title:
A Search for Spontaneous Radiation from Wavefunction Collapse in the
  Majorana Demonstrator

abstract:
  The \textsc{Majorana Demonstrator} neutrinoless double-beta decay search
experiment comprises a 44 kg (30 kg enriched in $^{76}$Ge) array of p-type,
point-contact germanium detectors. With its unprecedented energy resolution and
ultra-low backgrounds, \textsc{Majorana} also searches for rare event
signatures from beyond Standard Model physics in the low energy region below 50
keV. In this letter, we test the continuous spontaneous localization (CSL)
model, one of the mathematically well-motivated wavefunction collapse models
aimed at solving the long-standing unresolved quantum mechanical measurement
problem. While the CSL predicts the existence of a detectable radiation
signature in the X-ray domain, we find no evidence of such radiation in the
14.5-46 keV range in a 38.35 kg-y enriched germanium exposure collected between
Dec. 31, 2015 and Nov. 27, 2018 with the \textsc{Demonstrator}. We explored
both the mass-proportional (m-p) and the non-mass-proportional (n-m-p) versions
of the CSL with two different assumptions: that only the quasi-free electrons
can emit the X-ray radiation and that the nucleus can coherently emit an
amplified radiation. In all cases, we set the most stringent upper limit to
date on the collapse rate, $\lambda$, in the parameter space below
$r_\mathrm{C}=10^{-6}$ m for the white CSL model, where $r_\mathrm{C}$ is the
correlation length of the collapse noise field. If the result is interpreted in
terms of the Di\`{o}si-Penrose (DP) gravitational wavefunction collapse model,
the lower bound with a 95\% confidence level is more than two orders of
magnitude improvement over the previous best limit.


title: Formal Mathematics Statement Curriculum Learning

abstract:
  We explore the use of expert iteration in the context of language modeling
applied to formal mathematics. We show that at same compute budget, expert
iteration, by which we mean proof search interleaved with learning,
dramatically outperforms proof search only. We also observe that when applied
to a collection of formal statements of sufficiently varied difficulty, expert
iteration is capable of finding and solving a curriculum of increasingly
difficult problems, without the need for associated ground-truth proofs.
Finally, by applying this expert iteration to a manually curated set of problem
statements, we achieve state-of-the-art on the miniF2F benchmark, automatically
solving multiple challenging problems drawn from high school olympiads.


title:
Fluctuation scaling limits for positive recurrent jumping-in diffusions
  with large jumps

abstract:
  For positive recurrent jumping-in diffusions with large jumps, we study
scaling limits of the fluctuations of inverse local times and occupation times.
We generalize the eigenfunctions with modified Neumann boundary condition,
which have been introduced in the previous study for small jumps, to a class of
diffusions of stronger singularity.


title: Spectroscopy on the eEDM-sensitive states of ThF$^+$

abstract:
  An excellent candidate molecule for the measurement of the electron's
electric dipole moment (eEDM) is thorium monofluoride (ThF$^+$) because the
eEDM-sensitive state, $^3\Delta_1$, is the electronic ground state, and thus is
immune to decoherence from spontaneous decay. We perform spectroscopy on
$X\,^3\Delta_1$ to extract three spectroscopic constants crucial to the eEDM
experiment: the hyperfine coupling constant, the molecular frame electric
dipole moment, and the magnetic $g$-factor. To understand the impact of thermal
blackbody radiation on the vibrational ground state, we study the lifetime of
the first excited vibrational manifold of $X\,^3\Delta_1$. We perform ab initio
calculations, compare them to our results, and discuss prospects for using
ThF$^+$ in a new eEDM experiment at JILA.


title:
GMM Clustering for In-depth Food Accessibility Pattern Exploration and
  Prediction Model of Food Demand Behavior

abstract:
  Understanding the dynamics of food banks' demand from food insecurity is
essential in optimizing operational costs and equitable distribution of food,
especially when demand is uncertain. Hence, Gaussian Mixture Model (GMM)
clustering is selected to extract patterns. The novelty is that GMM clustering
is applied to identify the possible causes of food insecurity in a given
region, understanding the characteristics and structure of the food assistance
network in a particular region, and the clustering result is further utilized
to explore the patterns of uncertain food demand behavior and its significant
importance in inventory management and redistribution of surplus food thereby
developing a two-stage hybrid food demand estimation model. Data obtained from
a food bank network in Cleveland, Ohio, is used, and the clusters developed are
studied and visualized. The results reveal that this proposed framework can
make an in-depth identification of food accessibility and assistance patterns
and provides better prediction accuracies of the leveraged statistical and
machine learning algorithms by utilizing the GMM clustering results. Also,
implementing the proposed framework for case studies based on different levels
of planning led to practical results with remarkable ease and comfort intended
for the respective planning team.


title:
VindiCo: Privacy Safeguard Against Adaptation Based Spyware in
  Human-in-the-Loop IoT

abstract:
  Personalized IoT adapts their behavior based on contextual information, such
as user behavior and location. Unfortunately, the fact that personalized IoT
adapts to user context opens a side-channel that leaks private information
about the user. To that end, we start by studying the extent to which a
malicious eavesdropper can monitor the actions taken by an IoT system and
extract users' private information. In particular, we show two concrete
instantiations (in the context of mobile phones and smart homes) of a new
category of spyware which we refer to as Context-Aware Adaptation Based Spyware
(SpyCon). Experimental evaluations show that the developed SpyCon can predict
users' daily behavior with an accuracy of 90.3%. The rest of this paper is
devoted to introducing VindiCo, a software mechanism designed to detect and
mitigate possible SpyCon. Being new spyware with no known prior signature or
behavior, traditional spyware detection that is based on code signature or app
behavior is not adequate to detect SpyCon. Therefore, VindiCo proposes a novel
information-based detection engine along with several mitigation techniques to
restrain the ability of the detected SpyCon to extract private information. By
having general detection and mitigation engines, VindiCo is agnostic to the
inference algorithm used by SpyCon. Our results show that VindiCo reduces the
ability of SpyCon to infer user context from 90.3% to the baseline accuracy
(accuracy based on random guesses) with negligible execution overhead.


title: Twist and Turn Squeezing in a Multi-Mode Bose-Einstein Condensate

abstract:
  Here we examine the generation of Twist and Turn (TNT) Squeezing in a large
atom-number Bose-Einstein Condensate for the purposes of generating
quantum-enhanced states for atom interferometry. Unlike previous analysis, we
examine situations where the multi-mode dynamics is significant, and cannot be
captured by a simple single-mode model. We find that in some regimes, with
careful choice of the rotation parameter, we can still obtain squeezing much
more rapidly than via one-axis twisting (OAT).


title: Cerebral oxygen extraction fraction MRI: techniques and applications

abstract:
  The human brain constitutes 2% of the total body mass, but consumes 20% of
the oxygen. The rate of the brain's oxygen utilization can be determined from
the knowledge of cerebral blood flow and oxygen extraction fraction (OEF).
Therefore, OEF is a key physiological parameter of the brain's function and
metabolism. OEF has been suggested to be a useful biomarker in a number of
brain diseases. With recent advances in MRI techniques, several MRI-based
methods have been developed to measure OEF in the human brain. These MRI OEF
techniques are based on T2 of blood, phase of blood signal, susceptibility of
blood-containing voxel, effect of deoxyhemoglobin on signal behavior in
extravascular tissue, and calibration of BOLD signal using gas-inhalation.
Compared to 15O positron emission tomography, which is considered the "gold
standard" for OEF measurement, MRI-based techniques are non-invasive,
radiation-free, and have broader availabilities. This article provides a review
of these emerging MRI-based OEF techniques. We first briefly introduce the role
of OEF in brain oxygen homeostasis. We then review the methodological aspects
of different categories of MRI OEF techniques, including their signal
mechanisms, acquisition methods, and data analyses. Advantages and limitations
of the techniques are discussed. Finally, we review key applications of these
techniques in physiological and pathological conditions.


title: Technology Ethics in Action: Critical and Interdisciplinary Perspectives

abstract:
  This special issue interrogates the meaning and impacts of "tech ethics": the
embedding of ethics into digital technology research, development, use, and
governance. In response to concerns about the social harms associated with
digital technologies, many individuals and institutions have articulated the
need for a greater emphasis on ethics in digital technology. Yet as more groups
embrace the concept of ethics, critical discourses have emerged questioning
whose ethics are being centered, whether "ethics" is the appropriate frame for
improving technology, and what it means to develop "ethical" technology in
practice. This interdisciplinary issue takes up these questions, interrogating
the relationships among ethics, technology, and society in action. This special
issue engages with the normative and contested notions of ethics itself, how
ethics has been integrated with technology across domains, and potential paths
forward to support more just and egalitarian technology. Rather than starting
from philosophical theories, the authors in this issue orient their articles
around the real-world discourses and impacts of tech ethics--i.e., tech ethics
in action.


title: Asteroids and life: How special is the solar system?

abstract:
  Asteroid impacts with the Earth may have played an essential role in the
emergence of life on Earth through their creation of favorable niches for life,
changes to the atmosphere and delivery of water. Consequently, we suggest two
potential requirements for life in an exoplanetary system: first, that the
system has an asteroid belt, and second, that there is a mechanism to drive
asteroids to impact the terrestrial habitable planet. Since in the solar
system, the $\nu_6$ secular resonance has been shown to have been important in
driving these impacts, we explore how the masses and locations of two giant
planets determine the location and strength of this secular resonance.
Examining observed exoplanetary systems with two giant planets, we find that a
secular resonance within the asteroid belt region may not be uncommon. Hence
the solar system is somewhat special, but the degree of fine-tuning that may be
necessary for the emergence of life is not excessive. Finally, with $n$-body
simulations, we show that when the two giant planets are close to the 2:1 mean
motion resonance, the asteroid belt is unstable but this does not lead to
increased asteroid delivery.


title:
Asymptotic expected $T$-functionals of random polytopes with
  applications to $L_p$ surface areas

abstract:
  Asymptotic formulas are proved for the expected $T$-functional of the convex
hull of independent and identically distributed random points sampled from the
Euclidean unit sphere in $\mathbb{R}^n$ according to an arbitrary positive
continuous density. As an application, the approximation of the sphere by
random polytopes in terms of $L_p$ surface area differences is discussed.


title: Dissecting BFT Consensus: In Trusted Components we Trust!

