title:
US Salary History Bans -- Strategic Disclosure by Job Applicants and the
  Gender Pay Gap

abstract:
  I study the effects of US salary history bans which restrict employers from
inquiring about job applicants' pay history during the hiring process, but
allow candidates to voluntarily share information. Using a
difference-in-differences design, I show that these policies narrowed the
gender pay gap significantly by 2 p.p., driven almost entirely by an increase
in female earnings. The bans were also successful in weakening the
auto-correlation between current and future earnings, especially among
job-changers. I provide novel evidence showing that when employers could no
longer nudge candidates for information, the likelihood of voluntarily
disclosing salary history decreased among job applicants and by 2 p.p. more
among women. I then develop a salary negotiation model with asymmetric
information, where I allow job applicants to choose whether to reveal pay
history, and use this framework to explain my empirical findings on disclosure
behavior and gender pay gap.


title: The Restricted Partition and q-Partial Fractions

abstract:
  In this paper, we provide direct formulae for q-partial fraction
decomposition of the generating function associated with the restricted
partition of n into parts none of which exceed N. As a consequence of our
formulae we also derive some closed-form expressions for some coefficients of
the q-partial fractions. We completely determine the top-order terms of each
Sylvester wave by a special trigonometric sum which we term Gaussian-Ramanujan
sum. We also notice a surprising appearance of sum of products expressions of
degenerate Bernoulli and degenerate (modified) Euler numbers for the
coefficients associated with the first and the second waves. Our methodology is
based on an algebraic approach for partial fractions associated with the
cyclotomic polynomials. A SageMath code of our formulae is made publicly
available.


title: A cut-by-curves criterion for overconvergent of $F$-isocrystals

abstract:
  Let $X$ be a smooth scheme over a finite field. It is conjectured that a
convergent $F$-isocrystal on $X$ is overconvergent if its restriction to every
curve contained in $X$ is overconvergent. Using the theory of \'etale and
crystalline companions, we establish a weaker version of this criterion in
which we also assume that the wild local monodromy of the restrictions to
curves is trivialized by pullback along a single dominant morphism to $X$.


title: Universal logic with encoded spin qubits in silicon

abstract:
  Qubits encoded in a decoherence-free subsystem and realized in
exchange-coupled silicon quantum dots are promising candidates for
fault-tolerant quantum computing. Benefits of this approach include excellent
coherence, low control crosstalk, and configurable insensitivity to certain
error sources. Key difficulties are that encoded entangling gates require a
large number of control pulses and high-yielding quantum dot arrays. Here we
show a device made using the single-layer etch-defined gate electrode
architecture that achieves both the required functional yield needed for full
control and the coherence necessary for thousands of calibrated exchange pulses
to be applied. We measure an average two-qubit Clifford fidelity of $97.1 \pm
0.2\%$ with randomized benchmarking. We also use interleaved randomized
benchmarking to demonstrate the controlled-NOT gate with $96.3 \pm 0.7\%$
fidelity, SWAP with $99.3 \pm 0.5\%$ fidelity, and a specialized entangling
gate that limits spreading of leakage with $93.8 \pm 0.7\%$ fidelity.


title:
Ordered domains in sheared dense suspensions: the link to viscosity and
  the disruptive effect of friction

abstract:
  Monodisperse suspensions of Brownian colloidal spheres crystallize at high
densities, and ordering under shear has been observed at densities below the
crystallization threshold. We perform large-scale simulations of a model
suspension containing over $10^5$ particles to quantitatively study the
ordering under shear and to investigate its link to the rheological properties
of the suspension. We find that at high rates, for $Pe>1$, the shear flow
induces an ordering transition that significantly decreases the measured
viscosity. This ordering is analyzed in terms of the development of layering
and planar order, and we determine that particles are packed into hexagonal
crystal layers (with numerous defects) that slide past each other. By computing
local $\psi_6$ and $\psi_4$ order parameters, we determine that the defects
correspond to chains of particles in a square-like lattice. We compute the
individual particle contributions to the stress tensor and discover that the
largest contributors to the shear stress are primarily located in these lower
density, defect regions. The defect structure enables the formation of
compressed chains of particles to resist the shear, but these chains are
transient and short-lived. The inclusion of a contact friction force allows the
stress-bearing structures to grow into a system-spanning network, thereby
disrupting the order and drastically increasing the suspension viscosity.


title: On quantum states over time

abstract:
  In 2017, D. Horsman, C. Heunen, M. Pusey, J. Barrett, and R. Spekkens proved
that there is no physically reasonable assignment that takes a quantum channel
and an initial state and produces a joint state on the tensor product of the
input and output spaces. The interpretation was that there is a clear
distinction between space and time in the quantum setting that is not visible
classically, where in the latter, one can freely use Bayes' theorem to go
between joint states and marginals with noisy channels. In this paper, we prove
that there actually is such a physically reasonable assignment, bypassing the
no-go result of Horsman et al., and we illustrate that this is achievable by
restricting the domain of their assignment to a domain which represents the
given data more faithfully. This answers an open question at the end of their
work, thus indicating the possibility that such a symmetry between space and
time may exist in the quantum setting.


title:
Electromagnetic response of cuprate superconductors with coexisting
  electronic nematicity

abstract:
  The electronically nematic order has emerged as a key feature of cuprate
superconductors, however, its correlation with the fundamental properties such
as the electromagnetic response remains unclear. Here the nematic-order state
strength dependence of the electromagnetic response in cuprate superconductors
is investigated within the framework of the kinetic-energy-driven
superconductivity. It is shown that a significant anisotropy of the
electromagnetic response is caused by the electronic nematicity. In particular,
in addition to the pure d-wave component of the superconducting gap, the pure
s-wave component of the superconducting gap is generated by the electronic
nematicity, therefore there is a coexistence and competition of the d-wave
component and the s-wave component. This coexistence and competition leads to
that the maximal condensation energy appears at around the optimal strength of
the electronic nematicity, and then decreases in both the weak and strong
strength regions, which in turn induces the enhancement of superconductivity,
and gives rise to the dome-like shape of the nematic-order state strength
dependence of the superfluid density.


title: Backdoor Detection in Reinforcement Learning

abstract:
  While the real world application of reinforcement learning (RL) is becoming
popular, the safety concern and the robustness of an RL system require more
attention. A recent work reveals that, in a multi-agent RL environment,
backdoor trigger actions can be injected into a victim agent (a.k.a. trojan
agent), which can result in a catastrophic failure as soon as it sees the
backdoor trigger action. We propose the problem of RL Backdoor Detection,
aiming to address this safety vulnerability. An interesting observation we drew
from extensive empirical studies is a trigger smoothness property where normal
actions similar to the backdoor trigger actions can also trigger low
performance of the trojan agent. Inspired by this observation, we propose a
reinforcement learning solution TrojanSeeker to find approximate trigger
actions for the trojan agents, and further propose an efficient approach to
mitigate the trojan agents based on machine unlearning. Experiments show that
our approach can correctly distinguish and mitigate all the trojan agents
across various types of agents and environments.


title:
Codebook Design for Composite Beamforming in Next-generation mmWave
  Systems

abstract:
  In pursuance of the unused spectrum in higher frequencies, millimeter wave
(mmWave) bands have a pivotal role. However, the high path-loss and poor
scattering associated with mmWave communications highlight the necessity of
employing effective beamforming techniques. In order to efficiently search for
the beam to serve a user and to jointly serve multiple users it is often
required to use a composite beam which consists of multiple disjoint lobes. A
composite beam covers multiple desired angular coverage intervals (ACIs) and
ideally has maximum and uniform gain (smoothness) within each desired ACI,
negligible gain (leakage) outside the desired ACIs, and sharp edges. We propose
an algorithm for designing such ideal composite codebook by providing an
analytical closed-form solution with low computational complexity. There is a
fundamental trade-off between the gain, leakage and smoothness of the beams.
Our design allows to achieve different values in such trade-off based on
changing the design parameters. We highlight the shortcomings of the uniform
linear arrays (ULAs) in building arbitrary composite beams. Consequently, we
use a recently introduced twin-ULA (TULA) antenna structure to effectively
resolve these inefficiencies. Numerical results are used to validate the
theoretical findings.


title: Do Language Models Learn Position-Role Mappings?

abstract:
  How is knowledge of position-role mappings in natural language learned? We
explore this question in a computational setting, testing whether a variety of
well-performing pertained language models (BERT, RoBERTa, and DistilBERT)
exhibit knowledge of these mappings, and whether this knowledge persists across
alternations in syntactic, structural, and lexical alternations. In Experiment
1, we show that these neural models do indeed recognize distinctions between
theme and recipient roles in ditransitive constructions, and that these
distinct patterns are shared across construction type. We strengthen this
finding in Experiment 2 by showing that fine-tuning these language models on
novel theme- and recipient-like tokens in one paradigm allows the models to
make correct predictions about their placement in other paradigms, suggesting
that the knowledge of these mappings is shared rather than independently
learned. We do, however, observe some limitations of this generalization when
tasks involve constructions with novel ditransitive verbs, hinting at a degree
of lexical specificity which underlies model performance.


title:
HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic
  Analysis

abstract:
  Contextualized word embeddings have demonstrated state-of-the-art performance
in various natural language processing tasks including those that concern
historical semantic change. However, language models such as BERT was trained
primarily on contemporary corpus data. To investigate whether training on
historical corpus data improves diachronic semantic analysis, we present a
pre-trained BERT-based language model, HistBERT, trained on the balanced Corpus
of Historical American English. We examine the effectiveness of our approach by
comparing the performance of the original BERT and that of HistBERT, and we
report promising results in word similarity and semantic shift analysis. Our
work suggests that the effectiveness of contextual embeddings in diachronic
semantic analysis is dependent on the temporal profile of the input text and
care should be taken in applying this methodology to study historical semantic
change.


