Abstract:
  Let $\zeta^*(s)=\sum_{n=1}^{+\infty}(-1)^n/n^s$ and $\tau$ the operator
defined on the Frechet space of holomorphic functions in $\{s\in \mathbb C
:1/2< Re \, s<1\}$ by $\tau f(s)= f(s-2i\pi/\log 2)$. We show that the Riemann
Hypothesis is equivalent to the strong recurrence of $\zeta^*(s)$ for $\tau$.
It follows that a sufficient condition for $RH$ would be that every sum of a
series of eigenvectors with unimodular eigenvalues for an operator $u$ is
strongly recurrent for $u$. But we give a counterexample showing that it is not
the case.


Abstract:
  We present the results of a weekly monitoring of the new black hole candidate
X-ray binary MAXI J1631-472 carried out with the MeerKAT radio interferometer,
the Neil Gehrels Swift Observatory, and the Monitor of All-sky X-ray Image
(MAXI) instrument, during its 2018-2019 outburst. The source exhibits a number
of X-ray states, in particular both high- and low-luminosity hard states
bracketed by extended soft states. Radio flaring is observed shortly after a
transition from hard/intermediate states to the soft state. This is broadly in
agreement with existing empirical models, but its extended duration hints at
multiple unresolved flares and/or jet-ISM interactions. In the hard state
radio:X-ray plane, the source is revealed to be 'radio quiet' at high
luminosities, but to rejoin the `standard' track at lower luminosities, an
increasingly commonly-observed pattern of behaviour.


Abstract:
  Automated surgical gesture recognition is of great importance in
robot-assisted minimally invasive surgery. However, existing methods assume
that training and testing data are from the same domain, which suffers from
severe performance degradation when a domain gap exists, such as the simulator
and real robot. In this paper, we propose a novel unsupervised domain
adaptation framework which can simultaneously transfer multi-modality
knowledge, i.e., both kinematic and visual data, from simulator to real robot.
It remedies the domain gap with enhanced transferable features by using
temporal cues in videos, and inherent correlations in multi-modal towards
recognizing gesture. Specifically, we first propose an MDO-K to align
kinematics, which exploits temporal continuity to transfer motion directions
with smaller gap rather than position values, relieving the adaptation burden.
Moreover, we propose a KV-Relation-ATT to transfer the co-occurrence signals of
kinematics and vision. Such features attended by correlation similarity are
more informative for enhancing domain-invariance of the model. Two feature
alignment strategies benefit the model mutually during the end-to-end learning
process. We extensively evaluate our method for gesture recognition using DESK
dataset with peg transfer procedure. Results show that our approach recovers
the performance with great improvement gains, up to 12.91% in ACC and 20.16% in
F1score without using any annotations in real robot.


Abstract:
  Personalized recommendation system has become pervasive in various video
platform. Many effective methods have been proposed, but most of them didn't
capture the user's multi-level interest trait and dependencies between their
viewed micro-videos well. To solve these problems, we propose a Self-over-Co
Attention module to enhance user's interest representation. In particular, we
first use co-attention to model correlation patterns across different levels
and then use self-attention to model correlation patterns within a specific
level. Experimental results on filtered public datasets verify that our
presented module is useful.


Abstract:
  Deep neural networks (DNNs) are known to be vulnerable to adversarial
examples/attacks, raising concerns about their reliability in safety-critical
applications. A number of defense methods have been proposed to train robust
DNNs resistant to adversarial attacks, among which adversarial training has so
far demonstrated the most promising results. However, recent studies have shown
that there exists an inherent tradeoff between accuracy and robustness in
adversarially-trained DNNs. In this paper, we propose a novel technique Dual
Head Adversarial Training (DH-AT) to further improve the robustness of existing
adversarial training methods. Different from existing improved variants of
adversarial training, DH-AT modifies both the architecture of the network and
the training strategy to seek more robustness. Specifically, DH-AT first
attaches a second network head (or branch) to one intermediate layer of the
network, then uses a lightweight convolutional neural network (CNN) to
aggregate the outputs of the two heads. The training strategy is also adapted
to reflect the relative importance of the two heads. We empirically show, on
multiple benchmark datasets, that DH-AT can bring notable robustness
improvements to existing adversarial training methods. Compared with TRADES,
one state-of-the-art adversarial training method, our DH-AT can improve the
robustness by 3.4% against PGD40 and 2.3% against AutoAttack, and also improve
the clean accuracy by 1.8%.


Abstract:
  Context. The Sagittarius (Sgr) dwarf galaxy is merging with the Milky Way,
and the study of its globular clusters (GCs) is important to understand the
history and outcome of this ongoing process. Aims. Our main goal is to
characterize the GC system of the Sgr dwarf galaxy. This task is hampered by
high foreground stellar contamination, mostly from the Galactic bulge. Methods.
We performed a GC search specifically tailored to find new GC members within
the main body of this dwarf galaxy using the combined data of the VISTA
Variables in the Via Lactea Extended Survey (VVVX) near-infrared survey and the
Gaia Early Data Release 3 (EDR3) optical database. Results. We applied proper
motion (PM) cuts to discard foreground bulge and disk stars, and we found a
number of GC candidates in the main body of the Sgr dwarf galaxy. We selected
the best GCs as those objects that have significant overdensities above the
stellar background of the Sgr galaxy and that possess color-magnitude diagrams
(CMDs) with well-defined red giant branches (RGBs) consistent with the distance
and reddening of this galaxy. Conclusions. We discover eight new GC members of
the Sgr galaxy, which adds up to 29 total GCs known in this dwarf galaxy. This
total number of GCs shows that the Sgr dwarf galaxy hosts a rather rich GC
system. Most of the new GCs appear to be predominantly metal-rich and have low
luminosity. In addition, we identify ten other GC candidates that are more
uncertain and need more data for proper confirmation.


Abstract:
  Nano-optic imagers that modulate light at sub-wavelength scales could unlock
unprecedented applications in diverse domains ranging from robotics to
medicine. Although metasurface optics offer a path to such ultra-small imagers,
existing methods have achieved image quality far worse than bulky refractive
alternatives, fundamentally limited by aberrations at large apertures and low
f-numbers. In this work, we close this performance gap by presenting the first
neural nano-optics. We devise a fully differentiable learning method that
learns a metasurface physical structure in conjunction with a novel, neural
feature-based image reconstruction algorithm. Experimentally validating the
proposed method, we achieve an order of magnitude lower reconstruction error.
As such, we present the first high-quality, nano-optic imager that combines the
widest field of view for full-color metasurface operation while simultaneously
achieving the largest demonstrated 0.5 mm, f/2 aperture.


Abstract:
  In this paper, we address Novel Class Discovery (NCD), the task of unveiling
new classes in a set of unlabeled samples given a labeled dataset with known
classes. We exploit the peculiarities of NCD to build a new framework, named
Neighborhood Contrastive Learning (NCL), to learn discriminative
representations that are important to clustering performance. Our contribution
is twofold. First, we find that a feature extractor trained on the labeled set
generates representations in which a generic query sample and its neighbors are
likely to share the same class. We exploit this observation to retrieve and
aggregate pseudo-positive pairs with contrastive learning, thus encouraging the
model to learn more discriminative representations. Second, we notice that most
of the instances are easily discriminated by the network, contributing less to
the contrastive loss. To overcome this issue, we propose to generate hard
negatives by mixing labeled and unlabeled samples in the feature space. We
experimentally demonstrate that these two ingredients significantly contribute
to clustering performance and lead our model to outperform state-of-the-art
methods by a large margin (e.g., clustering accuracy +13% on CIFAR-100 and +8%
on ImageNet).


Abstract:
  Reinforcement learning is a powerful approach to learn behaviour through
interactions with an environment. However, behaviours are usually learned in a
purely reactive fashion, where an appropriate action is selected based on an
observation. In this form, it is challenging to learn when it is necessary to
execute new decisions. This makes learning inefficient, especially in
environments that need various degrees of fine and coarse control. To address
this, we propose a proactive setting in which the agent not only selects an
action in a state but also for how long to commit to that action. Our TempoRL
approach introduces skip connections between states and learns a skip-policy
for repeating the same action along these skips. We demonstrate the
effectiveness of TempoRL on a variety of traditional and deep RL environments,
showing that our approach is capable of learning successful policies up to an
order of magnitude faster than vanilla Q-learning.


Abstract:
  We study a two-player Stackelberg game with incomplete information such that
the follower's strategy belongs to a known family of parameterized functions
with an unknown parameter vector. We design an adaptive learning approach to
simultaneously estimate the unknown parameter and minimize the leader's cost,
based on adaptive control techniques and hysteresis switching. Our approach
guarantees that the leader's cost predicted using the parameter estimate
becomes indistinguishable from its actual cost in finite time, up to a
preselected, arbitrarily small error threshold. Also, the first-order necessary
condition for optimality holds asymptotically for the predicted cost.
Additionally, if a persistent excitation condition holds, then the parameter
estimation error becomes bounded by a preselected, arbitrarily small threshold
in finite time as well. For the case where there is a mismatch between the
follower's strategy and the parameterized function that is known to the leader,
our approach is able to guarantee the same convergence results for error
thresholds larger than the size of the mismatch. The algorithms and the
convergence results are illustrated via a simulation example in the domain of
network security.


Abstract:
  A marked Petri net is lucent if there are no two different reachable markings
enabling the same set of transitions, i.e., states are fully characterized by
the transitions they enable. Characterizing the class of systems that are
lucent is a foundational and also challenging question. However, little
research has been done on the topic. In this paper, it is shown that all
free-choice nets having a home cluster are lucent. These nets have a so-called
home marking such that it is always possible to reach this marking again. Such
a home marking can serve as a regeneration point or as an end-point. The result
is highly relevant because in many applications, we want the system to be
lucent and many well-behaved process models fall into the class identified in
this paper. Unlike previous work, we do not require the marked Petri net to be
live and strongly connected. Most of the analysis techniques for free-choice
nets are tailored towards well-formed nets. The approach presented in this
paper provides a novel perspective enabling new analysis techniques for
free-choice nets that do not need to be well-formed. Therefore, we can also
model systems and processes that are terminating and/or have an initialization
phase.