abstract:
  The growing interest in secure multi-party database applications has led to
the widespread adoption of Byzantine Fault-Tolerant (BFT) consensus protocols
that can handle malicious attacks from byzantine replicas. Existing BFT
protocols permit byzantine replicas to equivocate their messages. As a result,
they need f more replicas than Paxos-style protocols to prevent safety
violations due to equivocation. This led to the design of Trust-BFT protocols,
which require each replica to host an independent, trusted component.
  In this work, we analyze the design of existing Trust-BFT and make the
following observations regarding these protocols: (i) they adopt weaker
quorums, which prevents them from providing service in scenarios supported by
their BFT counterparts, (ii) they rely on the data persistence of trusted
components at byzantine replicas, and (iii) they enforce sequential ordering of
client requests.
  To resolve these challenges, we present solutions that facilitate the
recovery of Trust-BFT protocols despite their weak quorums or data persistence
dependence. Further, we present the design of lightweight, fast, and flexible
protocols (FlexiTrust), which achieve up to 100% more throughput than their
Trust-BFT counterparts.


title:
Microphysics of Water Clouds in the Atmospheres of Y Dwarfs and
  Temperate Giant Planets

abstract:
  Water clouds are expected to form on Y dwarfs and giant planets with
equilibrium temperatures near or below that of Earth, drastically altering
their atmospheric compositions and their albedos and thermal emission spectra.
Here we use the 1D Community Aerosol and Radiation Model for Atmospheres
(CARMA) to investigate the microphysics of water clouds on cool substellar
worlds to constrain their typical particle sizes and vertical extent, taking
into consideration nucleation and condensation, which have not been considered
in detail for water clouds in H/He atmospheres. We compute a small grid of Y
dwarf and temperate giant exoplanet atmosphere models with water clouds forming
through homogeneous nucleation and heterogeneous nucleation on cloud
condensation nuclei composed of meteoritic dust, organic photochemical hazes,
and upwelled potassium chloride cloud particles. We present comparisons with
the Ackerman & Marley parameterization of cloud physics to extract the optimal
sedimentation efficiency parameter (f$_{sed}$) using Virga. We find that no
Virga model replicates the CARMA water clouds exactly and that a transition in
f$_{sed}$ occurs from the base of the cloud to the cloud top. Furthermore, we
generate simulated thermal emission and geometric albedo spectra and find
large, wavelength-dependent differences between the CARMA and Virga models,
with different gas absorption bands reacting differently to the different cloud
distributions and particularly large differences in the M band. Therefore,
constraining the vertically-dependent properties of water clouds will be
essential to estimating the gas abundances in these atmospheres.


title: Direct Molecular Conformation Generation

abstract:
  Molecular conformation generation aims to generate three-dimensional
coordinates of all the atoms in a molecule and is an important task in
bioinformatics and pharmacology. Previous distance-based methods first predict
interatomic distances and then generate conformations based on them, which
could result in conflicting distances. In this work, we propose a method that
directly predicts the coordinates of atoms. We design a dedicated loss function
for conformation generation, which is invariant to roto-translation of
coordinates of conformations and permutation of symmetric atoms in molecules.
We further design a backbone model that stacks multiple blocks, where each
block refines the conformation generated by its preceding block. Our method
achieves state-of-the-art results on four public benchmarks: on small-scale
GEOM-QM9 and GEOM-Drugs which have $200$K training data, we can improve the
previous best matching score by $3.5\%$ and $28.9\%$; on large-scale GEOM-QM9
and GEOM-Drugs which have millions of training data, those two improvements are
$47.1\%$ and $36.3\%$. This shows the effectiveness of our method and the great
potential of the direct approach. Our code is released at
\url{https://github.com/DirectMolecularConfGen/DMCG}.


title:
A shuttling-based two-qubit logic gate for linking distant silicon
  quantum processors

abstract:
  Control of entanglement between qubits at distant quantum processors using a
two-qubit gate is an essential function of a scalable, modular implementation
of quantum computation. Among the many qubit platforms, spin qubits in silicon
quantum dots are promising for large-scale integration along with their
nanofabrication capability. However, linking distant silicon quantum processors
is challenging as two-qubit gates in spin qubits typically utilize short-range
exchange coupling, which is only effective between nearest-neighbor quantum
dots. Here we overcome this problem by coupling spin qubits at distant quantum
processors via coherent spin shuttling and a two-qubit gate between them.
Coherent shuttling of a spin qubit enables efficient switching of the exchange
coupling with an on/off ratio exceeding 1,000, while preserving the spin
coherence by 99.6% for the single shuttling between neighboring dots. With this
shuttling-mode exchange control, we demonstrate a two-qubit controlled-phase
gate with a fidelity of 93%, assessed via randomized benchmarking. This result
shows a feasible path toward a quantum link between distant silicon quantum
processors, a key requirement for large-scale quantum computation.


title:
Safe Learning for Uncertainty-Aware Planning via Interval MDP
  Abstraction

abstract:
  We study the problem of refining satisfiability bounds for partially-known
stochastic systems against planning specifications defined using syntactically
co-safe Linear Temporal Logic (scLTL). We propose an abstraction-based approach
that iteratively generates high-confidence Interval Markov Decision Process
(IMDP) abstractions of the system from high-confidence bounds on the unknown
component of the dynamics obtained via Gaussian process regression. In
particular, we develop a synthesis strategy to sample the unknown dynamics by
finding paths which avoid specification-violating states using a product IMDP.
We further provide a heuristic to choose among various candidate paths to
maximize the information gain. Finally, we propose an iterative algorithm to
synthesize a satisfying control policy for the product IMDP system. We
demonstrate our work with a case study on mobile robot navigation.


title: Fourier transform inversion in the Alexiewicz norm

abstract:
  If $f\in L^1({\mathbb R})$ it is proved that $\lim_{S\to\infty}\lVert f-f\ast
D_S\rVert=0$, where $D_S(x)=\sin(Sx)/(\pi x)$ is the Dirichlet kernel and
$\lVert f\rVert = \sup_{\alpha<\beta}|\int_{\alpha}^{\beta}f(x)\,dx|$ is the
Alexiewicz norm. This gives a symmetric inversion of the Fourier transform on
the real line. An asymmetric inversion is also proved. The results also hold
for a measure given by $dF$ where $F$ is a continuous function of bounded
variation. Such measures need not be absolutely continuous with respect to
Lebesgue measure. An example shows there is $f\in L^1({\mathbb R})$ such that
$\lim_{S\to\infty} \rVert f-f\ast D_S\lVert_1\neq 0$.


title:
A Novel Single-Mode Microwave Assisted Synthesis of Metal Oxide as
  Visible-light Photocatalyst

abstract:
  Visible-light photocatalyst titanium dioxide (TiO2) was successfully prepared
via a novel and facile single-mode microwave assisted synthesis process. In
this one-step synthesis, Ti as target material selectively oxides in magnetic
field throughout rapid heating, whose process requires less energy consumption
and short time. In obtained TiO2, self-doping of Ti3+ was confirmed, which
makes TiO2 performed sufficient light absorption in visible region with
wavelength above 400 nm. Such Ti3+ self-doped TiO2 exhibits much narrower
optical bandgap (2.14 eV) to compare with stoichiometric TiO2 (3.0-3.2 eV). The
synthesized TiO2 also shows superior photocatalytic activity to commercially
available TiO2 towards the degradation of Rhodamine B under visible light
irradiation.


title: Generative Flow Networks for Discrete Probabilistic Modeling

abstract:
  We present energy-based generative flow networks (EB-GFN), a novel
probabilistic modeling algorithm for high-dimensional discrete data. Building
upon the theory of generative flow networks (GFlowNets), we model the
generation process by a stochastic data construction policy and thus amortize
expensive MCMC exploration into a fixed number of actions sampled from a
GFlowNet. We show how GFlowNets can approximately perform large-block Gibbs
sampling to mix between modes. We propose a framework to jointly train a
GFlowNet with an energy function, so that the GFlowNet learns to sample from
the energy distribution, while the energy learns with an approximate MLE
objective with negative samples from the GFlowNet. We demonstrate EB-GFN's
effectiveness on various probabilistic modeling tasks.


title:
VNE Solution for Network Differentiated QoS and Security Requirements:
  From the Perspective of Deep Reinforcement Learning

abstract:
  The rapid development and deployment of network services has brought a series
of challenges to researchers. On the one hand, the needs of Internet end
users/applications reflect the characteristics of travel alienation, and they
pursue different perspectives of service quality. On the other hand, with the
explosive growth of information in the era of big data, a lot of private
information is stored in the network. End users/applications naturally start to
pay attention to network security. In order to solve the requirements of
differentiated quality of service (QoS) and security, this paper proposes a
virtual network embedding (VNE) algorithm based on deep reinforcement learning
(DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate
network. DRL agent is trained in the network environment constructed by the
above attributes. The purpose is to deduce the mapping probability of each
substrate node and map the virtual node according to this probability. Finally,
the breadth first strategy (BFS) is used to map the virtual links. In the
experimental stage, the algorithm based on DRL is compared with other
representative algorithms in three aspects: long term average revenue, long
term revenue consumption ratio and acceptance rate. The results show that the
algorithm proposed in this paper has achieved good experimental results, which
proves that the algorithm can be effectively applied to solve the end
user/application differentiated QoS and security requirements.


title:
Synthesis of Core/Shell Ti/TiOx Photocatalyst via Single-Mode Magnetic
  Microwave Assisted Direct Oxidation of TiH2

abstract:
  Submicron core/shell Ti/TiOx photocatalyst is successfully synthesized via
single-mode magnetic microwave (SMMW) assisted direct oxidation of planetary
ball-milled TiH2. The thickness of TiOx shell including highly concentrated
defects such as Ti3+ and/or oxygen vacancies is controllable in the range from
6 to over 18 nm by varying the treatment time in the SMMW assisted reaction. In
addition to its quite narrow optical bandgap (1.34-2.69 eV) and efficient
visible-light absorption capacity, the submicron Ti/TiOx particle exhibits
superior photocatalytic performance towards H2 production from water under both
UV and visible-light irradiation to compare with a commercial TiO2
photocatalyst (P-25). Such excellent performance can be achieved by the
synergetic effect of enhancement in visible light absorption capacity and
photo-excited carrier separation because of the highly concentrated surface
defects and the specific Ti/TiOx core/shell structure, respectively.


title: Vorticity-strain rate dynamics and the smallest scales of turbulence

abstract:
  Building upon the intrinsic properties of Navier-Stokes dynamics, namely the
prevalence of intense vortical structures and the interrelationship between
vorticity and strain rate, we propose a simple framework to quantify the
extreme events and the smallest scales of turbulence. We demonstrate that our
approach is in excellent agreement with the best available data from direct
numerical simulations of isotropic turbulence, with Taylor-scale Reynolds
number up to 1300. We additionally highlight a shortcoming of prevailing
intermittency models due to their disconnection from observed correlation
between vorticity and strain. Our work accentuates the importance of this
correlation as a crucial step in developing an accurate understanding of
intermittency in turbulence.


