title: Achieving Reliable Human Assessment of Open-Domain Dialogue Systems

abstract:
  Evaluation of open-domain dialogue systems is highly challenging and
development of better techniques is highlighted time and again as desperately
needed. Despite substantial efforts to carry out reliable live evaluation of
systems in recent competitions, annotations have been abandoned and reported as
too unreliable to yield sensible results. This is a serious problem since
automatic metrics are not known to provide a good indication of what may or may
not be a high-quality conversation. Answering the distress call of competitions
that have emphasized the urgent need for better evaluation techniques in
dialogue, we present the successful development of human evaluation that is
highly reliable while still remaining feasible and low cost. Self-replication
experiments reveal almost perfectly repeatable results with a correlation of
$r=0.969$. Furthermore, due to the lack of appropriate methods of statistical
significance testing, the likelihood of potential improvements to systems
occurring due to chance is rarely taken into account in dialogue evaluation,
and the evaluation we propose facilitates application of standard tests. Since
we have developed a highly reliable evaluation method, new insights into system
performance can be revealed. We therefore include a comparison of
state-of-the-art models (i) with and without personas, to measure the
contribution of personas to conversation quality, as well as (ii) prescribed
versus freely chosen topics. Interestingly with respect to personas, results
indicate that personas do not positively contribute to conversation quality as
expected.


title: Identifiability of Causal-based Fairness Notions: A State of the Art

abstract:
  Machine learning algorithms can produce biased outcome/prediction, typically,
against minorities and under-represented sub-populations. Therefore, fairness
is emerging as an important requirement for the large scale application of
machine learning based technologies. The most commonly used fairness notions
(e.g. statistical parity, equalized odds, predictive parity, etc.) are
observational and rely on mere correlation between variables. These notions
fail to identify bias in case of statistical anomalies such as Simpson's or
Berkson's paradoxes. Causality-based fairness notions (e.g. counterfactual
fairness, no-proxy discrimination, etc.) are immune to such anomalies and hence
more reliable to assess fairness. The problem of causality-based fairness
notions, however, is that they are defined in terms of quantities (e.g. causal,
counterfactual, and path-specific effects) that are not always measurable. This
is known as the identifiability problem and is the topic of a large body of
work in the causal inference literature. This paper is a compilation of the
major identifiability results which are of particular relevance for machine
learning fairness. The results are illustrated using a large number of examples
and causal graphs. The paper would be of particular interest to fairness
researchers, practitioners, and policy makers who are considering the use of
causality-based fairness notions as it summarizes and illustrates the major
identifiability results


title:
Gaia0007-1605: an old triple system with an inner brown dwarf-white
  dwarf binary and an outer white dwarf companion

abstract:
  We identify Gaia0007-1605AC as the first inner brown dwarf-white dwarf binary
of a hierarchical triple system in which the outer component is another white
dwarf (Gaia0007-1605B). From optical/near-infrared spectroscopy obtained at the
Very Large Telescope with the X-Shooter instrument and/or from Gaia photometry
plus SED fitting, we determine the effective temperatures and masses of the two
white dwarfs (12018+-68 K, 0.54+-0.01 Msun for Gaia0007-1605A and 4445+-116 K,
0.56+-0.05 Msun for Gaia0007-1605B) and the effective temperature of the brown
dwarf (1850+-50 K; corresponding to a spectral type L3+-1). By analysing the
available TESS light curves of Gaia0007-1605AC we detect a signal at
1.0446+-0.0015 days with an amplitude of 6.25 ppt, which we interpret as the
orbital period modulated from irradiation effects of the white dwarf on the
brown dwarf's surface. This drives us to speculate that the inner binary
evolved through a common envelope phase in the past. Using the outer white
dwarf as a cosmochronometer and analysing the kinematic properties of the
system, we conclude that the triple system is about 10 Gyr old.


title:
Beamforming in Hybrid RIS assisted Integrated Sensing and Communication
  Systems

abstract:
  In this paper, we consider a hybrid reconfigurable intelligent surface (RIS)
comprising of active and passive elements to aid an integrated sensing and
communication (ISAC) system serving multiple users and targets. Active elements
in a hybrid RIS include amplifiers and phase shifters, whereas passive elements
include only phase shifters. We jointly design transmit beamformers and RIS
coefficients, i.e., amplifier gains and phase shifts, to maximize the
worst-case target illumination power while ensuring a desired
signal-to-interference-plus-noise ratio for communication links and
constraining the RIS noise power due to the active elements. Since this design
problem is not convex, we propose a solver based on alternating optimization to
design the transmit beamformers and RIS coefficients. Through numerical
simulations, we demonstrate that the performance of the proposed hybrid RIS
assisted ISAC system is significantly better than that of passive RIS assisted
ISAC systems as well as ISAC systems without RIS even when only a small
fraction of the hybrid RIS contains active elements.


title: Formal Control Synthesis for Stochastic Neural Network Dynamic Models

abstract:
  Neural networks (NNs) are emerging as powerful tools to represent the
dynamics of control systems with complicated physics or black-box components.
Due to complexity of NNs, however, existing methods are unable to synthesize
complex behaviors with guarantees for NN dynamic models (NNDMs). This work
introduces a control synthesis framework for stochastic NNDMs with performance
guarantees. The focus is on specifications expressed in linear temporal logic
interpreted over finite traces (LTLf), and the approach is based on finite
abstraction. Specifically, we leverage recent techniques for convex relaxation
of NNs to formally abstract a NNDM into an interval Markov decision process
(IMDP). Then, a strategy that maximizes the probability of satisfying a given
specification is synthesized over the IMDP and mapped back to the underlying
NNDM. We show that the process of abstracting NNDMs to IMDPs reduces to a set
of convex optimization problems, hence guaranteeing efficiency. We also present
an adaptive refinement procedure that makes the framework scalable. On several
case studies, we illustrate the our framework is able to provide non-trivial
guarantees of correctness for NNDMs with architectures of up to 5 hidden layers
and hundreds of neurons per layer.


title:
Processing Induced Distinct Charge Carrier Dynamics of Bulky Organic
  Halide Treated Perovskites

abstract:
  State-of-the-art metal halide perovskite-based photovoltaics often employ
organic ammonium salts, AX, as a surface passivator, where A is a large organic
cation and X is a halide. These surface treatments passivate the perovskite by
forming layered perovskites (e.g., A2PbX4) or by AX itself serving as a surface
passivation agent on the perovskite photoactive film. It remains unclear
whether layered perovskites or AX is the ideal passivator due to an incomplete
understanding of the interfacial impact and resulting photoexcited carrier
dynamics of AX treatment. In the present study, we use TRPL measurements to
selectively probe the different interfaces of glass/perovskite/AX to
demonstrate the vastly distinct interfacial photoexcited state dynamics with
the presence of A2PbX4 or AX. Coupling the TRPL results with X-ray diffraction
and nanoscale microscopy measurements, we find that the presence of AX not only
passivates the traps at the surface and the grain boundaries, but also induces
an {\alpha}/{\delta}-FAPbI3 phase mixing that alters the carrier dynamics near
the glass/perovskite interface and enhances the photoluminescence quantum
yield. In contrast, the passivation with A2PbI4 is mostly localized to the
surface and grain boundaries near the top surface where the availability of
PbI2 directly determines the formation of A2PbI4. Such distinct mechanisms
significantly impact the corresponding solar cell performance, and we find AX
passivation that has not been converted to a layered perovskite allows for a
much larger processing window (e.g., larger allowed variance of AX
concentration which is critical for improving the eventual manufacturing yield)
and more reproducible condition to realize device performance improvements,
while A2PbI4 as a passivator yields a much narrower processing window. We
expect these results to enable a more rational route for developing AX for
perovskite.


title:
On the existence and uniqueness of solutions for non-autonomous
  semi-linear systems with non-instantaneous impulses, delay, and non-local
  conditions

abstract:
  A non-autonomous evolution semi-linear differential system under
non-instantaneous impulses, delays, and perturbed by non-local conditions is
studied. Its piece-wise continuous solutions belong to a finite-dimensional
Banach space. The existence and uniqueness of solutions on the interval
$[-r,\tau]$ are obtained by applying Karakostas fixed-point theorem. Further
results concerning solution prolongation are developed. An example is
presented, and several remarks on the infinite-dimensional case are included.


title: Communication-aware Drone Delivery Problem

abstract:
  The drone delivery problem (DDP) has been introduced to include aerial
vehicles in last-mile delivery operations to increase efficiency. However, the
existing studies have not incorporated the communication quality requirements
of such a delivery operation. This study introduces the Communication-aware DDP
(C-DDP), which incorporates handover and outage constraints. In particular, any
trip of a drone to deliver a customer package must require less than a certain
number of handover operations and cannot exceed a predefined outage duration
threshold. The authors develop a Mixed Integer Programming (MIP) model to
minimize the total flight distance while satisfying communication constraints
as well as the time windows of customers. We present a Genetic Algorithm (GA)
that can solve large instances, and compare its performance with an
off-the-shelf MIP solver. Computational results show that the GA can outperform
the MIP solver for solving larger instances and is a better option.


title:
Detectability of a spatial correlation between stellar-mass black hole
  mergers and Active Galactic Nuclei in the Local Universe

abstract:
  The origin of the Binary Black Hole (BBH) mergers detected through
Gravitational Waves (GWs) by the LIGO-Virgo-KAGRA (LVK) collaboration remains
debated. One fundamental reason is our ignorance of their host environment, as
the typical size of an event's localization volume can easily contain thousands
of galaxies. A strategy around this is to exploit statistical approaches to
assess the spatial correlation between these mergers and astrophysically
motivated host galaxy types, such as Active Galactic Nuclei (AGN). We use a
Likelihood ratio method to infer the degree of GW-AGN connection out to
$z=0.2$. We simulate BBH mergers whose components' masses are sampled from a
realistic distribution of the underlying population of Black Holes (BHs).
Localization volumes for these events are calculated assuming two different
interferometric network configurations. These correspond to the configuration
of the third (O3) and of the upcoming fourth (O4) LVK observing runs. We
conclude that the 13 BBH mergers detected during the third observing run at
$z\leq0.2$ are not enough to reject with a \(3\sigma\) significance the
hypothesis according to which there is no connection between GW and AGN more
luminous than $\approx 10^{44.3}\rm{erg}\ \rm{s}^{-1}$, that have number
density higher than \(10^{-4.75}\textrm{Mpc}^{-3}\). However, 13 detections are
enough to reject this no-connection hypothesis when rarer categories of AGN are
considered, with bolometric luminosities greater than $\approx
10^{45.5}\rm{erg}\ \rm{s}^{-1}$. We estimate that O4 results will potentially
allow us to test fractional contributions to the total BBH merger population
from AGN of any luminosity higher than \(80\%\).


