title:
Corrector estimates and homogenization error of unsteady flow ruled by
  Darcy's law

abstract:
  Concerned with Darcy's law with memory, originally developed by J.-L. Lions
\cite{Lions81}, this paper is devoted to studying non-stationary Stokes
equations on perforated domains. We obtain a sharp homogenization error in the
sense of energy norms, where the main challenge is to control the boundary
layers caused by the incompressibility condition. Our method is to construct
boundary-layer correctors associated with Bogovskii's operator, which is robust
enough to be applied to other problems of fluid mechanics on homogenization and
porous medium.


title: Bias-Eliminated Semantic Refinement for Any-Shot Learning

abstract:
  When training samples are scarce, the semantic embedding technique, ie,
describing class labels with attributes, provides a condition to generate
visual features for unseen objects by transferring the knowledge from seen
objects. However, semantic descriptions are usually obtained in an external
paradigm, such as manual annotation, resulting in weak consistency between
descriptions and visual features. In this paper, we refine the coarse-grained
semantic description for any-shot learning tasks, ie, zero-shot learning (ZSL),
generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new
model, namely, the semantic refinement Wasserstein generative adversarial
network (SRWGAN) model, is designed with the proposed multihead representation
and hierarchical alignment techniques. Unlike conventional methods, semantic
refinement is performed with the aim of identifying a bias-eliminated condition
for disjoint-class feature generation and is applicable in both inductive and
transductive settings. We extensively evaluate model performance on six
benchmark datasets and observe state-of-the-art results for any-shot learning;
eg, we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset
and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the
standard GZSL setting. Various visualizations are also provided to show the
bias-eliminated generation of SRWGAN. Our code is available.


title: Learning Latent Causal Dynamics

abstract:
  One critical challenge of time-series modeling is how to learn and quickly
correct the model under unknown distribution shifts. In this work, we propose a
principled framework, called LiLY, to first recover time-delayed latent causal
variables and identify their relations from measured temporal data under
different distribution shifts. The correction step is then formulated as
learning the low-dimensional change factors with a few samples from the new
environment, leveraging the identified causal structure. Specifically, the
framework factorizes unknown distribution shifts into transition distribution
changes caused by fixed dynamics and time-varying latent causal relations, and
by global changes in observation. We establish the identifiability theories of
nonparametric latent causal dynamics from their nonlinear mixtures under fixed
dynamics and under changes. Through experiments, we show that time-delayed
latent causal influences are reliably identified from observed variables under
different distribution changes. By exploiting this modular representation of
changes, we can efficiently learn to correct the model under unknown
distribution shifts with only a few samples.


title: Target-aware Molecular Graph Generation

abstract:
  Generating molecules with desired biological activities has attracted growing
attention in drug discovery. Previous molecular generation models are designed
as chemocentric methods that hardly consider the drug-target interaction,
limiting their practical applications. In this paper, we aim to generate
molecular drugs in a target-aware manner that bridges biological activity and
molecular design. To solve this problem, we compile a benchmark dataset from
several publicly available datasets and build baselines in a unified framework.
Building on the recent advantages of flow-based molecular generation models, we
propose SiamFlow, which forces the flow to fit the distribution of target
sequence embeddings in latent space. Specifically, we employ an alignment loss
and a uniform loss to bring target sequence embeddings and drug graph
embeddings into agreements while avoiding collapse. Furthermore, we formulate
the alignment into a one-to-many problem by learning spaces of target sequence
embeddings. Experiments quantitatively show that our proposed method learns
meaningful representations in the latent space toward the target-aware
molecular graph generation and provides an alternative approach to bridge
biology and chemistry in drug discovery.


title: Impact of Device Thermal Performance on 5G mmWave Communication Systems

abstract:
  5G millimeter wave (mmWave) cellular networks have been deployed by carriers
mostly in dense urban areas of the US, and have been reported to deliver a
throughput of around 1 - 2 Gbps per device. These throughput numbers are
captured via speed-testing applications which run for only a few seconds at a
time and are not indicative of the sustained throughput obtained while
downloading a large volume of data that can take several minutes to complete.
In this paper we report the first detailed measurements in three cities, Miami,
Chicago, and San Francisco that study the impact of skin temperature, as
measured by the device, on the throughput that the device is able to sustain
over many minutes. We report results from experiments conducted on deployed 5G
mmWave networks that show the effect of thermal throttling on a 5G mmWave
communication link when a large file download is initiated that saturates the
link, i.e., the device is connected to 4 mmWave downlink channels each 100 MHz
wide. We observe a gradual increase of skin temperature within 1-2 minutes
which correlates to a decrease in the data rate due to the number of aggregated
mmWave channels reducing from 4 to 1 before finally switching to 4G LTE.
Finally, we identify messaging in the Radio Resource Control (RRC) layer
confirming that the reduction in throughput was due to throttling due to the
skin temperature rise and further demonstrate that cooling the device restores
throughput performance. These results are extremely important since they
indicate that the Gbps throughput deliverable by 5G mmWave to an end-user 5G
device will be limited not by network characteristics but by device thermal
management.


title: The coefficients of the Jones polynomial

abstract:
  It has been known that, the coefficients of the series expansion of the Jones
polynomial evaluated at $e^x$ are rational valued Vassiliev invariants . In
this article, we calculate minimal multiplying factor, {\lambda}, needed for
these rational valued invariants to become integer valued Vassiliev invariants.
By doing that we obtain a set of integer-valued Vassiliev invariants.


title: Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals

abstract:
  Many real-world optimisation problems are defined over both categorical and
continuous variables, yet efficient optimisation methods such asBayesian
Optimisation (BO) are not designed tohandle such mixed-variable search spaces.
Recent approaches to this problem cast the selection of the categorical
variables as a bandit problem, operating independently alongside a BO component
which optimises the continuous variables. In this paper, we adopt a holistic
view and aim to consolidate optimisation of the categorical and continuous
sub-spaces under a single acquisition metric. We derive candidates from the
ExpectedImprovement criterion, which we call value proposals, and use these
proposals to make selections on both the categorical and continuous components
of the input. We show that this unified approach significantly outperforms
existing mixed-variable optimisation approaches across several mixed-variable
black-box optimisation tasks.


title: Revisiting mixed geometry

abstract:
  We provide a uniform construction of "mixed versions" or "graded lifts" in
the sense of Beilinson-Ginzburg-Soergel which works for arbitrary Artin stacks.
In particular, we obtain a general construction of graded lifts of many
categories arising in geometric representation theory and categorified knot
invariants. Our new theory associates to each Artin stack of finite type
$\mathcal{Y}$ over $\overline{\mathbb{F}}_q$ a symmetric monoidal
$\mathsf{DG}$-category $\mathsf{Shv}_{\mathsf{gr}, c}(\mathcal{Y})$ of
constructible graded sheaves on $\mathcal{Y}$ along with the six-functor
formalism, a perverse $t$-structure, and a weight (or co-$t$-)structure in the
sense of Bondarko and Pauksztello. This category sits in between the category
$\mathsf{Shv}_{\mathsf{m}, c}(\mathcal{Y}_n)$ of constructible mixed
$\ell$-adic sheaves in the sense of Beilinson-Bernstein-Deligne-Gabber for any
$\mathbb{F}_{q^n}$-form $\mathcal{Y}_n$ of $\mathcal{Y}$ and the category
$\mathsf{Shv}_c(\mathcal{Y})$ of constructible $\ell$-adic sheaves on
$\mathcal{Y}$, compatible with the six-functor formalism, perverse
$t$-structures, and Frobenius weights.
  Classically, mixed versions were only constructed in very special cases.
However, the category $\mathsf{Shv}_{\mathsf{gr}, c}(\mathcal{Y})$ agrees with
those previously constructed when they are available. For example, for any
reductive group $G$ with a fixed pair $T\subset B$ of a maximal torus and a
Borel subgroup, we have an equivalence of monoidal $\mathsf{DG}$ weight
categories $\mathsf{Shv}_{\mathsf{gr}, c}(B\backslash G/B) \simeq
\mathsf{Ch}^b(\mathsf{SBim}_W)$, where $\mathsf{Ch}^b(\mathsf{SBim}_W)$ is the
monoidal $\mathsf{DG}$-category of bounded chain complexes of Soergel bimodules
and $W$ is the Weyl group of $G$.


title:
Geometric Digital Twinning of Industrial Facilities: Retrieval of
  Industrial Shapes

abstract:
  This paper devises, implements and benchmarks a novel shape retrieval method
that can accurately match individual labelled point clusters (instances) of
existing industrial facilities with their respective CAD models. It employs a
combination of image and point cloud deep learning networks to classify and
match instances to their geometrically similar CAD model. It extends our
previous research on geometric digital twin generation from point cloud data,
which currently is a tedious, manual process. Experiments with our joint
network reveal that it can reliably retrieve CAD models at 85.2\% accuracy. The
proposed research is a fundamental framework to enable the geometric Digital
Twin (gDT) pipeline and incorporate the real geometric configuration into the
Digital Twin.


title: Robophysical modeling of spacetime dynamics

abstract:
  Systems consisting of spheres rolling on elastic membranes have been used as
educational tools to introduce a core conceptual idea of General Relativity
(GR): how curvature guides the movement of matter. However, previous studies
have revealed that such schemes cannot accurately represent relativistic
dynamics in the laboratory. Dissipative forces cause the initially GR-like
dynamics to be transient and consequently restrict experimental study to only
the beginnings of trajectories; dominance of Earth's gravity forbids the
difference between spatial and temporal spacetime curvatures. Here by
developing a mapping between dynamics of a wheeled vehicle on a spandex
membrane, we demonstrate that an active object that can prescribe its speed can
not only obtain steady-state orbits, but also use the additional parameters
such as speed to tune the orbits towards relativistic dynamics. Our mapping
demonstrates how activity mixes space and time in a metric, shows how active
particles do not necessarily follow geodesics in the real space but instead
follow geodesics in a fiducial spacetime. The mapping further reveals how
parameters such as the membrane elasticity and instantaneous speed allow
programming a desired spacetime such as the Schwarzschild metric near a
non-rotating black hole. Our mapping and framework point the way to the
possibility to create a robophysical analog gravity system in the laboratory at
low cost and provide insights into active matter in deformable environments and
robot exploration in complex landscapes.


