title:
A Quantitative Analysis of Physical Security and Path Loss With
  Frequency for IBOB Channel

abstract:
  Security vulnerabilities demonstrated in implantable medical devices have
opened the door for research into physically secure and low power communication
methodologies. In this study, we perform a comparative analysis of commonly
used ISM frequency bands and human body communication (HBC) for data transfer
from in-body to out-of-body (IBOB). We develop a figure of merit (FoM) that
comprises of the critical parameters to quantitatively compare the
communication methodologies. We perform finite-element method (FEM)-based
simulations and experiments to validate the FoM developed.


title:
On the Use of Dimension Reduction or Signal Separation Methods for
  Nitrogen River Pollution Source Identification

abstract:
  Identification of the current and expected future pollution sources to rivers
is crucial for sound environmental management. For this purpose numerous
approaches were proposed that can be clustered under physical based models,
stable isotope analysis and mixing methods, mass balance methods, time series
analysis, land cover analysis, and spatial statistics. Another extremely common
method is Principal Component Analysis, as well as its modifications, such as
Absolute Principal Component Score. they have been applied to the source
identification problems for nitrogen entry to rivers. This manuscript is
checking whether PCA can really be a powerful method to uncover nitrogen
pollution sources considering its theoretical background and assumptions.
Moreover, slightly similar techniques, Independent Component Analysis and
Factor Analysis will also be considered.


title: MetFI: Model-driven Fault Simulation Framework

abstract:
  Safety-critical designs need to ensure reliable operations under hostile
conditions with a certain degree of confidence. The continuously higher
complexity of these designs makes them more susceptible to the risk of failure.
ISO26262 recommends fault injection as the proper technique to verify and
measure the dependability of safety-critical designs. To cope with the
complexity, a lot of effort and stringent verification flow is needed.
Moreover, many fault injection tools offer only a limited degree of
controllability.
  We propose MetaFI, a model-driven simulator-independent fault simulation
framework that provides multi-purpose fault injection strategies such as
Statistical Fault Injection, Direct Fault Injection, Exhaustive Fault
Injection, and at the same time reduces manual efforts. The framework enables
injection of Stuck-at faults, Single-Event Transient faults, Single-Event Upset
faults as well as Timing faults. The fault simulation is performed at the
Register Transfer Level (RTL) of a design, in which parts of the design
targeted for fault simulation are represented with Gate-level (GL) granularity.
MetaFI is scalable with a full System-on-Chip (SoC) design and to demonstrate
the applicability of the framework, fault simulation was applied to various
components of two different SoCs. One SoC is running the Dhrystone application
and the other one is running a Fingerprint calculation application. A minimal
effort of 2 persondays was required to run 38 various fault injection campaigns
on both the designs. The framework provided significant data regarding failure
rates of the components. Results concluded that Prefetcher, a component of the
SoC processor, is more susceptible to failures than the other targeted
components on both the SoCs, regardless of the running application.


title:
Channel Modeling for Physically Secure Electro-Quasistatic In-Body to
  Out-of-Body Communication with Galvanic Tx and Multimodal Rx

abstract:
  Increasing number of devices being used in and around the human body has
resulted in the exploration of the human body as a communication medium. In
this paper, we design a channel model for implantable devices communicating
outside the body using physically secure Electro-Quasistatic Human Body
Communication. A galvanic receiver shows 5dB lower path loss than capacitive
receiver when placed close to transmitter whereas a capacitive receiver has
around 15dB lower path loss for larger separation between the transmitter and
receiver. Finite Element Method (FEM) based simulations are used to analyze the
communication channel for different receiver topologies and experimental data
is used to validate the simulation results.


title: Cartan's method and its applications in sheaf cohomology

abstract:
  This paper aims to use Cartan's original method in proving Theorem A and B on
closed cubes to provide a different proof of the vanishing of sheaf cohomology
over a closed cube if either (i) the degree exceeds its real dimension or (ii)
the sheaf is (locally) constant and the degree is positive. In the first case,
we can further use Godement's argument to show the topological dimension of a
paracompact topological manifold is less than or equal to its real dimension.


title: The $M$-matrix group inverse problem for distance-biregular graphs

abstract:
  In this paper we provide the group inverse of the combinatorial Laplacian
matrix of distance-biregular graphs using the so-called equilibrium measures
for sets obtained by deleting a vertex. We also show that the two equilibrium
arrays characterizing a distance-biregular graph can be expressed in terms of
the mentioned equilibrium measures. As a consequence of the minimum principle,
we show a characterization of when the group inverse of the combinatorial
Laplacian matrix of a distance-biregular graph is an $M$-matrix.


title: Structure of Axion Miniclusters

abstract:
  The Peak-Patch algorithm is used to identify the densest minicluster seeds in
the initial axion density field simulated from string decay. The fate of these
dense seeds is found by tracking the subsequent gravitational collapse in
cosmological $N$-body simulations. We find that miniclusters at late times are
well described by NFW profiles, although for around 70\% of simulated
miniclusters a single power law density profile of $r^{-2.9}$ is an equally
good fit due to the unresolved scale radius. Under the assumption that all
miniclusters with an unresolved scale radius are described by a power-law plus
axion star density profile, we identify a significant number of miniclusters
that might be dense enough to give rise to gravitational microlensing if the
axion mass is $0.2 \,\mathrm{meV}\lesssim m_a \lesssim 3\,\mathrm{meV}$. Higher
resolution simulations resolving the inner structure and axion star formation
are necessary to explore this possibility further.


title:
Interpretable Graph Convolutional Network of Multi-Modality Brain
  Imaging for Alzheimer's Disease Diagnosis

abstract:
  Identification of brain regions related to the specific neurological
disorders are of great importance for biomarker and diagnostic studies. In this
paper, we propose an interpretable Graph Convolutional Network (GCN) framework
for the identification and classification of Alzheimer's disease (AD) using
multi-modality brain imaging data. Specifically, we extended the Gradient Class
Activation Mapping (Grad-CAM) technique to quantify the most discriminative
features identified by GCN from brain connectivity patterns. We then utilized
them to find signature regions of interest (ROIs) by detecting the difference
of features between regions in healthy control (HC), mild cognitive impairment
(MCI), and AD groups. We conducted the experiments on the ADNI database with
imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET,
and showed that the ROI features learned by our method were effective for
enhancing the performances of both clinical score prediction and disease status
identification. It also successfully identified biomarkers associated with AD
and MCI.


title:
Surviving Early Career Research and Beyond in the Physics of Life: A
  concise user guide

abstract:
  Early Career Researcher (ECR) development is a dynamic challenge that
tensions the urge to perform ground-breaking research against an ultimate
practical aspiration of establishing an acceptable level of job security. There
is no typical career path for an ECR, least of all in the area of the Physics
of Life or Biophysics/Biological Physics. Being explicitly interdisciplinary
across the physical-life sciences interface presents more opportunities for a
multiplicity of career trajectories through different home academic
institutions and departments, as well as offering a broader range of
alternative future career trajectories in non-academic sectors. That said,
there are key common features, such as the transient nature of fixed-term
postdoc contracts, the substantial research and domestic challenges that these
present, and the often overwhelming pressures of the realities of competition
in the job market. In this short article I outline the key challenges to ECRs
in the Physics of Life and discuss simple strategies to manage and potentially
overcome them.


title:
Online Distributed Evolutionary Optimization of Time Division Multiple
  Access Protocols

abstract:
  With the advent of cheap, miniaturized electronics, ubiquitous networking has
reached an unprecedented level of complexity, scale and heterogeneity, becoming
the core of several modern applications such as smart industry, smart buildings
and smart cities. A crucial element for network performance is the protocol
stack, namely the sets of rules and data formats that determine how the nodes
in the network exchange information. A great effort has been put to devise
formal techniques to synthesize (offline) network protocols, starting from
system specifications and strict assumptions on the network environment.
However, offline design can be hard to apply in the most modern network
applications, either due to numerical complexity, or to the fact that the
environment might be unknown and the specifications might not available. In
these cases, online protocol design and adaptation has the potential to offer a
much more scalable and robust solution. Nevertheless, so far only a few
attempts have been done towards online automatic protocol design. Here, we
envision a protocol as an emergent property of a network, obtained by an
environment-driven Distributed Hill Climbing algorithm that uses node-local
reinforcement signals to evolve, at runtime and without any central
coordination, a network protocol from scratch. We test this approach with a
3-state Time Division Multiple Access (TDMA) Medium Access Control (MAC)
protocol and we observe its emergence in networks of various scales and with
various settings. We also show how Distributed Hill Climbing can reach
different trade-offs in terms of energy consumption and protocol performance.


title:
Virtual element method for modeling the deformation of multiphase
  composites

abstract:
  In this paper, we study applications of the virtual element method (VEM) for
simulating the deformation of multiphase composites. The VEM is a Galerkin
approach that is applicable to meshes that consist of arbitrarily-shaped
polygonal and polyhedral (simple and nonsimple) elements. In the VEM, the basis
functions are defined as the solution of a local elliptic partial differential
equation, and are never explicitly computed in the implementation of the
method. The stifness matrix of each element is built by using the elliptic
projection operator of the internal virtual work (bilinear form) and it
consists of two terms: a consistency term that is exactly computed (linear
patch test is satisfied) and a correction term (ensures stability) that is
orthogonal to affine displacement fields and has the right scaling. The VEM
simplifies mesh generation for a multiphase composite: a stiff inclusion can be
modeled using a single polygonal or polyhedral element. Attributes of the
virtual element approach are highlighted through comparisons with Voronoi-cell
lattice models, which provide discrete representations of material structure.
The comparisons involve a suite of two-dimensional linear elastic problems:
patch test, axisymmetric circular inclusion problem, and the deformation of a
three-phase composite. The simulations demonstrate the accuracy and flexibility
of the virtual element method.


title: Counterfactual Explanations for Natural Language Interfaces

abstract:
  A key challenge facing natural language interfaces is enabling users to
understand the capabilities of the underlying system. We propose a novel
approach for generating explanations of a natural language interface based on
semantic parsing. We focus on counterfactual explanations, which are post-hoc
explanations that describe to the user how they could have minimally modified
their utterance to achieve their desired goal. In particular, the user provides
an utterance along with a demonstration of their desired goal; then, our
algorithm synthesizes a paraphrase of their utterance that is guaranteed to
achieve their goal. In two user studies, we demonstrate that our approach
substantially improves user performance, and that it generates explanations
that more closely match the user's intent compared to two ablations.


