title:
Differential Secrecy for Distributed Data and Applications to Robust
  Differentially Secure Vector Summation

abstract:
  Computing the noisy sum of real-valued vectors is an important primitive in
differentially private learning and statistics. In private federated learning
applications, these vectors are held by client devices, leading to a
distributed summation problem. Standard Secure Multiparty Computation (SMC)
protocols for this problem are susceptible to poisoning attacks, where a client
may have a large influence on the sum, without being detected.
  In this work, we propose a poisoning-robust private summation protocol in the
multiple-server setting, recently studied in PRIO. We present a protocol for
vector summation that verifies that the Euclidean norm of each contribution is
approximately bounded. We show that by relaxing the security constraint in SMC
to a differential privacy like guarantee, one can improve over PRIO in terms of
communication requirements as well as the client-side computation. Unlike SMC
algorithms that inevitably cast integers to elements of a large finite field,
our algorithms work over integers/reals, which may allow for additional
efficiencies.


title: An Architecture for Web 3.0 and the Emergence of Spontaneous Time Order

abstract:
  In this study, we proposed an architecture for Web 3.0, which is based on the
hashed interactions among user nodes that can transform bilateral trusts into
collective time order, which is the major achievement of blockchain technology,
without the expensive Proof of Work or the questionable Proof of Stake.


title:
Analysis of Personal Data Visualization Reviews On mHealth Apps (short
  paper)

abstract:
  Mobile devices, specifically, smartphones proved easy and quick access to
data visualisations throughout various tracking apps. Mobile health (mHealth)
apps have given non-expert users access to data visualisation to track their
activities and health-related issues such as heart tracking and medication.
However, no work is done on user experience or perception of data
visualisations in mHealth apps. App reviews offer an indirect anchor for
researchers to examine how non-expert users perceive and interact with data
visualisations and identify the key challenges and recommendations. This paper
introduces an analysis of app reviews on data visualisations reported on a
dataset of 250 mHealth apps on the Google Play Store. We identified 8,406
comments related to data visualisations. 919 neutral comments, 1,557 negative
comments and 5,930 positive comments. From analysing the user reviews,
functional requirements turned out to be the most common problem across these
app reviews, followed by the look and feel and then data problems. A complete
set of data visualisations seem to be the most well-received capability of
mHealth apps. We used these comments to develop classification and data
visualisation guidelines when developing mobile data visualisations.


title:
Universal classical optical computing inspired by quantum information
  process

abstract:
  Quantum computing has attracted much attention in recent decades, since it is
believed to solve certain problems substantially faster than traditional
computing methods. Theoretically, such an advance can be obtained by networks
of the quantum operators in universal gate sets, one famous example of which is
formed by CNOT gate and single qubit gates. However, realizing a device that
performs practical quantum computing is tricky. This is because it requires a
scalable qubit system with long coherence time and good controls, which is
harsh for most current platforms. Here, we demonstrate that the information
process based on a relatively stable system -- classical optical system, can be
considered as an analogy of universal quantum computing. By encoding the
information via the polarization state of classical beams, the optical
computing elements that corresponds to the universal gate set are presented and
their combination for a general information process are theoretically
illustrated. Taking the analogy of two-qubit processor as an example, we
experimentally verify that our proposal works well. Considering the potential
of optical system for reliable and low-energy-consuming computation, our
results open a new way towards advanced information processing with high
quality and efficiency.


title:
Implications of the first evidence for coherent elastic scattering of
  reactor neutrinos

abstract:
  The recent evidence for coherent elastic neutrino-nucleus scattering
(CE$\nu$NS) in the NCC-1701 germanium detector using antineutrinos from the
Dresden-II nuclear reactor is in good agreement with standard model
expectations. However, we show that a $2\sigma$ improvement in the fit to the
data can be achieved if the quenching factor is described by a modified
Lindhard model. We also place constraints on the parameter space of a light
vector or scalar mediator that couples to neutrinos and quarks, and on a
neutrino magnetic moment. We demonstrate that the constraints are quite
sensitive to the quenching factor at low recoil energies by comparing
constraints for the standard Lindhard model with those by marginalizing over
the two parameters of the modified Lindhard model.


title:
On financial market correlation structures and diversification benefits
  across and within equity sectors

abstract:
  We study how to assess the potential benefit of diversifying an equity
portfolio by investing within and across equity sectors. We analyse 20 years of
US stock price data, which includes the global financial crisis (GFC) and the
COVID-19 market crash, as well as periods of financial stability, to determine
the `all weather' nature of equity portfolios. We establish that one may use
the leading eigenvalue of the cross-correlation matrix of log returns as well
as graph-theoretic diagnostics such as modularity to quantify the collective
behaviour of the market or a subset of it. We confirm that financial crises are
characterised by a high degree of collective behaviour of equities, whereas
periods of financial stability exhibit less collective behaviour. We argue that
during times of increased collective behaviour, risk reduction via sector-based
portfolio diversification is ineffective. Using the degree of collectivity as a
proxy for the benefit of diversification, we perform an extensive sampling of
equity portfolios to confirm the old financial adage that 30-40 stocks provide
sufficient diversification. Using hierarchical clustering, we discover a `best
value' equity portfolio for diversification consisting of 36 equities sampled
uniformly from 9 sectors. We further show that it is typically more beneficial
to diversify across sectors rather than within. Our findings have implications
for cost-conscious retail investors seeking broad diversification across equity
markets.


title: Passive verification protocol for thermal graph states

abstract:
  Graph states are entangled resource states for universal measurement-based
quantum computation. Although matter qubits such as superconducting circuits
and trapped ions are promising candidates to generate graph states, it is
technologically hard to entangle a large number of them due to several types of
noise. Since they must be sufficiently cooled to maintain their quantum
properties, thermal noise is one of major ones. In this paper, we show that for
any temperature $T$, the fidelity $\langle G|\rho_T|G\rangle$ between an ideal
graph state $|G\rangle$ at zero temperature and a thermal graph state $\rho_T$,
which is a graph state at temperature $T$, can be efficiently estimated by
using only one measurement setting. A remarkable property of our protocol is
that it is passive, while existing protocols are active, namely they switch
between at least two measurement settings. Since thermal noise is equivalent to
an independent phase-flip error, our estimation protocol also works for that
error. By generalizing our protocol to hypergraph states, we apply our protocol
to the quantum-computational-supremacy demonstration with instantaneous quantum
polynomial time circuits. Our results should make the characterization of
entangled matter qubits extremely feasible under thermal noise.


title:
Gradient estimate of the solutions to Hessian equations with oblique
  boundary value

abstract:
  In this paper, we study Hessian equations with prescribed contact angle
boundary value or oblique derivative boundary value and finally derive the a
priori global gradient estimate for the admissible solutions.


title: On the Schur Multiplier of finite $p$-groups of maximal class

abstract:
  In this article, we prove that the Schur Multiplier of a finite $p$-group of
maximal class of order $p^n ~(4 \leq n \leq p+1)$ is elementary abelian. The
case $n = p+1$ settles a question raised by Primo\v{z} Moravec in an earlier
article.


title: On the Effectiveness of Adversarial Training against Backdoor Attacks

abstract:
  DNNs' demand for massive data forces practitioners to collect data from the
Internet without careful check due to the unacceptable cost, which brings
potential risks of backdoor attacks. A backdoored model always predicts a
target class in the presence of a predefined trigger pattern, which can be
easily realized via poisoning a small amount of data. In general, adversarial
training is believed to defend against backdoor attacks since it helps models
to keep their prediction unchanged even if we perturb the input image (as long
as within a feasible range). Unfortunately, few previous studies succeed in
doing so. To explore whether adversarial training could defend against backdoor
attacks or not, we conduct extensive experiments across different threat models
and perturbation budgets, and find the threat model in adversarial training
matters. For instance, adversarial training with spatial adversarial examples
provides notable robustness against commonly-used patch-based backdoor attacks.
We further propose a hybrid strategy which provides satisfactory robustness
across different backdoor attacks.


title:
A Survey on Offloading in Federated Cloud-Edge-Fog Systems with
  Traditional Optimization and Machine Learning

abstract:
  The huge amount of data generated by the Internet of things (IoT) devices
needs the computational power and storage capacity provided by cloud, edge, and
fog computing paradigms. Each of these computing paradigms has its own pros and
cons. Cloud computing provides enhanced data storage and computing power but
causes high communication latency. Edge and fog computing provide similar
services with lower latency but with limited capacity, capability, and
coverage. A single computing paradigm cannot fulfil all the requirements of IoT
devices and a federation between them is needed to extend their capacity,
capability, and services. This federation is beneficial to both subscribers and
providers and also reveals research issues in traffic offloading between
clouds, edges, and fogs. Optimization has traditionally been used to solve the
problem of traffic offloading. However, in such a complex federated system,
traditional optimization cannot keep up with the strict latency requirements of
decision making, ranging from milliseconds to sub-seconds. Machine learning
approaches, especially reinforcement learning, are consequently becoming
popular because they can quickly solve offloading problems in dynamic
environments with large amounts of unknown information. This study provides a
novel federal classification between cloud, edge, and fog and presents a
comprehensive research roadmap on offloading for different federated scenarios.
We survey the relevant literature on the various optimization approaches used
to solve this offloading problem, and compare their salient features. We then
provide a comprehensive survey on offloading in federated systems with machine
learning approaches and the lessons learned as a result of these surveys.
Finally, we outline several directions for future research and challenges that
have to be faced in order to achieve such a federation.


title: Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning

abstract:
  In data-rich domains such as vision, language, and speech, deep learning
prevails to deliver high-performance task-specific models and can even learn
general task-agnostic representations for efficient finetuning to downstream
tasks. However, deep learning in resource-limited domains still faces the
following challenges including (i) limited data, (ii) constrained model
development cost, and (iii) lack of adequate pre-trained models for effective
finetuning. This paper introduces a new technique called model reprogramming to
bridge this gap. Model reprogramming enables resource-efficient cross-domain
machine learning by repurposing and reusing a well-developed pre-trained model
from a source domain to solve tasks in a target domain without model
finetuning, where the source and target domains can be vastly different. In
many applications, model reprogramming outperforms transfer learning and
training from scratch. This paper elucidates the methodology of model
reprogramming, summarizes existing use cases, provides a theoretical
explanation on the success of model reprogramming, and concludes with a
discussion on open-ended research questions and opportunities. A list of model
reprogramming studies is actively maintained and updated at
https://github.com/IBM/model-reprogramming.