title: Deconstructing the Inductive Biases of Hamiltonian Neural Networks

abstract:
  Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian
NNs, dramatically outperform other learned dynamics models by leveraging strong
inductive biases. These models, however, are challenging to apply to many real
world systems, such as those that don't conserve energy or contain contacts, a
common setting for robotics and reinforcement learning. In this paper, we
examine the inductive biases that make physics-inspired models successful in
practice. We show that, contrary to conventional wisdom, the improved
generalization of HNNs is the result of modeling acceleration directly and
avoiding artificial complexity from the coordinate system, rather than
symplectic structure or energy conservation. We show that by relaxing the
inductive biases of these models, we can match or exceed performance on
energy-conserving systems while dramatically improving performance on
practical, non-conservative systems. We extend this approach to constructing
transition models for common Mujoco environments, showing that our model can
appropriately balance inductive biases with the flexibility required for
model-based control.


title:
Heterogeneous Calibration: A post-hoc model-agnostic framework for
  improved generalization

abstract:
  We introduce the notion of heterogeneous calibration that applies a post-hoc
model-agnostic transformation to model outputs for improving AUC performance on
binary classification tasks. We consider overconfident models, whose
performance is significantly better on training vs test data and give intuition
onto why they might under-utilize moderately effective simple patterns in the
data. We refer to these simple patterns as heterogeneous partitions of the
feature space and show theoretically that perfectly calibrating each partition
separately optimizes AUC. This gives a general paradigm of heterogeneous
calibration as a post-hoc procedure by which heterogeneous partitions of the
feature space are identified through tree-based algorithms and post-hoc
calibration techniques are applied to each partition to improve AUC. While the
theoretical optimality of this framework holds for any model, we focus on deep
neural networks (DNNs) and test the simplest instantiation of this paradigm on
a variety of open-source datasets. Experiments demonstrate the effectiveness of
this framework and the future potential for applying higher-performing
partitioning schemes along with more effective calibration techniques.


title:
Electronic structure and spin-lattice relaxation in superconducting
  vortex states on the kagome lattice near van Hove filling

abstract:
  Starting from a tight-binding model on the kagome lattice near the van Hove
filling, the superconducting (SC) properties are investigated self-consistently
using the Bogoliubov-de Gennes equation with the consideration of the
inequivalent third-neighbor (TN) bonds. Near the van Hove filling, the most
favorable SC pairings are found to derive from the electrons belonging to the
same sublattice sites, including the on-site $s$-wave and the
spin-singlet/triplet TN pairings. The inequivalent TN bonds will result in
multiple SC components with different orbital angular momentums (OAM) for the
TN SC pairings. While the density of states (DOS) and the temperature ($T$)
dependence of the spin-lattice relaxation rate ($T^{-1}_{1}$) exhibit distinct
line shapes in the SC state for the three cases, a peak structure in the $T$
dependence of $T^{-1}_{1}$ can be found for both cases just below $T_{c}$ as a
result of the van Hove singularity, even though the SC gap has nodes. The
effects of magnetic vortices on the low energy excitations and on the $T$
dependence of $T^{-1}_{1}$ with the implications of the results are also
discussed for both cases.


title:
Minimal generating sets of moves for diagrams of isotopic knots and
  spatial trivalent graphs

abstract:
  Polyak proved that all oriented versions of Reidemeister moves for knot and
link diagrams can be generated by a set of just four oriented Reidemeister
moves, and that no fewer than four oriented Reidemeister moves generate them
all. Polyak also proved that a certain set containing two Reidemeister moves of
type 1, one move of type 2, and one move of type 3 form a minimal generating
set for all oriented Reidemeister moves. We expand upon Polyak's work by
providing an additional eleven minimal generating sets of oriented Reidemeister
moves, and we prove that these twelve sets represent all possible minimal
generating sets of oriented Reidemeister moves. We also consider the
Reidemeister-type moves that relate oriented spatial trivalent graph diagrams
with trivalent vertices that are sources and sinks and prove that a minimal
generating set of oriented Reidemeister-type moves for spatial trivalent graph
diagrams contains ten moves.


title: Recycling nonlocality in a quantum network

abstract:
  It has recently been discovered that the nonlocality of an entangled qubit
pair can be recycled for several Bell experiments. Here, we go beyond standard
Bell scenarios and investigate the recycling of nonlocal resources in a quantum
network. We realise a photonic quantum 3-branch star network in which three
sources of entangled pairs independently connect three outer parties with a
central node. After measuring, each outer party respectively relays their
system to an independent secondary measuring party. We experimentally
demonstrate that the outer parties can perform unsharp measurements that are
strong enough to violate a network Bell inequality with the central party, but
weak enough to maintain sufficient entanglement in the network to allow the
three secondary parties to do the same. Moreover, the violations are strong
enough to exclude any model based on standard projective measurements on the
EPR pairs emitted in the network. Our experiment brings together the research
program of recycling quantum resources with that of Bell nonlocality in
networks.


title: Collaborative analysis of genomic data: vision and challenges

abstract:
  The cost of DNA sequencing has resulted in a surge of genetic data being
utilised to improve scientific research, clinical procedures, and healthcare
delivery in recent years. Since the human genome can uniquely identify an
individual, this characteristic also raises security and privacy concerns. In
order to balance the risks and benefits, governance mechanisms including
regulatory and ethical controls have been established, which are prone to human
errors and create hindrance for collaboration. Over the past decade,
technological methods are also catching up that can support critical
discoveries responsibly. In this paper, we explore regulations and ethical
guidelines and propose our visions of secure/private genomic data
storage/processing/sharing platforms. Then, we present some available
techniques and a conceptual system model that can support our visions. Finally,
we highlight the open issues that need further investigation.


title:
Networks and Identity Drive Geographic Properties of the Diffusion of
  Linguistic Innovation

abstract:
  Adoption of cultural innovation (e.g., music, beliefs, language) is often
geographically correlated, with adopters largely residing within the boundaries
of relatively few well-studied, socially significant areas. These cultural
regions are often hypothesized to be the result of either (i) identity
performance driving the adoption of cultural innovation, or (ii) homophily in
the networks underlying diffusion. In this study, we show that demographic
identity and network topology are both required to model the diffusion of
innovation, as they play complementary roles in producing its spatial
properties. We develop an agent-based model of cultural adoption, and validate
geographic patterns of transmission in our model against a novel dataset of
innovative words that we identify from a 10% sample of Twitter. Using our
model, we are able to directly compare a combined network + identity model of
diffusion to simulated network-only and identity-only counterfactuals --
allowing us to test the separate and combined roles of network and identity.
While social scientists often treat either network or identity as the core
social structure in modeling culture change, we show that key geographic
properties of diffusion actually depend on both factors as each one influences
different mechanisms of diffusion. Specifically, the network principally drives
spread among urban counties via weak-tie diffusion, while identity plays a
disproportionate role in transmission among rural counties via strong-tie
diffusion. Diffusion between urban and rural areas, a key component in
innovation diffusing nationally, requires both network and identity. Our work
suggests that models must integrate both factors in order to understand and
reproduce the adoption of innovation.


title: A Stieltjes algorithm for generating multivariate orthogonal polynomials

abstract:
  Orthogonal polynomials of several variables have a vector-valued three-term
recurrence relation, much like the corresponding one-dimensional relation. This
relation requires only knowledge of certain recurrence matrices, and allows
simple and stable evaluation of multivariate orthogonal polynomials. In the
univariate case, various algorithms can evaluate the recurrence coefficients
given the ability to compute polynomial moments, but such a procedure is absent
in multiple dimensions. We present a new Multivariate Stieltjes (MS) algorithm
that fills this gap in the multivariate case, allowing computation of
recurrence matrices assuming moments are available. The algorithm is
essentially explicit in two and three dimensions, but requires the numerical
solution to a non-convex problem in more than three dimensions. Compared to
direct Gram-Schmidt-type orthogonalization, we demonstrate on several examples
in up to three dimensions that the MS algorithm is far more stable, and allows
accurate computation of orthogonal bases in the multivariate setting, in
contrast to direct orthogonalization approaches.


title: Multi-relation Message Passing for Multi-label Text Classification

abstract:
  A well-known challenge associated with the multi-label classification problem
is modelling dependencies between labels. Most attempts at modelling label
dependencies focus on co-occurrences, ignoring the valuable information that
can be extracted by detecting label subsets that rarely occur together. For
example, consider customer product reviews; a product probably would not
simultaneously be tagged by both "recommended" (i.e., reviewer is happy and
recommends the product) and "urgent" (i.e., the review suggests immediate
action to remedy an unsatisfactory experience). Aside from the consideration of
positive and negative dependencies, the direction of a relationship should also
be considered. For a multi-label image classification problem, the "ship" and
"sea" labels have an obvious dependency, but the presence of the former implies
the latter much more strongly than the other way around. These examples
motivate the modelling of multiple types of bi-directional relationships
between labels. In this paper, we propose a novel method, entitled
Multi-relation Message Passing (MrMP), for the multi-label classification
problem. Experiments on benchmark multi-label text classification datasets show
that the MrMP module yields similar or superior performance compared to
state-of-the-art methods. The approach imposes only minor additional
computational and memory overheads.


title:
Optical superradiance of a pair of color centers in an integrated
  silicon-carbide-on-insulator microresonator

abstract:
  An outstanding challenge for color center-based quantum information
processing technologies is the integration of optically-coherent emitters into
scalable thin-film photonics. Here, we report on the integration of
near-transform-limited silicon vacancy (V$_{\text{Si}}$) defects into microdisk
resonators fabricated in a CMOS-compatible 4H-Silicon Carbide-on-Insulator
platform. We demonstrate a single-emitter cooperativity of up to 0.8 as well as
optical superradiance from a pair of color centers coupled to the same cavity
mode. We investigate the effect of multimode interference on the photon
scattering dynamics from this multi-emitter cavity quantum electrodynamics
system. These results are crucial for the development of quantum networks in
silicon carbide and bridge the classical-quantum photonics gap by uniting
optically-coherent spin defects with wafer-scalable, state-of-the-art
photonics.