title: On randomization inference after inexact Mahalanobis matching

abstract:
  In observational causal inference, matched-pairs studies are often analyzed
using randomization tests. While intuitively appealing, these tests are not
formally justified by the randomness in the treatment assignment process (the
"design") unless all matches are exact. This paper asks whether these tests can
instead be justified by the random sampling of experimental units. We find that
under fairly restrictive sampling assumptions, the paired randomization test
based on a regression-adjusted test statistic may be asymptotically valid
despite inexact matching. We propose a new randomization test based on matching
with replacement that can be justified under weaker sampling assumptions.


title:
Exploring How Anomalous Model Input and Output Alerts Affect
  Decision-Making in Healthcare

abstract:
  An important goal in the field of human-AI interaction is to help users more
appropriately trust AI systems' decisions. A situation in which the user may
particularly benefit from more appropriate trust is when the AI receives
anomalous input or provides anomalous output. To the best of our knowledge,
this is the first work towards understanding how anomaly alerts may contribute
to appropriate trust of AI. In a formative mixed-methods study with 4
radiologists and 4 other physicians, we explore how AI alerts for anomalous
input, very high and low confidence, and anomalous saliency-map explanations
affect users' experience with mockups of an AI clinical decision support system
(CDSS) for evaluating chest x-rays for pneumonia. We find evidence suggesting
that the four anomaly alerts are desired by non-radiologists, and the
high-confidence alerts are desired by both radiologists and non-radiologists.
In a follow-up user study, we investigate how high- and low-confidence alerts
affect the accuracy and thus appropriate trust of 33 radiologists working with
AI CDSS mockups. We observe that these alerts do not improve users' accuracy or
experience and discuss potential reasons why.


title:
Stream Iterative Distributed Coded Computing for Learning Applications
  in Heterogeneous Systems

abstract:
  To improve the utility of learning applications and render machine learning
solutions feasible for complex applications, a substantial amount of heavy
computations is needed. Thus, it is essential to delegate the computations
among several workers, which brings up the major challenge of coping with
delays and failures caused by the system's heterogeneity and uncertainties. In
particular, minimizing the end-to-end job in-order execution delay, from
arrival to delivery, is of great importance for real-world delay-sensitive
applications. In this paper, for computation of each job iteration in a
stochastic heterogeneous distributed system where the workers vary in their
computing and communicating powers, we present a novel joint scheduling-coding
framework that optimally split the coded computational load among the workers.
This closes the gap between the workers' response time, and is critical to
maximize the resource utilization. To further reduce the in-order execution
delay, we also incorporate redundant computations in each iteration of a
distributed computational job. Our simulation results demonstrate that the
delay obtained using the proposed solution is dramatically lower than the
uniform split which is oblivious to the system's heterogeneity and, in fact, is
very close to an ideal lower bound just by introducing a small percentage of
redundant computations.


title:
Higher H\"older regularity for mixed local and nonlocal degenerate
  elliptic equations

abstract:
  We consider equations involving a combination of local and nonlocal
degenerate $p$-Laplace operators. The main contribution of the paper is almost
Lipschitz regularity for the homogeneous equation and H\"older continuity with
an explicit H\"older exponent in the general case. For certain parameters, our
results also imply H\"older continuity of the gradient. In addition, we
establish existence, uniqueness and local boundedness. The approach is based on
an iteration in the spirit of Moser combined with an approximation method.


title:
Stopping time detection of wood panel compression: A functional time
  series approach

abstract:
  We consider determining the optimal stopping time for the glue curing of wood
panels in an automatic process environment. Using the near-infrared
spectroscopy technology to monitor the manufacturing process ensures
substantial savings in energy and time. We collect a time series of curves from
a near-infrared spectrum probe consisting of 72 spectra and aim to detect an
optimal stopping time. We propose an estimation procedure to determine the
optimal stopping time of wood panel compression and the estimation uncertainty
associated with the estimated stopping time. Our method first divides the
entire data set into a training sample and a testing sample, then iteratively
computes integrated squared forecast errors based on the testing sample. We
then apply a structural break detection method with one breakpoint to determine
an estimated optimal stopping time from a univariate time series of the
integrated squared forecast errors. We also investigate the finite-sample
performance of the proposed method via a series of simulation studies.


title: TDD frame design for interference handling in mobile IAB networks

abstract:
  Integrated access and backhaul (IAB) is envisioned as a possible solution to
address the need for network densification in situations where fiber connection
is not viable. In Release 18, 3rd generation partnership project (3GPP) will
take another step on extending IAB capabilities. With mobile IAB (mIAB), IAB
nodes can be deployed within vehicles, e.g., buses. As in every new technology,
performance assessment and evaluation of the impact of interference is of
interest for industry and academia. In this paper, we present contributions on
those topics. First, we evaluate the performance of mIAB compared to fiber
connected deployments. Moreover, we study the impact of interference on the
performance of mIAB networks and propose a solution based on inserting silent
slots on the time division duplex (TDD) frame pattern. According to our
simulation results, mIAB is capable of improving performance of onboard user
equipments (UEs) without harming too much the quality of service (QoS) of
surrounding UEs. Furthermore, we show that the TDD frame pattern should be
carefully designed to account for scenarios with different levels of
interference.


title: Simulation study of betatron radiation in AWAKE Run 2 experiment

abstract:
  The spectroscopy of betatron radiation from the focusing plasma column can
work as a powerful non-invasive beam diagnostic method for plasma wakefield
acceleration experiments such as the AWAKE. In this paper, the effects of
radial size mismatch and off-axis injection on the beam dynamics, as well as
the spectral features of the betatron radiation emitted by the witness electron
bunch in the quasi-linear proton-driven plasma wakefield are studied. It is
shown that the evolution of the critical betatron photon energy and the overall
photon angular distribution can effectively reveal the initial injection
conditions of the witness electron bunch. The possibility of using this method
for the diagnostics of the seed electron bunch in the proton self-modulation
stage of AWAKE Run 2 is also discussed.


title:
Deviation of nonradial geodesics in a static spherically symmetric
  space-time

abstract:
  The article generalizes the description of tidal forces to the case of
geodesics with non-zero angular momentum in the metric of static spherically
symmetric black holes. We show that the geodesic deviation equation can be
diagonalized even with non-radial free motion of a test body in the
gravitational field. We present expressions for the spatial components of the
tidal force in a spherically symmetric metric. We also solve geodesic deviation
equation in the Schwarzschild metric and demonstrate how the presence of
angular momentum and its magnitude affect the solution.


title: Adjoint-based phase reduction analysis of incompressible periodic flows

abstract:
  We establish the theoretical framework for adjoint-based phase reduction
analysis for incompressible periodic flows. Through this adjoint-based method,
we obtain spatiotemporal phase sensitivity fields through a single pair of
forward and backward direct numerical simulations, as opposed to the
impulse-based method that requires a very large number of simulations.
Phase-based analysis involves perturbation analysis about a periodically
varying base state and hence is tailored for the analysis of periodic flows. We
formulate the phase description of periodic flows with respect to the potential
and vortical perturbations in the flow field. The current phase-reduction
analysis can also be implemented consistently in the immersed boundary
projection method, which facilitates the analysis over arbitrarily-shaped
bodies. We demonstrate the strength of the phase-based analysis for periodic
flows over circular cylinder and symmetric airfoils at high incidence angles.
The critical regions for phase modification in the cylinder flow are
investigated and the locations of flow separation are shown to be the most
sensitive regions. Further, the results reveal the influence of the angle of
attack and airfoil thickness on the phase-sensitivity distribution of flows
over various airfoils. The phase for such flows is defined based on the lift
coefficient, and hence is influenced by the vortical structures responsible for
lift production. The present framework sheds light on the connection between
phase-sensitivity and vortex formation dynamics.


title: Hilbert's 13th Problem for Algebraic Groups

abstract:
  The algebraic form of Hilbert's 13th Problem asks for the resolvent degree
$\text{rd}(n)$ of the general polynomial $f(x) = x^n + a_1 x^{n-1} + \ldots +
a_n$ of degree $n$, where $a_1, \ldots, a_n$ are independent variables. The
resolvent degree is the minimal integer $d$ such that every root of $f(x)$ can
be obtained in a finite number of steps, starting with $\mathbb C(a_1, \ldots,
a_n)$ and adjoining algebraic functions in $\leq d$ variables at each step.
Recently Farb and Wolfson defined the resolvent degree $\text{rd}_k(G)$ of any
finite group $G$ and any base field $k$ of characteristic $0$. In this setting
$\text{rd}(n) = \text{rd}_{\mathbb C}(S_n)$, where $S_n$ denotes the symmetric
group. In this paper we define $\text{rd}_k(G)$ for every algebraic group $G$
over an arbitrary field $k$, investigate the dependency of this quantity on $k$
and show that $\text{rd}_k(G) \leq 5$ for any field $k$ and any connected group
$G$. The question of whether $\text{rd}_k(G)$ can be bigger than $1$ for any
field $k$ and any algebraic group $G$ over $k$ (not necessarily connected)
remains open.


title: Tidal Forces in Kottler Spacetimes

abstract:
  The article considers tidal forces in the vicinity of the Kottler black hole.
We find a solution of the geodesic deviation equation for radially falling
bodies, which is determined by elliptic integrals. And also the asymptotic
behavior of all spatial geodesic deviation vector components were found. We
demonstrate that the radial component of the tidal force changes sign outside
the single event horizon for any negative values of the cosmological constant,
in contrast to the Schwarzschild black hole, where all the components of the
tidal force are sign-constant. We also find the similarity between the Kottler
black hole and the Reissner-Nordstrom black hole, because we indicate the value
of the cosmological constant, which ensures the existence of two horizons of
the black hole, between which the angular components of the tidal force change
sign. It was possible to detect non-analytical behavior of geodesic deviation
vector components in Anti-de Sitter spacetime and to describe it locally.