title: Feasibility of Interactive 3D Map for Remote Sighted Assistance

abstract:
  Remote sighted assistance (RSA) has emerged as a conversational assistive
technology, where remote sighted workers, i.e., agents, provide real-time
assistance to users with vision impairments via video-chat-like communication.
Researchers found that agents' lack of environmental knowledge, the difficulty
of orienting users in their surroundings, and the inability to estimate
distances from users' camera feeds are key challenges to sighted agents. To
address these challenges, researchers have suggested assisting agents with
computer vision technologies, especially 3D reconstruction. This paper presents
a high-fidelity prototype of such an RSA, where agents use interactive 3D maps
with localization capability. We conducted a walkthrough study with thirteen
agents and one user with simulated vision impairment using this prototype. The
study revealed that, compared to baseline RSA, the agents were significantly
faster in providing navigational assistance to users, and their mental workload
was significantly reduced -- all indicate the feasibility and prospect of 3D
maps in RSA.


title: Nucleon Form Factors from the Feynman-Hellmann Method in Lattice QCD

abstract:
  Lattice QCD calculations of the nucleon electromagnetic form factors are of
interest at both the high and low momentum transfer regions. For high momentum
transfers especially there are open questions which require more intense study,
such as the potential zero crossing in the proton's electric form factor. We
will present recent progress from the QCDSF/UKQCD/CSSM collaboration on the
calculation of these form factors using the Feynman-Hellmann method in lattice
QCD. The Feynman-Hellmann method allows for greater control over excited states
which we take advantage of by going to high values of the momentum transfer. In
this proceeding we present results of the form factors up to $6
\textrm{GeV}^{2}$, using $N_{f}=2+1$ flavour fermions for three different pion
masses in the range 310-470 $\textrm{MeV}$. The results are extrapolated to the
physical pion mass through the use of a flavour breaking expansion.


title: Real-time Emergency Vehicle Event Detection Using Audio Data

abstract:
  In this work, we focus on detecting emergency vehicles using only audio data.
Improved and quick detection can help in faster preemption of these vehicles at
signalized intersections thereby reducing overall response time in case of
emergencies. Important audio features were extracted from raw data and passed
into extreme learning machines (ELM) for training. ELMs have been used in this
work because of its simplicity and shorter run-time which can therefore be used
for online learning. Recently, there have been many studies that focus on sound
classification but most of the methods used are complex to train and implement.
The results from this paper show that ELM can achieve similar performance with
exceptionally shorter training times. The accuracy reported for ELM is about
97% for emergency vehicle detection (EVD).


title:
TiO2 with Super Narrow Bandgap Achieved in One-Step Single-Mode Magnetic
  Microwave Induced Plasma Treatment

abstract:
  TiO2 with super narrow bandgap (1.1 eV~) are successfully synthesized via a
facile and novel one-step single-mode magnetic microwave induced plasma
treatment. The selectively surface Ti3+-doping on obtained TiO2 as trapping
centers which significantly restrain the photo-excited carrier recombination
and achieve the enhancement of visible-light photocatalytic performance. In
addition, the surface chemical composition of TiOx is precisely controlled in a
wide region of 1.19 < x < 1.92 during one-step reaction. A detailed XPS
analysis reveals that the surface formed Ti3+ shows highly thermal/chemical
stability even through high-temperature treatment (~800 oC) in oxidative
atmosphere and photocatalytic reaction.


title:
Quasi-balanced weighing matrices, signed strongly regular graphs and
  association schemes

abstract:
  A weighing matrix $W$ is quasi-balanced if $|W||W|^\top=|W|^\top|W|$ has at
most two off-diagonal entries, where $|W|_{ij}=|W_{ij}|$. A quasi-balanced
weighing matrix $W$ signs a strongly regular graph if $|W|$ coincides with its
adjacency matrix. Among other things, signed strongly regular graphs and their
equivalent association schemes are presented.


title:
Field-induced Topological Hall effect in antiferromagnetic axion
  insulator candidate EuIn$_2$As$_2$

abstract:
  The magnetic topological materials have attracted significant attention due
to their potential realization of variety of novel quantum phenomena.
EuIn$_2$As$_2$ has recently been theoretically recognized as a long awaited
intrinsic antiferromagnetic bulk axion insulator. However, the experimental
study on transport properties arising from the topological states in this
material is scarce. In this paper, we perform the detailed magnetoresistance
(MR) and Hall measurements to study the magnetotransport properties of this
material. We find that the transport is strongly influenced by the spin
configuration of the Eu moments from the concomitant change in the field
dependence of the MR and that of the magnetization below the N\'eel
temperature. Most importantly, an anomalous Hall effect (AHE) and a large
topological Hall effect (THE) are observed. We suggest that the AHE is
originated from a nonvanishing net Berry curvature due to the helical spin
structure and that the THE is attributed to the formation of a noncoplanar spin
texture with a finite scalar spin chirality induced by the external magnetic
field in EuIn$_2$As$_2$. Our studies provide a platform to understand the
influence of the interplay between the topology of electronic bands and the
field-induced magnetic structure on magnetoelectric transport properties. In
addition, our observations give a hint to realize axion insulator states and
high-order topological insulator states through manipulating the magnetic state
of EuIn$_2$As$_2$.


title:
Observational constraints on varying fundamental constants in a minimal
  CPC model

abstract:
  A minimal model based on the Covariant Physical Couplings (CPC) framework for
gravity is proposed. The CPC framework is based on the assumptions of a
metric-compatible four-dimensional Riemannian manifold where a covariantly
conserved stress-energy tensor acts as source of the field equations which are
formally the same as Einstein field equations, but where the couplings $\{ G,
c,\Lambda \}$ are allowed to vary simultaneously. The minimal CPC model takes
$\Lambda$ as a genuine constant while $c$ and $G$ vary in an entangled way that
is consistent with Bianchi identity and the aforementioned assumptions. The
model is constrained using the most recent galaxy cluster gas mass fraction
observational data. Our result indicates that the functions $c(z)$ and
$G\left(z\right)=G_{0}\left(c/c_{0}\right)^{4}$ are compatible with constant
couplings for the two different parameterizations of $c=c(z)$ adopted here.


title: No Ensemble Averaging Below the Black Hole Threshold

abstract:
  In the AdS/CFT correspondence, amplitudes associated to connected bulk
manifolds with disconnected boundaries have presented a longstanding mystery. A
possible interpretation is that they reflect the effects of averaging over an
ensemble of boundary theories. But in examples in dimension $D\geq 3$, an
appropriate ensemble of boundary theories does not exist. Here we sharpen the
puzzle by identifying a class of "sub-threshold" observables that we claim do
not show effects of ensemble averaging. These are amplitudes that do not
involve black hole states. To support our claim, we explore the example of
$D=3$, and show that connected solutions of Einstein's equations with
disconnected boundary never contribute to sub-threshold observables. To
demonstrate this requires some novel results about the renormalized volume of a
hyperbolic three-manifold, which we prove using modern methods in hyperbolic
geometry. Why then do any observables show apparent ensemble averaging? We
propose that this reflects the chaotic nature of black hole physics and the
fact that the Hilbert space describing a black hole does not have a large $N$
limit.


title:
Data-driven discovery of high performance layered van der Waals
  piezoelectric NbOI2

abstract:
  Using high-throughput first-principles calculations to search for layered van
der Waals materials with the largest piezoelectric stress coefficients, we
discover NbOI2 to be the one among 2940 monolayers screened. The piezoelectric
performance of NbOI2 is independent of thickness, and its electromechanical
coupling factor of near unity is a hallmark of optimal interconversion between
electrical and mechanical energy. Laser scanning vibrometer studies on bulk and
few-layer NbOI2 crystals verify their huge piezoelectric responses, which
exceed internal references such as In2Se3 and CuInP2S6. Furthermore, we provide
insights into the atomic origins of anti-correlated piezoelectric and
ferroelectric responses in NbOX2 (X = Cl, Br, I), based on bond covalency and
structural distortions in these materials. Our discovery that NbOI2 has the
largest piezoelectric stress coefficients among 2D materials calls for the
development of NbOI2-based flexible nanoscale piezoelectric devices.


title: mSLAM: Massively multilingual joint pre-training for speech and text

abstract:
  We present mSLAM, a multilingual Speech and LAnguage Model that learns
cross-lingual cross-modal representations of speech and text by pre-training
jointly on large amounts of unlabeled speech and text in multiple languages.
mSLAM combines w2v-BERT pre-training on speech with SpanBERT pre-training on
character-level text, along with Connectionist Temporal Classification (CTC)
losses on paired speech and transcript data, to learn a single model capable of
learning from and representing both speech and text signals in a shared
representation space. We evaluate mSLAM on several downstream speech
understanding tasks and find that joint pre-training with text improves quality
on speech translation, speech intent classification and speech language-ID
while being competitive on multilingual ASR, when compared against speech-only
pre-training. Our speech translation model demonstrates zero-shot text
translation without seeing any text translation data, providing evidence for
cross-modal alignment of representations. mSLAM also benefits from multi-modal
fine-tuning, further improving the quality of speech translation by directly
leveraging text translation data during the fine-tuning process. Our empirical
analysis highlights several opportunities and challenges arising from
large-scale multimodal pre-training, suggesting directions for future research.


title:
Resource Management and Security Scheme of ICPSs and IoT Based on VNE
  Algorithm

abstract:
  The development of Intelligent Cyber-Physical Systems (ICPSs) in virtual
network environment is facing severe challenges. On the one hand, the Internet
of things (IoT) based on ICPSs construction needs a large amount of reasonable
network resources support. On the other hand, ICPSs are facing severe network
security problems. The integration of ICPSs and network virtualization (NV) can
provide more efficient network resource support and security guarantees for IoT
users. Based on the above two problems faced by ICPSs, we propose a virtual
network embedded (VNE) algorithm with computing, storage resources and security
constraints to ensure the rationality and security of resource allocation in
ICPSs. In particular, we use reinforcement learning (RL) method as a means to
improve algorithm performance. We extract the important attribute
characteristics of underlying network as the training environment of RL agent.
Agent can derive the optimal node embedding strategy through training, so as to
meet the requirements of ICPSs for resource management and security. The
embedding of virtual links is based on the breadth first search (BFS) strategy.
Therefore, this is a comprehensive two-stage RL-VNE algorithm considering the
constraints of computing, storage and security three-dimensional resources.
Finally, we design a large number of simulation experiments from the
perspective of typical indicators of VNE algorithms. The experimental results
effectively illustrate the effectiveness of the algorithm in the application of
ICPSs.