title:
Anomalous magnetic interference of cross-type Josephson junctions
  exposed to oblique magnetic fields

abstract:
  Gauge-invariant phase difference and critical currents of cross-type
Josephson junctions with thin and narrow superconducting strips exposed to
three-dimensional magnetic fields are theoretically investigated. When a
sandwich-type Josephson junction in the xy plane is exposed to parallel
magnetic fields Hx and Hy, the phase difference linearly depends on the spatial
coordinates, x and y, and the critical currents exhibit the standard
Fraunhofer-type magnetic interference. The perpendicular field Hz, on the other
hand, nonlinearly modulates the distribution of the phase difference and the
critical currents as the functions of the oblique field exhibit anomalous
magnetic interference. We obtain simple analytical expressions for critical
currents of small cross-type junctions by neglecting the effects of self-field
and trapped vortices. The resulting dc critical currents show anomalous and
diverse interference patterns depending on the parallel and perpendicular
magnetic fields


title:
A Survey on Artificial Intelligence for Source Code: A Dialogue Systems
  Perspective

abstract:
  In this survey paper, we overview major deep learning methods used in Natural
Language Processing (NLP) and source code over the last 35 years. Next, we
present a survey of the applications of Artificial Intelligence (AI) for source
code, also known as Code Intelligence (CI) and Programming Language Processing
(PLP). We survey over 287 publications and present a software-engineering
centered taxonomy for CI placing each of the works into one category describing
how it best assists the software development cycle. Then, we overview the field
of conversational assistants and their applications in software engineering and
education. Lastly, we highlight research opportunities at the intersection of
AI for code and conversational assistants and provide future directions for
researching conversational assistants with CI capabilities.


title:
Ion size effect on electrostatic and electroosmotic properties in soft
  nanochannels with pH-dependent charge density

abstract:
  We report a theoretical study of ion size effect on various properties in a
soft nanochannel with pH-dependent charge density. We develop a free energy
based mean-field theory taking into account ion size as well as pH-dependence
of charged polyelectrolyte layer grafted on a rigid surface in an electrolyte.
The influence of ion size on properties in a soft nanochannel is evaluated by
numerically calculating ion number densities and electrostatic potential. We
demonstrate that unlike in point-like ions, for finite sizes of ions, a uniform
distribution of chargeable sites within the polyelectrolyte layer causes
unphysical discontinuities in ion number densities not only for hydrogen ion
but also for other kinds of ions. It is shown that the same cubic spatial
distribution of chargeable sites as for point-like ions is necessary to ensure
continuity of ion number density and zero ion transport at the polyelectrolyte
layer - rigid solid interface. We find that considering finite ion size causes
an increase in electrostatic potential and electroosmotic velocity and a
decrease in ion number densities. More importantly, we demonstrate that in
polyelectrolyte layer, pH-dependence of polyelectrolyte charge density makes
accumulation of hydrogen ions stronger than for the other positive ion species
in the electrolyte and such a tendency is further enhanced by considering
finite ion size. In addition, we discuss how consideration of finite ion size
affects the role of various parameters on electrostatic and electroosmotic
properties.


title:
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill
  Acquisition

abstract:
  Though many reinforcement learning (RL) problems involve learning policies in
settings with difficult-to-specify safety constraints and sparse rewards,
current methods struggle to acquire successful and safe policies. Methods that
extract useful policy primitives from offline datasets using generative
modeling have recently shown promise at accelerating RL in these more complex
settings. However, we discover that current primitive-learning methods may not
be well-equipped for safe policy learning and may promote unsafe behavior due
to their tendency to ignore data from undesirable behaviors. To overcome these
issues, we propose SAFEty skill pRiors (SAFER), an algorithm that accelerates
policy learning on complex control tasks under safety constraints. Through
principled training on an offline dataset, SAFER learns to extract safe
primitive skills. In the inference stage, policies trained with SAFER learn to
compose safe skills into successful policies. We theoretically characterize why
SAFER can enforce safe policy learning and demonstrate its effectiveness on
several complex safety-critical robotic grasping tasks inspired by the game
Operation, in which SAFER outperforms baseline methods in learning successful
policies and enforcing safety.


title:
Electric double layer of colloidal particles in salt-free concentrated
  suspensions including non-uniform size effects and orientational ordering of
  water dipoles

abstract:
  The response of a suspension under a variety of static or alternating
external fields strongly depends on the equilibrium electric double layer that
surrounds the colloidal particles in the suspension. The theoretical models for
salt-free suspensions can be improved by incorporating non-uniform size effects
and orientational ordering of water dipoles neglected in previous mean-field
approaches, which are based on the Poisson-Boltzmann approach. Our model
including non-uniform size effects and orientational ordering of water dipoles
seems to have quite a promising effect because the model can predict the
phenomena like a heavy decrease in relative permittivity of the suspension and
counterion stratification near highly charged colloidal particle. In this work
we numerically obtain the electric potential, the counterions concentration and
the relative permittivity around a charged particle in a concentrated salt-free
suspension corrected by non-uniform size effects and orientational ordering of
water dipoles. The results show the worth of such corrections for medium to
high particle charges at every particle volume fraction. We conclude that
non-uniform size effects and orientational ordering of water dipoles are
necessary for the development of new theoretical models to study
non-equilibrium properties in concentrated colloidal suspensions.


title:
SkyMapper colours of Seyfert galaxies and Changing-look AGN-II. Newly
  discovered Changing-look AGN

abstract:
  Changing-look Active Galactic Nuclei (CLAGN) are AGN that change type as
their broad emission lines appear or disappear, which is usually accompanied by
strong flux changes inn their blue featureless continuum. We search for Turn-On
CLAGN by selecting type-2 AGN from the spectroscopic 6dF Galaxy Survey, whose
photometry, as observed over a decade later by the SkyMapper Southern Survey,
is consistent with type-1 AGN. Starting from a random sample of 235 known
type-2 AGN we select 18 candidates and confirm 13 AGN to have changed into
type-1 spectra; observations of an incomplete sample reveal nine further
Turn-On CLAGN. While our search was not intended to reliably discover Turn-Off
CLAGN, we discover four such cases as well. This result suggests a Turn-On
CLAGN rate of 12% over ~15 years and imply a total CLAGN rate of ~25% over this
period. Finally, we present observations of AGN that are atypical for the CLAGN
phenomenology, including J1109146 - a CLAGN that did not appear as an AGN in
6dFGS; J1406507 - the second reported Changinglook NLS1; and J1340153 - a CLAGN
with a change timescale of three months.


title: GeV $\gamma$-ray Emission of Compact Steep-Spectrum Source 4C +39.23B

abstract:
  Thirteen yr observation data of 4FGL J0824.9+3915 with the Large Area
Telescope on board the Fermi Gamma Ray Space Telescope (Fermi/LAT) are analyzed
for revisiting whether 4C +39.23B, a compact steep-spectrum (CSS) source closed
to a flat-spectrum radio quasar (FSRQ) 4C +39.23A in the $\gamma$-ray emitting
region of 4FGL J0824.9+3915, is a $\gamma$-ray emitter. We find that the
time-integrated $\gamma$-ray emission of 4FGL J0824.9+3915 is overwhelmingly
dominated by 4C +39.23A. It shows significant variability at a 6.7$\sigma$
confidence level and the average $\gamma$-ray flux in the 0.1--300 GeV energy
band is $(1.60\pm 0.15)\times10^{-8}$ ph cm$^{-2}$ s$^{-1}$ with a power-law
photon spectral index of $2.48\pm0.05$. During MJD 57500--58500, 4FGL
J0824.9+3915 is in a low state with a steady $\gamma$-ray flux. Analyzed the
Fermi/LAT observation data in this time interval, it is found that the TS
values of the $\gamma$-ray emission from 4C +39.23A and 4C +39.23B are $\sim5$
and $\sim 31$, respectively, indicating that the $\gamma$-ray emission in this
time interval is dominated by the CSS 4C +39.23B. The derived average flux in
this time interval for 4C +39.23B is $(9.40\pm4.10)\times 10^{-9}$ ph cm
$^{-2}$ s$^{-1}$ with $\Gamma_{\gamma}=2.45\pm0.17$. Attributing the spectral
energy distribution (SED) of 4C +39.23B to the radiations from its core and
extended region, we show that the SED can be represented with a two-zone
leptonic model. Its $\gamma$-ray emission is contributed by the core region.
The derived magnetic field strength and Doppler boosting factor of the core are
0.13 G and 6.5. Comparing 4C +39.23B with other $\gamma$-emitting CSSs and
compact symmetric objects (CSOs) in the $\Gamma_{\gamma}-L_{\gamma}$ plane, it
resembles CSSs.


title: Maximizing MeV x-ray dose in relativistic laser-solid interactions

abstract:
  Bremsstrahlung x-rays generated in laser-solid interactions can be used as
light sources for high-energy-density science. We present electron and x-ray
spectra from particle-in-cell and Monte Carlo simulations, varying laser pulse
intensity and duration at fixed energy of 200$\,$J. Superponderomotive electron
temperatures are observed at low intensity; a new temperature scaling is given
that depends on pulse duration and density scale length. Short, high-intensity
pulses create low-divergence electron beams before self-generated magnetic
fields evolve, resulting in more forward-going MeV x-rays.


title:
Optical interaction of the NV- centre in diamond with a plasmonic metal
  nanoparticle

abstract:
  We present a rigorous theoretical model for the optical interaction between a
nitrogen-vacancy (NV) centre in diamond and a plasmonic metal nanoparticle
(MNP), accompanied by a computationally efficient procedure to solve the
evolution. The proposed model enables us to successfully explain existing
optical emission measurements of NV centres both in the presence and absence of
a metal nanoparticle. We show that the NV-plasmon interaction provides a
versatile new avenue to enhance and control the optical emission of an NV
centre. Changes to the MNP type and size, NV-MNP centre separation, submerging
medium permittivity, and NV orientation with respect to the MNP surface can be
exploited to improve a plethora of NV centre based nanodevices.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.optics quant-ph


title:
The USTC-Ximalaya system for the ICASSP 2022 multi-channel multi-party
  meeting transcription (M2MeT) challenge

abstract:
  We propose two improvements to target-speaker voice activity detection
(TS-VAD), the core component in our proposed speaker diarization system that
was submitted to the 2022 Multi-Channel Multi-Party Meeting Transcription
(M2MeT) challenge. These techniques are designed to handle multi-speaker
conversations in real-world meeting scenarios with high speaker-overlap ratios
and under heavy reverberant and noisy condition. First, for data preparation
and augmentation in training TS-VAD models, speech data containing both real
meetings and simulated indoor conversations are used. Second, in refining
results obtained after TS-VAD based decoding, we perform a series of
post-processing steps to improve the VAD results needed to reduce diarization
error rates (DERs). Tested on the ALIMEETING corpus, the newly released
Mandarin meeting dataset used in M2MeT, we demonstrate that our proposed system
can decrease the DER by up to 66.55/60.59% relatively when compared with
classical clustering based diarization on the Eval/Test set.