Abstract:
  It is becoming increasingly popular for distributed systems to exploit
offload to reduce load on the CPU. Remote Direct Memory Access (RDMA) offload,
in particular, has become popular. However, RDMA still requires CPU
intervention for complex offloads that go beyond simple remote memory access.
As such, the offload potential is limited and RDMA-based systems usually have
to work around such limitations.
  We present RedN, a principled, practical approach to implementing complex
RDMA offloads, without requiring any hardware modifications. Using
self-modifying RDMA chains, we lift the existing RDMA verbs interface to a
Turing complete set of programming abstractions. We explore what is possible in
terms of offload complexity and performance with a commodity RDMA NIC. We show
how to integrate these RDMA chains into applications, such as the Memcached
key-value store, allowing us to offload complex tasks such as key lookups. RedN
can reduce the latency of key-value get operations by up to 2.6x compared to
state-of-the-art KV designs that use one-sided RDMA primitives (e.g., FaRM-KV),
as well as traditional RPC-over-RDMA approaches. Moreover, compared to these
baselines, RedN provides performance isolation and, in the presence of
contention, can reduce latency by up to 35x while providing applications with
failure resiliency to OS and process crashes.


Abstract:
  Let $\mathcal{E}$ be a $\mathbb{Q}$-isogeny class of elliptic curves defined
over $\mathbb{Q}$. The isogeny graph associated to $\mathcal{E}$ is a graph
which has a vertex for each element of $\mathcal{E}$ and an edge for each
$\mathbb{Q}$-isogeny of prime degree that maps one element of $\mathcal{E}$ to
another element of $\mathcal{E}$, with the degree recorded as a label of the
edge. The isogeny-torsion graph associated to $\mathcal{E}$ is the isogeny
graph associated to $\mathcal{E}$ where, in addition, we label each vertex with
the abstract group structure of the torsion subgroup over $\mathbb{Q}$ of the
corresponding elliptic curve. The main result of the article is a determination
of which isogeny-torsion graphs associated to $\mathbb{Q}$-isogeny classes of
elliptic curves defined over $\mathbb{Q}$ correspond to infinitely many
$\textit{j}$-invariants.


Abstract:
  The mechanism of thermal driving for launching mass outflows is
interconnected with classical thermal instability (TI). In a recent paper, we
demonstrated that as a result of this interconnectedness, radial wind solutions
of X-ray heated flows are prone to becoming clumpy. In this paper, we first
show that the Bernoulli function determines whether or not the entropy mode can
grow due to TI in dynamical flows. Based on this finding, we identify a
critical `unbound' radius beyond which TI should accompany thermal driving. Our
numerical disk wind simulations support this result and reveal that clumpiness
is a consequence of buoyancy disrupting the stratified structure of steady
state solutions. Namely, instead of a smooth transition layer separating the
highly ionized disk wind from the cold phase atmosphere below, hot bubbles
formed from TI rise up and fragment the atmosphere. These bubbles first appear
within large scale vortices that form below the transition layer, and they
result in the episodic production of distinctive cold phase structures referred
to as irradiated atmospheric fragments (IAFs). Upon interacting with the wind,
IAFs advect outward and develop extended crests. The subsequent disintegration
of the IAFs takes place within a turbulent wake that reaches high elevations
above the disk. We show that this dynamics has the following observational
implications: dips in the absorption measure distribution are no longer
expected within TI zones and there can be a less sudden desaturation of X-ray
absorption lines such as \OVIII as well as multiple absorption troughs in
\FeXXVK.


Abstract:
  Advances in high-precision dielectric spectroscopy has enabled access to
non-linear susceptibilities of polar molecular liquids. The observed
non-monotonic behavior has been claimed to provide strong support for theories
of dynamic arrest based on thermodynamic amorphous order. Here we approach this
question from the perspective of dynamic facilitation, an alternative view
focusing on emergent kinetic constraints underlying the dynamic arrest of a
liquid approaching its glass transition. We derive explicit expressions for the
frequency-dependent higher-order dielectric susceptibilities exhibiting a
non-monotonic shape, the height of which increases as temperature is lowered.
We demonstrate excellent agreement with the experimental data for glycerol,
challenging the idea that non-linear response functions reveal correlated
relaxation in supercooled liquids.


Abstract:
  We give concrete, "infinitesimal" conditions for a proper geodesically
complete CAT(0) space to have semistable fundamental group at infinity.


Abstract:
  We investigate the potential of type II supernovae (SNe) to constrain
axion-like particles (ALPs) coupled simultaneously to nucleons and electrons.
ALPs coupled to nucleons can be efficiently produced in the SN core via
nucleon-nucleon bremsstrahlung and, for a wide range of parameters, leave the
SN unhindered, producing a large ALP flux. For masses exceeding 1 MeV, these
ALPs would decay into electron-positron pairs, generating a positron flux. In
the case of Galactic SNe, the annihilation of the created positrons with the
electrons present in the Galaxy would contribute to the 511 keV annihilation
line. Using the SPI (SPectrometer on INTEGRAL) observation of this line, allows
us to exclude a wide range of the axion-electron coupling, $10^{-19} \lesssim
g_{ae} \lesssim 10^{-11}$, for $g_{ap}\sim 10^{-9}$. Additionally, ALPs from
extra-galactic SNe decaying into electron-positron pairs would yield a
contribution to the cosmic X-ray background. In this case, we constrain the
ALP-electron coupling down to $g_{ae} \sim 10^{-20}$.


Abstract:
  The point-to-set principle \cite{LutLut17} characterizes the Hausdorff
dimension of a subset $E\subseteq\R^n$ by the \textit{effective} (or
algorithmic) dimension of its individual points. This characterization has been
used to prove several results in classical, i.e., without any computability
requirements, analysis. Recent work has shown that algorithmic techniques can
be fruitfully applied to Marstrand's projection theorem, a fundamental result
in fractal geometry.
  In this paper, we introduce an extension of point-to-set principle - the
notion of \textit{optimal oracles} for subsets $E\subseteq\R^n$. One of the
primary motivations of this definition is that, if $E$ has optimal oracles,
then the conclusion of Marstrand's projection theorem holds for $E$. We show
that every analytic set has optimal oracles. We also prove that if the
Hausdorff and packing dimensions of $E$ agree, then $E$ has optimal oracles.
Moreover, we show that the existence of sufficiently nice outer measures on $E$
implies the existence of optimal Hausdorff oracles. In particular, the
existence of exact gauge functions for a set $E$ is sufficient for the
existence of optimal Hausdorff oracles, and is therefore sufficient for
Marstrand's theorem. Thus, the existence of optimal oracles extends the
currently known sufficient conditions for Marstrand's theorem to hold.
  Under certain assumptions, every set has optimal oracles. However, assuming
the axiom of choice and the continuum hypothesis, we construct sets which do
not have optimal oracles. This construction naturally leads to a generalization
of Davies theorem on projections.


Abstract:
  In this study, we tried to see and characterize potential threats to digital
activism in the internet-active nation of Indonesia by doing network analysis
on a recent digital activism event on Twitter, which protested against a recent
law related to alcoholic beverage investment. We hoped insights from the study
can help the nation moving forward as public discourses are likely to stay
online post-COVID. From this study, we found that threats in form of hashtag
hijackings happen often in digital activism, and there were traces of a
systematic information campaign in our observed case. We also found that the
usage of bots is prevalent in and they showed significant activity, although
the extent to which they influenced the conversation needs to be followed
through more. These threats are something to think about as activism goes
increasingly digital after COVID-19 as it can imbue unwanted messages, sow
polarization, and distract the conversation from the real issue.


Abstract:
  Diffusion tensor imaging (DTI) is a prevalent neuroimaging tool in analyzing
the anatomical structure. The distinguishing feature of DTI is that the
voxel-wise variable is a 3x3 positive definite matrix other than a scalar,
describing the diffusion process at the voxel. Recently, several statistical
methods have been proposed to analyze the DTI data. This paper focuses on the
statistical inference of eigenvalues of DTI because it provides more
transparent clinical interpretations. However, the statistical inference of
eigenvalues is statistically challenging because few treat these responses as
random eigenvalues. In our paper, we rely on the distribution of the Wishart
matrix's eigenvalues to model the random eigenvalues. A hierarchical model
which captures the eigenvalues' randomness and spatial auto-correlation is
proposed to infer the local covariate effects. The Monte-Carlo
Expectation-Maximization algorithm is implemented for parameter estimation.
Both simulation studies and application to IXI data-set are used to demonstrate
our proposal. The results show that our proposal is more proper in analyzing
auto-correlated random eigenvalues compared to alternatives.


Abstract:
  Structured stochastic multi-armed bandits provide accelerated regret rates
over the standard unstructured bandit problems. Most structured bandits,
however, assume the knowledge of the structural parameter such as Lipschitz
continuity, which is often not available. To cope with the latent structural
parameter, we consider a transfer learning setting in which an agent must learn
to transfer the structural information from the prior tasks to the next task,
which is inspired by practical problems such as rate adaptation in wireless
link. We propose a novel framework to provably and accurately estimate the
Lipschitz constant based on previous tasks and fully exploit it for the new
task at hand. We analyze the efficiency of the proposed framework in two folds:
(i) the sample complexity of our estimator matches with the
information-theoretic fundamental limit; and (ii) our regret bound on the new
task is close to that of the oracle algorithm with the full knowledge of the
Lipschitz constant under mild assumptions. Our analysis reveals a set of useful
insights on transfer learning for latent Lipschitzconstants such as the
fundamental challenge a learner faces. Our numerical evaluations confirm our
theoretical findings and show the superiority of the proposed framework
compared to baselines.


