title: A Natural Model of Spontaneous CP Violation

abstract:
  We examine the possibility of building a natural non-supersymmetric model of
spontaneous CP violation equipped with the Nelson-Barr (NB) mechanism to
address the strong CP problem. Our approach is to utilize a doubly composite
dynamics where the first confinement of the CFT occurs at the scale of
spontaneous CP violation (SCPV) and the second confinement at the TeV scale. A
holographic dual description of this 4D set-up via a warped extra dimension
with three 3-branes provides an explicit realization of this idea. In this
model, radiative corrections to the strong CP phase are well under control, and
the coincidence of mass scales, which we generally encounter in NB models, is
addressed. Our model also provides an explanation to the quark Yukawa
hierarchies with a percent level of tuning, and a solution to the gauge
hierarchy problem just as in the usual Randall-Sundrum model with the Higgs
being localized on the TeV brane. We show that in our model the naturalness
criteria points to a low-scale spontaneous CP violation, potentially testable
at future colliders.


title: Self-Testing of a Single Quantum System: Theory and Experiment

abstract:
  Certifying individual quantum devices with minimal assumptions is crucial for
the development of quantum technologies. Here, we investigate how to leverage
single-system contextuality to realize self-testing. We develop a robust
self-testing protocol based on the simplest contextuality witness for the
simplest contextual quantum system, the Klyachko-Can-Binicio\u{g}lu-Shumovsky
(KCBS) inequality for the qutrit. We establish a lower bound on the fidelity of
the state and the measurements (to an ideal configuration) as a function of the
value of the witness under a pragmatic assumption on the measurements we call
the KCBS orthogonality condition. We apply the method in an experiment with
randomly chosen measurements on a single trapped $^{40}{\rm Ca}^+$ and
near-perfect detection efficiency. The observed statistics allow us to
self-test the system and provide the first experimental demonstration of
quantum self-testing of a single system. Further, we quantify and report that
deviations from our assumptions are minimal, an aspect previously overlooked by
contextuality experiments.


title: Neutron star mass formula with nuclear saturation parameters

abstract:
  We derive the empirical formulas for the neutron star mass and gravitational
redshift as a function of the central density and specific combination of the
nuclear saturation parameters, which are applicable to the stellar models
constructed with the central density up to threefold nuclear saturation
density. Combining the both empirical formulas, one also estimates the neutron
star radius. In practice, we find that the neutron star mass (radius) can be
estimated within $\sim 10\%$ (a few percent) accuracy by comparing the mass and
radius evaluated with our empirical formulas to those determined with the
specific equation of state. Since our empirical formulas directly connect the
neutron star mass and radius to the nuclear saturation parameters, one can
discuss the neutron star properties with the specific values of nuclear
saturation parameters constrained via nuclear experiments.


title:
Traveling Wave Form Description for Dirac Field and Its Deduction To
  Pauli Equation Type Forms in Quantum Mechanics

abstract:
  We derive an equivalent traveling wave form description for Dirac field. In
the non-relativistic limit, such form can reduce to inverse-Galilean
transformed Schrodinger-type equation. We find that, the resulting
two-component Schrodinger-type equation from the reduction of traveling wave
form description of Dirac field is different to the naive Galilean transformed
Schrodinger equation. Taking into account the interactions of the system to
electromagnetic field by adding proper forms of covariant derivative, the
traveling wave form description for Pauli equation can be similarly obtained in
the non-relativistic limit. Such descriptions allow one to choose arbitrary
convenient reference frame for quantum system involving spins. Using
Bargmann-Wigner formalism for field with arbitrary spin $s\geq 1/2$, which
satisfy Dirac-type equations in all its indices, the traveling wave description
for such a field can be similarly obtained from the traveling wave form
description of Dirac field, for example, for the spin-3/2 Rarita-Schwinger
field and spin-2 gravitational field.


title: The Data Airlock: infrastructure for restricted data informatics

abstract:
  Data science collaboration is problematic when access to operational data or
models from outside the data-holding organisation is prohibited, for a variety
of legal, security, ethical, or practical reasons. There are significant data
privacy challenges when performing collaborative data science work against such
restricted data. In this paper we describe a range of causes and risks
associated with restricted data along with the social, environmental, data, and
cryptographic measures that may be used to mitigate such issues. We then show
how these are generally inadequate for restricted data contexts and introduce
the 'Data Airlock' - secure infrastructure that facilitates 'eyes-off' data
science workloads. After describing our use-case we detail the architecture and
implementation of a first, single-organisation version of the Data Airlock
infrastructure. We conclude with outcomes and learning from this
implementation, and outline requirements for a second, federated version.


title: A categorification of the Lusztig--Vogan module

abstract:
  We construct two categorifications of the Lusztig--Vogan module associated to
a real reductive algebraic group. The first categorification is given by
semisimple complexes in an equivariant derived category, and the second is
constructed as a module category over Soergel bimodules. Our categorifications
are related by taking equivariant hypercohomology.