title:
Non-Equilibrium Statistical Physics Beyond the Ideal Heat Bath
  Approximation

abstract:
  Important models of nonequilibrium statistical physics (NESP) are limited by
a commonly used, but often unrecognized, near-equilibrium approximation.
Fokker-Planck and Langevin equations, the Einstein and random-flight diffusion
models, and the Schnakenberg model of biochemical networks suppose that
fluctuations are due to an ideal equilibrium bath. But far from equilibrium,
this perfect bath concept does not hold. A more principled approach should
derive the rate fluctuations from an underlying dynamical model, rather than
assuming a particular form. Here, using Maximum Caliber as the underlying
principle, we derive corrections for NESP processes in an imperfect - but more
realistic - environment, corrections which become particularly important far
from equilibrium. Beyond characterizing a heat bath by the single equilibrium
property of temperature, we must also consider finite bath size and finite bath
speed to fully describe dynamics further from equilibrium.


title: Dark $SU(2)_D$ Gauge Symmetry and Scotogenic Dirac Neutrinos

abstract:
  Dark matter is postulated to transform under the non-Abelian $SU(2)_D$ gauge
symmetry. Its connection to Dirac neutrino masses is explored.


title:
Improving Multimodal Speech Recognition by Data Augmentation and Speech
  Representations

abstract:
  Multimodal speech recognition aims to improve the performance of automatic
speech recognition (ASR) systems by leveraging additional visual information
that is usually associated to the audio input. While previous approaches make
crucial use of strong visual representations, e.g. by finetuning pretrained
image recognition networks, significantly less attention has been paid to its
counterpart: the speech component. In this work, we investigate ways of
improving the base speech recognition system by following similar techniques to
the ones used for the visual encoder, namely, transferring representations and
data augmentation. First, we show that starting from a pretrained ASR
significantly improves the state-of-the-art performance; remarkably, even when
building upon a strong unimodal system, we still find gains by including the
visual modality. Second, we employ speech data augmentation techniques to
encourage the multimodal system to attend to the visual stimuli. This technique
replaces previously used word masking and comes with the benefits of being
conceptually simpler and yielding consistent improvements in the multimodal
setting. We provide empirical results on three multimodal datasets, including
the newly introduced Localized Narratives.


title:
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning
  Framework

abstract:
  Current contrastive learning frameworks focus on leveraging a single
supervisory signal to learn representations, which limits the efficacy on
unseen data and downstream tasks. In this paper, we present a hierarchical
multi-label representation learning framework that can leverage all available
labels and preserve the hierarchical relationship between classes. We introduce
novel hierarchy preserving losses, which jointly apply a hierarchical penalty
to the contrastive loss, and enforce the hierarchy constraint. The loss
function is data driven and automatically adapts to arbitrary multi-label
structures. Experiments on several datasets show that our
relationship-preserving embedding performs well on a variety of tasks and
outperform the baseline supervised and self-supervised approaches. Code is
available at https://github.com/salesforce/hierarchicalContrastiveLearning.


title: ELM: Embedding and Logit Margins for Long-Tail Learning

abstract:
  Long-tail learning is the problem of learning under skewed label
distributions, which pose a challenge for standard learners. Several recent
approaches for the problem have proposed enforcing a suitable margin in logit
space. Such techniques are intuitive analogues of the guiding principle behind
SVMs, and are equally applicable to linear models and neural models. However,
when applied to neural models, such techniques do not explicitly control the
geometry of the learned embeddings. This can be potentially sub-optimal, since
embeddings for tail classes may be diffuse, resulting in poor generalization
for these classes. We present Embedding and Logit Margins (ELM), a unified
approach to enforce margins in logit space, and regularize the distribution of
embeddings. This connects losses for long-tail learning to proposals in the
literature on metric embedding, and contrastive learning. We theoretically show
that minimising the proposed ELM objective helps reduce the generalisation gap.
The ELM method is shown to perform well empirically, and results in tighter
tail class embeddings.


title: Neural network controllers for uncertain linear systems

abstract:
  We consider the design of reliable neural network (NN)-based approximations
of traditional stabilizing controllers for linear systems affected by polytopic
uncertainty, including controllers with variable structure and those based on a
minimal selection policy. We develop a systematic procedure to certify the
closed-loop stability and performance of a polytopic system when a rectified
linear unit (ReLU)-based approximation replaces such traditional controllers.
We provide sufficient conditions to ensure stability involving the worst-case
approximation error and the Lipschitz constant characterizing the error
function between ReLU-based and traditional controller-based state-to-input
mappings, and further provide offline, mixed-integer optimization-based methods
that allow us to compute those quantities exactly.


title:
Quantifying societal emotional resilience to natural disasters from
  geo-located social media content

abstract:
  Natural disasters can have devastating and long-lasting effects on a
community's emotional well-being. These effects may be distributed unequally,
affecting some communities more profoundly and possibly over longer time
periods than others. Here, we analyze the effects of four major US hurricanes,
namely, Irma, Harvey, Florence, and Dorian on the emotional well-being of the
affected communities and regions. We show that a community's emotional response
to a hurricane event can be measured from the content of social media that its
population posted before, during, and after the hurricane. For each hurricane
making landfall in the US, we observe a significant decrease in sentiment in
the affected areas before and during the hurricane followed by a rapid return
to pre-hurricane baseline, often within 1-2 weeks. However, some communities
exhibit markedly different rates of decline and return to previous equilibrium
levels. This points towards the possibility of measuring the emotional
resilience of communities from the dynamics of their online emotional response.


title:
The Volatile Carbon to Oxygen Ratio as a Tracer for the Formation
  Locations of Interstellar Comets

abstract:
  Based on the occurrence rates implied by the discoveries of 1I/`Oumuamua and
2I/Borisov, the forthcoming Rubin Observatory Legacy Survey of Space and Time
(LSST) should detect $\ge1$ interstellar comets every year (Hoover et al.
2021). We advocate for future measurements of the production rates of H$_2$O,
CO$_2$ and CO in these comets to estimate their carbon to oxygen ratios, which
traces formation locations within their original protoplanetary disks. We
review similar measurements for Solar System comets, which indicate formation
interior to the CO snowline. By quantifying the relative processing in the
interstellar medium and Solar System, we estimate that production rates will
not be representative of primordial compositions for the majority of
interstellar comets. Preferential desorption of CO and CO$_2$ relative to
H$_2$O in the interstellar medium implies that measured C/O ratios represent
lower limits on the primordial ratios. Specifically, production rate ratios of
${\rm Q}({\rm CO})/{\rm Q}({\rm H_2O})<.2$ and ${\rm Q}({\rm CO})/{\rm Q}({\rm
H_2O})>1$ likely indicate formation interior and exterior to the CO snowline,
respectively. The high C/O ratio of 2I/Borisov implies that it formed exterior
to the CO snowline. We provide an overview of the currently operational
facilities capable of obtaining these measurements that will constrain the
fraction of ejected comets that formed exterior to the CO snowline. This
fraction will provide key insights into the efficiency of and mechanisms for
cometary ejection in exoplanetary systems.


title:
Radiation-induced D/H Exchange Rate Constants in Aliphatics Embedded in
  Water Ice

abstract:
  Gas-phase and solid-state chemistry in low-temperature interstellar clouds
and cores leads to a D/H enhancement in interstellar ices, which is eventually
inherited by comets, meteorites, and even planetary satellites. Hence, the D/ H
ratio has been widely used as a tracer for the origins of extraterrestrial
chemistry. However, the D/H ratio can also be influenced by cosmic rays, which
are ubiquitous and can penetrate even dense interstellar molecular cores. The
effects of such high-energy radiation on deuterium fractionation have not been
studied in a quantitative manner. In this study, we present rate constants for
radiation-induced D-to-H exchange for fully deuterated small (1-2 C)
hydrocarbons embedded in H2O ice at 20 K and H-to-D exchange for the protiated
forms of these molecules in D2O ice at 20 K. We observed larger rate constants
for H-to-D exchange in the D2O ice versus D-to-H exchange in H2O ice, which we
have attributed to the greater bond strength of C-D versus C-H. We find that
the H-to-D exchange rate constants are smaller for protiated methane than
ethane, in agreement with bond energies from the literature. We are unable to
obtain rate constants for the unsaturated and reactive hydrocarbons ethylene
and acetylene. Interpretation of the rate constants suggest that D/H exchange
products are formed in abundance alongside radiolysis products. We discuss how
our quantitative and qualitative data can be used to interpret the D/ H ratios
of aliphatic compounds observed throughout space.


title:
Towards a High Efficiency of Native NDN over Wi-Fi 6 for the Internet of
  Vehicles

abstract:
  Named Data Networking (NDN) is a top-notched architecture to deal with
content distribution over the Internet. With the explosion of video streaming
transmission and future massive Internet of Things and Vehicles (IoT/IoV)
traffic, evolving Wi-Fi networks will play an essential role in such
ecosystems. However, Native NDN deployment over wireless networks may not
perform well. Wi-Fi broadcasts/multicasts result in reduced throughput due to
the usage of basic service mode. Despite recent initial works addressing that
issue, further studies and proposals are required to boost the adoption of
Native NDN. We advocate that an initial step towards designing a feasible
Native NDN over wireless networks should be understanding the challenges in
emerging scenarios and providing a uniform baseline to compare and advance
proposals. To this end, first, we highlight some challenges and directions to
improve throughput and energy efficiency, reduce processing overhead, and
security issues. Next, we propose a variant of NDN that minimizes the problems
identified by performing transmission via unicast to avoid storms in wireless
networks. Finally, we conducted a performance evaluation to compare Standard
Native NDN with our proposal on Wi-Fi 6 vehicular networks. The results show
that our proposal outperforms the Standard NDN in the evaluated scenarios,
reaching values close to 89% of satisfied requests, achieving more than 200% of
data received than Standard NDN.


title:
Calculation of energies and hyperfine structure constants of 233U^+ and
  233U

abstract:
  We carried out calculations of the energies and magnetic dipole hyperfine
structure constants of the low-lying states of 233U^+ and 233U using two
different approaches. With six valence electrons and a very heavy core, uranium
represents a major challenge for precision atomic theory even using large-scale
computational resources. The first approach combines configuration interaction
(CI) with a method allowing us to include core-valence correlations to all
orders of the perturbation theory over residual Coulomb interaction. The second
approach is a pure CI method which allows the use of different initial
approximations. We present a detailed analysis of all calculated properties and
discuss the advantages and disadvantages of each of these methods. We report a
preliminary value of U nuclear magnetic moment and outline the need for further
experiments.