title:
Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven
  Multi-Objective Deep Reinforcement Learning Approach

abstract:
  Penetration Testing plays a critical role in evaluating the security of a
target network by emulating real active adversaries. Deep Reinforcement
Learning (RL) is seen as a promising solution to automating the process of
penetration tests by reducing human effort and improving reliability. Existing
RL solutions focus on finding a specific attack path to impact the target
hosts. However, in reality, a diverse range of attack variations are needed to
provide comprehensive assessments of the target network's security level.
Hence, the attack agents must consider multiple objectives when penetrating the
network. Nevertheless, this challenge is not adequately addressed in the
existing literature. To this end, we formulate the automatic penetration
testing in the Multi-Objective Reinforcement Learning (MORL) framework and
propose a Chebyshev decomposition critic to find diverse adversary strategies
that balance different objectives in the penetration test. Additionally, the
number of available actions increases with the agent consistently probing the
target network, making the training process intractable in many practical
situations. Thus, we introduce a coverage-based masking mechanism that reduces
attention on previously selected actions to help the agent adapt to future
exploration. Experimental evaluation on a range of scenarios demonstrates the
superiority of our proposed approach when compared to adapted algorithms in
terms of multi-objective learning and performance efficiency.


title:
Hidden bawls, whispers, and yelps: can text be made to sound more than
  just its words?

abstract:
  Whether a word was bawled, whispered, or yelped, captions will typically
represent it in the same way. If they are your only way to access what is being
said, subjective nuances expressed in the voice will be lost. Since so much of
communication is carried by these nuances, we posit that if captions are to be
used as an accurate representation of speech, embedding visual representations
of paralinguistic qualities into captions could help readers use them to better
understand speech beyond its mere textual content. This paper presents a model
for processing vocal prosody (its loudness, pitch, and duration) and mapping it
into visual dimensions of typography (respectively, font-weight, baseline
shift, and letter-spacing), creating a visual representation of these lost
vocal subtleties that can be embedded directly into the typographical form of
text. An evaluation was carried out where participants were exposed to this
speech-modulated typography and asked to match it to its originating audio,
presented between similar alternatives. Participants (n=117) were able to
correctly identify the original audios with an average accuracy of 65%, with no
significant difference when showing them modulations as animated or static
text. Additionally, participants' comments showed their mental models of
speech-modulated typography varied widely.


title:
Local well-posedness of the Skew mean curvature flow for small data in
  $d\geq 2$ dimensions

abstract:
  The skew mean curvature flow is an evolution equation for $d$ dimensional
manifolds embedded in $\mathbb{R}^{d+2}$ (or more generally, in a Riemannian
manifold). It can be viewed as a Schr\"odinger analogue of the mean curvature
flow, or alternatively as a quasilinear version of the Schr\"odinger Map
equation. In an earlier paper, the authors introduced a harmonic/Coulomb gauge
formulation of the problem, and used it to prove small data local
well-posedness in dimensions $d \geq 4$. In this article, we prove small data
local well-posedness in low-regularity Sobolev spaces for the skew mean
curvature flow in dimension $d\geq 2$. This is achieved by introducing a new,
heat gauge formulation of the equations, which turns out to be more robust in
low dimensions.


title: Pulse-echo speed-of-sound imaging using convex probes

abstract:
  Computed ultrasound tomography in echo mode (CUTE) is a new ultrasound
(US)-based medical imaging modality with promise for diagnosing various types
of disease based on the tissue's speed of sound (SoS). It is developed for
conventional pulse-echo US using handheld probes and can thus be implemented in
state-of-the-art medical US systems. One promising application is the
quantification of the liver fat fraction in fatty liver disease. So far, CUTE
was demonstrated using linear array probes where the imaging depth is
comparable to the aperture size. For liver imaging, however, convex probes are
preferred since they provide a larger penetration depth and a wider view angle
allowing to capture a large area of the liver. With the goal of liver imaging
in mind, we adapt CUTE to convex probes, with a special focus on discussing
strategies that make use of the convex geometry in order to make our
implementation computationally efficient. We then demonstrate in an abdominal
imaging phantom that accurate quantitative SoS using convex probes is feasible,
in spite of the smaller aperture size in relation to the image area compared to
linear arrays. A preliminary in vivo result of liver imaging confirms this
outcome, but also indicates that deep quantitative imaging in the real liver
can be more challenging, probably due to the increased complexity of the tissue
compared to phantoms.


title:
Time Division Multiplexing Ising Computer Using Single Tunable True
  Random Number Generator Based on Spin Torque Nano-Oscillator

abstract:
  Ising computer is a powerful computation scheme to deal with NP-hard
optimization problems that cannot be efficiently addressed by conventional
computers. A robust probabilistic bit (P-Bit) which is realized by a hardware
entity fluctuating in time between -1 and 1 plays a key role in the success of
Ising computer. Spintronics technology, such as stochastic nanomagnet, is
recently proposed as a good platform for the hardware emulation of P-Bit. Here,
we report, for the first time, a Time Division Multiplexing (TDM) Ising
computer using single tunable true random number generator which is comprised
of a Spin Torque Nano-Oscillator (STNO). First, the intrinsic frequency
fluctuation of the STNO is utilized to design a simple digital true random
number generator (TRNG). The true random number generator is further evolved
into a tunable random number generator to act as a P-Bit. Second, in order to
accomplish combinational optimization with our proposed P-Bit under Ising
model, a novel incremental coupling rule is proposed. With such coupling rule
between P-Bit array, high fidelity NOT and XOR logic gate is demonstrated.
Third, it is proposed that our digital TRNG can be simply reused acting as a
P-Bit array by time division multiplexing. The whole Ising computer can be
implemented by one single STNO, and integer factorization of as high as 87%
accuracy rate is achieved.


title:
Online Learning of Trellis Diagram Using Neural Network for Robust
  Detection and Decoding

abstract:
  This paper studies machine learning-assisted maximum likelihood (ML) and
maximum a posteriori (MAP) receivers for a communication system with memory,
which can be modelled by a trellis diagram. The prerequisite of the ML/MAP
receiver is to obtain the likelihood of the received samples under different
state transitions of the trellis diagram, which relies on the channel state
information (CSI) and the distribution of the channel noise. We propose to
learn the trellis diagram real-time using an artificial neural network (ANN)
trained by a pilot sequence. This approach, termed as the online learning of
trellis diagram (OLTD), requires neither the CSI nor statistics of the noise,
and can be incorporated into the classic Viterbi and the BCJR algorithm.
%Compared with the state-of-the-art ViterbiNet and BCJRNet algorithms in the
literature, it It is shown to significantly outperform the model-based methods
in non-Gaussian channels. It requires much less training overhead than the
state-of-the-art methods, and hence is more feasible for real implementations.
As an illustrative example, the OLTD-based BCJR is applied to a Bluetooth low
energy (BLE) receiver trained only by a 256-sample pilot sequence. Moreover,
the OLTD-based BCJR can accommodate for turbo equalization, while the
state-of-the-art BCJRNet/ViterbiNet cannot. As an interesting by-product, we
propose an enhancement to the BLE standard by introducing a bit interleaver to
its physical layer; the resultant improvement of the receiver sensitivity can
make it a better fit for some Internet of Things (IoT) communications.


title: The spherical Plateau problem for group homology

abstract:
  Given a group homology class $h$ of a countable group $\Gamma$, we study a
corresponding homological Plateau problem inside a canonical classifying space
of $\Gamma$, which is defined using the regular representation of $\Gamma$ and
which is locally isometric to a Hilbert unit sphere. We investigate the
relation between group theoretic properties of the pair $(\Gamma,h)$ and the
geometry of its Plateau solutions. For instance, we prove that for a closed
oriented $3$-manifold $M$, the intrinsic geometry of any Plateau solution is
given by the hyperbolic part of $M$ endowed with one third of its hyperbolic
metric.


title:
BRST Ghost-Vertex Operator in Witten's Cubic Open String Field Theory on
  Multiple $Dp$-branes

abstract:
  The Becchi-Rouet-Stora-Tyutin (BRST) ghost field is a key element in
constructing Witten's cubic open string field theory. However, to date, the
ghost sector of the string field theory has not received a great deal of
attention.
  In this study, we address the BRST ghost on multiple $Dp$-branes, which
carries non-Abelian indices and couples to a non-Ablelian gauge field. We found
that the massless components of the BRST ghost field can play the role of the
Faddeev-Popov ghost in the non-Alelian gauge field, such that the string field
theory maintains the local non-Abelian gauge invariance.


title:
Invariance Learning in Deep Neural Networks with Differentiable Laplace
  Approximations

abstract:
  Data augmentation is commonly applied to improve performance of deep learning
by enforcing the knowledge that certain transformations on the input preserve
the output. Currently, the correct data augmentation is chosen by human effort
and costly cross-validation, which makes it cumbersome to apply to new
datasets. We develop a convenient gradient-based method for selecting the data
augmentation. Our approach relies on phrasing data augmentation as an
invariance in the prior distribution and learning it using Bayesian model
selection, which has been shown to work in Gaussian processes, but not yet for
deep neural networks. We use a differentiable Kronecker-factored Laplace
approximation to the marginal likelihood as our objective, which can be
optimised without human supervision or validation data. We show that our method
can successfully recover invariances present in the data, and that this
improves generalisation on image datasets.


title: A Heuristic Proof Procedure for Propositional Logic

abstract:
  Theorem proving is one of the oldest applications which require heuristics to
prune the search space. Invertible proof procedures has been the major tool. In
this paper, we present a novel and powerful heuristic called $nongshim$ which
can be seen as an underlying principle of invertible proof procedures. Using
this heuristic, we derive an invertible sequent calculus\cite{Ketonen,Troe}
from sequent calculus for propositional logic.


title: Convergence of online $k$-means

abstract:
  We prove asymptotic convergence for a general class of $k$-means algorithms
performed over streaming data from a distribution: the centers asymptotically
converge to the set of stationary points of the $k$-means cost function. To do
so, we show that online $k$-means over a distribution can be interpreted as
stochastic gradient descent with a stochastic learning rate schedule. Then, we
prove convergence by extending techniques used in optimization literature to
handle settings where center-specific learning rates may depend on the past
trajectory of the centers.