title:
Cold Gas in Massive Galaxies as A Critical Test of Black Hole Feedback
  Models

abstract:
  Black hole feedback has been widely implemented as the key recipe to quench
star formation in massive galaxies in modern semi-analytic models and
hydrodynamical simulations. As the theoretical details surrounding the
accretion and feedback of black holes continue to be refined, various feedback
models have been implemented across simulations, with notable differences in
their outcomes. Yet, most of these simulations have successfully reproduced
some observations, such as stellar mass function and star formation rate
density in the local Universe. We use the recent observation on the change of
neutral hydrogen gas mass (including both ${\rm H_2}$ and ${\rm HI}$) with star
formation rate of massive central disc galaxies as a critical constraint of
black hole feedback models across several simulations. We find that the
predictions of IllustrisTNG agree with the observations much better than the
other models tested in this work. This favors IllustrisTNG's treatment of
active galactic nuclei - where kinetic winds are driven by black holes at low
accretion rates - as more plausible amongst those we test. In turn, this also
indirectly supports the idea that the massive central disc galaxy population in
the local Universe was likely quenched by AGN feedback.


title: Constructing knots with specified geometric limits

abstract:
  It is known that any tame hyperbolic 3-manifold with infinite volume and a
single end is the geometric limit of a sequence of finite volume hyperbolic
knot complements. Purcell and Souto showed that if the original manifold embeds
in the 3-sphere, then such knots can be taken to lie in the 3-sphere. However,
their proof was nonconstructive; no examples were produced. In this paper, we
give a constructive proof in the geometrically finite case. That is, given a
geometrically finite, tame hyperbolic 3-manifold with one end, we build an
explicit family of knots whose complements converge to it geometrically. Our
knots lie in the (topological) double of the original manifold. The
construction generalises the class of fully augmented links to a Kleinian
groups setting.


title:
On the separability of subgroups of nilpotent groups by root classes of
  groups

abstract:
  Suppose that $\mathcal{C}$ is a class of groups consisting only of periodic
groups and $\mathfrak{P}(\mathcal{C})^{\prime}$ is the set of prime numbers
each of which does not divide the order of any element of a
$\mathcal{C}$-group. A subgroup $Y$ of a group $X$ is called a)
$\mathcal{C}$-separable in this group if, for each $x \in X \setminus Y$, there
exists a homomorphism $\sigma$ of $X$ onto a group from $\mathcal{C}$ such that
$x\sigma \notin Y\sigma$; b) $\mathfrak{P}(\mathcal{C})^{\prime}$-isolated in
$X$ if, for any $x \in X$, $q \in \mathfrak{P}(\mathcal{C})^{\prime}$, the
inclusion $x^{q} \in Y$ implies that $x \in Y$. It is easy to see that if $Y$
is $\mathcal{C}$-separable in $X$, then it is
$\mathfrak{P}(\mathcal{C})^{\prime}$-isolated in this group. Let us say that
$X$ has the property $\mathcal{C}\mbox{-}\mathfrak{Sep}$ if all its
$\mathfrak{P}(\mathcal{C})^{\prime}$-isolated subgroups are
$\mathcal{C}$-separable. We find a condition that is sufficient for a nilpotent
group $N$ to have the property $\mathcal{C}\mbox{-}\mathfrak{Sep}$ provided
$\mathcal{C}$ is a root class (i.e., it contains non-trivial groups and is
closed under taking subgroups, extensions, and Cartesian products of the form
$\prod_{v \in V}U_{v}$, where $U, V \in \mathcal{C}$ and $U_{v}$ is an
isomorphic copy of $U$ for each $v \in V$). We also prove that if $N$ is
torsion-free, then the indicated condition is necessary for this group to have
$\mathcal{C}\mbox{-}\mathfrak{Sep}$.


title: A Very Elementary Introduction to Sheaves

abstract:
  This paper is a very non-rigorous, loose, and extremely basic introduction to
sheaves. This is meant to be a a guide to gaining intuition about sheaves, what
they look like, and how they work, so that after reading this paper, someone
can jump into the extremely abstract definitions and examples seen in textbooks
with at least some idea of what is going on. Most of this material is inspired
and built from the work of Dr. Michael Robinson, and that of Dr. Robert Ghrist
and Dr. Jakob Hansen, as well as Dr. Justin Curry's PhD thesis, who are some of
the only applied sheaf theorists out there and they do an amazing job of
explaining sheaves in a concrete way through their research. The rest of this
paper is populated by mathematical definitions found in textbooks that I have
stretched from two lines into multiple pages, as well as some analogies for
thinking of sheaves I have thought of myself. This paper only assumes knowledge
of basic linear algebra, basic group theory, and the very fundamentals of
topology. If there is anything in the setup that you do not understand it is
probably a quick Wikipedia search away. I hope this paper provides insight,
intuition, and helpful examples of why sheaves are such powerful tools in both
math and science.


title:
Learning Mechanically Driven Emergent Behavior with Message Passing
  Neural Networks

abstract:
  From designing architected materials to connecting mechanical behavior across
scales, computational modeling is a critical tool in solid mechanics. Recently,
there has been a growing interest in using machine learning to reduce the
computational cost of physics-based simulations. Notably, while machine
learning approaches that rely on Graph Neural Networks (GNNs) have shown
success in learning mechanics, the performance of GNNs has yet to be
investigated on a myriad of solid mechanics problems. In this work, we examine
the ability of GNNs to predict a fundamental aspect of mechanically driven
emergent behavior: the connection between a column's geometric structure and
the direction that it buckles. To accomplish this, we introduce the Asymmetric
Buckling Columns (ABC) dataset, a dataset comprised of three sub-datasets of
asymmetric and heterogeneous column geometries where the goal is to classify
the direction of symmetry breaking (left or right) under compression after the
onset of instability. Because of complex local geometry, the "image-like" data
representations required for implementing standard convolutional neural network
based metamodels are not ideal, thus motivating the use of GNNs. In addition to
investigating GNN model architecture, we study the effect of different input
data representation approaches, data augmentation, and combining multiple
models as an ensemble. While we were able to obtain good results, we also
showed that predicting solid mechanics based emergent behavior is non-trivial.
Because both our model implementation and dataset are distributed under
open-source licenses, we hope that future researchers can build on our work to
create enhanced mechanics-specific machine learning pipelines for capturing the
behavior of complex geometric structures.


title:
ETSformer: Exponential Smoothing Transformers for Time-series
  Forecasting

abstract:
  Transformers have been actively studied for time-series forecasting in recent
years. While often showing promising results in various scenarios, traditional
Transformers are not designed to fully exploit the characteristics of
time-series data and thus suffer some fundamental limitations, e.g., they
generally lack of decomposition capability and interpretability, and are
neither effective nor efficient for long-term forecasting. In this paper, we
propose ETSFormer, a novel time-series Transformer architecture, which exploits
the principle of exponential smoothing in improving Transformers for
time-series forecasting. In particular, inspired by the classical exponential
smoothing methods in time-series forecasting, we propose the novel exponential
smoothing attention (ESA) and frequency attention (FA) to replace the
self-attention mechanism in vanilla Transformers, thus improving both accuracy
and efficiency. Based on these, we redesign the Transformer architecture with
modular decomposition blocks such that it can learn to decompose the
time-series data into interpretable time-series components such as level,
growth and seasonality. Extensive experiments on various time-series benchmarks
validate the efficacy and advantages of the proposed method. The code and
models of our implementations will be released.


title:
Asymptotic behaviour of rotating convection-driven dynamos in the plane
  layer geometry

abstract:
  Dynamos driven by rotating convection in the plane layer geometry are
investigated numerically over a range of Ekman number ($E$), Rayleigh number
($Ra$) and magnetic Prandtl number ($Pm$). The asymptotic behaviour of the
system is investigated. All of the simulations are in the quasi-geostrophic
regime in which the Coriolis and pressure gradient forces are approximately
balanced at leading order, whereas all other forces, including the Lorentz
force, act as perturbations. The flow regimes range from laminar to turbulent.
Scalings for the energetics, flow speeds, magnetic field amplitude and length
scales are presented. The transition from large scale dynamos to small scale
dynamos is well described by the magnetic Reynolds number based on the small
convective length scale, $\widetilde{Rm}$; large scale dynamos dominate when
$\widetilde{Rm} \lesssim O(1)$. The strength of the large scale magnetic field
saturates at a finite Rayleigh number. Energy spectra show that all length
scales of the flow field and magnetic field scale are consistent with a scaling
of $E^{1/3}$, irrespective of flow regime. The viscous dissipation length scale
is approximately constant over the investigated parameter range. The ohmic
dissipation length scale shows two regimes: it remains approximately constant
and comparable to the viscous dissipation length scale in the large scale
dynamo regime; it then transitions to a $\widetilde{Rm}^{-1/2}$ scaling in the
small scale dynamo regime.


title:
Machine Learning Solar Wind Driving Magnetospheric Convection in Tail
  Lobes

abstract:
  To quantitatively study the driving mechanisms of magnetospheric convection
in the magnetotail lobes on a global scale, we utilize data from the ARTEMIS
spacecraft in the deep tail and the Cluster spacecraft in the near tail.
Previous work demonstrated that, in the lobes near the Moon, we can estimate
the convection by utilizing ARTEMIS measurements of lunar ions velocity. In
this paper, we analyze these datasets with machine learning models to determine
what upstream factors drive the lobe convection in different magnetotail
regions and thereby understand the mechanisms that control the dynamics of the
tail lobes. Our results show that the correlations between the predicted and
test convection velocities for the machine learning models (>0.75) are much
better than those of the multiple linear regression model (~ 0.23 - 0.43). The
systematic analysis reveals that the IMF and magnetospheric activity play an
important role in influencing plasma convection in the global magnetotail
lobes.

categories: astro-ph.SR astro-ph.EP cs.LG physics.space-ph


title:
Element Doping Enhanced Charge-to-Spin Conversion Efficiency in
  Amorphous PtSn4 Dirac Semimetal

abstract:
  Topological semimetals (TSs) are promising candidates for low-power
spin-orbit torque (SOT) devices due to their large charge-to-spin conversion
efficiency. Here, we investigated the charge-to-spin conversion efficiency of
amorphous PtSn4 (5 nm)/CoFeB (2.5-12.5 nm) layered structures prepared by a
magnetron sputtering method at room temperature. The charge-to-spin ratio of
PtSn4/CoFeB bilayers was 0.08, characterized by a spin torque ferromagnetic
resonance (ST-FMR) technique. This ratio can further increase to 0.14 by
inducing dopants, like Al and CoSi, into PtSn4. The dopants can also decrease
(Al doping) or increase (CoSi doping) the resistivity of PtSn4. The work
proposed a way to enhance the spin-orbit coupling (SOC) in amorphous TSs with
dopants.