title: BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs

abstract:
  We present a 3D face reconstruction system that aims at recovering the 3D
facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D
facial geometry of babies differs substantially from that of adults,
baby-specific facial reconstruction systems are needed. BabyNet consists of two
stages: 1) a 3D graph convolutional autoencoder learns a latent space of the
baby 3D facial shape; and 2) a 2D encoder that maps photographs to the 3D
latent space based on representative features extracted using transfer
learning. In this way, using the pre-trained 3D decoder, we can recover a 3D
face from 2D images. We evaluate BabyNet and show that 1) methods based on
adult datasets cannot model the 3D facial geometry of babies, which proves the
need for a baby-specific method, and 2) BabyNet outperforms classical
model-fitting methods even when a baby-specific 3D morphable model, such as
BabyFM, is used.


title: Future stability of self-gravitating dust balls in an expanding universe

abstract:
  We consider a system representing self-gravitating balls of dust in an
expanding Universe. It is demonstrated that one can prescribe data for such a
system at infinity and evolve it backward in time without the development of
shocks or singularities. The resulting solution to the Einstein-lambda-dust
equations exists for an infinite amount of time in the asymptotic region of the
spacetime. Furthermore, we find that if the density is small compared to the
Cosmological constant, then it is possible to construct Cosmological solutions
to the Einstein constraint equations on a standard Cauchy hypersurface
representing self-gravitating balls of dust. If, in addition, the density is
assumed to be sufficiently small, then this initial data gives rise to a future
geodesically complete solution to the Einstein-lambda-dust equations admitting
a smooth conformal extension at infinity which can be regarded as a
perturbation of de Sitter spacetime. The main technical tool in this analysis
are Friedrichs conformal Einstein field equations for the Einstein-lambda-dust
written in terms of a gauge in which the flow lines of the dust are recast as
conformal geodesics.


title:
Development and Commissioning of a Compact Cosmic Ray Muon Imaging
  Prototype

abstract:
  Due to the muon tomography's capability of imaging high Z materials, some
potential applications have been reported on inspecting smuggled nuclear
materials in customs. A compact Cosmic Ray Muons (CRM) imaging prototype,
Lanzhou University Muon Imaging System (LUMIS), is comprehensively introduced
in this paper including the structure design, assembly, data acquisition and
analysis, detector performance test, and material imaging commissioning etc.
Casted triangular prism plastic scintillators (PS) were coupled with Si-PMs for
sensitive detector components in system. LUMIS's experimental results show that
the detection efficiency of an individual detector layer is about 98%, the
position resolution for vertical incident muons is 2.5 mm and the angle
resolution is 8.73 mrad given a separation distance of 40.5 cm. Moreover, the
image reconstruction software was developed based on the Point of Closest
Approach (PoCA) to detect lead bricks as our target. The reconstructed images
indicate that the profile of the lead bricks in the image is highly consistent
with the target. Subsequently, the capability of LUMIS to distinguish different
materials, such as Pb, Cu, Fe, and Al, was investigated as well. The lower
limit of response time for rapidly alarming high-Z materials is also given and
discussed. The successful development and commissioning of the LUMIS prototype
have provided a new solution option in technology and craftsmanship for
developing compact CRM imaging systems that can be used in many applications.


title:
The invariant-based shortcut to adiabaticity for qubit heat engine
  operates under quantum Otto cycle

abstract:
  In this paper, we study the role and relevance of the cost for an
invariant-based shortcut to adiabaticity enabled qubit heat engine operates in
a quantum Otto cycle. We consider a qubit heat engine with Landau-Zener
Hamiltonian and improve its performance using the Lewis-Riesenfeld
invariant-based shortcut to adiabaticity method. Addressing the importance of
cost for better performance, the paper explores its relationship with the work
and efficiency of the heat engine. We analyze the cost variation with the time
duration of non-adiabatic unitary processes involved in the heat engine cycle.
The cost required to attain the quasi-static performance of the qubit heat
engine is also discussed. We found the efficiency lost due to non-adiabaticity
of the engine can be revived using the shortcut method and it is even possible
to attain quasi-static performance under finite time with higher cost.


title:
Microwave-frequency scanning gate microscopy of a Si/SiGe double quantum
  dot

abstract:
  Conventional quantum transport methods can provide quantitative information
on spin, orbital, and valley states in quantum dots, but often lack spatial
resolution. Scanning tunneling microscopy, on the other hand, provides
exquisite spatial resolution of the local electronic density of states, but
often at the expense of speed. Working to combine the spatial resolution and
energy sensitivity of scanning probe microscopy with the speed of microwave
measurements, we couple a metallic probe tip to a Si/SiGe double quantum dot
that is integrated with a local charge detector. We first demonstrate that a
dc-biased tip can be used to change the charge occupancy of the double dot. We
then apply microwave excitation through the scanning tip to drive
photon-assisted tunneling transitions in the double dot. We infer the double
dot energy level diagram from the frequency and detuning dependence of the
photon-assisted tunneling resonance condition. These measurements allow us to
resolve $\sim$65 $\mu$eV excited states, an energy scale consistent with
typical valley splittings in Si/SiGe. Future extensions of this approach may
allow spatial mapping of the valley splitting in Si devices, which is of
fundamental importance for spin-based quantum processors.


title:
A note on the rearrangement of functions in time and on the parabolic
  Talenti inequality

abstract:
  Talenti inequalities are a central feature in the qualitative analysis of PDE
constrained optimal control as well as in calculus of variations. The classical
parabolic Talenti inequality states that if we consider the parabolic equation
${\frac{\partial u}{\partial t}}-\Delta u=f=f(t,x)$ then, replacing, for any
time $t$, $f(t,\cdot)$ with its Schwarz rearrangement $f^\#(t,\cdot)$ increases
the concentration of the solution in the following sense: letting $v$ be the
solution of ${\frac{\partial v}{\partial t}}-\Delta v=f^\#$ in the ball, then
the solution $u$ is less concentrated than $v$. This property can be rephrased
in terms of the existence of a maximal element for a certain order
relationship. It is natural to try and rearrange the source term not only in
space but also in time, and thus to investigate the existence of such a maximal
element when we rearrange the function with respect to the two variables. In
the present paper we prove that this is not possible.


title:
Recoil imaging for dark matter, neutrinos, and physics beyond the
  Standard Model

abstract:
  Recoil imaging entails the detection of spatially resolved ionization tracks
generated by particle interactions. This is a highly sought-after capability in
many classes of detector, with broad applications across particle and
astroparticle physics. However, at low energies, where ionization signatures
are small in size, recoil imaging only seems to be a practical goal for
micro-pattern gas detectors. This white paper outlines the physics case for
recoil imaging, and puts forward a decadal plan to advance towards the
directional detection of low-energy recoils with sensitivity and resolution
close to fundamental performance limits. The science case covered includes: the
discovery of dark matter into the neutrino fog, directional detection of
sub-MeV solar neutrinos, the precision study of coherent-elastic
neutrino-nucleus scattering, the detection of solar axions, the measurement of
the Migdal effect, X-ray polarimetry, and several other applied physics goals.
We also outline the R&D programs necessary to test concepts that are crucial to
advance detector performance towards their fundamental limit: single primary
electron sensitivity with full 3D spatial resolution at the $\sim$100
micron-scale. These advancements include: the use of negative ion drift,
electron counting with high-definition electronic readout, time projection
chambers with optical readout, and the possibility for nuclear recoil tracking
in high-density gases such as argon. We also discuss the readout and
electronics systems needed to scale-up such detectors to the ton-scale and
beyond.

categories: physics.ins-det astro-ph.CO hep-ex hep-ph


title: Cross-Layer Approximation For Printed Machine Learning Circuits

abstract:
  Printed electronics (PE) feature low non-recurring engineering costs and low
per unit-area fabrication costs, enabling thus extremely low-cost and on-demand
hardware. Such low-cost fabrication allows for high customization that would be
infeasible in silicon, and bespoke architectures prevail to improve the
efficiency of emerging PE machine learning (ML) applications. However, even
with bespoke architectures, the large feature sizes in PE constraint the
complexity of the ML models that can be implemented. In this work, we bring
together, for the first time, approximate computing and PE design targeting to
enable complex ML models, such as Multi-Layer Perceptrons (MLPs) and Support
Vector Machines (SVMs), in PE. To this end, we propose and implement a
cross-layer approximation, tailored for bespoke ML architectures. At the
algorithmic level we apply a hardware-driven coefficient approximation of the
ML model and at the circuit level we apply a netlist pruning through a full
search exploration. In our extensive experimental evaluation we consider 14
MLPs and SVMs and evaluate more than 4300 approximate and exact designs. Our
results demonstrate that our cross approximation delivers Pareto optimal
designs that, compared to the state-of-the-art exact designs, feature 47% and
44% average area and power reduction, respectively, and less than 1% accuracy
loss.


title: Revisiting the chiral effective action in holographic models

abstract:
  We obtain the pion decay constant and coefficients of fourth derivative terms
in the chiral Lagrangian for massless quarks in the Witten-Sakai-Sugimoto
model. We extract these quantities from the two-pion scattering amplitude,
which we compute directly in the holographic dual through tree-level Witten
diagrams. We compare our results with the existing standard procedure of
constructing the chiral action with radial modes in the gravity dual. While the
pion decay constant agrees, we point out that the values of the other
coefficients have not been correctly identified. This suggests that past
derivations of effective actions from holographic models may have to be
revisited and future derivations more carefully considered.