title: Viscous-inertial transition in dense granular suspension

abstract:
  Granular suspensions present a transition from a Newtonian rheology in the
Stokes limit to a Bagnoldian rheology when inertia is increased. A custom
rheometer which can be run in a pressure or a volume-imposed mode is used to
examine this transition in the dense regime close to jamming. By varying
systematically the interstitial fluid, shear rate, and packing fraction in
volume-imposed measurements, we show that the transition takes place at a
Stokes number of 10 independent of the packing fraction. Using pressure-imposed
rheometry, we investigate whether the inertial and viscous regimes can be
unified as a function of a single dimensionless number based on stress
additivity.


title:
Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face
  Recognition

abstract:
  We present a new method for face recognition from digital images acquired
under varying illumination conditions. The method is based on mathematical
modeling of local gradient distributions using the Radon Cumulative
Distribution Transform (R-CDT). We demonstrate that lighting variations cause
certain types of deformations of local image gradient distributions which, when
expressed in R-CDT domain, can be modeled as a subspace. Face recognition is
then performed using a nearest subspace in R-CDT domain of local gradient
distributions. Experiment results demonstrate the proposed method outperforms
other alternatives in several face recognition tasks with challenging
illumination conditions. Python code implementing the proposed method is
available, which is integrated as a part of the software package PyTransKit.


title: Equivariant Graph Hierarchy-Based Neural Networks

abstract:
  Equivariant Graph neural Networks (EGNs) are powerful in characterizing the
dynamics of multi-body physical systems. Existing EGNs conduct flat message
passing, which, yet, is unable to capture the spatial/dynamical hierarchy for
complex systems particularly, limiting substructure discovery and global
information fusion. In this paper, we propose Equivariant Hierarchy-based Graph
Networks (EGHNs) which consist of the three key components: generalized
Equivariant Matrix Message Passing (EMMP) , E-Pool and E-UpPool. In particular,
EMMP is able to improve the expressivity of conventional equivariant message
passing, E-Pool assigns the quantities of the low-level nodes into high-level
clusters, while E-UpPool leverages the high-level information to update the
dynamics of the low-level nodes. As their names imply, both E-Pool and E-UpPool
are guaranteed to be equivariant to meet physic symmetry. Considerable
experimental evaluations verify the effectiveness of our EGHN on several
applications including multi-object dynamics simulation, motion capture, and
protein dynamics modeling.


title:
Multiview Scattering Scanning Imaging Confocal Microscopy through a
  Multimode Fiber

abstract:
  Confocal and multiphoton microscopy are effective techniques to obtain
high-contrast images of 2-D sections within bulk tissue. However, scattering
limits their application to depths only up to ~1 millimeter. Multimode fibers
make excellent ultrathin endoscopes that can penetrate deep inside the tissue
with minimal damage. Here, we present Multiview Scattering Scanning Imaging
Confocal (MUSSIC) Microscopy that enables high signal-to-noise ratio (SNR)
imaging through a multimode fiber, hence combining the optical sectioning and
resolution gain of confocal microscopy with the minimally invasive penetration
capability of multimode fibers. The key advance presented here is the high SNR
image reconstruction enabled by employing multiple coplanar virtual pinholes to
capture multiple perspectives of the object, re-shifting them appropriately and
combining them to obtain a high-contrast and high-resolution confocal image. We
present the theory for the gain in contrast and resolution in MUSSIC microscopy
and validate the concept through experimental results.


title: Combining the Silhouette and Skeleton Data for Gait Recognition

abstract:
  Gait recognition, a promising long-distance biometric technology, has aroused
intense interest in computer vision. Existing works on gait recognition can be
divided into appearance-based methods and model-based methods, which extract
features from silhouettes and skeleton data, respectively. However, since
appearance-based methods are greatly affected by clothing changing and carrying
condition, and model-based methods are limited by the accuracy of pose
estimation approaches, gait recognition remains challenging in practical
applications. In order to integrate the advantages of such two approaches, a
two-branch neural network (NN) is proposed in this paper. Our method contains
two branches, namely a CNN-based branch taking silhouettes as input and a
GCN-based branch taking skeletons as input. In addition, two new modules are
proposed in the GCN-based branch for better gait representation. First, we
present a simple yet effective fully connected graph convolution operator to
integrate the multi-scale graph convolutions and alleviate the dependence on
natural human joint connections. Second, we deploy a multi-dimension attention
module named STC-Att to learn spatial, temporal and channel-wise attention
simultaneously. We evaluated the proposed two-branch neural network on the
CASIA-B dataset. The experimental results show that our method achieves
state-of-the-art performance in various conditions.


title: Photomolecular Effect: Visible Light Absorption at Water-Vapor Interface

abstract:
  The evaporation of water is ubiquitous in nature and industrial technologies.
The known mechanism for evaporation is "thermal evaporation" which highlights
the energy input for evaporation is via heat. Due to the weak absorption of
water to visible light, the first step to using solar energy to evaporate water
is usually by converting it into thermal energy through photothermal processes
via additional absorbing materials. Contrary to this conventional wisdom, we
report here strong absorption of photons in the visible spectrum at the
water-vapor interface by direct cleavage of water clusters via a process we
call photomolecular effect. We show that this process happens at the
water-vapor interface by measuring the dependence of the photomolecular
evaporation rate on the wavelength, the angle of incidence, and the
polarization of the incident light. The spectra signatures in the vapor phase
further support the photomolecular effect. Despite the long propagation lengths
of visible light in bulk water, we demonstrate that they can heat a thin layer
of fog easily, suggesting that this process is ubiquitous. The photomolecular
effect will have significant implications for the earth's water cycle, global
warming, plant transpiration, as well as different technologies involving the
evaporation of liquids from drying to power generation

categories: physics.atm-clus cond-mat.mes-hall physics.flu-dyn physics.optics


title:
Efficient numerical algorithm for multi-level ionization of
  high-atomic-number gases

abstract:
  An efficient numerical algorithm for the laser driven multi-level ionization
of high-atomic-number gases is proposed and implemented in an electromagnetic
particle-in-cell code SPACE. The algorithm is based on analytical solutions to
the system of differential equations describing ionization evolution. Using
analytical solutions resolves the multiscale issue of ionization due to
different characteristic time scales of ionization processes and the main code
time step. Algorithm efficiency is improved by using a locally-reduced system
of differential equations. The effects of the orbital quantum numbers and their
projections have been examined. The algorithm is applied to the study of
ionization injection of electrons into laser-driven plasma wakefields.


title: Overcoming Van der Waals Forces in reconfigurable nanostructures

abstract:
  Reconfigurable metamaterials require constituent nanostructures to
demonstrate switching of shapes with external stimuli. For generality, such
nanostructures would touch and stick to other surfaces in one of its
configurations. Yet, a longstanding challenge is in overcoming this stiction
caused by Van der Waals forces, which impedes shape recovery. Here, we
introduce a stiff yet self-recovering material system based on acrylic acid,
and tested it in high-aspect ratio structures, where recovery is weak. This
designer material has a storage modulus of ~5.2 GPa at room temperature and ~90
MPa in the rubbery state at 150 Celsius, an order of magnitude higher than
previous reports. A high-resolution resin for two-photon lithography was
developed based on this polymer system, enabling 3D printing of nanopillars
with diameters of ~400 nm and aspect ratio as high as ~10. Experimentally, we
observed self-recovery as collapsed and touching structures overcome stiction
to stand back up. We developed a theoretical model to explain the
recoverability of these sub-micron structures. Reconfigurable structural colour
prints and holograms were demonstrated, indicating potential applications of
the material system as a shape memory polymer suitable for sub-micron
reconfigurable metamaterials.


title: On Local Distributions in Graph Signal Processing

abstract:
  Graph filtering is the cornerstone operation in graph signal processing
(GSP). Thus, understanding it is key in developing potent GSP methods. Graph
filters are local and distributed linear operations, whose output depends only
on the local neighborhood of each node. Moreover, a graph filter's output can
be computed separately at each node by carrying out repeated exchanges with
immediate neighbors. Graph filters can be compactly written as polynomials of a
graph shift operator (typically, a sparse matrix description of the graph).
This has led to relating the properties of the filters with the spectral
properties of the corresponding matrix -- which encodes global structure of the
graph. In this work, we propose a framework that relies solely on the local
distribution of the neighborhoods of a graph. The crux of this approach is to
describe graphs and graph signals in terms of a measurable space of rooted
balls. Leveraging this, we are able to seamlessly compare graphs of different
sizes and coming from different models, yielding results on the convergence of
spectral densities, transferability of filters across arbitrary graphs, and
continuity of graph signal properties with respect to the distribution of local
substructures.


title: Movies2Scenes: Learning Scene Representations Using Movie Similarities

abstract:
  Labeling movie-scenes is a time-consuming process which makes applying
end-to-end supervised methods for scene-understanding a challenging problem.
Moreover, directly using image-based visual representations for
scene-understanding tasks does not prove to be effective given the large gap
between the two domains. To address these challenges, we propose a novel
contrastive learning approach that uses commonly available movie-level
information (e.g., co-watch, genre, synopsis) to learn a general-purpose
scene-level representation. Our learned representation comfortably outperforms
existing state-of-the-art approaches on eleven downstream tasks evaluated using
multiple benchmark datasets. To further demonstrate generalizability of our
learned representation, we present its comparative results on a set of
video-moderation tasks evaluated using a newly collected large-scale internal
movie dataset.


title:
Tail Asymptotics in any direction of the stationary distribution in a
  two-dimensional discrete-time QBD process

abstract:
  We consider a discrete-time two-dimensional quasi-birth-and-death process
(2d-QBD process for short) $\{(\boldsymbol{X}_n,J_n)\}$ on
$\mathbb{Z}_+^2\times S_0$, where $\boldsymbol{X}_n=(X_{1,n},X_{2,n})$ is the
level state, $J_n$ the phase state (background state) and $S_0$ a finite set,
and study asymptotic properties of the stationary tail distribution. The 2d-QBD
process is an extension of usual one-dimensional QBD process. By using the
matrix analytic method of the queueing theory and the complex analytic method,
we obtain the asymptotic decay rate of the stationary tail distribution in any
direction. This result is an extension of the corresponding result for a
certain two-dimensional reflecting random work without background processes,
obtained by using the large deviation techniques. We also present a condition
ensuring the sequence of the stationary probabilities geometrically decays
without power terms, asymptotically. Asymptotic properties of the stationary
tail distribution in the coordinate directions in a 2d-QBD process have already
been studied in the literature. The results of this paper are also important
complements to those results.


