Abstract:
  Real-world graphs are massive in size and we need a huge amount of space to
store them. Graph compression allows us to compress a graph so that we need a
lesser number of bits per link to store it. Of many techniques to compress a
graph, a typical approach is to find clique-like caveman or traditional
communities in a graph and encode those cliques to compress the graph. On the
other side, an alternative approach is to consider graphs as a collection of
hubs connecting spokes and exploit it to arrange the nodes such that the
resulting adjacency matrix of the graph can be compressed more efficiently. We
perform an empirical comparison of these two approaches and show that both
methods can yield good results under favorable conditions. We perform our
experiments on ten real-world graphs and define two cost functions to present
our findings.


Abstract:
  The lifetimes of localized nonlinear modes in both the
$\beta$-Fermi-Pasta-Ulam-Tsingou ($\beta$-FPUT) chain and a cubic $\beta$-FPUT
lattice are studied as functions of perturbation amplitude, and by extension,
the relative strength of the nonlinear interactions compared to the linear
part. We first recover the well known result that localized nonlinear
excitations (LNEs) produced by a bond squeeze can be reduced to an approximate
two-frequency solution and then show that the nonlinear term in the potential
can lead to the production of secondary frequencies within the phonon band.
This can affect the stability and lifetime of the LNE by facilitating
interactions between the LNE and a low energy acoustic background which can be
regarded as "noise" in the system. In the one dimensional FPUT chain, the LNE
is stabilized by low energy acoustic emissions at early times; in some cases
allowing for lifetimes several orders of magnitude larger than the oscillation
period. The longest lived LNEs are found to satisfy the parameter dependence
$\mathcal{A}\sqrt{\beta}\approx1.1$ where $\beta$ is the relative nonlinear
strength and $\mathcal{A}$ is the displacement amplitude of the center
particles in the LNE. In the cubic FPUT lattice, the LNE lifetime $T$ decreases
rapidly with increasing amplitude $\mathcal{A}$ and is well described by the
double log relationship $\log_{10}\log_{10}(T)\approx
-(0.15\pm0.01)\mathcal{A}\sqrt{\beta}+(0.62\pm0.02)$.


Abstract:
  Link prediction is a fundamental challenge in network science. Among various
methods, similarity-based algorithms are popular for their simplicity,
interpretability, high efficiency and good performance. In this paper, we show
that the most elementary local similarity index Common Neighbor (CN) can be
linearly decomposed by eigenvectors of the adjacency matrix of the target
network, with each eigenvector's contribution being proportional to the square
of the corresponding eigenvalue. As in many real networks, there is a huge gap
between the largest eigenvalue and the second largest eigenvalue, the CN index
is thus dominated by the leading eigenvector and much useful information
contained in other eigenvectors may be overlooked. Accordingly, we propose a
parameter-free algorithm that ensures the contributions of the leading
eigenvector and the secondary eigenvector the same. Extensive experiments on
real networks demonstrate that the prediction performance of the proposed
algorithm is remarkably better than well-performed local similarity indices in
the literature. A further proposed algorithm that can adjust the contribution
of leading eigenvector shows the superiority over state-of-the-art algorithms
with tunable parameters for its competitive accuracy and lower computational
complexity.


Abstract:
  The great influence of Bitcoin has promoted the rapid development of
blockchain-based digital currencies, especially the altcoins, since 2013.
However, most altcoins share similar source codes, resulting in concerns about
code innovations. In this paper, an empirical study on existing altcoins is
carried out to offer a thorough understanding of various aspects associated
with altcoin innovations. Firstly, we construct the dataset of altcoins,
including source code repositories, GitHub fork relations, and market
capitalizations (cap). Then, we analyze the altcoin innovations from the
perspective of source code similarities. The results demonstrate that more than
85% of altcoin repositories present high code similarities. Next, a temporal
clustering algorithm is proposed to mine the inheritance relationship among
various altcoins. The family pedigrees of altcoin are constructed, in which the
altcoin presents similar evolution features as biology, such as power-law in
family size, variety in family evolution, etc. Finally, we investigate the
correlation between code innovations and market capitalization. Although we
fail to predict the price of altcoins based on their code similarities, the
results show that altcoins with higher innovations reflect better market
prospects.


Abstract:
  Reinforcement Learning (RL) is a semi-supervised learning paradigm which an
agent learns by interacting with an environment. Deep learning in combination
with RL provides an efficient method to learn how to interact with the
environment is called Deep Reinforcement Learning (deep RL). Deep RL has gained
tremendous success in gaming - such as AlphaGo, but its potential have rarely
being explored for challenging tasks like Speech Emotion Recognition (SER). The
deep RL being used for SER can potentially improve the performance of an
automated call centre agent by dynamically learning emotional-aware response to
customer queries. While the policy employed by the RL agent plays a major role
in action selection, there is no current RL policy tailored for SER. In
addition, extended learning period is a general challenge for deep RL which can
impact the speed of learning for SER. Therefore, in this paper, we introduce a
novel policy - "Zeta policy" which is tailored for SER and apply Pre-training
in deep RL to achieve faster learning rate. Pre-training with cross dataset was
also studied to discover the feasibility of pre-training the RL Agent with a
similar dataset in a scenario of where no real environmental data is not
available. IEMOCAP and SAVEE datasets were used for the evaluation with the
problem being to recognize four emotions happy, sad, angry and neutral in the
utterances provided. Experimental results show that the proposed "Zeta policy"
performs better than existing policies. The results also support that
pre-training can reduce the training time upon reducing the warm-up period and
is robust to cross-corpus scenario.


Abstract:
  Decentralized cryptocurrency exchanges offer compelling security benefits
over centralized exchanges: users control their funds and avoid the risk of an
exchange hack or malicious operator. However, because user assets are fully
accessible by a secret key, decentralized exchanges pose significant internal
security risks for trading firms and automated trading systems, where a
compromised system can result in total loss of funds. Centralized exchanges
mitigate this risk through API key based security policies that allow
professional users to give individual traders or automated systems specific and
customizable access rights such as trading or withdrawal limits. Such policies,
however, are not compatible with decentralized exchanges, where all exchange
operations require a signature generated by the owner's secret key. This paper
introduces a protocol based upon multiparty computation that allows for the
creation of API keys and security policies that can be applied to any existing
decentralized exchange. Our protocol works with both ECDSA and EdDSA signature
schemes and prioritizes efficient computation and communication. We have
deployed this protocol on Nash exchange, as well as around several
Ethereum-based automated market maker smart contracts, where it secures the
trading accounts and wallets of thousands of users.


Abstract:
  In-phase synchronization is a stable state of identical Kuramoto oscillators
coupled on a network with identical positive connections, regardless of network
topology. However, this fact does not mean that the networks always synchronize
in-phase because other attractors besides the stable state may exist. The
critical connectivity $\mu_{\mathrm{c}}$ is defined as the network connectivity
above which only the in-phase state is stable for all the networks. In other
words, below $\mu_{\mathrm{c}}$, one can find at least one network which has a
stable state besides the in-phase sync. The best known evaluation of the value
so far is $0.6828\cdots\leq\mu_{\mathrm{c}}\leq0.75$. In this paper, focusing
on the twisted states of the circulant networks, we provide a method to
systematically analyze the linear stability of all possible twisted states on
all possible circulant networks. This method using integer programming enables
us to find the densest circulant network having a stable twisted state besides
the in-phase sync, which breaks a record of the lower bound of the
$\mu_{\mathrm{c}}$ from $0.6828\cdots$ to $0.6838\cdots$. We confirm the
validity of the theory by numerical simulations of the networks not converging
to the in-phase state.


Abstract:
  We study two-photon scattering in a mixed cavity optomechanical system, which
is composed of a single-mode cavity field coupled to a single-mode mechanical
oscillation via both the first-order and quadratic optomechanical interactions.
By solving the scattering problem within the Wigner-Weisskopf framework, we
obtain the analytical scattering state and find four physical processes
associated with the two-photon scattering in this system. We calculate the
two-photon scattering spectrum and find that two-photon frequency
anticorrelation can be induced in the scattering process. We also establish the
relationship between the parameters of the mixed cavity optomechanical system
and the characteristics of the two-photon scattering spectrum. This work not
only provides a scattering means to create correlated photon pairs, but also
presents a spectrometric method to characterize the optomechanical systems.


Abstract:
  Survey scientists increasingly face the problem of high-dimensionality in
their research as digitization makes it much easier to construct
high-dimensional (or "big") data sets through tools such as online surveys and
mobile applications. Machine learning methods are able to handle such data, and
they have been successfully applied to solve \emph{predictive} problems.
However, in many situations, survey statisticians want to learn about
\emph{causal} relationships to draw conclusions and be able to transfer the
findings of one survey to another. Standard machine learning methods provide
biased estimates of such relationships. We introduce into survey statistics the
double machine learning approach, which gives approximately unbiased estimators
of causal parameters, and show how it can be used to analyze survey nonresponse
in a high-dimensional panel setting.


Abstract:
  Scanning tunneling microscope lithography can be used to create
nanoelectronic devices in which dopant atoms are precisely positioned in a Si
lattice within $\sim$1 nm of a target position. This exquisite precision is
promising for realizing various quantum technologies. However, a potentially
impactful form of disorder is due to incorporation kinetics, in which the
number of P atoms that incorporate into a single lithographic window is
manifestly uncertain. We present experimental results indicating that the
likelihood of incorporating into an ideally written three-dimer single-donor
window is $63 \pm 10\%$ for room-temperature dosing, and corroborate these
results with a model for the incorporation kinetics. Nevertheless, further
analysis of this model suggests conditions that might raise the incorporation
rate to near-deterministic levels. We simulate bias spectroscopy on a chain of
comparable dimensions to the array in our yield study, indicating that such an
experiment may help confirm the inferred incorporation rate.