title:
Lipschitz metric isometries between Outer Spaces of virtually free
  groups

abstract:
  Dowdall and Taylor observed that given a finite-index subgroup of a free
group, taking covers induces an embedding from the Outer Space of the free
group to the Outer Space of the subgroup, that this embedding is an isometry
with respect to the (asymmetric) Lipschitz metric, and that the embedding sends
folding paths to folding paths. The purpose of this note is to extend this
result to virtually free groups. We further extend a result Francaviglia and
Martino, proving the existence of "candidates" for the Lipschitz distance
between points in the Outer Space of the virtually free group. Additionally we
identify a deformation retraction of the spine of the Outer Space for the
virtually free group with the space considered by Krsti\'c and Vogtmann.


title: Example-Based Vulnerability Detection and Repair in Java Code

abstract:
  The Java libraries JCA and JSSE offer cryptographic APIs to facilitate secure
coding. When developers misuse some of the APIs, their code becomes vulnerable
to cyber-attacks. To eliminate such vulnerabilities, people built tools to
detect security-API misuses via pattern matching. However, most tools do not
(1) fix misuses or (2) allow users to extend tools' pattern sets. To overcome
both limitations, we created Seader-an example-based approach to detect and
repair security-API misuses. Given an exemplar <insecure, secure>code pair,
Seader compares the snippets to infer any API-misuse template and corresponding
fixing edit. Based on the inferred info, given a program, Seader performs
inter-procedural static analysis to search for security-API misuses and to
propose customized fixes. For evaluation, we applied Seader to 28 <insecure,
secure> codepairs; Seader successfully inferred 21 unique API-misuse templates
and related fixes. With these <vulnerability, fix> patterns, we applied SEADER
to a program benchmark that has 86 known vulnerabilities. Seader detected
vulnerabilities with 95% precision, 72% recall, and82% F-score. We also applied
Seader to 100 open-source projects and manually checked 77 suggested repairs;
76 of the repairs were correct. Seader can help developers correctly use
security APIs.


title:
Solving Critical Problems of the Muon Collider Higgs Factory: Optics,
  Magnets and their Protection, Detector Backgrounds

abstract:
  A low-energy medium-luminosity Muon Collider (MC) is being studied as a
possible Higgs Factory (HF). Electrons from muon decays will deposit more than
300 kW in superconducting magnets of the HF collider ring. This imposes
significant challenges to superconducting (SC) magnets used in the MC storage
ring (SR) and interaction regions (IR). Magnet designs are proposed which
provide high operating gradient and magnetic field in a large aperture to
accommodate the large size of muon beams (due to low beta*), as well as a
cooling system to intercept the large heat deposition from the showers induced
by decay electrons. The distribution of heat deposition in the MC SR lattice
elements requires large-aperture magnets in order to accommodate thick high-Z
absorbers to protect the SC coils. Based on the developed MARS15 model and
intensive simulations, a sophisticated radiation protection system was designed
for the collider SR and IR to bring the peak power density in the
superconducting coils below the quench limit and reduce the dynamic heat
deposition in the cold mass by a factor of 100. The system consists of tight
tungsten masks in the magnet interconnect regions and elliptical tungsten
liners in the magnet aperture optimized individually for each magnet. These
also reduce the background particle fluxes in the collider detector.


title: Effects of photon field on entanglement generation in charged particles

abstract:
  The Bose-Marletto-Vedral (BMV) experiment [3, 4] is a proposal for testing
the quantum nature of gravity with entanglement due to Newtonian gravity. This
proposal has stimulated controversy on how the entanglement due to Newtonian
gravity is related to the essence of quantum gravity and the existence of
gravitons. Motivated by this, we analyze the entanglement generation between
two charged particles coupled to a photon field. We assume that each particle
is in a superposition of two trajectories and that the photon field is
initially in a coherent state. Based on covariant quantum electrodynamics, the
formula for the entanglement negativity of the charged particles is derived for
the first time. Adopting simple analytic trajectories of the particles, we
demonstrate the entanglement between them. It is observed that the entanglement
is suppressed by the decoherence due to the vacuum fluctuations of the photon
field. We also find that the effect of quantum superposition of bremsstrahlung
appears in the entanglement negativity formula. The similar structures between
the gravity theory and electromagnetic theory suggests that a similar feature
may be observed in the entanglement generation by quantum gravitational
radiation.


title: $(\mathcal{F},\mathcal{A})$-Gorenstein flat homological dimensions

abstract:
  In this paper we develop the homological properties of the $(\mathcal{L},
\mathcal{A})$-Gorenstein flat $R$-modules $\mathcal{GF}_{(\mathcal{F}(R),
\mathcal{A})}$ proposed by Gillespie. Where the class $\mathcal{A} \subseteq
\mathrm{Mod} (R^{op})$ sometimes corresponds to a duality pair $(\mathcal{L},
\mathcal{A})$. We study the weak global and finitistic dimensions that comes
with $\mathcal{GF}_{(\mathcal{F}(R), \mathcal{A})}$ and show that over a
$(\mathcal{L}, \mathcal{A})$-Gorenstein ring, the functor $-\otimes _R -$ is
left balanced over $\mathrm{Mod} (R^{op}) \times \mathrm{Mod} (R)$ by the
classes $\mathcal{GF}_{(\mathcal{F}(R^{op}), \mathcal{A})} \times
\mathcal{GF}_{(\mathcal{F}(R), \mathcal{A})}$. When the duality pair is
$(\mathcal{F} (R), \mathcal{FP}Inj (R^{op}))$ we recover the G. Yang's result
over a Ding-Chen ring, and we see that is new for $(\mathrm{Lev} (R),
\mathrm{AC} (R^{op}))$ among others.