title: Fairness and promptness in Muller formulas

abstract:
  In this paper we consider two different views of the model checking problems
for the Linear Temporal Logic (LTL). On the one hand, we consider the universal
model checking problem for LTL, where one asks that for a given system and a
given formula all the runs of the system satisfy the formula. On the other
hand, the fair model checking problem for LTL asks that for a given system and
a given formula almost all the runs of the system satisfy the formula. It was
shown that these two problems have the same theoretical complexity i.e.
PSPACE-complete. The question arises whether one can find a fragment of LTL for
which the complexity of these two problems differs. One such fragment was
identified in a previous work, namely the Muller fragment. We address a similar
comparison for the prompt fragment of LTL (pLTL). pLTL extends LTL with an
additional operator, i.e. the prompt-eventually. This operator ensures the
existence of a bound such that liveness properties are satisfied within this
bound. We show that the corresponding Muller fragment for pLTL does not enjoy
the same algorithmic properties with respect to the comparison considered. We
also identify a new expressive fragment for which the fair model checking is
faster than the universal one.


title: Map-based cosmology inference with lognormal cosmic shear maps

abstract:
  Most cosmic shear analyses to date have relied on summary statistics (e.g.
$\xi_+$ and $\xi_-$). These types of analyses are necessarily sub-optimal, as
the use of summary statistics is lossy. In this paper, we forward-model the
convergence field of the Universe as a lognormal random field conditioned on
the observed shear data. This new map-based inference framework enables us to
recover the joint posterior of the cosmological parameters and the convergence
field of the Universe. Our analysis properly accounts for the covariance in the
mass maps across tomographic bins, which significantly improves the fidelity of
the maps relative to single-bin reconstructions. We verify that applying our
inference pipeline to Gaussian random fields recovers posteriors that are in
excellent agreement with their analytical counterparts. At the resolution of
our maps -- and to the extent that the convergence field can be described by
the lognormal model -- our map posteriors allow us to reconstruct \it all \rm
summary statistics (including non-Gaussian statistics). We forecast that a
map-based inference analysis of LSST-Y10 data can improve cosmological
constraints in the $\sigma_8$--$\Omega_{\rm m}$ plane by $\approx 30\%$
relative to the currently standard cosmic shear analysis. This improvement
happens almost entirely along the $S_8=\sigma_8\Omega_{\rm m}^{1/2}$
directions, meaning map-based inference fails to significantly improve
constraints on $S_8$.


title:
Understanding User Perceptions, Collaborative Experience and User
  Engagement in Different Human-AI Interaction Designs for Co-Creative Systems

abstract:
  Human-AI co-creativity involves humans and AI collaborating on a shared
creative product as partners. In a creative collaboration, communication is an
essential component among collaborators. In many existing co-creative systems
users can communicate with the AI, usually using buttons or sliders. Typically,
the AI in co-creative systems cannot communicate back to humans, limiting their
potential to be perceived as partners rather than just a tool. This paper
presents a study with 38 participants to explore the impact of two interaction
designs, with and without AI-to-human communication, on user engagement,
collaborative experience and user perception of a co-creative AI. The study
involves user interaction with two prototypes of a co-creative system that
contributes sketches as design inspirations during a design task. The results
show improved collaborative experience and user engagement with the system
incorporating AI-to-human communication. Users perceive co-creative AI as more
reliable, personal, and intelligent when the AI communicates to users. The
findings can be used to design effective co-creative systems, and the insights
can be transferred to other fields involving human-AI interaction and
collaboration.


title:
Traveling along horizontal broken geodesics of a homogenous Finsler
  submersion

abstract:
  In this paper, we discuss how to travel along horizontal broken geodesics of
a homogenous Finsler submersion, i.e., we study, what in Riemannian geometry
was called by Wilking, the dual leaves. More precisely, we investigate the
attainable sets $\mathcal{A}_{q}(\mathcal{C})$ of the set of analytic vector
fields $\mathcal{C}$ determined by the family of horizontal unit geodesic
vector fields $\mathcal{C}$ to the fibers $\mathcal{F}=\{\rho^{-1}(c)\}$ of a
homogenous analytic Finsler submersion $\rho: M\to B$. Since reverse of
geodesics don't need to be geodesics in Finsler geometry, one can have examples
on non compact Finsler manifolds $M$ where the attainable sets (the dual
leaves) are no longer orbits or even submanifolds. Nevertheless we prove that,
when $M$ is compact and the orbits of $\mathcal{C}$ are embedded, then the
attainable sets coincide with the orbits. Furthermore, if the flag curvature is
positive then $M$ coincides with the attainable set of each point. In other
words, fixed two points of $M$, one can travel from one point to the other
along horizontal broken geodesics.
  In addition, we show that each orbit $\mathcal{O}(q)$ of $\mathcal{C}$
associated to a singular Finsler foliation coincides with $M$, when the flag
curvature is positive, i.e, we prove Wilking's result in Finsler context. In
particular we review Wilking's transversal Jacobi fields in Finsler case.


title:
Structural Cumulative Survival Models for Robust Estimation of Treatment
  Effects Accounting for Treatment Switching in Randomized Experiments

abstract:
  We propose an instrumental variable estimator to estimate the treatment
causal effect when treatment switching is present in a randomized experiment,
under a structural cumulative survival model. Our estimator is robust to
violation of the exclusion restriction, a commonly adopted assumption for IV
methods that is untestable and usually subject to questioning in practice,
especially in an open-label randomized trial. We derive large-sample properties
of our proposed estimator, along with inferential tools. We apply the estimator
to evaluate the treatment effect of Nucleoside Reverse Transcriptase Inhibitors
on a safety outcome in the Optimized Treatment That Includes or Omits NRTIs
trial.


title: Black holes in the limiting curvature theory of gravity

abstract:
  We discuss a recently proposed limiting curvature theory of gravity and its
application to the problem of singularities inside black holes. In this theory
the growth of the curvature is suppressed by specially chosen inequality
constraints included in the gravity action. We consider a case of a spherically
symmetric four dimensional black hole and demonstrated that imposed curvature
constraints modify a solution in the black hole interior. Instead of forming
the curvature singularity the modified metric describes a space which is
exponentially expanding in one direction and oscillating the other two
directions. The spacetime is complete and its polynomial curvature invariants
are uniformly bounded.


title:
TransHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal
  Restriction

abstract:
  Knowledge graph embedding methods are important for knowledge graph
completion (link prediction) due to their robust performance and efficiency on
large-magnitude datasets. One state-of-the-art method, PairRE, leverages two
separate vectors for relations to model complex relations (i.e., 1-to-N,
N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly
restricts entities on the hyper-ellipsoid surface and thus limits the
optimization of entity distribution, which largely hinders the performance of
knowledge graph completion. To address this problem, we propose a novel score
function TransHER, which leverages relation-specific translations between head
and tail entities restricted on separate hyper-ellipsoids. Specifically, given
a triplet, our model first maps entities onto two separate hyper-ellipsoids and
then conducts a relation-specific translation on one of them. The
relation-specific translation provides TransHER with more direct guidance in
optimization and the ability to learn semantic characteristics of entities with
complex relations. Experimental results show that TransHER can achieve
state-of-the-art performance and generalize to datasets in different domains
and scales. All our code will be publicly available.


title: Determining the aberrations in a nearly diffraction-limited spectrograph

abstract:
  We present a method to determine the static aberrations in a nearly
diffraction-limited spectrograph introduced, for example, by alignment or
manufacturing errors. We consider an instrument with two stages separated by a
slit or image slicer located in the intermediate focal plane. In such a
spectrograph, it is not trivial to distinguish aberrations in the first stage,
before the slit, from those in the second, after the slit. However, our method
achieves this. Measuring these aberrations separately opens the possibility of
reducing them, by realignment or other means, and thereby improving the optical
performance of the instrument. The method is based on fitting models to
multiple images of a point source, with controlled displacements of the source
perpendicular to the slit and controlled defocuses of the second stage or the
detector. Fitting models to these images allows the determination of the
aberrations in both stages. Our key discovery is that the displaced and
defocused images provide additional information which allows us to break the
ambiguity between the two stages. We present simulations that validate the
performance of the method.


title:
Smart sensors using artificial intelligence for on-detector electronics
  and ASICs

abstract:
  Cutting edge detectors push sensing technology by further improving spatial
and temporal resolution, increasing detector area and volume, and generally
reducing backgrounds and noise. This has led to a explosion of more and more
data being generated in next-generation experiments. Therefore, the need for
near-sensor, at the data source, processing with more powerful algorithms is
becoming increasingly important to more efficiently capture the right
experimental data, reduce downstream system complexity, and enable faster and
lower-power feedback loops. In this paper, we discuss the motivations and
potential applications for on-detector AI. Furthermore, the unique requirements
of particle physics can uniquely drive the development of novel AI hardware and
design tools. We describe existing modern work for particle physics in this
area. Finally, we outline a number of areas of opportunity where we can advance
machine learning techniques, codesign workflows, and future microelectronics
technologies which will accelerate design, performance, and implementations for
next generation experiments.