Abstract:
  We compute the phase diagram of the simplest holographic bottom-up model of
conformal interfaces. The model consists of a thin domain wall between
three-dimensional Anti-de Sitter (AdS) vacua, anchored on a boundary circle. We
distinguish five phases depending on the existence of a black hole, the
intersection of its horizon with the wall, and the fate of inertial observers.
We show that, like the Hawking-Page phase transition, the capture of the wall
by the horizon is also a first order transition and comment on its field-theory
interpretation. The static solutions of the domain-wall equations include
gravitational avatars of the Faraday cage, black holes with negative specific
heat, and an intriguing phenomenon of suspended vacuum bubbles corresponding to
an exotic interface/anti-interface fusion. Part of our analysis overlaps with
recent work by Simidzija and Van Raamsdonk but the interpretation is different.


Abstract:
  We study the extent to which it is possible to approximate the optimal value
of a Unique Games instance in Fixed-Point Logic with Counting (FPC). We prove
two new FPC-inexpressibility results for Unique Games: the existence of a (1/2,
1/3 + $\delta$)-inapproximability gap, and inapproximability to within any
constant factor. Previous recent work has established similar
FPC-inapproximability results for a small handful of other problems. Our
construction builds upon some of these ideas, but contains a novel technique.
While most FPC-inexpressibility results are based on variants of the
CFI-construction, ours is significantly different.


Abstract:
  We report some recent results on analytic pseudodifferential operators, also
known as Wick operators. An important tool in our study is the Bargmann
transform which provides a coupling between the classical (real) and analytic
pseudodifferential calculus. Since the Bargmann transform of Hermite functions
gives rise to formal power series in the complex domain, the results are
formulated in terms of the Bargmann images of Pilipovi\'c spaces.


Abstract:
  We study the shape of the normalized stable L\'{e}vy tree $\mathcal{T}$ near
its root. We show that, when zooming in at the root at the proper speed with a
scaling depending on the index of stability, we get the unnormalized Kesten
tree. In particular the limit is described by a tree-valued Poisson point
process which does not depend on the initial normalization. We apply this to
study the asymptotic behavior of additive functionals of the form
\[\mathbf{Z}_{\alpha,\beta}=\int_{\mathcal{T}} \mu(\mathrm{d} x) \int_0^{H(x)}
\sigma_{r,x}^\alpha \mathfrak{h}_{r,x}^\beta\,\mathrm{d} r\]as
$\max(\alpha,\beta) \to \infty$, where $\mu$ is the mass measure on
$\mathcal{T}$, $H(x)$ is the height of $x$ and $\sigma_{r,x}$ (resp.
$\mathfrak{h}_{r,x}$) is the mass (resp. height) of the subtree of
$\mathcal{T}$ above level $r$ containing $x$. Such functionals arise as scaling
limits of additive functionals of the size and height on conditioned
Bienaym{\'e}-Galton-Watson trees.


Abstract:
  Order-agnostic autoregressive distribution (density) estimation (OADE), i.e.,
autoregressive distribution estimation where the features can occur in an
arbitrary order, is a challenging problem in generative machine learning. Prior
work on OADE has encoded feature identity by assigning each feature to a
distinct fixed position in an input vector. As a result, architectures built
for these inputs must strategically mask either the input or model weights to
learn the various conditional distributions necessary for inferring the full
joint distribution of the dataset in an order-agnostic way. In this paper, we
propose an alternative approach for encoding feature identities, where each
feature's identity is included alongside its value in the input. This feature
identity encoding strategy allows neural architectures designed for sequential
data to be applied to the OADE task without modification. As a proof of
concept, we show that a Transformer trained on this input (which we refer to as
"the DEformer", i.e., the distribution estimating Transformer) can effectively
model binarized-MNIST, approaching the performance of fixed-order
autoregressive distribution estimating algorithms while still being entirely
order-agnostic. Additionally, we find that the DEformer surpasses the
performance of recent flow-based architectures when modeling a tabular dataset.


Abstract:
  The long fascination antiferromagnetic materials have exerted on the
scientific community over about a century has been entirely renewed recently
with the discovery of several unexpected phenomena including various classes of
anomalous spin and charge Hall effects and unconventional magnonic transport,
but also homochiral magnetic entities such as skyrmions. With these
breakthroughs, antiferromagnets standout as a rich playground for the
investigation of novel topological behaviors, and as promising candidate
materials for disruptive low-power microelectronic applications. Remarkably,
the newly discovered phenomena are all related to the topology of the magnetic,
electronic or magnonic ground state of the antiferromagnets. This review
exposes how non-trivial topology emerges at different levels in
antiferromagnets and explores the novel mechanisms that have been discovered
recently. We also discuss how novel classes of quantum magnets could enrich the
currently expanding field of antiferromagnetic spintronics and how spin
transport can in turn favor a better understanding of exotic quantum
excitations.


Abstract:
  This study presents a physically consistent displacement-driven reformulation
of the concept of action-at-a-distance, which is at the foundation of nonlocal
elasticity. In contrast to existing approaches that adopts an integral
stress-strain constitutive relation, the displacement-driven approach is
predicated on an integral strain-displacement relation. The most remarkable
consequence of this reformulation is that the (total) strain energy is
guaranteed to be convex and positive-definite without imposing any constraint
on the symmetry of the kernels. This feature is critical to enable the
application of nonlocal formulations to general continua exhibiting asymmetric
interactions; ultimately a manifestation of material heterogeneity. Remarkably,
the proposed approach also enables a strong satisfaction of the locality
recovery condition and of the laws of thermodynamics, which are not foregone
conclusions in most classical nonlocal elasticity theories. Additionally, the
formulation is frame-invariant and the nonlocal operator remains physically
consistent at boundaries. The study is complemented by a detailed analysis of
the dynamic response of the nonlocal continuum and of its intrinsic dispersion
leading to the consideration that the choice of nonlocal kernels should depend
on the specific material. Examples of exponential or power-law kernels are
presented in order to demonstrate the applicability of the method to different
classes of nonlocal media. The ability to admit generalized kernels reinforces
the generalized nature of the displacement-driven approach over existing
integral methodologies, which typically lead to simplified differential models
based on exponential kernels. The theoretical formulation is also leveraged to
simulate the static response of nonlocal beams and plates illustrating the
intrinsic consistency of the approach, which is free from unwanted boundary
effects.


Abstract:
  Recent studies on semantic frame induction show that relatively high
performance has been achieved by using clustering-based methods with
contextualized word embeddings. However, there are two potential drawbacks to
these methods: one is that they focus too much on the superficial information
of the frame-evoking verb and the other is that they tend to divide the
instances of the same verb into too many different frame clusters. To overcome
these drawbacks, we propose a semantic frame induction method using masked word
embeddings and two-step clustering. Through experiments on the English FrameNet
data, we demonstrate that using the masked word embeddings is effective for
avoiding too much reliance on the surface information of frame-evoking verbs
and that two-step clustering can improve the number of resulting frame clusters
for the instances of the same verb.


Abstract:
  A recent strand of research in structural proof theory aims at exploring the
notion of analytic calculi (i.e. those calculi that support general and modular
proof-strategies for cut elimination), and at identifying classes of logics
that can be captured in terms of these calculi. In this context, Wansing
introduced the notion of proper display calculi as one possible design
framework for proof calculi in which the analiticity desiderata are realized in
a particularly transparent way. Recently, the theory of properly displayable
logics (i.e. those logics that can be equivalently presented with some proper
display calculus) has been developed in connection with generalized Sahlqvist
theory (aka unified correspondence). Specifically, properly displayable logics
have been syntactically characterized as those axiomatized by analytic
inductive axioms, which can be equivalently and algorithmically transformed
into analytic structural rules so that the resulting proper display calculi
enjoy a set of basic properties: soundness, completeness, conservativity, cut
elimination and subformula property. In this context, the proof that the given
calculus is complete w.r.t. the original logic is usually carried out
syntactically, i.e. by showing that a (cut free) derivation exists of each
given axiom of the logic in the basic system to which the analytic structural
rules algorithmically generated from the given axiom have been added. However,
so far this proof strategy for syntactic completeness has been implemented on a
case-by-case base, and not in general. In this paper, we address this gap by
proving syntactic completeness for properly displayable logics in any normal
(distributive) lattice expansion signature. Specifically, we show that for
every analytic inductive axiom a cut free derivation can be effectively
generated which has a specific shape, referred to as pre-normal form.


Abstract:
  We initiate the study of fine-grained completeness theorems for exact and
approximate optimization in the polynomial-time regime. Inspired by the first
completeness results for decision problems in P (Gao, Impagliazzo, Kolokolova,
Williams, TALG 2019) as well as the classic class MaxSNP and
MaxSNP-completeness for NP optimization problems (Papadimitriou, Yannakakis,
JCSS 1991), we define polynomial-time analogues MaxSP and MinSP, which contain
a number of natural optimization problems in P, including Maximum Inner
Product, general forms of nearest neighbor search and optimization variants of
the $k$-XOR problem. Specifically, we define MaxSP as the class of problems
definable as $\max_{x_1,\dots,x_k} \#\{ (y_1,\dots,y_\ell) :
\phi(x_1,\dots,x_k, y_1,\dots,y_\ell) \}$, where $\phi$ is a quantifier-free
first-order property over a given relational structure (with MinSP defined
analogously). On $m$-sized structures, we can solve each such problem in time
$O(m^{k+\ell-1})$. Our results are:
  - We determine (a sparse variant of) the Maximum/Minimum Inner Product
problem as complete under *deterministic* fine-grained reductions: A strongly
subquadratic algorithm for Maximum/Minimum Inner Product would beat the
baseline running time of $O(m^{k+\ell-1})$ for *all* problems in MaxSP/MinSP by
a polynomial factor.
  - This completeness transfers to approximation: Maximum/Minimum Inner Product
is also complete in the sense that a strongly subquadratic $c$-approximation
would give a $(c+\varepsilon)$-approximation for all MaxSP/MinSP problems in
time $O(m^{k+\ell-1-\delta})$, where $\varepsilon > 0$ can be chosen
arbitrarily small. Combining our completeness with~(Chen, Williams, SODA 2019),
we obtain the perhaps surprising consequence that refuting the OV Hypothesis is
*equivalent* to giving a $O(1)$-approximation for all MinSP problems in
faster-than-$O(m^{k+\ell-1})$ time.