title: Upper bound on the number of collisions of pinned billiard balls

abstract:
  We consider systems of "pinned balls," i.e., balls that have fixed positions
and pseudo-velocities. Pseudo-velocities change according to the same rules as
those for velocities of totally elastic collisions between moving balls. The
times of possible pseudo-collisions for different pairs of pinned balls are
chosen in an exogenous way. We give an explicit upper bound for the maximum
number of pseudo-collisions for a system of $n$ pinned balls in a
$d$-dimensional space. The proof is based on analysis of foldings, i.e.,
mappings that formalize the idea of folding a piece of paper along a crease. We
prove an upper bound for the size of an orbit of a point subjected to foldings.


title:
National Radio Dynamic Zone Concept with Autonomous Aerial and Ground
  Spectrum Sensors

abstract:
  National radio dynamic zone (NRDZs) are intended to be geographically bounded
areas within which controlled experiments can be carried out while protecting
the nearby licensed users of the spectrum. An NRDZ will facilitate research and
development of new spectrum technologies, waveforms, and protocols, in typical
outdoor operational environments of such technologies. In this paper, we
introduce and describe an NRDZ concept that relies on a combination of
autonomous aerial and ground sensor nodes for spectrum sensing and radio
environment monitoring (REM). We elaborate on key characteristics and features
of an NRDZ to enable advanced wireless experimentation while also coexisting
with licensed users. Some preliminary results based on simulation and
experimental evaluations are also provided on out-of-zone leakage monitoring
and real-time REMs.


title: Multivariate Stochastic Volatility Models and Large Deviation Principles

abstract:
  We establish a comprehensive sample path large deviation principle (LDP) for
log-processes associated with multivariate time-inhomogeneous stochastic
volatility models. Examples of models for which the new LDP holds include
Gaussian models, non-Gaussian fractional models, mixed models, models with
reflection, and models in which the volatility process is a solution to a
Volterra type stochastic integral equation. The LDP for log-processes is used
to obtain large deviation style asymptotic formulas for the distribution
function of the first exit time of a log-process from an open set and for the
price of a multidimensional binary barrier option. We also prove a sample path
LDP for solutions to Volterra type stochastic integral equations with
predictable coefficients depending on auxiliary stochastic processes.


title: Natural Language Communication with a Teachable Agent

abstract:
  Conversational teachable agents offer a promising platform to support
learning, both in the classroom and in remote settings. In this context, the
agent takes the role of the novice, while the student takes on the role of
teacher. This framing is significant for its ability to elicit the Prot\'eg\'e
effect in the student-teacher, a pedagogical phenomenon known to increase
engagement in the teaching task, and also improve cognitive outcomes. In prior
work, teachable agents often take a passive role in the learning interaction,
and there are few studies in which the agent and student engage in natural
language dialogue during the teaching task. This work investigates the effect
of teaching modality when interacting with a virtual agent, via the web-based
teaching platform, the Curiosity Notebook. A method of teaching the agent by
selecting sentences from source material is compared to a method paraphrasing
the source material and typing text input to teach. A user study has been
conducted to measure the effect teaching modality on the learning outcomes and
engagement of the participants. The results indicate that teaching via
paraphrasing and text input has a positive effect on learning outcomes for the
material covered, and also on aspects of affective engagement. Furthermore,
increased paraphrasing effort, as measured by the similarity between the source
material and the material the teacher conveyed to the robot, improves learning
outcomes for participants.


title:
Semantic-diversity transfer network for generalized zero-shot learning
  via inner disagreement based OOD detector

abstract:
  Zero-shot learning (ZSL) aims to recognize objects from unseen classes, where
the kernel problem is to transfer knowledge from seen classes to unseen classes
by establishing appropriate mappings between visual and semantic features. The
knowledge transfer in many existing works is limited mainly due to the facts
that 1) the widely used visual features are global ones but not totally
consistent with semantic attributes; 2) only one mapping is learned in existing
works, which is not able to effectively model diverse visual-semantic
relations; 3) the bias problem in the generalized ZSL (GZSL) could not be
effectively handled. In this paper, we propose two techniques to alleviate
these limitations. Firstly, we propose a Semantic-diversity transfer Network
(SetNet) addressing the first two limitations, where 1) a multiple-attention
architecture and a diversity regularizer are proposed to learn multiple local
visual features that are more consistent with semantic attributes and 2) a
projector ensemble that geometrically takes diverse local features as inputs is
proposed to model visual-semantic relations from diverse local perspectives.
Secondly, we propose an inner disagreement based domain detection module (ID3M)
for GZSL to alleviate the third limitation, which picks out unseen-class data
before class-level classification. Due to the absence of unseen-class data in
training stage, ID3M employs a novel self-contained training scheme and detects
out unseen-class data based on a designed inner disagreement criterion.
Experimental results on three public datasets demonstrate that the proposed
SetNet with the explored ID3M achieves a significant improvement against $30$
state-of-the-art methods.