Abstract:
  Aiming at the traditional grasping method for manipulators based on 2D
camera, when faced with the scene of gathering or covering, it can hardly
perform well in unstructured scenes that appear as gathering and covering, for
the reason that can't recognize objects accurately in cluster scenes from a
single perspective and the manipulators can't make the environment better for
grasping. In this case, a novel method of pushing-grasping collaborative based
on the deep Q-network in dual perspectives is proposed in this paper. This
method adopts an improved deep Q network algorithm, with an RGB-D camera to
obtain the information of objects' RGB images and point clouds from two
perspectives, and combines the pushing and grasping actions so that the trained
manipulator can make the scenes better for grasping so that it can perform well
in more complicated grasping scenes. What's more, we improved the reward
function of the deep Q-network and propose the piecewise reward function to
speed up the convergence of the deep Q-network. We trained different models and
tried different methods in the V-REP simulation environment, and it concluded
that the method proposed in this paper converges quickly and the success rate
of grasping objects in unstructured scenes raises up to 83.5%. Besides, it
shows the generalization ability and well performance when novel objects appear
in the scenes that the manipulator has never grasped before.


Abstract:
  Several calibration techniques have been proposed in the literature for the
calibration of two-component two-dimensional (2C-2D) particle image velocimetry
(PIV) and three-component two-dimensional (3C-2D) stereoscopic PIV (SPIV)
systems. These techniques generally involve the use of a calibration target
that is assumed to be at the exact centre of the laser sheet within the field
of view (FOV), which in practice is very difficult to achieve. In 3C-2D SPIV,
several methods offer different correction schemes based on the computation of
a disparity map, which are aimed at correcting errors produced due to this
misalignment. These techniques adjust the calibration of individual cameras to
reduce the disparity error, but in doing so can create unintended errors in the
measurement position and/or the velocity measurements, such as introducing a
bias in the measured three-component (3-C) displacements. This paper introduces
a novel method to ensure accurate alignment of the laser sheet with the
calibration target so that the uncertainty in displacement measurements is less
than or equal to the uncertainty inherent to the PIV and hence, no correction
scheme is required. The proposed method has been validated with a simple
experiment in which true displacements are given to a particle container
(illuminated by an aligned laser sheet) and the measured 3C displacements are
compared with the given true displacements. An uncertainty of less than 7.6
micrometres (equivalent to 0.114 pixels) in the measured 3C displacements
demonstrates the effectiveness of the new alignment method and eliminates the
need for any ad hoc post-correction scheme.


Abstract:
  This note is a short description of TeCoMiner, an interactive tool for
exploring the topic content of text collections. Unlike other topic modeling
tools, TeCoMiner is not based on some generative probabilistic model but on
topological considerations about co-occurrence networks of terms. We outline
the methods used for identifying topics, describe the features of the tool, and
sketch an application, using a corpus of policy related scientific news on
environmental issues published by the European Commission over the last decade.


Abstract:
  Light fidelity (LiFi), which is based on visible light communications (VLC),
is celebrated as a cutting-edge technological paradigm that is envisioned to be
an indispensable part of 6G systems. Nonetheless, LiFi performance is subject
to efficiently overcoming the line-of-sight blockage, whose adverse effect on
wireless reception reliability becomes even more pronounced in highly dynamic
environments, such as vehicular application scenarios. Meanwhile,
reconfigurable intelligent surfaces (RIS) emerged recently as a revolutionary
concept that transfers the physical propagation environment into a fully
controllable and customisable space in a low-cost low-power fashion. We
anticipate that the integration of RIS in LiFi-enabled networks will not only
support blockage mitigation but will also provision complex interactions among
network entities, and is hence manifested as a promising platform that enables
a plethora of technological trends and new applications. In this article, for
the first time in the open literature, we set the scene for a holistic overview
of RIS-assisted LiFi systems. Specifically, we explore the underlying RIS
architecture from the perspective of physics and present a forward-looking
vision that outlines potential operational elements supported by RIS-enabled
transceivers and RIS-enabled environments. Finally, we highlight major
associated challenges and offer a look ahead toward promising future
directions.


Abstract:
  We consider a system of charged one-dimensional spin-$\frac{1}{2}$ fermions
at low temperature. We study how the energy of a highly-excited quasiparticle
(or hole) relaxes toward the chemical potential in the regime of weak
interactions. The dominant relaxation processes involve collisions with two
other fermions. We find a dramatic enhancement of the relaxation rate at low
energies, with the rate scaling as the inverse sixth power of the excitation
energy. This behavior is caused by the long-range nature of the Coulomb
interaction.


Abstract:
  In order to objectively assess new medical imaging technologies via
computer-simulations, it is important to account for all sources of variability
that contribute to image data. One important source of variability that can
significantly limit observer performance is associated with the variability in
the ensemble of objects to-be-imaged. This source of variability can be
described by stochastic object models (SOMs), which are generative models that
can be employed to sample from a distribution of to-be-virtually-imaged
objects. It is generally desirable to establish SOMs from experimental imaging
measurements acquired by use of a well-characterized imaging system, but this
task has remained challenging. Deep generative neural networks, such as
generative adversarial networks (GANs) hold potential for such tasks. To
establish SOMs from imaging measurements, an AmbientGAN has been proposed that
augments a GAN with a measurement operator. However, the original AmbientGAN
could not immediately benefit from modern training procedures and GAN
architectures, which limited its ability to be applied to realistically sized
medical image data. To circumvent this, in this work, a modified AmbientGAN
training strategy is proposed that is suitable for modern progressive or
multi-resolution training approaches such as employed in the Progressive
Growing of GANs and Style-based GANs. AmbientGANs established by use of the
proposed training procedure are systematically validated in a controlled way by
use of computer-simulated measurement data corresponding to a stylized imaging
system. Finally, emulated single-coil experimental magnetic resonance imaging
data are employed to demonstrate the methods under less stylized conditions.


Abstract:
  In this paper, we study the evolution of opinions over social networks with
bounded confidence in social cliques. Node initial opinions are independently
and identically distributed; at each time step, nodes review the average
opinions of a randomly selected local clique. The clique averages may represent
local group pressures on peers. Then nodes update their opinions under bounded
confidence: only when the difference between an agent individual opinion and
the corresponding local clique pressure is below a threshold, this agent
opinion is updated according to the DeGroot rule as a weighted average of the
two values. As a result, this opinion dynamics is a generalization of the
classical Deffuant-Weisbuch model in which only pairwise interactions take
place. First of all, we prove conditions under which all node opinions converge
to finite limits. We show that in the limits the event that all nodes achieve a
consensus, and the event that all nodes achieve pairwise distinct limits, i.e.,
social disagreements, are both nontrivial events. Next, we show that opinion
fluctuations may take place in the sense that at least one agent in the network
fails to hold a converging opinion trajectory. In fact, we prove that this
fluctuation event happens with a strictly positive probability, and also
constructively present an initial value event under which the fluctuation event
arises with probability one. These results add to the understanding of the role
of bounded confidence in social opinion dynamics, and the possibility of
fluctuation reveals that bringing in cliques in Deffuant-Weisbuch models have
fundamentally changed the behavior of such opinion dynamical processes.


Abstract:
  Modeling complex systems and data using the language of graphs and networks
has become an essential topic across a range of different disciplines.
Arguably, this network-based perspective derives is success from the relative
simplicity of graphs: A graph consists of nothing more than a set of vertices
and a set of edges, describing relationships between pairs of such vertices.
This simple combinatorial structure makes graphs interpretable and flexible
modeling tools. The simplicity of graphs as system models, however, has been
scrutinized in the literature recently. Specifically, it has been argued from a
variety of different angles that there is a need for higher-order networks,
which go beyond the paradigm of modeling pairwise relationships, as
encapsulated by graphs. In this survey article we take stock of these recent
developments. Our goals are to clarify (i) what higher-order networks are, (ii)
why these are interesting objects of study, and (iii) how they can be used in
applications.


Abstract:
  At sufficiently low temperatures magnetic materials often enter a correlated
phase hosting collective, coherent magnetic excitations such as magnons or
triplons. Drawing on the enormous progress on topological materials of the last
few years, recent research has led to new insights into the geometry and
topology of these magnetic excitations. Berry phases associated to magnetic
dynamics can lead to observable consequences in heat and spin transport while
analogues of topological insulators and semimetals can arise within magnon band
structures from natural magnetic couplings. Magnetic excitations offer a
platform to explore the interplay of magnetic symmetries and topology, to drive
topological transitions using magnetic fields. examine the effects of
interactions on topological bands and to generate topologically protected spin
currents at interfaces. In this review, we survey progress on all these topics,
highlighting aspects of topological matter that are unique to magnon systems
and the avenues yet to be fully investigated.


Abstract:
  Parameter estimation procedures provide valuable guidance in the
understanding and improvement of organic solar cells and other devices. They
often rely on one-dimensional models, but in the case of bulk-heterojunction
(BHJ) designs, it is not straightforward that these models' parameters have a
consistent physical interpretation. Indeed, contrarily to two- or
three-dimensional models, the BHJ morphology is not explicitly described in
one-dimensional models and must be implicitly expressed through effective
parameters. In order to inform experimental decisions, a helpful parameter
estimation method must establish that one can correctly interpret the provided
parameters. However, only a few works have been undertaken to reach that
objective in the context of BHJ organic solar cells. In this work, a realistic
two-dimensional model of BHJ solar cells is used to investigate the behavior of
state-of-the-art parameter estimation procedures in situations that emulate
experimental conditions. We demonstrate that fitting solely current-voltage
characteristics by an effective medium one-dimensional model can yield
nonsensical results, which may lead to counter-productive decisions about
future design choices. In agreement with previously published literature, we
explicitly demonstrate that fitting several characterization results together
can drastically improve the robustness of the parameter estimation. Based on a
detailed analysis of parameter estimation results, a set of recommendations is
formulated to avoid the most problematic pitfalls and increase awareness about
the limitations that cannot be circumvented.


Abstract:
  In this work, we investigate dynamic oversampling techniques for large-scale
multiple-antenna systems equipped with low-cost and low-power 1-bit
analog-to-digital converters at the base stations. To compensate for the
performance loss caused by the coarse quantization, oversampling is applied at
the receiver. Unlike existing works that use uniform oversampling, which
samples the signal at a constant rate, a novel dynamic oversampling scheme is
proposed. The basic idea is to perform time-varying nonuniform oversampling,
which selects samples with nonuniform patterns that vary over time. We consider
two system design criteria: a design that maximizes the achievable sum rate and
another design that minimizes the mean square error of detected symbols.
Dynamic oversampling is carried out using a dimension reduction matrix
$\mathbf{\Delta}$, which can be computed by the generalized eigenvalue
decomposition or by novel submatrix-level feature selection algorithms.
Moreover, the proposed scheme is analyzed in terms of convergence,
computational complexity and power consumption at the receiver. Simulations
show that systems with the proposed dynamic oversampling outperform those with
uniform oversampling in terms of computational cost, achievable sum rate and
symbol error rate performance.