Abstract:
  We study baryonic matter with isospin asymmetry, including fully dynamically
its interplay with pion condensation. To this end, we employ the holographic
Witten-Sakai-Sugimoto model and the so-called homogeneous ansatz for the gauge
fields in the bulk to describe baryonic matter. Within the confined geometry
and restricting ourselves to the chiral limit, we map out the phase structure
in the presence of baryon and isospin chemical potentials, showing that for
sufficiently large chemical potentials condensed pions and isospin-asymmetric
baryonic matter coexist. We also present first results of the same approach in
the deconfined geometry and demonstrate that this case, albeit technically more
involved, is better suited for comparisons with and predictions for real-world
QCD. Our study lays the ground for future improved holographic studies aiming
towards a realistic description of charge neutral, beta-equilibrated matter in
compact stars, and also for more refined comparisons with lattice studies at
nonzero isospin chemical potential.


Abstract:
  This paper studies recurrence phenomena in iterative holomorphic dynamics of
certain multi-valued maps. In particular, we prove an analogue of the
Poincar\'e recurrence theorem for meromorphic correspondences with respect to
certain dynamically interesting measures associated with them. Meromorphic
correspondences present a significant measure-theoretic obstacle: the image of
a Borel set under a meromorphic correspondence need not be Borel. We manage
this issue using the Measurable Projection Theorem, which is an aspect of
descriptive set theory. We also prove a result concerning invariance properties
of the supports of the measures mentioned.


Abstract:
  Polynomial chaos expansions (PCEs) have been used in many real-world
engineering applications to quantify how the uncertainty of an output is
propagated from inputs. PCEs for models with independent inputs have been
extensively explored in the literature. Recently, different approaches have
been proposed for models with dependent inputs to expand the use of PCEs to
more real-world applications. Typical approaches include building PCEs based on
the Gram-Schmidt algorithm or transforming the dependent inputs into
independent inputs. However, the two approaches have their limitations
regarding computational efficiency and additional assumptions about the input
distributions, respectively. In this paper, we propose a data-driven approach
to build sparse PCEs for models with dependent inputs. The proposed algorithm
recursively constructs orthonormal polynomials using a set of monomials based
on their correlations with the output. The proposed algorithm on building
sparse PCEs not only reduces the number of minimally required observations but
also improves the numerical stability and computational efficiency. Four
numerical examples are implemented to validate the proposed algorithm.


Abstract:
  Gaia provided the largest-ever catalogue of white dwarf stars. We use this
catalogue, along with the third public data release of the Zwicky Transient
Facility (ZTF), to identify new eclipsing white dwarf binaries. Our method
exploits light curve statistics and the Box Least Squares algorithm to detect
periodic light curve variability. The search revealed 18 new binaries, of which
17 are eclipsing. We use the position in the Gaia H-R diagram to classify these
binaries and find that the majority of these white dwarfs have main sequence
companions. We identify one system as a candidate eclipsing white dwarf--brown
dwarf binary and a further two as extremely low mass (ELM) white dwarf
binaries. We also provide identification spectroscopy for 17 of our 18
binaries. Running our search method on mock light curves with real ZTF
sampling, we estimate our efficiency of detecting objects with light curves
similar to the ones of the newly discovered binaries. Many more binaries are to
be found in the ZTF footprint as the data releases grow, so our survey is
ongoing.


Abstract:
  Proton radiography is a widely-fielded diagnostic used to measure magnetic
structures in plasma. The deflection of protons with multi-MeV kinetic energy
by the magnetic fields is used to infer their path-integrated field strength.
Here, the use of tomographic methods is proposed for the first time to lift the
degeneracy inherent in these path-integrated measurements, allowing full
reconstruction of spatially resolved magnetic field structures in three
dimensions. Two techniques are proposed which improve the performance of
tomographic reconstruction algorithms in cases with severely limited numbers of
available probe beams, as is the case in laser-plasma interaction experiments
where the probes are created by short, high-power laser pulse irradiation of
secondary foil targets. The methods are equally applicable to optical probes
such as shadowgraphy and interferometry [M. Kasim et al. Phys. Rev. E 95,
023306 (2017)], thereby providing a disruptive new approach to three
dimensional imaging across the physical sciences and engineering disciplines.


Abstract:
  This paper proposes a method to probabilistically quantify the moments (mean
and variance) of excavated material during excavation by aggregating the prior
moments of the grade blocks around the given bucket dig location. By modelling
the moments as random probability density functions (pdf) at sampled locations,
a formulation of the sums of Gaussian based uncertainty estimation is presented
that jointly estimates the location pdfs, as well as the prior values for
uncertainty coming from ore body knowledge (obk) sub block models. The moments
calculated at each random location is a single Gaussian and they are the
components of Gaussian mixture distribution. The overall uncertainty of the
excavated material at the given bucket location is represented by the Gaussian
Mixture Model (GMM) and therefore moment matching method is proposed to
estimate the moments of the reduced GMM. The method was tested in a region at a
Pilbara iron ore deposit situated in the Brockman Iron Formation of the
Hamersley Province, Western Australia, and suggests a frame work to quantify
the uncertainty in the excavated material that hasn't been studied anywhere in
the literature yet.


Abstract:
  We describe a physics program at the Relativistic Heavy Ion Collider (RHIC)
with tagged forward protons. The program started with the proton-proton elastic
scattering experiment (PP2PP), for which a set of Roman Pot stations was build.
The PP2PP experiment took data at RHIC as a dedicated experiment at the
beginning of RHIC operations. To expand the physics program to include
non-elastic channels with forward protons, like Central Exclusive Production
(CEP), Central Production (CP) and Single Diffraction Dissociation (SD), the
experiment with its equipment was merged with the STAR experiment at RHIC.
Consequently the expanded program, which included both elastic and inelastic
channels became part of the physics program and operations of the STAR
experiment. In this paper we shall describe the physics results obtained by the
PP2PP and STAR experiments to date.


Abstract:
  In the present paper, we study the existence of best proximity pair in
ultrametric spaces. We show, under suitable assumptions, that the proximinal
pair $(A,B)$ has a best proximity pair. As a consequence we generalize a well
known best approximation result and we derive some fixed point theorems.
Moreover, we provide examples to illustrate the obtained results.


Abstract:
  By using the quantum extremal island formula, we perform a simple calculation
of the generalized entanglement entropy of Hawking radiation from the two
dimensional Liouville black hole. No reasonable island was found when
extremizing the generalized entropy. We explain qualitatively the reason why
the page curve cannot be reproduced in the present model. This suggests that
the islands may not necessarily save the information paradox for the Liouville
black holes.


Abstract:
  We substantially extend our relaxation theory for perturbed many-body quantum
systems from [Phys. Rev. Lett. 124, 120602 (2020)] by establishing an
analytical prediction for the time-dependent observable expectation values
which depends on only two characteristic parameters of the perturbation
operator: its overall strength and its range or band width. Compared to the
previous theory, a significantly larger range of perturbation strengths is
covered. The results are obtained within a typicality framework by solving the
pertinent random matrix problem exactly for a certain class of banded
perturbations and by demonstrating the (approximative) universality of these
solutions, which allows us to adopt them to considerably more general classes
of perturbations. We also verify the prediction by comparison with several
numerical examples.


Abstract:
  With an increase in low-cost machine learning APIs, advanced machine learning
models may be trained on private datasets and monetized by providing them as a
service. However, privacy researchers have demonstrated that these models may
leak information about records in the training dataset via membership inference
attacks. In this paper, we take a closer look at another inference attack
reported in literature, called attribute inference, whereby an attacker tries
to infer missing attributes of a partially known record used in the training
dataset by accessing the machine learning model as an API. We show that even if
a classification model succumbs to membership inference attacks, it is unlikely
to be susceptible to attribute inference attacks. We demonstrate that this is
because membership inference attacks fail to distinguish a member from a nearby
non-member. We call the ability of an attacker to distinguish the two (similar)
vectors as strong membership inference. We show that membership inference
attacks cannot infer membership in this strong setting, and hence inferring
attributes is infeasible. However, under a relaxed notion of attribute
inference, called approximate attribute inference, we show that it is possible
to infer attributes close to the true attributes. We verify our results on
three publicly available datasets, five membership, and three attribute
inference attacks reported in literature.


Abstract:
  We have investigated the magneto-transport properties of beta-Bi4I4 bulk
crystal, which was recently theoretically proposed and experimentally
demonstrated to be a topological insulator. At low temperature T and magnetic
field B, a series of Shubnikov-De Haas(SdH) oscillations are observed on the
magnetoresistivity (MR). The detailed analysis reveals a light cyclotron mass
of 0.1 me, and the field angle dependence of MR reveals that the SdH
oscillations originate from a convex Fermi surface. In the extreme quantum
limit (EQL) region, there is a metal-insulator transition occurring soon after
the EQL. We perform the scaling analysis, and all the isotherms fall onto a
universal scaling with a fitted critical exponent of 6.5. The enormous value of
critical exponent implies this insulating quantum phase originated from strong
electron-electron interactions in high fields. However, in the far end of EQL,
both the longitudinal and Hall resistivity increase exponentially with B, and
the temperature dependence of the MR reveals an energy gap induced by the high
magnetic field, signifying a magnetic freeze-out effect. Our findings indicate
that bulk beta-Bi4I4 is an excellent candidate for a 3D topological system for
exploring EQL physics and relevant exotic quantum phases.