title: Kan Extensions in Data Science and Machine Learning

abstract:
  A common problem in data science is "use this function defined over this
small set to generate predictions over that larger set." Extrapolation,
interpolation, statistical inference and forecasting all reduce to this
problem. The Kan extension is a powerful tool in category theory that
generalizes this notion. In this work we explore several applications of Kan
extensions to data science. We begin by deriving a simple classification
algorithm as a Kan extension and experimenting with this algorithm on real
data. Next, we use the Kan extension to derive a procedure for learning
clustering algorithms from labels and explore the performance of this procedure
on real data. We then investigate how Kan extensions can be used to learn a
general mapping from datasets of labeled examples to functions and to
approximate a complex function with a simpler one.


title: Permanence properties of splinters via ultrapower

abstract:
  We show that the splinter property ascends along regular residue field
extensions, and along arbitrary regular maps in equal characteristic under an
excellence hypothesis. We also study the splinter property of non-Noetherian
rings, especially those related to ultrapowers, to the extent necessary for our
main results.


title: Graph Augmentation Learning

abstract:
  Graph Augmentation Learning (GAL) provides outstanding solutions for graph
learning in handling incomplete data, noise data, etc. Numerous GAL methods
have been proposed for graph-based applications such as social network analysis
and traffic flow forecasting. However, the underlying reasons for the
effectiveness of these GAL methods are still unclear. As a consequence, how to
choose optimal graph augmentation strategy for a certain application scenario
is still in black box. There is a lack of systematic, comprehensive, and
experimentally validated guideline of GAL for scholars. Therefore, in this
survey, we in-depth review GAL techniques from macro (graph), meso (subgraph),
and micro (node/edge) levels. We further detailedly illustrate how GAL enhance
the data quality and the model performance. The aggregation mechanism of
augmentation strategies and graph learning models are also discussed by
different application scenarios, i.e., data-specific, model-specific, and
hybrid scenarios. To better show the outperformance of GAL, we experimentally
validate the effectiveness and adaptability of different GAL strategies in
different downstream tasks. Finally, we share our insights on several open
issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability,
and generalization.


title: Structure-Preserving Model Reduction for Nonlinear Power Grid Network

abstract:
  We develop a structure-preserving system-theoretic model reduction framework
for nonlinear power grid networks. First, via a lifting transformation, we
convert the original nonlinear system with trigonometric nonlinearities to an
equivalent quadratic nonlinear model. This equivalent representation allows us
to employ the $\mathcal{H}_2$-based model reduction approach, Quadratic
Iterative Rational Krylov Algorithm (Q-IRKA), as an intermediate model
reduction step. Exploiting the structure of the underlying power network model,
we show that the model reduction bases resulting from Q-IRKA have a special
subspace structure, which allows us to effectively construct the final model
reduction basis. This final basis is applied on the original nonlinear
structure to yield a reduced model that preserves the physically meaningful
(second-order) structure of the original model. The effectiveness of our
proposed framework is illustrated via two numerical examples.


title:
Robust Control Approaches for Minimizing the Bandwidth Ratio in
  Multi-Loop Control

abstract:
  Conventional dc-dc, dc-ac converter control entail an inner current and outer
voltage (ICOV) control loop. Stability of multi-loop control is achieved by
ensuring faster dynamics of inner loops, often separating the bandwidths by
large factors. Heuristically a factor-of-ten has often been used to separate
the bandwidths of two adjacent loops in the nested architecture. In this paper,
we present a numerical method to optimally select the ratio of bandwidths for a
nested controller. We first manipulate the robust framework to show that the
optimal $H_\infty$ subsumes the classical inner current outer voltage control.
We then use optimality of the proposed $H_\infty$ controller to inspect
feasibility of different ratios of bandwidths of the inner and outer
controllers. We finally select the numerically closest bandwidths of the two
nested loops which the guarantee stability. Simulation models are used to
verify the optimal bandwidth separation for a candidate grid-forming dc-ac
converter with inner current-outer voltage control.


title: Measuring Consumer Perceived Warm-Glow for Technology Adoption Modeling

abstract:
  In this paper, we adapt and validate two constructs, perceived extrinsic
warm-glow (PIWG) and perceived intrinsic warm-glow (PIWG), to measure the two
dimensions of consumer perceived warm-glow (i.e., extrinsic and intrinsic) for
use with the practice of technology adoption model-ing. Taking an experimental
approach, participants were exposed to one of four vignettes de-signed to
simulate the absence or the presence of warm-glow (specifically, extrinsic
warm-glow, intrinsic warm-glow, and concurrently extrinsic and intrinsic
warm-glow). The results revealed that both constructs measured their respective
forms of warm-glow with two caveats. The first that singularly trying to evoke
extrinsic warm-glow led to only a slight increase in consumer perception of
extrinsic warm-glow. We attributed this finding to individuals not being
attracted to technology products that overtly target and seek to satisfy their
vanity, instead preferring technology that does so subtly. The second that
singularly trying to evoke intrinsic warm-glow also resulted in the
manifestation of extrinsic warm-glow. Thus, warm-glow appears as a blend of
extrinsic and intrinsic dimensions. A finding serves to reinforce what has
already been reported in the warm-glow literature and the idea of impure
altruism.