title: Effective estimates for some functions defined over primes

abstract:
  In the first part this paper we give effective estimates for some classical
arithmetic functions defined over prime numbers. First we find the smallest
real number $x_0$ so that some inequality involving Chebyshev's
$\vartheta$-function holds for every $x \geq x_0$. Then we give some new
results concerning the existence of prime numbers in short intervals. Also we
derive new upper and lower bounds for some functions defined over prime
numbers, for instance the prime counting function $\pi(x)$, which improve
current best estimates of similar shape.


title:
Integrating Dependency Tree Into Self-attention for Sentence
  Representation

abstract:
  Recent progress on parse tree encoder for sentence representation learning is
notable. However, these works mainly encode tree structures recursively, which
is not conducive to parallelization. On the other hand, these works rarely take
into account the labels of arcs in dependency trees. To address both issues, we
propose Dependency-Transformer, which applies a relation-attention mechanism
that works in concert with the self-attention mechanism. This mechanism aims to
encode the dependency and the spatial positional relations between nodes in the
dependency tree of sentences. By a score-based method, we successfully inject
the syntax information without affecting Transformer's parallelizability. Our
model outperforms or is comparable to the state-of-the-art methods on four
tasks for sentence representation and has obvious advantages in computational
efficiency.


title: Graph Summarization with Graph Neural Networks

abstract:
  The goal of graph summarization is to represent large graphs in a structured
and compact way. A graph summary based on equivalence classes preserves
pre-defined features of a graph's vertex within a $k$-hop neighborhood such as
the vertex labels and edge labels. Based on these neighborhood characteristics,
the vertex is assigned to an equivalence class. The calculation of the assigned
equivalence class must be a permutation invariant operation on the pre-defined
features. This is achieved by sorting on the feature values, e. g., the edge
labels, which is computationally expensive, and subsequently hashing the
result. Graph Neural Networks (GNN) fulfill the permutation invariance
requirement. We formulate the problem of graph summarization as a subgraph
classification task on the root vertex of the $k$-hop neighborhood. We adapt
different GNN architectures, both based on the popular message-passing protocol
and alternative approaches, to perform the structural graph summarization task.
We compare different GNNs with a standard multi-layer perceptron (MLP) and
Bloom filter as non-neural method. For our experiments, we consider four
popular graph summary models on a large web graph. This resembles challenging
multi-class vertex classification tasks with the numbers of classes ranging
from $576$ to multiple hundreds of thousands. Our results show that the
performance of GNNs are close to each other. In three out of four experiments,
the non-message-passing GraphMLP model outperforms the other GNNs. The
performance of the standard MLP is extraordinary good, especially in the
presence of many classes. Finally, the Bloom filter outperforms all neural
architectures by a large margin, except for the dataset with the fewest number
of $576$ classes.


title:
Generalized Bandit Regret Minimizer Framework in Imperfect Information
  Extensive-Form Game

abstract:
  Regret minimization methods are a powerful tool for learning approximate Nash
equilibrium (NE) in two-player zero-sum imperfect information extensive-form
games (IIEGs). We consider the problem in the interactive bandit-feedback
setting where we don't know the dynamics of the IIEG. In general, only the
interactive trajectory and the reached terminal node value $v(z^t)$ are
revealed. To learn NE, the regret minimizer is required to estimate the
full-feedback loss gradient $\ell^t$ by $v(z^t)$ and minimize the regret. In
this paper, we propose a generalized framework for this learning setting. It
presents a theoretical framework for the design and the modular analysis of the
bandit regret minimization methods. We demonstrate that the most recent bandit
regret minimization methods can be analyzed as a particular case of our
framework. Following this framework, we describe a novel method SIX-OMD to
learn approximate NE. It is model-free and extremely improves the best existing
convergence rate from the order of $O(\sqrt{X B/T}+\sqrt{Y C/T})$ to $O(\sqrt{
M_{\mathcal{X}}/T} +\sqrt{ M_{\mathcal{Y}}/T})$. Moreover, SIX-OMD is
computationally efficient as it needs to perform the current strategy and
average strategy updates only along the sampled trajectory.


title:
Supporting Schema References in Keyword Queries over Relational
  Databases

abstract:
  Relational Keyword Search (R-KwS) systems enable naive/informal users to
explore and retrieve information from relational databases without knowing
schema details or query languages. These systems take the keywords from the
input query, locate the elements of the target database that correspond to
these keywords, and look for ways to "connect" these elements using information
on referential integrity constraints, i.e., key/foreign key pairs. Although
several such systems have been proposed in the literature, most of them only
support queries whose keywords refer to the contents of the target database and
just very few support queries in which keywords refer to elements of the
database schema. This paper proposes LATHE, a novel R-KwS designed to support
such queries. To this end, in our work, we first generalize the well-known
concepts of Query Matches (QMs) and Candidate Joining Networks (CJNs) to handle
keywords referring to schema elements and propose new algorithms to generate
them. Then, we introduce an approach to automatically select the CJNs that are
more likely to represent the user intent when issuing a keyword query. This
approach includes two major innovations: a ranking algorithm for selecting
better QMs, yielding the generation of fewer but better CJNs, and an eager
evaluation strategy for pruning void useless CJNs. We present a comprehensive
set of experiments performed with query sets and datasets previously used in
experiments with state-of-the-art R-KwS systems and methods. Our results
indicate that LATHE can handle a wider variety of keyword queries while
remaining highly effective, even for large databases with intricate schemas.


title: Visualizing and Understanding Patch Interactions in Vision Transformer

abstract:
  Vision Transformer (ViT) has become a leading tool in various computer vision
tasks, owing to its unique self-attention mechanism that learns visual
representations explicitly through cross-patch information interactions.
Despite having good success, the literature seldom explores the explainability
of vision transformer, and there is no clear picture of how the attention
mechanism with respect to the correlation across comprehensive patches will
impact the performance and what is the further potential. In this work, we
propose a novel explainable visualization approach to analyze and interpret the
crucial attention interactions among patches for vision transformer.
Specifically, we first introduce a quantification indicator to measure the
impact of patch interaction and verify such quantification on attention window
design and indiscriminative patches removal. Then, we exploit the effective
responsive field of each patch in ViT and devise a window-free transformer
architecture accordingly. Extensive experiments on ImageNet demonstrate that
the exquisitely designed quantitative method is shown able to facilitate ViT
model learning, leading the top-1 accuracy by 4.28% at most. Moreover, the
results on downstream fine-grained recognition tasks further validate the
generalization of our proposal.


title:
On the small noise limit in the Smoluchowski-Kramers approximation of
  nonlinear wave equations with variable friction

abstract:
  We study the validity of a large deviation principle for a class of
stochastic nonlinear damped wave equations, of Klein-Gordon type, in the joint
small mass and small noise limit. The friction term is assumed to be state
dependent.


title: Real-time Cosmology with High Precision Spectroscopy and Astrometry

abstract:
  Breakthroughs in physics and astrophysics are often driven by technological
advances, with the recent detection of gravitational waves being one such
example. This white paper focuses upon how improved astrometric and
spectroscopic measurements from a new generation of precise, accurate, and
stable astronomical instrumentation can address two of the fundamental
mysteries of our time -- dark energy and dark matter -- and probe the nature of
spacetime. Instrumentation is now on the cusp of enabling new cosmological
measurements based on redshifts (cosmic redshift drift) and extremely precise
time-series measurements of accelerations, astrophysical source positions
(astrometry), and angles (cosmic parallax). These allow tests of the
fundamental framework of the universe (the Friedmann equations of general
relativity and whether cosmic expansion is physically accelerating) and its
contents (dark energy evolution and dark matter behavior), while also anchoring
the cosmic distance scale ($H_0$).


title:
Formalizing Cost Fairness for Two-Party Exchange Protocols using Game
  Theory and Applications to Blockchain (Extended Version)

abstract:
  Existing fair exchange protocols usually neglect consideration of cost when
assessing their fairness. However, in an environment with non-negligible
transaction cost, e.g., public blockchains, high or unexpected transaction cost
might be an obstacle for wide-spread adoption of fair exchange protocols in
business applications. For example, as of 2021-12-17, the initialization of the
FairSwap protocol on the Ethereum blockchain requires the selling party to pay
a fee of approx. 349.20 USD per exchange. We address this issue by defining
cost fairness, which can be used to assess two-party exchange protocols
including implied transaction cost. We show that in an environment with
non-negligible transaction cost where one party has to initialize the exchange
protocol and the other party can leave the exchange at any time cost fairness
cannot be achieved.


title:
Optimal Covariate Weighting Increases Discoveries in High-throughput
  Biology

abstract:
  The large-scale multiple testing inherent to high throughput biological data
necessitates very high statistical stringency and thus true effects in data are
difficult to detect unless they have high effect sizes. One promising approach
for reducing the multiple testing burden is to use independent information to
prioritize the features most likely to be true effects. However, using the
independent data effectively is challenging and often does not lead to
substantial gains in power. Current state-of-the-art methods sort features into
groups by the independent information and calculate weights for each group.
However, when true effects are weak and rare (the typical situation for high
throughput biological studies), all groups will contain many null tests and
thus their weights are diluted, and performance suffers. We introduce Covariate
Rank Weighting (CRW), a method for calculating approximate optimal weights
conditioned on the ranking of tests by an external covariate. This approach
uses the probabilistic relationship between covariate ranking and test effect
size to calculate individual weights for each test that are more informative
than group weights and are not diluted by null effects. We show how this
relationship can be calculated theoretically for normally distributed
covariates. It can be estimated empirically in other cases. We show via
simulations and applications to data that this method outperforms existing
methods by as much as 10-fold in the rare/low effect size scenario common to
biological data and has at least comparable performance in all scenarios.