title:
Top-k Community Similarity Search Over Large-Scale Road Networks
  (Technical Report)

abstract:
  With the urbanization and development of infrastructure, the community search
over road networks has become increasingly important in many real applications
such as urban/city planning, social study on local communities, and community
recommendations by real estate agencies. In this paper, we propose a novel
problem, namely top-k community similarity search (Top-kCS2) over road
networks, which efficiently and effectively obtains k spatial communities that
are the most similar to a given query community in road-network graphs. In
order to efficiently and effectively tackle the Top-kCS2 problem, in this
paper, we will design an effective similarity measure between spatial
communities, and propose a framework for retrieving Top-kCS2 query answers,
which integrates offline pre-processing and online computation phases.
Moreover, we also consider a variant, namely continuous top-k community
similarity search (CTop-kCS2), where the query community continuously moves
along a query line segment. We develop an efficient algorithm to split query
line segments into intervals, incrementally obtain similar candidate
communities for each interval and define actual CTop-kCS2 query answers.
Extensive experiments have been conducted on real and synthetic data sets to
confirm the efficiency and effectiveness of our proposed Top-kCS2 and CTop-kCS2
approaches under various parameter setting


title: Categorical aspects of the Koll\'ar--Shepherd-Barron correspondence

abstract:
  Any smoothing of a $2$-dimensional cyclic quotient singularity (c.q.s.) is
also a $\mathbb Q$-Gorenstein smoothing of one of its M-resolutions (partial
resolutions with Wahl singularities and nef canonical divisor). We categorify
the Milnor fiber of the smoothing to a hereditary algebra $\hat R$, which can
be viewed as a smoothing of the Kalck-Karmazyn algebra of the c.q.s. Kawamata
considered the case when the smoothing of the c.q.s. is itself $\mathbb
Q$-Gorenstein, in which case $\hat R$ is semi-simple. In general, $\hat R$ is
the endomorphism algebra of a strong exceptional collection of Hacking vector
bundles associated to the N-resolution of the c.q.s. introduced in this paper.
The N-resolution, which has a nef anticanonical divisor, is connected to the
M-resolution via a geometric action of the braid group on all Wahl resolutions
constructed using the universal family of k1A and k2A extremal neighborhoods.
One of the consequences of the existence of this braid group action is that the
categorified Milnor fiber is a hereditary algebra, and in particular is rigid
in families. We give applications to the derived categories of Dolgachev
surfaces.


title: Offline Visual Representation Learning for Embodied Navigation

abstract:
  How should we learn visual representations for embodied agents that must see
and move? The status quo is tabula rasa in vivo, i.e. learning visual
representations from scratch while also learning to move, potentially augmented
with auxiliary tasks (e.g. predicting the action taken between two successive
observations). In this paper, we show that an alternative 2-stage strategy is
far more effective: (1) offline pretraining of visual representations with
self-supervised learning (SSL) using large-scale pre-rendered images of indoor
environments (Omnidata), and (2) online finetuning of visuomotor
representations on specific tasks with image augmentations under long learning
schedules. We call this method Offline Visual Representation Learning (OVRL).
We conduct large-scale experiments - on 3 different 3D datasets (Gibson, HM3D,
MP3D), 2 tasks (ImageNav, ObjectNav), and 2 policy learning algorithms (RL, IL)
- and find that the OVRL representations lead to significant across-the-board
improvements in state of art, on ImageNav from 29.2% to 54.2% (+25% absolute,
86% relative) and on ObjectNav from 18.1% to 23.2% (+5.1% absolute, 28%
relative). Importantly, both results were achieved by the same visual encoder
generalizing to datasets that were not seen during pretraining. While the
benefits of pretraining sometimes diminish (or entirely disappear) with long
finetuning schedules, we find that OVRL's performance gains continue to
increase (not decrease) as the agent is trained for 2 billion frames of
experience.


title: The essential l-dimension of finite groups of Lie type, l $\neq$ 2

abstract:
  In this paper, we compute the essential l-dimension of the finite groups of
classical Lie type for l not equal to the defining prime, l $\neq$ 2,
specifically the general linear and special linear groups, the symplectic
groups, the orthogonal groups, and the unitary groups, and the simple factors
in their Jordan-H\"older series.


title: Qudit lattice surgery

abstract:
  We observe that lattice surgery, a model of fault-tolerant qubit computation,
generalises straightforwardly to arbitrary finite-dimensional qudits. The
generalised model is based on the group algebras $\mathbb{C}\mathbb{Z}_d$ for
$d \geq 2$. It still requires magic state injection for universal quantum
computation. We relate the model to the ZX-calculus, a diagrammatic language
based on Hopf-Frobenius algebras.


title:
The Bimodal Absorption System Imaging Campaign (BASIC) I. A Dual
  Population of Low-metallicity Absorbers at z $<1$

abstract:
  The bimodal absorption system imaging campaign (BASIC) aims to characterize
the galaxy environments of a sample of 36 HI-selected partial Lyman limit
systems (pLLSs) and Lyman limit systems (LLSs) in 23 QSO fields at $z \lesssim
1$. These pLLSs/LLSs provide a unique sample of absorbers with unbiased and
well-constrained metallicities, allowing us to explore the origins of
metal-rich and low-metallicity circumgalactic medium (CGM) at $z<1$. Here we
present Keck/KCWI and VLT/MUSE observations of 11 of these QSO fields (19
pLLSs) that we combine with HST/ACS imaging to identify and characterize the
absorber-associated galaxies. We find 23 unique absorber-associated galaxies,
with an average of one associated galaxy per absorber. For seven absorbers, all
with $<10\%$ solar metallicities, we find no associated galaxies with $\log
M_\star \gtrsim 9.0$ within $\rho/R_{vir}$ and $|\Delta v|/v_{esc} \le$ 1.5
with respect to the absorber. We do not find any strong correlations between
the metallicities or HI column densities of the gas and most of the galaxy
properties, except for the stellar mass of the galaxies: the low-metallicity
([X/H] $\le -1.4$) systems have a probability of $0.39^{+0.16}_{-0.15}$ for
having a host galaxy with $\log M_\star \ge 9.0$ within $\rho/R_{vir} \le 1.5$,
while the higher metallicity absorbers have a probability of
$0.78^{+0.10}_{-0.13}$. This implies metal-enriched pLLSs/LLSs at $z<1$ are
typically associated with the CGM of galaxies with $\log M_\star > 9.0$,
whereas low-metallicity pLLSs/LLSs are found in more diverse locations, with
one population arising in the CGM of galaxies and another more broadly
distributed in overdense regions of the universe. Using absorbers not
associated with galaxies, we estimate the unweighted geometric mean metallicity
of the intergalactic medium to be [X/H] $\lesssim -2.1$ at $z<1$, which is
lower than previously estimated.


title:
Connecting Arrow's Theorem, voting theory, and the traveling salesperson
  problem

abstract:
  Problems with majority voting over pairs as represented by Arrow's Theoremand
those of finding the lengths of closed paths as captured by the Traveling
Salesperson Problem (TSP) appear to have nothing in common. In fact, they are
connected. As shown, pairwise voting and a version of the TSP share the same
domain where each system can be simplified by restricting it to complementary
regions to eliminate extraneous terms. Central for doing so is the Borda Count,
where it is shown that its outcome most accurately refects the voter
preferences.


title: Asymptotic Inference for Infinitely Imbalanced Logistic Regression

abstract:
  In this paper we extend the work of Owen (2007) by deriving a second order
expansion for the slope parameter in logistic regression, when the size of the
majority class is unbounded and the minority class is finite. More precisely,
we demonstrate that the second order term converges to a normal distribution
and explicitly compute its variance, which surprisingly once again depends only
on the mean of the minority class points and not their arrangement under mild
regularity assumptions. In the case that the majority class is normally
distributed, we illustrate that the variance of the the limiting slope depends
exponentially on the z-score of the average of the minority class's points with
respect to the majority class's distribution. We confirm our results by Monte
Carlo simulations.


title: Adversarial Fine-tune with Dynamically Regulated Adversary

abstract:
  Adversarial training is an effective method to boost model robustness to
malicious, adversarial attacks. However, such improvement in model robustness
often leads to a significant sacrifice of standard performance on clean images.
In many real-world applications such as health diagnosis and autonomous
surgical robotics, the standard performance is more valued over model
robustness against such extremely malicious attacks. This leads to the
question: To what extent we can boost model robustness without sacrificing
standard performance? This work tackles this problem and proposes a simple yet
effective transfer learning-based adversarial training strategy that
disentangles the negative effects of adversarial samples on model's standard
performance. In addition, we introduce a training-friendly adversarial attack
algorithm, which facilitates the boost of adversarial robustness without
introducing significant training complexity. Extensive experimentation
indicates that the proposed method outperforms previous adversarial training
algorithms towards the target: to improve model robustness while preserving
model's standard performance on clean data.


title: Searching and Sorting Algorithms for Quantum Annealing Computers

abstract:
  Algorithms for searching and sorting data sets on quantum annealing systems
are presented. Search algorithms for unordered data sets are developed. A
sorting algorithm for data sets is provided, with a consideration of sort
stability. Scalability of the algorithms, considering both the number of qubits
required and the qubit connectivity, is characterized as a function of problem
size.


title: Hydrodynamics of $N$-urn susceptible-infected-removed epidemics

abstract:
  In this paper we are concerned with $N$-urn susceptible-infected-removed
epidemics, where each urn is in one of three states, namely `susceptible',
`infected' and `removed'. We assume that recovery rates of infected urns and
infection rates between infected and susceptible urns are all
coordinate-dependent. We show that the hydrodynamic limit of our model is
driven by a deterministic $(C[0, 1])^\prime$-valued process with density which
is the solution to a nonlinear $C[0, 1]$-valued ordinary differential equation
consistent with a mean-field analysis. We further show that the fluctuation of
our process is driven by a generalized Ornstein-Uhlenbeck process. A key step
in proofs of above main results is to show that sates of different urns are
approximately independent as $N\rightarrow+\infty$.


title: Metamaterial Thermoelectric Conversion

abstract:
  We propose a thermoelectric device that can produce a thermal gradient even
in a uniform-temperature environment. We introduced a metamaterial absorber
(MA), which comprised a transparent calcium fluoride layer sandwiched between a
silver mirror layer and silver microdisk arrays, at the one-end of a
thermoelectric device made of bismuth antimony telluride. The heating
efficiencies of the MA and opposite electrodes of the device were unbalanced,
which induced the Seebeck effect and resulted in electricity generation. We
fabricated the MA on a copper electrode loaded on a thermoelectric device and
the device was placed it in a uniform temperature environment at 364 K. The
0.14 K thermal gradient across the device and the electric power density of
0.04 microW/cm2 were experimentally measured.