title: Conformal prediction for the design problem

abstract:
  In many real-world deployments of machine learning, we use a prediction
algorithm to choose what data to test next. For example, in the protein design
problem, we have a regression model that predicts some real-valued property of
a protein sequence, which we use to propose new sequences believed to exhibit
higher property values than observed in the training data. Since validating
designed sequences in the wet lab is typically costly, it is important to know
how much we can trust the model's predictions. In such settings, however, there
is a distinct type of distribution shift between the training and test data:
one where the training and test data are statistically dependent, as the latter
is chosen based on the former. Consequently, the model's error on the test data
-- that is, the designed sequences -- has some non-trivial relationship with
its error on the training data. Herein, we introduce a method to quantify
predictive uncertainty in such settings. We do so by constructing confidence
sets for predictions that account for the dependence between the training and
test data. The confidence sets we construct have finite-sample guarantees that
hold for any prediction algorithm, even when a trained model chooses the
test-time input distribution. As a motivating use case, we demonstrate how our
method quantifies uncertainty for the predicted fitness of designed protein
using several real data sets.


title: An Exact Method for the Daily Package Shipment Problem with Outsourcing

abstract:
  The package shipment problem requires to optimally co-design paths for both
packages and a heterogeneous fleet in a transit center network (TCN). Instances
arising from the package delivery industry in China usually involve more than
ten thousand origin-destination (OD) pairs and have to be solved daily within
an hour. Motivated by the fact that there is no interaction among different
origin centers due to their competitive relationship, we propose a novel
two-layer localized package shipment on a TCN (LPS-TCN) model that exploits
outsourcing for cost saving. Consequently, the original problem breaks into a
set of much smaller shipment problems, each of which has hundreds of OD pairs
and is subsequently modelled as a mixed integer program (MIP). Since the
LPS-TCN model is proved to be Strongly NP-hard and contains tens of thousands
of feasible paths, an off-the-shelf MIP solver cannot produce a reliable
solution in a practically acceptable amount of time. We develop a column
generation based algorithm that iteratively adds "profitable" paths and further
enhance it by problem-specific cutting planes and variable bound tightening
techniques. Computational experiments on realistic instances from a major
Chinese package express company demonstrate that the LPS-TCN model can yield
solutions that bring daily economic cost reduction up to 1 million CNY for the
whole TCN. In addition, our proposed algorithm solves the LPS-TCN model
substantially faster than CPLEX, one of the state-of-the-art commercial MIP
solvers.


title:
Third-order $k$-Jacobsthal matrix sequence: Another way of demonstrating
  their properties

abstract:
  Recently, Cerda-Morales \cite{Ce6} introduced commutative matrices derived
from the third-order Jacobsthal matrix sequence and the third-order
Jacobsthal--Lucas matrix sequence. In the present work, through the
identification of certain special matrices, we can identify other forms of
demonstration and also the description of commutative matrix properties for
negative indices. A new generalization of this sequence is used for our
purpose.


title: Towards Property-Based Tests in Natural Language

abstract:
  We consider a new approach to generate tests from natural language. Rather
than relying on machine learning or templated extraction from structured
comments, we propose to apply classic ideas from linguistics to translate
natural-language sentences into executable tests. This paper explores the
application of combinatory categorial grammars (CCGs) to generating
property-based tests. Our prototype is able to generate tests from English
descriptions for each example in a textbook chapter on property-based testing.


title:
The role of Blockchain in DDoS attacks mitigation: techniques, open
  challenges and future directions

abstract:
  With the proliferation of new technologies such as Internet of Things (IOT)
and Software-Defined Networking(SDN) in the recent years, the distributed
denial of service (DDoS)attack vector has broadened and opened new
opportunities for more sophisticated DDoS attacks on the targeted victims. The
new attack vector includes unsecured and vulnerable IoT devices connected to
the internet, denial of service vulnerabilities like southbound channel
saturation in the SDN architecture. Given the high-volume and pervasive nature
of these attacks, it is beneficial for stakeholders to collaborate in detecting
and mitigating the denial of service attacks in a timely manner. The blockchain
technology is considered to improve the security aspects owing to the
decentralized design, secured distributed storage and privacy. A thorough
exploration and classification of blockchain techniques used for DDoS attack
mitigation is not explored in the prior art. This paper reviews and categorizes
the existed state-of-the-art DDoS mitigation solutions based on blockchain
technology. The DDoS mitigation techniques are classified based on the solution
deployment location i.e. network based, near attacker location, near victim
location and hybrid solutions in the network architecture with emphasis on the
IoT and SDN architectures. Additionally, based on our study, the research
challenges and future directions to implement the blockchain based DDoS
mitigation solutions are discussed. We believe that this paper could serve as a
starting point and reference resource for future researchers working on denial
of service attacks detection and mitigation using blockchain technology.


title:
On the Convergence of Gradient Extrapolation Methods for Unbalanced
  Optimal Transport

abstract:
  We study the Unbalanced Optimal Transport (UOT) between two measures of
possibly different masses with at most $n$ components, where marginal
constraints of the standard Optimal Transport (OT) are relaxed via
Kullback-Leibler divergence with regularization factor $\tau$. We propose a
novel algorithm based on Gradient Extrapolation Method (GEM-UOT) to find an
$\varepsilon$-approximate solution to the UOT problem in $O\big( \kappa n^2
\log\big(\frac{\tau n}{\varepsilon}\big) \big)$, where $\kappa$ is the
condition number depending on only the two input measures. Compared to the only
known complexity ${O}\big(\tfrac{\tau n^2 \log(n)}{\varepsilon}
\log\big(\tfrac{\log(n)}{{\varepsilon}}\big)\big)$ for solving the UOT problem
via the Sinkhorn algorithm, ours is better in $\varepsilon$ and lifts
Sinkhorn's linear dependence on $\tau$, which hindered its practicality to
approximate the standard OT via UOT. Our proof technique is based on a novel
dual formulation of the squared $\ell_2$-norm regularized UOT objective, which
is of independent interest and also leads to a new characterization of
approximation error between UOT and OT in terms of both the transportation plan
and transport distance. To this end, we further present an algorithm, based on
GEM-UOT with fine tuned $\tau$ and a post-process projection step, to find an
$\varepsilon$-approximate solution to the standard OT problem in $O\big( \kappa
n^2 \log\big(\frac{ n}{\varepsilon}\big) \big)$, which is a new complexity in
the literature of OT. Extensive experiments on synthetic and real datasets
validate our theories and demonstrate the favorable performance of our methods
in practice.


title:
An Evolutionary Pathway for the Quantum Internet Relying on Secure
  Classical Repeaters

abstract:
  Until quantum repeaters become mature, quantum networks remain restricted
either to limited areas of directly connected nodes or to nodes connected to a
common node. We circumvent this limitation by conceiving quantum networks using
secure classical repeaters combined with the quantum secure direct
communication (QSDC) principle, which is a compelling form of quantum
communication that directly transmits information over quantum channel. The
final component of this promising solution is our classical quantum-resistant
algorithm. Explicitly, in these networks, the ciphertext gleaned from a
quantum-resistant algorithm is transmitted using QSDC along the nodes, where it
is read out and then transmitted to the next node. At the repeaters, the
information is protected by our quantum-resistant algorithm, which is secure
even in the face of a quantum computer. Hence, our solution offers secure
end-to-end communication across the entire network, since it is capable of both
eavesdropping detection and prevention in the emerging quantum internet. It is
compatible with operational networks, and will enjoy the compelling services of
the popular Internet, including authentication. Hence, it smoothens the
transition from the classical Internet to the Quantum Internet (Qinternet) by
following a gradual evolutionary upgrade. It will act as an alternative network
in quantum computing networks in the future. We have presented the first
experimental demonstration of a secure classical repeater based hybrid quantum
network constructed by a serial concatenation of an optical fiber and
free-space communication link. In conclusion, secure repeater networks may
indeed be constructed using existing technology and continue to support a
seamless evolutionary pathway to the future Qinternet of quantum computers.


title: Optimal Control with Learning on the Fly: System with Unknown Drift

abstract:
  This paper derives an optimal control strategy for a simple stochastic
dynamical system with constant drift and an additive control input. Motivated
by the example of a physical system with an unexpected change in its dynamics,
we take the drift parameter to be unknown, so that it must be learned while
controlling the system. The state of the system is observed through a linear
observation model with Gaussian noise. In contrast to most previous work, which
focuses on a controller's asymptotic performance over an infinite time horizon,
we minimize a quadratic cost function over a finite time horizon. The
performance of our control strategy is quantified by comparing its cost with
the cost incurred by an optimal controller that has full knowledge of the
parameters. This approach gives rise to several notions of "regret." We derive
a set of control strategies that provably minimize the worst-case regret; these
arise from Bayesian strategies that assume a specific fixed prior on the drift
parameter. This work suggests that examining Bayesian strategies may lead to
optimal or near-optimal control strategies for a much larger class of realistic
dynamical models with unknown parameters.


title: Bandit Sampling for Multiplex Networks

abstract:
  Graph neural networks have gained prominence due to their excellent
performance in many classification and prediction tasks. In particular, they
are used for node classification and link prediction which have a wide range of
applications in social networks, biomedical data sets, and financial
transaction graphs. Most of the existing work focuses primarily on the monoplex
setting where we have access to a network with only a single type of connection
between entities. However, in the multiplex setting, where there are multiple
types of connections, or \emph{layers}, between entities, performance on tasks
such as link prediction has been shown to be stronger when information from
other connection types is taken into account. We propose an algorithm for
scalable learning on multiplex networks with a large number of layers. The
efficiency of our method is enabled by an online learning algorithm that learns
how to sample relevant neighboring layers so that only the layers with relevant
information are aggregated during training. This sampling differs from prior
work, such as MNE, which aggregates information across \emph{all} layers and
consequently leads to computational intractability on large networks. Our
approach also improves on the recent layer sampling method of \textsc{DeePlex}
in that the unsampled layers do not need to be trained, enabling further
increases in efficiency.We present experimental results on both synthetic and
real-world scenarios that demonstrate the practical effectiveness of our
proposed approach.