title: Signature of electromagnetic quantum fluctuations in exciton physics

abstract:
  Quantum fluctuations of the electromagnetic field are known to produce the
atomic Lamb shift. We here reveal their iconic signature in semiconductor
physics, through the blue-shift they produce to optically bright excitons, thus
lifting the energy of these excitons above their dark counterparts. The
electromagnetic field here acts in its full complexity: in addition to the
longitudinal part via interband \textit{virtual Coulomb} processes, the
transverse part -- which has been missed up to now -- also acts via resonant
and nonresonant \textit{virtual photons}. These two parts beautifully combine
to produce a bright exciton blue-shift independent of the exciton wave-vector
direction. Our work readily leads to a striking prediction: long-lived excitons
must have a small bright-dark splitting. Although the analogy between exciton
and hydrogen atom could lead us to see the bright exciton shift as a Lamb
shift, this is not fully so: the atom shift entirely comes from virtual
photons, whereas the Coulomb interaction also contributes to the exciton shift
through the so-called "electron-hole exchange".


title:
Multiplicative functions commutable with binary quadratic forms $x^2 \pm
  xy + y^2$

abstract:
  If a multiplicative function $f$ is commutable with a quadratic form
$x^2+xy+y^2$, i.e., \[ f(x^2+xy+y^2) = f(x)^2 + f(x)\,f(y) + f(y)^2, \] then
$f$ is the identity function. In other hand, if $f$ is commutable with a
quadratic form $x^2-xy+y^2$, then $f$ is one of three kinds of functions: the
identity function, the constant function, and an indicator function for
$\mathbb{N}\setminus p\mathbb{N}$ with a prime $p$.


title: M2-branes and $\mathfrak{q}$-Painlev\'e equations

abstract:
  In this paper we investigate a novel connection between the effective theory
of M2-branes on $(\mathbb{C}^2/\mathbb{Z}_2\times
\mathbb{C}^2/\mathbb{Z}_2)/\mathbb{Z}_k$ and the $\mathfrak{q}$-deformed
Painlev\'e equations, by proposing that the grand canonical partition function
of the corresponding four-nodes circular quiver $\mathcal{N}=4$ Chern-Simons
matter theory solves the $\mathfrak{q}$-Painlev\'e VI equation. We analyse how
this describes the moduli space of the topological string on local
$\text{dP}_5$ and, via geometric engineering, five dimensional $N_f=4$
$\text{SU}(2)$ $\mathcal{N}=1$ gauge theory on a circle. The results we find
extend the known relation between ABJM theory, $\mathfrak{q}$-Painlev\'e
$\text{III}_3$, and topological strings on local ${\mathbb P}^1\times{\mathbb
P}^1$. From the mathematical viewpoint the quiver Chern-Simons theory provides
a conjectural Fredholm determinant realisation of the $\mathfrak{q}$-Painlev\'e
VI $\tau$-function. We provide evidence for this proposal by analytic and
numerical checks and discuss in detail the successive decoupling limits down to
$N_f=0$, corresponding to $\mathfrak{q}$-Painlev\'e$\,\,$III${}_3$.


title:
Shape-Haptics: Planar & Passive Force Feedback Mechanisms for Physical
  Interfaces

abstract:
  We present Shape-Haptics, an approach for designers to rapidly design and
fabricate passive force feedback mechanisms for physical interfaces. Such
mechanisms are used in everyday interfaces and tools, and they are challenging
to design. Shape-Haptics abstracts and broadens the haptic expression of this
class of force feedback systems through 2D laser cut configurations that are
simple to fabricate. They leverage the properties of polyoxymethylene plastic
and comprise a compliant spring structure that engages with a sliding profile
during tangible interaction. By shaping the sliding profile, designers can
easily customize the haptic force feedback delivered by the mechanism. We
provide a computational design sandbox to facilitate designers to explore and
fabricate Shape-Haptics mechanisms. We also propose a series of applications
that demonstrate the utility of Shape-Haptics in creating and customizing
haptics for different physical interfaces.


title:
Giant spin torque efficiency in naturally oxidized polycrystalline TaAs
  thin films

abstract:
  We report the measurement of efficient charge-to-spin conversion at room
temperature in Weyl semimetal/ferromagnet heterostructures with both oxidized
and pristine interfaces. Polycrystalline films of the Weyl semimetal, TaAs, are
grown by molecular beam epitaxy on (001) GaAs and interfaced with a metallic
ferromagnet (Ni$_{0.8}$Fe$_{0.2}$). Spin torque ferromagnetic resonance
(ST-FMR) measurements in samples with an oxidized interface yield a spin torque
efficiency as large as $\xi_{\mathrm{FMR}}=0.45\pm 0.25$ for a 8 nm
Ni$_{0.8}$Fe$_{0.2}$ layer thickness. By studying ST-FMR in these samples with
varying Ni$_{0.8}$Fe$_{0.2}$ layer thickness, we extract a damping-like torque
efficiency as high as $\xi_{\mathrm{DL}}=1.36\pm 0.66$. In samples with a
pristine (unoxidized) interface, the spin torque efficiency has opposite sign
to that observed in oxidized samples ($\xi_{\mathrm{FMR}}=-0.27\pm 0.14$ for a
5 nm Ni$_{0.8}$Fe$_{0.2}$ layer thickness). We also find a lower bound on the
spin Hall conductivity ($424 \pm 110 \frac{\hbar}{e}$ S/cm) which is
surprisingly consistent with theoretical predictions for the single crystal
Weyl semimetal state of TaAs.


title:
Hidden spatiotemporal sequence in transition to shear band in amorphous
  solids

abstract:
  Shear banding is a fundamental non-equilibrium phenomenon in amorphous
solids. Due to the intrinsic entangling of three local atomic motions: shear,
dilatation and rotation, the precise physical process of shear band emergence
is still an enigma. To unveil this mystery, we formulate for the first time a
theoretical protocol covering both affine and non-affine components of
deformation, to decode these highly entangled local atomic-scale events. In
contrast to the broad concept of shear transformation zone, plastic behavior
can be demonstrated comprehensively as the operative manipulation of more exact
shear-dominated zones (SDZs), dilatation-dominated zones (DDZs) and
rotation-dominated zones (RDZs). Their spatiotemporal evolution exhibits a
novel transition from synchronous motion to separate distribution at the onset
of shear band, which is in striking resemblance with the transition from
laminar flow to turbulent flow in flow dynamics. The hidden mechanism is
revealed with the help of extreme value theory (EVT) and percolation analysis.
Analysis of EVT indicates dilatation is the dominant mode at the embryo of
initial plastic units. The percolation analysis points towards the critical
power-law scaling nature at the transition from stochastic activation to
percolation of plastic regions. Then the comprehensive pictures of shear
banding emergence are uncovered. Firstly, dilatation triggers initial shear and
rotation in soft regions, leading to embryo of flow units, which is followed by
the inhomogeneous turbulent-like pattern manifesting as the secondary
activation of rotation in neighboring hard material. Such rotation activation
contributes to further perturbation, and ultimately, leads to shear band
formation. Our findings also reinforce that the discussion of plastic behavior
in disordered materials must take into account both affine and non-affine
component deformation.


title:
Decentralized Safe Multi-agent Stochastic Optimal Control using Deep
  FBSDEs and ADMM

abstract:
  In this work, we propose a novel safe and scalable decentralized solution for
multi-agent control in the presence of stochastic disturbances. Safety is
mathematically encoded using stochastic control barrier functions and safe
controls are computed by solving quadratic programs. Decentralization is
achieved by augmenting to each agent's optimization variables, copy variables,
for its neighbors. This allows us to decouple the centralized multi-agent
optimization problem. However, to ensure safety, neighboring agents must agree
on "what is safe for both of us" and this creates a need for consensus. To
enable safe consensus solutions, we incorporate an ADMM-based approach.
Specifically, we propose a Merged CADMM-OSQP implicit neural network layer,
that solves a mini-batch of both, local quadratic programs as well as the
overall consensus problem, as a single optimization problem. This layer is
embedded within a Deep FBSDEs network architecture at every time step, to
facilitate end-to-end differentiable, safe and decentralized stochastic optimal
control. The efficacy of the proposed approach is demonstrated on several
challenging multi-robot tasks in simulation. By imposing requirements on safety
specified by collision avoidance constraints, the safe operation of all agents
is ensured during the entire training process. We also demonstrate superior
scalability in terms of computational and memory savings as compared to a
centralized approach.


title:
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit
  Detection & Multi-Task Learning Challenges

abstract:
  This paper describes the third Affective Behavior Analysis in-the-wild (ABAW)
Competition, held in conjunction with IEEE International Conference on Computer
Vision and Pattern Recognition (CVPR), 2022. The 3rd ABAW Competition is a
continuation of the Competitions held at ICCV 2021, IEEE FG 2020 and IEEE CVPR
2017 Conferences, and aims at automatically analyzing affect. This year the
Competition encompasses four Challenges: i) uni-task Valence-Arousal
Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit
Detection, and iv) Multi-Task-Learning. All the Challenges are based on a
common benchmark database, Aff-Wild2, which is a large scale in-the-wild
database and the first one to be annotated in terms of valence-arousal,
expressions and action units. In this paper, we present the four Challenges,
with the utilized Competition corpora, we outline the evaluation metrics and
present the baseline systems along with their obtained results.


title: Batched Dueling Bandits

abstract:
  The $K$-armed dueling bandit problem, where the feedback is in the form of
noisy pairwise comparisons, has been widely studied. Previous works have only
focused on the sequential setting where the policy adapts after every
comparison. However, in many applications such as search ranking and
recommendation systems, it is preferable to perform comparisons in a limited
number of parallel batches. We study the batched $K$-armed dueling bandit
problem under two standard settings: (i) existence of a Condorcet winner, and
(ii) strong stochastic transitivity and stochastic triangle inequality. For
both settings, we obtain algorithms with a smooth trade-off between the number
of batches and regret. Our regret bounds match the best known sequential regret
bounds (up to poly-logarithmic factors), using only a logarithmic number of
batches. We complement our regret analysis with a nearly-matching lower bound.
Finally, we also validate our theoretical results via experiments on synthetic
and real data.