title:
Technical Report: A Hierarchical Deliberative-Reactive System
  Architecture for Task and Motion Planning in Partially Known Environments

abstract:
  We describe a task and motion planning architecture for highly dynamic
systems that combines a domain-independent sampling-based deliberative planning
algorithm with a global reactive planner. We leverage the recent development of
a reactive, vector field planner that provides guarantees of reachability to
large regions of the environment even in the face of unknown or unforeseen
obstacles. The reachability guarantees can be formalized using contracts that
allow a deliberative planner to reason purely in terms of those contracts and
synthesize a plan by choosing a sequence of reactive behaviors and their target
configurations, without evaluating specific motion plans between targets. This
reduces both the search depth at which plans will be found, and the number of
samples required to ensure a plan exists, while crucially preserving
correctness guarantees. The result is reduced computational cost of
synthesizing plans, and increased robustness of generated plans to actuator
noise, model misspecification, or unknown obstacles. Simulation studies show
that our hierarchical planning and execution architecture can solve complex
navigation and rearrangement tasks, even when faced with narrow passageways or
incomplete world information.


title: Viscous droplet impingement on soft substrates

abstract:
  Viscous droplets impinging on soft substrates may exhibit several distinct
behaviours including repeated bouncing, wetting, and hovering, i.e., spreading
and retracting after impact without bouncing back or wetting. We experimentally
study the conditions enabling these characteristic behaviours by systematically
varying the substrate elasticity, impact velocity and the liquid viscosity. For
each substrate elasticity, the transition to wetting is determined as the
dependence of the Weber number We, which measures the droplet's kinetic energy
against its surface energy, on the Ohnesorge number Oh, which compares
viscosity to inertia and capillarity. We find that while We at the wetting
transition monotonically decreases with Oh for relatively rigid substrates, it
exhibits a counter-intuitive behaviour in which it first increases then
gradually decreases for softer substrates. We experimentally determine the
dependence of the maximum Weber number allowing non-wetting impacts on the
substrate elasticity and show that it provides an excellent quantitative
measure of liquid repellency for a wide range of surfaces, from liquid to soft
surfaces and non-deformable surfaces.


title: Data-Driven Stochastic Optimal Control using Linear Transfer Operators

abstract:
  We provide a data-driven framework for optimal control of a continuous-time
stochastic dynamical system. The proposed framework relies on the linear
operator theory involving linear Perron-Frobenius (P-F) and Koopman operators.
Our first results involving the P-F operator provide a convex formulation to
the optimal control problem in the dual space of densities. This convex
formulation of the stochastic optimal control problem leads to an
infinite-dimensional convex program. The finite-dimensional approximation of
the convex program is obtained using a data-driven approximation of the P-F
operator. Our second results demonstrate the use of the Koopman operator, which
is dual to the P-F operator, for the stochastic optimal control design. We show
that the Hamilton Jacobi Bellman (HJB) equation can be expressed using the
Koopman operator. We provide an iterative procedure along the lines of a
popular policy iteration algorithm based on the data-driven approximation of
the Koopman operator for solving the HJB equation. The two formulations, namely
the convex formulation involving P-F operator and Koopman based formulation
using HJB equation, can be viewed as dual to each other where the duality
follows due to the dual nature of P-F and Koopman operators. Finally, we
present several numerical examples to demonstrate the efficacy of the developed
framework.


title:
Self-consistent Gradient-like Eigen Decomposition in Solving
  Schr\"odinger Equations

abstract:
  The Schr\"odinger equation is at the heart of modern quantum mechanics. Since
exact solutions of the ground state are typically intractable, standard
approaches approximate Schr\"odinger equation as forms of nonlinear generalized
eigenvalue problems $F(V)V = SV\Lambda$ in which $F(V)$, the matrix to be
decomposed, is a function of its own top-$k$ smallest eigenvectors $V$, leading
to a "self-consistency problem". Traditional iterative methods heavily rely on
high-quality initial guesses of $V$ generated via domain-specific heuristics
methods based on quantum mechanics. In this work, we eliminate such a need for
domain-specific heuristics by presenting a novel framework, Self-consistent
Gradient-like Eigen Decomposition (SCGLED) that regards $F(V)$ as a special
"online data generator", thus allows gradient-like eigendecomposition methods
in streaming $k$-PCA to approach the self-consistency of the equation from
scratch in an iterative way similar to online learning. With several critical
numerical improvements, SCGLED is robust to initial guesses, free of
quantum-mechanism-based heuristics designs, and neat in implementation. Our
experiments show that it not only can simply replace traditional
heuristics-based initial guess methods with large performance advantage
(achieved averagely 25x more precise than the best baseline in similar wall
time), but also is capable of finding highly precise solutions independently
without any traditional iterative methods.


title:
An Empirical Review of Optimization Techniques for Quantum Variational
  Circuits

abstract:
  Quantum Variational Circuits (QVCs) are often claimed as one of the most
potent uses of both near term and long term quantum hardware. The standard
approaches to optimizing these circuits rely on a classical system to compute
the new parameters at every optimization step. However, this process can be
extremely challenging, due to the nature of navigating the exponentially
scaling complex Hilbert space, barren plateaus, and the noise present in all
foreseeable quantum hardware. Although a variety of optimization algorithms are
employed in practice, there is often a lack of theoretical or empirical
motivations for this choice. To this end we empirically evaluate the potential
of many common gradient and gradient free optimizers on a variety of
optimization tasks. These tasks include both classical and quantum data based
optimization routines. Our evaluations were conducted in both noise free and
noisy simulations. The large number of problems and optimizers evaluated yields
strong empirical guidance for choosing optimizers for QVCs that is currently
lacking.


title:
Exploring Sub-skeleton Trajectories for Interpretable Recognition of
  Sign Language

abstract:
  Recent advances in tracking sensors and pose estimation software enable smart
systems to use trajectories of skeleton joint locations for supervised
learning. We study the problem of accurately recognizing sign language words,
which is key to narrowing the communication gap between hard and non-hard of
hearing people.
  Our method explores a geometric feature space that we call `sub-skeleton'
aspects of movement. We assess similarity of feature space trajectories using
natural, speed invariant distance measures, which enables clear and insightful
nearest neighbor classification. The simplicity and scalability of our basic
method allows for immediate application in different data domains with little
to no parameter tuning.
  We demonstrate the effectiveness of our basic method, and a boosted
variation, with experiments on data from different application domains and
tracking technologies. Surprisingly, our simple methods improve sign
recognition over recent, state-of-the-art approaches.


title: Fair Representation Clustering with Several Protected Classes

abstract:
  We study the problem of fair $k$-median where each cluster is required to
have a fair representation of individuals from different groups. In the fair
representation $k$-median problem, we are given a set of points $X$ in a metric
space. Each point $x\in X$ belongs to one of $\ell$ groups. Further, we are
given fair representation parameters $\alpha_j$ and $\beta_j$ for each group
$j\in [\ell]$. We say that a $k$-clustering $C_1, \cdots, C_k$ fairly
represents all groups if the number of points from group $j$ in cluster $C_i$
is between $\alpha_j |C_i|$ and $\beta_j |C_i|$ for every $j\in[\ell]$ and
$i\in [k]$. The goal is to find a set $\mathcal{C}$ of $k$ centers and an
assignment $\phi: X\rightarrow \mathcal{C}$ such that the clustering defined by
$(\mathcal{C}, \phi)$ fairly represents all groups and minimizes the
$\ell_1$-objective $\sum_{x\in X} d(x, \phi(x))$.
  We present an $O(\log k)$-approximation algorithm that runs in time
$n^{O(\ell)}$. Note that the known algorithms for the problem either (i)
violate the fairness constraints by an additive term or (ii) run in time that
is exponential in both $k$ and $\ell$. We also consider an important special
case of the problem where $\alpha_j = \beta_j = \frac{f_j}{f}$ and $f_j, f \in
\mathbb{N}$ for all $j\in [\ell]$. For this special case, we present an $O(\log
k)$-approximation algorithm that runs in $(kf)^{O(\ell)}\log n + poly(n)$ time.


title: The Madelung Constant in $N$ Dimensions

abstract:
  We introduce two convergent series expansions (direct and recursive) in terms
of Bessel functions and representations of sums $r_N(m)$ of squares for
$N$-dimensional Madelung constants, $M_N(s)$, where $s$ is the exponent of the
Madelung series (usually chosen as $s=1/2$). The functional behavior including
analytical continuation, and the convergence of the Bessel function expansion
is discussed in detail. Recursive definitions are used to evaluate $r_N(m)$.
Values for $M_N(s)$ for $s=\tfrac{1}{2}, \tfrac{3}{2}, 3$ and 6 for dimension
up to $N=20$ and for $M_N(1/2)$ up to $N=100$ are presented. Zucker's original
analysis on $N$-dimensional Madelung constants for even dimensions up to $N=8$
and their possible continuation into higher dimensions is briefly analyzed.


title: Anomaly flow by an Aharonov-Bohm phase

abstract:
  In gauge-Higgs unification (GHU), gauge symmetry is dynamically broken by an
Aharonov-Bohm (AB) phase, $\theta_H$, in the fifth dimension. We analyze
$SU(2)$ GHU with an $SU(2)$ doublet fermion in flat $M^4 \times (S^1/Z_2)$
spacetime and in the Randall-Sundrum (RS) warped space. With orbifold boundary
conditions the $U(1)$ part of gauge symmetry remains unbroken at $\theta_H = 0$
and $\pi$. The fermion multiplet has chiral zero modes at $\theta_H = 0$, which
become massive at $\theta_H = \pi$. In other words chiral fermions are
transformed to vectorlike fermions by the AB phase $\theta_H$. Chiral anomaly
at $\theta_H = 0$ continuously varies as $\theta_H$ and vanishes at
$\theta_H=\pi$. We demonstrate this intriguing phenomenon in the RS space in
which there occurs no level crossing in the mass spectrum and everything varies
smoothly. The flat spacetime limit is singular as the AdS curvature of the RS
space diminishes, and reproduces the result in the flat spacetime. Anomalies
appear for various combinations of Kaluza-Klein excitation modes of gauge
fields as well. Although the magnitude of anomalies depends on $\theta_{H}$ and
the warp factor of the RS space, it does not depend on the bulk mass parameter
of the fermion field controlling its mass and wave function at general
$\theta_H$.