title:
The synergistic modulation of electronic and geometry structures leads
  to ultra-low thermal conductivity of graphene-like borides (g-B3X5, X=N, P,
  As)

abstract:
  The design of novel devices with specific technical interests through
modulating structural properties and bonding characteristics promotes the
vigorous development of materials informatics. Herein, we propose a synergy
strategy of component reconstruction by combining geometric configuration and
bonding characteristics. With the synergy strategy, we designed a novel
two-dimensional (2D) graphene-like borides, e.g. g-B3N5, which possesses
counter-intuitive ultra-low thermal conductivity of 21.08 W/mK despite the
small atomic mass. The ultra-low thermal conductivity is attributed to the
synergy effect of electronics and geometry on thermal transport due to the
combining reconstruction of g-BN and nitrogene. With the synergy effect, the
dominant acoustic branches are strongly softened, and the scattering absorption
and Umklapp process are simultaneously suppressed. Thus, the thermal
conductivity is significantly lowered. To verify the component reconstruction
strategy, we further constructed g-B3P5 and g-B3As5, and uncovered the
ultra-low thermal conductivity of 2.50 and 1.85 W/mK, respectively. The synergy
effect and the designed ultra-low thermal conductivity materials with
lightweight atomic mass cater to the demand for light development of momentum
machinery and heat protection, such as aerospace vehicles, high-speed rail,
automobiles.


title:
Emergence of Crystalline Few-body Correlations in Mass-imbalanced Fermi
  Polarons

abstract:
  Identifying few-body correlations is an efficient tool to solve complex
many-body problems. Polarons, interpolating between few- and many-body systems,
serve as an ideal platform to achieve the goal. In this work, we reveal the
emergence of various crystalline few-body correlations from the mass-imbalanced
Fermi polarons in two dimension. A unified variational approach up to three
particle-hole excitations allows us to extract the dominant $n$-body
correlations for $n$ ranging from $2$ (dimer), $3$ (trimer) to $4$ (tetramer).
When the fermion-impurity mass ratio is beyond certain critical value, the
Fermi polaron is found to undergo a smooth crossover, instead of a sharp
transition, from the polaronic to trimer and tetramer regimes as increasing the
fermion-impurity attraction. The emergent trimer and tetramer correlations
result in the {\it momentum-space crystallization} of particle-hole excitations
featuring a stable diagonal or triangular structure, as can be directly probed
through the density-density correlation of majority fermions. Our results shed
light on the intriguing quantum phases in the mass-imbalanced Fermi-Fermi
mixtures beyond the pairing superfluid paradigm.


title:
Understanding the Trustworthiness Management in the Social Internet of
  Things: A Survey

abstract:
  The next generation of the Internet of Things (IoT) facilitates the
integration of the notion of social networking into smart objects (i.e.,
things) in a bid to establish the social network of interconnected objects.
This integration has led to the evolution of a promising and emerging paradigm
of Social Internet of Things (SIoT), wherein the smart objects act as social
objects and intelligently impersonate the social behaviour similar to that of
humans. These social objects are capable of establishing social relationships
with the other objects in the network and can utilize these relationships for
service discovery. Trust plays a significant role to achieve the common goal of
trustworthy collaboration and cooperation among the objects and provide
systems' credibility and reliability. In SIoT, an untrustworthy object can
disrupt the basic functionality of a service by delivering malicious messages
and adversely affect the quality and reliability of the service. In this
survey, we present a holistic view of trustworthiness management for SIoT. The
essence of trust in various disciplines has been discussed along with the Trust
in SIoT followed by a detailed study on trust management components in SIoT.
Furthermore, we analyzed and compared the trust management schemes by primarily
categorizing them into four groups in terms of their strengths, limitations,
trust management components employed in each of the referred trust management
schemes, and the performance of these studies vis-a-vis numerous trust
evaluation dimensions. Finally, we have discussed the future research
directions of the emerging paradigm of SIoT, particularly for trustworthiness
management in SIoT.


title:
Hitting probabilities of Gaussian random fields and collision of
  eigenvalues of random matrices

abstract:
  Let $X= \{X(t), t \in \mathbb R^N\}$ be a centered Gaussian random field with
values in $\mathbb R^d$ satisfying certain conditions and let $F \subset
\mathbb R^d$ be a Borel set. In our main theorem, we provide a sufficient
condition for $F$ to be polar for $X$, i.e. $\mathbb P \big( X(t) \in F \hbox{
for some } t \in \mathbb R^N \big) = 0$, which improves significantly the main
result in Dalang et al [7], where the case of $F$ being a singleton was
considered. We provide a variety of examples of Gaussian random field for which
our result is applicable. Moreover, by using our main theorem, we solve a
problem on the existence of collisions of the eigenvalues of random matrices
with Gaussian random field entries that was left open in Jaramillo and Nualart
[14] and Song et al [21].


title:
Covariant BRST Quantization of Unimodular Gravity I -- Formulation with
  antisymmetric tensor ghosts --

abstract:
  Unimodular gravity (UG) is an interesting theory that may explain why the
cosmological constant is extremely small, in contrast to general relativity
(GR). The theory has only the transverse diffeomorphism invariance and this
causes lots of debate as to the equivalence of UG to GR in the covariant
quantization. We study the covariant BRST quantization of UG by gauge fixing
only the transverse diffeomorphism and show that the remaining physical degrees
of freedom are two, the same number as GR. This is achieved by using
antisymmetric tensor ghost fields which automatically satisfy the transverse
condition without nonlocal projection operator. The theory exhibits the ghosts
for ghosts phenomenon, which requires further gauge fixing and introduction of
more ghosts. We identify the BRST quartet structure among the various fields
and single out the remaining physical degrees of freedom.


title:
Single-frame label-free cell tomography at speed of more than 10,000
  volumes per second

abstract:
  Three-dimensional (3D) image cytometers may significantly improve the cell
analysis accuracy to facilitate biological discoveries and clinical diagnosis,
but their development is curbed by the low imaging throughput. Here we report
SIngle-frame LAbel-free Cell Tomography (SILACT) with diffraction-limited
resolution and unprecedented imaging speed of over 10,000 volumes/second.
SILACT is built on a unique interferometric microscope with angle-multiplexing
illumination and a pre-trained physics-incorporating Deep Neural Network for
efficient 3D Refractive Index (RI) reconstruction, from which 3D morphological
and biophysical parameters of cells are extracted. With microfluidics and a
high-speed camera, SILACT is capable of imaging over 20,000 cells/second and
distinguishing different cell species during rapid measurements of large cell
quantities, as well as visualizing shear-induced 3D transient deformation of
red blood cells on a sub-millisecond scale.


title: Graph-Relational Domain Adaptation

abstract:
  Existing domain adaptation methods tend to treat every domain equally and
align them all perfectly. Such uniform alignment ignores topological structures
among different domains; therefore it may be beneficial for nearby domains, but
not necessarily for distant domains. In this work, we relax such uniform
alignment by using a domain graph to encode domain adjacency, e.g., a graph of
states in the US with each state as a domain and each edge indicating
adjacency, thereby allowing domains to align flexibly based on the graph
structure. We generalize the existing adversarial learning framework with a
novel graph discriminator using encoding-conditioned graph embeddings.
Theoretical analysis shows that at equilibrium, our method recovers classic
domain adaptation when the graph is a clique, and achieves non-trivial
alignment for other types of graphs. Empirical results show that our approach
successfully generalizes uniform alignment, naturally incorporates domain
information represented by graphs, and improves upon existing domain adaptation
methods on both synthetic and real-world datasets. Code will soon be available
at https://github.com/Wang-ML-Lab/GRDA.


title: Survey of Hallucination in Natural Language Generation

abstract:
  Natural Language Generation (NLG) has improved exponentially in recent years
thanks to the development of sequence-to-sequence deep learning technologies
such as Transformer-based language models. This advancement has led to more
fluent and coherent NLG, leading to improved development in downstream tasks
such as abstractive summarization, dialogue generation and data-to-text
generation. However, it is also apparent that deep learning based generation is
prone to hallucinate unintended text, which degrades the system performance and
fails to meet user expectations in many real-world scenarios. To address this
issue, many studies have been presented in measuring and mitigating
hallucinated texts, but these have never been reviewed in a comprehensive
manner before. In this survey, we thus provide a broad overview of the research
progress and challenges in the hallucination problem in NLG. The survey is
organized into two parts: (1) a general overview of metrics, mitigation
methods, and future directions; and (2) an overview of task-specific research
progress on hallucinations in the following downstream tasks, namely
abstractive summarization, dialogue generation, generative question answering,
data-to-text generation, and machine translation. This survey serves to
facilitate collaborative efforts among researchers in tackling the challenge of
hallucinated texts in NLG.


title:
Domain Adversarial Spatial-Temporal Network: A Transferable Framework
  for Short-term Traffic Forecasting across Cities

abstract:
  Accurate real-time traffic forecast is critical for intelligent
transportation systems (ITS) and it serves as the cornerstone of various smart
mobility applications. Though this research area is dominated by deep learning,
recent studies indicate that the accuracy improvement by developing new model
structures is becoming marginal. Instead, we envision that the improvement can
be achieved by transferring the "forecasting-related knowledge" across cities
with different data distributions and network topologies. To this end, this
paper aims to propose a novel transferable traffic forecasting framework:
Domain Adversarial Spatial-Temporal Network (DASTNet). DASTNet is pre-trained
on multiple source networks and fine-tuned with the target network's traffic
data. Specifically, we leverage the graph representation learning and
adversarial domain adaptation techniques to learn the domain-invariant node
embeddings, which are further incorporated to model the temporal traffic data.
To the best of our knowledge, we are the first to employ adversarial
multi-domain adaptation for network-wide traffic forecasting problems. DASTNet
consistently outperforms all state-of-the-art baseline methods on three
benchmark datasets. The trained DASTNet is applied to Hong Kong's new traffic
detectors, and accurate traffic predictions can be delivered immediately
(within one day) when the detector is available. Overall, this study suggests
an alternative to enhance the traffic forecasting methods and provides
practical implications for cities lacking historical traffic data.