title: A simple analytic example of the gravitational wave memory effect

abstract:
  We report an analytical example of the gravitational wave memory effect in
exact plane wave spacetimes. A square pulse profile is chosen which gives rise
to a curved wave region sandwiched between two flat Minkowski spacetimes.
Working in the Brinkmann coordinate system, we solve the geodesic equations
exactly in all three regions. Issues related to the continuity and
differentiability of the solutions at the boundaries of the pulse are
addressed. The evolution of the geodesic separation reveals displacement and
velocity memory effects with quantitative estimates depending on initial values
and the amplitude, width of the pulse. The deformation caused by the pulse on a
ring of particles is then examined in detail. Formation of caustics is found in
both scenarios i.e. evolution of separation for a pair of geodesics and shape
deformation of a ring of particles -- a feature consistent with previous work
on geodesic congruences in this spacetime. In summary, our analysis provides a
useful illustration of memory effects involving closed-form exact expressions.


title: Random Graph Matching in Geometric Models: the Case of Complete Graphs

abstract:
  This paper studies the problem of matching two complete graphs with edge
weights correlated through latent geometries, extending a recent line of
research on random graph matching with independent edge weights to geometric
models. Specifically, given a random permutation $\pi^*$ on $[n]$ and $n$ iid
pairs of correlated Gaussian vectors $\{X_{\pi^*(i)}, Y_i\}$ in $\mathbb{R}^d$
with noise parameter $\sigma$, the edge weights are given by
$A_{ij}=\kappa(X_i,X_j)$ and $B_{ij}=\kappa(Y_i,Y_j)$ for some link function
$\kappa$. The goal is to recover the hidden vertex correspondence $\pi^*$ based
on the observation of $A$ and $B$. We focus on the dot-product model with
$\kappa(x,y)=\langle x, y \rangle$ and Euclidean distance model with
$\kappa(x,y)=\|x-y\|^2$, in the low-dimensional regime of $d=o(\log n)$ wherein
the underlying geometric structures are most evident. We derive an approximate
maximum likelihood estimator, which provably achieves, with high probability,
perfect recovery of $\pi^*$ when $\sigma=o(n^{-2/d})$ and almost perfect
recovery with a vanishing fraction of errors when $\sigma=o(n^{-1/d})$.
Furthermore, these conditions are shown to be information-theoretically optimal
even when the latent coordinates $\{X_i\}$ and $\{Y_i\}$ are observed,
complementing the recent results of [DCK19] and [KNW22] in geometric models of
the planted bipartite matching problem. As a side discovery, we show that the
celebrated spectral algorithm of [Ume88] emerges as a further approximation to
the maximum likelihood in the geometric model.


title: The Open Problem of Finding a General Classification of Geodetic Graphs

abstract:
  This note describes some open problems that can be examined with the purpose
of gaining additional insight of how to solve the problem of finding a general
classification of geodetic graphs


title:
Detailed magneto-heat capacity analysis of SnAs topological
  superconductor

abstract:
  In this article, we report magneto-heat capacity analysis of superconducting
SnAs, which is characterized through X-ray diffraction (XRD), X-ray
photoelectron spectroscopy (XPS), and magneto-transport measurements. The
studied SnAs superconductor evidenced the presence of superconductivity at
around 4K, and the same is seen to persist up to an applied field of 250Oe. The
bulk nature of superconductivity is determined through AC susceptibility along
with heat capacity measurements. Magneto-heat capacity measurements show SnAs
to be a fully gapped s wave superconductor. This finding is well supported by
calculated superconducting physical parameters. Further, the calculation of the
residual Sommerfeld coefficient at different fields confirms node-less
superconductivity in SnAs.


title:
Partial Identification with Noisy Covariates: A Robust Optimization
  Approach

abstract:
  Causal inference from observational datasets often relies on measuring and
adjusting for covariates. In practice, measurements of the covariates can often
be noisy and/or biased, or only measurements of their proxies may be available.
Directly adjusting for these imperfect measurements of the covariates can lead
to biased causal estimates. Moreover, without additional assumptions, the
causal effects are not point-identifiable due to the noise in these
measurements. To this end, we study the partial identification of causal
effects given noisy covariates, under a user-specified assumption on the noise
level. The key observation is that we can formulate the identification of the
average treatment effects (ATE) as a robust optimization problem. This
formulation leads to an efficient robust optimization algorithm that bounds the
ATE with noisy covariates. We show that this robust optimization approach can
extend a wide range of causal adjustment methods to perform partial
identification, including backdoor adjustment, inverse propensity score
weighting, double machine learning, and front door adjustment. Across synthetic
and real datasets, we find that this approach provides ATE bounds with a higher
coverage probability than existing methods.


title: ClF diatomic molecule: rovibrational spectra

abstract:
  Following the first principles the analytic Born-Oppenheimer (BO) potential
curve for the ground state $X^1\Sigma^+$ of the molecule ClF is proposed for
whole range of internuclear distances $R \in [0,\infty)$. It is based on
matching the perturbation theory at small internuclear distances $R$ and
multipole expansion at large distances $R$, it has the form of two-point Pade
approximant and provides 3-4 figures in rovibrational energies. It supports
5719 rovibrational states with maximal vibrational number $\nu_{max} = 47$ and
maximal angular momentum $L_{max} = 210$ including 36 weakly-bound states close
to threshold (to dissociation limit) with the energies $\lesssim 10^{-4}$
Hartree. The van der Waals constant $C^{(ClF)}_6\ \sim\ 29.3$ a.u. is
predicted.


title: Visually Grounded Task and Motion Planning for Mobile Manipulation

abstract:
  Task and motion planning (TAMP) algorithms aim to help robots achieve
task-level goals, while maintaining motion-level feasibility. This paper
focuses on TAMP domains that involve robot behaviors that take extended periods
of time (e.g., long-distance navigation). In this paper, we develop a visual
grounding approach to help robots probabilistically evaluate action
feasibility, and introduce a TAMP algorithm, called GROP, that optimizes both
feasibility and efficiency. We have collected a dataset that includes 96,000
simulated trials of a robot conducting mobile manipulation tasks, and then used
the dataset to learn to ground symbolic spatial relationships for action
feasibility evaluation. Compared with competitive TAMP baselines, GROP
exhibited a higher task-completion rate while maintaining lower or comparable
action costs. In addition to these extensive experiments in simulation, GROP is
fully implemented and tested on a real robot system.


title: Automatically Generating Counterfactuals for Relation Classification

abstract:
  The goal of relation classification (RC) is to extract the semantic relations
between/among entities in the text. As a fundamental task in natural language
processing, it is crucial to ensure the robustness of RC models. Despite the
high accuracy current deep neural models have achieved in RC tasks, they are
easily affected by spurious correlations. One solution to this problem is to
train the model with counterfactually augmented data (CAD) such that it can
learn the causation rather than the confounding. However, no attempt has been
made on generating counterfactuals for RC tasks. In this paper, we formulate
the problem of automatically generating CAD for RC tasks from an entity-centric
viewpoint, and develop a novel approach to derive contextual counterfactuals
for entities. Specifically, we exploit two elementary topological properties,
i.e., the centrality and the shortest path, in syntactic and semantic
dependency graphs, to first identify and then intervene on the contextual
causal features for entities. We conduct a comprehensive evaluation on four RC
datasets by combining our proposed approach with a variety of backbone RC
models. The results demonstrate that our approach not only improves the
performance of the backbones, but also makes them more robust in the
out-of-domain test.


title:
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential
  Model-based Optimization

abstract:
  Sequential model-based optimization sequentially selects a candidate point by
constructing a surrogate model with the history of evaluations, to solve a
black-box optimization problem. Gaussian process (GP) regression is a popular
choice as a surrogate model, because of its capability of calculating
prediction uncertainty analytically. On the other hand, an ensemble of
randomized trees is another option and has practical merits over GPs due to its
scalability and easiness of handling continuous/discrete mixed variables. In
this paper we revisit various ensembles of randomized trees to investigate
their behavior in the perspective of prediction uncertainty estimation. Then,
we propose a new way of constructing an ensemble of randomized trees, referred
to as BwO forest, where bagging with oversampling is employed to construct
bootstrapped samples that are used to build randomized trees with random
splitting. Experimental results demonstrate the validity and good performance
of BwO forest over existing tree-based models in various circumstances.


title: Connecting Optimization and Generalization via Gradient Flow Path Length

abstract:
  Optimization and generalization are two essential aspects of machine
learning. In this paper, we propose a framework to connect optimization with
generalization by analyzing the generalization error based on the length of
optimization trajectory under the gradient flow algorithm after convergence.
Through our approach, we show that, with a proper initialization, gradient flow
converges following a short path with an explicit length estimate. Such an
estimate induces a length-based generalization bound, showing that short
optimization paths after convergence are associated with good generalization,
which also matches our numerical results. Our framework can be applied to broad
settings. For example, we use it to obtain generalization estimates on three
distinct machine learning models: underdetermined $\ell_p$ linear regression,
kernel regression, and overparameterized two-layer ReLU neural networks.


title: Fast Eye Detector Using Metric Learning for Iris on The Move

abstract:
  This paper proposes a fast eye detection method based on fully-convolutional
Siamese networks for iris recognition. The iris on the move system requires to
capture high resolution iris images from a moving subject for iris recognition.
Therefore, capturing images contains both eyes at high-frame-rate increases the
chance of iris imaging. In order to output the authentication result in real
time, the system requires a fast eye detector extracting the left and right eye
regions from the image. Our method extracts features of a partial face image
and a reference eye image using Siamese network frameworks. Similarity heat
maps of both eyes are created by calculating the spatial cosine similarity
between extracted features. Besides, we use CosFace as a loss function for
training to discriminate the left and right eyes with high accuracy even with a
shallow network. Experimental results show that our method trained by CosFace
is fast and accurate compared with conventional generic object detection
methods.