title: PPA: Preference Profiling Attack Against Federated Learning

abstract:
  Federated learning (FL) trains a global model across a number of
decentralized participants, each with a local dataset. Compared to traditional
centralized learning, FL does not require direct local datasets access and thus
mitigates data security and privacy concerns. However, data privacy concerns
for FL still exist due to inference attacks, including known membership
inference, property inference, and data inversion.
  In this work, we reveal a new type of privacy inference attack, coined
Preference Profiling Attack (PPA), that accurately profiles private preferences
of a local user. In general, the PPA can profile top-k, especially for top-1,
preferences contingent on the local user's characteristics. Our key insight is
that the gradient variation of a local user's model has a distinguishable
sensitivity to the sample proportion of a given class, especially the
majority/minority class. By observing a user model's gradient sensitivity to a
class, the PPA can profile the sample proportion of the class in the user's
local dataset and thus the user's preference of the class is exposed. The
inherent statistical heterogeneity of FL further facilitates the PPA. We have
extensively evaluated the PPA's effectiveness using four datasets from the
image domains of MNIST, CIFAR10, Products-10K and RAF-DB. Our results show that
the PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10,
respectively. More importantly, in the real-world commercial scenarios of
shopping (i.e., Products-10K) and the social network (i.e., RAF-DB), the PPA
gains a top-1 attack accuracy of 78% in the former case to infer the most
ordered items, and 88% in the latter case to infer a victim user's emotions.
Although existing countermeasures such as dropout and differential privacy
protection can lower the PPA's accuracy to some extent, they unavoidably incur
notable global model deterioration.


title:
Influence of asymmetric depletion of solvents on the electric double
  layer of charged objects in binary polar solvent mixture

abstract:
  For binary solvent mixtures composed of ions and two kinds of polar solvents,
electric double layer near a charged object is strongly affected by not only
the binary solvent composition but also nature of solvents such as volume and
dipole moment of a solvent molecule. Accounting for difference in sizes of
solvents and orientational ordering of solvent dipoles, we theoretically obtain
general expressions for the spatial distribution functions of solvents and
ions, in planar geometry and within the mean-field approach. Although focusing
on long-range electrostatic interaction, we neglect short-range interactions
such as preferential solvation, our approach predicts an asymmetric depletion
of the two solvents from the charged surface and a behavior of decreased
permittivity of the binary solvent mixture. Furthermore, we suggest that the
key factor for the depletion is the ratio of the solvent dipole moment to the
solvent volume. The influence of binary solvent composition, volume of solvent
and dipole moment of solvent on the number density of solvents, permittivity
and differential capacitance are presented and discussed, respectively. We
conclude that accounting for difference in the volume and dipole moment between
polar solvents is necessary for new approach to represent more realistic
situations such as preferential solvation.


title:
Impurity effects on binding energy, diamagnetic susceptibility and
  photoionization cross-section of chalcopyrite AgInSe2 Nanotadpole

abstract:
  Recently the interest in chalcopyrite semiconductor nanostructures has
increased because of their non-toxicity and their wide direct bandgap.
Likewise, structures with non-trivial geometry are particularly interesting
because of their electronic, optical, and magnetic properties. In the current
article, the finite element method was used in conjunction with the effective
mass approximation to theoretically investigate the properties of a
chalcopyrite AgInSe2 nanotadpole in the presence of an off-center impurity. The
morphology of the nanotadpole gives it excellent hydrodynamic properties and is
ideal for a wide range of applications. The probability densities for various
impurity positions and energy levels were obtained. The results suggested a
strong dependence of the behavior of the electron on the impurity positions and
the orientation of the wave function. The binding energy's dependence on the
nanotadpole's size and the impurity position was obtained. The dependence of
the diamagnetic susceptibility on the impurity position was calculated. An
extensive investigation of the photoionization cross-section and the
oscillatory strength was carried out for the ground and the first two excited
states as the initial states and the first twenty excited states as the final
states.


title: A Trust Region Method for Pareto Front Approximation

abstract:
  In this paper, we consider black-box multiobjective optimization problems in
which all objective functions are not given analytically. In multiobjective
optimization, it is important to produce a set of uniformly distributed
discrete solutions over the Pareto front to build a good approximation. In
black-box biobjective optimization, one can evaluate distances between
solutions with the ordering property of the Pareto front. These distances allow
to be able to evaluate distribution of all solution points, so it is not
difficult to maintain uniformity of solutions distribution. However, problems
with more than two objectives do not have ordering property, so it is noted
that these problems require other techniques to measure the coverage and
maintain uniformity of solutions distribution. In this paper, we propose an
algorithm based on a trust region method for the Pareto front approximation in
black-box multiobjective optimization. In the algorithm, we select a reference
point in the set of non-dominated points by employing the density function and
explore area around this point. This ensures uniformity of solutions
distribution even for problems with more than two objective functions. We also
prove that the iteration points of the algorithm converge to Pareto critical
points. Finally, we present numerical results suggesting that the algorithm
generates the set of well-distributed solutions approximating the Pareto front,
even in the case for the problems with three objective functions.


title:
Influence of solvent polarization and non-uniform ion size on
  electrostatic properties between charged surfaces in an electrolyte solution

abstract:
  In this paper, we study electrostatic properties between two similar or
oppositely charged surfaces immersed in an electrolyte solution by using
mean-field approach accounting for solvent polarization and non-uniform size
effect. Applying a free energy formalism accounting for unequal ion sizes and
orientational ordering of water dipoles, we derive coupled and self-consistent
equations to calculate electrostatic properties between charged surfaces.
Electrostatic properties for similarly charged surfaces depend on counterion
size but not coion size. Moreover, electrostatic potential and osmotic pressure
between similarly charged surfaces are found to be increased with increasing
counterion size. On the other hand, the corresponding ones between oppositely
charged surfaces are related to both sizes of positive and negative ions. For
oppositely charged surfaces, the electrostatic potential, number density of
solvent molecules and relative permittivity of an electrolyte having unequal
ion sizes are not symmetric about the centerline between the charged surfaces.
For either case, the consideration of solvent polarization results in an
decrease in the electrostatic potential and the osmotic pressure compared to
the case without the effect.


title: Consistency and Diversity induced Human Motion Segmentation

abstract:
  Subspace clustering is a classical technique that has been widely used for
human motion segmentation and other related tasks. However, existing
segmentation methods often cluster data without guidance from prior knowledge,
resulting in unsatisfactory segmentation results. To this end, we propose a
novel Consistency and Diversity induced human Motion Segmentation (CDMS)
algorithm. Specifically, our model factorizes the source and target data into
distinct multi-layer feature spaces, in which transfer subspace learning is
conducted on different layers to capture multi-level information. A
multi-mutual consistency learning strategy is carried out to reduce the domain
gap between the source and target data. In this way, the domain-specific
knowledge and domain-invariant properties can be explored simultaneously.
Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion
(HSIC) is introduced to ensure the diversity of multi-level subspace
representations, which enables the complementarity of multi-level
representations to be explored to boost the transfer learning performance.
Moreover, to preserve the temporal correlations, an enhanced graph regularizer
is imposed on the learned representation coefficients and the multi-level
representations of the source data. The proposed model can be efficiently
solved using the Alternating Direction Method of Multipliers (ADMM) algorithm.
Extensive experimental results on public human motion datasets demonstrate the
effectiveness of our method against several state-of-the-art approaches.


title:
Settling the Communication Complexity for Distributed Offline
  Reinforcement Learning

abstract:
  We study a novel setting in offline reinforcement learning (RL) where a
number of distributed machines jointly cooperate to solve the problem but only
one single round of communication is allowed and there is a budget constraint
on the total number of information (in terms of bits) that each machine can
send out. For value function prediction in contextual bandits, and both
episodic and non-episodic MDPs, we establish information-theoretic lower bounds
on the minimax risk for distributed statistical estimators; this reveals the
minimum amount of communication required by any offline RL algorithms.
Specifically, for contextual bandits, we show that the number of bits must
scale at least as $\Omega(AC)$ to match the centralised minimax optimal rate,
where $A$ is the number of actions and $C$ is the context dimension; meanwhile,
we reach similar results in the MDP settings. Furthermore, we develop learning
algorithms based on least-squares estimates and Monte-Carlo return estimates
and provide a sharp analysis showing that they can achieve optimal risk up to
logarithmic factors. Additionally, we also show that temporal difference is
unable to efficiently utilise information from all available devices under the
single-round communication setting due to the initial bias of this method. To
our best knowledge, this paper presents the first minimax lower bounds for
distributed offline RL problems.


title:
The Gaia-ESO Survey: Age-chemical-clock relations spatially resolved in
  the Galactic disc

abstract:
  The last decade has seen a revolution in our knowledge of the Galaxy thanks
to the Gaia and asteroseismic space missions and the ground-based spectroscopic
surveys. To complete this picture, it is necessary to map the ages of its
stellar populations. During recent years, the dependence on time of abundance
ratios involving slow (s) neutron-capture and $\alpha$ elements (called
chemical-clocks) has been used to provide estimates of stellar ages, usually in
a limited volume close to the Sun. We aim to analyse the relations of chemical
clocks in the Galactic disc extending the range to R$_{\rm GC}\sim$6-20~kpc.
Using the sixth internal data release of the Gaia-ESO survey, we calibrated
several relations between stellar ages and abundance ratios [s/$\alpha$] using
a sample of open clusters, the largest one so far used with this aim. Thanks to
their wide galactocentric coverage, we investigated the radial variations of
the shape of these relations, confirming their non-universality. We estimated
our accuracy and precision in recovering the global ages of open clusters, and
the ages of their individual members. We applied the multi-variate relations
with the highest correlation coefficients to the field star population. We
confirm that there is no single age-chemical clock relationship valid for the
whole disc, but that there is a dependence on the galactocentric position,
which is related to the radial variation of the star formation history combined
with the non-monotonic dependence on metallicity of the yields of the s-process
elements from low- and intermediate-mass stars. Finally, the abundance ratios
[Ba/$\alpha$] are more sensitive to age than those with [Y/$\alpha$] for young
disc stars, and their slopes vary less with galactocentric distance.