title:
Magnetic domain engineering in bilayer CrI$_3$ by applying external
  electric fields

abstract:
  Motivated by manipulating the magnetic order of bilayer CrI$_3$, we carry out
microscopic calculations to find the magnetic order and various magnetic
domains of the system in the presence of an electric field. Making use of
density functional simulations, a spin model Hamiltonian is introduced
consisting of isotropic exchange couplings, Dzyaloshinskii-Moriya (DM)
interaction, and on-site magnetic anisotropy. The spin dynamics of two
well-known states of bilayer CrI$_3$, low temperature (LT) and high temperature
(HT) phases, are obtained by solving the Landau-Lifshitz-Gilbert equation. We
show the magnetic texture is stacking-dependent in bilayer CrI$_3$ and stable
magnetic domains can appear in the HT stack which are tunable by external
electric and magnetic fields. Therefore, we suggest that the HT phase
represents a promising candidate for data storage in the modern generation of
spintronic devices which work based on magnetic domain engineering.


title:
SDEX: Monte Carlo Simulation of Stochastic Differential Equations on
  Memristor Crossbars

abstract:
  Here we present stochastic differential equations (SDEs) on a memristor
crossbar, where the source of gaussian noise is derived from the random
conductance due to ion drift in the devices during programming. We examine the
effects of line resistance on the generation of normal random vectors, showing
the skew and kurtosis are within acceptable bounds. We then show the
implementation of a stochastic differential equation solver for the
Black-Scholes SDE, and compare the distribution with the analytic solution. We
determine that the random number generation works as intended, and calculate
the energy cost of the simulation.


title:
Strongly lensed type Ia supernovae as a precise late-universe probe of
  measuring the Hubble constant and cosmic curvature

abstract:
  Strongly lensed type Ia supernovae (SNe Ia) are expected to have some
advantages in measuring time delays of multiple images, and so they have a
great potential to be developed into a powerful late-universe cosmological
probe. In this paper, we simulate a sample of lensed SNe Ia with time-delay
measurements in the era of the Legacy Survey of Space and Time (LSST). Based on
the distance sum rule, we use lensed SNe Ia to implement model-independent
constraints on the Hubble constant $H_0$ and cosmic curvature parameter
$\Omega_K$ in the late universe. We find that if 20 lensed SNe Ia could be
observed, the constraint on $H_{0}$ is better than the measurement by the SH0ES
collaboration. When the event number of lensed SNe Ia increases to 100, the
constraint precision of $H_{0}$ is comparable with the result from
\emph{Planck} 2018 data. Considering 200 lensed SNe Ia events as the optimistic
estimation, we obtain $\Delta H_0=0.33$ $\rm km\ s^{-1}\ Mpc^{-1}$ and
$\Delta\Omega_K=0.053$. In addition, we also simulate lensed quasars in
different scenarios to make a comparison and we find that they are still a
useful cosmological probe even though the constraint precision from them is
much less than that obtained from lensed SNe Ia. In the era of LSST, the
measurements of time delay from both lensed SNe Ia and lensed quasars are
expected to yield the results of $\Delta H_0=0.26 ~\rm km\ s^{-1}\ Mpc^{-1}$
and $\Delta\Omega_K=0.044$.


title:
Learning with Asymmetric Kernels: Least Squares and Feature
  Interpretation

abstract:
  Asymmetric kernels naturally exist in real life, e.g., for conditional
probability and directed graphs. However, most of the existing kernel-based
learning methods require kernels to be symmetric, which prevents the use of
asymmetric kernels. This paper addresses the asymmetric kernel-based learning
in the framework of the least squares support vector machine named AsK-LS,
resulting in the first classification method that can utilize asymmetric
kernels directly. We will show that AsK-LS can learn with asymmetric features,
namely source and target features, while the kernel trick remains applicable,
i.e., the source and target features exist but are not necessarily known.
Besides, the computational burden of AsK-LS is as cheap as dealing with
symmetric kernels. Experimental results on the Corel database, directed graphs,
and the UCI database will show that in the case asymmetric information is
crucial, the proposed AsK-LS can learn with asymmetric kernels and performs
much better than the existing kernel methods that have to do symmetrization to
accommodate asymmetric kernels.


title: Topological state transfer in Kresling origami

abstract:
  Topological mechanical metamaterials have been widely explored for their
boundary states, which can be robustly isolated or transported in a controlled
manner. However, such systems often require pre-configured design or complex
active actuation for wave manipulation. Here, we present the possibility of
in-situ transfer of topological boundary modes by leveraging the
reconfigurability intrinsic in twisted origami lattices. In particular, we
employ a dimer Kresling origami system consisting of unit cells with opposite
chirality, which couples longitudinal and rotational degrees of freedom in
elastic waves. The quasi-static twist imposed on the lattice alters the strain
landscape of the lattice, thus significantly affecting the wave dispersion
relations and the topology of the underling bands. This in turn facilitates an
efficient topological state transfer from one edge to the other. This simple
and practical approach of energy transfer in origami-inspired lattices can thus
inspire a new class of efficient energy manipulation devices.


title:
Effective Boundary Conditions for the Heat Equation with
  Three-dimensional Interior Inclusion

abstract:
  This paper is motivated by a heat equation on a domain containing an inside
layer, which is thin compared to the scale of the domain. Moreover, the thermal
conductivity in the layer is drastically different from that in the other part.
We study the effects of the layer by investigating the asymptotic behavior of
the solution of the heat equation with Dirichlet condition imposed on the outer
boundary, as the thickness of the layer shrinks. In this paper, particularly,
we focus on the case where the domain is three dimensional, and derive the
related "effective boundary conditions" (EBCs), which reveal the effects of the
inclusion.


title:
Constraining Dark matter annihilation with Dark Energy Survey Y3 LSBG
  sample

abstract:
  To reveal natures of the dark matter (DM) particles, a gamma-ray signal
produced in annihilation processes of DM into the standard model particles has
been one of the major probes. The cross-correlation between highly DM dominated
structures, such as local dwarf galaxies, and observed photons in the direction
of the structures, has been explored and provided stringent constraints on the
annihilation rate. In our previous work, we have shown that it is sufficient to
know the distance distribution of the galaxy sample and individual distance
measurement is not required for constraining the annihilation rate. In this
work, we apply the method to low-surface brightness galaxies (LSBGs) with
unknown individual redshifts provided from the Dark Energy Survey (DES) Year 3
data. With all DES-LSBGs of about 24,000 objects, we find that the upper limits
of the cross section for bb channel with 95% C.L. is 3*10^-25 cm^3/s at DM mass
of 100 GeV. To be more conservative, we remove about 7000 LSBGs within 1 degree
from resolved gamma-ray point sources and then the constraint becomes 30 %
weaker than the one with all samples in all DM mass ranges.


title:
Discovery and Long-term Broadband X-ray monitoring of Galactic Black
  Hole Candidate MAXI J1803-298

abstract:
  We report the results from the broad-band X-ray monitoring of the new
Galactic black hole candidate MAXI J1803$-$298 with the MAXI/GSC and Swift/BAT
during its outburst. After the discovery on 2021 May 1, the soft X-ray flux
below 10 keV rapidly increased for $\sim 10$ days and then have been gradually
decreasing over 5 months. At the brightest phase, the source exhibited the
state transition from the low/hard state to the high/soft state via the
intermediate state. The broad-band X-ray spectrum during the outburst was well
described with a disk blackbody plus its thermal or non-thermal Comptonization.
Before the transition the source spectrum was described by a thermal
Comptonization component with a photon index of $\sim 1.7$ and an electron
temperature of $\sim 30$ keV, whereas a strong disk blackbody component was
observed after the transition. The spectral properties in these periods are
consistent with the low/hard state and the high/soft state, respectively. A
sudden flux drop with a few days duration, unassociated with a significant
change in the hardness ratio, was found in the intermediate state. A possible
cause of this variation is that the mass accretion rate rapidly increased at
the disk transition, which induced a strong Compton-thick outflow and scattered
out the X-ray flux. Assuming a non-spinning black hole, we estimated a black
hole mass of MAXI J1803$-$298 as $5.8 \pm 0.4~(\cos i/\cos 70^\circ)^{-1/2}
(D/8~\mathrm{kpc})~M_\odot$ (where $i$ and $D$ are the inclination angle and
the distance) from the inner disk radius obtained in the high/soft state.


title: GALAXY: Graph-based Active Learning at the Extreme

abstract:
  Active learning is a label-efficient approach to train highly effective
models while interactively selecting only small subsets of unlabelled data for
labelling and training. In "open world" settings, the classes of interest can
make up a small fraction of the overall dataset -- most of the data may be
viewed as an out-of-distribution or irrelevant class. This leads to extreme
class-imbalance, and our theory and methods focus on this core issue. We
propose a new strategy for active learning called GALAXY (Graph-based Active
Learning At the eXtrEme), which blends ideas from graph-based active learning
and deep learning. GALAXY automatically and adaptively selects more
class-balanced examples for labeling than most other methods for active
learning. Our theory shows that GALAXY performs a refined form of uncertainty
sampling that gathers a much more class-balanced dataset than vanilla
uncertainty sampling. Experimentally, we demonstrate GALAXY's superiority over
existing state-of-art deep active learning algorithms in unbalanced vision
classification settings generated from popular datasets.


title: Quasi-normal g-modes of neutron stars with quarks

abstract:
  Quasi-normal oscillation modes of neutron stars provide a means to probe
their interior composition using gravitational wave astronomy. We compute the
frequencies and damping times of composition-dependent core g-modes of neutron
stars containing quark matter employing linearized perturbative equations of
general relativity. We find that ignoring background metric perturbations due
to the oscillating fluid, as in the Cowling approximation, underestimates the
g-mode frequency by up to 10% for higher mass stars, depending on the
parameters of the nuclear equation of state and how the mixed phase is
constructed. The g-mode frequencies are well-described by a linear scaling with
the central lepton (or combined lepton and quark) fraction for nucleonic
(hybrid) stars. Our findings suggest that neutron stars with and without quarks
are manifestly different with regards to their quasi-normal g-mode spectrum,
and may thus be distinguished from one another in future observations of
gravitational waves from merging neutron stars.