title: Robotic Grasping from Classical to Modern: A Survey

abstract:
  Robotic Grasping has always been an active topic in robotics since grasping
is one of the fundamental but most challenging skills of robots. It demands the
coordination of robotic perception, planning, and control for robustness and
intelligence. However, current solutions are still far behind humans,
especially when confronting unstructured scenarios. In this paper, we survey
the advances of robotic grasping, starting from the classical formulations and
solutions to the modern ones. By reviewing the history of robotic grasping, we
want to provide a complete view of this community, and perhaps inspire the
combination and fusion of different ideas, which we think would be helpful to
touch and explore the essence of robotic grasping problems. In detail, we
firstly give an overview of the analytic methods for robotic grasping. After
that, we provide a discussion on the recent state-of-the-art data-driven
grasping approaches rising in recent years. With the development of computer
vision, semantic grasping is being widely investigated and can be the basis of
intelligent manipulation and skill learning for autonomous robotic systems in
the future. Therefore, in our survey, we also briefly review the recent
progress in this topic. Finally, we discuss the open problems and the future
research directions that may be important for the human-level robustness,
autonomy, and intelligence of robots.


title:
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based
  on Multiagent Dual-core Learning

abstract:
  Enzyme Commission (EC) numbers, which associate a protein sequence with the
biochemical reactions it catalyzes, are essential for the accurate
understanding of enzyme functions and cellular metabolism. Many ab-initio
computational approaches were proposed to predict EC numbers for given input
sequences directly. However, the prediction performance (accuracy, recall,
precision), usability, and efficiency of existing methods still have much room
to be improved. Here, we report ECRECer, a cloud platform for accurately
predicting EC numbers based on novel deep learning techniques. To build
ECRECer, we evaluate different protein representation methods and adopt a
protein language model for protein sequence embedding. After embedding, we
propose a multi-agent hierarchy deep learning-based framework to learn the
proposed tasks in a multi-task manner. Specifically, we used an extreme
multi-label classifier to perform the EC prediction and employed a greedy
strategy to integrate and fine-tune the final model. Comparative analyses
against four representative methods demonstrate that ECRECer delivers the
highest performance, which improves accuracy and F1 score by 70% and 20% over
the state-of-the-the-art, respectively. With ECRECer, we can annotate numerous
enzymes in the Swiss-Prot database with incomplete EC numbers to their full
fourth level. Take UniPort protein "A0A0U5GJ41" as an example (1.14.-.-),
ECRECer annotated it with "1.14.11.38", which supported by further protein
structure analysis based on AlphaFold2. Finally, we established a webserver
(https://ecrecer.biodesign.ac.cn) and provided an offline bundle to improve
usability.


title: An Exploration of a New Group of Channel Symmetries

abstract:
  We study a certain symmetry group associated to any given communication
channel, which can informally be viewed as the set of transformations of the
set of inputs that "commute" with the action of the channel. In a general
setting, we show that the distribution over the inputs that maximizes the
mutual information between the input and output of a given channel is a "fixed
point" of the action of the channel's group. We consider as examples the groups
associated with the binary symmetric channel and the binary deletion channel.
We show that the group of the binary symmetric channel is extremely large (it
contains a number of elements that grows faster than any exponential function
of $n$), and we give empirical evidence that the group of the binary deletion
channel is extremely small (it contains a number of elements constant in $n$).
This serves as some formal justification for why the analysis of the binary
deletion channel has proved much more difficult than its memoryless
counterparts.


title: Multi-Agent Path Finding with Prioritized Communication Learning

abstract:
  Multi-agent pathfinding (MAPF) has been widely used to solve large-scale
real-world problems, e.g., automation warehouses. The learning-based, fully
decentralized framework has been introduced to alleviate real-time problems and
simultaneously pursue optimal planning policy. However, existing methods might
generate significantly more vertex conflicts (or collisions), which lead to a
low success rate or more makespan. In this paper, we propose a PrIoritized
COmmunication learning method (PICO), which incorporates the \textit{implicit}
planning priorities into the communication topology within the decentralized
multi-agent reinforcement learning framework. Assembling with the classic
coupled planners, the implicit priority learning module can be utilized to form
the dynamic communication topology, which also builds an effective
collision-avoiding mechanism. PICO performs significantly better in large-scale
MAPF tasks in success rates and collision rates than state-of-the-art
learning-based planners.


title:
On the classification of non-aCM curves on quintic hypersurfaces in
  $\mathbb{P}^3$

abstract:
  In this paper, we call a sub-scheme of dimension one in $\mathbb{P}^3$ a
curve. It is well known that the arithmetic genus and the degree of an aCM
curve $D$ in $\mathbb{P}^3$ is computed by the $h$-vector of $D$. However, for
a given curve $D$ in $\mathbb{P}^3$, the two invariants of $D$ do not tell us
whether $D$ is aCM or not. In this paper, we give a classification of curves on
a smooth quintic hypersurface in $\mathbb{P}^3$ which are not aCM.


title:
Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular
  Data Synthesis

abstract:
  Tabular data synthesis has received wide attention in the literature. This is
because available data is often limited, incomplete, or cannot be obtained
easily, and data privacy is becoming increasingly important. In this work, we
present a generalized GAN framework for tabular synthesis, which combines the
adversarial training of GANs and the negative log-density regularization of
invertible neural networks. The proposed framework can be used for two
distinctive objectives. First, we can further improve the synthesis quality, by
decreasing the negative log-density of real records in the process of
adversarial training. On the other hand, by increasing the negative log-density
of real records, realistic fake records can be synthesized in a way that they
are not too much close to real records and reduce the chance of potential
information leakage. We conduct experiments with real-world datasets for
classification, regression, and privacy attacks. In general, the proposed
method demonstrates the best synthesis quality (in terms of task-oriented
evaluation metrics, e.g., F1) when decreasing the negative log-density during
the adversarial training. If increasing the negative log-density, our
experimental results show that the distance between real and fake records
increases, enhancing robustness against privacy attacks.


title: Boolean Observation Games

abstract:
  We introduce Boolean Observation Games, a subclass of multi-player finite
strategic games with incomplete information and qualitative objectives. In
Boolean observation games, each player is associated with a finite set of
propositional variables of which only it can observe the value, and it controls
whether and to whom it can reveal that value. It does not control the given,
fixed, value of variables. Boolean observation games are a generalization of
Boolean games, a well-studied subclass of strategic games but with complete
information, and wherein each player controls the value of its variables.
  In Boolean observation games player goals describe multi-agent knowledge of
variables. As in classical strategic games, players choose their strategies
simultaneously and therefore observation games capture aspects of both
imperfect and incomplete information. They require reasoning about sets of
outcomes given sets of indistinguishable valuations of variables. What a Nash
equilibrium is, depends on an outcome relation between such sets. We present
various outcome relations, including a qualitative variant of ex-post
equilibrium. We identify conditions under which, given an outcome relation,
Nash equilibria are guaranteed to exist. We also study the complexity of
checking for the existence of Nash equilibria and of verifying if a strategy
profile is a Nash equilibrium. We further study the subclass of Boolean
observation games with `knowing whether' goal formulas, for which the
satisfaction does not depend on the value of variables. We show that each such
Boolean observation game corresponds to a Boolean game and vice versa, by a
different correspondence, and that both correspondences are precise in terms of
existence of Nash equilibria.


title: A Model for Daily Global Stock Market Returns

abstract:
  Most stock markets are open for 6-8 hours per trading day. The Asian,
European and American stock markets are separated in time by time-zone
differences. We propose a statistical dynamic factor model for a large number
of daily returns across multiple time zones. Our model has a common global
factor as well as continent factors. Under a mild fixed-signs assumption, our
model is identified and has a structural interpretation. We derive the
asymptotic theories of the quasi-maximum likelihood estimator (QMLE) of our
model. As QMLE is inefficient by definition in this article, we outline three
related estimators for practical use; Monte Carlo simulations reveal that two
of them work well. We then apply our model to two real data sets - the equity
portfolio returns of Japan, Europe and US and MSCI equity indices of 41
developed and emerging markets. Some new insights about linkages between
different markets are drawn. Last, a Bayesian estimator (i.e., the Gibbs
sampling) is also explained and suitable for estimation when the number of
stocks is not too big.


title:
Contrastive predictive coding for Anomaly Detection in Multi-variate
  Time Series Data

abstract:
  Anomaly detection in multi-variate time series (MVTS) data is a huge
challenge as it requires simultaneous representation of long term temporal
dependencies and correlations across multiple variables. More often, this is
solved by breaking the complexity through modeling one dependency at a time. In
this paper, we propose a Time-series Representational Learning through
Contrastive Predictive Coding (TRL-CPC) towards anomaly detection in MVTS data.
First, we jointly optimize an encoder, an auto-regressor and a non-linear
transformation function to effectively learn the representations of the MVTS
data sets, for predicting future trends. It must be noted that the context
vectors are representative of the observation window in the MTVS. Next, the
latent representations for the succeeding instants obtained through non-linear
transformations of these context vectors, are contrasted with the latent
representations of the encoder for the multi-variables such that the density
for the positive pair is maximized. Thus, the TRL-CPC helps to model the
temporal dependencies and the correlations of the parameters for a healthy
signal pattern. Finally, fitting the latent representations are fit into a
Gaussian scoring function to detect anomalies. Evaluation of the proposed
TRL-CPC on three MVTS data sets against SOTA anomaly detection methods shows
the superiority of TRL-CPC.


title: Special purpose quantum graphs for guaranteed and fast search times

abstract:
  We design monitored quantum walks with the aim of optimizing state transfer
and target search. We show how to construct walks with the property that all
the eigenvalues of the non-Hermitian survival operator, describing the mixed
effect of unitary dynamics and the back-action of measurement, coalesce to
zero, corresponding to an exceptional point whose degree is the size of the
Hilbert space. Generally, this search is guaranteed to succeed in a bounded
time for any initial condition. It also performs better than the classical
random walk search or quantum search on typical graphs. For example, a crawler
can be designed such that, starting on a node of the graph, the walker is
detected on any of the nodes with probability one at predetermined times. It
also allows perfect quantum state transfer from one node of the system to any
other, with or without monitoring. Interestingly, this crawler is described as
a massless Dirac quasi-particle.