title: Split or unsplit electron beams?

abstract:
  Non-linear effects have become increasingly relevant in modern circular
particle accelerators, and in recent years a change of paradigm has appeared,
the attitude towards nonlinear effects having shifted from fighting them to
exploiting them with the goal of devising new beam manipulations, such as the
splitting of the beam in the transverse phase space by crossing a stable
resonance. In the field of hadron accelerators, well-established operational
techniques based on nonlinear effects exist, whereas for the case of
synchrotron light sources these new techniques are only beginning their way
into the field. In this paper, we discuss novel techniques aimed at providing
split beams in synchrotron light sources that are obtained by using stable
islands in the transverse phase space or unsplit beams with AC dipoles to
generate periodic closed orbits. The results of detailed numerical simulations,
which support the proposed methods, are presented and discussed in this paper,
together with possible applications.


title: Reciprocal symmetry breaking in Pareto sampling

abstract:
  Let $W_1,\ldots,W_N$ be a sample of $\mathrm{Pareto}(\alpha)$ random
variables normalized by their sum, such that $\sum_i W_i=1$. The $W_i$ may
represent the weights of valleys in a spin glass (if $0<\alpha<1$), or the
frequency of different lineages (families) in a genealogy. This paper considers
a population in which there are $N$ individuals reproducing with
$\mathrm{Pareto}(\alpha)$ offspring-number distribution ($1<\alpha<2$). The
probability of two randomly-chosen individuals being siblings, $Y_2=\sum_i
W_i^2$, gives the sample mean of the normalized size of families, and its
reciprocal gives the effective number of families (or reproducing lineages) in
the population, $N_{\mathrm{e}}=1/Y_2$. The typical sample mean is very
different from the average over all possible samples, i.e. $Y_2$ is not a
self-averaging quantity. The typical $Y_2$ and its reciprocal do not vary with
$N$ in opposite ways. Non-self-averaging effects are crucial in understanding
genetic diversity in mass spawning species such as marine fishes.


title: Shortcut in codimension-2 brane cosmology in light of GW170817

abstract:
  In this paper, our universe is regarded as a codimension-2 brane embedded in
a noncompact six-dimensional Anti-de Sitter (AdS) spacetime. We derive the
gravitational horizon radius on the brane under the low-energy approximation,
which reflects how the extra dimensions cause the shortcut effect of
gravitational waves (GWs). We also study the time delay between a GW signal and
an electromagnetic (EM) wave signal in the low-redshift limit by combining with
the joint observations of GW170817/GRB 170817A, which gives an upper limit to
the $\text{AdS}_{6}^{~}$ radius as $\ell~\!^{2}_{~} \lesssim
3.84\,\text{Mpc}^{2}_{~}$. For a high-redshift source, the time delay is
converted into the discrepancy between the source redshift derived from the GW
signal and the one derived from the EM counterpart. It is found that if one
expects to detect the EM counterpart of a high-redshift GW event within a
reasonable observation time, it requires a stronger constraint on the
$\text{AdS}_{6}^{~}$ radius. Our research shows that the $\text{AdS}_{6}^{~}$
radius should satisfy $\ell~\!_{~}^{2}\lesssim0.02\,\text{Mpc}^{2}_{~}$ for the
DECIGO and BBO.


title: Self-organized Kagome-lattice in a metal-organic monolayer

abstract:
  We report on the successful on-surface synthesis of metal-organic covalent
coordination networks with a dense Kagome lattice of metallic centers. In the
case of Mn centers ab-initio calculations show that the adsorbed monolayer on
Ag(111) has all the characteristic features of a strictly two-dimensional (2D)
ferromagnetic Kagome metal. Tetrahydroxyquinone (THQ) and metal atoms (M=Cu or
Mn) are co-deposited on the Ag(111) substrate to build well-ordered 2D lattices
M$_3$C$_6$O$_6$. The surface is studied by scanning tunneling microscopy (STM),
low energy electron diffraction (LEED) and X-ray photoelectron spectroscopy
(XPS) to optimize the growth conditions like fluxes and temperatures. The
details of the atomic, electronic and magnetic structures are clarified by
density functional theory (DFT) calculations. XPS and DFT reveal a Cu$^+$
charge state and no local magnetic moments for the Cu-organic network. For the
Mn-organic network, we find the charge state Mn$^{2+}$ and a local spin S=5/2.
Charge transfer stabilizes the Cu$^+$ and Mn$^{2+}$ charge states. We find two
different modifications of the M$_3$C$_6$O$_6$ lattice. DFT calculations which
neglect the small spin-orbit coupling show a Dirac point, i.e. a band crossing
with linear electron dispersion at the K-point of the Brillouin zone. This
Dirac point is at the Fermi level if there is no charge transfer but drops by
100 meV if electron doping of Cu$_3$C$_6$O$_6$ on Ag(111) surface is
acknowledged. We predict the magnetic couplings of an isolated M$_3$C$_6$O$_6$
monolayer to be short range and antiferromagnetic leading to high frustration
at the Kagome lattice and a tendency towards a spin-liquid ground state. In the
case of hole transfer from the substrates ferromagnetic ordering is introduced,
making M$_3$C$_6$O$_6$ an interesting candidate for the quantum anomalous Hall
effect.


title:
Understanding Value Decomposition Algorithms in Deep Cooperative
  Multi-Agent Reinforcement Learning

abstract:
  Value function decomposition is becoming a popular rule of thumb for scaling
up multi-agent reinforcement learning (MARL) in cooperative games. For such a
decomposition rule to hold, the assumption of the individual-global max (IGM)
principle must be made; that is, the local maxima on the decomposed value
function per every agent must amount to the global maximum on the joint value
function. This principle, however, does not have to hold in general. As a
result, the applicability of value decomposition algorithms is concealed and
their corresponding convergence properties remain unknown. In this paper, we
make the first effort to answer these questions. Specifically, we introduce the
set of cooperative games in which the value decomposition methods find their
validity, which is referred as decomposable games. In decomposable games, we
theoretically prove that applying the multi-agent fitted Q-Iteration algorithm
(MA-FQI) will lead to an optimal Q-function. In non-decomposable games, the
estimated Q-function by MA-FQI can still converge to the optimum under the
circumstance that the Q-function needs projecting into the decomposable
function space at each iteration. In both settings, we consider value function
representations by practical deep neural networks and derive their
corresponding convergence rates. To summarize, our results, for the first time,
offer theoretical insights for MARL practitioners in terms of when value
decomposition algorithms converge and why they perform well.


title: Double inflation via non-minimally coupled spectator

abstract:
  We argue that double inflation may occur when a spectator field is
non-minimally coupled to gravity. As a concrete example, we study a two-field
inflationary model where the initial spectator field is non-minimally coupled
to gravity while the initial inflaton field is minimally coupled. The
non-minimal coupling results in the growth of the spectator field which, in
turn, drives the second stage of inflation in a significant region of parameter
space. The isocurvature fluctuations originating from the spectator field
source adiabatic ones, and hence the spectator non-minimal coupling can modify
the inflationary predictions for the spectral index and the tensor-to-scalar
ratio even though the initial inflaton field is minimally coupled to gravity.
We explicitly show that quadratic chaotic inflation can become viable by the
introduction of the spectator non-minimal coupling.


title: Online Learning for Min Sum Set Cover and Pandora's Box

abstract:
  Two central problems in Stochastic Optimization are Min Sum Set Cover and
Pandora's Box. In Pandora's Box, we are presented with n boxes, each containing
an unknown value and the goal is to open the boxes in some order to minimize
the sum of the search cost and the smallest value found. Given a distribution
of value vectors, we are asked to identify a near-optimal search order. Min Sum
Set Cover corresponds to the case where values are either 0 or infinity. In
this work, we study the case where the value vectors are not drawn from a
distribution but are presented to a learner in an online fashion. We present a
computationally efficient algorithm that is constant-competitive against the
cost of the optimal search order. We extend our results to a bandit setting
where only the values of the boxes opened are revealed to the learner after
every round. We also generalize our results to other commonly studied variants
of Pandora's Box and Min Sum Set Cover that involve selecting more than a
single value subject to a matroid constraint.


title: Quench dynamics of noninteracting fermions with a delta impurity

abstract:
  We study the out-of-equilibrium dynamics of noninteracting fermions in one
dimension and in continuum space, in the presence of a delta impurity potential
at the origin whose strength $g$ is varied at time $t=0$. The system is
prepared in its ground state with $g=g_0=+\infty$, with two different densities
and Fermi wave-vectors $k_L$ and $k_R$ on the two half-spaces $x>0$ and $x<0$
respectively. It then evolves for $t>0$ as an isolated system, with a finite
impurity strength $g$. We compute exactly the time dependent density and
current. For a fixed position $x$ and in the large time limit $t \to \infty$,
the system reaches a non-equilibrium stationary state (NESS). We obtain
analytically the correlation kernel, density, particle current, and energy
current in the NESS, and characterize their relaxation, which is algebraic in
time. In particular, in the NESS, we show that, away from the impurity, the
particle density displays oscillations which are the non-equilibrium analog of
the Friedel oscillations. In the regime of "rays", $x/t=\xi$ fixed with $x, t
\to \infty$, we compute the same quantities and observe the emergence of two
light cones, associated to the Fermi velocities $k_L$ and $k_R$ in the initial
state. Interestingly, we find non trivial quantum correlations between two
opposite rays with velocities $\xi$ and $-\xi$ which we compute explicitly. We
extend to a continuum setting and to a correlated initial state the analytical
methods developed in a recent work of Ljubotina, Sotiriadis and Prosen, in the
context of a discrete fermionic chain with an impurity. We also generalize our
results to an initial state at finite temperature, recovering, via explicit
calculations, some predictions of conformal field theory in the low energy
limit.