Abstract:
  We propose a novel codimension-n holography, called cone holography, between
a gravitational theory in $(d+1)$-dimensional conical spacetime and a CFT on
the $(d+1-n)$-dimensional defects. Similar to wedge holography, the cone
holography can be obtained by taking the zero-volume limit of holographic
defect CFT. Remarkably, it can be regarded as a holographic dual of the edge
modes on the defects. For one class of solutions, we prove that the cone
holography is equivalent to AdS/CFT, by showing that the classical
gravitational action and thus the CFT partition function in large N limit are
the same for the two theories. In general, cone holography and AdS/CFT are
different due to the infinite towers of massive Kaluza-Klein modes on the
branes. We test cone holography by studying Weyl anomaly, Entanglement/R\'enyi
entropy and correlation functions, and find good agreements between the
holographic and the CFT results. In particular, the c-theorem is obeyed by cone
holography. These are strong supports for our proposal. We discuss two kinds of
boundary conditions, the mixed boundary condition and Neumann boundary
condition, and find that they both define a consistent theory of cone
holography. We also analyze the mass spectrum on the brane and find that the
larger the tension is, the more continuous the mass spectrum is. The cone
holography can be regarded as a generalization of the wedge holography, and it
is closely related to the defect CFT, entanglement/R\'enyi entropy and
AdS/BCFT(dCFT). Thus it is expected to have a wide range of applications.


Abstract:
  We consider the monotone inclusion problems in real Hilbert spaces. Proximal
splitting algorithms are very popular technique to solve it and generally
achieve weak convergence under mild assumptions. Researchers assume strong
conditions like strong convexity or strong monotonicity on the considered
operators to prove strong convergence of the algorithms. Mann iteration method
and normal S-iteration method are popular methods to solve fixed point
problems. We propose a new common fixed point algorithm based on normal
S-iteration method {using Tikhonov regularization }to find common fixed point
of nonexpansive operators and prove strong convergence of the generated
sequence to the set of common fixed points without assuming strong convexity
and strong monotonicity. Based on the proposed fixed point algorithm, we
propose a forward-backward-type algorithm and a Douglas-Rachford algorithm in
connection with Tikhonov regularization to find the solution of monotone
inclusion problems. Further, we consider the complexly structured monotone
inclusion problems which are very popular these days. We also propose a
strongly convergent forward-backward-type primal-dual algorithm and a
Douglas-Rachford-type primal-dual algorithm to solve the monotone inclusion
problems. Finally, we conduct a numerical experiment to solve image deblurring
problems.


Abstract:
  Evolved low- to intermediate-mass stars are known to shed their gaseous
envelope into a large, dusty, molecule-rich circumstellar nebula which
typically develops a high degree of structural complexity. Most of the
large-scale, spatially correlated structures in the nebula are thought to
originate from the interaction of the stellar wind with a companion. As part of
the Atomium large programme, we observed the M-type asymptotic giant branch
(AGB) star R Hydrae with ALMA. The morphology of the inner wind of R Hya, which
has a known companion at ~3500 au, was determined from maps of CO and SiO
obtained at high angular resolution. A map of the CO emission reveals a
multi-layered structure consisting of a large elliptical feature at an angular
scale of ~10'' that is oriented along the north-south axis. The wind morphology
within the elliptical feature is dominated by two hollow bubbles. The bubbles
are on opposite sides of the AGB star and lie along an axis with a position
angle of ~115 deg. Both bubbles are offset from the central star, and their
appearance in the SiO channel maps indicates that they might be shock waves
travelling through the AGB wind. An estimate of the dynamical age of the
bubbles yields an age of the order of 100 yr, which is in agreement with the
previously proposed elapsed time since the star last underwent a thermal pulse.
When the CO and SiO emission is examined on subarcsecond angular scales, there
is evidence for an inclined, differentially rotating equatorial density
enhancement, strongly suggesting the presence of a second nearby companion. The
position angle of the major axis of this disc is ~70 deg in the plane of the
sky. We tentatively estimate that a lower limit on the mass of the nearby
companion is ~0.65 Msol on the basis of the highest measured speeds in the disc
and the location of its inner rim at ~6 au from the AGB star.


Abstract:
  Synthesized speech from articulatory movements can have real-world use for
patients with vocal cord disorders, situations requiring silent speech, or in
high-noise environments. In this work, we present EMA2S, an end-to-end
multimodal articulatory-to-speech system that directly converts articulatory
movements to speech signals. We use a neural-network-based vocoder combined
with multimodal joint-training, incorporating spectrogram, mel-spectrogram, and
deep features. The experimental results confirm that the multimodal approach of
EMA2S outperforms the baseline system in terms of both objective evaluation and
subjective evaluation metrics. Moreover, results demonstrate that joint
mel-spectrogram and deep feature loss training can effectively improve system
performance.


Abstract:
  Neural Ordinary Differential Equations (NODEs) use a neural network to model
the instantaneous rate of change in the state of a system. However, despite
their apparent suitability for dynamics-governed time-series, NODEs present a
few disadvantages. First, they are unable to adapt to incoming data points, a
fundamental requirement for real-time applications imposed by the natural
direction of time. Second, time series are often composed of a sparse set of
measurements that could be explained by many possible underlying dynamics.
NODEs do not capture this uncertainty. In contrast, Neural Processes (NPs) are
a family of models providing uncertainty estimation and fast data adaptation
but lack an explicit treatment of the flow of time. To address these problems,
we introduce Neural ODE Processes (NDPs), a new class of stochastic processes
determined by a distribution over Neural ODEs. By maintaining an adaptive
data-dependent distribution over the underlying ODE, we show that our model can
successfully capture the dynamics of low-dimensional systems from just a few
data points. At the same time, we demonstrate that NDPs scale up to challenging
high-dimensional time-series with unknown latent dynamics such as rotating
MNIST digits.


Abstract:
  We show that the permanent of a matrix can be written as the expectation
value of a function of random variables each with zero mean and unit variance.
This result is used to show that Glynn's theorem and a simplified MacMahon
theorem extend from a common probabilistic interpretation of the permanent.
Combining the methods in these two proofs, we prove a new result that relates
the permanent of a matrix to the expectation value of a product of hyperbolic
trigonometric functions, or, equivalently, the partition function of a spin
system. We conclude by discussing how the main theorem can be generalized and
how the techniques used to prove it can be applied to more general problems in
combinatorics.


Abstract:
  To improve the detection accuracy and generalization of steganalysis, this
paper proposes the Steganalysis Contrastive Framework (SCF) based on
contrastive learning. The SCF improves the feature representation of
steganalysis by maximizing the distance between features of samples of
different categories and minimizing the distance between features of samples of
the same category. To decrease the computing complexity of the contrastive loss
in supervised learning, we design a novel Steganalysis Contrastive Loss
(StegCL) based on the equivalence and transitivity of similarity. The StegCL
eliminates the redundant computing in the existing contrastive loss. The
experimental results show that the SCF improves the generalization and
detection accuracy of existing steganalysis DNNs, and the maximum promotion is
2% and 3% respectively. Without decreasing the detection accuracy, the training
time of using the StegCL is 10% of that of using the contrastive loss in
supervised learning.


Abstract:
  We prove the existence and the Besov regularity of the density of the
solution to a general parabolic SPDE which includes the stochastic Burgers
equation on an unbounded domain. We use an elementary approach based on the
fractional integration by parts.


Abstract:
  In the new era of very large telescopes, where data is crucial to expand
scientific knowledge, we have witnessed many deep learning applications for the
automatic classification of lightcurves. Recurrent neural networks (RNNs) are
one of the models used for these applications, and the LSTM unit stands out for
being an excellent choice for the representation of long time series. In
general, RNNs assume observations at discrete times, which may not suit the
irregular sampling of lightcurves. A traditional technique to address irregular
sequences consists of adding the sampling time to the network's input, but this
is not guaranteed to capture sampling irregularities during training.
Alternatively, the Phased LSTM unit has been created to address this problem by
updating its state using the sampling times explicitly. In this work, we study
the effectiveness of the LSTM and Phased LSTM based architectures for the
classification of astronomical lightcurves. We use seven catalogs containing
periodic and nonperiodic astronomical objects. Our findings show that LSTM
outperformed PLSTM on 6/7 datasets. However, the combination of both units
enhances the results in all datasets.


Abstract:
  Recurrent event analyses have found a wide range of applications in
biomedicine, public health, and engineering, among others, where study subjects
may experience a sequence of event of interest during follow-up. The R package
reReg (Chiou and Huang 2021) offers a comprehensive collection of practical and
easy-to-use tools for regression analysis of recurrent events, possibly with
the presence of an informative terminal event. The regression framework is a
general scale-change model which encompasses the popular Cox-type model, the
accelerated rate model, and the accelerated mean model as special cases.
Informative censoring is accommodated through a subject-specific frailty
without no need for parametric specification. Different regression models are
allowed for the recurrent event process and the terminal event. Also included
are visualization and simulation tools.


Abstract:
  Segmentation of additive manufacturing (AM) defects in X-ray Computed
Tomography (XCT) images is challenging, due to the poor contrast, small sizes
and variation in appearance of defects. Automatic segmentation can, however,
provide quality control for additive manufacturing. Over recent years,
three-dimensional convolutional neural networks (3D CNNs) have performed well
in the volumetric segmentation of medical images. In this work, we leverage
techniques from the medical imaging domain and propose training a 3D U-Net
model to automatically segment defects in XCT images of AM samples. This work
not only contributes to the use of machine learning for AM defect detection but
also demonstrates for the first time 3D volumetric segmentation in AM. We train
and test with three variants of the 3D U-Net on an AM dataset, achieving a mean
intersection of union (IOU) value of 88.4%.