title: Proton Decay in SUSY GUTs

abstract:
  I review proton decay in the SUSY SU(5) GUTs assuming the mini-split SUSY
breaking model. In the mini-split SUSY breaking model, the squark and slepton
masses are O(10^{(2-3)}) TeV while the gaugino masses are O(1) TeV. As the
result, even the minimal SUSY SU(5) GUT is still viable in the model. We
present the motivation of the mini-split SUSY model and discuss the future
prospects of proton decay searches in the SUSY SU(5) GUTs.


title: Joint Speech Recognition and Audio Captioning

abstract:
  Speech samples recorded in both indoor and outdoor environments are often
contaminated with secondary audio sources. Most end-to-end monaural speech
recognition systems either remove these background sounds using speech
enhancement or train noise-robust models. For better model interpretability and
holistic understanding, we aim to bring together the growing field of automated
audio captioning (AAC) and the thoroughly studied automatic speech recognition
(ASR). The goal of AAC is to generate natural language descriptions of contents
in audio samples. We propose several approaches for end-to-end joint modeling
of ASR and AAC tasks and demonstrate their advantages over traditional
approaches, which model these tasks independently. A major hurdle in evaluating
our proposed approach is the lack of labeled audio datasets with both speech
transcriptions and audio captions. Therefore we also create a multi-task
dataset by mixing the clean speech Wall Street Journal corpus with multiple
levels of background noises chosen from the AudioCaps dataset. We also perform
extensive experimental evaluation and show improvements of our proposed methods
as compared to existing state-of-the-art ASR and AAC methods.


title:
Asteroid Measurements at Millimeter Wavelengths with the South Pole
  Telescope

abstract:
  We present the first measurements of asteroids in millimeter wavelength (mm)
data from the South Pole Telescope (SPT), which is used primarily to study the
cosmic microwave background (CMB). We analyze maps of two $\sim270$ deg$^2$ sky
regions near the ecliptic plane, each observed with the SPTpol camera $\sim100$
times over one month. We subtract the mean of all maps of a given field,
removing static sky signal, and then average the mean-subtracted maps at known
asteroid locations. We detect three asteroids$\text{ -- }$(324) Bamberga, (13)
Egeria, and (22) Kalliope$\text{ -- }$with signal-to-noise ratios (S/N) of
11.2, 10.4, and 6.1, respectively, at 2.0 mm (150 GHz); we also detect (324)
Bamberga with S/N of 4.1 at 3.2 mm (95 GHz). We place constraints on these
asteroids' effective emissivities, brightness temperatures, and light curve
modulation amplitude. Our flux density measurements of (324) Bamberga and (13)
Egeria roughly agree with predictions, while our measurements of (22) Kalliope
suggest lower flux, corresponding to effective emissivities of $0.66 \pm 0.11$
at 2.0 mm and $<0.47$ at 3.2mm. We predict the asteroids detectable in other
SPT datasets and find good agreement with detections of (772) Tanete and (1093)
Freda in recent data from the SPT-3G camera, which has $\sim10 \times$ the
mapping speed of SPTpol. This work is the first focused analysis of asteroids
in data from CMB surveys, and it demonstrates we can repurpose historic and
future datasets for asteroid studies. Future SPT measurements can help
constrain the distribution of surface properties over a larger asteroid
population.


title:
DigiQ: A Scalable Digital Controller for Quantum Computers Using SFQ
  Logic

abstract:
  The control of cryogenic qubits in today's superconducting quantum computer
prototypes presents significant scalability challenges due to the massive costs
of generating/routing the analog control signals that need to be sent from a
classical controller at room temperature to the quantum chip inside the
dilution refrigerator. Thus, researchers in industry and academia have focused
on designing in-fridge classical controllers in order to mitigate these
challenges. Superconducting Single Flux Quantum (SFQ) is a classical logic
family proposed for large-scale in-fridge controllers. SFQ logic has the
potential to maximize scalability thanks to its ultra-high speed and very low
power consumption. However, architecture design for SFQ logic poses challenges
due to its unconventional pulse-driven nature and lack of dense memory and
logic. Thus, research at the architecture level is essential to guide
architects to design SFQ-based classical controllers for large-scale quantum
machines.
  In this paper, we present DigiQ, the first system-level design of a Noisy
Intermediate Scale Quantum (NISQ)-friendly SFQ-based classical controller. We
perform a design space exploration of SFQ-based controllers and co-design the
quantum gate decompositions and SFQ-based implementation of those
decompositions to find an optimal SFQ-friendly design point that trades area
and power for latency and control while ensuring good quantum algorithmic
performance. Our co-design results in a single instruction, multiple data
(SIMD) controller architecture, which has high scalability (>42,000-qubit
scales), but imposes new challenges on the calibration of control pulses. We
present software-level solutions to address these challenges, which if
unaddressed would degrade quantum circuit fidelity given the imperfections of
qubit hardware.


title: One-sided composite cavity on an optical nanofiber for cavity QED

abstract:
  We demonstrate a one-sided cavity on an optical nanofiber (ONF) using a
composite method. The one-sided composite cavity is created by mounting an
asymmetric defect mode grating on an ONF. We design the one-sided composite
cavity on an ONF to enhance channeling efficiency into one side of ONF while
operating from under- to critical- and over-coupling regimes using numerical
simulations. Experimentally, we demonstrate coupling characteristics of the
one-sided composite cavity, showing good correspondence with simulation
results.


title:
Classification of solutions to equations involving Higher-order
  fractional Laplacian

abstract:
  In this paper, we are concerned with the following equation involving
higher-order fractional Lapalacian \begin{equation*} \left\{\begin{aligned}
&(-\Delta)^{p+{\frac{\alpha}{2}}}u(x)=u_+^\gamma~~ \mbox{ in }\mathbb{R}^n,\\
&\int_{\mathbb{R}^n}u_+^\gamma dx<+\infty, \end{aligned}\right. \end{equation*}
where $p\geq 1$ is an integer, $0<\alp<2$, $n> 2p+\alpha$ and $\gamma \in
(1,\frac{n}{n-2p-\alp})$. We establish an integral representation formula for
any nonconstant classical solution satisfying certain growth at infinity. From
this we prove that these solutions are radially symmetric about some point in
$\R^n$ and monotone decreasing in the radial direction via method of moving
planes in integral forms.


title: Sobolev spaces revisited

abstract:
  We describe a recent, one-parameter family of characterizations of Sobolev
and BV functions on $\mathbb{R}^n$, using sizes of superlevel sets of suitable
difference quotients. This provides an alternative point of view to the BBM
formula by Bourgain, Brezis and Mironescu, and complements in the case of BV
some results of Cohen, Dahmen, Daubechies and DeVore about the sizes of wavelet
coefficients of such functions. An application towards Gagliardo-Nirenberg
interpolation inequalities is then given. We also establish a related
one-parameter family of formulae for the $L^p$ norm of functions in
$L^p(\mathbb{R}^n)$.


title: Practical implications of SFQ-based two-qubit gates

abstract:
  Scalability of today's superconducting quantum computers is limited due to
the huge costs of generating/routing microwave control pulses per qubit from
room temperature. One active research area in both industry and academia is to
push the classical controllers to the dilution refrigerator in order to
increase the scalability of quantum computers. Superconducting Single Flux
Quantum (SFQ) is a classical logic technology with low power consumption and
ultra-high speed, and thus is a promising candidate for in-fridge classical
controllers with maximized scalability. Prior work has demonstrated
high-fidelity SFQ-based single-qubit gates. However, little research has been
done on SFQ-based multi-qubit gates, which are necessary to realize SFQ-based
universal quantum computing.
  In this paper, we present the first thorough analysis of SFQ-based two-qubit
gates. Our observations show that SFQ-based two-qubit gates tend to have high
leakage to qubit non-computational subspace, which presents severe design
challenges. We show that despite these challenges, we can realize gates with
high fidelity by carefully designing optimal control methods and qubit
architectures. We develop optimal control methods that suppress leakage, and
also investigate various qubit architectures that reduce the leakage. After
carefully engineering our SFQ-friendly quantum system, we show that it can
achieve similar gate fidelity and gate time to microwave-based quantum systems.
The promising results of this paper show that (1) SFQ-based universal quantum
computation is both feasible and effective; and (2) SFQ is a promising approach
in designing classical controller for quantum machines because it can increase
the scalability while preserving gate fidelity and performance.


title: Circle Squaring with Pieces of Small Boundary and Low Borel Complexity

abstract:
  Tarski's Circle Squaring Problem from 1925 asks whether it is possible to
partition a disk in the plane into finitely many pieces and reassemble them via
isometries to yield a partition of a square of the same area. It was finally
resolved by Laczkovich in 1990 in the affirmative. Recently, several new proofs
have emerged which achieve circle squaring with better structured pieces:
namely, pieces which are Lebesgue measurable and have the property of Baire
(Grabowski-M\'ath\'e-Pikhurko) or even are Borel (Marks-Unger).
  In this paper, we show that circle squaring is possible with Borel pieces of
positive Lebesgue measure whose boundaries have upper Minkowski dimension less
than 2 (in particular, each piece is Jordan measurable). We also improve the
Borel complexity of the pieces: namely, we show that each piece can be taken to
be a Boolean combination of $F_\sigma$ sets. This is a consequence of our more
general result that applies to any two bounded subsets of $R^k$, $k\ge 1$, of
equal positive measure whose boundaries have upper Minkowski dimension smaller
than $k$.


title:
New Insights into the Evolution of Massive Stars and Their Effects on
  Our Understanding of Early Galaxies

abstract:
  The observable characteristics and subsequent evolution of young stellar
populations is dominated by their massive stars. As our understanding of those
massive stars and the factors affecting their evolution improves, so our
interpretation of distant, unresolved stellar systems can also advance. As
observations increasingly probe the distant Universe, and the rare low
metallicity starbursts nearby, so the opportunity arises for these two fields
to complement one another, and lead to an improved conception of both stars and
galaxies. Here we review the current state of the art in modelling of massive
star dominated stellar populations, and discuss their applications and
implications for interpreting the distant Universe. Our principle findings
include:
  - Binary evolutionary pathways must be included to understand the stellar
populations in early galaxies.
  - Observations constraining the extreme ultraviolet spectrum of early
galaxies are showing that current models are incomplete. The best current guess
is that some form of accretion onto compact remnants is required.
  - The evolution and fates of very massive stars, of the order of 100Msun and
above, may be key to fully understand aspects of early galaxies.