title:
On Intra Video Coding and In-loop Filtering for Neural Object Detection
  Networks

abstract:
  Classical video coding for satisfying humans as the final user is a widely
investigated field of studies for visual content, and common video codecs are
all optimized for the human visual system (HVS). But are the assumptions and
optimizations also valid when the compressed video stream is analyzed by a
machine? To answer this question, we compared the performance of two
state-of-the-art neural detection networks when being fed with deteriorated
input images coded with HEVC and VVC in an autonomous driving scenario using
intra coding. Additionally, the impact of the three VVC in-loop filters when
coding images for a neural network is examined. The results are compared using
the mean average precision metric to evaluate the object detection performance
for the compressed inputs. Throughout these tests, we found that the
Bj{\o}ntegaard Delta Rate savings with respect to PSNR of 22.2 % using VVC
instead of HEVC cannot be reached when coding for object detection networks
with only 13.6% in the best case. Besides, it is shown that disabling the VVC
in-loop filters SAO and ALF results in bitrate savings of 6.4 % compared to the
standard VTM at the same mean average precision.


title:
TFCNet: Temporal Fully Connected Networks for Static Unbiased Temporal
  Reasoning

abstract:
  Temporal Reasoning is one important functionality for vision intelligence. In
computer vision research community, temporal reasoning is usually studied in
the form of video classification, for which many state-of-the-art Neural
Network structures and dataset benchmarks are proposed in recent years,
especially 3D CNNs and Kinetics. However, some recent works found that current
video classification benchmarks contain strong biases towards static features,
thus cannot accurately reflect the temporal modeling ability. New video
classification benchmarks aiming to eliminate static biases are proposed, with
experiments on these new benchmarks showing that the current clip-based 3D CNNs
are outperformed by RNN structures and recent video transformers.
  In this paper, we find that 3D CNNs and their efficient depthwise variants,
when video-level sampling strategy is used, are actually able to beat RNNs and
recent vision transformers by significant margins on static-unbiased temporal
reasoning benchmarks. Further, we propose Temporal Fully Connected Block (TFC
Block), an efficient and effective component, which approximates fully
connected layers along temporal dimension to obtain video-level receptive
field, enhancing the spatiotemporal reasoning ability. With TFC blocks inserted
into Video-level 3D CNNs (V3D), our proposed TFCNets establish new
state-of-the-art results on synthetic temporal reasoning benchmark, CATER, and
real world static-unbiased dataset, Diving48, surpassing all previous methods.


title:
A Posteriori Estimates of Taylor-Hood Element for Stokes Problem Using
  Auxiliary Subspace Techniques

abstract:
  Based on the auxiliary subspace techniques, a hierarchical basis a posteriori
error estimator is proposed for the Stokes problem in two and three dimensions.
For the error estimator, we need to solve only two global diagonal linear
systems corresponding to the degree of freedom of velocity and pressure
respectively, which reduces the computational cost sharply. The upper and lower
bounds up to an oscillation term of the error estimator are also shown to
address the reliability of the adaptive method without saturation assumption.
Numerical simulations are performed to demonstrate the effectiveness and
robustness of our algorithm.


title: A short proof of Rubin's theorem

abstract:
  In a remarkable theorem, M. Rubin proved that if a group $G$ acts in a
locally dense way on a locally compact Hausdorff space $X$ without isolated
points, then the space $X$ and the action of $G$ on $X$ are unique up to
$G$-equivariant homeomorphism. Here we give a short, self-contained proof of
Rubin's theorem, using equivalence classes of ultrafilters on a poset to
reconstruct the points of the space $X$.


title: FedSyn: Synthetic Data Generation using Federated Learning

abstract:
  As Deep Learning algorithms continue to evolve and become more sophisticated,
they require massive datasets for model training and efficacy of models. Some
of those data requirements can be met with the help of existing datasets within
the organizations. Current Machine Learning practices can be leveraged to
generate synthetic data from an existing dataset. Further, it is well
established that diversity in generated synthetic data relies on (and is
perhaps limited by) statistical properties of available dataset within a single
organization or entity. The more diverse an existing dataset is, the more
expressive and generic synthetic data can be. However, given the scarcity of
underlying data, it is challenging to collate big data in one organization. The
diverse, non-overlapping dataset across distinct organizations provides an
opportunity for them to contribute their limited distinct data to a larger pool
that can be leveraged to further synthesize. Unfortunately, this raises data
privacy concerns that some institutions may not be comfortable with.
  This paper proposes a novel approach to generate synthetic data - FedSyn.
FedSyn is a collaborative, privacy preserving approach to generate synthetic
data among multiple participants in a federated and collaborative network.
FedSyn creates a synthetic data generation model, which can generate synthetic
data consisting of statistical distribution of almost all the participants in
the network. FedSyn does not require access to the data of an individual
participant, hence protecting the privacy of participant's data. The proposed
technique in this paper leverages federated machine learning and generative
adversarial network (GAN) as neural network architecture for synthetic data
generation. The proposed method can be extended to many machine learning
problem classes in finance, health, governance, technology and many more.


title:
Detecting local earthquakes via fiber-optic cables in telecommunication
  conduits under Stanford University campus using deep learning

abstract:
  With fiber-optic seismic acquisition development, continuous dense seismic
monitoring is becoming increasingly more accessible. Repurposing fiber cables
in telecommunication conduits makes it possible to run seismic studies at low
cost, even in locations where traditional seismometers are not easily
installed, such as in urban areas. However, due to the large volume of
continuous streaming data, data collected in such a manner will go to waste
unless we significantly automate the processing workflow. We train a
convolutional neural network (CNN) for earthquake detection using data acquired
over three years by fiber cables in telecommunication conduits under Stanford
University campus. We demonstrate that fiber-optic systems can effectively
complement sparse seismometer networks to detect local earthquakes. The CNN
allows for reliable earthquake detection despite a low signal-to-noise ratio
and even detects small-amplitude previously-uncataloged events.


title:
A fast, high-order scheme for evaluating volume potentials on complex 2D
  geometries via area-to-line integral conversion and domain mappings

abstract:
  This article presents a new high-order accurate algorithm for finding a
particular solution to a linear, constant-coefficient partial differential
equation (PDE) by means of a convolution of the volumetric source function with
the Green's function in complex geometries. Utilizing volumetric domain
decomposition, the integral is computed over a union of regular boxes (lending
the scheme compatibility with adaptive box codes) and triangular regions (which
may be potentially curved near boundaries). Singular and near-singular
quadrature is handled by converting integrals on volumetric regions to line
integrals bounding a reference volume cell using cell mappings and elements of
the Poincar\'e lemma, followed by leveraging existing one-dimensional
near-singular and singular quadratures appropriate to the singular nature of
the kernel. The scheme achieves compatibility with fast multipole methods
(FMMs) and thereby optimal asymptotic complexity by coupling global rules for
target-independent quadrature of smooth functions to local target-dependent
singular quadrature corrections, and it relies on orthogonal polynomial systems
on each cell for well-conditioned, high-order and efficient (with respect to
number of required volume function evaluations) approximation of arbitrary
volumetric sources. Our domain discretization scheme is naturally compatible
with standard meshing software such as Gmsh, which are employed to discretize a
narrow region surrounding the domain boundaries. We present 8th-order accurate
results, demonstrate the success of the method with examples showing up to
12-digit accuracy on complex geometries, and, for static geometries, our
numerical examples show well over $99\%$ of evaluation time of the particular
solution is spent in the FMM step.


title: Moir\'e dispersion of edge states in spin chains on superconductors

abstract:
  Our calculations of ferromagnetic spin chains on s-wave superconductors show
that the energy oscillations of edge states with the chain's length are due to
a moir\'e pattern emerging from Friedel-like oscillations and the discreteness
of the spin-chain lattice. By modifying the spin lattice, the moir\'e
dispersion of edge states can be controlled. In particular, we can engineer
non-dispersive edge states that remain at fixed energy regardless of the size
distribution of the spin chains. This is an important step in the study of edge
states of spin chains that can be fabricated with a certain size dispersion.


title: Analytic spread of filtrations on two dimensional normal local rings

abstract:
  In this paper we prove that a classical theorem by McAdam about the analytic
spread of an ideal in a Noetherian local ring continues to be true for
divisorial filtrations on a two dimensional normal excellent local ring $R$,
and that the Hilbert polynomial of the fiber cone of a divisorial filtration on
$R$ has a Hilbert function which is the sum of a linear polynomial and a
bounded function. We prove these theorems by first studying asymptotic
properties of divisors on a resolution of singularities of the spectrum of $R$.
The filtration of the symbolic powers of an ideal is an example of a divisorial
filtration. Divisorial filtrations are often not Noetherian, giving a
significant difference in the classical case of filtrations of powers of ideals
and divisorial filtrations.


title: Are discrete units necessary for Spoken Language Modeling?

abstract:
  Recent work in spoken language modeling shows the possibility of learning a
language unsupervisedly from raw audio without any text labels. The approach
relies first on transforming the audio into a sequence of discrete units (or
pseudo-text) and then training a language model directly on such pseudo-text.
Is such a discrete bottleneck necessary, potentially introducing irreversible
errors in the encoding of the speech signal, or could we learn a language model
without discrete units at all? In this work, show that discretization is indeed
essential for good results in spoken language modeling, but that can omit the
discrete bottleneck if we use using discrete target features from a higher
level than the input features. We also show that an end-to-end model trained
with discrete target like HuBERT achieves similar results as the best language
model trained on pseudo-text on a set of zero-shot spoken language modeling
metrics from the Zero Resource Speech Challenge 2021.


title:
Validation of standardized data formats and tools for ground-level
  particle-based gamma-ray observatories

abstract:
  Ground-based gamma-ray astronomy is still a rather young field of research,
with strong historical connections to particle physics. This is why most
observations are conducted by experiments with proprietary data and analysis
software, as it is usual in the particle physics field. However in recent
years, this paradigm has been slowly shifting towards the development and use
of open-source data formats and tools, driven by upcoming observatories such as
the Cherenkov Telescope Array (CTA). In this context, a community-driven,
shared data format (the gamma-astro-data-format or GADF) and analysis tools
such as Gammapy and ctools have been developed. So far these efforts have been
led by the IACT community, leaving out other types of ground-based gamma-ray
instruments.We aim to show that the data from ground particle arrays, such as
the High-Altitude Water Cherenkov (HAWC) observatory, is also compatible with
the GADF and can thus be fully analysed using the related tools, in this case
Gammapy. We reproduce several published HAWC results using Gammapy and data
products compliant with GADF standard. We also illustrate the capabilities of
the shared format and tools by producing a joint fit of the Crab spectrum
including data from six different gamma-ray experiments. We find excellent
agreement with the reference results, a powerful check of both the published
results and the tools involved. The data from particle detector arrays such as
the HAWC observatory can be adapted to the GADF and thus analysed with Gammapy.
A common data format and shared analysis tools allow multi-instrument joint
analysis and effective data sharing. Given the complementary nature of pointing
and wide-field instruments, this synergy will be distinctly beneficial for the
joint scientific exploitation of future observatories such as the Southern
Wide-field Gamma-ray Observatory and CTA.