Abstract:
  Retinal artery/vein (A/V) classification is a critical technique for
diagnosing diabetes and cardiovascular diseases. Although deep learning based
methods achieve impressive results in A/V classification, their performances
usually degrade severely when being directly applied to another database, due
to the domain shift, e.g., caused by the variations in imaging protocols. In
this paper, we propose a novel vessel-mixing based consistency regularization
framework, for cross-domain learning in retinal A/V classification. Specially,
to alleviate the severe bias to source domain, based on the label smooth prior,
the model is regularized to give consistent predictions for unlabeled
target-domain inputs that are under perturbation. This consistency
regularization implicitly introduces a mechanism where the model and the
perturbation is opponent to each other, where the model is pushed to be robust
enough to cope with the perturbation. Thus, we investigate a more difficult
opponent to further inspire the robustness of model, in the scenario of retinal
A/V, called vessel-mixing perturbation. Specially, it effectively disturbs the
fundus images especially the vessel structures by mixing two images regionally.
We conduct extensive experiments on cross-domain A/V classification using four
public datasets, which are collected by diverse institutions and imaging
devices. The results demonstrate that our method achieves the state-of-the-art
cross-domain performance, which is also close to the upper bound obtained by
fully supervised learning on target domain.


Abstract:
  This paper studies equilibrium quality of semi-separable position auctions
(known as the Ad Types setting) with greedy or optimal allocation combined with
generalized second-price (GSP) or Vickrey-Clarke-Groves (VCG) pricing. We make
three contributions: first, we give upper and lower bounds on the Price of
Anarchy (PoA) for auctions which use greedy allocation with GSP pricing, greedy
allocations with VCG pricing, and optimal allocation with GSP pricing. Second,
we give Bayes-Nash equilibrium characterizations for two-player, two-slot
instances (for all auction formats) and show that there exists both a revenue
hierarchy and revenue equivalence across some formats. Finally, we use
no-regret learning algorithms and bidding data from a large online advertising
platform and no-regret learning algorithms to evaluate the performance of the
mechanisms under semi-realistic conditions. For welfare, we find that the
optimal-to-realized welfare ratio (an empirical PoA analogue) is broadly better
than our upper bounds on PoA; For revenue, we find that the hierarchy in
practice may sometimes agree with simple theory, but generally appears
sensitive to the underlying distribution of bidder valuations.


Abstract:
  This paper introduces the notion of an Input Constrained Control Barrier
Function (ICCBF), as a method to synthesize safety-critical controllers for
non-linear control affine systems with input constraints. The method identifies
a subset of the safe set of states, and constructs a controller to render the
subset forward invariant. The feedback controller is represented as the
solution to a quadratic program, which can be solved efficiently for real-time
implementation. Furthermore, we show that ICCBFs are a generalization of Higher
Order Control Barrier Functions, and thus are applicable to systems of
non-uniform relative degree. Simulation results are presented for the adaptive
cruise control problem, and a spacecraft rendezvous problem.


Abstract:
  Electron-hole asymmetry is a fundamental property in solids that can
determine the nature of quantum phase transitions and the regime of operation
for devices. The observation of electron-hole asymmetry in graphene and
recently in the phase diagram of bilayer graphene has spurred interest into
whether it stems from disorder or from fundamental interactions such as
correlations. Here, we report an effective new way to access electron-hole
asymmetry in 2D materials by directly measuring the quasiparticle self-energy
in graphene/Boron Nitride field effect devices. As the chemical potential moves
from the hole to the electron doped side, we see an increased strength of
electronic correlations manifested by an increase in the band velocity and
inverse quasiparticle lifetime. These results suggest that electronic
correlations play an intrinsic role in driving electron hole asymmetry in
graphene and provide a new insight for asymmetries in more strongly correlated
materials.


Abstract:
  Finding shortest paths in a given network (e.g., a computer network or a road
network) is a well-studied task with many applications. We consider this task
under the presence of an adversary, who can manipulate the network by
perturbing its edge weights to gain an advantage over others. Specifically, we
introduce the Force Path Problem as follows. Given a network, the adversary's
goal is to make a specific path the shortest by adding weights to edges in the
network. The version of this problem in which the adversary can cut edges is
NP-complete. However, we show that Force Path can be solved to within arbitrary
numerical precision in polynomial time. We propose the PATHPERTURB algorithm,
which uses constraint generation to build a set of constraints that require
paths other than the adversary's target to be sufficiently long. Across a
highly varied set of synthetic and real networks, we show that the optimal
solution often reduces the required perturbation budget by about half when
compared to a greedy baseline method.


Abstract:
  We explore a two-qubit system defined on valley isospins of two electrons
confined in a gate-defined double quantum dot created within a MoS$_2$
monolayer flake. We show how to initialize, control, interact and read out such
valley qubits only by electrical means using voltages applied to the local
planar gates, which are layered on the top of the flake. By demonstrating the
two-qubit exchange or readout via the Pauli blockade, we prove that valley
qubits in transition-metal-dichalcogenide semiconductors family fulfill the
universality criteria and represent a scalable quantum computing platform. Our
numerical experiments are based on the tight-binding model for a MoS$_2$
monolayer, which gives single-electron eigenstates that are then used to
construct a basis of Slater-determinants for the two-electron configuration
space. We express screened electron-electron interactions in this basis by
calculating the Coulomb matrix elements using localized Slater-type orbitals.
Then we solve the time-dependent Schr\"odinger equation and obtain an exact
time-evolution of the two-electron system. During the evolution we
simultaneously solve the Poison equation, finding the confinement potential
controlled via voltages applied to the gates.


Abstract:
  This paper is concerned with the problem of representing and learning the
optimal control law for the linear quadratic Gaussian (LQG) optimal control
problem. In recent years, there is a growing interest in re-visiting this
classical problem, in part due to the successes of reinforcement learning (RL).
The main question of this body of research (and also of our paper) is to
approximate the optimal control law {\em without} explicitly solving the
Riccati equation. For this purpose, a novel simulation-based algorithm, namely
an ensemble Kalman filter (EnKF), is introduced in this paper. The algorithm is
used to obtain formulae for optimal control, expressed entirely in terms of the
EnKF particles. For the general partially observed LQG problem, the proposed
EnKF is combined with a standard EnKF (for the estimation problem) to obtain
the optimal control input based on the use of the separation principle. A
nonlinear extension of the algorithm is also discussed which clarifies the
duality roots of the proposed EnKF. The theoretical results and algorithms are
illustrated with numerical experiments.


Abstract:
  Computational couplings of Markov chains provide a practical route to
unbiased Monte Carlo estimation that can utilize parallel computation. However,
these approaches depend crucially on chains meeting after a small number of
transitions. For models that assign data into groups, e.g. mixture models, the
obvious approaches to couple Gibbs samplers fail to meet quickly. This failure
owes to the so-called "label-switching" problem; semantically equivalent
relabelings of the groups contribute well-separated posterior modes that impede
fast mixing and cause large meeting times. We here demonstrate how to avoid
label switching by considering chains as exploring the space of partitions
rather than labelings. Using a metric on this space, we employ an optimal
transport coupling of the Gibbs conditionals. This coupling outperforms
alternative couplings that rely on labelings and, on a real dataset, provides
estimates more precise than usual ergodic averages in the limited time regime.
Code is available at github.com/tinnguyen96/coupling-Gibbs-partition.


Abstract:
  Standard machine learning approaches require centralizing the users' data in
one computer or a shared database, which raises data privacy and
confidentiality concerns. Therefore, limiting central access is important,
especially in healthcare settings, where data regulations are strict. A
potential approach to tackling this is Federated Learning (FL), which enables
multiple parties to collaboratively learn a shared prediction model by using
parameters of locally trained models while keeping raw training data locally.
In the context of AI-assisted pain-monitoring, we wish to enable
confidentiality-preserving and unobtrusive pain estimation for long-term
pain-monitoring and reduce the burden on the nursing staff who perform frequent
routine check-ups. To this end, we propose a novel Personalized Federated Deep
Learning (PFDL) approach for pain estimation from face images. PFDL performs
collaborative training of a deep model, implemented using a lightweight CNN
architecture, across different clients (i.e., subjects) without sharing their
face images. Instead of sharing all parameters of the model, as in standard FL,
PFDL retains the last layer locally (used to personalize the pain estimates).
This (i) adds another layer of data confidentiality, making it difficult for an
adversary to infer pain levels of the target subject, while (ii) personalizing
the pain estimation to each subject through local parameter tuning. We show
using a publicly available dataset of face videos of pain (UNBC-McMaster
Shoulder Pain Database), that PFDL performs comparably or better than the
standard centralized and FL algorithms, while further enhancing data privacy.
This, has the potential to improve traditional pain monitoring by making it
more secure, computationally efficient, and scalable to a large number of
individuals (e.g., for in-home pain monitoring), providing timely and
unobtrusive pain measurement.


Abstract:
  We study the emptiness and $\lambda$-reachability problems for unary and
binary Probabilistic Finite Automata (PFA) and characterise the complexity of
these problems in terms of the degree of ambiguity of the automaton and the
size of its alphabet. Our main result is that emptiness and
$\lambda$-reachability are solvable in EXPTIME for polynomially ambiguous unary
PFA and if, in addition, the transition matrix is over $\{0, 1\}$, we show they
are in NP. In contrast to the Skolem-hardness of the $\lambda$-reachability and
emptiness problems for exponentially ambiguous unary PFA, we show that these
problems are NP-hard even for finitely ambiguous unary PFA. For binary
polynomially ambiguous PFA with fixed and commuting transition matrices, we
prove NP-hardness of the $\lambda$-reachability (dimension $9$), nonstrict
emptiness (dimension $37$) and strict emptiness (dimension $40$) problems.