title: Thermal Energy Transport in Oxide Nuclear Fuel

abstract:
  To efficiently capture the energy of the nuclear bond, advanced nuclear
reactor concepts seek solid fuels that must withstand unprecedented temperature
and radiation extremes. In these advanced fuels, thermal energy transport under
irradiation is directly related to reactor performance as well as reactor
safety. The science of thermal transport in nuclear fuel is a grand challenge
due to both computational and experimental complexities. Here, we provide a
comprehensive review of thermal transport research on two actinide oxides: one
currently in use in commercial nuclear reactors, uranium dioxide (UO2), and one
advanced fuel candidate material, thorium dioxide (ThO2). In both materials,
heat is carried by lattice waves or phonons. Crystalline defects caused by
fission events effectively scatter phonons and lead to a degradation in fuel
performance over time. Bolstered by new computational and experimental tools,
researchers are now developing the foundational work necessary to accurately
model and ultimately control thermal transport in advanced nuclear fuel. We
begin by reviewing research aimed at understanding thermal transport in perfect
single crystals. The absence of defects enables studies that focus on the
fundamental aspects of phonon transport. Next, we review research that targets
defect generation and evolution. Here, the focus is on ion irradiation studies
used as surrogates for damage caused by fission products. We end this review
with a discussion of modeling and experimental efforts directed at predicting
and validating mesoscale thermal transport in the presence of irradiation
defects. While efforts into these research areas have been robust, challenging
work remains in developing holistic tools to capture and predict thermal energy
transport across widely varying environmental conditions.


title: Spatio-Temporal Graph Localization Networks for Image-based Navigation

abstract:
  Localization in topological maps is essential for image-based navigation
using an RGB camera. Localization using only one camera can be challenging in
medium-to-large-sized environments because similar-looking images are often
observed repeatedly, especially in indoor environments. To overcome this issue,
we propose a learning-based localization method that simultaneously utilizes
the spatial consistency from topological maps and the temporal consistency from
time-series images captured by the robot. Our method combines a convolutional
neural network (CNN) to embed image features and a recurrent-type graph neural
network to perform accurate localization. When training our model, it is
difficult to obtain the ground truth pose of the robot when capturing images in
real-world environments. Hence, we propose a sim2real transfer approach with
semi-supervised learning that leverages simulator images with the ground truth
pose in addition to real images. We evaluated our method quantitatively and
qualitatively and compared it with several state-of-the-art baselines. The
proposed method outperformed the baselines in environments where the map
contained similar images. Moreover, we evaluated an image-based navigation
system incorporating our localization method and confirmed that navigation
accuracy significantly improved in the simulator and real environments when
compared with the other baseline methods.


title: A Near-Infrared look at AGN Feedback in Bulgeless Galaxies

abstract:
  While it is generally believed that supermassive black holes (SMBH) lie in
most galaxies with bulges, few SMBHs have been confirmed in bulgeless galaxies.
Identifying such a population could provide important insights to the BH seed
population and secular BH growth. To this end, we obtained near-infrared
spectroscopic observations of a sample of low-redshift bulgeless galaxies with
mid-infrared colors suggestive of AGN. We find additional evidence of AGN
activity (such as coronal lines and broad permitted lines) in 69$\%$ (9/13) of
the sample, demonstrating that mid-infrared selection is a powerful tool to
detect AGN. More than half of the galaxies with confirmed AGN activity show
fast outflows in [O III] in the optical and/or [Si VI] in the NIR, with the
latter generally having much faster velocities that are also correlated to
their spatial extent. We are also able to obtain virial BH masses for some
targets and find they fall within the scatter of other late-type galaxies in
the $M_{\rm{BH}}$-$M_{\rm{stellar}}$ relation. The fact that they lack a
significant bulge component indicates that secular processes, likely
independent of major mergers, grew these BHs to supermassive sizes. Finally, we
analyze the rotational gas kinematics and find two notable exceptions: two AGN
hosts with outflows that appear to be rotating faster than expected. There is
an indication that these two galaxies have stellar masses significantly lower
than expected from their dark matter halo masses. This, combined with the
observed AGN activity and strong gas outflows may be evidence of the effects of
AGN feedback.


title:
Mathematical formulae for neutron self-shielding properties of media in
  an isotropic neutron field

abstract:
  The complexity of the neutron transport phenomenon throws its shadows on
every physical system wherever neutron is produced or used. In the current
study, an \emph{ab initio} derivation of the neutron self-shielding factor, the
problem of the decrease of the neutron flux as it penetrates into a material
placed in an isotropic neutron field, has been attempted by employing the
theory of steady-state neutron transport, starting from Stuart's formula.
Simple formulae were derived based on the integral cross-section parameters,
which can be adopted by the user according to various variables, such as the
neutron flux distribution and geometry of the simulation at hand. The concluded
formulae of the self-shielding factors comprise an inverted sigmoid function
normalized a weight representing the ratio between the total and scattering
macroscopic cross-sections of the medium. The general convex volume geometries
are reduced to a set of chord lengths, while the neutron interactions
probabilities within the volume are parameterized to the epithermal and thermal
neutron energies. The arguments of the inverted-sigmoid function were derived
from a simplified version of neutron transport formulation. Accordingly, the
obtained general formulae were successful in giving the experimental values of
the neutron self-shielding factor for different elements and different
geometries. The results were validated by comparing them to the experimental
data as well as a variety of data extracted from the literature


title: FAIR data enabling new horizons for materials research

abstract:
  The prosperity and lifestyle of our society are very much governed by
achievements in condensed matter physics, chemistry and materials science,
because new products for sectors such as energy, the environment, health,
mobility and information technology (IT) rely largely on improved or even new
materials. Examples include solid-state lighting, touchscreens, batteries,
implants, drug delivery and many more. The enormous amount of research data
produced every day in these fields represents a gold mine of the twenty-first
century. This gold mine is, however, of little value if these data are not
comprehensively characterized and made available. How can we refine this
feedstock; that is, turn data into knowledge and value? For this, a FAIR
(findable, accessible, interoperable and reusable) data infrastructure is a
must. Only then can data be readily shared and explored using data analytics
and artificial intelligence (AI) methods. Making data 'findable and AI ready'
(a forward-looking interpretation of the acronym) will change the way in which
science is carried out today. In this Perspective, we discuss how we can
prepare to make this happen for the field of materials science.


title:
Computational Approaches to Model X-ray Photon Correlation Spectroscopy
  from Molecular Dynamics

abstract:
  X-ray photon correlation spectroscopy (XPCS) allows for the resolution of
dynamic processes within a material across a wide range of length and time
scales. X-ray speckle visibility spectroscopy (XSVS) is a related method that
uses a single diffraction pattern to probe ultrafast dynamics. Interpretation
of the XPCS and XSVS data in terms of underlying physical processes is
necessary to establish the connection between the macroscopic responses and the
microstructural dynamics. To aid the interpretation of the XPCS and XSVS data,
we present a computational framework to model these experiments by computing
the X-ray scattering intensity directly from the atomic positions obtained from
molecular dynamics (MD) simulations. We compare the efficiency and accuracy of
two alternative computational methods: the direct method computing the
intensity at each diffraction vector separately, and a method based on fast
Fourier transform that computes the intensities at all diffraction vectors at
once. The computed X-ray speckle patterns capture the density fluctuations over
a range of length and time scales and are shown to reproduce the known
properties and relations of experimental XPCS and XSVS for liquids.


title:
Ultrafast Charge Migration Dynamics in Enol Keto Tautomerization
  Monitored with a Local Soft-X-Ray Probe

abstract:
  Proton-coupled electron transfer (PCET) is the underlying mechanism governing
important reactions ranging from water splitting in photosynthesis to oxygen
reduction in hydrogen fuel cells. The interplay of proton and electronic charge
distribution motions can vary from sequential to concerted schemes, with
elementary steps occurring on ultrafast time scales. We demonstrate with a
simulation study that femtosecond soft-X-ray spectroscopy provides key insight
into the PCET mechanism of a photoinduced intramolecular enol* $\rightarrow$
keto* tautomerization reaction. A full quantum treatment of electronic and
nuclear dynamics of 2-(2-hydroxyphenyl-)benzothiazole upon electronic
excitation reveals how spectral signatures of local excitations from core to
frontier orbitals display the distinct stages of charge migration for the H
atom, donating, and accepting sites. Our findings indicate UV/X-ray pump-probe
spectroscopy provides a unique way to probe ultrafast electronic structure
rearrangements in photoinduced chemical reactions essential to understanding
the mechanism of PCET.


title:
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on
  Tabular and Textual Data

abstract:
  A pressing challenge in current dialogue systems is to successfully converse
with users on topics with information distributed across different modalities.
Previous work in multiturn dialogue systems has primarily focused on either
text or table information. In more realistic scenarios, having a joint
understanding of both is critical as knowledge is typically distributed over
both unstructured and structured forms. We present a new dialogue dataset,
HybriDialogue, which consists of crowdsourced natural conversations grounded on
both Wikipedia text and tables. The conversations are created through the
decomposition of complex multihop questions into simple, realistic multiturn
dialogue interactions. We propose retrieval, system state tracking, and
dialogue response generation tasks for our dataset and conduct baseline
experiments for each. Our results show that there is still ample opportunity
for improvement, demonstrating the importance of building stronger dialogue
systems that can reason over the complex setting of information-seeking
dialogue grounded on tables and text.


title: Microlocal Theory of Legendrian Links and Cluster Algebras

abstract:
  We show the existence of quasi-cluster $\mathcal{A}$-structures and cluster
Poisson structures on moduli stacks of sheaves with singular support in the
alternating strand diagram of grid plabic graphs by studying the microlocal
parallel transport of sheaf quantizations of Lagrangian fillings of Legendrian
links. The construction is in terms of contact and symplectic topology, showing
that there exists an initial seed associated to a canonical relative Lagrangian
skeleton. In particular, mutable cluster $\mathcal{A}$-variables are
intrinsically characterized via the symplectic topology of Lagrangian fillings
in terms of dually $\mathbb{L}$-compressible cycles. New ingredients are
introduced throughout this work, including the initial weave associated to a
grid plabic graph, cluster mutation along a non-square face of a plabic graph,
the concept of the sugar-free hull, and the notion of microlocal merodromy.