Abstract:
  In an active power distribution system, Volt-VAR optimization (VVO) methods
are employed to achieve network-level objectives such as minimization of
network power losses. The commonly used model-based centralized and distributed
VVO algorithms perform poorly in the absence of a communication system and with
model and measurement uncertainties. In this paper, we proposed a model-free
local Volt-VAR control approach for network-level optimization that does not
require communication with other decision-making agents. The proposed algorithm
is based on extremum-seeking approach that uses only local measurements to
minimize the network power losses. To prove that the proposed extremum-seeking
controller converges to the optimum solution, we also derive mathematical
conditions for which the loss minimization problem is convex with respect to
the control variables. Local controllers pose stability concerns during highly
variable scenarios. Thus, the proposed extremum-seeking controller is
integrated with an adaptive-droop control module to provide a stable local
control response. The proposed approach is validated using IEEE 4-bus and IEEE
123-bus systems and achieves the loss minimization objective while maintaining
the voltage within the pre-specific limits even during highly variable DER
generation scenarios.


Abstract:
  Technology has changed both our way of life and the way in which we learn.
Students now attend lectures with laptops and mobile phones, and this situation
is accentuated in the case of students on Computer Science degrees, since they
require their computers in order to participate in both theoretical and
practical lessons. Problems, however, arise when the students' social networks
are opened on their computers and they receive notifications that interrupt
their work. We set up a workshop regarding time, thoughts and attention
management with the objective of teaching our students techniques that would
allow them to manage interruptions, concentrate better and definitively make
better use of their time. Those who took part in the workshop were then
evaluated to discover its effects. The results obtained are quite optimistic
and are described in this paper with the objective of encouraging other
universities to perform similar initiatives.


Abstract:
  Multi-scale 3D characterization is widely used by materials scientists to
further their understanding of the relationships between microscopic structure
and macroscopic function. Scientific computed tomography (CT) instruments are
one of the most popular choices for 3D non-destructive characterization of
materials at length scales ranging from the angstrom-scale to the micron-scale.
These instruments typically have a source of radiation that interacts with the
sample to be studied and a detector assembly to capture the result of this
interaction. A collection of such high-resolution measurements are made by
re-orienting the sample which is mounted on a specially designed stage/holder
after which reconstruction algorithms are used to produce the final 3D volume
of interest. The end goal of scientific CT scans include determining the
morphology,chemical composition or dynamic behavior of materials when subjected
to external stimuli. In this article, we will present an overview of recent
advances in reconstruction algorithms that have enabled significant
improvements in the performance of scientific CT instruments - enabling faster,
more accurate and novel imaging capabilities. In the first part, we will focus
on model-based image reconstruction algorithms that formulate the inversion as
solving a high-dimensional optimization problem involving a data-fidelity term
and a regularization term. In the last part of the article, we will present an
overview of recent approaches using deep-learning based algorithms for
improving scientific CT instruments.


Abstract:
  Random K-out graphs, denoted $\mathbb{H}(n;K)$, are generated by each of the
$n$ nodes drawing $K$ out-edges towards $K$ distinct nodes selected uniformly
at random, and then ignoring the orientation of the arcs. Recently, random
K-out graphs have been used in applications as diverse as random (pairwise) key
predistribution in ad-hoc networks, anonymous message routing in
crypto-currency networks, and differentially-private federated averaging. In
many applications, connectivity of the random K-out graph when some of its
nodes are dishonest, have failed, or have been captured is of practical
interest. We provide a comprehensive set of results on the connectivity and
giant component size of $\mathbb{H}(n;K_n,\gamma_n)$, i.e., random K-out graph
when $\gamma_n$ of its nodes, selected uniformly at random, are deleted. First,
we derive conditions for $K_n$ and $n$ that ensure, with high probability
(whp), the connectivity of the remaining graph when the number of deleted nodes
is $\gamma_n=\Omega(n)$ and $\gamma_n=o(n)$, respectively. Next, we derive
conditions for $\mathbb{H}(n;K_n,\gamma_n)$ to have a giant component, i.e., a
connected subgraph with $\Omega(n)$ nodes, whp. This is also done for different
scalings of $\gamma_n$ and upper bounds are provided for the number of nodes
outside the giant component. Simulation results are presented to validate the
usefulness of the results in the finite node regime.


Abstract:
  The increasing complexity of modern robotic systems and the environments they
operate in necessitates the formal consideration of safety in the presence of
imperfect measurements. In this paper we propose a rigorous framework for
safety-critical control of systems with erroneous state estimates. We develop
this framework by leveraging Control Barrier Functions (CBFs) and unifying the
method of Backup Sets for synthesizing control invariant sets with robustness
requirements -- the end result is the synthesis of Measurement-Robust Control
Barrier Functions (MR-CBFs). This provides theoretical guarantees on safe
behavior in the presence of imperfect measurements and improved robustness over
standard CBF approaches. We demonstrate the efficacy of this framework both in
simulation and experimentally on a Segway platform using an onboard
stereo-vision camera for state estimation.


Abstract:
  We collect data at all frequencies for the new sources classified as unknown
active galactic nuclei (AGNs) in the latest Burst Alert Telescope (BAT) all-sky
hard X-ray catalog. Focusing on the 36 sources with measured redshift, we
compute their spectral energy distribution (SED) from radio to $\gamma$-rays
with the aim to classify these objects. We apply emission models that attempt
to reproduce the obtained SEDs, including: i) a standard thin accretion disk
together with an obscuring torus and a X-ray corona; ii) a two temperature
thick advection-dominated flow; iii) an obscured AGN model, accounting for
absorption along the line of sight at kiloelectronvolt energies and in the
optical band; and iv) a phenomenological model to describe the jet emission in
blazar-like objects. We integrate the models with the SWIRE template libraries
to account for the emission of the host galaxy. For every source we found a
good agreement between data and our model. Considering that the sources were
selected in the hard X-ray band, which is rather unaffected by absorption, we
expected and found a large fraction of absorbed radio-quiet AGNs (31 out of 36)
and some additional rare radio-loud sources (5 out of 36), since the jet
emission in hard X-rays is important for aligned jets owing to the boost
produced by the beaming effect. With our work we can confirm the hypothesis
that a number of galaxies, whose optical spectra lack AGN emission features,
host an obscured active nucleus. The approach we used proved to be efficient in
rapidly identifying objects, which commonly used methods were not able to
classify.


Abstract:
  Most asteroids are somewhat elongated and have non-zero lightcurve
amplitudes. Such asteroids can be detected in large-scale sky surveys even if
their mean magnitudes are fainter than the stated sensitivity limits. We
explore the detection of elongated asteroids under a set of idealized but
useful approximations. We find that objects up to 1 magnitude fainter than a
survey's sensitivity limit are likely to be detected, and that the effect is
most pronounced for asteroids with lightcurve amplitudes 0.1-0.4 mag.This
imposes a bias on the derived size and shape distributions of the population
that must be properly accounted for.


Abstract:
  This paper investigates the problem of online statistical inference of model
parameters in stochastic optimization problems via the Kiefer-Wolfowitz
algorithm with random search directions. We first present the asymptotic
distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW)
estimators, whose asymptotic covariance matrices depend on the function-value
query complexity and the distribution of search directions. The distributional
result reflects the trade-off between statistical efficiency and function query
complexity. We further analyze the choices of random search directions to
minimize the asymptotic covariance matrix, and conclude that the optimal search
direction depends on the optimality criteria with respect to different summary
statistics of the Fisher information matrix. Based on the asymptotic
distribution result, we conduct online statistical inference by providing two
construction procedures of valid confidence intervals. We provide numerical
experiments verifying our theoretical results with the practical effectiveness
of the procedures.


Abstract:
  Adversarial training is actively studied for learning robust models against
adversarial examples. A recent study finds that adversarially trained models
degenerate generalization performance on adversarial examples when their weight
loss landscape, which is loss changes with respect to weights, is sharp.
Unfortunately, it has been experimentally shown that adversarial training
sharpens the weight loss landscape, but this phenomenon has not been
theoretically clarified. Therefore, we theoretically analyze this phenomenon in
this paper. As a first step, this paper proves that adversarial training with
the L2 norm constraints sharpens the weight loss landscape in the linear
logistic regression model. Our analysis reveals that the sharpness of the
weight loss landscape is caused by the noise aligned in the direction of
increasing the loss, which is used in adversarial training. We theoretically
and experimentally confirm that the weight loss landscape becomes sharper as
the magnitude of the noise of adversarial training increases in the linear
logistic regression model. Moreover, we experimentally confirm the same
phenomena in ResNet18 with softmax as a more general case.


Abstract:
  Cross-lingual text classification aims at training a classifier on the source
language and transferring the knowledge to target languages, which is very
useful for low-resource languages. Recent multilingual pretrained language
models (mPLM) achieve impressive results in cross-lingual classification tasks,
but rarely consider factors beyond semantic similarity, causing performance
degradation between some language pairs. In this paper we propose a simple yet
effective method to incorporate heterogeneous information within and across
languages for cross-lingual text classification using graph convolutional
networks (GCN). In particular, we construct a heterogeneous graph by treating
documents and words as nodes, and linking nodes with different relations, which
include part-of-speech roles, semantic similarity, and document translations.
Extensive experiments show that our graph-based method significantly
outperforms state-of-the-art models on all tasks, and also achieves consistent
performance gain over baselines in low-resource settings where external tools
like translators are unavailable.


Abstract:
  We consider the problem of computing the Fr\'echet distance between two
curves for which the exact locations of the vertices are unknown. Each vertex
may be placed in a given uncertainty region for that vertex, and the objective
is to place vertices so as to minimise the Fr\'echet distance. This problem was
recently shown to be NP-hard in 2D, and it is unclear how to compute an optimal
vertex placement at all.
  We present the first general algorithmic framework for this problem. We prove
that it results in a polynomial-time algorithm for curves in 1D with intervals
as uncertainty regions. In contrast, we show that the problem is NP-hard in 1D
in the case that vertices are placed to maximise the Fr\'echet distance.
  We also study the weak Fr\'echet distance between uncertain curves. While
finding the optimal placement of vertices seems more difficult than the regular
Fr\'echet distance -- and indeed we can easily prove that the problem is
NP-hard in 2D -- the optimal placement of vertices in 1D can be computed in
polynomial time. Finally, we investigate the discrete weak Fr\'echet distance,
for which, somewhat surprisingly, the problem is NP-hard already in 1D.