Abstract:
  Federated learning (FL) has emerged with increasing popularity to collaborate
distributed medical institutions for training deep networks. However, despite
existing FL algorithms only allow the supervised training setting, most
hospitals in realistic usually cannot afford the intricate data labeling due to
absence of budget or expertise. This paper studies a practical yet challenging
FL problem, named \textit{Federated Semi-supervised Learning} (FSSL), which
aims to learn a federated model by jointly utilizing the data from both labeled
and unlabeled clients (i.e., hospitals). We present a novel approach for this
problem, which improves over traditional consistency regularization mechanism
with a new inter-client relation matching scheme. The proposed learning scheme
explicitly connects the learning across labeled and unlabeled clients by
aligning their extracted disease relationships, thereby mitigating the
deficiency of task knowledge at unlabeled clients and promoting discriminative
information from unlabeled samples. We validate our method on two large-scale
medical image classification datasets. The effectiveness of our method has been
demonstrated with the clear improvements over state-of-the-arts as well as the
thorough ablation analysis on both tasks\footnote{Code will be made available
at \url{https://github.com/liuquande/FedIRM}}.


Abstract:
  The quasi-one-dimensional spin ladder compounds, BaFe$_2$S$_3$ and
BaFe$_2$Se$_3$, are investigated by infrared spectroscopy and density
functional theory (DFT) calculations. We observe strong anisotropic electronic
properties and an optical gap in the leg direction that is gradually filled
above the antiferromagnetic (afm) ordering temperature, turning the systems
into a metallic phase. Combining the optical data with the DFT calculations we
associate the optical gap feature with the $p$-$d$ transition that appears only
in the afm ordered state. Hence, the insulating ground state along the leg
direction is attributed to Slater physics rather than Mott-type correlations.


Abstract:
  The on-going COVID-19 pandemic highlights the severe health risks posed by
deep submicron sized airborne viruses and particulates in the spread of
infectious diseases. There is an urgent need for the development of efficient,
durable and reusable filters for this size range. Here we report the
realization of efficient particulate filters using nanowire-based low-density
metal foams which combine extremely large surface areas with excellent
mechanical properties. The metal foams exhibit outstanding filtration
efficiencies (>96.6%) in the PM_{0.3} regime, with potentials for further
improvement. Their mechanical stability and light weight, chemical and
radiation resistance, ease of cleaning and reuse, and recyclability further
make such metal foams promising filters for combating COVID-19 and other types
of airborne particulates.


Abstract:
  Mendelian randomization (MR) is a statistical method exploiting genetic
variants as instrumental variables to estimate the causal effect of modifiable
risk factors on an outcome of interest. Despite wide uses of various popular
two-sample MR methods based on genome-wide association study summary level
data, however, those methods could suffer from potential power loss or/and
biased inference when the chosen genetic variants are in linkage disequilibrium
(LD), and also have relatively large direct effects on the outcome whose
distribution might be heavy-tailed which is commonly referred to as the
idiosyncratic pleiotropy phenomenon. To resolve those two issues, we propose a
novel Robust Bayesian Mendelian Randomization (RBMR) model that uses the more
robust multivariate generalized t-distribution to model such direct effects in
a probabilistic model framework which can also incorporate the LD structure
explicitly. The generalized t-distribution can be represented as a Gaussian
scaled mixture so that our model parameters can be estimated by the EM-type
algorithms. We compute the standard errors by calibrating the evidence lower
bound using the likelihood ratio test. Through extensive simulation studies, we
show that our RBMR has robust performance compared to other competing methods.
We also apply our RBMR method to two benchmark data sets and find that RBMR has
smaller bias and standard errors. Using our proposed RBMR method, we find that
coronary artery disease is associated with increased risk of critically ill
coronavirus disease 2019 (COVID-19). We also develop a user-friendly R package
RBMR for public use.


Abstract:
  On the space of $\pm 1$ spin configurations on the 3d-square lattice, we
consider the \emph{shaken dynamics}, a parallel Markovian dynamics that can be
interpreted in terms of Probabilistic Cellular Automata, whose transition
probabilities are defined in terms of a pair ferromagnetic Ising-type
Hamiltonian with nearest neighbor interaction $J$, depending on an additional
parameter $q$, measuring the tendency of the system to remain locally in the
same state. We compute the stationary measure of the shaken dynamics and we
investigate its relation with the Gibbs measure for the Ising model. It turns
out that the two parameters $J$ and $q$ tune the geometry of the underlying
lattice. By a judicious use of perturbative methods we show rigorously that our
model exhibits a line of critical points in $J-q$ plane that separates the
ordered phase from the disordered one, and we perform numerical simulation to
determine the phase transition curve. Our method allows us to find in a unified
way the critical values of $J$ for Ising model with first neighbors
interaction, defined on a whole class of lattice, intermediate between the
two-dimensional hexagonal and the three-dimensional cubic one, such as, for
example, the tetrahedral lattice. Finally we estimate the critical exponents of
the magnetic susceptibility and show that our model captures a phase transition
in the geometry of the system at $q = 0$.


Abstract:
  Current supervised sketch-based image retrieval (SBIR) methods achieve
excellent performance. However, the cost of data collection and labeling
imposes an intractable barrier to practical deployment of real applications. In
this paper, we present the first attempt at unsupervised SBIR to remove the
labeling cost (category annotations and sketch-photo pairings) that is
conventionally needed for training. Existing single-domain unsupervised
representation learning methods perform poorly in this application, due to the
unique cross-domain (sketch and photo) nature of the problem. We therefore
introduce a novel framework that simultaneously performs unsupervised
representation learning and sketch-photo domain alignment. Technically this is
underpinned by exploiting joint distribution optimal transport (JDOT) to align
data from different domains during representation learning, which we extend
with trainable cluster prototypes and feature memory banks to further improve
scalability and efficacy. Extensive experiments show that our framework
achieves excellent performance in the new unsupervised setting, and performs
comparably or better than state-of-the-art in the zero-shot setting.


Abstract:
  We use first principles molecular dynamics simulations coupled to the
thermodynamic integration method to study the hcp-bcc transition and melting of
beryllium up to a pressure of 1600~GPa. We derive the melting line by equating
solid and liquid Gibbs free energies, and represent it by a Simon Glatzel fit
$T_m= 1564~\text{K} (1 + P/(15.6032 ~\text{GPa}))^{0.383}$, which is in good
agreement with previous two-phase simulations below 6000~K. We also derive the
hcp-bcc solid-solid phase boundary and show the quasiharmonic approximation
underestimates the stability of the hcp structure, predicting lower transition
pressures between hcp and bcc phases. However, our results are consistent with
the stability regime predicted by the phonon quasiparticle method. We also
predict that hcp-bcc-liquid triple point is located at 164.7~GPa and 4314~K. In
addition, we compute the shock Hugoniot curve, and show that it is in good
agreement with experiments, intersecting our derived melting curve at
$\sim$235~GPa at 4900~K. Finally, we show that an isentropic compression path
that intersects the melting curve at both low and high temperature in the
liquid regime, can reappear in the solid after a gap as large as 7000~K.
Therefore, we predict that a large section of the melting curve could be
sampled, in principle, by a ramp compression experiment, where solid and liquid
Be would coexist as the sample is compressed.


Abstract:
  After a few microseconds of the creation of our Universe through the Big
Bang, the primordial matter was believed to be a soup of the fundamental
constituents of matter -- quarks and gluons. This is expected to be created in
the laboratory by colliding heavy nuclei at ultra-relativistic speeds. A plasma
of quarks and gluons, called Quark-Gluon Plasma (QGP) can be created at the
energy and luminosity frontiers in the Relativistic Heavy Ion Collider (RHIC),
at Brookhaven National Laboratory, New York, USA, and the Large Hadron Collider
(LHC) at CERN, Geneva, Switzerland. Heavy quarks, namely the charm and bottom
quarks, are considered as novel probes to characterize QGP, and hence the
produced Quantum Chromodynamics (QCD) matter. Heavy quark transport
coefficients play a significant role in understanding the properties of QGP.
Experimental measurements of nuclear suppression factor and elliptic flow can
constrain the heavy quark transport coefficients, which are key ingredients for
phenomenological studies, and they help to disentangle different energy loss
mechanisms. We give a general perspective of the heavy quark drag and diffusion
coefficients in QGP and discuss their potentials as probes to disentangle
different hadronization mechanisms, as well as to probe the initial
electromagnetic fields produced in non-central heavy-ion collisions.
Experimental perspectives on future measurements are discussed with special
emphasis on heavy-flavors as next-generation probes in view of new
technological developments.


Abstract:
  We present simulations which predict significantly higher laser to X-ray
efficiencies than those previously found in high intensity (1e20-1e22 W/cm2)
laser-solid simulations. The bremsstrahlung emission is shown to last for
10-100 ps, which is difficult to model with conventional particle-in-cell (PIC)
codes. The importance of collective effects is also demonstrated, showing the
limitations of Monte Carlo modelling in these systems. A new, open-source
hybrid-PIC code with bremsstrahlung routines has been developed to model this
X-ray production in 3D. Special boundary conditions are used to emulate complex
electron refluxing behaviour, which has been characterised in 2D full-PIC
simulations. The peak X-ray efficiency was recorded in thick gold targets, with
7.4% conversion of laser energy into X-rays of energy 1 MeV or higher. The
target size is shown to play a role in the conversion efficiency and angular
distribution of emitted X-rays, and a simple analytic model is presented for
estimating these efficiencies.


Abstract:
  Studying the diffusion and kinetic equilibration of heavy quarks within a hot
QCD medium profits from the knowledge of a coloured Lorentz force that acts on
them. Starting from the spatial components of the vector current, and carrying
out two matching computations, one for the heavy quark mass scale ($M$) and
another for thermal scales ($\sqrt{MT}$, $T$), we determine 1-loop matching
coefficients for the electric and magnetic parts of a Lorentz force. The
magnetic part has a non-zero anomalous dimension, which agrees with that
extracted from two other considerations, one thermal and the other in vacuum.
The matching coefficient could enable a lattice study of a colour-magnetic
2-point correlator.