categories: cond-mat.stat-mech cond-mat.quant-gas math-ph math.MP


title:
A Note on the Misinterpretation of the US Census Re-identification
  Attack

abstract:
  In 2018, the US Census Bureau designed a new data reconstruction and
re-identification attack and tested it against their 2010 data release. The
specific attack executed by the Bureau allows an attacker to infer the race and
ethnicity of respondents with average 75% precision for 85% of the respondents,
assuming that the attacker knows the correct age, sex, and address of the
respondents. They interpreted the attack as exceeding the Bureau's privacy
standards, and so introduced stronger privacy protections for the 2020 Census
in the form of the TopDown Algorithm (TDA).
  This paper demonstrates that race and ethnicity can be inferred from the
TDA-protected census data with substantially better precision and recall, using
less prior knowledge: only the respondents' address. Race and ethnicity can be
inferred with average 75% precision for 98% of the respondents, and can be
inferred with 100% precision for 11% of the respondents. The inference is done
by simply assuming that the race/ethnicity of the respondent is that of the
majority race/ethnicity for the respondent's census block.
  The conclusion to draw from this simple demonstration is NOT that the
Bureau's data releases lack adequate privacy protections. Indeed it is the
purpose of the data releases to allow this kind of inference. The problem,
rather, is that the Bureau's criteria for measuring privacy is flawed and
overly pessimistic.


title:
Chiral condensates for neutron stars in hadron-quark crossover; from a
  parity doublet nucleon model to an NJL quark model

abstract:
  In this contribution, we summarize our recent studies on the chiral invariant
mass and the chiral condensates in neutron star matter. We construct a unified
equations of state assuming the crossover phase transition from hadronic matter
described by a parity doublet model to quark matter by an Nambu--Jona-Lasinio
type quark model. We first show that the chiral invariant mass is constrained
to be 600 MeV $\lesssim m_0 \lesssim$ 900 MeV from recent observations of
neutron stars. We then determine the density dependence of the chiral
condensate in the crossover description, and show that the chiral condensates
are actually smoothly connected from the hadronic matter where the change is
driven by the positive chiral scalar charge in a nucleon, to the quark matter
where the change is by the modification of the quark Dirac sea, reflecting the
hadron-quark crossover.


title: Constrained Neutrino Mass Matrix and Majorana Phases

abstract:
  We endeavour to constrain the neutrino mass matrix on the phenomenological
ground and procure simple model-independent textures of the latter predicting
the two Majorana phases. In this regard, two types of parametrization of
neutrino mass matrix: general and exponential are employed. We obtain
forty-seven predictive neutrino mass matrix textures, out of which twenty-five
are associated with the general parametrization, and the rest belong to the
exponential one. Apart from Type-A/P textures, the rest deal with the
prediction of a few other oscillation parameters as well.


title: Abnormal Bound Systems

abstract:
  It is taken for granted that bound systems are made of massive constituents
that interact through particle exchanges (charged particles interacting via
photon exchanges, quarks in elementary particles interacting via gluon
exchanges, and nucleons in nuclei interacting via meson exchanges). However, as
was recently theoretically found, there exist systems dominated by exchange
particles (at least for the zero exchange masses). In these systems, the
contribution of massive constituents is negligible. These systems have a
relativistic nature (since they are mainly made of massless particles moving at
the speed of light), and therefore, they cannot be described by the
Schroedinger equation. Though these results were found so far in the simple
Wick--Cutkosky model (spinless constituents interacting via the ladder of
spinless massless exchanges), the physical ground for their existence seems to
be rather general.


title:
Distilling Hypernymy Relations from Language Models: On the
  Effectiveness of Zero-Shot Taxonomy Induction

abstract:
  In this paper, we analyze zero-shot taxonomy learning methods which are based
on distilling knowledge from language models via prompting and sentence
scoring. We show that, despite their simplicity, these methods outperform some
supervised strategies and are competitive with the current state-of-the-art
under adequate conditions. We also show that statistical and linguistic
properties of prompts dictate downstream performance.


title:
Memory-based gaze prediction in deep imitation learning for robot
  manipulation

abstract:
  Deep imitation learning is a promising approach that does not require
hard-coded control rules in autonomous robot manipulation. The current
applications of deep imitation learning to robot manipulation have been limited
to reactive control based on the states at the current time step. However,
future robots will also be required to solve tasks utilizing their memory
obtained by experience in complicated environments (e.g., when the robot is
asked to find a previously used object on a shelf). In such a situation, simple
deep imitation learning may fail because of distractions caused by complicated
environments. We propose that gaze prediction from sequential visual input
enables the robot to perform a manipulation task that requires memory. The
proposed algorithm uses a Transformer-based self-attention architecture for the
gaze estimation based on sequential data to implement memory. The proposed
method was evaluated with a real robot multi-object manipulation task that
requires memory of the previous states.


title:
Space-Time Adaptive Processing Using Random Matrix Theory Under Limited
  Training Samples

abstract:
  Space-time adaptive processing (STAP) is one of the most effective approaches
to suppressing ground clutters in airborne radar systems. It basically takes
two forms, i.e., full-dimension STAP (FD-STAP) and reduced-dimension STAP
(RD-STAP). When the numbers of clutter training samples are less than two times
their respective system degrees-of-freedom (DOF), the performances of both
FD-STAP and RD-STAP degrade severely due to inaccurate clutter estimation. To
enhance STAP performance under the limited training samples, this paper
develops a STAP theory with random matrix theory (RMT). By minimizing the
output clutter-plus-noise power, the estimate of the inversion of clutter plus
noise covariance matrix (CNCM) can be obtained through optimally manipulating
its eigenvalues, and thus producing the optimal STAP weight vector. Two STAP
algorithms, FD-STAP using RMT (RMT-FD-STAP) and RD-STAP using RMT
(RMT-RD-STAP), are proposed. It is found that both RMT-FD-STAP and RMT-RD-STAP
greatly outperform other-related STAP algorithms when the numbers of training
samples are larger than their respective clutter DOFs, which are much less than
the corresponding system DOFs. Theoretical analyses and simulation demonstrate
the effectiveness and the performance advantages of the proposed STAP
algorithms.


title:
PVSeRF: Joint Pixel-, Voxel- and Surface-Aligned Radiance Field for
  Single-Image Novel View Synthesis

abstract:
  We present PVSeRF, a learning framework that reconstructs neural radiance
fields from single-view RGB images, for novel view synthesis. Previous
solutions, such as pixelNeRF, rely only on pixel-aligned features and suffer
from feature ambiguity issues. As a result, they struggle with the
disentanglement of geometry and appearance, leading to implausible geometries
and blurry results. To address this challenge, we propose to incorporate
explicit geometry reasoning and combine it with pixel-aligned features for
radiance field prediction. Specifically, in addition to pixel-aligned features,
we further constrain the radiance field learning to be conditioned on i)
voxel-aligned features learned from a coarse volumetric grid and ii) fine
surface-aligned features extracted from a regressed point cloud. We show that
the introduction of such geometry-aware features helps to achieve a better
disentanglement between appearance and geometry, i.e. recovering more accurate
geometries and synthesizing higher quality images of novel views. Extensive
experiments against state-of-the-art methods on ShapeNet benchmarks demonstrate
the superiority of our approach for single-image novel view synthesis.


title:
Stochastic Linear Quadratic Optimal Control Problems with Random
  Coefficients and Markovian Regime Switching System

abstract:
  This paper develops a thorough investigation for the stochastic
linear-quadratic optimal control problems for systems under the Markovian
regime switching, where coefficients in the state equation and the weighting
matrices in the cost functional are random. We first present a representation
of the cost functional, which is characterized as a bilinear form on a Hilbert
space in terms of the adapted solutions of some forward-backward stochastic
differential equations by the technique of It\^{o}'s formula with jumps. We
show that a necessary condition of open-loop optimal control is the convexity
of the cost functional, and a sufficient condition of it is the uniform
convexity of the cost functional. Also, we investigate the properties of the
stochastic value flow of the stochastic linear-quadratic optimal control
problem. We obtain the solvability of the corresponding stochastic Riccati
equation under the uniform convexity condition. Furthermore, we show that the
open-loop optimal control admits a closed-loop representation by using the
unique solvability of the stochastic Riccati equation.


title:
AstroSat Detection of a Quasi-periodic Oscillation at ~ 42 Hz in Cygnus
  X-2

abstract:
  We report the results of AstroSat observations of Cygnus X-2 during February
2016. The source's power density spectrum generated using LAXPC data revealed
the presence of a prominent Quasi-periodic Oscillation (QPO) at $\sim42$ Hz
with broadband continuum noise at lower frequencies at $\sim10$ Hz. The large
effective area of LAXPC at $\gtrsim$30 keV allowed for an unprecedented study
of the energy dependence of the QPO and the broad noise continuum. The
fractional r.m.s increases with energy, and its shape is similar for both the
QPO and the continuum noise, suggesting a common radiative origin. However,
while the QPO exhibits hard time lags, with the high energy photons lagging the
low ones by a few milliseconds, the continuum noise shows the opposite
behavior. The photon spectrum from SXT and LAXPC in $0.7-30$ keV band comprises
the soft component from a disc and a hard Comptonized component from a hot
corona. While the energy dependence of the r.m.s shows that the QPO and the
continuum noise variability are dominated by the Comptonized component, the
change in sign of the time-lag suggests that the dynamic origin of the QPO may
be in the disk while the noise continuum may originate from the corona.


title:
Auditory Model based Phase-Aware Bayesian Spectral Amplitude Estimator
  for Single-Channel Speech Enhancement

abstract:
  Bayesian estimation of short-time spectral amplitude is one of the most
predominant approaches for the enhancement of the noise corrupted speech. The
performance of these estimators are usually significantly improved when any
perceptually relevant cost function is considered. On the other hand, the
recent progress in the phase-based speech signal processing have shown that the
phase-only enhancement based on spectral phase estimation methods can also
provide joint improvement in the perceived speech quality and intelligibility,
even in low SNR conditions. In this paper, to take advantage of both the
perceptually motivated cost function involving STSAs of estimated and true
clean speech and utilizing the prior spectral phase information, we have
derived a phase-aware Bayesian STSA estimator. The parameters of the cost
function are chosen based on the characteristics of the human auditory system,
namely, the dynamic compressive nonlinearity of the cochlea, the perceived
loudness theory and the simultaneous masking properties of the ear. This type
of parameter selection scheme results in more noise reduction while limiting
the speech distortion. The derived STSA estimator is optimal in the MMSE sense
if the prior phase information is available. In practice, however, typically
only an estimate of the clean speech phase can be obtained via employing
different types of spectral phase estimation techniques which have been
developed throughout the last few years. In a blind setup, we have evaluated
the proposed Bayesian STSA estimator with different types of standard phase
estimation methods available in the literature. Experimental results have shown
that the proposed estimator can achieve substantial improvement in performance
than the traditional phase-blind approaches.