title:
Kinetic Monte Carlo Modelling of Nano-oxide Precipitation and its
  Associated Stability under Neutron Irradiation for the Fe-Ti-Y-O system

abstract:
  It is difficult to predict material behavior under decades long irradiation
regimes. Machine and deep learning can be very important tools that can help
predict resulting material properties. These tools require strong and robust
models that can carefully reproduce the microstructural evolution against
experiments so researchers can understand the material processes and to
validate the model for future use with other materials. Thus, a newly developed
Kinetic Monte Carlo (KMC) model was constructed to investigate the
precipitation behavior of Y-Ti-O oxides along grain boundaries in
nanostructured ferritic alloys (NFA) and the oxide response to neutron
irradiation. The level of radiation damage simulated reached the long dpa range
required for reactor components. Simulations were conducted to investigate the
influence of temperature as well as the influence of grain boundary sinks on
the resulting oxide characteristics of a 12YWT-like alloy during heat
treatments at 1023K - 1223K. There is good agreement of the oxide
characteristics from simulation with literature values and the influence of the
grain boundary on precipitation is slight. The same oxide configuration and
positions were input into a new simulation and placed under simulated neutron
irradiation to a total accumulated dose of 8 dpa at temperatures, 673K - 873K,
and at dose rates of 10^-3, 10^-4, and 10^-5. The expected inverse relationship
of oxide size with dose rate was observed. A long-term irradiation simulation
at 873K and 10^-3 dpa/s was taken to 66 dpa and it was found that the oxides in
the vicinity of the grain boundary were more susceptible to dissolution.
Additionally, irradiation simulations of a 14YWT-like alloy were conducted to
replicate findings from neutron irradiation experiments. The larger oxides in
the 14YWT-like alloy did not dissolve and showed the same stability as in the
experiments.


title:
A Flexible Divergence Elimination Method for Calculating Lepton Magnetic
  Moments in Quantum Electrodynamics

abstract:
  A precise calculation of the lepton anomalous magnetic moments (AMM) requires
an evaluation of the quantum electrodynamics (QED) Feynman diagrams up to five
independent loops. The complicated structure of ultraviolet (UV), infrared (IR)
and mixed divergences in the corresponding integrals makes it difficult to
calculate these high-order contributions in reasonable computer time frame.
  We demonstrate a method that eliminates all divergences point by point in
Feynman parametric space (before integration) and possesses a flexibility that
can be used for improving the precision of the numerical integration. This
flexibility is especially actual for calculating the contributions of the
Feynman diagrams with electron loops to the muon AMM. 3-loop and 4-loop
numerical test results are provided.
  The subtraction procedure is based on a forest formula with linear operators
applied to the Feynman amplitudes of UV divergent subdiagrams. It is similar to
BPHZ; the difference is in the linear operators used and in the way of
combining them. It is equivalent to the on-shell renormalization after
summation over diagrams: no residual renormalization is required.


title:
SNPSFuzzer: A Fast Greybox Fuzzer for Stateful Network Protocols using
  Snapshots

abstract:
  Greybox fuzzing has been widely used in stateless programs and has achieved
great success. However, most state-of-the-art greybox fuzzers generally have
the problems of slow speed and shallow state depth coverage in the process of
fuzzing stateful network protocol programs which are able to remember and store
details of the interactions. The existing greybox fuzzers for network protocol
programs send a series of well-defined prefix sequences of input messages first
and then send mutated messages to test the target state of a stateful network
protocol. The process mentioned above causes a high time cost. In this paper,
we propose SNPSFuzzer, a fast greybox fuzzer for stateful network protocol
using snapshots. SNPSFuzzer dumps the context information when the network
protocol program is under a specific state and restores it when the state needs
to be fuzzed. Furthermore, we design a message chain analysis algorithm to
explore more and deeper network protocol states. Our evaluation shows that,
compared with the state-of-the-art network protocol greybox fuzzer AFLNET,
SNPSFuzzer increases the speed of network protocol fuzzing by 112.0%-168.9% and
improves path coverage by 21.4%-27.5% within 24 hours. Moreover, SNPSFuzzer
exposes a previously unreported vulnerability in program Tinydtls.


title:
Proper Motions of Water Masers in the Star-forming Region IRAS
  23139+5939

abstract:
  We observed H$_2$O (6$_{16}$ $\rightarrow$ 5$_{23}$) maser emission
associated with the high-mass star-forming region IRAS 23139+5959 using the
KaVA a combination of VLBI arrays between the KVN (Korea) and VERA (Japan).
Through multi-epoch KaVA observations, we detected three groups of maser
features, two of which coincide with those previously detected by the Karl G.
Jansky Very Large Array (VLA). By determining the maser proper motions, we
found that the first of maser groups exhibit an expanding motion that traces a
wide-angle outflow almost along the line of sight, while the second one seems
to be associated with the envelope of an \hii region. We discuss the star
formation activity in IRAS 23139+5939, which may be reflected in the high
variability of H$_2$O masers associated with an outflow seen from the front.


title: NxtPost: User to Post Recommendations in Facebook Groups

abstract:
  In this paper, we present NxtPost, a deployed user-to-post content-based
sequential recommender system for Facebook Groups. Inspired by recent advances
in NLP, we have adapted a Transformer-based model to the domain of sequential
recommendation. We explore causal masked multi-head attention that optimizes
both short and long-term user interests. From a user's past activities
validated by defined safety process, NxtPost seeks to learn a representation
for the user's dynamic content preference and to predict the next post user may
be interested in. In contrast to previous Transformer-based methods, we do not
assume that the recommendable posts have a fixed corpus. Accordingly, we use an
external item/token embedding to extend a sequence-based approach to a large
vocabulary. We achieve 49% abs. improvement in offline evaluation. As a result
of NxtPost deployment, 0.6% more users are meeting new people, engaging with
the community, sharing knowledge and getting support. The paper shares our
experience in developing a personalized sequential recommender system, lessons
deploying the model for cold start users, how to deal with freshness, and
tuning strategies to reach higher efficiency in online A/B experiments.


title: Automatic Interpretative Image-Focusing Analysis

abstract:
  The focusing of a seismic image is directly linked to the accuracy of the
velocity model. Therefore, a critical step in a seismic imaging workflow is to
perform a focusing analysis on a seismic image to determine velocity errors.
While the offset/aperture-angle axis is frequently used for focusing analysis,
the physical (i.e., midpoint) axes of seismic images tend to be ignored as
focusing analysis of geological structures is highly interpretative and
difficult to automate. We have developed an automatic data-driven approach
using convolutional neural networks to automate image-focusing analysis. Using
focused and unfocused geological faults, we show that our method can make use
of both spatial and offset/angle focusing information to robustly estimate
velocity errors within seismic images. We demonstrate that our method correctly
estimates velocity errors from a 2D Gulf of Mexico limited-aperture image where
a traditional semblance-based approach fails. We also show that our method has
the added benefit of improving the interpretation of faults within the image.


title:
Summary On The ICASSP 2022 Multi-Channel Multi-Party Meeting
  Transcription Grand Challenge

abstract:
  The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand
Challenge (M2MeT) focuses on one of the most valuable and the most challenging
scenarios of speech technologies. The M2MeT challenge has particularly set up
two tracks, speaker diarization (track 1) and multi-speaker automatic speech
recognition (ASR) (track 2). Along with the challenge, we released 120 hours of
real-recorded Mandarin meeting speech data with manual annotation, including
far-field data collected by 8-channel microphone array as well as near-field
data collected by each participants' headset microphone. We briefly describe
the released dataset, track setups, baselines and summarize the challenge
results and major techniques used in the submissions.


title:
Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT
  Network

abstract:
  Mobile Edge Computing (MEC) has recently emerged as a promising technology in
the 5G era. It is deemed an effective paradigm to support computation-intensive
and delay critical applications even at energy-constrained and
computation-limited Internet of Things (IoT) devices. To effectively exploit
the performance benefits enabled by MEC, it is imperative to jointly allocate
radio and computational resources by considering non-stationary computation
demands, user mobility, and wireless fading channels. This paper aims to study
the tradeoff between energy efficiency (EE) and service delay for multi-user
multi-server MEC-enabled IoT systems when provisioning offloading services in a
user mobility scenario. Particularly, we formulate a stochastic optimization
problem with the objective of minimizing the long-term average network EE with
the constraints of the task queue stability, peak transmit power, maximum
CPU-cycle frequency, and maximum user number. To tackle the problem, we propose
an online offloading and resource allocation algorithm by transforming the
original problem into several individual subproblems in each time slot based on
Lyapunov optimization theory, which are then solved by convex decomposition and
submodular methods. Theoretical analysis proves that the proposed algorithm can
achieve a $[O(1/V), O(V)]$ tradeoff between EE and service delay. Simulation
results verify the theoretical analysis and demonstrate our proposed algorithm
can offer much better EE-delay performance in task offloading challenges,
compared to several baselines.


title: Non-Abelian Anyon Collider

abstract:
  A collider, where particles are injected to a beam splitter from opposite
sides, has been used for identifying quantum statistics of identical particles.
The collision leads to bunching of the particles for bosons and antibunching
for fermions. In recent experiments, a collider was applied to a fractional
quantum Hall regime hosting Abelian anyons. The observed negative cross
correlation of electrical currents cannot be understood with fermionic
antibunching. Here we predict, based on a conformal field theory and
non-perturbative treatment of non-equilibrium anyon injection, that the
collider provides a tool for direct observation of the braiding statistics of
various Abelian and non-Abelian anyons. Its dominant process is not direct
collision between injected anyons, contrary to common expectation, but braiding
between injected anyons and an anyon excited at the collider. The dependence of
the resulting negative cross correlation on the injection currents
distinguishes non-Abelian SU(2)$_k$ anyons, Ising anyons, and Abelian Laughlin
anyons.