title:
Enhanced and controllable reflected group delay based on Tamm surface
  plasmons with Dirac semimetals

abstract:
  In this paper, the reflected group delay from a multilayer structure where
Dirac semimetal is coated on one-dimensional photonic crystal (1D PC) separated
by a spacer layer is investigated theoretically. It is shown that the group
delayof reflected beam in this structure can be significant enhanced negatively
and can be switched from negative to positive. The enhanced group delay
originates from the steep phase change caused by the excitation of Tamm
plasmons at the interface between the Dirac semimetal and spacer layer. It is
clear that the positive and negative group delay can be actively tuned through
the Fermi energy and the relaxation time of the Dirac semimetal. We believe
this enhanced and tunable delay scheme is promising for fabricating optical
delay devices and other applications at middle infrared band.


title: Opportunities for new physics searches with heavy ions at colliders

abstract:
  Opportunities for searches for phenomena beyond the Standard Model (BSM)
using heavy-ions beams at high energies are outlined. Different BSM searches
proposed in the last years in collisions of heavy ions, mostly at the Large
Hadron Collider, are summarized. A few concrete selected cases are reviewed
including searches for axion-like particles, anomalous $\tau$ electromagnetic
moments, magnetic monopoles, and dark photons. Expectations for the achievable
sensitivities of these searches in the coming years are given. Studies of CP
violation in hot and dense QCD matter and connections to ultrahigh-energy
cosmic rays physics are also mentioned.


title: PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows

abstract:
  Point cloud denoising aims to restore clean point clouds from raw
observations corrupted by noise and outliers while preserving the fine-grained
details. We present a novel deep learning-based denoising model, that
incorporates normalizing flows and noise disentanglement techniques to achieve
high denoising accuracy. Unlike existing works that extract features of point
clouds for point-wise correction, we formulate the denoising process from the
perspective of distribution learning and feature disentanglement. By
considering noisy point clouds as a joint distribution of clean points and
noise, the denoised results can be derived from disentangling the noise
counterpart from latent point representation, and the mapping between Euclidean
and latent spaces is modeled by normalizing flows. We evaluate our method on
synthesized 3D models and real-world datasets with various noise settings.
Qualitative and quantitative results show that our method outperforms previous
state-of-the-art deep learning-based approaches. %in terms of detail
preservation and distribution uniformity.


title: An extended mixed finite element method for elliptic interface problems

abstract:
  In this paper, we propose an extended mixed finite element method for
elliptic interface problems. By adding some stabilization terms, we present a
mixed approximation form based on Brezzi-Douglas-Marini element space and the
piecewise constant function space, and show that the discrete inf-sup constant
is independent of how the interface intersects the triangulation. Furthermore,
we derive that the optimal convergence holds independent of the location of the
interface relative to the mesh. Finally, some numerical examples are presented
to verify our theoretical results.


title:
New Buckled Honeycomb Lattice Compound Sr3CaOs2O9 Exhibiting
  Antiferromagnetism above Room Temperature

abstract:
  Synthesis, crystal structure and magnetic properties of a new 2:1 ordered
triple perovskite Sr3CaOs2O9 are reported. The compound crystallizes in P21/c
space group and features a unique buckled honeycomb lattice of osmium. It
exhibits long-range antiferromagnetic ordering with a high Neel temperature of
385 K as confirmed by susceptibility, heat capacity and neutron diffraction
measurements and is electrically insulating. This compound is also the first
example of a 2:1 ordered osmate perovskite. Theoretical investigations indicate
that Sr3CaOs2O9 features a sizeable antiferromagnetic exchange between the
puckered planes resulting in a high TN. The magnetic properties of the known
compound Sr3CaRu2O9 are elucidated in comparison. It shows antiferromagnetic
order below TN = 200 K.


title: Flat Delaunay Complexes for Homeomorphic Manifold Reconstruction

abstract:
  Given a smooth submanifold of the Euclidean space, a finite point cloud and a
scale parameter, we introduce a construction which we call the flat Delaunay
complex (FDC). This is a variant of the tangential Delaunay complex (TDC)
introduced by Boissonnat et al.. Building on their work, we provide a short and
direct proof that when the point cloud samples sufficiently nicely the
submanifold and is sufficiently safe (a notion which we define in the paper),
our construction is homeomorphic to the submanifold. Because the proof works
even when data points are noisy, this allows us to propose a perturbation
scheme that takes as input a point cloud sufficiently nice and returns a point
cloud which in addition is sufficiently safe. Equally importantly, our
construction provides the framework underlying a variational formulation of the
reconstruction problem which we present in a companion paper.


title: Saliency-Driven Versatile Video Coding for Neural Object Detection

abstract:
  Saliency-driven image and video coding for humans has gained importance in
the recent past. In this paper, we propose such a saliency-driven coding
framework for the video coding for machines task using the latest video coding
standard Versatile Video Coding (VVC). To determine the salient regions before
encoding, we employ the real-time-capable object detection network You Only
Look Once~(YOLO) in combination with a novel decision criterion. To measure the
coding quality for a machine, the state-of-the-art object segmentation network
Mask R-CNN was applied to the decoded frame. From extensive simulations we find
that, compared to the reference VVC with a constant quality, up to 29 % of
bitrate can be saved with the same detection accuracy at the decoder side by
applying the proposed saliency-driven framework. Besides, we compare YOLO
against other, more traditional saliency detection methods.


title:
Terahertz frequency selective surfaces using heterostructures based on
  two-dimensional diffraction grating of single-walled carbon nanotubes

abstract:
  For single-walled carbon nanotubes (SWCNTs) with a length of 1-50 nm, the
surface plasmon-polariton (SPP) resonance is within the terahertz frequency
range; therefore, SWCNT lattices can be used to design frequency-selective
surface (FSS). The numerical model of electromagnetic wave diffraction on a
two-dimensional periodic SWCNT lattice can be described by an
integro-differential equation of the second-order with respect to the surface
current along SWCNT. The equation can be solved by the Bubnov-Galerkin method.
Frequency dependence of reflecting and transmitting electromagnetic waves for
FSSs near the SPP resonance are studied numerically. It is shown that the
resonances are within the lower-frequency part of the terahertz range. Also, we
estimate the relaxation frequency of an individual SWCNT and demonstrate the
applicability of the Kubo formula for graphene conductivity to array of strips
similar in size with SWCNTs under consideration.


title: The geometry of controlled rough paths

abstract:
  We prove that the spaces of controlled (branched) rough paths of arbitrary
order form a continuous field of Banach spaces. This structure has many
similarities to an (infinite-dimensional) vector bundle and allows to define a
topology on the total space, the collection of all controlled path spaces,
which turns out to be Polish in the geometric case. The construction is
intrinsic and based on a new approximation result for controlled rough paths.
This framework turns well-known maps such as the rough integration map and the
It\^o-Lyons map into continuous (structure preserving) mappings. Moreover, it
is compatible with previous constructions of interest in the stability theory
for rough integration.


title:
Hybrid Artifact Detection System for Minute Resolution Blood Pressure
  Signals from ICU

abstract:
  Physiological monitoring in intensive care units generates data that can be
used to aid clinical decision making facilitating early interventions. However,
the low data quality of physiological signals due to the recording conditions
in clinical settings limits the automated extraction of relevant information
and leads to significant numbers of false alarms. This paper investigates the
utilization of a hybrid artifact detection system that combines a Variational
Autoencoder with a statistical detection component for the labeling of
artifactual samples to automate the costly process of cleaning physiological
recordings. The system is applied to mean blood pressure signals from an
intensive care unit dataset recorded within the scope of the KidsBrainIT
project. Its performance is benchmarked to manual annotations made by trained
researchers. Our preliminary results indicate that the system is capable of
consistently achieving sensitivity and specificity levels that surpass 90%.
Thus, it provides an initial foundation that can be expanded upon to partially
automate data cleaning in offline applications and reduce false alarms in
online applications.


title:
Block-Sparse Adversarial Attack to Fool Transformer-Based Text
  Classifiers

abstract:
  Recently, it has been shown that, in spite of the significant performance of
deep neural networks in different fields, those are vulnerable to adversarial
examples. In this paper, we propose a gradient-based adversarial attack against
transformer-based text classifiers. The adversarial perturbation in our method
is imposed to be block-sparse so that the resultant adversarial example differs
from the original sentence in only a few words. Due to the discrete nature of
textual data, we perform gradient projection to find the minimizer of our
proposed optimization problem. Experimental results demonstrate that, while our
adversarial attack maintains the semantics of the sentence, it can reduce the
accuracy of GPT-2 to less than 5% on different datasets (AG News, MNLI, and
Yelp Reviews). Furthermore, the block-sparsity constraint of the proposed
optimization problem results in small perturbations in the adversarial example.


title: Peng Cheng Object Detection Benchmark for Smart City

abstract:
  Object detection is an algorithm that recognizes and locates the objects in
the image and has a wide range of applications in the visual understanding of
complex urban scenes. Existing object detection benchmarks mainly focus on a
single specific scenario and their annotation attributes are not rich enough,
these make the object detection model is not generalized for the smart city
scenes. Considering the diversity and complexity of scenes in intelligent city
governance, we build a large-scale object detection benchmark for the smart
city. Our benchmark contains about 500K images and includes three scenarios:
intelligent transportation, intelligent security, and drones. For the
complexity of the real scene in the smart city, the diversity of weather,
occlusion, and other complex environment diversity attributes of the images in
the three scenes are annotated. The characteristics of the benchmark are
analyzed and extensive experiments of the current state-of-the-art target
detection algorithm are conducted based on our benchmark to show their
performance.