Abstract:
  In this paper, we provide a general framework for the construction of the
Einstein frame within non-linear extensions of the teleparallel equivalents of
General Relativity. These include the metric teleparallel and the symmetric
teleparallel, but also the general teleparallel theories. We write the actions
in a form where we separate the Einstein--Hilbert term, the conformal mode due
to the non-linear nature of the theories (which is analogous to the extra
degree of freedom in $f(R)$ theories), and the sector that manifestly shows the
dynamics arising from the breaking of local symmetries. This frame is then used
to study the theories around the Minkowski background, and we show how all the
non-linear extensions share the same quadratic action around Minkowski. As a
matter of fact, we find that the gauge symmetries that are lost by going to the
non-linear generalisations of the teleparallel General Relativity equivalents
arise as accidental symmetries in the linear theory around Minkowski.
Remarkably, we also find that the conformal mode can be absorbed into a Weyl
rescaling of the metric at this order and, consequently, it disappears from the
linear spectrum so only the usual massless spin 2 perturbation propagates.
These findings unify in a common framework the known fact that no additional
modes propagate on Minkowski backgrounds, and we can trace it back to the
existence of accidental gauge symmetries of such a background.


Abstract:
  For a caching system with multiple users, we aim to characterize the
memory-rate tradeoff for caching with uncoded cache placement, under nonuniform
file popularity. Focusing on the modified coded caching scheme (MCCS) recently
proposed by Yu, etal., we formulate the cache placement optimization problem
for the MCCS to minimize the average delivery rate under nonuniform file
popularity, restricting to a class of popularity-first placements. We then
present two information-theoretic lower bounds on the average rate for caching
with uncoded placement, one for general cache placements and the other
restricted to the popularity-first placements. By comparing the average rate of
the optimized MCCS with the lower bounds, we prove that the optimized MCCS
attains the general lower bound for the two-user case, providing the exact
memory-rate tradeoff. Furthermore, it attains the popularity-first-based lower
bound for the case of general K users with distinct file requests. In these two
cases, our results also reveal that the popularity-first placement is optimal
for the MCCS, and zero-padding used in coded delivery incurs no loss of
optimality. For the case of K users with redundant file requests, our analysis
shows that there may exist a gap between the optimized MCCS and the lower
bounds due to zero-padding. We next fully characterize the optimal
popularity-first cache placement for the MCCS, which is shown to possess a
simple file-grouping structure and can be computed via an efficient algorithm
using closed-form expressions. Finally, we extend our study to accommodate both
nonuniform file popularity and sizes, where we show that the optimized MCCS
attains the lower bound for the two-user case, providing the exact memory-rate
tradeoff. Numerical results show that, for general settings, the gap between
the optimized MCCS and the lower bound only exists in limited cases and is very
small.


Abstract:
  Novelty detection using deep generative models such as autoencoder,
generative adversarial networks mostly takes image reconstruction error as
novelty score function. However, image data, high dimensional as it is,
contains a lot of different features other than class information which makes
models hard to detect novelty data. The problem gets harder in multi-modal
normality case. To address this challenge, we propose a new way of measuring
novelty score in multi-modal normality cases using orthogonalized latent space.
Specifically, we employ orthogonal low-rank embedding in the latent space to
disentangle the features in the latent space using mutual class information.
With the orthogonalized latent space, novelty score is defined by the change of
each latent vector. Proposed algorithm was compared to state-of-the-art novelty
detection algorithms using GAN such as RaPP and OCGAN, and experimental results
show that ours outperforms those algorithms.


Abstract:
  The concept of k-spectrum for genomes is here investigated as a basic tool to
analyze genomes. Related spectral notions based on k-mers are introduced with
some related mathematical properties which are relevant for informational
analysis of genomes. Procedures to generate spectral segmentations of genomes
are provided and are tested (under several values of length k for k-mers) on
cases of real genomes, such as some human chromosomes and Saccharomyces
cerevisiae.


Abstract:
  In this paper we consider the strategic asset allocation of an insurance
company. This task can be seen as a special case of portfolio optimization. In
the 1950s, Markowitz proposed to formulate portfolio optimization as a
bicriteria optimization problem considering risk and return as objectives.
However, recent developments in the field of insurance require four and more
objectives to be considered, among them the so-called solvency ratio that stems
from the Solvency II directive of the European Union issued in 2009. Moreover,
the distance to the current portfolio plays an important role. While literature
on portfolio optimization with three objectives is already scarce, applications
with four and more objectives have not yet been solved so far by
multi-objective approaches based on scalarizations. However, recent algorithmic
improvements in the field of exact multi-objective methods allow the
incorporation of many objectives and the generation of well-spread
representations within few iterations. We describe the implementation of such
an algorithm for a strategic asset allocation with four objective functions and
demonstrate its usefulness for the practitioner. Our approach is in operative
use in a German insurance company. Our partners report a significant
improvement in their decision making process since, due to the proper
integration of the new objectives, the software proposes portfolios of much
better quality than before within short running time.


Abstract:
  Programming languages with algebraic effects often track the computations'
effects using type-and-effect systems. In this paper, we propose to view an
algebraic effect theory of a computation as a variable context; consequently,
we propose to track algebraic effects of a computation with \emph{contextual
modal types}. We develop ECMTT, a novel calculus which tracks algebraic effects
by a contextualized variant of the modal $\Box$ (necessity) operator, that it
inherits from Contextual Modal Type Theory (CMTT).
  Whereas type-and-effect systems add effect annotations on top of a prior
programming language, the effect annotations in ECMTT are inherent to the
language, as they are managed by programming constructs corresponding to the
logical introduction and elimination forms for the $\Box$ modality. Thus, the
type-and-effect system of ECMTT is actually just a type system.
  Our design obtains the properties of local soundness and completeness, and
determines the operational semantics solely by $\beta$-reduction, as customary
in other logic-based calculi. In this view, effect handlers arise naturally as
a witness that one context (i.e., algebraic theory) can be reached from
another, generalizing explicit substitutions from CMTT.
  To the best of our knowledge, ECMTT is the first system to relate algebraic
effects to modal types. We also see it as a step towards providing a
correspondence in the style of Curry and Howard that may transfer a number of
results from the fields of modal logic and modal type theory to that of
algebraic effects.


Abstract:
  We extend a classical test of subsphericity, based on the first two moments
of the eigenvalues of the sample covariance matrix, to the high-dimensional
regime where the signal eigenvalues of the covariance matrix diverge to
infinity and either $p/n \rightarrow 0$ or $p/n \rightarrow \infty$. In the
latter case we further require that the divergence of the eigenvalues is
suitably fast in a specific sense. Our work can be seen to complement that of
Schott (2006) who established equivalent results in the case $p/n \rightarrow
\gamma \in (0, \infty)$. As our second main contribution, we use the test to
derive a consistent estimator for the latent dimension of the model.
Simulations and a real data example are used to demonstrate the results,
providing also evidence that the test might be further extendable to a wider
asymptotic regime.


Abstract:
  Recent works on ride-sharing order dispatching have highlighted the
importance of taking into account both the spatial and temporal dynamics in the
dispatching process for improving the transportation system efficiency. At the
same time, deep reinforcement learning has advanced to the point where it
achieves superhuman performance in a number of fields. In this work, we propose
a deep reinforcement learning based solution for order dispatching and we
conduct large scale online A/B tests on DiDi's ride-dispatching platform to
show that the proposed method achieves significant improvement on both total
driver income and user experience related metrics. In particular, we model the
ride dispatching problem as a Semi Markov Decision Process to account for the
temporal aspect of the dispatching actions. To improve the stability of the
value iteration with nonlinear function approximators like neural networks, we
propose Cerebellar Value Networks (CVNet) with a novel distributed state
representation layer. We further derive a regularized policy evaluation scheme
for CVNet that penalizes large Lipschitz constant of the value network for
additional robustness against adversarial perturbation and noises. Finally, we
adapt various transfer learning methods to CVNet for increased learning
adaptability and efficiency across multiple cities. We conduct extensive
offline simulations based on real dispatching data as well as online AB tests
through the DiDi's platform. Results show that CVNet consistently outperforms
other recently proposed dispatching methods. We finally show that the
performance can be further improved through the efficient use of transfer
learning.


Abstract:
  Let $G$ be a graph on $n$ nodes. In the stochastic population protocol model,
a collection of $n$ indistinguishable, resource-limited nodes collectively
solve tasks via pairwise interactions. In each interaction, two randomly chosen
neighbors first read each other's states, and then update their local states. A
rich line of research has established tight upper and lower bounds on the
complexity of fundamental tasks, such as majority and leader election, in this
model, when $G$ is a clique. Specifically, in the clique, these tasks can be
solved fast, i.e., in $n \operatorname{polylog} n$ pairwise interactions, with
high probability, using at most $\operatorname{polylog} n$ states per node.
  In this work, we consider the more general setting where $G$ is an arbitrary
graph, and present a technique for simulating protocols designed for
fully-connected networks in any connected regular graph. Our main result is a
simulation that is efficient on many interesting graph families: roughly, the
simulation overhead is polylogarithmic in the number of nodes, and quadratic in
the conductance of the graph. As a sample application, we show that, in any
regular graph with conductance $\phi$, both leader election and exact majority
can be solved in $\phi^{-2} \cdot n \operatorname{polylog} n$ pairwise
interactions, with high probability, using at most $\phi^{-2} \cdot
\operatorname{polylog} n$ states per node. This shows that there are fast and
space-efficient population protocols for leader election and exact majority on
graphs with good expansion properties. We believe our results will prove
generally useful, as they allow efficient technology transfer between the
well-mixed (clique) case, and the under-explored spatial setting.


Abstract:
  We present a technique for a complete 3D reconstruction of small objects
moving in front of a textured background. It is a particular variation of
multibody structure from motion, which specializes to two objects only. The
scene is captured in several static configurations between which the relative
pose of the two objects may change. We reconstruct every static configuration
individually and segment the points locally by finding multiple poses of
cameras that capture the scene's other configurations. Then, the local
segmentation results are combined, and the reconstructions are merged into the
resulting model of the scene. In experiments with real artifacts, we show that
our approach has practical advantages when reconstructing 3D objects from all
sides. In this setting, our method outperforms the state-of-the-art. We
integrate our method into the state of the art 3D reconstruction pipeline
COLMAP.


Abstract:
  We propose a probe for the analysis of deep learning architectures that is
based on machine learning and approximation theoretical principles. Given a
deep learning architecture and a training set, during or after training, the
Sparsity Probe allows to analyze the performance of intermediate layers by
quantifying the geometrical features of representations of the training set. We
show how the Sparsity Probe enables measuring the contribution of adding depth
to a given architecture, to detect under-performing layers, etc., all this
without any auxiliary test data set.