title: Analytical study on magnetic component of geodesic acoustic mode

abstract:
  The magnetic components of geodesic acoustic mode (GAM) are analytically
investigated under the gyrokinetic framework with both the m=1 and m=2
harmonics are considered, where m is the poloidal mode number. With the
quasi-neutrality condition and Ampere's law, the amplitudes of various poloidal
magnetic components are derived. It is shown that both m=1 and m=2 magnetic
components exist and are dominated by the cosine and sine components,
respectively. In addition, it is found that the amplitudes of all magnetic
components increase with respect to the ratio of plasma pressure to magnetic
pressure \b{eta} and safety factor q. Most importantly, the amplitude of m=1
magnetic component is significantly enhanced due to the coupling of magnetic
drift frequency with the first and second harmonics of the distribution
functions, thus it can be comparable to that of m=2 magnetic component under
certain conditions.


title:
Sensitivity analysis in longitudinal clinical trials via distributional
  imputation

abstract:
  Missing data is inevitable in longitudinal clinical trials. Conventionally,
the missing at random assumption is assumed to handle missingness, which
however is unverifiable empirically. Thus, sensitivity analysis is critically
important to assess the robustness of the study conclusions against untestable
assumptions. Toward this end, regulatory agencies often request using
imputation models such as return-to-baseline, control-based, and washout
imputation. Multiple imputation is popular in sensitivity analysis; however, it
may be inefficient and result in an unsatisfying interval estimation by Rubin's
combining rule. We propose distributional imputation (DI) in sensitivity
analysis, which imputes each missing value by samples from its target
imputation model given the observed data. Drawn on the idea of Monte Carlo
integration, the DI estimator solves the mean estimating equations of the
imputed dataset. It is fully efficient with theoretical guarantees. Moreover,
we propose weighted bootstrap to obtain a consistent variance estimator, taking
into account the variabilities due to model parameter estimation and target
parameter estimation. The finite-sample performance of DI inference is assessed
in the simulation study. We apply the proposed framework to an antidepressant
longitudinal clinical trial involving missing data to investigate the
robustness of the treatment effect. Our proposed DI approach detects a
statistically significant treatment effect in both the primary analysis and
sensitivity analysis under certain prespecified sensitivity models in terms of
the average treatment effect, the risk difference, and the quantile treatment
effect in lower quantiles of the responses, uncovering the benefit of the test
drug for curing depression.


title: Complex Network Analysis of the Bitcoin Transaction Network

abstract:
  In this brief, we conduct a complex-network analysis of the Bitcoin
transaction network. In particular, we design a new sampling method, namely
random walk with flying-back (RWFB), to conduct effective data sampling. We
then conduct a comprehensive analysis of the Bitcoin network in terms of the
degree distribution, clustering coefficient, the shortest-path length,
connected component, centrality, assortativity, and the rich-club coefficient.
We obtain several important observations including the small-world phenomenon,
multi-center status, preferential attachment, and non-rich-club effect of the
current network. This work brings up an in-depth understanding of the current
Bitcoin blockchain network and offers implications for future directions in
malicious activity and fraud detection in cryptocurrency blockchain networks.


title:
Triangular Transfer: Freezing the Pivot for Triangular Machine
  Translation

abstract:
  Triangular machine translation is a special case of low-resource machine
translation where the language pair of interest has limited parallel data, but
both languages have abundant parallel data with a pivot language. Naturally,
the key to triangular machine translation is the successful exploitation of
such auxiliary data. In this work, we propose a transfer-learning-based
approach that utilizes all types of auxiliary data. As we train auxiliary
source-pivot and pivot-target translation models, we initialize some parameters
of the pivot side with a pre-trained language model and freeze them to
encourage both translation models to work in the same pivot language space, so
that they can be smoothly transferred to the source-target translation model.
Experiments show that our approach can outperform previous ones.


title:
Spurious Radial Migration from Relativistic Effects in the Milky-Way
  Disk

abstract:
  The gradient of the gravitational redshift in the potential of the Milky-Way
induces an apparent spurious radial migration. I show that this effect is
simply related to the local acceleration, which was measured recently by Gaia
eDR3, implying a spectroscopic shift of $[-2.4x10^{-2}/(r/8kpc)] km/s/kpc. The
transverse Doppler effect yields a comparable contribution. The spurious radial
velocity from both relativistic effects amounts to crossing a major portion of
the Milky-Way disk during the age of the universe, and must be corrected for in
any future measurement of the actual radial migration of stars.


title: Sub-Terahertz Wireless Coverage Analysis at 142 GHz in Urban Microcell

abstract:
  Small-cell cellular base stations are going to be used for mmWave and sub-THz
communication systems to provide multi-Gbps data rates and reliable coverage to
mobile users. This paper analyzes the base station coverage of sub-THz
communication systems and the system performance in terms of spectral
efficiency through Monte Carlo simulations for both single-cell and multi-cell
cases. The simulations are based on realistic channel models derived from
outdoor field measurements at 142 GHz in urban microcell (UMi) environments
conducted in downtown Brooklyn, New York. The single-cell base station can
provide a downlink coverage area with a radius of 200 m and the 7-cell system
can provide a downlink coverage area with a radius of 400 m at 142 GHz. Using a
1 GHz downlink bandwidth and 100 MHz uplink bandwidth, the 7-cell system can
provide about 4.5 Gbps downlink average data rate and 410 Mbps uplink average
data rate at 142 GHz.