title: DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts

abstract:
  Digitization of newspapers is of interest for many reasons including
preservation of history, accessibility and search ability, etc. While
digitization of documents such as scientific articles and magazines is
prevalent in literature, one of the main challenges for digitization of
newspaper lies in its complex layout (e.g. articles spanning multiple columns,
text interrupted by images) analysis, which is necessary to preserve human
read-order. This work provides a major breakthrough in the digitization of
newspapers on three fronts: first, releasing a dataset of 3000 fully-annotated,
real-world newspaper images from 21 different U.S. states representing an
extensive variety of complex layouts for document layout analysis; second,
proposing layout segmentation as a precursor to existing optical character
recognition (OCR) engines, where multiple state-of-the-art image segmentation
models and several post-processing methods are explored for document layout
segmentation; third, providing a thorough and structured evaluation protocol
for isolated layout segmentation and end-to-end OCR.


title:
Synthesis of Modeling, Visualization, and Programming in GeoGebra as an
  Effective Approach for Teaching and Learning STEM Topics

abstract:
  GeoGebra is an interactive geometry, algebra, statistics, and calculus
application designed for teaching and learn-ing math, science, and engineering.
Its dynamic interface allows its users to accurately and interactively
visualize their work, models, and results. GeoGebra employs the synthesis of
three key features: modeling, visualization, and programming (MVP). Many
studies have shown the positive effects of GeoGebra on the efficiency and
effectiveness of learning and teaching topics related to science, technology,
engineering, and mathematics. In this study, we dis-cuss how GeoGebra provides
an environment for learning that is very interactive and collaborative between
the learner and the instructor. We also show how integrating GeoGebra into the
learning scheme can help improve the skills and knowledge of school and
university students in numerous advanced mathematical courses, such as
calcu-lus, mathematical statistics, linear algebra, linear programming,
computer-aided design, computer-aided geomet-ric design, analytic and
projective geometry, and graphical representation. Therefore, this study shows
the effec-tiveness of GeoGebra and its MVP key features in science and
engineering, particularly in topics related to mathe-matics. Each key feature
of GeoGebra is thoroughly analyzed, and further analyses, along with how
GeoGebra can be helpful in different topics, are discussed.


title: Broken Bracelets and Kostant's Partition Function

abstract:
  Inspired by the work of Amdeberhan, Can, and Moll on broken necklaces, we
define a broken bracelet as a linear arrangement of marked and unmarked
vertices and introduce a generalization called $n$-stars, which is a collection
of $n$ broken bracelets whose final (unmarked) vertices are identified. Through
these combinatorial objects, we provide a new framework for the study of
Kostant's partition function, which counts the number of ways to express a
vector as a nonnegative integer linear combination of the positive roots of a
Lie algebra. Our main result establishes that (up to reflection) the number of
broken bracelets with a fixed number of unmarked vertices with nonconsecutive
marked vertices gives an upper bound for the value of Kostant's partition
function for multiples of the highest root of a Lie algebra of type $A$. We
connect this work to multiplex juggling sequences, as studied by Benedetti,
Hanusa, Harris, Morales, and Simpson, by providing a correspondence to an
equivalence relation on $n$-stars.


title:
Generalized Omega Turn Gait Enables Agile Limbless Robot Turning in
  Complex Environments

abstract:
  Reorientation (turning in plane) plays a critical role for all robots in any
field application, especially those that in confined spaces. While important,
reorientation remains a relatively unstudied problem for robots, including
limbless mechanisms, often called snake robots. Instead of looking at snakes,
we take inspiration from observations of the turning behavior of tiny nematode
worms C. elegans. Our previous work presented an in-place and in-plane turning
gait for limbless robots, called an omega turn, and prescribed it using a novel
two-wave template. In this work, we advance omega turn-inspired controllers in
three aspects: 1) we use geometric methods to vary joint angle amplitudes and
forward wave spatial frequency in our turning equation to establish a wide and
precise amplitude modulation and frequency modulation on omega turn; 2) we use
this new relationship to enable robots with fewer internal degrees of freedom
(i.e., fewer joints in the body) to achieve desirable performance, and 3) we
apply compliant control methods to this relationship to handle unmodelled
effects in the environment. We experimentally validate our approach on a
limbless robot that the omega turn can produce effective and robust turning
motion in various types of environments, such as granular media and rock pile.


title:
A hard spectrum diffuse $\gamma$-ray component associated with HII gas
  in the Galactic plane

abstract:
  We analyzed 12-year Fermi Large Area Telescope $\gamma$-ray data in the inner
Galaxy centered at (l=30$^{\deg}$, b=0$^{\deg}$.) and (l=330$^{\deg}$,
b=0$^{\deg}$). We found significant hardening of the spectrum of the diffuse
$\gamma$-ray emission in these regions as previously reported. We further
deduced that the diffuse $\gamma$ rays can be divided into two components from
the likelihood analysis. One component is associated with the total gas column
density and reveals a soft spectrum, while the other is associated with the HII
gas and presents a hard spectrum. Assuming the diffuse $\gamma$-ray emissions
are mainly produced through the interaction between cosmic rays (CRs) and the
ambient gas, these two components are produced by the CR populations with
spectral indices of 2.8 ("soft") and 2.3 ("hard"), respectively. We argue that
the hard CR population may come from the vicinity of the CR accelerators. The
soft CR population has a similar spectral shape and density as measured in the
solar neighborhood, which implies a uniform CR "sea" with a similar density and
spectral shape in the Galaxy.


title:
A quasinonexpansive extension of a mapping with an attractive point in a
  Hilbert space

abstract:
  In this paper, we show that, under appropriate conditions, there exists a
quasinonexpansive extension of a mapping with an attractive point in the sense
of Takahashi and Takeuchi (2011) such that the fixed point set of the extension
equals the attractive point set of the given mapping. Then using the
quasinonexpansive extension, we establish some convergence theorems for
approximating attractive points of a generalized hybrid mapping in the sense of
Kocourek, Takahashi, and Yao (2010).


title:
Anomalous sorption kinetics of self-interacting particles by a spherical
  trap

abstract:
  In this paper we propose a computational framework for the investigation of
the correlated motion between positive and negative ions exposed to the
attraction of a bubble surface that mimics the (oscillating) cell membrane. The
correlated diffusion of surfactants is described by a Poisson-Nernst-Planck
(PNP) system, in which the drift term is given by the gradient of a potential
which includes both the effect of the bubble and the Coulomb interaction
between the carriers. The latter term is obtained from the solution of a
self-consistent Poisson equation. For very short Debye lengths one can adopt
the so called Quasi-Neutral limit which drastically simplifies the system, thus
allowing for much faster numerical simulations. The paper has four main
objectives. The first one is to present a PNP model that describes ion charges
in presence of a trap. The second one is to provide benchmark tests for the
validation of simplified multiscale models under current development [1]. The
third one is to explore the relevance of the term describing the interaction
among the apolar tails of the anions. The last one is to quantitatively explore
the validity of the Quasi-Neutral limit by comparison with detailed numerical
simulation for smaller and smaller Debye lengths. In order to reach these
goals, we propose a simple and efficient Alternate Direction Implicit method
for the numerical solution of the non-linear PNP system, which guarantees
second order accuracy both in space and time, without requiring solution of
nonlinear equation at each time step. New semi-implicit scheme for a simplified
PNP system near quasi neutrality is also proposed.


title:
Characterization of Semantic Segmentation Models on Mobile Platforms for
  Self-Navigation in Disaster-Struck Zones

abstract:
  The role of unmanned vehicles for searching and localizing the victims in
disaster impacted areas such as earthquake-struck zones is getting more
important. Self-navigation on an earthquake zone has a unique challenge of
detecting irregularly shaped obstacles such as road cracks, debris on the
streets, and water puddles. In this paper, we characterize a number of
state-of-the-art FCN models on mobile embedded platforms for self-navigation at
these sites containing extremely irregular obstacles. We evaluate the models in
terms of accuracy, performance, and energy efficiency. We present a few
optimizations for our designed vision system. Lastly, we discuss the trade-offs
of these models for a couple of mobile platforms that can each perform
self-navigation. To enable vehicles to safely navigate earthquake-struck zones,
we compiled a new annotated image database of various earthquake impacted
regions that is different than traditional road damage databases. We train our
database with a number of state-of-the-art semantic segmentation models in
order to identify obstacles unique to earthquake-struck zones. Based on the
statistics and tradeoffs, an optimal CNN model is selected for the mobile
vehicular platforms, which we apply to both low-power and extremely low-power
configurations of our design. To our best knowledge, this is the first study
that identifies unique challenges and discusses the accuracy, performance, and
energy impact of edge-based self-navigation mobile vehicles for
earthquake-struck zones. Our proposed database and trained models are publicly
available.


title: Generation Alpha: Understanding the Next Cohort of University Students

abstract:
  Technology is changing at a blistering pace and is impacting on the way we
consider knowledge as a free commodity, along with the ability to apply skills,
concepts and understandings. Technology is aiding the way the world is
evolving, and its contributions to education are not an exemption. While
technology advances will play a crucial part in future teaching-learning
approaches, educators will also be challenged by the next higher-education
generation, the Alpha Generation. This entrepreneurial generation will embrace
the innovation, progressiveness, and advancement with the expectation that one
in two Generation Alphas will obtain a university degree. In anticipating the
educational challenges and opportunities of the future higher education
environment, this research reflected on Generation Alpha as the next cohort of
university students, considering their preferred learning styles, perceptions
and expectations relating to education. The research employed a theoretical
analysis based on the characteristics and traits that distinguishes Generation
Alpha, spearheaded by technology advances. The empirical investigation
considered three independent studies that were previous conducted by authors
from Slovakia, Hungary, Australia, and Turkey to understand the challenges and
opportunities pertaining to Generation Alpha. The research identified the
influence of social media, social connections, high levels of perceptions and
the Generation Alpha's ability to interpret information as strengths to
consider in future teaching-learning approaches in the higher education
environment. This research concluded with recommendations on how universities
could be transformed to ensure a better learning experience for Generation
Alpha students, aligned with their characteristics, perceptions and
expectations.


title:
Do new investment strategies take existing strategies' returns -- An
  investigation into agent-based models

abstract:
  Commodity trading advisors (CTAs), who mainly trade commodity futures, showed
good returns in the 2000s. However, since the 2010's, they have not performed
very well. One possible reason of this phenomenon is the emergence of
short-term reversal traders (STRTs) who prey on CTAs for profit. In this study,
I built an artificial market model by adding a CTA agent (CTAA) and STRT agent
(STRTA) to a prior model and investigated whether emerging STRTAs led to a
decrease in CTAA revenue to determine whether STRTs prey on CTAs for profit. To
the contrary, my results showed that a CTAA and STRTA are more likely to trade
and earn more when both exist. Therefore, it is possible that they have a
mutually beneficial relationship.