title: Contrastive-mixup learning for improved speaker verification

abstract:
  This paper proposes a novel formulation of prototypical loss with mixup for
speaker verification. Mixup is a simple yet efficient data augmentation
technique that fabricates a weighted combination of random data point and label
pairs for deep neural network training. Mixup has attracted increasing
attention due to its ability to improve robustness and generalization of deep
neural networks. Although mixup has shown success in diverse domains, most
applications have centered around closed-set classification tasks. In this
work, we propose contrastive-mixup, a novel augmentation strategy that learns
distinguishing representations based on a distance metric. During training,
mixup operations generate convex interpolations of both inputs and virtual
labels. Moreover, we have reformulated the prototypical loss function such that
mixup is enabled on metric learning objectives. To demonstrate its
generalization given limited training data, we conduct experiments by varying
the number of available utterances from each speaker in the VoxCeleb database.
Experimental results show that applying contrastive-mixup outperforms the
existing baseline, reducing error rate by 16% relatively, especially when the
number of training utterances per speaker is limited.


title:
Seeing is Living? Rethinking the Security of Facial Liveness
  Verification in the Deepfake Era

abstract:
  Facial Liveness Verification (FLV) is widely used for identity authentication
in many security-sensitive domains and offered as Platform-as-a-Service (PaaS)
by leading cloud vendors. Yet, with the rapid advances in synthetic media
techniques (e.g., deepfake), the security of FLV is facing unprecedented
challenges, about which little is known thus far.
  To bridge this gap, in this paper, we conduct the first systematic study on
the security of FLV in real-world settings. Specifically, we present
LiveBugger, a new deepfake-powered attack framework that enables customizable,
automated security evaluation of FLV. Leveraging LiveBugger, we perform a
comprehensive empirical assessment of representative FLV platforms, leading to
a set of interesting findings. For instance, most FLV APIs do not use
anti-deepfake detection; even for those with such defenses, their effectiveness
is concerning (e.g., it may detect high-quality synthesized videos but fail to
detect low-quality ones). We then conduct an in-depth analysis of the factors
impacting the attack performance of LiveBugger: a) the bias (e.g., gender or
race) in FLV can be exploited to select victims; b) adversarial training makes
deepfake more effective to bypass FLV; c) the input quality has a varying
influence on different deepfake techniques to bypass FLV. Based on these
findings, we propose a customized, two-stage approach that can boost the attack
success rate by up to 70%. Further, we run proof-of-concept attacks on several
representative applications of FLV (i.e., the clients of FLV APIs) to
illustrate the practical implications: due to the vulnerability of the APIs,
many downstream applications are vulnerable to deepfake. Finally, we discuss
potential countermeasures to improve the security of FLV. Our findings have
been confirmed by the corresponding vendors.


title: Extending structures for Gel'fand-Dorfman bialgebras

abstract:
  Gel'fand-Dorfman bialgebra, which is both a Lie algebra and a Novikov algebra
with some compatibility condition, appears in the study of Hamiltonian pairs in
completely integrable systems and a class of special Lie conformal algebras
called quadratic Lie conformal algebras. In this paper, we investigate the
extending structures problem for Gel'fand-Dorfman bialgebras, which is
equivalent to some extending structures problem of quadratic Lie conformal
algebras. Explicitly, given a Gel'fand-Dorfman bialgebra $(A, \circ,
[\cdot,\cdot])$, this problem asks that how to describe and classify all
Gel'fand-Dorfman bialgebraic structures on a vector space $E$ $(A\subset E$)
such that $(A, \circ, [\cdot,\cdot])$ is a subalgebra of $E$ up to an
isomorphism whose restriction on $A$ is the identity map. Motivated by the
theories of extending structures for Lie algebras and Novikov algebras, we
construct an object $\mathcal{GH}^2(V,A)$ to answer the extending structures
problem by introducing a definition of unified product for Gel'fand-Dorfman
bialgebras, where $V$ is a complement of $A$ in $E$. In particular, we
investigate the special case when $\text{dim}(V)=1$ in detail.


title:
The Transition Region between Brightest Cluster Galaxies and
  Intra-Cluster Light in Galaxy Groups and Clusters

abstract:
  We take advantage of a state-of-art semi analytic model of galaxy formation,
and the model presented in \citet{contini21a}, to investigate the mass
distribution of Brightest Cluster Galaxies (BCGs) and Intra-Cluster Light (ICL)
by addressing two points: (1) the region of transition between a BCG dominated
distribution and an ICL dominated one, and; (2) the relation between the total
BCG+ICL mass and the ICL one alone. We find the transition radius to be
independent of both BCG+ICL and halo masses, with an average of 60$\pm$40 kpc,
in good agreement with previous observational measurements, but given the large
scatter, it can be considered as a sort of physical separation between the two
components only on cluster scale. From the analysis of $M_{ICL}-M_{BCG+ICL}$
relation, we build a method able to extract the ICL mass directly from the
knowledge of the BCG+ICL one. Given the large scatter on low mass systems, such
method under/overpredicts the true value of the ICL in a significant way, up to
a factor of three in the worst cases. On the other hand, for $\log
M_{BCG+ICL}>12$ or $\log M_{Halo}>14$, the difference between the true value
and the one extracted from the $M_{ICL}-M_{BCG+ICL}$ relation ranges between
$\pm$30\%. We therefore suggest this relation as a reliable test for
observational works aiming to isolate the ICL from the BCG, for systems hosted
by haloes on cluster scale.


title:
Tracking the 3D evolution of a halo coronal mass ejection using the
  revised cone model

abstract:
  This paper aims to track the 3D evolution of a full halo CME on 2011 June 21.
The CME results from a non-radial eruption of a filament-carrying flux rope in
NOAA active region 11236. The eruption is observed in EUV wavelengths by the
EUVI on board the ahead and behind STEREO spacecrafts and the AIA on board SDO.
The CME is observed by the COR1 coronagraph on board STEREO and the C2
coronagraph on board SOHO/LASCO. The revised cone model is slightly modified,
with the top of the cone becoming a sphere, which is internally tangent to the
legs. Using the multi-point observations, the cone model is applied to derive
the morphological and kinematic properties of the CME. The cone shape fits
nicely with the CME observed by EUVI and COR1 on board STEREO twin spacecraft
and LASCO/C2 coronagraph. The cone angle increases sharply from 54$^{\circ}$ to
130$^{\circ}$ in the initial phase, indicating a rapid expansion. A relation
between the cone angle and heliocentric distance of CME leading front is
derived, $\omega=130\degr-480d^{-5}$, where $d$ is in unit of $R_{\odot}$. The
inclination angle decreases gradually from $\sim$51$^{\circ}$ to
$\sim$18$^{\circ}$, suggesting a trend of radial propagation. The heliocentric
distance increases gradually in the initial phase and quickly in the later
phase up to $\sim$11\,$R_{\odot}$. The true speed of CME reaches $\sim$1140 km
s$^{-1}$, which is $\sim$1.6 times higher than the apparent speed in the
LASCO/C2 field of view. The revised model is promising in tracking the complete
evolution of CMEs.


title: Transition Matrix Representation of Trees with Transposed Convolutions

abstract:
  How can we effectively find the best structures in tree models? Tree models
have been favored over complex black box models in domains where
interpretability is crucial for making irreversible decisions. However,
searching for a tree structure that gives the best balance between the
performance and the interpretability remains a challenging task. In this paper,
we propose TART (Transition Matrix Representation with Transposed
Convolutions), our novel generalized tree representation for optimal structural
search. TART represents a tree model with a series of transposed convolutions
that boost the speed of inference by avoiding the creation of transition
matrices. As a result, TART allows one to search for the best tree structure
with a few design parameters, achieving higher classification accuracy than
those of baseline models in feature-based datasets.


title:
Sequential Information Design: Markov Persuasion Process and Its
  Efficient Reinforcement Learning

abstract:
  In today's economy, it becomes important for Internet platforms to consider
the sequential information design problem to align its long term interest with
incentives of the gig service providers. This paper proposes a novel model of
sequential information design, namely the Markov persuasion processes (MPPs),
where a sender, with informational advantage, seeks to persuade a stream of
myopic receivers to take actions that maximizes the sender's cumulative
utilities in a finite horizon Markovian environment with varying prior and
utility functions. Planning in MPPs thus faces the unique challenge in finding
a signaling policy that is simultaneously persuasive to the myopic receivers
and inducing the optimal long-term cumulative utilities of the sender.
Nevertheless, in the population level where the model is known, it turns out
that we can efficiently determine the optimal (resp. $\epsilon$-optimal) policy
with finite (resp. infinite) states and outcomes, through a modified
formulation of the Bellman equation.
  Our main technical contribution is to study the MPP under the online
reinforcement learning (RL) setting, where the goal is to learn the optimal
signaling policy by interacting with with the underlying MPP, without the
knowledge of the sender's utility functions, prior distributions, and the
Markov transition kernels. We design a provably efficient no-regret learning
algorithm, the Optimism-Pessimism Principle for Persuasion Process (OP4), which
features a novel combination of both optimism and pessimism principles. Our
algorithm enjoys sample efficiency by achieving a sublinear $\sqrt{T}$-regret
upper bound. Furthermore, both our algorithm and theory can be applied to MPPs
with large space of outcomes and states via function approximation, and we
showcase such a success under the linear setting.


title: Physics-Informed Graph Learning: A Survey

abstract:
  An expeditious development of graph learning in recent years has found
innumerable applications in several diversified fields. Of the main associated
challenges are the volume and complexity of graph data. A lot of research has
been evolving around the preservation of graph data in a low dimensional space.
The graph learning models suffer from the inability to maintain original graph
information. In order to compensate for this inability, physics-informed graph
learning (PIGL) is emerging. PIGL incorporates physics rules while performing
graph learning, which enables numerous potentials. This paper presents a
systematic review of PIGL methods. We begin with introducing a unified
framework of graph learning models, and then examine existing PIGL methods in
relation to the unified framework. We also discuss several future challenges
for PIGL. This survey paper is expected to stimulate innovative research and
development activities pertaining to PIGL.


title: Submodlib: A Submodular Optimization Library

abstract:
  Submodular functions are a special class of set functions which naturally
model the notion of representativeness, diversity, coverage etc. and have been
shown to be computationally very efficient. A lot of past work has applied
submodular optimization to find optimal subsets in various contexts. Some
examples include data summarization for efficient human consumption, finding
effective smaller subsets of training data to reduce the model development time
(training, hyper parameter tuning), finding effective subsets of unlabeled data
to reduce the labeling costs, etc. A recent work has also leveraged submodular
functions to propose submodular information measures which have been found to
be very useful in solving the problems of guided subset selection and guided
summarization. In this work, we present Submodlib which is an open-source,
easy-to-use, efficient and scalable Python library for submodular optimization
with a C++ optimization engine. Submodlib finds its application in
summarization, data subset selection, hyper parameter tuning, efficient
training and more. Through a rich API, it offers a great deal of flexibility in
the way it can be used. Source of Submodlib is available at
https://github.com/decile-team/submodlib.