Abstract:
  While reinforcement learning (RL) is gaining popularity in energy systems
control, its real-world applications are limited due to the fact that the
actions from learned policies may not satisfy functional requirements or be
feasible for the underlying physical system. In this work, we propose PROjected
Feasibility (PROF), a method to enforce convex operational constraints within
neural policies. Specifically, we incorporate a differentiable projection layer
within a neural network-based policy to enforce that all learned actions are
feasible. We then update the policy end-to-end by propagating gradients through
this differentiable projection layer, making the policy cognizant of the
operational constraints. We demonstrate our method on two applications:
energy-efficient building operation and inverter control. In the building
operation setting, we show that PROF maintains thermal comfort requirements
while improving energy efficiency by 4% over state-of-the-art methods. In the
inverter control setting, PROF perfectly satisfies voltage constraints on the
IEEE 37-bus feeder system, as it learns to curtail as little renewable energy
as possible within its safety set.


Abstract:
  The chiral magnetic effect (CME) is a novel transport phenomenon, arising
from the interplay between quantum anomalies and strong magnetic fields in
chiral systems. In high-energy nuclear collisions, the CME may survive the
expansion of the quark-gluon plasma fireball and be detected in experiments.
Over the past decade, the experimental searches for the CME have aroused
extensive interest at the Relativistic Heavy Ion Collider (RHIC) and the Large
Hadron Collider (LHC). The main goal of this article is to investigate three
pertinent experimental approaches: the $\gamma$ correlator, the $R$ correlator
and the signed balance functions. We will exploit both simple Monte Carlo
simulations and a realistic event generator (EBE-AVFD) to verify the
equivalence in the kernel-component observables among these methods and to
ascertain their sensitivities to the CME signal for the isobaric collisions at
RHIC.


Abstract:
  AN Cam is a little-studied eclipsing binary containing somewhat evolved
components in an orbit with a period of 21.0 d and an eccentricity of 0.47. A
spectroscopic orbit based on photoelectric radial velocities was published in
1977. AN Cam has been observed using the TESS satellite in three sectors: the
data were obtained in long-cadence mode and cover nine eclipses. By modelling
these data and published radial velocities we obtain masses of 1.380 +/- 0.021
Msun and 1.402 +/- 0.025 Msun, and radii of 2.159 +/- 0.012 Rsun and 2.646 +/-
0.014 Rsun. We also derive a precise orbital ephemeris from these data and
recent times of minimum light, but find that the older times of minimum light
cannot be fitted assuming a constant orbital period. This could be caused by
astrophysical or instrumental effects; forthcoming TESS observations will help
the investigation of this issue. We use the Gaia EDR3 parallax and
optical/infrared apparent magnitudes to measure effective temperatures of 6050
+/- 150 K and 5750 +/- 150 K: the primary star is hotter but smaller and less
massive than its companion. A comparison with theoretical models indicates that
the system has an approximately solar chemical composition and an age of 3.3
Gyr. Despite the similarity of their masses the two stars are in different
evolutionary states: the primary is near the end of its main-sequence lifetime
and the secondary is now a subgiant. AN Cam is a promising candidate for
constraining the strength of convective core overshooting in 1.4 Msun stars.


Abstract:
  We develop a framework to study posterior contraction rates in sparse high
dimensional generalized linear models (GLM). We introduce a new family of GLMs,
denoted by clipped GLM, which subsumes many standard GLMs and makes minor
modification of the rest. With a sparsity inducing prior on the regression
coefficients, we delineate sufficient conditions on true data generating
density that leads to minimax optimal rates of posterior contraction of the
coefficients in $\ell_1$ norm. Our key contribution is to develop sufficient
conditions commensurate with the geometry of the clipped GLM family, propose
prior distributions which do not require any knowledge of the true parameters
and avoid any assumption on the growth rate of the true coefficient vector.


Abstract:
  Although abbreviations are fairly common in handwritten sources, particularly
in medieval and modern Western manuscripts, previous research dealing with
computational approaches to their expansion is scarce. Yet abbreviations
present particular challenges to computational approaches such as handwritten
text recognition and natural language processing tasks. Often, pre-processing
ultimately aims to lead from a digitised image of the source to a normalised
text, which includes expansion of the abbreviations. We explore different
setups to obtain such a normalised text, either directly, by training HTR
engines on normalised (i.e., expanded, disabbreviated) text, or by decomposing
the process into discrete steps, each making use of specialist models for
recognition, word segmentation and normalisation. The case studies considered
here are drawn from the medieval Latin tradition.


Abstract:
  A 1d random geometric graph (1d RGG) is built by joining a random sample of
$n$ points from an interval of the real line with probability $p$. We count the
number of $k$-hop paths between two vertices of the graph in the case where the
space is the 1d interval $[0,1]$. We show how the $k$-hop path count between
two vertices at Euclidean distance $|x-y|$ is in bijection with the volume
enclosed by a uniformly random $d$-dimensional lattice path joining the corners
of a $(k-1)$-dimensional hyperrectangular lattice. We are able to provide the
probability generating function and distribution of this $k$-hop path count as
a sum over lattice paths, incorporating the idea of restricted integer
partitions with limited number of parts. We therefore demonstrate and describe
an important link between spatial random graphs, and lattice path
combinatorics, where the $d$-dimensional lattice paths correspond to spatial
permutations of the geometric points on the line.


Abstract:
  We experimentally demonstrate the steady-state generation of propagating
Wigner-negative states from a continuously driven superconducting qubit. We
reconstruct the Wigner function of the radiation emitted into propagating modes
defined by their temporal envelopes, using digital filtering. For an optimized
temporal filter, we observe a large Wigner logarithmic negativity, in excess of
0.08, in agreement with theory. The fidelity between the theoretical
predictions and the states generated experimentally is up to 99%, reaching
state-of-the-art realizations in the microwave frequency domain. Our results
provide a new way to generate and control nonclassical states, and may enable
promising applications such as quantum networks and quantum computation based
on waveguide quantum electrodynamics.


Abstract:
  We use numerical bootstrap techniques to study correlation functions of
scalars transforming in the adjoint representation of $SU(N)$ in three
dimensions. We obtain upper bounds on operator dimensions for various
representations and study their dependence on $N$. We discover new families of
kinks, one of which could be related to bosonic QED${}_3$. We then specialize
to the cases $N=3,4$, which have been conjectured to describe a phase
transition respectively in the ferromagnetic complex projective model $CP^2$
and the antiferromagnetic complex projective model $ACP^{3}$. Lattice
simulations provide strong evidence for the existence of a second order phase
transition, while an effective field theory approach does not predict any fixed
point. We identify a set of assumptions that constrain operator dimensions to
small regions overlapping with the lattice predictions.


Abstract:
  Electronic health records (EHRs), digital collections of patient healthcare
events and observations, are ubiquitous in medicine and critical to healthcare
delivery, operations, and research. Despite this central role, EHRs are
notoriously difficult to process automatically. Well over half of the
information stored within EHRs is in the form of unstructured text (e.g.
provider notes, operation reports) and remains largely untapped for secondary
use. Recently, however, newer neural network and deep learning approaches to
Natural Language Processing (NLP) have made considerable advances,
outperforming traditional statistical and rule-based systems on a variety of
tasks. In this survey paper, we summarize current neural NLP methods for EHR
applications. We focus on a broad scope of tasks, namely, classification and
prediction, word embeddings, extraction, generation, and other topics such as
question answering, phenotyping, knowledge graphs, medical dialogue,
multilinguality, interpretability, etc.


Abstract:
  This paper is a primer on cryptographic accumulators and how to apply them
practically. A cryptographic accumulator is a space- and time-efficient data
structure used for set-membership tests. Since it is possible to represent any
computational problem where the answer is yes or no as a set-membership
problem, cryptographic accumulators are invaluable data structures in computer
science and engineering. But, to the best of our knowledge, there is neither a
concise survey comparing and contrasting various types of accumulators nor a
guide for how to apply the most appropriate one for a given application.
Therefore, we address that gap by describing cryptographic accumulators while
presenting their fundamental and so-called optional properties. We discuss the
effects of each property on the given accumulator's performance in terms of
space and time complexity, as well as communication overhead.


Abstract:
  The combination of topology and quantum criticality can give rise to an
exotic mix of counterintuitive effects. Here, we show that unexpected
topological properties take place in a paradigmatic strongly-correlated
Hamiltonian: the 1D extended Bose-Hubbard model. In particular, we reveal the
presence of two distinct topological quantum critical points with localized
edge states and gapless bulk excitations. Our results show that the topological
critical points separate two phases, one topologically protected and the other
topologically trivial, both characterized by a long-range ordered string
correlation function. The long-range order persists also at the topological
critical points and it reflects the presence of localized edge states protected
by a finite charge gap. Finally, we introduce a super-resolution quantum gas
microscopy scheme for dipolar dysprosium atoms, which provides a reliable route
towards the experimental study of topological quantum critical points.


Abstract:
  Accurately segmenting a variety of clinically significant lesions from whole
body computed tomography (CT) scans is a critical task on precision oncology
imaging, denoted as universal lesion segmentation (ULS). Manual annotation is
the current clinical practice, being highly time-consuming and inconsistent on
tumor's longitudinal assessment. Effectively training an automatic segmentation
model is desirable but relies heavily on a large number of pixel-wise labelled
data. Existing weakly-supervised segmentation approaches often struggle with
regions nearby the lesion boundaries. In this paper, we present a novel
weakly-supervised universal lesion segmentation method by building an attention
enhanced model based on the High-Resolution Network (HRNet), named AHRNet, and
propose a regional level set (RLS) loss for optimizing lesion boundary
delineation. AHRNet provides advanced high-resolution deep image features by
involving a decoder, dual-attention and scale attention mechanisms, which are
crucial to performing accurate lesion segmentation. RLS can optimize the model
reliably and effectively in a weakly-supervised fashion, forcing the
segmentation close to lesion boundary. Extensive experimental results
demonstrate that our method achieves the best performance on the publicly
large-scale DeepLesion dataset and a hold-out test set.