Abstract:
  We propose a stochastic SIR model, specified as a system of stochastic
differential equations, to analyse the data of the Italian COVID-19 epidemic,
taking also into account the under-detection of infected and recovered
individuals in the population. We find that a correct assessment of the amount
of under-detection is important to obtain reliable estimates of the critical
model parameters. Moreover, a single SIR model over the whole epidemic period
is unable to correctly describe the behaviour of the pandemic. Then, the
adaptation of the model in every time-interval between relevant government
decrees that implement contagion mitigation measures, provides short-term
predictions and a continuously updated assessment of the basic reproduction
number.


Abstract:
  The article is devoted to questions concerning the problems of compactness of
solutions of the Dirichlet problem for the Beltrami equation in some simply
connected domain. In terms of prime ends, we have proved results of a detailed
form for the case when the maximal dilations of these solutions satisfy certain
integral constraints. In addition, in this article we have proved theorems on
the local and global behavior of plane and spatial mappings with direct and
inverse modulus conditions.


Abstract:
  Process digitization and integration is an increasing need for enterprises,
while cyber-attacks denote a growing threat. Using the Business Process
Management Notation (BPMN) is common to handle the digital and integration
focus within and across organizations. In other parts of the same companies,
threat modeling and attack graphs are used for analyzing the security posture
and resilience.
  In this paper, we propose a novel approach to use attack graph simulations on
processes represented in BPMN. Our contributions are the identification of
BPMN's attack surface, a mapping of BPMN elements to concepts in a Meta Attack
Language (MAL)-based Domain-Specific Language (DSL), called coreLang, and a
prototype to demonstrate our approach in a case study using a real-world
invoice integration process. The study shows that non-invasively enriching BPMN
instances with cybersecurity analysis through attack graphs is possible without
much human expert input. The resulting insights into potential vulnerabilities
could be beneficial for the process modelers.


Abstract:
  We identify infinite classes of potentials for which the Coleman instantons
do not exist. For these potentials, the decay of a false vacuum must be
described by the new instantons introduced in [7,8].


Abstract:
  Systematic differences in the the proton's charge radius, as determined by
ordinary atoms and muonic atoms, have caused a resurgence of interest in
elastic lepton scattering measurements. The proton's charge radius, defined as
the slope of the charge form factor at Q$^2$=0, does not depend on the probe.
Any difference in the apparent size of the proton, when determined from
ordinary versus muonic hydrogen, could point to new physics or need for the
higher order corrections. While recent measurements seem to now be in
agreement, there is to date no high precision elastic scattering data with both
electrons and positrons. A high precision proton radius measurement could be
performed in Hall B at Jefferson Lab with a positron beam and the calorimeter
based setup of the PRad experiment. This measurement could also be extended to
deuterons where a similar discrepancy has been observed between the muonic and
electronic determination of deuteron charge radius. A new, high precision
measurement with positrons, when viewed alongside electron scattering
measurements and the forthcoming MUSE muon scattering measurement, could help
provide new insights into the origins of the proton radius puzzle, and also
provide new experimental constraints on radiative correction calculations.


Abstract:
  Lithium iron phosphate (LixFePO4), a cathode material used in rechargeable
Li-ion batteries, phase separates upon de/lithiation under equilibrium. The
interfacial structure and chemistry within these cathode materials affects
Li-ion transport, and therefore battery performance. Correlative imaging of
LixFePO4 was performed using four-dimensional scanning transmission electron
microscopy (4D-STEM), scanning transmission X-ray microscopy (STXM), and X-ray
ptychography in order to analyze the local structure and chemistry of the same
particle set. Over 50,000 diffraction patterns from 10 particles provided
measurements of both structure and chemistry at a nanoscale spatial resolution
(16.6-49.5 nm) over wide (several micron) fields-of-view with statistical
robustness.LixFePO4 particles at varying stages of delithiation were measured
to examine the evolution of structure and chemistry as a function of
delithiation. In lithiated and delithiated particles, local variations were
observed in the degree of lithiation even while local lattice structures
remained comparatively constant, and calculation of linear coefficients of
chemical expansion suggest pinning of the lattice structures in these
populations. Partially delithiated particles displayed broadly core-shell-like
structures, however, with highly variable behavior both locally and per
individual particle that exhibited distinctive intermediate regions at the
interface between phases, and pockets within the lithiated core that correspond
to FePO4 in structure and chemistry.The results provide insight into the
LixFePO4 system, subtleties in the scope and applicability of Vegards law
(linear lattice parameter-composition behavior) under local versus global
measurements, and demonstrate a powerful new combination of experimental and
analytical modalities for bridging the crucial gap between local and
statistical characterization.


Abstract:
  Large Transformers pretrained over clinical notes from Electronic Health
Records (EHR) have afforded substantial gains in performance on predictive
clinical tasks. The cost of training such models (and the necessity of data
access to do so) coupled with their utility motivates parameter sharing, i.e.,
the release of pretrained models such as ClinicalBERT. While most efforts have
used deidentified EHR, many researchers have access to large sets of sensitive,
non-deidentified EHR with which they might train a BERT model (or similar).
Would it be safe to release the weights of such a model if they did? In this
work, we design a battery of approaches intended to recover Personal Health
Information (PHI) from a trained BERT. Specifically, we attempt to recover
patient names and conditions with which they are associated. We find that
simple probing methods are not able to meaningfully extract sensitive
information from BERT trained over the MIMIC-III corpus of EHR. However, more
sophisticated "attacks" may succeed in doing so: To facilitate such research,
we make our experimental setup and baseline probing models available at
https://github.com/elehman16/exposing_patient_data_release


Abstract:
  In ultrasound tomography, the speed of sound inside an object is estimated
based on acoustic measurements carried out by sensors surrounding the object.
An accurate forward model is a prominent factor for high-quality image
reconstruction, but it can make computations far too time-consuming in many
applications. Using approximate forward models, it is possible to speed up the
computations, but the quality of the reconstruction may have to be compromised.
In this paper, a neural network -based approach is proposed, that can
compensate for modeling errors caused by the approximate forward models. The
approach is tested with various different imaging scenarios in a simulated
two-dimensional domain. The results show that with fairly small training
datasets, the proposed approach can be utilized to approximate the modelling
errors, and to significantly improve the image reconstruction quality in
ultrasound tomography, compared to commonly used inversion algorithms.


Abstract:
  This paper deals with the topological entropy for hom Markov shifts
$\mathcal{T}_M$ on $d$-tree. If $M$ is a reducible adjacency matrix with $q$
irreducible components $M_1, \cdots, M_q$, we show that
$h(\mathcal{T}_{M})=\max_{1\leq i\leq q}h(\mathcal{T}_{M_{i}})$ fails
generally, and present a case study with full characterization in terms of the
equality. Though that it is likely the sets $\{h(\mathcal{T}_{M}):M\text{ is
binary and irreducible}\}$ and $\{h(\mathcal{T}_{X}):X\text{ is a one-sided
shift}\}$ are not coincident, we show the two sets share the common closure.
Despite the fact that such closure is proved to contain the interval $[d \log
2, \infty)$, numerical experiments suggest its complement contain open
intervals.


Abstract:
  In this note, we study the holographic CFT in the de Sitter static patch at
finite temperature $T$ and chemical potential. We find that butterfly velocity
$v_B$ in such field theory degenerates for all values of the Hubble parameter
$H$ and $T$. We interpret this as a chaos disruption caused by the interplay
between the expansion of chaotic correlations constrained by $v_B$ and effects
caused by de Sitter curvature. The chemical potential restores healthy
butterfly velocity for some range of temperatures. Also, we provide some
analogy of this chaos suppression with the Schwinger effect in de Sitter and
black hole formation from shock wave collision.


Abstract:
  A rotation curve inequality that holds for spherically symmetric mass
distributions is derived, and tested against the SPARC galaxy rotation curves
dataset. We identify several Galaxies, eg NGC7793 and UGC05253, which are
candidates for hosting non-spherical dark matter structures that could be
detected by more precise measurements.


Abstract:
  Wide-area synchrophasor ambient measurements provide a valuable data source
for real-time oscillation mode monitoring and analysis. This paper introduces a
novel method for identifying inter-area oscillation modes using wide-area
ambient measurements. Based on multivariate empirical mode decomposition
(MEMD), which can analyze multi-channel non-stationary and nonlinear signals,
the proposed method is capable of detecting the common oscillation mode that
exists in multiple synchrophasor measurements at low amplitudes. Test results
based on two real-world datasets validate the effectiveness of the proposed
method.


Abstract:
  We demonstrate an individual single-walled carbon nanotube light emitter
integrated onto a microcavity and a waveguide operating in the telecom
wavelength regime. Light emission from the carbon nanotube is enhanced at the
cavity resonance and is efficiently extracted from the waveguide facet. We have
transferred carbon nanotubes to a nanobeam cavity with a dry process, ensuring
that an individual carbon nanotube is used. The guided light emission from a
chirality-identified single carbon nanotube has a narrow linewidth of less than
1.3 nm and an off-resonance rejection of $\sim$17 dB. The waveguide-coupled
device configuration is compatible with fully integrated on-chip designs and is
promising for carbon-nanotube-based photonics.


Abstract:
  This paper suggests the use of multiple distributed intelligent reflecting
surfaces (IRSs) towards a smarter control of the propagation environment.
Notably, we also take into account the inevitable correlated Rayleigh fading in
IRS-assisted systems. In particular, in a single-input and single-output (SISO)
system, we consider and compare two insightful scenarios, namely, a finite
number of large IRSs and a large number of finite size IRSs to show which
implementation method is more advantageous. In this direction, we derive the
coverage probability in closed-form for both cases contingent on statistical
channel state information (CSI) by using the deterministic equivalent (DE)
analysis. Next, we obtain the optimal coverage probability. Among others,
numerical results reveal that the addition of more surfaces outperforms the
design scheme of adding more elements per surface. Moreover, in the case of
uncorrelated Rayleigh fading, statistical CSI-based IRS systems do not allow
the optimization of the coverage probability.