title:
First-principles study on the specific heat of glass at glass transition
  with a case study on glycerol

abstract:
  The standard method to determine the transition temperature (Tg) of glass
transition is the jump in the specific heat. Despite this importance, standard
theory for this jump is lacking. The difficulties encompass from lack of proper
treatments of specific heat of liquids, hysteresis, to the timescale issue. The
first part of this paper provides a non-empirical method to calculate specific
heat of glass. The method consists of molecular dynamics (MD) simulations based
on density-functional theory (DFT) and thermodynamics methods. The total-energy
approach based on DFT enables us to calculate specific heat, irrespective of
solids or liquids. A serious problem for glass-transition states is involvement
of complicated energy dissipation processes, which is resolved by adiabatic MD
simulations. The problems of hysteresis and the timescale issue are alleviated
by restricting the scope of calculations to equilibrium states only. The second
part of this paper describes an application of the theory to the specific-heat
jump (Delta Cp) of glycerol in order to show the validity of the methods.
Despite severe limitations due to the small size of supercells, a reasonable
value for the specific-heat jump is obtained. By decomposing Delta Cp into
contributions of the structural energy, phonon, and thermal expansion parts, we
have a sound interpretation for the Delta Cp: the major contribution to Delta
Cp comes from the change in the structural energy. From this, a neat energy
diagram about the glass transition is obtained. An outcome of this study is
verification of the empirical relationship between the fragility and
specific-heat jump. These two energies are scaled by the ratio k=Tg/ Delta T,
where Delta T is the width of the transition, through which the two quantities
are interrelated. This is useful for organizing various relationships that are
found empirically.


title:
Causal Scene BERT: Improving object detection by searching for
  challenging groups of data

abstract:
  Modern computer vision applications rely on learning-based perception modules
parameterized with neural networks for tasks like object detection. These
modules frequently have low expected error overall but high error on atypical
groups of data due to biases inherent in the training process. In building
autonomous vehicles (AV), this problem is an especially important challenge
because their perception modules are crucial to the overall system performance.
After identifying failures in AV, a human team will comb through the associated
data to group perception failures that share common causes. More data from
these groups is then collected and annotated before retraining the model to fix
the issue. In other words, error groups are found and addressed in hindsight.
Our main contribution is a pseudo-automatic method to discover such groups in
foresight by performing causal interventions on simulated scenes. To keep our
interventions on the data manifold, we utilize masked language models. We
verify that the prioritized groups found via intervention are challenging for
the object detector and show that retraining with data collected from these
groups helps inordinately compared to adding more IID data. We also plan to
release software to run interventions in simulated scenes, which we hope will
benefit the causality community.


title:
Real-time disease prediction with local differential privacy in Internet
  of Medical Things

abstract:
  The rapid development in Internet of Medical Things (IoMT) boosts the
opportunity for real-time health monitoring using various data types such as
electroencephalography (EEG) and electrocardiography (ECG). Security issues
have significantly impeded the e-healthcare system implementation. Three
important challenges for privacy preserving system need to be addressed:
accurate matching, privacy enhancement without compromising security, and
computation efficiency. It is essential to guarantee prediction accuracy since
disease diagnosis is strongly related to health and life. In this paper, we
propose efficient disease prediction that guarantees security against malicious
clients and honest-but-curious server using matrix encryption technique. A
biomedical signal provided by the client is diagnosed such that the server does
not get any information about the signal as well as the final result of the
diagnosis while the client does not learn any information about the server's
medical data. Thorough security analysis illustrates the disclosure resilience
of the proposed scheme and the encryption algorithm satisfies local
differential privacy. After result decryption performed by the client's device,
performance is not degraded to perform prediction on encrypted data. The
proposed scheme is efficient to implement real-time health monitoring.


title: Contribution of directedness in graph spectra

abstract:
  In graph analyses, directed edges are often approximated to undirected ones
so that the adjacency matrices may be symmetric. However, such simplification
has not been thoroughly verified. In this study, we investigate how
directedness affects the graph spectra by introducing random directization,
which is an opposite operation of neglecting edge directions. We analytically
reveal that uniformly random directization typically conserves the relative
spectral structure of the adjacency matrix in the perturbative regime. The
result of random directization implies that the spectrum of the adjacency
matrix can be conserved after the directedness is ignored.


title:
Low-Complexity Decoding of a Class of Reed-Muller Subcodes for
  Low-Capacity Channels

abstract:
  We present a low-complexity and low-latency decoding algorithm for a class of
Reed-Muller (RM) subcodes that are defined based on the product of smaller RM
codes. More specifically, the input sequence is shaped as a multi-dimensional
array, and the encoding over each dimension is done separately via a smaller RM
encoder. Similarly, the decoding is performed over each dimension via a
low-complexity decoder for smaller RM codes. The proposed construction is of
particular interest to low-capacity channels that are relevant to emerging
low-rate communication scenarios. We present an efficient soft-input
soft-output (SISO) iterative decoding algorithm for the product of RM codes and
demonstrate its superiority compared to hard decoding over RM code components.
The proposed coding scheme has decoding (as well as encoding) complexity of
$\mathcal{O}(n\log n)$ and latency of $\mathcal{O}(\log n)$ for blocklength
$n$. This research renders a general framework toward efficient decoding of RM
codes.


title: Linear Time Kernel Matrix Approximation via Hyperspherical Harmonics

abstract:
  We propose a new technique for constructing low-rank approximations of
matrices that arise in kernel methods for machine learning. Our approach pairs
a novel automatically constructed analytic expansion of the underlying kernel
function with a data-dependent compression step to further optimize the
approximation. This procedure works in linear time and is applicable to any
isotropic kernel. Moreover, our method accepts the desired error tolerance as
input, in contrast to prevalent methods which accept the rank as input.
Experimental results show our approach compares favorably to the commonly used
Nystrom method with respect to both accuracy for a given rank and computational
time for a given accuracy across a variety of kernels, dimensions, and
datasets. Notably, in many of these problem settings our approach produces
near-optimal low-rank approximations. We provide an efficient open-source
implementation of our new technique to complement our theoretical developments
and experimental results.


title:
Predicting the Exoplanet Yield of the TESS Prime and Extended Missions
  Through Years 1-7

abstract:
  The Transiting Exoplanet Survey Satellite (TESS) has discovered $\sim$5000
planets and planet candidates after three and a half years of observations.
With a planned second Extended Mission spanning Years 5 - 7 on the horizon, now
is the time to revise predictions of the TESS exoplanet yield. We present
simulations of the number of detectable planets around 9.4 million AFGKM stars
in the TESS Input Catalog Candidate Target List v8.01 through seven years of
the TESS mission. Our simulations take advantage of improved models for the
photometric performance and temporal window functions. The detection model was
also improved by relying on the results of inject-and-recovery testing by the
Kepler team. We estimate 4719$\pm$334 planets around these stars should be
detectable with data from the Prime Mission alone (Years 1 - 2), and another
3707$\pm$209 planets should be detectable by the end of the current Extended
Mission (Years 3 - 4). Based on a proposed pointing scenario for a second
Extended Mission (Years 5 - 7), we predict TESS should find a further
4093$\pm$180 planets, bringing the total TESS yield to 12519$\pm$678 planets.
We provide our predicted yields as functions of host star spectral type, planet
radius, orbital period, follow-up feasibility, and location relative to the
habitable zone. We also compare our predictions to the actual Prime Mission
yield, finding good agreement


title:
Effects of the refractive index of the X-ray corona on the emission
  lines in AGN

abstract:
  X-ray reflection from an accretion disc produces characteristic emission
lines allowing us to probe the innermost regions in AGN. We investigate these
emission lines under a framework of Riemannian geometrical optics where the
corona has a refractive index of $n \neq 1$. The empty space outside is a
vacuum with $n = 1$. The Kerr metric is modified to trace the light rays that
are bent due to not only the gravity of the black hole, but also the effects of
coronal plasma dependent on $n$. The choice of $n$ alters the null geodesics,
producing the effect which is analogous to the light deflection. For the corona
with $n > 1$, the disc on the far side within the corona covers a larger area
on the observer' sky, enhancing the blue wing of the line and producing more
flux difference between the blue peak and extended red tail. The inverse
effects are seen when $n < 1$. Moreover, the corona with $n > 1$ and $n < 1$
could induce extra shifts in the blue wing ($\Delta g_{max}$) to higher and
lower energy, respectively. These effects are more prominent when the
inclination angle is $\gtrsim 60^\circ$ and the corona extends to $\gtrsim
5r_g$. To obtain the deviation of the line shift of $\Delta g_{\rm max} \gtrsim
0.01$, the difference between the refractive index of the corona and that of
the empty space must be $\Delta n \gtrsim 0.5%$. Finally, the lensing corona
can influence the arrival time of photons that may affect the observed
variability of these emission lines


title:
100-mJ class, sub-two-cycle, carrier-envelope phase-stable dual-chirped
  optical parametric amplification

abstract:
  Based on the dual-chirped optical parametric amplification and type-I
BiB$_3$O$_6$(BiBO) crystals, the generation of $>$100 mJ, 10.4 fs, 10 Hz,
carrier-to-envelope phase (CEP)-stable laser pulses, which are centered at 1.7
$\mu$m, is demonstrated; it produces a peak power of 10 TW. CEP-dependent high
harmonic generation is implemented to confirm the sub-two-cycle pulse duration
and CEP stabilization of infrared (IR) laser pulses. As far as we know, the
obtained pulse energy and peak power represent the highest values for
sub-two-cycle CEP-stable IR optical parametric amplification. Additionally, the
prospects of achieving high-energy water window isolated attosecond pulses via
our developed laser source are discussed.


title: Cellular cosheaf homology are cosheaf homology

abstract:
  A cosheaf is the dual notion of a sheaf, but we cannot define its homology as
the formal dual of sheaf cohomology, in general, because of the lack of the
cosheafification. A cellular cosheaf is a contravariant functor from the face
poset of a CW complex to the category of abelian groups. We show that given a
cellular cosheaf $F$, there is a natural way to associate a cosheaf
$\widehat{F}$, for which we can define homology as the formal dual of sheaf
cohomology, such that the Borel-Moore homology of $F$ is isomorphic to the
homology of $\widehat{F}$ whenever the underlying CW complex of $F$ is a
simplicial complex.