Abstract:
  Limited by the small keyboard, most mobile apps support the automatic login
feature for better user experience. Therefore, users avoid the inconvenience of
retyping their ID and password when an app runs in the foreground again.
However, this auto-login function can be exploited to launch the so-called
"data-clone attack": once the locally-stored, auto-login depended data are
cloned by attackers and placed into their own smartphones, attackers can break
through the login-device number limit and log in to the victim's account
stealthily. A natural countermeasure is to check the consistency of
devicespecific attributes. As long as the new device shows different device
fingerprints with the previous one, the app will disable the auto-login
function and thus prevent data-clone attacks. In this paper, we develop
VPDroid, a transparent Android OS-level virtualization platform tailored for
security testing. With VPDroid, security analysts can customize different
device artifacts, such as CPU model, Android ID, and phone number, in a virtual
phone without user-level API hooking. VPDroid's isolation mechanism ensures
that user-mode apps in the virtual phone cannot detect device-specific
discrepancies. To assess Android apps' susceptibility to the data-clone attack,
we use VPDroid to simulate data-clone attacks with 234 most-downloaded apps.
Our experiments on five different virtual phone environments show that
VPDroid's device attribute customization can deceive all tested apps that
perform device-consistency checks, such as Twitter, WeChat, and PayPal. 19
vendors have confirmed our report as a zero-day vulnerability. Our findings
paint a cautionary tale: only enforcing a device-consistency check at client
side is still vulnerable to an advanced data-clone attack.


Abstract:
  Experimental results of the $p(^{13}{\rm B},d)^{12}{\rm B}$ transfer reaction
to the low-lying states in $^{12}$B are reported. The optical potential
parameters for the entrance channel are extracted from the elastic scattering
$p$($^{13}{\rm B}$, $p$) measured in the same experiment, while those for the
exit channel are global ones. Spectroscopic factors associated with the $p$-,
$s$-, and $d$-wave neutron transfer to the known $^{12}$B states, are extracted
by comparing the deuteron angular distributions with the calculation results.
The separated $s$- and $d$-wave intruder strengths in $^{13}{\rm B}_{\rm g.s.}$
were determined to be $10(2)\%$ and $6(1)\%$, respectively, which follow
roughly the systematics for the $N$ = 8 neutron-rich isotones. The measured
total intruder strength is in good agreement with the shell model calculation,
while the individual ones evolve quite differently. Particularly, the sudden
change of the $d$-wave intensity between $^{13}$B and $^{12}$Be needs further
theoretical interpretation.


Abstract:
  Traffic anomaly detection has played a crucial role in Intelligent
Transportation System (ITS). The main challenges of this task lie in the highly
diversified anomaly scenes and variational lighting conditions. Although much
work has managed to identify the anomaly in homogenous weather and scene, few
resolved to cope with complex ones. In this paper, we proposed a dual-modality
modularized methodology for the robust detection of abnormal vehicles. We
introduced an integrated anomaly detection framework comprising the following
modules: background modeling, vehicle tracking with detection, mask
construction, Region of Interest (ROI) backtracking, and dual-modality tracing.
Concretely, we employed background modeling to filter the motion information
and left the static information for later vehicle detection. For the vehicle
detection and tracking module, we adopted YOLOv5 and multi-scale tracking to
localize the anomalies. Besides, we utilized the frame difference and tracking
results to identify the road and obtain the mask. In addition, we introduced
multiple similarity estimation metrics to refine the anomaly period via
backtracking. Finally, we proposed a dual-modality bilateral tracing module to
refine the time further. The experiments conducted on the Track 4 testset of
the NVIDIA 2021 AI City Challenge yielded a result of 0.9302 F1-Score and
3.4039 root mean square error (RMSE), indicating the effectiveness of our
framework.


Abstract:
  In comparison to conventional traffic designs, shared spaces promote a more
pleasant urban environment with slower motorized movement, smoother traffic,
and less congestion. In the foreseeable future, shared spaces will be populated
with a mixture of autonomous vehicles (AVs) and vulnerable road users (VRUs)
like pedestrians and cyclists. However, a driver-less AV lacks a way to
communicate with the VRUs when they have to reach an agreement of a
negotiation, which brings new challenges to the safety and smoothness of the
traffic. To find a feasible solution to integrating AVs seamlessly into
shared-space traffic, we first identified the possible issues that the
shared-space designs have not considered for the role of AVs. Then an online
questionnaire was used to ask participants about how they would like a driver
of the manually driving vehicle to communicate with VRUs in a shared space. We
found that when the driver wanted to give some suggestions to the VRUs in a
negotiation, participants thought that the communications via the driver's body
behaviors were necessary. Besides, when the driver conveyed information about
her/his intentions and cautions to the VRUs, participants selected different
communication methods with respect to their transport modes (as a driver,
pedestrian, or cyclist). These results suggest that novel eHMIs might be useful
for AV-VRU communication when the original drivers are not present. Hence, a
potential eHMI design concept was proposed for different VRUs to meet their
various expectations. In the end, we further discussed the effects of the eHMIs
on improving the sociality in shared spaces and the autonomous driving systems.


Abstract:
  Lie algebroids provide a natural medium to discuss classical systems,
however, quantum systems have not been considered. In aim of this paper is to
attempt to rectify this situation. Lie algebroids are reviewed and their use in
classical systems is described. The geometric structure of the Schr\"{o}dinger
and Heisenberg representations of quantum systems is examined and their
relationship to Lie algebroids is explored. Geometrically, a quantum system is
seen to be a collection of bounded, linear, self-adjoint operators on a
Hilbert, or more precisely, a K\"{a}hler manifold. The geometry of the
Schr\"{o}dinger representation is given by the Poisson structure of the
co-adjoint orbits on the dual of the Lie algebra. Finally, it is shown that the
Schr\"{o}dinger and Heisenberg representations are equivalent.


Abstract:
  Security related questions for Cyber Physical Systems (CPS) have attracted
much research attention in searching for novel methods for attack-resilient
control and/or estimation. Specifically, false data injection attacks (FDIAs)
have been shown to be capable of bypassing bad data detection (BDD), while
arbitrarily compromising the integrity of state estimators and robust
controller even with very sparse measurements corruption. Moreover, based on
the inherent sparsity of pragmatic attack signals, $\ell_1$-minimization scheme
has been used extensively to improve the design of attack-resilient estimators.
For this, the theoretical maximum for the percentage of compromised nodes that
can be accommodated has been shown to be $50\%$. In order to guarantee correct
state recoveries for larger percentage of attacked nodes, researchers have
begun to incorporate prior information into the underlying resilient observer
design framework. For the most pragmatic cases, this prior information is often
obtained through some data-driven machine learning process. Existing results
have shown strong positive correlation between the tolerated attack percentages
and the precision of the prior information. In this paper, we present a pruning
method to improve the precision of the prior information, given corresponding
stochastic uncertainty characteristics of the underlying machine learning
model. Then a weighted $\ell_1$-minimization is proposed based on the pruned
prior. The theoretical and simulation results show that the pruning method
significantly improves the observer performance for much larger attack
percentages, even when moderately accurate machine learning model used.


Abstract:
  Many modern imaging applications can be modeled as compressed sensing linear
inverse problems. When the measurement operator involved in the inverse problem
is sufficiently random, denoising Scalable Message Passing (SMP) algorithms
have a potential to demonstrate high efficiency in recovering compressed data.
One of the key components enabling SMP to achieve fast convergence, stability
and predictable dynamics is the Onsager correction that must be updated at each
iteration of the algorithm. This correction involves the denoiser's divergence
that is traditionally estimated via the Black-Box Monte Carlo (BB-MC) method
\cite{MC-divergence}. While the BB-MC method demonstrates satisfying accuracy
of estimation, it requires executing the denoiser additional times at each
iteration and might lead to a substantial increase in computational cost of the
SMP algorithms. In this work we develop two Large System Limit models of the
Onsager correction for denoisers operating within SMP algorithms and use these
models to propose two practical classes of divergence estimators that require
no additional executions of the denoiser and demonstrate similar or superior
correction compared to the BB-MC method.


Abstract:
  Object detection is widely studied in computer vision filed. In recent years,
certain representative deep learning based detection methods along with solid
benchmarks are proposed, which boosts the development of related researchs.
However, existing detection methods still suffer from undesirable performance
under challenges such as camouflage, blur, inter-class similarity, intra-class
variance and complex environment. To address this issue, we propose LGA-RCNN
which utilizes a loss-guided attention (LGA) module to highlight representative
region of objects. Then, those highlighted local information are fused with
global information for precise classification and localization.


Abstract:
  We theoretically investigate the formation of $W$ states in a tripartite
system composed of three charge qubits coupled to vibrational modes. The
electromechanical coupling is responsable for second order virtual processes
that result in an effective electron-electron interaction between neighbor
qubits, which yields to the formation of $W$ states. Based on the Lang-Firsov
transformation and perturbation theory, we analytically solve the quantum
dynamics, providing a mathematical expression for the maximally entangled $W$
state. Dephasing is also taken into accout, paying particular attention on the
robustness of bipartite entanglement against local dephasing processes.


Abstract:
  We study estimating inherent human disagreement (annotation label
distribution) in natural language inference task. Post-hoc smoothing of the
predicted label distribution to match the expected label entropy is very
effective. Such simple manipulation can reduce KL divergence by almost half,
yet will not improve majority label prediction accuracy or learn label
distributions. To this end, we introduce a small amount of examples with
multiple references into training. We depart from the standard practice of
collecting a single reference per each training example, and find that
collecting multiple references can achieve better accuracy under the fixed
annotation budget. Lastly, we provide rich analyses comparing these two methods
for improving label distribution estimation.


