guarantees resemble the classical quasi-Monte Carlo error
rates for uniform $\mathbb{P}$ on $[0,1]^d$ but apply to general distributions
on $\mathbb{R}^d$ and a wide range of common kernels. We use our results to
derive explicit non-asymptotic maximum mean discrepancy bounds for Gaussian,
Mat\'ern, and B-spline kernels and present two vignettes illustrating the
practical benefits of kernel thinning over i.i.d.\ sampling and standard Markov
chain Monte Carlo thinning, in dimensions $d=2$ through $100$.


Abstract:
  In heat exchangers, an incompressible fluid is heated initially and cooled at
the boundary. The goal is to transfer the heat to the boundary as efficiently
as possible. In this paper we study a related steady version of this problem
where a steadily stirred fluid is uniformly heated in the interior and cooled
on the boundary. For a given large P\'eclet number, how should one stir to
minimize some norm of the temperature? This version of the problem was
previously studied by Marcotte, Doering et\ al.\ (SIAM Appl.\ Math '18) in a
disk, where the authors used matched asymptotics to show that when the P\'eclet
number, $\pe$, is sufficiently large one can stir the fluid in a manner that
ensures the total heat is $O(1/\pe)$. In this paper we confirm their results
with rigorous proofs, and also provide an almost matching lower bound. For
simplicity, we work on the infinite strip instead of the unit disk and the
proof uses probabilistic techniques.


Abstract:
  Due to recent improvements in image resolution and acquisition speed,
materials microscopy is experiencing an explosion of published imaging data.
The standard publication format, while sufficient for traditional data
ingestion scenarios where a select number of images can be critically examined
and curated manually, is not conducive to large-scale data aggregation or
analysis, hindering data sharing and reuse. Most images in publications are
presented as components of a larger figure with their explicit context buried
in the main body or caption text, so even if aggregated, collections of images
with weak or no digitized contextual labels have limited value. To solve the
problem of curating labeled microscopy data from literature, this work
introduces the EXSCLAIM! Python toolkit for the automatic EXtraction,
Separation, and Caption-based natural Language Annotation of IMages from
scientific literature. We highlight the methodology behind the construction of
EXSCLAIM! and demonstrate its ability to extract and label open-source
scientific images at high volume.


Abstract:
  By generalizing a geometric argument for frictionless spheres, a model is
proposed for the jamming density $\phi_J$ of mechanically stable packings of
bidisperse, frictional spheres. The monodisperse, $\mu_s$-dependent jamming
density $\phi_J^{\mathrm{mono}}(\mu_s)$ is the only input required in the
model, where $\mu_s$ is the coefficient of friction. The predictions of the
model are validated by robust estimates of $\phi_J$ obtained from computer
simulations of up to $10^7$ particles for a wide range of $\mu_s$, and size
ratios up to 40:1. Although $\phi_J$ varies nonmonotonically with the volume
fraction of small spheres $f^s$ for all $\mu_s$, its maximum value
$\phi_{J,\mathrm{max}}$ at an optimal $f^{s}_{\mathrm{max}}$ are both
$\mu_s$-dependent. The optimal $f^{s}_{\mathrm{max}}$ is characterized by a
sharp transition in the fraction of small rattler particles.


Abstract:
  We consider a heat conduction problem $S$ with mixed boundary conditions in a
$n$-dimensional domain $\Omega$ with regular boundary and a family of problems
$S_{\alpha}$ with also mixed boundary conditions in $\Omega$, where $\alpha>0$
is the heat transfer coefficient on the portion of the boundary $\Gamma_{1}$.
In relation to these state systems, we formulate Neumann boundary optimal
control problems on the heat flux $q$ which is definite on the complementary
portion $\Gamma_{2}$ of the boundary of $\Omega$. We obtain existence and
uniqueness of the optimal controls, the first order optimality conditions in
terms of the adjoint state and the convergence of the optimal controls, the
system state and the adjoint state when the heat transfer coefficient $\alpha$
goes to infinity. Furthermore, we formulate particular boundary optimal control
problems on a real parameter $\lambda$, in relation to the parabolic problems
$S$ and $S_{\alpha}$ and to mixed elliptic problems $P$ and $P_{\alpha}$. We
find a explicit form for the optimal controls, we prove monotony properties and
we obtain convergence results when the parameter time goes to infinity.