title: Basis-Function Models in Spatial Statistics

abstract:
  Spatial statistics is concerned with the analysis of data that have spatial
locations associated with them, and those locations are used to model
statistical dependence between the data. The spatial data are treated as a
single realisation from a probability model that encodes the dependence through
both fixed effects and random effects, where randomness is manifest in the
underlying spatial process and in the noisy, incomplete, measurement process.
The focus of this review article is on the use of basis functions to provide an
extremely flexible and computationally efficient way to model spatial processes
that are possibly highly non-stationary. Several examples of basis-function
models are provided to illustrate how they are used in Gaussian, non-Gaussian,
multivariate, and spatio-temporal settings, with applications in geophysics.
Our aim is to emphasise the versatility of these spatial statistical models and
to demonstrate that they are now centre-stage in a number of application
domains. The review concludes with a discussion and illustration of software
currently available to fit spatial-basis-function models and implement
spatial-statistical prediction.


title:
$\textit{Ab initio}$ downfolding based on the GW approximation for
  infinite-layer nickelates

abstract:
  We derive an effective three-orbital model for the infinite-layer nickelates
based on the band structure obtained by the GW approximation (GWA), where we
consider the Ni 3$d_{x^2-y^2}$ and O 2$p$ orbitals forming the $\sigma$-bond.
In the GWA, the self-energy correction to the local density approximation (LDA)
increases the energy difference between Ni $3d_{x^2-y^2}$ and O $2p$, which
reduces the bandwidth of the antibonding 3$d_{x^2-y^2}$ orbitals. The isolation
of the Ni $3d_{x^2-y^2}$ around the Fermi level suppresses the screening
effect. As a result, the correlation effect becomes more significant than that
in the model constructed by the LDA-based downfolding. Furthermore, the
Mott-Hubbard type character is enhanced in the GWA-based effective model,
because the charge-transfer energy increases more rapidly compared to the
increase in the interaction parameters.


title:
Generalised Parton Distributions from Lattice Feynman-Hellmann
  Techniques

abstract:
  We report on the use of Feynman-Hellmann techniques to calculate the
off-forward Compton amplitude (OFCA) in lattice QCD. At leading-twist, the
Euclidean OFCA is parameterised by the Mellin moments of generalised parton
distributions (GPDs). Hence we extract GPD moments for two values of the soft
momentum transfer, $t=-1.10, -2.20\;\text{GeV}^2$ and zero-skewness kinematics
at an unphysical pion mass of $m_{\pi}\approx 470\;\text{MeV}$. This includes
the first determination of the $n=4$ moments.


title:
Emergent skyrmion-based chiral order in zero-field Heisenberg
  antiferromagnets on the breathing kagome lattice

abstract:
  We show that classical Heisenberg antiferromagnets on the breathing kagome
lattice can be a platform to realize a zero-field topological order of the
scalar spin chirality which can be viewed as a miniature skyrmion crystal (SkX)
of discrete form with a small number of spins in its magnetic unit cell. In the
model, a third nearest-neighbor (NN) antiferromagnetic interaction along the
bond direction $J_3$ and the breathing bond-alternation characterized by the
ratio of the NN interaction for large triangles to that for small ones,
$J_1'/J_1$, are essential. It is found by means of Monte Carlo simulations that
a commensurate triple-${\bf Q}$ state appearing for relatively strong $J_3$ at
zero field is the noncoplanar state with the SkX structure in the breathing
case of $J_1'/J_1 \neq 1$, while in the uniform case of $J_1'/J_1 =1$, it is a
collinear state favored by thermal fluctuations. The origin of this chiral
order and experimental implications of our result are also discussed.


title:
Superconductivity with the enhanced upper critical field in the
  Pt-Doping CuRh2Se4 spinel

abstract:
  We report the effect of Pt doping on the superconductivity in CuRh2Se4 spinel
using a combined experimental and theoretical study. Our XRD results reveal
that the Cu(Rh1-xPtx)2Se4 crystallizes in the structure with a space group of
Fd3-m (No. 227), and the lattice parameter a increases with Pt doping. The
resistivity and magnetic susceptibility measurement results verify that the
superconducting transition temperature (Tc) forms a dome-like shape with a
maximum value of 3.84 K at x = 0.06. It is also observed that the Pt-doping
slightly reduces the lower critical magnetic field from 220 Oe in CuRh2Se4 to
168 Oe in Cu(Rh0.94Pt0.06)2Se4, while it significantly enhances the upper
critical magnetic field, reaching the maximum of 4.93 T in the
Cu(Rh0.94Pt0.06)2Se4 sample. The heat capacity result indicates that the sample
Cu(Rh0.91Pt0.09)2Se4 is a bulk superconductor. First-principles calculations
suggest that the Pt-doping leads to a red-shift of a density of state peak near
the Fermi level, consistent with the dome-like Tc observed experimentally.


title:
Accelerating Part-Scale Simulation in Liquid Metal Jet Additive
  Manufacturing via Operator Learning

abstract:
  Predicting part quality for additive manufacturing (AM) processes requires
high-fidelity numerical simulation of partial differential equations (PDEs)
governing process multiphysics on a scale of minimum manufacturable features.
This makes part-scale predictions computationally demanding, especially when
they require many small-scale simulations. We consider drop-on-demand liquid
metal jetting (LMJ) as an illustrative example of such computational
complexity. A model describing droplet coalescence for LMJ may include coupled
incompressible fluid flow, heat transfer, and phase change equations.
Numerically solving these equations becomes prohibitively expensive when
simulating the build process for a full part consisting of thousands to
millions of droplets. Reduced-order models (ROMs) based on neural networks (NN)
or k-nearest neighbor (kNN) algorithms have been built to replace the original
physics-based solver and are computationally tractable for part-level
simulations. However, their quick inference capabilities often come at the
expense of accuracy, robustness, and generalizability. We apply an operator
learning (OL) approach to learn a mapping between initial and final states of
the droplet coalescence process for enabling rapid and accurate part-scale
build simulation. Preliminary results suggest that OL requires
order-of-magnitude fewer data points than a kNN approach and is generalizable
beyond the training set while achieving similar prediction error.


title:
Approximating Gradients for Differentiable Quality Diversity in
  Reinforcement Learning

abstract:
  Consider the problem of training robustly capable agents. One approach is to
generate a diverse collection of agent polices. Training can then be viewed as
a quality diversity (QD) optimization problem, where we search for a collection
of performant policies that are diverse with respect to quantified behavior.
Recent work shows that differentiable quality diversity (DQD) algorithms
greatly accelerate QD optimization when exact gradients are available. However,
agent policies typically assume that the environment is not differentiable. To
apply DQD algorithms to training agent policies, we must approximate gradients
for performance and behavior. We propose two variants of the current
state-of-the-art DQD algorithm that compute gradients via approximation methods
common in reinforcement learning (RL). We evaluate our approach on four
simulated locomotion tasks. One variant achieves results comparable to the
current state-of-the-art in combining QD and RL, while the other performs
comparably in two locomotion tasks. These results provide insight into the
limitations of current DQD algorithms in domains where gradients must be
approximated. Source code is available at https://github.com/icaros-usc/dqd-rl


title:
Non-radial weights and polynomial approximation in spaces of analytic
  functions

abstract:
  We study sufficient conditions on weight functions under which norm
approximations by analytic polynomials are possible. The weights we study
include radial, non-radial, and angular weights.


title:
Multiparameter simultaneous optimal estimation with an SU(2) coding
  unitary evolution

abstract:
  In a ubiquitous $SU(2)$ dynamics, achieving the simultaneous optimal
estimation of multiple parameters is significant but difficult. Using quantum
control to optimize this $SU(2)$ coding unitary evolution is one of solutions.
We propose a method, characterized by the nested cross-products of the
coefficient vector $\mathbf{X}$ of $SU(2)$ generators and its partial
derivative $\partial_\ell \mathbf{X}$, to investigate the control-enhanced
quantum multiparameter estimation. Our work reveals that quantum control is not
always functional in improving the estimation precision, which depends on the
characterization of an $SU(2)$ dynamics with respect to the objective
parameter. This characterization is quantified by the angle $\alpha_\ell$
between $\mathbf{X}$ and $\partial_\ell \mathbf{X}$. For an $SU(2)$ dynamics
featured by $\alpha_\ell=\pi/2$, the promotion of the estimation precision can
get the most benefits from the controls. When $\alpha_\ell$ gradually closes to
$0$ or $\pi$, the precision promotion contributed to by quantum control
correspondingly becomes inconspicuous. Until a dynamics with $\alpha_\ell=0$ or
$\pi$, quantum control completely loses its advantage. In addition, we find a
set of conditions restricting the simultaneous optimal estimation of all the
parameters, but fortunately, which can be removed by using a maximally
entangled two-qubit state as the probe state and adding an ancillary channel
into the configuration. Lastly, a spin-$1/2$ system is taken as an example to
verify the above-mentioned conclusions. Our proposal sufficiently exhibits the
hallmark of control-enhancement in fulfilling the multiparameter estimation
mission, and it is applicable to an arbitrary $SU(2)$ parametrization process.


title: Fluid inertia and the scallop theorem

abstract:
  In Stokes flow, Purcell's scallop theorem forbids objects with
time-reversible (reciprocal) swimming strokes from moving. In the presence of
inertia, this restriction is eased and reciprocally deforming bodies can swim.
Recent works that investigated dimer models with finite inertia show
interesting results (e.g. switches in swim direction as a function of inertia)
but the results vary and seem to be case-specific. Here, we introduce a general
model and investigate the behaviour of a simple swimmer, an asymmetric
spherical dimer of oscillating length, at small amplitudes at intermediate
Reynolds numbers. In our analysis we make the important distinction between
particle and fluid inertia, both of which need to be considered separately. We
asymptotically expand the Navier-Stokes equations in the small amplitude limit
to obtain a system of linear PDEs. Using a combination of numerical (Finite
Element) and analytical (reciprocal theorem, method of reflections) methods we
solve the system to obtain the dimer's swim speed and show that there are two
mechanisms that give rise to motion: due to boundary conditions (effective slip
velocity) and due to Reynolds stresses. Each mechanism is driven by two classes
of sphere--sphere interactions, between one sphere's motion and 1) the
oscillating background flow induced by the other's motion, and 2) a geometrical
asymmetry induced by the other's presence. We can thus unify and explain
behaviours observed in other works. Our results show how sensitive,
counter-intuitive and rich motility is in the parameter space of finite inertia
of particles and fluid.