title:
CNN-Aided Factor Graphs with Estimated Mutual Information Features for
  Seizure Detection

abstract:
  We propose a convolutional neural network (CNN) aided factor graphs assisted
by mutual information features estimated by a neural network for seizure
detection. Specifically, we use neural mutual information estimation to
evaluate the correlation between different electroencephalogram (EEG) channels
as features. We then use a 1D-CNN to extract extra features from the EEG
signals and use both features to estimate the probability of a seizure
event.~Finally, learned factor graphs are employed to capture the temporal
correlation in the signal. Both sets of features from the neural mutual
estimation and the 1D-CNN are used to learn the factor nodes. We show that the
proposed method achieves state-of-the-art performance using 6-fold
leave-four-patients-out cross-validation.


title:
Photoionization and core resonances from range-separated
  density-functional theory: General formalism and example of the beryllium
  atom

abstract:
  We explore the merits of linear-response range-separated time-dependent
density-functional theory (TDDFT) for the calculation of photoionization
spectra. We consider two variants of range-separated TDDFT, namely the
timedependent range-separated hybrid (TDRSH) scheme which uses a global
range-separation parameter and the timedependent locally range-separated hybrid
(TDLRSH) which uses a local range-separation parameter, and compare with
standard time-dependent local-density approximation (TDLDA) and time-dependent
Hartree-Fock (TDHF). We show how to calculate photoionization spectra with
these methods using the Sternheimer approach formulated in a nonorthogonal
B-spline basis set with appropriate frequency-dependent boundary conditions. We
illustrate these methods on the photoionization spectrum of the Be atom,
focusing in particular on the core resonances. Both the TDRSH and TDLRSH
photoionization spectra are found to constitute a large improvement over the
TDLDA photoionization spectrum and a more modest improvement over the TDHF
photoionization spectrum.


title:
Balancing Consumer and Business Value of Recommender Systems: A
  Simulation-based Analysis

abstract:
  Automated recommendations can nowadays be found on many online platforms, and
such recommendations can create substantial value for consumers and providers.
Often, however, not all recommendable items have the same profit margin, and
providers might thus be tempted to promote items that maximize their profit. In
the short run, consumers might accept non-optimal recommendations, but they may
lose their trust in the long run. Ultimately, this leads to the problem of
designing balanced recommendation strategies, which consider both consumer and
provider value and lead to sustained business success. This work proposes a
simulation framework based on Agent-based Modeling designed to help providers
explore longitudinal dynamics of different recommendation strategies. In our
model, consumer agents receive recommendations from providers, and the
perceived quality of the recommendations influences the consumers' trust over
time. In addition, we consider network effects where positive and negative
experiences are shared with others on social media. Simulations with our
framework show that balanced strategies that consider both stakeholders indeed
lead to stable consumer trust and sustained profitability. We also find that
social media can reinforce phenomena like the loss of trust in the case of
negative experiences. To ensure reproducibility and foster future research, we
publicly share our flexible simulation framework.


title:
Fluid-rigid body interaction in an incompressible electrically
  conducting fluid

abstract:
  We analyze a mathematical model that describes the interaction between an
insulating rigid body and an incompressible electrically conducting fluid, in
which the body is located. The model as well as the mathematical analysis
involve the fields of fluid-structure interaction and of magnetohydrodynamics.
Our main result is the proof of the existence of a weak solution to the
corresponding system of partial differential equations. The proof relies on a
use of a time discretization via the Rothe method to decouple the system. This
allows us to deal with test functions, depending on the position of the moving
body and therefore on the solution of the system, in the weak formulation of
the induction equation. The proof moreover makes use of the Brinkman
penalization in order to cope with the mechanical part of the problem.


title:
An Adaptive Hybrid Active Learning Strategy with Free Ratings in
  Collaborative Filtering

abstract:
  Recommender systems are information retrieval methods that predict user
preferences to personalize services. These systems use the feedback and the
ratings provided by users to model the behavior of users and to generate
recommendations. Typically, the ratings are quite sparse, i.e., only a small
fraction of items are rated by each user. To address this issue and enhance the
performance, active learning strategies can be used to select the most
informative items to be rated. This rating elicitation procedure enriches the
interaction matrix with informative ratings and therefore assists the
recommender system to better model the preferences of the users. In this paper,
we evaluate various non-personalized and personalized rating elicitation
strategies. We also propose a hybrid strategy that adaptively combines a
non-personalized and a personalized strategy. Furthermore, we propose a new
procedure to obtain free ratings based on the side information of the items. We
evaluate these ideas on the MovieLens dataset. The experiments reveal that our
proposed hybrid strategy outperforms the strategies from the literature. We
also propose the extent to which free ratings are obtained, improving further
the performance and also the user experience.


title:
Tactile-Sensitive NewtonianVAE for High-Accuracy Industrial
  Connector-Socket Insertion

abstract:
  An industrial connector-socket insertion task requires sub-millimeter
positioning and compensation of grasp pose of a connector. Thus high accurate
estimation of relative pose between socket and connector is a key factor to
achieve the task. World models are promising technology for visuo-motor
control. They obtain appropriate state representation for control to jointly
optimize feature extraction and latent dynamics model. Recent study shows
NewtonianVAE, which is a kind of the world models, acquires latent space which
is equivalent to mapping from images to physical coordinate. Proportional
control can be achieved in the latent space of NewtonianVAE. However,
application of NewtonianVAE to high accuracy industrial tasks in physical
environments is open problem. Moreover, there is no general frameworks to
compensate goal position in the obtained latent space considering the grasp
pose. In this work, we apply NewtonianVAE to USB connector insertion with grasp
pose variation in the physical environments. We adopt a GelSight type tactile
sensor and estimate insertion position compensated by the grasp pose of the
connector. Our method trains the latent space in an end-to-end manner, and
simple proportional control is available. Therefore, it requires no additional
engineering and annotation. Experimental results show that the proposed method,
Tactile-Sensitive NewtonianVAE, outperforms naive combination of
regression-based grasp pose estimator and coordinate transformation. Moreover,
we reveal the original NewtonianVAE does not work in some situation, and
demonstrate that domain knowledge induction improves model accuracy. This
domain knowledge is easy to be known from specification of robots or
measurement.


title:
Label-efficient Hybrid-supervised Learning for Medical Image
  Segmentation

abstract:
  Due to the lack of expertise for medical image annotation, the investigation
of label-efficient methodology for medical image segmentation becomes a heated
topic. Recent progresses focus on the efficient utilization of weak annotations
together with few strongly-annotated labels so as to achieve comparable
segmentation performance in many unprofessional scenarios. However, these
approaches only concentrate on the supervision inconsistency between strongly-
and weakly-annotated instances but ignore the instance inconsistency inside the
weakly-annotated instances, which inevitably leads to performance degradation.
To address this problem, we propose a novel label-efficient hybrid-supervised
framework, which considers each weakly-annotated instance individually and
learns its weight guided by the gradient direction of the strongly-annotated
instances, so that the high-quality prior in the strongly-annotated instances
is better exploited and the weakly-annotated instances are depicted more
precisely. Specially, our designed dynamic instance indicator (DII) realizes
the above objectives, and is adapted to our dynamic co-regularization (DCR)
framework further to alleviate the erroneous accumulation from distortions of
weak annotations. Extensive experiments on two hybrid-supervised medical
segmentation datasets demonstrate that with only 10% strong labels, the
proposed framework can leverage the weak labels efficiently and achieve
competitive performance against the 100% strong-label supervised scenario.


title: How to accomplish inflation and a constant dark energy density

abstract:
  It is commonly assumed that the Lagrangian of multi-field theories of gravity
contains the sum of kinetic terms of scalar fields. However, we propose here
the Lagrangian contains not the sum but the quotient of kinetic terms. With
this novel change, we find the inflationary universe can be acquired with two
scalars without the slow-roll approximation. On the other hand, a constant dark
energy density can also be achieved with two scalars while without the need of
Einstein cosmological constant.


title: The Category of Linear Optical Quantum Computing

abstract:
  This note reviews the model of computation generated by photonic circuits,
comprising edges that are traversed by photons in a single time-bin and
vertices given by idealised lossless beam splitters and phase shifters. The
circuit model is abstracted as a representation of the symmetric monoidal
category of unitary matrices on the bosonic Fock space of multimode photons. A
diagrammatic language, designed to aid with the understanding and development
of photonic algorithms, is presented that encapsulates the category properties
of this representation. As demonstrations of the formalism, the boson sampling
scheme and the protocol of Knill, Laflamme and Milburn are developed on the
single-rail photonic computer, and a parity-based model for the qudit is
investigated.


title:
Symbol based convergence analysis in multigrid methods for saddle point
  problems

abstract:
  Saddle point problems arise in a variety of applications, e.g., when solving
the Stokes equations. They can be formulated such that the system matrix is
symmetric, but indefinite, so the variational convergence theory that is
usually used to prove multigrid convergence cannot be applied. In a 2016 paper
in Numerische Mathematik Notay has presented a different algebraic approach
that analyzes properly preconditioned saddle point problems, proving
convergence of the Two-Grid method. In the present paper we analyze saddle
point problems where the blocks are circulant within this framework. We are
able to derive sufficient conditions for convergence and provide optimal
parameters for the preconditioning of the saddle point problem and for the
point smoother that is used. The analysis is based on the generating symbols of
the circulant blocks. Further, we show that the structure can be kept on the
coarse level, allowing for a recursive application of the approach in a W- or
V-cycle and proving the "level independency" property. Numerical results
demonstrate the efficiency of the proposed method in the circulant and the
Toeplitz case.


title: Policy Architectures for Compositional Generalization in Control

abstract:
  Many tasks in control, robotics, and planning can be specified using desired
goal configurations for various entities in the environment. Learning
goal-conditioned policies is a natural paradigm to solve such tasks. However,
current approaches struggle to learn and generalize as task complexity
increases, such as variations in number of environment entities or compositions
of goals. In this work, we introduce a framework for modeling entity-based
compositional structure in tasks, and create suitable policy designs that can
leverage this structure. Our policies, which utilize architectures like Deep
Sets and Self Attention, are flexible and can be trained end-to-end without
requiring any action primitives. When trained using standard reinforcement and
imitation learning methods on a suite of simulated robot manipulation tasks, we
find that these architectures achieve significantly higher success rates with
less data. We also find these architectures enable broader and compositional
generalization, producing policies that extrapolate to different numbers of
entities than seen in training, and stitch together (i.e. compose) learned
skills in novel ways. Videos of the results can be found at
https://sites.google.com/view/comp-gen-rl.