Abstract:
  An antiproton plasma confined in a quasi-1D device is described in terms of a
self-consistent fluid formulation using a variational approach. Unlike previous
treatments, the use of the time-dependent variational method allows to retain
the thermal and Coulomb effects. A certain Ansatz is proposed for the number
density and fluid velocity fields, which reduces the problem essentially to
ordinary nonlinear differential equations. In adiabatic cooling, the frequency
of the trap potential is slowly decreased. An adiabatic equation of state is
assumed for closure. The numerical simulation of the nonlinear dynamics is
performed, for realistic parameters.


Abstract:
  In this article we have showed that a gradient $\rho$-Einstein soliton with a
vector field of bounded norm and satisfying some other conditions is isometric
to the Euclidean sphere. Later, we have proved that a non-trivial complete
gradient $\rho$-Einstein soliton with finite weighted Dirichlet integral and
certain restriction on Ricci curvature must be of constant scalar curvature and
steady Ricci flat. Finally, we have proved that a non-shrinking or
non-expanding gradient traceless Ricci soliton possessing some conditions must
be steady.


Abstract:
  The two-stage methods for instance segmentation, e.g. Mask R-CNN, have
achieved excellent performance recently. However, the segmented masks are still
very coarse due to the downsampling operations in both the feature pyramid and
the instance-wise pooling process, especially for large objects. In this work,
we propose a new method called RefineMask for high-quality instance
segmentation of objects and scenes, which incorporates fine-grained features
during the instance-wise segmenting process in a multi-stage manner. Through
fusing more detailed information stage by stage, RefineMask is able to refine
high-quality masks consistently. RefineMask succeeds in segmenting hard cases
such as bent parts of objects that are over-smoothed by most previous methods
and outputs accurate boundaries. Without bells and whistles, RefineMask yields
significant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN on COCO, LVIS, and
Cityscapes benchmarks respectively at a small amount of additional
computational cost. Furthermore, our single-model result outperforms the winner
of the LVIS Challenge 2020 by 1.3 points on the LVIS test-dev set and
establishes a new state-of-the-art. Code will be available at
https://github.com/zhanggang001/RefineMask.


Abstract:
  Model-based deep reinforcement learning has achieved success in various
domains that require high sample efficiencies, such as Go and robotics.
However, there are some remaining issues, such as planning efficient
explorations to learn more accurate dynamic models, evaluating the uncertainty
of the learned models, and more rational utilization of models. To mitigate
these issues, we present MEEE, a model-ensemble method that consists of
optimistic exploration and weighted exploitation. During exploration, unlike
prior methods directly selecting the optimal action that maximizes the expected
accumulative return, our agent first generates a set of action candidates and
then seeks out the optimal action that takes both expected return and future
observation novelty into account. During exploitation, different discounted
weights are assigned to imagined transition tuples according to their model
uncertainty respectively, which will prevent model predictive error propagation
in agent training. Experiments on several challenging continuous control
benchmark tasks demonstrated that our approach outperforms other model-free and
model-based state-of-the-art methods, especially in sample complexity.


Abstract:
  Considering a strong field limit, we investigate the retrolensing phenomenon
in the vicinity of a Janis-Newman-Winicour (JNW) naked singularity embedded in
a scalar field. We assume that the light rays from a nearby source are
reflected by the photon sphere of the naked singularity, acting as a lens, to
create a pair of images. The analytic expressions of the lensing coefficients
$\bar{a}$ and $\bar{b}$ are obtained, which are scalar dependent and discordant
with \cite{Bozza:2002b}. Moreover, considering the powerful supermassive black
hole candidates Sgr A* and M87*, we examined the influence of the scalar field
on the apparent brightness and angular positions of the parity images. Our
results are highlighted in correspondence with a non-scalar field gravity,
specifically the Schwarzschild gravity. We have found that the brightness
increases in the presence of the scalar field, while, the lensing coefficients
$\bar{a}$, $\bar{b}$, the angular positions and the angular separation of the
relativistic images experience a reverse effect.