Abstract:
  In this paper, we develop and study approximately smooth basis constructions
for isogeometric analysis over two-patch domains. One key element of
isogeometric analysis is that it allows high order smoothness within one patch.
However, for representing complex geometries, a multi-patch construction is
needed. In this case, a $C^0$-smooth basis is easy to obtain, whereas
$C^1$-smooth isogeometric functions require a special construction. Such spaces
are of interest when solving numerically fourth-order PDE problems, such as the
biharmonic equation and the Kirchhoff-Love plate or shell formulation, using an
isogeometric Galerkin method.
  With the construction of so-called analysis-suitable $G^1$ (in short,
AS-$G^1$) parametrizations, as introduced in (Collin, Sangalli, Takacs; CAGD,
2016), it is possible to construct $C^1$ isogeometric spaces which possess
optimal approximation properties. These geometries need to satisfy certain
constraints along the interfaces and additionally require that the regularity
$r$ and degree $p$ of the underlying spline space satisfy $1 \leq r \leq p-2$.
The problem is that most complex geometries are not AS-$G^1$ geometries.
Therefore, we define basis functions for isogeometric spaces by enforcing
approximate $C^1$ conditions following the basis construction from (Kapl,
Sangalli, Takacs; CAGD, 2017). For this reason, the defined function spaces are
not exactly $C^1$ but only approximately.
  We study the convergence behavior and define function spaces that converge
optimally under $h$-refinement, by locally introducing functions of higher
polynomial degree and lower regularity. The convergence rate is optimal in
several numerical tests performed on domains with non-trivial interfaces. While
an extension to more general multi-patch domains is possible, we restrict
ourselves to the two-patch case and focus on the construction over a single
interface.


Abstract:
  We show that a closed orientable 3--dimensional manifold admits a round fold
map into the plane, i.e. a fold map whose critical value set consists of
disjoint simple closed curves isotopic to concentric circles, if and only if it
is a graph manifold, generalizing the characterization for simple stable maps
into the plane. Furthermore, we also give a characterization of closed
orientable graph manifolds that admit directed round fold maps into the plane,
i.e.\ round fold maps such that the number of regular fiber components of a
regular value increases toward the central region in the plane.


Abstract:
  Inflexible combined heat and power (CHP) plants and uncertain wind power
production result in excess power in distribution networks, which leads to
inverse power flow challenging grid operations. Power-to-X facilities such as
electrolysers and electric boilers can offer extra flexibility to the
integrated energy system. In this regard, we aim to jointly determine the
optimal Power-to-X facility sizing and integrated energy system operations in
this study. To account for wind power uncertainties, a distributionally robust
chance-constrained model is developed to characterize wind power uncertainties
using ambiguity sets. Linear decision rules are applied to analytically express
real-time recourse actions when uncertainties are exposed, which allows the
propagation of wind power uncertainties to gas and heat systems. Accordingly,
the developed three-stage distributionally robust chance-constrained model is
converted into a computationally tractable single-stage mixed-integer conic
model. A case study validates the effectiveness of introducing the electrolyser
and electric boiler into the integrated energy system, with respect to the
decreased system cost, expanded CHP plant flexibility and reduced inverse power
flow. The developed distributionally robust optimization model exhibits better
effectiveness and robustness compared to a chance-constrained optimization
model assuming wind forecast errors follow Gaussian distribution. Detailed
profit analysis reveals that although the overall system cost is minimized, the
profit is distributed unevenly across various stakeholders in the system.


Abstract:
  Among exoplanets, the small-size population constitutes the dominant one,
with a diversity of properties and compositions ranging from rocky to gas
dominated envelope. While a large fraction of them have masses and radii
similar to or smaller than Neptune, yet none share common properties in term of
orbital period and insulation with our ice giants. These exoplanets belong to
multi-planet systems where planets are closely packed within the first tenth of
AU and often exposed to strong irradiation from their host star. Their
formation process, subsequent evolution, and fate are still debated and trigger
new developments of planet formation models. This paper reviews the
characteristics and properties of this extended sample of planets with radii
between $\sim$ 1.6 and 4.0$_\oplus$. Even though we still lack real
Neptune/Uranus analogues, these exoplanets provide us with key observational
constraints that allow the formation of our ice giants to be placed in a more
general framework than the sole example of our solar system.


Abstract:
  A new robust stochastic volatility (SV) model having Student-t marginals is
proposed. Our process is defined through a linear normal regression model
driven by a latent gamma process that controls temporal dependence. This gamma
process is strategically chosen to enable us to find an explicit expression for
the pairwise joint density function of the Student-t response process. With
this at hand, we propose a composite likelihood (CL) based inference for our
model, which can be straightforwardly implemented with a low computational
cost. This is a remarkable feature of our Student-t SV process over existing SV
models in the literature that involve computationally heavy algorithms for
estimating parameters. Aiming at a precise estimation of the parameters related
to the latent process, we propose a CL Expectation-Maximization algorithm and
discuss a bootstrap approach to obtain standard errors. The finite-sample
performance of our composite likelihood methods is assessed through Monte Carlo
simulations. The methodology is motivated by an empirical application in the
financial market. We analyze the relationship, across multiple time periods,
between various US sector Exchange-Traded Funds returns and individual
companies' stock price returns based on our novel Student-t model. This
relationship is further utilized in selecting optimal financial portfolios.


Abstract:
  Efficient generation of spin polarization is very important for spintronics
and quantum computation. We propose a new mechanism of Chiral Phonon activated
Spin Seebeck (CPASS) effect in nonmagnetic materials in the absence of magnetic
field and spin-orbital coupling. Owing to nonequilibrium distribution of chiral
phonons under temperature gradient, we investigate the resulted chiral phonon
activated spin polarization by solving the Boltzmann transport equation. The
CPASS coefficients, with both band and phonon-drag contributions, exhibit
linear dependence on the temperature gradient. The above two contributions are
opposite for negative charge carriers and their relative magnitude is tunable
by the chemical potential modulation. The CPASS effect where the spin
accumulation is induced by chiral phonons, provides opportunities for the
exploration of advanced spintronic devices based on chiral materials even in
the absence of magnetic order and spin-orbit coupling.


Abstract:
  Real-world networks and knowledge graphs are usually heterogeneous networks.
Representation learning on heterogeneous networks is not only a popular but a
pragmatic research field. The main challenge comes from the heterogeneity --
the diverse types of nodes and edges. Besides, for a given node in a HIN, the
significance of a neighborhood node depends not only on the structural distance
but semantics. How to effectively capture both structural and semantic
relations is another challenge. The current state-of-the-art methods are based
on the algorithm of meta-path and therefore have a serious disadvantage -- the
performance depends on the arbitrary choosing of meta-path(s). However, the
selection of meta-path(s) is experience-based and time-consuming. In this work,
we propose a novel meta-path-free representation learning on heterogeneous
networks, namely Heterogeneous graph Convolutional Networks (HCN). The proposed
method fuses the heterogeneity and develops a $k$-strata algorithm ($k$ is an
integer) to capture the $k$-hop structural and semantic information in
heterogeneous networks. To the best of our knowledge, this is the first attempt
to break out of the confinement of meta-paths for representation learning on
heterogeneous networks. We carry out extensive experiments on three real-world
heterogeneous networks. The experimental results demonstrate that the proposed
method significantly outperforms the current state-of-the-art methods in a
variety of analytic tasks.


Abstract:
  Discrete max-linear Bayesian networks are directed graphical models specified
by the same recursive structural equations as max-linear models but with
discrete innovations. When all of the random variables in the model are binary,
these models are isomorphic to the conjunctive Bayesian network (CBN) models of
Beerenwinkel, Eriksson, and Sturmfels. Many of the techniques used to study CBN
models can be extended to discrete max-linear models and similar results can be
obtained. In particular, we extend the fact that CBN models are toric varieties
after linear change of coordinates to all discrete max-linear models.


Abstract:
  Mars northern polar latitudes are known to harbor an enhanced 3 ${\mu}$m
spectral signature when observed from orbit. This may indicate a greater amount
of surface adsorbed or bound water, although it has not yet been possible to
easily reconcile orbital observations with ground measurements by Phoenix. Here
we re-analyzed OMEGA/Mars Express observations acquired during the Northern
summer to further characterize this 3 ${\mu}$m absorption band increase. We
identify the presence of a new specific spectral signature composed of an
additional narrow absorption feature centered at 3.03 ${\mu}$m coupled with an
absorption at ${\lambda}$ ${\geq}$ 3.8 ${\mu}$m. This signature is
homogeneously distributed over a high-albedo open ring surrounding the
circumpolar low-albedo terrains between ~ 68{\deg}N and 76{\deg}N and ~
0{\deg}E and 270{\deg}E. This location includes the Phoenix landing site. This
feature shows no time variability and can be confidently attributed to a
seasonally stable surface component. All together, the stability, spectral
shape and absence of significant correlation with other signatures in the 1 $-$
2.5 ${\mu}$m range discard interpretations relying on water ice or easily
exchangeable adsorbed water. Sulfates, notably anhydrite, provide interesting
comparisons to several sections of the spectrum. Analogies with Earth samples
also show that the spectral signature could result from a latitudinal
modification of the hydration state and/or grains size of salts contaminants.
While the exact full spectral shape cannot be easily reproduced, plausible
explanations to this observation seem to involve geologically recent water
alteration at high northern latitudes.


Abstract:
  This paper discusses the current critique against neural network-based
Natural Language Understanding (NLU) solutions known as language models. We
argue that much of the current debate rests on an argumentation error that we
will refer to as the singleton fallacy: the assumption that language, meaning,
and understanding are single and uniform phenomena that are unobtainable by
(current) language models. By contrast, we will argue that there are many
different types of language use, meaning and understanding, and that (current)
language models are build with the explicit purpose of acquiring and
representing one type of structural understanding of language. We will argue
that such structural understanding may cover several different modalities, and
as such can handle several different types of meaning. Our position is that we
currently see no theoretical reason why such structural knowledge would be
insufficient to count as "real" understanding.


Abstract:
  We classify 2+1 dimensional integrable systems with nonlocality of the
intermediate long wave type. Links to the 2+1 dimensional waterbag system
  are established. Dimensional reductions of integrable systems constructed in
this paper provide dispersive regularisations of hydrodynamic equations
governing propagation of long nonlinear waves in a shear flow with piecewise
linear velocity profile (for special values of vorticities).