title:
Towards the automated large-scale reconstruction of past road networks
  from historical maps

abstract:
  Transportation infrastructure, such as road or railroad networks, represent a
fundamental component of our civilization. For sustainable planning and
informed decision making, a thorough understanding of the long-term evolution
of transportation infrastructure such as road networks is crucial. However,
spatially explicit, multi-temporal road network data covering large spatial
extents are scarce and rarely available prior to the 2000s. Herein, we propose
a framework that employs increasingly available scanned and georeferenced
historical map series to reconstruct past road networks, by integrating
abundant, contemporary road network data and color information extracted from
historical maps. Specifically, our method uses contemporary road segments as
analytical units and extracts historical roads by inferring their existence in
historical map series based on image processing and clustering techniques. We
tested our method on over 300,000 road segments representing more than 50,000
km of the road network in the United States, extending across three study areas
that cover 53 historical topographic map sheets dated between 1890 and 1950. We
evaluated our approach by comparison to other historical datasets and against
manually created reference data, achieving F-1 scores of up to 0.95, and showed
that the extracted road network statistics are highly plausible over time,
i.e., following general growth patterns. We demonstrated that contemporary
geospatial data integrated with information extracted from historical map
series open up new avenues for the quantitative analysis of long-term
urbanization processes and landscape changes far beyond the era of operational
remote sensing and digital cartography.


title: Two-Photon Interference of Single Photons from Dissimilar Sources

abstract:
  Entanglement swapping and heralding are at the heart of many protocols for
distributed quantum information. For photons, this typically involves Bell
state measurements based on two-photon interference effects. In this context,
hybrid systems that combine high rate, ultra-stable and pure quantum sources
with long-lived quantum memories are particularly interesting. Here, we develop
a theoretical description of pulsed two-photon interference of photons from
dissimilar sources to predict the outcomes of second-order cross-correlation
measurements. These are directly related to, and hence used to quantify, photon
indistinguishability. We study their dependence on critical system parameters
such as quantum state lifetime and frequency detuning, and quantify the impact
of emission time jitter, pure dephasing and spectral wandering. Our results
show that for fixed lifetime of emitter one, for each frequency detuning there
is an optimal lifetime of emitter two that leads to highest photon
indistinguishability. Expectations for different hybrid combinations involving
III-V quantum dots, color centers in diamond, 2D materials and atoms are
quantitatively compared for real-world system parameters. Our work both
provides a theoretical basis for the treatment of dissimilar emitters and
enables assessment of which imperfections can be tolerated in hybrid photonic
quantum networks.


title:
Rationalizable Implementation of Social Choice Functions: Complete
  Characterization

abstract:
  We provide a necessary and sufficient condition for rationalizable
implementation of social choice functions, i.e., we offer a complete answer
regarding what social choice functions can be rationalizably implemented.


title:
New-physics signatures via $C\!P$ violation in
  $\eta^{(\prime)}\to\pi^0\mu^+\mu^-$ and $\eta^\prime\to\eta\mu^+\mu^-$ decays

abstract:
  In this work we investigate the prospect of observing new-physics signatures
via $C\!P$ violation in $\eta^{(\prime)}\to\pi^0\mu^+\mu^-$ and
$\eta^\prime\to\eta\mu^+\mu^-$ decays at the REDTOP experiment. We make use of
the SMEFT to parametrise the new-physics $C\!P$-violating effects and find that
the projected REDTOP statistics are not competitive with respect to nEDM
experiments. This reasserts the $\eta\to\mu^+\mu^-$ process as the most
promising channel to find $C\!P$-violation at this experimental facility.


title:
TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic
  Representations

abstract:
  Taxonomies are fundamental to many real-world applications in various
domains, serving as structural representations of knowledge. To deal with the
increasing volume of new concepts needed to be organized as taxonomies,
researchers turn to automatically completion of an existing taxonomy with new
concepts. In this paper, we propose TaxoEnrich, a new taxonomy completion
framework, which effectively leverages both semantic features and structural
information in the existing taxonomy and offers a better representation of
candidate position to boost the performance of taxonomy completion.
Specifically, TaxoEnrich consists of four components: (1)
taxonomy-contextualized embedding which incorporates both semantic meanings of
concept and taxonomic relations based on powerful pretrained language models;
(2) a taxonomy-aware sequential encoder which learns candidate position
representations by encoding the structural information of taxonomy; (3) a
query-aware sibling encoder which adaptively aggregates candidate siblings to
augment candidate position representations based on their importance to the
query-position matching; (4) a query-position matching model which extends
existing work with our new candidate position representations. Extensive
experiments on four large real-world datasets from different domains show that
\TaxoEnrich achieves the best performance among all evaluation metrics and
outperforms previous state-of-the-art methods by a large margin.


title: Quantum algorithms for matrix operations and linear systems of equations

abstract:
  Fundamental matrix operations and solving linear systems of equations are
ubiquitous in scientific investigations. Using the "Sender-Receiver" model, we
propose quantum algorithms for matrix operations such as matrix-vector product,
matrix-matrix product, the sum of two matrices, and calculation of determinant
and inverse of a matrix. We encode the matrix entries into the probability
amplitudes of pure initial states of senders. After applying a proper unitary
transformation to the complete quantum system, the desired result can be found
in certain blocks of the receiver's density matrix. These quantum protocols can
be used as subroutines in other quantum schemes. Furthermore, we present an
alternative quantum algorithm for solving linear systems of equations.


title: Limits of real bivariate rational functions

abstract:
  Given two nonzero polynomials $f, g \in\mathbb R[x,y]$ and a point $(a, b)
\in \mathbb{R}^2,$ we give some necessary and sufficient conditions for the
existence of the limit $\displaystyle \lim_{(x, y) \to (a, b)} \frac{f(x,
y)}{g(x, y)}.$ We also show that, if the denominator $g$ has an isolated zero
at the given point $(a, b),$ then the set of possible limits of $\displaystyle
\lim_{(x, y) \to (a, b)} \frac{f(x, y)}{g(x, y)}$ is a closed interval in
$\overline{\mathbb{R}}$ and can be explicitly determined. As an application, we
propose an effective algorithm to verify the existence of the limit and compute
the limit (if it exists). Our approach is geometric and is based on Puiseux
expansions.


title:
Improving performance of aircraft detection in satellite imagery while
  limiting the labelling effort: Hybrid active learning

abstract:
  The earth observation industry provides satellite imagery with high spatial
resolution and short revisit time. To allow efficient operational employment of
these images, automating certain tasks has become necessary. In the defense
domain, aircraft detection on satellite imagery is a valuable tool for
analysts. Obtaining high performance detectors on such a task can only be
achieved by leveraging deep learning and thus us-ing a large amount of labeled
data. To obtain labels of a high enough quality, the knowledge of military
experts is needed.We propose a hybrid clustering active learning method to
select the most relevant data to label, thus limiting the amount of data
required and further improving the performances. It combines diversity- and
uncertainty-based active learning selection methods. For aircraft detection by
segmentation, we show that this method can provide better or competitive
results compared to other active learning methods.


title: Case-based reasoning for rare events prediction on strategic sites

abstract:
  Satellite imagery is now widely used in the defense sector for monitoring
locations of interest. Although the increasing amount of data enables pattern
identification and therefore prediction, carrying this task manually is hardly
feasible. We hereby propose a cased-based reasoning approach for automatic
prediction of rare events on strategic sites. This method allows direct
incorporation of expert knowledge, and is adapted to irregular time series and
small-size datasets. Experiments are carried out on two use-cases using real
satellite images: the prediction of submarines arrivals and departures from a
naval base, and the forecasting of imminent rocket launches on two space bases.
The proposed method significantly outperforms a random selection of reference
cases on these challenging applications, showing its strong potential.


title: Vertex to vertex geodesics on platonic solids

abstract:
  We give a simple proof based on symmetries that there are no geodesics from a
vertex to itself in the cube, tetrahedron, octahedron, and icosahedron.


title:
Differential Private Knowledge Transfer for Privacy-Preserving
  Cross-Domain Recommendation

abstract:
  Cross Domain Recommendation (CDR) has been popularly studied to alleviate the
cold-start and data sparsity problem commonly existed in recommender systems.
CDR models can improve the recommendation performance of a target domain by
leveraging the data of other source domains. However, most existing CDR models
assume information can directly 'transfer across the bridge', ignoring the
privacy issues. To solve the privacy concern in CDR, in this paper, we propose
a novel two stage based privacy-preserving CDR framework (PriCDR). In the first
stage, we propose two methods, i.e., Johnson-Lindenstrauss Transform (JLT)
based and Sparse-awareJLT (SJLT) based, to publish the rating matrix of the
source domain using differential privacy. We theoretically analyze the privacy
and utility of our proposed differential privacy based rating publishing
methods. In the second stage, we propose a novel heterogeneous CDR model
(HeteroCDR), which uses deep auto-encoder and deep neural network to model the
published source rating matrix and target rating matrix respectively. To this
end, PriCDR can not only protect the data privacy of the source domain, but
also alleviate the data sparsity of the source domain. We conduct experiments
on two benchmark datasets and the results demonstrate the effectiveness of our
proposed PriCDR and HeteroCDR.


title:
A Directional Equispaced interpolation-based Fast Multipole Method for
  oscillatory kernels

abstract:
  Fast Multipole Methods (FMMs) based on the oscillatory Helmholtz kernel can
reduce the cost of solving N-body problems arising from Boundary Integral
Equations (BIEs) in acoustic or electromagnetics. However, their cost strongly
increases in the high-frequency regime. This paper introduces a new directional
FMM for oscillatory kernels (defmm - directional equispaced interpolation-based
fmm), whose precomputation and application are FFT-accelerated due to
polynomial interpolations on equispaced grids. We demonstrate the consistency
of our FFT approach, and show how symmetries can be exploited in the Fourier
domain. We also describe the algorithmic design of defmm, well-suited for the
BIE non-uniform particle distributions, and present performance optimizations
on one CPU core. Finally, we exhibit important performance gains on all test
cases for defmm over a state-of-the-art FMM library for oscillatory kernels.


title: Diffusion bridges vector quantized Variational AutoEncoders

abstract:
  Vector Quantised-Variational AutoEncoders (VQ-VAE) are generative models
based on discrete latent representations of the data, where inputs are mapped
to a finite set of learned embeddings.To generate new samples, an
autoregressive prior distribution over the discrete states must be trained
separately. This prior is generally very complex and leads to very slow
generation. In this work, we propose a new model to train the prior and the
encoder/decoder networks simultaneously. We build a diffusion bridge between a
continuous coded vector and a non-informative prior distribution. The latent
discrete states are then given as random functions of these continuous vectors.
We show that our model is competitive with the autoregressive prior on the
mini-Imagenet dataset and is very efficient in both optimization and sampling.
Our framework also extends the standard VQ-VAE and enables end-to-end training.