title:
Theoretical tools for neutrino scattering: interplay between lattice
  QCD, EFTs, nuclear physics, phenomenology, and neutrino event generators

abstract:
  Maximizing the discovery potential of increasingly precise neutrino
experiments will require an improved theoretical understanding of
neutrino-nucleus cross sections over a wide range of energies. Low-energy
interactions are needed to reconstruct the energies of astrophysical neutrinos
from supernovae bursts and search for new physics using increasingly precise
measurement of coherent elastic neutrino scattering. Higher-energy interactions
involve a variety of reaction mechanisms including quasi-elastic scattering,
resonance production, and deep inelastic scattering that must all be included
to reliably predict cross sections for energies relevant to DUNE and other
accelerator neutrino experiments. This white paper discusses the theoretical
status, challenges, required resources, and path forward for achieving precise
predictions of neutrino-nucleus scattering and emphasizes the need for a
coordinated theoretical effort involved lattice QCD, nuclear effective
theories, phenomenological models of the transition region, and event
generators.

categories: hep-ph hep-ex hep-lat nucl-th physics.comp-ph


title:
Geometric Effect of High-Resolution Electron Energy Loss Spectroscopy on
  the Identification of Plasmons: An Example of Graphene

abstract:
  High-resolution electron energy loss spectroscopy (HREELS) is one of the most
powerful methods to detect the dispersion of plasmons. However, we find that in
the HREELS measurement, the scattering geometric configuration will seriously
affect the identification of plasmons. Here, taking graphene as an example,
using the HREELS capable of two-dimensional energy-momentum mapping combined
with the intensity distribution calculations, we visually display the intensity
distribution of the scattering geometric factor. We demonstrate that the energy
loss peaks from the scattering geometric effect may be misinterpreted as the
features of an acoustic plasmon. In any HREELS measurement, it is necessary to
evaluate the effect of the scattering geometry quantitatively to identify the
intrinsic surface excitations.


title:
Coordinated Power Control for Network Integrated Sensing and
  Communication

abstract:
  This correspondence paper studies a network integrated sensing and
communication (ISAC) system that unifies the interference channel for
communication and distributed radar sensing. In this system, a set of
distributed ISAC transmitters send individual messages to their respective
communication users (CUs), and at the same time cooperate with multiple sensing
receivers to estimate the location of one target. We exploit the coordinated
power control among ISAC transmitters to minimize their total transmit power
while ensuring the minimum signal-to-interference-plus-noise ratio (SINR)
constraints at individual CUs and the maximum Cram\'{e}r-Rao lower bound (CRLB)
requirement for target location estimation. Although the formulated coordinated
power control problem is non-convex and difficult to solve in general, we
propose two efficient algorithms to obtain high-quality solutions based on the
semi-definite relaxation (SDR) and CRLB approximation, respectively. Numerical
results show that the proposed designs achieve substantial performance gains in
terms of power reduction, as compared to the benchmark with a heuristic
separate communication-sensing design.


title: Phased Flight Trajectory Prediction with Deep Learning

abstract:
  The unprecedented increase of commercial airlines and private jets over the
next ten years presents a challenge for air traffic control. Precise flight
trajectory prediction is of great significance in air transportation
management, which contributes to the decision-making for safe and orderly
flights. Existing research and application mainly focus on the sequence
generation based on historical trajectories, while the aircraft-aircraft
interactions in crowded airspace especially the airspaces near busy airports
have been largely ignored. On the other hand, there are distinct
characteristics of aerodynamics for different flight phases, and the trajectory
may be affected by various uncertainties such as weather and advisories from
air traffic controllers. However, there is no literature fully considers all
these issues. Therefore, we proposed a phased flight trajectory prediction
framework. Multi-source and multi-modal datasets have been analyzed and mined
using variants of recurrent neural network (RNN) mixture. To be specific, we
first introduce spatio temporal graphs into the low-altitude airway prediction
problem, and the motion constraints of an aircraft are embedded to the
inference process for reliable forecasting results. In the en-route phase, the
dual attention mechanism is employed to adaptively extract much more important
features from overall datasets to learn the hidden patterns in dynamical
environments. The experimental results demonstrate our proposed framework can
outperform state-of-the-art methods for flight trajectory prediction for large
passenger/transport airplanes.


title:
GATE: Graph CCA for Temporal SElf-supervised Learning for
  Label-efficient fMRI Analysis

abstract:
  In this work, we focus on the challenging task, neuro-disease classification,
using functional magnetic resonance imaging (fMRI). In population graph-based
disease analysis, graph convolutional neural networks (GCNs) have achieved
remarkable success. However, these achievements are inseparable from abundant
labeled data and sensitive to spurious signals. To improve fMRI representation
learning and classification under a label-efficient setting, we propose a novel
and theory-driven self-supervised learning (SSL) framework on GCNs, namely
Graph CCA for Temporal self-supervised learning on fMRI analysis GATE.
Concretely, it is demanding to design a suitable and effective SSL strategy to
extract formation and robust features for fMRI. To this end, we investigate
several new graph augmentation strategies from fMRI dynamic functional
connectives (FC) for SSL training. Further, we leverage canonical-correlation
analysis (CCA) on different temporal embeddings and present the theoretical
implications. Consequently, this yields a novel two-step GCN learning procedure
comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning
on a small labeled fMRI dataset for a classification task. Our method is tested
on two independent fMRI datasets, demonstrating superior performance on autism
and dementia diagnosis.