Abstract:
  Let $ 0\le f\in C^{0,1}(\mathbb R^n)$. Given a domain $\Omega\subset \mathbb
R^n$, we prove that any stable solution to the equation $-\Delta u=f(u)$ in
$\Omega$ satisfies a BMO interior regularity when $n=10$, and an Morrey
$M^{p_n,4+2/(p_n-2)}$ interior regularity when $n\ge 11$, where
$$p_n=\frac{2(n-2\sqrt{n-1}-2)}{n-2\sqrt{n-1}-4}. $$
  This result is optimal as hinted by earlier results, and answers an open
question raised by Cabr\'e, Figalli, Ros-Oton and Serra. As an application, we
show a sharp Liouville property: Any stable solution $u \in C^2(\mathbb R^n)$
to $-\Delta u=f(u)$ in $\mathbb R^n$ satisfying the growth condition, i.e.\
$|u(x)|= o\left( \log|x| \right)$ as $|x|\to+\infty$ when $n=10$; or $|u(x)|=
o\left( |x| ^{ -\frac n2+\sqrt{n-1}+2 }\right)$ as $x|\to+\infty$ when $n\ge
11$, must be a constant. This extends the well-known Liouville property for
radial stable solutions obtained by Villegas.


Abstract:
  The aim of quantum metrology is to estimate target parameters as precisely as
possible. In this paper, we consider quantum metrology based on
symmetry-protected adiabatic transformation. We introduce a ferromagnetic Ising
model with a transverse field as a probe and consider the estimation of a
longitudinal field. Without the transverse field, the ground state of the probe
is given by the Greenberger-Horne-Zeilinger state, and thus the Heisenberg
limit estimation of the longitudinal field can be achieved through parity
measurement. In our scheme, full information of the longitudinal field encoded
on parity is exactly mapped to global magnetization by symmetry-protected
adiabatic transformation, and thus the parity measurement can be replaced with
global magnetization measurement. Moreover, this scheme requires neither
accurate control of individual qubits nor that of interaction strength. We
discuss the effects of the finite transverse field and nonadiabatic transitions
as imperfection of adiabatic transformation. By taking into account finite time
duration for state preparation, sensing, and readout, we also compare
performance of the present scheme with a classical scheme in the absence and
presence of dephasing.


Abstract:
  Postprocessing ensemble weather predictions to correct systematic errors has
become a standard practice in research and operations. However, only few recent
studies have focused on ensemble postprocessing of wind gust forecasts, despite
its importance for severe weather warnings. Here, we provide a comprehensive
review and systematic comparison of eight statistical and machine learning
methods for probabilistic wind gust forecasting via ensemble postprocessing,
that can be divided in three groups: State of the art postprocessing techniques
from statistics (ensemble model output statistics (EMOS), member-by-member
postprocessing, isotonic distributional regression), established machine
learning methods (gradient-boosting extended EMOS, quantile regression forests)
and neural network-based approaches (distributional regression network,
Bernstein quantile network, histogram estimation network). The methods are
systematically compared using six years of data from a high-resolution,
convection-permitting ensemble prediction system that was run operationally at
the German weather service, and hourly observations at 175 surface weather
stations in Germany. While all postprocessing methods yield calibrated
forecasts and are able to correct the systematic errors of the raw ensemble
predictions, incorporating information from additional meteorological predictor
variables beyond wind gusts leads to significant improvements in forecast
skill. In particular, we propose a flexible framework of locally adaptive
neural networks with different probabilistic forecast types as output, which
not only significantly outperform all benchmark postprocessing methods but also
learn physically consistent relations associated with the diurnal cycle,
especially the evening transition of the planetary boundary layer.


Abstract:
  The linearized Korteweg-De Vries equation can be written as a Hamilton-like
system. However, the Hamilton energy depends on the time, and is a nonsymmetric
operator on $L^2({\bf R})$. By performing suitable unitary transforms on the
Hamilton energy, we can reduce this operator into one that is not independent
on the time but nonsymmetric. In this study, we consider the $L^2$-stability
issues and smoothing estimates for this operator, and prove that it has no
eigenvalues.


Abstract:
  Consider an analytical function $f:V\subset\mathbb R^2\rightarrow\mathbb R$
having $0$ as its regular value, a switching manifold $\Sigma=f^{-1}(0)$ and a
piecewise analytical vector field $X=(X^+,X^-)$, i.e. $X^\pm$ are analytical
vector fields defined on $\Sigma^\pm=\{p\in V: \pm f(p)>0\}$. We characterize
when the vector field $X$ has a monodromic singular point in $\Sigma$, called
$\Sigma$-monodromic singular point. Moreover, under certain conditions, we show
that a $\Sigma$-monodromic singular point of $X$ has a neighborhood free of
limit cycles.