title: Positron Sources for Future High Energy Physics Colliders

abstract:
  An unprecedented positron average current is required to fit the luminosity
demands of future $e^+e^-$ high energy physics colliders. In addition, in order
to access precision-frontier physics, these machines require positron
polarization to enable exploring the polarization dependence in many HEP
processes cross sections, reducing backgrounds and extending the reach of
chiral physics studies beyond the standard model. The ILC has a mature plan for
the polarized positron source based on conversion in a thin target of
circularly polarized gammas generated by passing the main high energy e-beam in
a long superconducting helical undulator. Compact colliders (CLIC, C3 and
advanced accelerator-based concepts) adopt a simplified approach and currently
do not plan to use polarized positrons in their baseline design, but could
greatly benefit from the development of compact alternative solutions to
polarized positron production. Increasing the positron current, the
polarization purity and simplifying the engineering design are all
opportunities where advances in accelerator technology have the potential to
make a significant impact. This white-paper describes the current status of the
field and provides R\&D short-term and long-term pathways for polarized
positron sources.


title:
Stimulated perturbation on the neutron flux distribution in the
  mutually-dependent source-to-absorber geometry

abstract:
  The complexity of the neutron transport phenomenon throws its shadows on
every physical system wherever neutron is produced or absorbed. The Monte Carlo
N-Particle Transport Code (MCNP) was used to investigate the flux perturbations
in the neutron field caused by an absorber. The geometry of the present
experiment was designed to reach a simulation of an isotopic neutron field. The
neutron source was a ${}^{241}$AmBe with the production physics of neutrons is
dependent only on alpha-beryllium interaction and is independent of what
happened to the neutron after it was generated. The geometries have been
designed to get a volume of uniform neutron densities within a spherical volume
of radius 15 cm in every neutron energy group up to 10 MeV. Absorbers of
different dimensions were placed within the volume to investigate the field
perturbation. Different neutron absorbers were used to correlate the phenomenon
to the integral cross-section of the absorber. Flux density inside and outside
the absorber samples was determined, while the spatial neutron flux
distribution produced by the AmBe source without an absorber was taken as a
reference. This study displayed that absorbers of various dimensions perturb
the neutron field in a way that is dependent on the absorption and scattering
cross-sections, particularly in the neutron resonance region. Unlike the simple
picture of reducing the number density of neutrons, the perturbation was found
to influence the moderation of neutrons in the medium, significantly above 1
MeV.


title: BI-GreenNet: Learning Green's functions by boundary integral network

abstract:
  Green's function plays a significant role in both theoretical analysis and
numerical computing of partial differential equations (PDEs). However, in most
cases, Green's function is difficult to compute. The troubles arise in the
following three folds. Firstly, compared with the original PDE, the dimension
of Green's function is doubled, making it impossible to be handled by
traditional mesh-based methods. Secondly, Green's function usually contains
singularities which increase the difficulty to get a good approximation.
Lastly, the computational domain may be very complex or even unbounded. To
override these problems, we leverage the fundamental solution, boundary
integral method and neural networks to develop a new method for computing
Green's function with high accuracy in this paper. We focus on Green's function
of Poisson and Helmholtz equations in bounded domains, unbounded domains. We
also consider Poisson equation and Helmholtz domains with interfaces. Extensive
numerical experiments illustrate the efficiency and the accuracy of our method
for solving Green's function. In addition, we also use the Green's function
calculated by our method to solve a class of PDE, and also obtain
high-precision solutions, which shows the good generalization ability of our
method on solving PDEs.


title: Controlling the False Discovery Rate via knockoffs: is the +1 needed?

abstract:
  Barber and Cand\`es (2015) control of the FDR in feature selection relies on
estimating the FDR by the number of knockoff wins +1 divided by the number of
original wins. We show that the +1 is necessary in typical scenarios.


title:
Sub-percent Precision Measurement of Neutrino Oscillation Parameters
  with JUNO

abstract:
  JUNO is a multi-purpose neutrino observatory under construction in the south
of China. This publication presents new sensitivity estimates for the
measurement of the $\Delta m^2_{31}$, $\Delta m^2_{21}$, $\sin^2 \theta_{12}$,
and $\sin^2 \theta_{13}$ oscillation parameters using reactor antineutrinos,
which is one of the primary physics goals of the experiment. The sensitivities
are obtained using the best knowledge available to date on the location and
overburden of the experimental site, the nuclear reactors in the surrounding
area and beyond, the detector response uncertainties, and the reactor
antineutrino spectral shape constraints expected from the TAO satellite
detector. It is found that the $\Delta m^2_{31}$, $\Delta m^2_{21}$, and
$\sin^2 \theta_{12}$ oscillation parameters will be determined to better than
0.5% precision in six years of data collection, which represents approximately
an order of magnitude improvement over existing constraints.


title: Watts: Infrastructure for Open-Ended Learning

abstract:
  This paper proposes a framework called Watts for implementing, comparing, and
recombining open-ended learning (OEL) algorithms. Motivated by modularity and
algorithmic flexibility, Watts atomizes the components of OEL systems to
promote the study of and direct comparisons between approaches. Examining
implementations of three OEL algorithms, the paper introduces the modules of
the framework. The hope is for Watts to enable benchmarking and to explore new
types of OEL algorithms. The repo is available at
\url{https://github.com/aadharna/watts}


title:
Simultaneous Control and Trajectory Estimation for Collision Avoidance
  of Autonomous Robotic Spacecraft Systems

abstract:
  We propose factor graph optimization for simultaneous planning, control, and
trajectory estimation for collision-free navigation of autonomous systems in
environments with moving objects. The proposed online probabilistic motion
planning and trajectory estimation navigation technique generates optimal
collision-free state and control trajectories for autonomous vehicles when the
obstacle motion model is both unknown and known. We evaluate the utility of the
algorithm to support future autonomous robotic space missions.


title:
DMD-based generation of vector beams through a common-path
  interferometer

abstract:
  Complex vector modes of light, non-separable in their spatial and
polarisation degrees of freedom, are revolutionising a wide variety of research
fields. It is therefore not surprising that the generation techniques have
evolved quite dramatically since their inception. At present it is common to
use computer-controlled devices, among which digital micromirror devices have
become popular. Some of the reason for this are their low-cost, their
polarisation-insensitive and their high-refresh rates. As such, in this
manuscript we put forward a novel technique characterised by its high
stability, which is achieved through a common-path interferometer. We
demonstrate the capabilities of this technique experimentally, first by
generating arbitrary vector modes on a higher-order Poincar\'e sphere,
secondly, by generating vector modes in different coordinates systems and
finally, by generating various vector modes simultaneously. Our technique will
find applications in fields such as optical manipulations, optical
communications, optical metrology, among others.


title:
Temporal Analysis of Transaction Ego Networks with Different Labels on
  Ethereum

abstract:
  Due to the widespread use of smart contracts, Ethereum has become the
second-largest blockchain platform after Bitcoin. Many different types of
Ethereum accounts (ICO, Mining, Gambling, etc.) also have quite active trading
activities on Ethereum. Studying the transaction records of these specific
Ethereum accounts is very important for understanding their particular
transaction characteristics, and further labeling the pseudonymous accounts.
However, traditional methods are generally based on static and global
transaction networks to conduct research, ignoring useful information about
dynamic changes. Our work chooses six kinds of important account labels, and
builds ego networks for each kind of Ethereum account. We focus on the
interaction between the target node and neighbor nodes with temporal analysis.
Experiments show that there is a significant difference between various types
of accounts in terms of several network features, helping us better understand
their transaction patterns. To the best of our knowledge, this is the first
work to analyze the dynamic characteristics of Ethereum labeled accounts from
the perspective of transaction ego networks.


title:
Domain Decomposition Framework for Maxwell Finite Element Solvers and
  Application to PIC

abstract:
  The most popular methods for self-consistent simulation of fields interacting
with charged species is using finite difference time domain (FDTD) methods
together with Newton's laws of motion to evolve locations and velocities of
particles. Despite their popularity, the limitation of FDTD particle in cell
(EM-FDTDPIC) methods are well known. To address these, there has been
significant interest over the past decade in exploring alternatives. In the
past few years, the advances in electromagnetic finite element methods for
particle in cell (EM-FEMPIC) has advanced by leaps and bounds. The mathematics
necessary for implicit FEM methods that are unconditionally stable and charge
conserving are now well understood. Some of these advances are more recent. The
next bottleneck necessary to make EM-FEMPIC competitive with FDTD based scheme
is overcoming computational cost. Our approach to resolving this challenge is
develop two different finite element tearing and integration approaches, and
using these to create domain decomposition schemes for EM-FEMPIC. Details of
the proposed methodology are presented as well as a number of results that
demonstrates charge conservation as well as amelioration of costs for a number
of problems.


title:
Elliptic equations in Sobolev spaces with Morrey drift and the
  zeroth-order coefficients

abstract:
  We consider elliptic equations with operators $L=a^{ij}D_{ij}+b^{i}D_{i}-c$
with $a$ being almost in VMO, $b$ in a Morrey class containing $ L_{d}$, and
$c\geq0$ in a Morrey class containing $L_{d/2}$. We prove the solvability in
Sobolev spaces of $Lu=f\in L_{p}$ in bounded $C^{1,1}$-domains, and of $\lambda
u-Lu=f$ in the whole space for any $\lambda>0$. Weak uniqueness of the
martingale problem associated with such operators is also discussed.


title:
Shielding Federated Learning: Robust Aggregation with Adaptive Client
  Selection

abstract:
  Federated learning (FL) enables multiple clients to collaboratively train an
accurate global model while protecting clients' data privacy. However, FL is
susceptible to Byzantine attacks from malicious participants. Although the
problem has gained significant attention, existing defenses have several flaws:
the server irrationally chooses malicious clients for aggregation even after
they have been detected in previous rounds; the defenses perform ineffectively
against sybil attacks or in the heterogeneous data setting.
  To overcome these issues, we propose MAB-RFL, a new method for robust
aggregation in FL. By modelling the client selection as an extended multi-armed
bandit (MAB) problem, we propose an adaptive client selection strategy to
choose honest clients that are more likely to contribute high-quality updates.
We then propose two approaches to identify malicious updates from sybil and
non-sybil attacks, based on which rewards for each client selection decision
can be accurately evaluated to discourage malicious behaviors. MAB-RFL achieves
a satisfying balance between exploration and exploitation on the potential
benign clients. Extensive experimental results show that MAB-RFL outperforms
existing defenses in three attack scenarios under different percentages of
attackers.