title: Faulty isogenies: a new kind of leakage

abstract:
  In SIDH and SIKE protocols, public keys are defined over quadratic extensions
of prime fields. We present in this work a projective invariant property
characterizing affine Montgomery curves defined over prime fields. We then
force a secret 3-isogeny chain to repeatedly pass through a curve defined over
a prime field in order to exploit the new property and inject zeros in the
A-coefficient of an intermediate curve to successfully recover the isogeny
chain one step at a time. Our results introduce a new kind of fault attacks
applicable to SIDH and SIKE.


title:
InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail
  Entities

abstract:
  Knowledge graph embedding (KGE) models learn the representation of entities
and relations in knowledge graphs. Distance-based methods show promising
performance on link prediction task, which predicts the result by the distance
between two entity representations. However, most of these methods represent
the head entity and tail entity separately, which limits the model capacity. We
propose a novel distance-based method named InterHT that allows the head and
tail entities to interact better and get better entity representation.
Experimental results show that our proposed method achieves the best results on
ogbl-wikikg2 dataset.


title:
Nanostructured transition metal dichalcogenide multilayers for advanced
  nanophotonics

abstract:
  Transition metal dichalcogenides (TMDs) attract significant attention due to
their exceptional optical, excitonic, mechanical, and electronic properties.
Nanostructured multilayer TMDs were recently shown to be highly promising for
nanophotonic applications, as motivated by their exceptionally high refractive
indexes and optical anisotropy. Here, we extend this vision to more
sophisticated structures, such as periodic arrays of nanodisks and nanoholes,
as well as proof-of-concept waveguides and resonators. We specifically focus on
various advanced nanofabrication strategies, including careful selection of
resists for electron beam lithography and etching methods. The specific
materials studied here include semiconducting WS$_2$, in-plane anisotropic
ReS$_2$, and metallic TaSe$_2$, TaS$_2$ and NbSe$_2$. The resulting
nanostructures can potentially impact several nanophotonic and optoelectronic
areas, including high-index nanophotonics, plasmonics and on-chip optical
circuits. The knowledge of TMD material-dependent nanofabrication parameters
developed here will help broaden the scope of future applications of these
materials in all-TMD nanophotonics.


title:
Flocking of the Cucker-Smale and Motsch-Tadmor models on general
  weighted digraphs via a probabilistic method

abstract:
  In this paper, we discuss the flocking phenomenon for the Cucker-Smale and
Motsch-Tadmor models in continuous time on a general oriented and weighted
graph with a general communication function of the form
$\Psi_{ij}(t,(x_1(t),v_1(t)),\dots,(x_N(t),v_N(t))$. We present a new approach
for studying this problem based on a probabilistic interpretation of the
solutions. We establish flocking conditions for four assumptions on the
weighted graph. These results improve and generalize previous results on
particular cases of our models. Indeed, for the Cucker-Smale model, we refine
previous results on the minimal case where the graph admits a unique closed
communication class. Moreover, we improve the flocking conditions under two new
assumptions when the adjacency matrix is scrambling or when it admits a
positive reversible measure. In the last case, we characterize the asymptotic
speed. We also study the hierarchical leadership case where we are now able to
give general flocking conditions which allow to deal with the case
$\psi(r)\propto(1+r^2)^{-\beta/2}$ and $\beta\geq1$. Finally, we show that some
of the flocking results of the Cucker-Smale model can be extended to a
generalisation of the Motsch-Tadmor model on a general weighted digraph.
Notably, we are able to show that the unconditionnal flocking occurs for the
original model when the integral of $\psi$ diverges instead of $\psi^2$.


title:
Monte-Carlo Multiscale Simulation Study of Argon Adsorption/Desorption
  Hysteresis in Mesoporous Heterogeneous Tubular Pores like MCM-41 or Oxidized
  Porous Silicon

abstract:
  In a recent paper [J. Chem. Phys. 127, 154701 (2007)], a multiscale approach
was introduced which allowed the calculation of adsorption/desorption
hysteresis for fluid confined in a single


title: FILM: Frame Interpolation for Large Motion

abstract:
  We present a frame interpolation algorithm that synthesizes multiple
intermediate frames from two input images with large in-between motion. Recent
methods use multiple networks to estimate optical flow or depth and a separate
network dedicated to frame synthesis. This is often complex and requires scarce
optical flow or depth ground-truth. In this work, we present a single unified
network, distinguished by a multi-scale feature extractor that shares weights
at all scales, and is trainable from frames alone. To synthesize crisp and
pleasing frames, we propose to optimize our network with the Gram matrix loss
that measures the correlation difference between feature maps. Our approach
outperforms state-of-the-art methods on the Xiph large motion benchmark. We
also achieve higher scores on Vimeo-90K, Middlebury and UCF101, when comparing
to methods that use perceptual losses. We study the effect of weight sharing
and of training with datasets of increasing motion range. Finally, we
demonstrate our model's effectiveness in synthesizing high quality and
temporally coherent videos on a challenging near-duplicate photos dataset.
Codes and pre-trained models are available at
https://github.com/google-research/frame-interpolation.


title: Composite Spin Approach to the Blockade Effect in Rydberg Atom Arrays

abstract:
  The Rydberg blockade induces strongly correlated many-body effects in Rydberg
atom arrays, including rich ground-state phases and many-body scar states in
the excitation spectrum. In this letter, we propose a composite spin
representation that can provide a unified description for major features in
this system. The composite spin combines Rydberg excitation and the auxiliary
fermions, which are introduced to implement the Rydberg blockade constraint
automatically. First, we focus on the PXP model describing one-dimensional
arrays with the Rydberg blocking radius being a lattice spacing. Using
composite spins, the ground state is simply a ferromagnetic product state of
composite spins, and the magnon excitations of these composite spins can
accurately describe the many-body scar states and signal the quantum phase
transition driven by detuning. Then, we show that Rydberg atom arrays with
different blocking radii can share a universal description of the composite
spin representation, and the difference between different blockade radii can be
absorbed in the formation of composite spins.


title:
Investigating Explainability of Generative AI for Code through
  Scenario-based Design

abstract:
  What does it mean for a generative AI model to be explainable? The emergent
discipline of explainable AI (XAI) has made great strides in helping people
understand discriminative models. Less attention has been paid to generative
models that produce artifacts, rather than decisions, as output. Meanwhile,
generative AI (GenAI) technologies are maturing and being applied to
application domains such as software engineering. Using scenario-based design
and question-driven XAI design approaches, we explore users' explainability
needs for GenAI in three software engineering use cases: natural language to
code, code translation, and code auto-completion. We conducted 9 workshops with
43 software engineers in which real examples from state-of-the-art generative
AI models were used to elicit users' explainability needs. Drawing from prior
work, we also propose 4 types of XAI features for GenAI for code and gathered
additional design ideas from participants. Our work explores explainability
needs for GenAI for code and demonstrates how human-centered approaches can
drive the technical development of XAI in novel domains.


title:
Molecular dynamics study of shear-induced long-range correlations in
  simple fluids

abstract:
  We investigate long-range correlations (LRCs) induced by shear flow using the
molecular dynamics (MD) simulation. We observe the LRCs by comparing the MD
results with the linearized fluctuating hydrodynamics (LFH). We find that the
MD result has large finite-size effects, and it prevents the occurrence of LRCs
in small systems. We examine the finite-size effects using a sufficiently large
system consisting of more than ten million particles, and verify the existence
of shear-induced LRCs without ambiguity. Furthermore, we show that MD result is
quantitatively consistent with the LFH solution for the large system. As we
reduce the system size $L$ or increase the shear rate $\dot{\gamma}$, the
hydrodynamic description gradually breaks down in the long-wavelength region.
We define a characteristic wavenumber $k^{\rm vio}$ associated with the
breakdown and find the nontrivial scaling relations $k^{\rm vio} \propto
L^{-\omega}$ and $k^{\rm vio} \propto \dot{\gamma}$, where $\omega$ is an
exponent depending on $\dot{\gamma}$. These relations enable us to estimate the
finite-size effects in a larger-size simulation from a smaller system.


title: Moving Kinks and Their Wave Packets

abstract:
  Recently a linearized perturbation theory has been formulated for soliton
sectors of quantum field theories. While it is more economical than alternative
formalisms, such as collective coordinates, it is currently limited to solitons
which stay close to a base point, about which the theory is linearized. As a
result, so far this formalism has only been applied to stationary solitons. In
spite of this limitation, we construct kink states with fixed nonzero momenta
and also moving, normalizable kink wave packets. The former are
nonnormalizable, coherent superpositions of kinks at all spatial positions and
are simultaneous eigenstates of the Hamiltonian and the momentum operator. The
latter are localized about a single, moving classical solution. To understand
the wave packets, we calculate several simple matrix elements.


title: Number of distinct sites visited by a resetting random walker

abstract:
  We investigate the number $V_p(n)$ of distinct sites visited by an $n$-step
resetting random walker on a $d$-dimensional hypercubic lattice with resetting
probability $p$. In the case $p=0$, we recover the well-known result that the
average number of distinct sites grows for large $n$ as $\langle
V_0(n)\rangle\sim n^{d/2}$ for $d<2$ and as $\langle V_0(n)\rangle\sim n$ for
$d>2$. For $p>0$, we show that $\langle V_p(n)\rangle$ grows extremely slowly
as $\sim \left[\log(n)\right]^d$. We observe that the recurrence-transience
transition at $d=2$ for standard random walks (without resetting) disappears in
the presence of resetting. In the limit $p\to 0$, we compute the exact
crossover scaling function between the two regimes. In the one-dimensional
case, we derive analytically the full distribution of $V_p(n)$ in the limit of
large $n$. Moreover, for a one-dimensional random walker, we introduce a new
observable, which we call imbalance, that measures how much the visited region
is symmetric around the starting position. We analytically compute the full
distribution of the imbalance both for $p=0$ and for $p>0$. Our theoretical
results are verified by extensive numerical simulations.

categories: cond-mat.stat-mech math-ph math.MP math.PR