Abstract:
  Influenza, an infectious disease, causes many deaths worldwide. Predicting
influenza victims during epidemics is an important task for clinical, hospital,
and community outbreak preparation. On-line user-generated contents (UGC),
primarily in the form of social media posts or search query logs, are generally
used for prediction for reaction to sudden and unusual outbreaks. However, most
studies rely only on the UGC as their resource and do not use various UGCs. Our
study aims to solve these questions about Influenza prediction: Which model is
the best? What combination of multiple UGCs works well? What is the nature of
each UGC? We adapt some models, LASSO Regression, Huber Regression, Support
Vector Machine regression with Linear kernel (SVR) and Random Forest, to test
the influenza volume prediction in Japan during 2015 - 2018. For that, we use
on-line five data resources: (1) past flu patients, (2) SNS (Twitter), (3)
search engines (Yahoo! Japan), (4) shopping services (Yahoo! Shopping), and (5)
Q&A services (Yahoo! Chiebukuro) as resources of each model. We then validate
respective resources contributions using the best model, Huber Regression, with
all resources except one resource. Finally, we use Bayesian change point method
for ascertaining whether the trend of time series on any resources is reflected
in the trend of flu patient count or not. Our experiments show Huber Regression
model based on various data resources produces the most accurate results. Then,
from the change point analysis, we get the result that search query logs and
social media posts for three years represents these resources as a good
predictor. Conclusions: We show that Huber Regression based on various data
resources is strong for outliers and is suitable for the flu prediction.
Additionally, we indicate the characteristics of each resource for the flu
prediction.


Abstract:
  Social media marketing is an emerging marketing technique worldwide. This
research concentrates on how effectively social media can be used to promote a
product in tourism industry. The efficient use of social media develops a
tourism company in terms of sales, branding, reach and relationship management.
The study aims to find the best social media platform to promote and develop a
tourism company and the customer opinion towards planning a trip through
online. It also concentrates on customer response for online offers and
discounts in those social media platforms. The study attempts to understand and
create suitable model for social media marketing for tourism companies with a
sample size of 400. The sampling technique used in this study is purposive
sampling method. The purposive sample can also be called as judgemental sample.
Normally the sample will be selected based on the knowledge possessed by the
respondents on a particular phenomenon. Here, the study is been conducted among
the people who use social media. The sampling technique helped the researcher
to identify the target sample i.e., the social media users.


Abstract:
  In recent years, physiological signal based authentication has shown great
promises,for its inherent robustness against forgery. Electrocardiogram (ECG)
signal, being the most widely studied biosignal, has also received the highest
level of attention in this regard. It has been proven with numerous studies
that by analyzing ECG signals from different persons, it is possible to
identify them, with acceptable accuracy. In this work, we present, EDITH, a
deep learning-based framework for ECG biometrics authentication system.
Moreover, we hypothesize and demonstrate that Siamese architectures can be used
over typical distance metrics for improved performance. We have evaluated EDITH
using 4 commonly used datasets and outperformed the prior works using less
number of beats. EDITH performs competitively using just a single heartbeat
(96-99.75% accuracy) and can be further enhanced by fusing multiple beats (100%
accuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture
manages to reduce the identity verification Equal Error Rate (EER) to 1.29%. A
limited case study of EDITH with real-world experimental data also suggests its
potential as a practical authentication system.


Abstract:
  We prove that the 2D finite depth capillary water wave equations admit no
solitary wave solutions. This closes the existence/non-existence problem for
solitary water waves in 2D, under the classical assumptions of
incompressibility and irrotationality, and with the physical parameters being
gravity, surface tension and the fluid depth.


Abstract:
  We consider the punctured plane with volume density $|x|^\alpha$ and
perimeter density $|x|^\beta$. We show that centred balls are uniquely
isoperimetric for indices $(\alpha,\beta)$ which satisfy the conditions
$\alpha-\beta+1>0$, $\alpha\leq 2\beta$ and $\alpha(\beta+1)\leq\beta^2$ except
in the case $\alpha=\beta=0$ which corresponds to the classical isoperimetric
inequality.


Abstract:
  Dynamic languages, such as Python and Javascript, trade static typing for
developer flexibility and productivity. Lack of static typing can cause
run-time exceptions and is a major factor for weak IDE support. To alleviate
these issues, PEP 484 introduced optional type annotations for Python. As
retrofitting types to existing codebases is error-prone and laborious,
learning-based approaches have been proposed to enable automatic type
annotations based on existing, partially annotated codebases. However, it is
still quite challenging for learning-based approaches to give a relevant
prediction in the first suggestion or the first few ones. In this paper, we
present Type4Py, a deep similarity learning-based hierarchical neural network
model that learns to discriminate between types of the same kind and dissimilar
types in a high-dimensional space, which results in clusters of types. Nearest
neighbor search suggests a list of likely types for arguments, variables, and
functions' return. The results of the quantitative and qualitative evaluation
indicate that Type4Py significantly outperforms state-of-the-art approaches at
the type prediction task. Considering the Top-1 prediction, Type4Py obtains a
Mean Reciprocal Rank of 72.5%, which is 10.87% and 16.45% higher than that of
Typilus and TypeWriter, respectively.


Abstract:
  Quantifying entanglement properties of mixed states in quantum field theory
via entanglement of purification and reflected entropy is a new and challenging
subject. In this work, we study both quantities for two spherical subregions
far away from each other in the vacuum of a conformal field theory in any
number of dimensions. Using lattice techniques, we find an elementary proof
that the decay of both, the entanglement of purification and reflected entropy,
is enhanced with respect to the mutual information behaviour by a logarithm of
the distance between the subregions. In the case of the Ising spin chain at
criticality and the related free fermion conformal field theory, we compute
also the overall coefficients numerically for the both quantities of interest.


Abstract:
  Spin-dependent transport phenomena due to relativistic spin-orbit coupling
and broken space-inversion symmetry are often difficult to interpret
microscopically, in particular when occurring at surfaces or interfaces. Here
we present a theoretical and experimental study of spin-orbit torque and
unidirectional magnetoresistance in a model room-temperature ferromagnet NiMnSb
with inversion asymmetry in the bulk of this half-heusler crystal. Besides the
angular dependence on magnetization, the competition of Rashba and
Dresselhaus-like spin-orbit couplings results in the dependence of these
effects on the crystal direction of the applied electric field. The
phenomenology that we observe highlights potential inapplicability of commonly
considered approaches for interpreting experiments. We point out that, in
general, there is no direct link between the current-induced non-equilibrium
spin polarization inferred from the measured spin-orbit torque and the
unidirectional magnetiresistance. We also emphasize that the unidirectional
magnetoresistance has not only longitudinal but also transverse components in
the electric field -- current indices which complicates its separation from the
thermoelectric contributions to the detected signals in common experimental
techniques. We use the theoretical results to analyze our measurements of the
on-resonance and off-resonance mixing signals in microbar devices fabricated
from an epitaxial NiMnSb film along different crystal directions. Based on the
analysis we extract an experimental estimate of the unidirectional
magnetoresistance in NiMnSb.


Abstract:
  Medical visual question answering (Med-VQA) has tremendous potential in
healthcare. However, the development of this technology is hindered by the
lacking of publicly-available and high-quality labeled datasets for training
and evaluation. In this paper, we present a large bilingual dataset, SLAKE,
with comprehensive semantic labels annotated by experienced physicians and a
new structural medical knowledge base for Med-VQA. Besides, SLAKE includes
richer modalities and covers more human body parts than the currently available
dataset. We show that SLAKE can be used to facilitate the development and
evaluation of Med-VQA systems. The dataset can be downloaded from
http://www.med-vqa.com/slake.


Abstract:
  Recently, Space-Time Memory Network (STM) based methods have achieved
state-of-the-art performance in semi-supervised video object segmentation
(VOS). A crucial problem in this task is how to model the dependency both among
different frames and inside every frame. However, most of these methods neglect
the spatial relationships (inside each frame) and do not make full use of the
temporal relationships (among different frames). In this paper, we propose a
new transformer-based framework, termed TransVOS, introducing a vision
transformer to fully exploit and model both the temporal and spatial
relationships. Moreover, most STM-based approaches employ two separate encoders
to extract features of two significant inputs, i.e., reference sets (history
frames with predicted masks) and query frame (current frame), respectively,
increasing the models' parameters and complexity. To slim the popular
two-encoder pipeline while keeping the effectiveness, we design a single
two-path feature extractor to encode the above two inputs in a unified way.
Extensive experiments demonstrate the superiority of our TransVOS over
state-of-the-art methods on both DAVIS and YouTube-VOS datasets.


Abstract:
  In the present work, single- and segregated-network PINN architectures are
applied to predict momentum, species and temperature distributions of a dry air
humidification problem in a simple 2D rectangular domain. The created PINN
models account for variable fluid properties, species- and heat-diffusion and
convection. Both the mentioned PINN architectures were trained using different
hyperparameter settings, such as network width and depth to find the
best-performing configuration. It is shown that the segregated-network PINN
approach results in on-average 62% lower losses when compared to the
single-network PINN architecture for the given problem. Furthermore, the
single-network variant struggled to ensure species mass conservation in
different areas of the computational domain, whereas, the segregated approach
successfully maintained species conservation. The PINN predicted velocity,
temperature and species profiles for a given set of boundary conditions were
compared to results generated using OpenFOAM software. Both the single- and
segregated-network PINN models produced accurate results for temperature and
velocity profiles, with average percentage difference relative to the CFD
results of approximately 7.5% for velocity and 8% for temperature. The mean
error percentages for the species mass fractions are 9\% for the single-network
model and 1.5% for the segregated-network approach. To showcase the
applicability of PINNs for surrogate modelling of multi-species problems, a
parameterised version of the segregated-network PINN is trained which could
produce results for different water vapour inlet velocities. The normalised
mean absolute percentage errors, relative to the OpenFOAM results, across three
predicted cases for velocity and temperature are approximately 7.5% and 2.4%
for water vapour mass fraction.