title:
Joint User Association and Beamforming in Integrated
  Satellite-HAPS-Ground Networks

abstract:
  This paper proposes, and evaluates the benefit of, one particular hybrid
satellite-HAPS-ground network, where one high-altitude-platform-station (HAPS)
connected to one geo-satellite assists the ground base-stations (BSs) at
serving ground-level users. The paper assumes that the geo-satellite is
connected to the HAPS using free-space-optical backhaul link. The HAPS,
equipped with multiple antennas, aims at transmitting the geo-satellite data to
the users via radio-frequency (RF) links using spatial-multiplexing. Each
ground BS, on the other hand, is equipped with multiple antennas and serves
users through RF links. The paper then focuses on maximizing the network-wide
throughput, subject to user-connectivity constraints, HAPS and BSs power
constraints, and backhaul constraints, so as to jointly determine the
user-association strategy of each user (i.e., user to geo-satellite via HAPS,
or user to BS), and their associated beamforming vectors. We tackle such a
mixed discrete-continuous optimization problem using an iterative approach,
where the user-association is determined using a combination of linear integer
programming and generalized assignment problems, and where the beamforming
strategy is found using a weighted-minimum-mean-squared-error approach. The
simulations illustrate the appreciable gain of our proposed algorithm, and
highlight the prospects of augmenting the ground networks with
beamforming-empowered HAPS for connecting the unconnected, and super-connecting
the connected.


title: Cross-modal Memory Networks for Radiology Report Generation

abstract:
  Medical imaging plays a significant role in clinical practice of medical
diagnosis, where the text reports of the images are essential in understanding
them and facilitating later treatments. By generating the reports
automatically, it is beneficial to help lighten the burden of radiologists and
significantly promote clinical automation, which already attracts much
attention in applying artificial intelligence to medical domain. Previous
studies mainly follow the encoder-decoder paradigm and focus on the aspect of
text generation, with few studies considering the importance of cross-modal
mappings and explicitly exploit such mappings to facilitate radiology report
generation. In this paper, we propose a cross-modal memory networks (CMN) to
enhance the encoder-decoder framework for radiology report generation, where a
shared memory is designed to record the alignment between images and texts so
as to facilitate the interaction and generation across modalities. Experimental
results illustrate the effectiveness of our proposed model, where
state-of-the-art performance is achieved on two widely used benchmark datasets,
i.e., IU X-Ray and MIMIC-CXR. Further analyses also prove that our model is
able to better align information from radiology images and texts so as to help
generating more accurate reports in terms of clinical indicators.


title:
Detection of a broad 8 $\mu$m UIR feature in the mid-infrared spectrum
  of WR 125 observed with Subaru/COMICS

abstract:
  We present the detection of a broad 8 $\mu$m feature in newly formed dust
around the carbon-rich Wolf-Rayet (WC) binary WR 125 from N-band low-resolution
(NL; R$\sim$250) spectroscopy between 7.3-13.6 $\mu$m and N-band (11.7 $\mu$m)
and Q-band (18.8 $\mu$m) imaging with Subaru/COMICS in 2019 October. WR 125 is
a colliding wind binary (${\rm WC7+O9}$) that exhibited renewed dust formation
starting in 2018, $\sim$28 years after its first dust formation episode had
been observed. We also compare our infrared photometry with historical
observations and revise the dust-formation period of WR 125 to 28.1 years.
Archival infrared spectra of five dusty WC stars, WR 48a, WR 98a, WR 104, WR
112 and WR 118, obtained with ISO/SWS are reanalyzed and compared with the WR
125 spectrum to search for a similar feature. We analyze the dusty WC spectra
using two different extinction curves to investigate the impact of interstellar
extinction correction on the presence and/or properties of the 8 $\mu$m
feature. All of the dusty WC spectra dereddened with the two different
extinction curves show a broad feature around 8 $\mu$m (FWHM$\sim$1-2 $\mu$m).
We suggest that these 8 $\mu$m features seen in the dusty WC spectra are
related to the Class C unidentified infrared (UIR) features.


title:
Pattern recognition with neuromorphic computing using magnetic-field
  induced dynamics of skyrmions

abstract:
  Nonlinear phenomena in physical systems can be used for brain-inspired
computing with low energy consumption. Response from the dynamics of a
topological spin structure called skyrmion is one of the candidates for such a
neuromorphic computing. However, its ability has not been well explored
experimentally. Here, we experimentally demonstrate neuromorphic computing
using nonlinear response originating from magnetic-field induced dynamics of
skyrmions. We designed a simple-structured skyrmion-based neuromorphic device
and succeeded in handwritten digit recognition with the accuracy as large as
94.7 % and waveform recognition. Notably, there exists a positive correlation
between the recognition accuracy and the number of skyrmions in the devices.
The large degree of freedoms of skyrmion systems, such as the position and the
size, originate the more complex nonlinear mapping and the larger output
dimension, and thus high accuracy. Our results provide a guideline for
developing energy-saving and high-performance skyrmion neuromorphic computing
devices.


title:
Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass
  Sequences

abstract:
  Genetic improvement is a search technique that aims to improve a given
acceptable solution to a problem. In this paper, we present the novel use of
genetic improvement to find problem-specific optimized LLVM pass sequences. We
develop a pass-level patch representation in the linear genetic programming
framework, Shackleton, to evolve the modifications to be applied to the default
optimization pass sequences. Our GI-evolved solution has a mean of 3.7% runtime
improvement compared to the -O3 optimization level in the default code
generation options which optimizes on runtime. The proposed GI method provides
an automatic way to find a problem-specific optimization sequence that improves
upon a general solution without any expert domain knowledge. In this paper, we
discuss the advantages and limitations of the GI feature in the Shackleton
Framework and present our results.


title:
Understanding the Characteristics of Visual Contents in Open Source
  Issue Discussions: A Case Study of Jupyter Notebook

abstract:
  Most issue tracking systems for open source software (OSS) development
include features for community members to embed visual contents, such as images
and videos, to enhance the discussion. Although playing an important role,
there is little knowledge on the characteristics of the visual contents to
support their use. To address this gap, we conducted an empirical study on the
Jupyter Notebook project. We found that more than a quarter of the issues in
the Jupyter Notebook project included visual contents. Additionally, issues
that had visual contents in the comments but not in the issue posts tended to
have a longer discussion and to involve a larger number of discussants. In our
qualitative analysis, we identified eight types of visual contents in our
sample, with about 60% including screenshots or mockups of the main product. We
also found that visual contents served diverse purposes, touching both problem
and solution spaces of the issues. Our effort serves as an important step
towards a comprehensive understanding of the characteristics of visual contents
in OSS issue discussions. Our results provided several design implications for
issue tracking systems to better facilitate the use of visual contents.


title:
Covariance-aware Feature Alignment with Pre-computed Source Statistics
  for Test-time Adaptation

abstract:
  The accuracy of deep neural networks is degraded when the distribution of
features in the test environment (target domain) differs from that of the
training (source) environment. To mitigate the degradation, test-time
adaptation (TTA), where a model adapts to the target domain without access to
the source dataset, can be used in the test environment. However, the existing
TTA methods lack feature distribution alignment between the source and target
domains, which unsupervised domain adaptation mainly addresses, because
accessing the source dataset is prohibited in the TTA setting. In this paper,
we propose a novel TTA method, named Covariance-Aware Feature alignment (CAFe),
which explicitly aligns the source and target feature distributions at test
time. To perform alignment without accessing the source data, CAFe uses
auxiliary feature statistics (mean and covariance) pre-computed on the source
domain, which are lightweight and easily prepared. Further, to improve
efficiency and stability, we propose feature grouping, which splits the feature
dimensions into groups according to their correlations by using spectral
clustering to avoid degeneration of the covariance matrix. We empirically show
that CAFe outperforms prior TTA methods on a variety of distribution shifts.


title:
Evidence for quasi-two-dimensional superconductivity in infinite-layer
  nickelates

abstract:
  After being expected as a promising analogue to cuprates for decades,
superconductivity was recently discovered in infinite-layer nickelates,
providing new opportunities to explore mechanisms of high-temperature
superconductivity. However, in sharp contrast to the single-band
quasi-two-dimensional superconductivity in cuprates, nickelates exhibit a
multi-band electronic structure and an unexpected isotropic superconductivity
as reported recently, which challenges the cuprate-like picture in nickelates.
Here, we show the superconductivity in nickelates is actually anisotropic and
quasi-two-dimensional in nature, as that in cuprates. By synthesizing
high-quality lanthanide nickelate films with enhanced crystallinity and
superconductivity ($T_{c}^{onset}$ = 18.8 K, $T_{c}^{zero}$ = 16.5 K), strong
anisotropic magnetotransport behaviors have been observed. The
quasi-two-dimensional nature is further confirmed by the existence of a
cusp-like peak of the angle-dependent $T_{c}$, and a
Berezinskii-Kosterlitz-Thouless transition near $T_{c}$. Our work thus suggests
a quasi-two-dimensional superconductivity in infinite-layer nickelates,
implying a single-3$d_{x^2-y^2}$-band cuprate-like picture may remain valid in
these compounds.


title: Adaptive Multi-Strategy Market-Making Agent For Volatile Markets

abstract:
  Crypto-currency market uncertainty drives the need to find adaptive solutions
to maximise gain or at least to avoid loss throughout the periods of trading
activity. Given the high dimensionality and complexity of the state-action
space in this domain, it can be treated as a "Narrow AGI" problem with the
scope of goals and environments bound to financial markets. Adaptive
Multi-Strategy Agent approach for market-making introduces a new solution to
maximise positive "alpha" in long-term handling limit order book (LOB)
positions by using multiple sub-agents implementing different strategies with a
dynamic selection of these agents based on changing market conditions. AMSA
provides no specific strategy of its own while being responsible for segmenting
the periods of market-making activity into smaller execution sub-periods,
performing internal backtesting on historical data on each of the sub-periods,
doing sub- agent performance evaluation and re-selection of them at the end of
each sub- period, and collecting returns and losses incrementally. With this
approach, the return becomes a function of hyper-parameters such as market data
granularity (refresh rate), the execution sub-period duration, number of active
sub-agents, and their individual strategies. Sub-agent selection for the next
trading sub-period is made based on return/loss and alpha values obtained
during internal backtesting as well as real trading. Experiments with the AMSA
have been performed under different market conditions relying on historical
data and proved a high probability of positive alpha throughout the periods of
trading activity in the case of properly selected hyper-parameters.