Abstract:
  We prove a generalization of the strong subadditivity of the von Neumann
entropy for bosonic quantum Gaussian systems. Such generalization determines
the minimum values of linear combinations of the entropies of subsystems
associated to arbitrary linear functions of the quadratures, and holds for
arbitrary quantum states including the scenario where the entropies are
conditioned on a memory quantum system. We apply our result to prove new
entropic uncertainty relations with quantum memory, a generalization of the
quantum Entropy Power Inequality, and the linear time scaling of the
entanglement entropy produced by quadratic Hamiltonians.


Abstract:
  Advances in image-based dietary assessment methods have allowed nutrition
professionals and researchers to improve the accuracy of dietary assessment,
where images of food consumed are captured using smartphones or wearable
devices. These images are then analyzed using computer vision methods to
estimate energy and nutrition content of the foods. Food image segmentation,
which determines the regions in an image where foods are located, plays an
important role in this process. Current methods are data dependent, thus cannot
generalize well for different food types. To address this problem, we propose a
class-agnostic food image segmentation method. Our method uses a pair of eating
scene images, one before start eating and one after eating is completed. Using
information from both the before and after eating images, we can segment food
images by finding the salient missing objects without any prior information
about the food class. We model a paradigm of top down saliency which guides the
attention of the human visual system (HVS) based on a task to find the salient
missing objects in a pair of images. Our method is validated on food images
collected from a dietary study which showed promising results.


Abstract:
  We prove that the polar degree of an arbitrarily singular projective
hypersurface can be decomposed as a sum of non-negative numbers which represent
local vanishing cycles of two different types. This yields lower bounds for the
polar degree of any singular projective hypersurface.


Abstract:
  Predicting DNA-protein binding is an important and classic problem in
bioinformatics. Convolutional neural networks have outperformed conventional
methods in modeling the sequence specificity of DNA-protein binding. However,
none of the studies has utilized graph convolutional networks for motif
inference. In this work, we propose to use graph convolutional networks for
motif inference. We build a sequence k-mer graph for the whole dataset based on
k-mer co-occurrence and k-mer sequence relationship and then learn DNA Graph
Convolutional Network (DNA-GCN) for the whole dataset. Our DNA-GCN is
initialized with a one-hot representation for all nodes, and it then jointly
learns the embeddings for both k-mers and sequences, as supervised by the known
labels of sequences. We evaluate our model on 50 datasets from ENCODE. DNA-GCN
shows its competitive performance compared with the baseline model. Besides, we
analyze our model and design several different architectures to help fit
different datasets.


Abstract:
  Recent studies show that neural natural language processing (NLP) models are
vulnerable to backdoor attacks. Injected with backdoors, models perform
normally on benign examples but produce attacker-specified predictions when the
backdoor is activated, presenting serious security threats to real-world
applications. Since existing textual backdoor attacks pay little attention to
the invisibility of backdoors, they can be easily detected and blocked. In this
work, we present invisible backdoors that are activated by a learnable
combination of word substitution. We show that NLP models can be injected with
backdoors that lead to a nearly 100% attack success rate, whereas being highly
invisible to existing defense strategies and even human inspections. The
results raise a serious alarm to the security of NLP models, which requires
further research to be resolved. All the data and code of this paper are
released at https://github.com/thunlp/BkdAtk-LWS.


Abstract:
  We develop new methods for computing the precise Dehn functions of coabelian
subgroups of direct products of groups, that is, subgroups which arise as
kernels of homomorphisms from the direct product onto a free abelian group.
These improve and generalise previous results by Carter and Forester on Dehn
functions of level sets in products of simply connected cube complexes, by
Bridson on Dehn functions of cocyclic groups and by Dison on Dehn functions of
coabelian groups. We then provide several applications of our methods to
subgroups of direct products of free groups, to groups with interesting
geometric finiteness properties and to subgroups of direct products of
right-angled Artin groups.


Abstract:
  Quality estimation aims to measure the quality of translated content without
access to a reference translation. This is crucial for machine translation
systems in real-world scenarios where high-quality translation is needed. While
many approaches exist for quality estimation, they are based on supervised
machine learning requiring costly human labelled data. As an alternative, we
propose a technique that does not rely on examples from human-annotators and
instead uses synthetic training data. We train off-the-shelf architectures for
supervised quality estimation on our synthetic data and show that the resulting
models achieve comparable performance to models trained on human-annotated
data, both for sentence and word-level prediction.