title:
Reinforcing Local Feature Representation for Weakly-Supervised Dense
  Crowd Counting

abstract:
  Fully-supervised crowd counting is a laborious task due to the large amounts
of annotations. Few works focus on weekly-supervised crowd counting, where only
the global crowd numbers are available for training. The main challenge of
weekly-supervised crowd counting is the lack of local supervision information.
To address this problem, we propose a self-adaptive feature similarity learning
(SFSL) network and a global-local consistency (GLC) loss to reinforce local
feature representation. We introduce a feature vector which represents the
unbiased feature estimation of persons. The network updates the feature vector
self-adaptively and utilizes the feature similarity for the regression of crowd
numbers. Besides, the proposed GLC loss leverages the consistency between the
network estimations from global and local areas. The experimental results
demonstrate that our proposed method based on different backbones narrows the
gap between weakly-supervised and fully-supervised dense crowd counting.


title:
Observation of boundary induced chiral anomaly bulk states and their
  transport properties

abstract:
  The robust transport of edge modes is perhaps the most useful property of
topological materials. The existence of edge modes is guaranteed by the
bulk-edge correspondence, which states that the number of topological edge
modes is determined by the bulk topological invariants. To obtain robust
transport on the edge, we need to make volumetric changes to many bulk atoms to
control the properties of a few edge atoms in a lower dimension. We suggest
here that we can do the reverse in some cases: the properties of the edge can
guarantee chiral transport phenomena in some bulk modes, achieving phenomena
that are essentially the same as those observed in topological valley-Hall
systems. Specifically, we show that a topologically trivial 2D hexagonal
phononic crystal slab (waveguide) bounded by hardwall boundaries guarantees the
existence of bulk modes with chiral anomaly inside a pseudogap. We
experimentally observed robust valley-selected transport, complete valley state
conversion, and valley focusing of the chiral anomaly bulk states (CABSs) in
such phononic crystal waveguides.


title: Maximizing the electromomentum coupling in piezoelectric laminates

abstract:
  Asymmetric piezoelectric composites exhibit coupling between their
macroscopic linear momentum and electric field, a coupling that does not appear
at the microscopic scale. This electromomentum coupling constitutes an
additional knob to tailor the dynamic response of the medium, analogously to
the Willis coupling in elastic composites. Here, we employ topology- and free
material optimization approaches to maximize the electromomentum coupling of
periodic piezoelectric laminates in the low frequency, long-wavelength limit.
We find that the coupling can be enhanced by orders of magnitude, depending on
the degrees of freedom in the optimization process. The optimal compositions
that we find provide guidelines for the design of metamaterials with maximum
electromomentum coupling, paving the way for their integration in wave control
applications.


title: Holographic Complexity in dS$_{d+1}$

abstract:
  We study the CV, CA, and CV2.0 approaches to holographic complexity in
$(d+1)$-dimensional de Sitter spacetime. We find that holographic complexity
and corresponding growth rate presents universal behaviour for all three
approaches. In particular, the holographic complexity exhibits `hyperfast'
growth [arXiv:2109.14104] and appears to diverge with a universal power law at
a (finite) critical time. We introduce a cutoff surface to regulate this
divergence, and the subsequent growth of the holographic complexity is linear
in time.


title:
Discrimination Against Immigrants in the Criminal Justice System:
  Evidence from Pretrial Detentions

abstract:
  This paper tests for discrimination against immigrant defendants in the
criminal justice system in Chile using a decade of nationwide administrative
records on pretrial detentions. Observational benchmark regressions show that
immigrant defendants are 8.6 percentage points less likely to be released
pretrial relative to Chilean defendants with similar proxies for pretrial
misconduct potential. Diagnostics for omitted variable bias -- including a
novel test to assess the quality of the proxy vector based on comparisons of
pretrial misconduct rates among released defendants -- suggest that the
discrimination estimates are not driven by omitted variable bias and that, if
anything, failing to fully account for differences in misconduct potential
leads to an underestimation of discrimination. Our estimates suggest that
discrimination stems from an informational problem because judges do not
observe criminal records in origin countries, with stereotypes and taste-based
discrimination playing a role in the problem's resolution. We find that
discrimination is especially large for drug offenses and that discrimination
increased after a recent immigration wave.


title:
Torsionfreeness for divisor class groups of toric rings of integral
  polytopes

abstract:
  In the present paper, we give some sufficient conditions for
$\operatorname{Cl}(\Bbbk[P])$ to be torsionfree, where
$\operatorname{Cl}(\Bbbk[P])$ denote the divisor class group of the toric ring
$\Bbbk[P]$ of an integral polytope $P$. We prove that
$\operatorname{Cl}(\Bbbk[P])$ is torsionfree if $P$ is compressed, and
$\operatorname{Cl}(\Bbbk[P])$ is torsionfree if $P$ is a $(0,1)$-polytope which
has at most $\dim P+2$ facets. Moreover, we characterize the toric rings of
$(0,1)$-polytopes in the case $\operatorname{Cl}(\Bbbk[P])\cong \mathbb{Z}$.


title: Cut and Continuous Paste towards Real-time Deep Fall Detection

abstract:
  Deep learning based fall detection is one of the crucial tasks for
intelligent video surveillance systems, which aims to detect unintentional
falls of humans and alarm dangerous situations. In this work, we propose a
simple and efficient framework to detect falls through a single and small-sized
convolutional neural network. To this end, we first introduce a new image
synthesis method that represents human motion in a single frame. This
simplifies the fall detection task as an image classification task. Besides,
the proposed synthetic data generation method enables to generate a sufficient
amount of training dataset, resulting in satisfactory performance even with the
small model. At the inference step, we also represent real human motion in a
single image by estimating mean of input frames. In the experiment, we conduct
both qualitative and quantitative evaluations on URFD and AIHub airport
datasets to show the effectiveness of our method.


title: Graph Lifelong Learning: A Survey

abstract:
  Graph learning substantially contributes to solving artificial intelligence
(AI) tasks in various graph-related domains such as social networks, biological
networks, recommender systems, and computer vision. However, despite its
unprecedented prevalence, addressing the dynamic evolution of graph data over
time remains a challenge. In many real-world applications, graph data
continuously evolves. Current graph learning methods that assume graph
representation is complete before the training process begins are not
applicable in this setting. This challenge in graph learning motivates the
development of a continuous learning process called graph lifelong learning to
accommodate the future and refine the previous knowledge in graph data. Unlike
existing survey papers that focus on either lifelong learning or graph learning
separately, this survey paper covers the motivations, potentials,
state-of-the-art approaches (that are well categorized), and open issues of
graph lifelong learning. We expect extensive research and development interest
in this emerging field.


title:
Thermoelectric Hall Effect at High-Magnetic-Field Quantum Limit in
  Graphite as a Nodal-Line Semimetal

abstract:
  The high-magnetic-field thermoelectric effect in nodal-line semimetals with
straight nodal lines was investigated. Three-dimensional (3D) Dirac/Weyl
semimetals exhibit constant thermoelectric Hall conductivity at the
high-magnetic-field quantum limit, resulting in a boundless linear increase of
Seebeck coefficient. This is known as the quantized thermoelectric Hall effect
(QTHE), and is expected to lead to high-performance thermoelectricity at low
temperatures. Here, in addition to Dirac/Weyl semimetals, we demonstrated that
3D semimetals with straight nodal lines can also exhibit the QTHE under high
magnetic fields. As a candidate material for experimental validation, we
discussed the thermoelectric properties of bulk graphite. Furthermore, we
investigated the dimensional crossover of thermoelectricity to two-dimensional
behavior in thin-film graphite.


title:
An Energy-concentrated Wavelet Transform for Time Frequency Analysis of
  Transient Signals

abstract:
  Transient signals are often composed of a series of modes that have
multivalued time-dependent instantaneous frequency (IF), which brings
challenges to the development of signal processing technology. Fortunately, the
group delay (GD) of such signal can be well expressed as a single valued
function of frequency. By considering the frequency-domain signal model, we
present a postprocessing method called wavelet transform (WT)-based
time-reassigned synchrosqueezing transform (WTSST). Our proposed method embeds
a two-dimensional GD operator into a synchrosqueezing framework to generate a
time-frequency representation (TFR) of transient signal with high energy
concentration and allows to retrieve the whole or part of the signal. The
theoretical analyses of the WTSST are provided, including the analysis of GD
candidate accuracy and signal reconstruction accuracy. Moreover, based on
WTSST, the WT-based time-reassigned multisynchrosqueezing transform (WTMSST) is
proposed by introducing a stepwise refinement scheme, which further improves
the drawback that the WTSST method is unable to deal with strong
frequency-varying signal. Simulation and real signal analysis illustrate that
the proposed methods have the capacity to appropriately describe the features
of transient signals.


title: An Object Aware Hybrid U-Net for Breast Tumour Annotation

abstract:
  In the clinical settings, during digital examination of histopathological
slides, the pathologist annotate the slides by marking the rough boundary
around the suspected tumour region. The marking or annotation is generally
represented as a polygonal boundary that covers the extent of the tumour in the
slide. These polygonal markings are difficult to imitate through CAD techniques
since the tumour regions are heterogeneous and hence segmenting them would
require exhaustive pixel wise ground truth annotation. Therefore, for CAD
analysis, the ground truths are generally annotated by pathologist explicitly
for research purposes. However, this kind of annotation which is generally
required for semantic or instance segmentation is time consuming and tedious.
In this proposed work, therefore, we have tried to imitate pathologist like
annotation by segmenting tumour extents by polygonal boundaries. For polygon
like annotation or segmentation, we have used Active Contours whose vertices or
snake points move towards the boundary of the object of interest to find the
region of minimum energy. To penalize the Active Contour we used modified U-Net
architecture for learning penalization values. The proposed hybrid deep
learning model fuses the modern deep learning segmentation algorithm with
traditional Active Contours segmentation technique. The model is tested against
both state-of-the-art semantic segmentation and hybrid models for performance
evaluation against contemporary work. The results obtained show that the
pathologist like annotation could be achieved by developing such hybrid models
that integrate the domain knowledge through classical segmentation methods like
Active Contours and global knowledge through semantic segmentation deep
learning models.