title: Random Ensemble Reinforcement Learning for Traffic Signal Control

abstract:
  Traffic signal control is a significant part of the construction of
intelligent transportation. An efficient traffic signal control strategy can
reduce traffic congestion, improve urban road traffic efficiency and facilitate
people's lives. Existing reinforcement learning approaches for traffic signal
control mainly focus on learning through a separate neural network. Such an
independent neural network may fall into the local optimum of the training
results. Worse more, the collected data can only be sampled once, so the data
utilization rate is low. Therefore, we propose the Random Ensemble Double DQN
Light (RELight) model. It can dynamically learn traffic signal control
strategies through reinforcement learning and combine random ensemble learning
to avoid falling into the local optimum to reach the optimal strategy.
Moreover, we introduce the Update-To-Data (UTD) ratio to control the number of
data reuses to improve the problem of low data utilization. In addition, we
have conducted sufficient experiments on synthetic data and real-world data to
prove that our proposed method can achieve better traffic signal control
effects than the existing optimal methods.


title:
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain
  Analysis: From Theory to Practice

abstract:
  Vision Transformer (ViT) has recently demonstrated promise in computer vision
problems. However, unlike Convolutional Neural Networks (CNN), it is known that
the performance of ViT saturates quickly with depth increasing, due to the
observed attention collapse or patch uniformity. Despite a couple of empirical
solutions, a rigorous framework studying on this scalability issue remains
elusive. In this paper, we first establish a rigorous theory framework to
analyze ViT features from the Fourier spectrum domain. We show that the
self-attention mechanism inherently amounts to a low-pass filter, which
indicates when ViT scales up its depth, excessive low-pass filtering will cause
feature maps to only preserve their Direct-Current (DC) component. We then
propose two straightforward yet effective techniques to mitigate the
undesirable low-pass limitation. The first technique, termed AttnScale,
decomposes a self-attention block into low-pass and high-pass components, then
rescales and combines these two filters to produce an all-pass self-attention
matrix. The second technique, termed FeatScale, re-weights feature maps on
separate frequency bands to amplify the high-frequency signals. Both techniques
are efficient and hyperparameter-free, while effectively overcoming relevant
ViT training artifacts such as attention collapse and patch uniformity. By
seamlessly plugging in our techniques to multiple ViT variants, we demonstrate
that they consistently help ViTs benefit from deeper architectures, bringing up
to 1.1% performance gains "for free" (e.g., with little parameter overhead). We
publicly release our codes and pre-trained models at
https://github.com/VITA-Group/ViT-Anti-Oversmoothing.


title: Automatic Performance Estimation for Decentralized Optimization

abstract:
  We present a methodology to automatically compute worst-case performance
bounds for a large class of first-order decentralized optimization algorithms.
These algorithms aim at minimizing the average of local functions that are
distributed across a network of agents. They typically combine local
computations and consensus steps. Our methodology is based on the approach of
Performance Estimation Problem (PEP), which allow computing the worst-case
performance and worst-case instance of first-order optimization algorithms by
solving an SDP. We propose two ways of representing consensus steps in PEPs,
which allows writing and solving PEPs for decentralized optimization. The first
formulation is exact but specific to a given averaging matrix. The second
formulation is a relaxation that provides guarantees valid over an entire class
of averaging matrices, characterized by their spectral range. This formulation
often allows recovering the worst possible averaging matrix for the given
algorithm. We apply our methodology to three different decentralized methods.
For each of them, we obtain numerically tight worst-case performance bounds
that significantly improve on the existing ones, as well as insights about the
parameters tuning and the worst communication networks.


title:
Stability analysis of anisotropic Bianchi type-I cosmological model in
  teleparallel gravity

abstract:
  In this work, we study a cosmological model of Bianchi type-I Universe in
teleparallel gravity for a perfect fluid. To obtain the cosmological solution
of the model, we assume that the deceleration parameter is a linear function of
the Hubble parameter $H$ i.e. $q=-1+\beta H$ (where $\beta $ as a positive
constant). Consequently, we get a model of our Universe, where it goes from the
initial phase of deceleration to the current phase of acceleration. We have
discussed some physical and geometric properties such as Hubble parameter,
deceleration parameter, energy density, pressure, and equation of state (EoS)
parameter and study their behavior graphically in terms of redshift and compare
it with observational data such as Type Ia supernovae (SNIa). We also discussed
the behavior of other parameters such as the Jerk parameter, Statefinder
parameters and we tested the validity of the model by studying the stability
analysis and energy conditions.


title: Human-Like Navigation Behavior: A Statistical Evaluation Framework

abstract:
  Recent advancements in deep reinforcement learning have brought forth an
impressive display of highly skilled artificial agents capable of complex
intelligent behavior. In video games, these artificial agents are increasingly
deployed as non-playable characters (NPCs) designed to enhance the experience
of human players. However, while it has been shown that the convincing
human-like behavior of NPCs leads to increased engagement in video games, the
believability of an artificial agent's behavior is most often measured solely
by its proficiency at a given task. Recent work has hinted that proficiency
alone is not sufficient to discern human-like behavior. Motivated by this, we
build a non-parametric two-sample hypothesis test designed to compare the
behaviors of artificial agents to those of human players. We show that the
resulting $p$-value not only aligns with anonymous human judgment of human-like
behavior, but also that it can be used as a measure of similarity.


title: Online User Profiling to Detect Social Bots on Twitter

abstract:
  Social media platforms can expose influential trends in many aspects of
everyday life. However, the movements they represent can be contaminated by
disinformation. Social bots are one of the significant sources of
disinformation in social media. Social bots can pose serious cyber threats to
society and public opinion. This research aims to develop machine learning
models to detect bots based on the extracted user's profile from a Tweet's
text. Online users' profile shows the user's personal information, such as age,
gender, education, and personality. In this work, the user's profile is
constructed based on the user's online posts. This work's main contribution is
three-fold: First, we aim to improve bot detection through machine learning
models based on the user's personal information generated by the user's online
comments. When comparing two online posts, the similarity of personal
information makes it difficult to differentiate a bot from a human user.
However, this research turns personal information similarity among two online
posts into an advantage for the new bot detection model. The new proposed model
for bot detection creates user profiles based on personal information such as
age, personality, gender, education from users' online posts and introduces a
machine learning model to detect social bots with high prediction accuracy
based on personal information. Second, create a new public data set that shows
the user's profile for more than 6900 Twitter accounts in the Cresci 2017 data
set.


title: A Weibo Dataset for the 2022 Russo-Ukrainian Crisis

abstract:
  Online social networks such as Twitter and Weibo play an important role in
how people stay informed and exchange reactions. Each crisis encompasses a new
opportunity to study the portability of models for various tasks (e.g.,
information extraction, complex event understanding, misinformation detection,
etc.), due to differences in domain, entities, and event types. We present the
Russia-Ukraine Crisis Weibo (RUW) dataset, with over 3.5M user posts and
comments in the first release. Our data is available at
https://github.com/yrf1/RussiaUkraine_weibo_dataset.


title:
Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy
  CT Reconstruction

abstract:
  Objective. Dual-energy computed tomography (DECT) has the potential to
improve contrast, reduce artifacts and the ability to perform material
decomposition in advanced imaging applications. The increased number or
measurements results with a higher radiation dose and it is therefore essential
to reduce either number of projections per energy or the source X-ray
intensity, but this makes tomographic reconstruction more ill-posed.
  Approach. We developed the multi-channel convolutional analysis operator
learning (MCAOL) method to exploit common spatial features within attenuation
images at different energies and we propose an optimization method which
jointly reconstructs the attenuation images at low and high energies with a
mixed norm regularization on the sparse features obtained by pre-trained
convolutional filters through the convolutional analysis operator learning
(CAOL) algorithm.
  Main results. Extensive experiments with simulated and real computed
tomography (CT) data were performed to validate the effectiveness of the
proposed methods and we reported increased reconstruction accuracy compared to
CAOL and iterative methods with single and joint total-variation (TV)
regularization.
  Significance. Qualitative and quantitative results on sparse-views and
low-dose DECT demonstrate that the proposed MCAOL method outperforms both CAOL
applied on each energy independently and several existing state-of-the-art
model-based iterative reconstruction (MBIR) techniques, thus paving the way for
dose reduction.

categories: eess.IV cs.CV cs.LG cs.NA math.NA math.OC physics.med-ph


title:
Trapping wave fields in an expulsive potential by means of linear
  coupling

abstract:
  We demonstrate the existence of confined states in one- and two-dimensional
(1D and 2D) systems of two linearly-coupled components, with the confining
harmonic-oscillator (HO) potential acting upon one component, and an expulsive
anti-HO potential acting upon the other. The systems ca be implemented in
optical and BEC dual-core waveguides. In the 1D linear system, codimension-one
solutions are found in an exact form for the ground state (GS) and dipole mode
(the first excited state). Generic solutions are produced by means of the
variational approximation, and are found in a numerical form. Exact
codimension-one solutions and generic numerical ones are also obtained for the
GS and vortex states in the 2D system (the exact solutions are found for all
values of the vorticity). Both the trapped and anti-trapped components of the
bound states may be dominant ones, in terms of the norm. The localized modes
may be categorized as bound states in continuum, as they coexist with
delocalized ones. The 1D states, as well as the GS in 2D, are weakly affected
and remain stable if the self-attractive or repulsive nonlinearity is added to
the system. The self-attraction makes the vortex states unstable against
splitting, while they remain stable under the action of the self-repulsion.