title: How to Understand Masked Autoencoders

abstract:
  "Masked Autoencoders (MAE) Are Scalable Vision Learners" revolutionizes the
self-supervised learning method in that it not only achieves the
state-of-the-art for image pre-training, but is also a milestone that bridges
the gap between visual and linguistic masked autoencoding (BERT-style)
pre-trainings. However, to our knowledge, to date there are no theoretical
perspectives to explain the powerful expressivity of MAE. In this paper, we,
for the first time, propose a unified theoretical framework that provides a
mathematical understanding for MAE. Specifically, we explain the patch-based
attention approaches of MAE using an integral kernel under a non-overlapping
domain decomposition setting. To help the research community to further
comprehend the main reasons of the great success of MAE, based on our
framework, we pose five questions and answer them with mathematical rigor using
insights from operator theory.


title:
CAD-RADS Scoring using Deep Learning and Task-Specific Centerline
  Labeling

abstract:
  With coronary artery disease (CAD) persisting to be one of the leading causes
of death worldwide, interest in supporting physicians with algorithms to speed
up and improve diagnosis is high. In clinical practice, the severeness of CAD
is often assessed with a coronary CT angiography (CCTA) scan and manually
graded with the CAD-Reporting and Data System (CAD-RADS) score. The clinical
questions this score assesses are whether patients have CAD or not (rule-out)
and whether they have severe CAD or not (hold-out). In this work, we reach new
state-of-the-art performance for automatic CAD-RADS scoring. We propose using
severity-based label encoding, test time augmentation (TTA) and model
ensembling for a task-specific deep learning architecture. Furthermore, we
introduce a novel task- and model-specific, heuristic coronary segment
labeling, which subdivides coronary trees into consistent parts across
patients. It is fast, robust, and easy to implement. We were able to raise the
previously reported area under the receiver operating characteristic curve
(AUC) from 0.914 to 0.942 in the rule-out and from 0.921 to 0.950 in the
hold-out task respectively.


title:
APPFL: Open-Source Software Framework for Privacy-Preserving Federated
  Learning

abstract:
  Federated learning (FL) enables training models at different sites and
updating the weights from the training instead of transferring data to a
central location and training as in classical machine learning. The FL
capability is especially important to domains such as biomedicine and smart
grid, where data may not be shared freely or stored at a central location
because of policy challenges. Thanks to the capability of learning from
decentralized datasets, FL is now a rapidly growing research field, and
numerous FL frameworks have been developed. In this work, we introduce APPFL,
the Argonne Privacy-Preserving Federated Learning framework. APPFL allows users
to leverage implemented privacy-preserving algorithms, implement new
algorithms, and simulate and deploy various FL algorithms with
privacy-preserving techniques. The modular framework enables users to customize
the components for algorithms, privacy, communication protocols, neural network
models, and user data. We also present a new communication-efficient algorithm
based on an inexact alternating direction method of multipliers. The algorithm
requires significantly less communication between the server and the clients
than does the current state of the art. We demonstrate the computational
capabilities of APPFL, including differentially private FL on various test
datasets and its scalability, by using multiple algorithms and datasets on
different computing environments.


title: Calibrated Learning to Defer with One-vs-All Classifiers

abstract:
  The learning to defer (L2D) framework has the potential to make AI systems
safer. For a given input, the system can defer the decision to a human if the
human is more likely than the model to take the correct action. We study the
calibration of L2D systems, investigating if the probabilities they output are
sound. We find that Mozannar & Sontag's (2020) multiclass framework is not
calibrated with respect to expert correctness. Moreover, it is not even
guaranteed to produce valid probabilities due to its parameterization being
degenerate for this purpose. We propose an L2D system based on one-vs-all
classifiers that is able to produce calibrated probabilities of expert
correctness. Furthermore, our loss function is also a consistent surrogate for
multiclass L2D, like Mozannar & Sontag's (2020). Our experiments verify that
not only is our system calibrated, but this benefit comes at no cost to
accuracy. Our model's accuracy is always comparable (and often superior) to
Mozannar & Sontag's (2020) model's in tasks ranging from hate speech detection
to galaxy classification to diagnosis of skin lesions.


title:
Trained Model in Supervised Deep Learning is a Conditional Risk
  Minimizer

abstract:
  We proved that a trained model in supervised deep learning minimizes the
conditional risk for each input (Theorem 2.1). This property provided insights
into the behavior of trained models and established a connection between
supervised and unsupervised learning in some cases. In addition, when the
labels are intractable but can be written as a conditional risk minimizer, we
proved an equivalent form of the original supervised learning problem with
accessible labels (Theorem 2.2). We demonstrated that many existing works, such
as Noise2Score, Noise2Noise and score function estimation can be explained by
our theorem. Moreover, we derived a property of classification problem with
noisy labels using Theorem 2.1 and validated it using MNIST dataset.
Furthermore, We proposed a method to estimate uncertainty in image
super-resolution based on Theorem 2.2 and validated it using ImageNet dataset.
Our code is available on github.


title:
Interactions between a propagating detonation wave and water spray cloud
  in hydrogen/air mixture

abstract:
  Inhibition of hydrogen explosion is crucial to realize its wide applications
and fine water spray is an ideal mitigant due to numerous advantages. In this
work, interactions between a propagating hydrogen/air detonation wave and
circular water cloud are numerically studied. Eulerian-Lagrangian method
involving two-way gas-droplets coupling is applied, with a two-dimensional
configuration. Different droplet (diameter, concentration) and cloud (diameter)
properties are considered. Our results show that droplet size, concentration
and cloud radius have significant effects on peak pressure trajectory of the
detonation wave. After interacting with cloud, the detonation wave exhibits
three propagation modes, including perturbed propagation, leeward
re-detonation, and detonation extinction. Leeward re-detonation is analyzed
from unsteady evolutions of gas and liquid droplet quantities. The refracted
detonation wave inside the cloud is decoupled and propagates more slowly than
the one outside the cloud. The detonation re-initiation is from a local hot
spot, caused by shock focusing from upper and lower diffracted detonations.
Disintegration of water droplets proceeds when the detonation wave crosses the
cloud and multiphase interfacial instability is observed due to the difference
in effective density of the two fluids. Furthermore, detonation extinction is
observed when we consider various water cloud size. It is featured by quickly
fading peak pressure trajectories when the detonation passes the cloud, and no
local autoignition occurs in the shock focusing area. Evolutions of
thermochemical structures from the shocked area in an extinction process are
also studied. Moreover, parametric studies considering various droplet
concentrations and cloud radii are performed.


title: An Application of Singular Traces to Crystals and Percolation

abstract:
  For a certain class of discrete metric spaces, we provide a formula for the
density of states. This formula involves Dixmier traces and is proven using
recent advances in operator theory. Various examples are given of metric spaces
for which this formula holds, including crystals, quasicrystals and the
infinite cluster resulting from super-critical bond percolation on
$\mathbb{Z}^d$.

categories: math-ph cond-mat.other math.FA math.MP quant-ph


title:
A Novel Image Descriptor with Aggregated Semantic Skeleton
  Representation for Long-term Visual Place Recognition

abstract:
  In a Simultaneous Localization and Mapping (SLAM) system, a loop-closure can
eliminate accumulated errors, which is accomplished by Visual Place Recognition
(VPR), a task that retrieves the current scene from a set of pre-stored
sequential images through matching specific scene-descriptors. In urban scenes,
the appearance variation caused by seasons and illumination has brought great
challenges to the robustness of scene descriptors. Semantic segmentation images
can not only deliver the shape information of objects but also their categories
and spatial relations that will not be affected by the appearance variation of
the scene. Innovated by the Vector of Locally Aggregated Descriptor (VLAD), in
this paper, we propose a novel image descriptor with aggregated semantic
skeleton representation (SSR), dubbed SSR-VLAD, for the VPR under drastic
appearance-variation of environments. The SSR-VLAD of one image aggregates the
semantic skeleton features of each category and encodes the spatial-temporal
distribution information of the image semantic information. We conduct a series
of experiments on three public datasets of challenging urban scenes. Compared
with four state-of-the-art VPR methods- CoHOG, NetVLAD, LOST-X, and
Region-VLAD, VPR by matching SSR-VLAD outperforms those methods and maintains
competitive real-time performance at the same time.


title:
Quality Metric Guided Portrait Line Drawing Generation from Unpaired
  Training Data

abstract:
  Face portrait line drawing is a unique style of art which is highly abstract
and expressive. However, due to its high semantic constraints, many existing
methods learn to generate portrait drawings using paired training data, which
is costly and time-consuming to obtain. In this paper, we propose a novel
method to automatically transform face photos to portrait drawings using
unpaired training data with two new features; i.e., our method can (1) learn to
generate high quality portrait drawings in multiple styles using a single
network and (2) generate portrait drawings in a "new style" unseen in the
training data. To achieve these benefits, we (1) propose a novel quality metric
for portrait drawings which is learned from human perception, and (2) introduce
a quality loss to guide the network toward generating better looking portrait
drawings. We observe that existing unpaired translation methods such as
CycleGAN tend to embed invisible reconstruction information indiscriminately in
the whole drawings due to significant information imbalance between the photo
and portrait drawing domains, which leads to important facial features missing.
To address this problem, we propose a novel asymmetric cycle mapping that
enforces the reconstruction information to be visible and only embedded in the
selected facial regions. Along with localized discriminators for important
facial regions, our method well preserves all important facial features in the
generated drawings. Generator dissection further explains that our model learns
to incorporate face semantic information during drawing generation. Extensive
experiments including a user study show that our model outperforms
state-of-the-art methods.