title:
Generation and characterization of customized perfect Laguerre-Gaussian
  beams with arbitrary profiles

abstract:
  We experimentally demonstrate the generation of customized perfect
Laguerre-Gaussian (PLG) beams whose intensity maxima localized around any
desired curves. The principle is to act appropriate algebraic functions on the
angular spectra of PLG beams. We characterize the propagation properties of
these beams and compare them with non-diffraction caustic beams possessing the
same intensity profiles. The results manifest that the customized-PLG beams can
maintain their profiles during propagation and suffer less energy loss than the
non-diffraction caustic beams, and hence are able to propagate a longer
distance. This new structure beam would have potential applications in areas
such as optical communication, soliton routing and steering, optical tweezing
and trapping, atom optics, etc.


title: Universal adversarial perturbation for remote sensing images

abstract:
  Recently, with the application of deep learning in the remote sensing image
(RSI) field, the classification accuracy of the RSI has been greatly improved
compared with traditional technology. However, even state-of-the-art object
recognition convolutional neural networks are fooled by the universal
adversarial perturbation (UAP). To verify that UAP makes the RSI classification
model error classification, this paper proposes a novel method combining an
encoder-decoder network with an attention mechanism. Firstly, the former can
learn the distribution of perturbations better, then the latter is used to find
the main regions concerned by the RSI classification model. Finally, the
generated regions are used to fine-tune the perturbations making the model
misclassified with fewer perturbations. The experimental results show that the
UAP can make the RSI misclassify, and the attack success rate (ASR) of our
proposed method on the RSI data set is as high as 97.35%.


title:
Ensembling Handcrafted Features with Deep Features: An Analytical Study
  for Classification of Routine Colon Cancer Histopathological Nuclei Images

abstract:
  The use of Deep Learning (DL) based methods in medical histopathology images
have been one of the most sought after solutions to classify, segment, and
detect diseased biopsy samples. However, given the complex nature of medical
datasets due to the presence of intra-class variability and heterogeneity, the
use of complex DL models might not give the optimal performance up to the level
which is suitable for assisting pathologists. Therefore, ensemble DL methods
with the scope of including domain agnostic handcrafted Features (HC-F)
inspired this work. We have, through experiments, tried to highlight that a
single DL network (domain-specific or state of the art pre-trained models)
cannot be directly used as the base model without proper analysis with the
relevant dataset. We have used F1-measure, Precision, Recall, AUC, and
Cross-Entropy Loss to analyse the performance of our approaches. We observed
from the results that the DL features ensemble bring a marked improvement in
the overall performance of the model, whereas, domain agnostic HC-F remains
dormant on the performance of the DL models.


title: A Framework for Multi-stage Bonus Allocation in meal delivery Platform

abstract:
  Online meal delivery is undergoing explosive growth, as this service is
becoming increasingly popular. A meal delivery platform aims to provide
excellent and stable services for customers and restaurants. However, in
reality, several hundred thousand orders are canceled per day in the Meituan
meal delivery platform since they are not accepted by the crowd soucing
drivers. The cancellation of the orders is incredibly detrimental to the
customer's repurchase rate and the reputation of the Meituan meal delivery
platform. To solve this problem, a certain amount of specific funds is provided
by Meituan's business managers to encourage the crowdsourcing drivers to accept
more orders. To make better use of the funds, in this work, we propose a
framework to deal with the multi-stage bonus allocation problem for a meal
delivery platform. The objective of this framework is to maximize the number of
accepted orders within a limited bonus budget. This framework consists of a
semi-black-box acceptance probability model, a Lagrangian dual-based dynamic
programming algorithm, and an online allocation algorithm. The semi-black-box
acceptance probability model is employed to forecast the relationship between
the bonus allocated to order and its acceptance probability, the Lagrangian
dual-based dynamic programming algorithm aims to calculate the empirical
Lagrangian multiplier for each allocation stage offline based on the historical
data set, and the online allocation algorithm uses the results attained in the
offline part to calculate a proper delivery bonus for each order. To verify the
effectiveness and efficiency of our framework, both offline experiments on a
real-world data set and online A/B tests on the Meituan meal delivery platform
are conducted. Our results show that using the proposed framework, the total
order cancellations can be decreased by more than 25\% in reality.


title: Bounds on the precision of currents in underdamped Langevin dynamics

abstract:
  We derive bounds on the precision of fluctuating currents, which are valid
for the steady state of underdamped Langevin dynamics. These bounds provide a
generalization of the overdamped thermodynamic uncertainty relation to the
finite-mass regime. In the overdamped case, the precision of a current is
bounded by the entropy production. By contrast, the underdamped bound acquires
two additional positive terms, which characterize the local mean acceleration
and the fluctuations of the velocity. We generalize the bound to the cases of a
magnetic field and anisotropic temperature, and derive a joint bound for
several observables. Finally, we apply our results to biased free diffusion and
the Brownian gyrator (with and without magnetic field), as well as to diffusion
in periodic potentials. In the latter case, we show that the underdamped bound
can be tight when taking into account the correlations between the current and
other observables.


title: Automatic Test Pattern Generation for Robust Quantum Circuit Testing

abstract:
  Quantum circuit testing is essential for detecting potential faults in
realistic quantum devices, while the testing process itself also suffers from
the inexactness and unreliability of quantum operations. This paper alleviates
the issue by proposing a novel framework of automatic test pattern generation
(ATPG) for the robust quantum circuit testing. We introduce the stabilizer
projector decomposition (SPD) for representing the quantum test pattern, and
construct the test application using classical randomness and Clifford-only
circuits, which are rather robust and efficient as shown in the fault-tolerant
quantum computation. However, it is generally hard to generate SPDs due to the
super-exponentially growing number of the stabilizer projectors. To circumvent
this difficulty, we develop an SPD generation algorithm, as well as several
acceleration techniques which can exploit both locality and sparsity in
generating SPDs. Furthermore, we provide in-depth analyses for the proposed
algorithms, both theoretically and empirically. To demonstrate the
effectiveness and efficiency, we implement and evaluate our algorithms on
several commonly used benchmark circuits, such as Quantum Fourier Transform
(QFT), Quantum Volume (QV) and Bernstein-Vazirani (BV) in IBM Qiskit. For
example, test patterns are automatically generated by our algorithm for a
10-qubit QFT circuit, and then a fault is detected by simulating the test
application with detection accuracy higher than 91%.


title:
A general theory of specific binding: insights from a genetic,
  mechano-chemical protein model

abstract:
  Proteins need to selectively interact with specific targets among a multitude
of similar molecules in the cell. Despite a firm physical understanding of
binding interactions, we lack a general theory of how proteins evolve high
specificity. Here, we present a model that combines chemistry, mechanics and
genetics, and explains how their interplay governs the evolution of specific
protein-ligand interactions. The model shows that there are many routes to
achieving discrimination - by varying degrees of flexibility and
shape/chemistry complementarity - but the key ingredient is precision. Harder
discrimination tasks require more collective and precise coaction of structure,
forces and movements. Proteins can achieve this through correlated mutations
extending far from a binding site, which fine tune the localized interaction
with the ligand. Thus, the solution of more complicated tasks is aided by
increasing the protein, and proteins become more evolvable and robust when they
are larger than the bare minimum required for discrimination. Our model makes
testable, specific predictions about the role of flexibility in discrimination,
and how to independently tune affinity and specificity. The proposed theory of
molecular discrimination addresses the natural question "why are proteins so
big?" A possible answer is that molecular discrimination is often a hard task
best performed by adding more layers to the protein.


title: Maximally distributed random fields under sublinear expectation

abstract:
  This paper focuses on the maximal distribution on sublinear expectation space
and introduces a new type of random fields with the maximally distributed
finite-dimensional distribution. The corresponding spatial maximally
distributed white noise is constructed, which includes the temporal-spatial
situation as a special case due to the symmetrical independence property of
maximal distribution. In addition, the stochastic integrals with respect to the
spatial or temporal-spatial maximally distributed white noises are established
in a quite direct way without the usual assumption of adaptability for
integrand.


title:
TBA-like equations for non-planar scattering amplitude/Wilson lines
  duality at strong coupling

abstract:
  We compute the minimal area of a string worldsheet ending on two infinite
periodic light-like Wilson lines in the AdS$_3$ boundary, which is dual to the
first non-planar correction to the gluon scattering amplitude in
$\mathcal{N}=4$ SYM at the strong coupling. Using the connection between the
Hitchin system and the thermodynamic Bethe ansatz (TBA) equations, we present
an analytic method to compute the minimal area surface and express the
non-trivial part of the minimal area in terms of the free energy of the
TBA-like equations. Given the cross ratios as inputs, the area computed from
the TBA-like equations matches that calculated using the numerical integration.


title:
Bag of Visual Words (BoVW) with Deep Features -- Patch Classification
  Model for Limited Dataset of Breast Tumours

abstract:
  Currently, the computational complexity limits the training of high
resolution gigapixel images using Convolutional Neural Networks. Therefore,
such images are divided into patches or tiles. Since, these high resolution
patches are encoded with discriminative information therefore; CNNs are trained
on these patches to perform patch-level predictions. However, the problem with
patch-level prediction is that pathologist generally annotates at image-level
and not at patch level. Due to this limitation most of the patches may not
contain enough class-relevant features. Through this work, we tried to
incorporate patch descriptive capability within the deep framework by using Bag
of Visual Words (BoVW) as a kind of regularisation to improve generalizability.
Using this hypothesis, we aim to build a patch based classifier to discriminate
between four classes of breast biopsy image patches (normal, benign, \textit{In
situ} carcinoma, invasive carcinoma). The task is to incorporate quality deep
features using CNN to describe relevant information in the images while
simultaneously discarding irrelevant information using Bag of Visual Words
(BoVW). The proposed method passes patches obtained from WSI and microscopy
images through pre-trained CNN to extract features. BoVW is used as a feature
selector to select most discriminative features among the CNN features.
Finally, the selected feature sets are classified as one of the four classes.
The hybrid model provides flexibility in terms of choice of pre-trained models
for feature extraction. The pipeline is end-to-end since it does not require
post processing of patch predictions to select discriminative patches. We
compared our observations with state-of-the-art methods like ResNet50,
DenseNet169, and InceptionV3 on the BACH-2018 challenge dataset. Our proposed
method shows better performance than all the three methods.