title:
Solving Multi-Structured Problems by Introducing Linkage Kernels into
  GOMEA

abstract:
  Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue
of linkage (or variable interaction) learning. This requires, however, that the
linkage model can capture the exploitable structure of a problem. Usually, a
single type of linkage structure is attempted to be captured using models such
as a linkage tree. However, in practice, problems may exhibit multiple linkage
structures. This is for instance the case in multi-objective optimization when
the objectives have different linkage structures. This cannot be modelled
sufficiently well when using linkage models that aim at capturing a single type
of linkage structure, deteriorating the advantages brought by MBEAs. Therefore,
here, we introduce linkage kernels, whereby a linkage structure is learned for
each solution over its local neighborhood. We implement linkage kernels into
the MBEA known as GOMEA that was previously found to be highly scalable when
solving various problems. We further introduce a novel benchmark function
called Best-of-Traps (BoT) that has an adjustable degree of different linkage
structures. On both BoT and a worst-case scenario-based variant of the
well-known MaxCut problem, we experimentally find a vast performance
improvement of linkage-kernel GOMEA over GOMEA with a single linkage tree as
well as the MBEA known as DSMGA-II.


title: Penning-trap mass measurements of the deuteron and the HD+ molecular ion

abstract:
  The masses of the lightest atomic nuclei and the electron mass are
interlinked and are crucial in a wide range of research fields, with their
values affecting observables in atomic, molecular and neutrino physics as well
as metrology. The most precise values for these fundamental parameters come
from Penning-trap mass spectrometry, which achieves relative mass uncertainties
in the range of $10^{-11}$. However, redundancy checks using data from
different experiments reveal significant inconsistencies in the masses of the
proton ($m_p$), the deuteron ($m_d$) and helion ($m_\text{he}$), amounting to
$5$ standard deviations for the term $\Delta=m_p+m_d-m_{\text{he}}$, which
suggests that the uncertainty of these values may have been underestimated.
Here we present results from absolute mass measurements of the deuteron and the
${HD}^+$ molecular ion against $^{12}C$ as a mass reference. Our value for the
deuteron $m_d=2.013\,553\,212\,535 (17)$u supersedes the precision of the
literature value by a factor of $2.4$ and deviates from this by $4.8$ standard
deviations. With a relative uncertainty of $8$ parts per trillion (ppt) this is
the most precise mass value measured directly in atomic mass units.
Furthermore, the measurement of the ${HD}^+$ molecular ion,
$m({HD}^+)=3.021\,378\,241\,561\,(61)$u, not only allows for a rigorous
consistency check of our measurements of the masses of the deuteron (this work)
and proton, but also establishes an additional link for the masses of tritium
and helium-3 to the atomic mass unit. Combined with a recent measurement of the
deuteron-to-proton mass ratio the uncertainty of the reference value of $m_p$
can be reduced by a factor of three. This is a post-peer-review, pre-copyedit
version of an article published in Nature. The final authenticated version is
available online at https://doi.org/10.1038/s41586-020-2628-7


title: Mission planning for emergency rapid mapping with drones

abstract:
  We introduce a mission planning concept for routing unmanned aerial vehicles
(UAVs) through a set of sampling locations in the immediate aftermath of an
incident such as a fire or chemical accident. Using interpolation methods that
account for the spatial interdependencies inherent in the surveyed phenomenon,
these samples allow predicting the distribution of hazardous substances across
the affected area. We define the generalized correlated team orienteering
problem (GCorTOP) for selecting {informative} samples considering spatial
correlations between observed and unobserved locations as well as priorities in
the surveyed area. To quickly provide high-quality solutions in time-sensitive
situations, we propose a two-phase multi-start adaptive large neighborhood
search (2MLS). We show the competitiveness of the solution approach using
benchmark instances for the team orienteering problem and investigate the
performance of the proposed models and solution approach in an extensive study
based on newly introduced benchmark instances for the mission planning problem.


title:
Imitation and Adaptation Based on Consistency: A Quadruped Robot
  Imitates Animals from Videos Using Deep Reinforcement Learning

abstract:
  The essence of quadrupeds' movements is the movement of the center of
gravity, which has a pattern in the action of quadrupeds. However, the gait
motion planning of the quadruped robot is time-consuming. Animals in nature can
provide a large amount of gait information for robots to learn and imitate.
Common methods learn animal posture with a motion capture system or numerous
motion data points. In this paper, we propose a video imitation adaptation
network (VIAN) that can imitate the action of animals and adapt it to the robot
from a few seconds of video. The deep learning model extracts key points during
animal motion from videos. The VIAN eliminates noise and extracts key
information of motion with a motion adaptor, and then applies the extracted
movements function as the motion pattern into deep reinforcement learning
(DRL). To ensure similarity between the learning result and the animal motion
in the video, we introduce rewards that are based on the consistency of the
motion. DRL explores and learns to maintain balance from movement patterns from
videos, imitates the action of animals, and eventually, allows the model to
learn the gait or skills from short motion videos of different animals and to
transfer the motion pattern to the real robot.


title:
On The Scale Dependence and Spacetime Dimension of the Internet with
  Causal Sets

abstract:
  A statistical measure of dimension is used to compute the effective average
space dimension for the Internet and other graphs, based on typed edges (links)
from an ensemble of starting points. The method is applied to CAIDA's ITDK data
for the Internet. The effective dimension at different scales is calibrated to
the conventional Euclidean dimension using low dimensional hypercubes. Internet
spacetime has a 'foamy' multi-scale containment hierarchy, with interleaving
semantic types. There is an emergent scale for approximate long range order in
the device node spectrum, but this is not evident at the AS level, where there
is finite distance containment. Statistical dimension is thus a locally varying
measure, which is scale-dependent, giving an visual analogy for the hidden
scale-dependent dimensions of Kaluza-Klein theories. The characteristic
exterior dimensions of the Internet lie between 1.66 +- 0.00 and 6.12 +- 0.00,
and maximal interior dimensions rise to 7.7.


title: FExGAN-Meta: Facial Expression Generation with Meta Humans

abstract:
  The subtleness of human facial expressions and a large degree of variation in
the level of intensity to which a human expresses them is what makes it
challenging to robustly classify and generate images of facial expressions.
Lack of good quality data can hinder the performance of a deep learning model.
In this article, we have proposed a Facial Expression Generation method for
Meta-Humans (FExGAN-Meta) that works robustly with the images of Meta-Humans.
We have prepared a large dataset of facial expressions exhibited by ten
Meta-Humans when placed in a studio environment and then we have evaluated
FExGAN-Meta on the collected images. The results show that FExGAN-Meta robustly
generates and classifies the images of Meta-Humans for the simple as well as
the complex facial expressions.


title: Baire property of spaces of $[0,1]$-valued continuous functions

abstract:
  A topological space $X$ is Baire if the intersection of any sequence of open
dense subsets of $X$ is dense in $X$. Let $C_p(X,[0,1])$ denote the space of
all continuous $[0,1]$-valued functions on a Tychonoff space $X$ with the
topology of pointwise convergence. In this paper, we have obtained a
characterization when the function space $C_p(X,[0,1])$ is Baire for a
Tychonoff space $X$ all separable closed subsets of which are $C$-embedded. In
particular, this characterization is true for normal spaces and, hence, for
metrizable spaces. Moreover, we obtained that the space $C_p(X,[0,1])$ is
Baire, if and only if, the space $Cp(X,K)$ is Baire for a Peano continuum $K$.


title:
Large inverted band-gap in strained three-layer InAs/GaInSb quantum
  wells

abstract:
  Quantum spin Hall insulators (QSHIs) based on HgTe and three-layer InAs/GaSb
quantum wells (QWs) have comparable bulk band-gaps of about 10--18~meV. The
former however features a band-gap vanishing with temperature, while the gap in
InAs/GaSb QSHIs is rather temperature-independent.Here, we report on the
realization of large inverted band-gap in strained three-layer InAs/GaInSb QWs.
By temperature-dependent magnetotransport measurements of gated Hall bar
devices, we extract a gap as high as 45 meV. Combining local and non-local
measurements, we attribute the edge conductivity observed at temperatures up to
40 K to the topological edge channels with equilibration lengths of a few
micrometers. Our findings pave the way toward manipulating edge transport at
high temperatures in QW heterostructures.


title:
Full-plane persistent spin textures with cubic order intrinsic and
  anisotropic band splitting in bulk Lead-free materials

abstract:
  Spin-orbit coupling (SOC) effects occurring in noncentrosymmetric materials
are known to be responsible for nontrivial spin configurations and a number of
emergent physical phenomena such as electrical control of spin degrees of
freedom and spin-to-charge conversion. The materials preserving a uniform spin
configuration in the momentum-space, known as persistent spin texture (PST),
provide long carrier spin lifetimes through persistent spin helix (PSH)
mechanism. However, most of the PST studied till now are attributed to the
linear in \textbf{\textit{k}} splitting and cease to exist locally around
certain high-symmetry-point of first Brillouin Zone (FBZ). The persistent spin
textures with purely cubic spin splittings have drawn attention owing to unique
benefits in spin transport. Here, by using the relativistic first-principles
calculations supplemented with \textbf{\textit{k.p}} analysis, we report the
emergence of purely cubic splitting (PCS) belonging to $D_{3h}$ point group,
which is enforced by in-plane mirror and three-fold rotation operations. In
addition, the in-plane mirror symmetry operation sustains the PST in larger
region (i.e. full planes) of FBZ alongside giant spin splitting. Our results
also demonstrate how application of uniaxial strain could be envisaged to tune
the magnitude of the PCS, preserving the PST. The observed PSTs provide a route
to non-dephasing spin transport with larger spin-Hall conductivity, thus
offering a promising platform for future spintronics devices.


title:
Fixed point Floer cohomology of disjoint Dehn twists on a w+-monotone
  manifold with rational symplectic form

abstract:
  We give an explicit description of the Floer cohomology of a family of Dehn
twists about disjoint Lagrangian spheres in a w+ - monotone rational symplectic
manifold. As a byproduct of our framework, in a monotone symplectic manifold we
are able to define a class in the fixed point Floer cohomology of a Dehn twist
by counting half strips bound to the given Lagrangian sphere and prove it must
vanish. In subsequent work we plan on using this vanishing result to give a new
geometric proof of Seidel's long exact sequence.