title: HybridNets: End-to-End Perception Network

abstract:
  End-to-end Network has become increasingly important in multi-tasking. One
prominent example of this is the growing significance of a driving perception
system in autonomous driving. This paper systematically studies an end-to-end
perception network for multi-tasking and proposes several key optimizations to
improve accuracy. First, the paper proposes efficient segmentation head and
box/class prediction networks based on weighted bidirectional feature network.
Second, the paper proposes automatically customized anchor for each level in
the weighted bidirectional feature network. Third, the paper proposes an
efficient training loss function and training strategy to balance and optimize
network. Based on these optimizations, we have developed an end-to-end
perception network to perform multi-tasking, including traffic object
detection, drivable area segmentation and lane detection simultaneously, called
HybridNets, which achieves better accuracy than prior art. In particular,
HybridNets achieves 77.3 mean Average Precision on Berkeley DeepDrive Dataset,
outperforms lane detection with 31.6 mean Intersection Over Union with 12.83
million parameters and 15.6 billion floating-point operations. In addition, it
can perform visual perception tasks in real-time and thus is a practical and
accurate solution to the multi-tasking problem. Code is available at
https://github.com/datvuthanh/HybridNets.


title:
An Active Contour Model with Local Variance Force Term and Its Efficient
  Minimization Solver for Multi-phase Image Segmentation

abstract:
  In this paper, we propose an active contour model with a local variance force
(LVF) term that can be applied to multi-phase image segmentation problems. With
the LVF, the proposed model is very effective in the segmentation of images
with noise. To solve this model efficiently, we represent the regularization
term by characteristic functions and then design a minimization algorithm based
on a modification of the iterative convolution-thresholding method (ICTM),
namely ICTM-LVF. This minimization algorithm enjoys the energy-decaying
property under some conditions and has highly efficient performance in the
segmentation. To overcome the initialization issue of active contour models, we
generalize the inhomogeneous graph Laplacian initialization method (IGLIM) to
the multi-phase case and then apply it to give the initial contour of the
ICTM-LVF solver. Numerical experiments are conducted on synthetic images and
real images to demonstrate the capability of our initialization method, and the
effectiveness of the local variance force for noise robustness in the
multi-phase image segmentation.


title: Collision Avoidance of 3-Dimensional Objects in Dynamic Environments

abstract:
  Achieving collision avoidance between moving objects is an important
objective while determining robot trajectories. In performing collision
avoidance maneuvers, the relative shapes of the objects play an important role.
The literature largely models the shapes of the objects as spheres, and this
can make the avoidance maneuvers very conservative, especially when the objects
are of elongated shape and/or non-convex. In this paper, we model the shapes of
the objects using suitable combinations of ellipsoids and
one-sheeted/two-sheeted hyperboloids, and employ a collision cone approach to
achieve collision avoidance. We present a method to construct the 3-D collision
cone, and present simulation results demonstrating the working of the collision
avoidance laws.


title:
Optimal Control of Partially Observable Markov Decision Processes with
  Finite Linear Temporal Logic Constraints

abstract:
  Autonomous agents often operate in scenarios where the state is partially
observed. In addition to maximizing their cumulative reward, agents must
execute complex tasks with rich temporal and logical structures. These tasks
can be expressed using temporal logic languages like finite linear temporal
logic (LTL_f). This paper, for the first time, provides a structured framework
for designing agent policies that maximize the reward while ensuring that the
probability of satisfying the temporal logic specification is sufficiently
high. We reformulate the problem as a constrained partially observable Markov
decision process (POMDP) and provide a novel approach that can leverage
off-the-shelf unconstrained POMDP solvers for solving it. Our approach
guarantees approximate optimality and constraint satisfaction with high
probability. We demonstrate its effectiveness by implementing it on several
models of interest.


title:
Simulations of scrape-off layer power width for EAST H-mode plasma and
  ITER 15MA baseline scenario by 2D electrostatic turbulence code

abstract:
  The scrape-off layer power width (\lambda_q) is an important parameter for
characterizing the divertor heat loads. Many experimental, theoretical, and
numerical studies have been performed in recent years. In this paper, a 2D
electrostatic turbulence code, BOUT-HESEL, has been upgraded to simulate H-mode
plasmas for the first time. The code is validated against the previous
implementation and the experiments. The simulated \lambda_q is found to agree
quite well with the Eich scaling for the EAST H-mode discharge. The code is
utilized to simulate the ITER 15MA baseline scenario. The ITER simulation
reveals that the radial particle/heat transports are dominated by blobby
transports, and predicts \lambda_{q,ITER} = 9.6 mm, which is much larger than
the prediction by the Eich scaling. Based on the EAST modified cases, an
estimated HESEL H-mode scaling, \lambda_q=0.51R_c^1.1B_t^(-0.3)q_95^1.1 is
proposed. This scaling predicts \lambda_{q,ITER} = 9.3 mm, which agrees
surprisingly well with that for the ITER case. A further investigation combined
with the basic parameters in the database of the Eich scaling shows that the
missing positive scaling dependence on the machine size (Rc) in the Eich
scaling appears to be shaded by the negative scaling dependence on the toroidal
magnetic field (Bt) for current devices. This is however not the case for ITER,
explaining why simulations in recent studies and in this paper can reproduce
the Eich scaling for current devices, but predict a much larger \lambda_q for
ITER. According to the simulation results, the strong positive scaling
dependence of \lambda_q on Rc is due to a combination of slowing down the
parallel heat transports by increasing the parallel connection length and the
enhancement of the radial ExB turbulent heat transports when the machine size
is increased.


title: A Survey of Multi-Tenant Deep Learning Inference on GPU

abstract:
  Deep Learning (DL) models have achieved superior performance. Meanwhile,
computing hardware like NVIDIA GPUs also demonstrated strong computing scaling
trends with 2x throughput and memory bandwidth for each generation. With such
strong computing scaling of GPUs, multi-tenant deep learning inference by
co-locating multiple DL models onto the same GPU becomes widely deployed to
improve resource utilization, enhance serving throughput, reduce energy cost,
etc. However, achieving efficient multi-tenant DL inference is challenging
which requires thorough full-stack system optimization. This survey aims to
summarize and categorize the emerging challenges and optimization opportunities
for multi-tenant DL inference on GPU. By overviewing the entire optimization
stack, summarizing the multi-tenant computing innovations, and elaborating the
recent technological advances, we hope that this survey could shed light on new
optimization perspectives and motivate novel works in future large-scale DL
system optimization.


title: DATA: Domain-Aware and Task-Aware Self-supervised Learning

abstract:
  The paradigm of training models on massive data without label through
self-supervised learning (SSL) and finetuning on many downstream tasks has
become a trend recently. However, due to the high training costs and the
unconsciousness of downstream usages, most self-supervised learning methods
lack the capability to correspond to the diversities of downstream scenarios,
as there are various data domains, different vision tasks and latency
constraints on models. Neural architecture search (NAS) is one universally
acknowledged fashion to conquer the issues above, but applying NAS on SSL seems
impossible as there is no label or metric provided for judging model selection.
In this paper, we present DATA, a simple yet effective NAS approach specialized
for SSL that provides Domain-Aware and Task-Aware pre-training. Specifically,
we (i) train a supernet which could be deemed as a set of millions of networks
covering a wide range of model scales without any label, (ii) propose a
flexible searching mechanism compatible with SSL that enables finding networks
of different computation costs, for various downstream vision tasks and data
domains without explicit metric provided. Instantiated With MoCo v2, our method
achieves promising results across a wide range of computation costs on
downstream tasks, including image classification, object detection and semantic
segmentation. DATA is orthogonal to most existing SSL methods and endows them
the ability of customization on downstream needs. Extensive experiments on
other SSL methods demonstrate the generalizability of the proposed method. Code
is released at https://github.com/GAIA-vision/GAIA-ssl


title: Highly scalable, wearable surface-enhanced Raman spectroscopy

abstract:
  The last two decades have witnessed a dramatic growth of wearable sensor
technology, mainly represented by flexible, stretchable, on-skin electronic
sensors that provide rich information of the wearer's health conditions and
surroundings. A recent breakthrough in the field is the development of wearable
chemical sensors based on surface-enhanced Raman spectroscopy (SERS) that can
detect molecular fingerprints universally, sensitively, and noninvasively.
However, while their sensing properties are excellent, these sensors are not
scalable for widespread use beyond small-scale human health monitoring due to
their cumbersome fabrication process and limited multifunctional sensing
capabilities. Here we demonstrate a highly scalable, wearable SERS sensor based
on an easy-to-fabricate, low-cost, ultrathin, flexible, stretchable, adhesive,
and bio-integratable gold nanomesh. It can be fabricated in any shape and worn
on virtually any surface for label-free, large-scale, in-situ sensing of
diverse analytes from low to high concentrations (10 nM - 1 mM). To show the
practical utility of the wearable SERS sensor, we test the sensor for the
detection of sweat biomarkers, drugs of abuse, and microplastics. This wearable
SERS sensor represents a significant step toward the generalizability and
practicality of wearable sensing technology.


title:
Latent Image Animator: Learning to Animate Images via Latent Space
  Navigation

abstract:
  Due to the remarkable progress of deep generative models, animating images
has become increasingly efficient, whereas associated results have become
increasingly realistic. Current animation-approaches commonly exploit structure
representation extracted from driving videos. Such structure representation is
instrumental in transferring motion from driving videos to still images.
However, such approaches fail in case the source image and driving video
encompass large appearance variation. Moreover, the extraction of structure
information requires additional modules that endow the animation-model with
increased complexity. Deviating from such models, we here introduce the Latent
Image Animator (LIA), a self-supervised autoencoder that evades need for
structure representation. LIA is streamlined to animate images by linear
navigation in the latent space. Specifically, motion in generated video is
constructed by linear displacement of codes in the latent space. Towards this,
we learn a set of orthogonal motion directions simultaneously, and use their
linear combination, in order to represent any displacement in the latent space.
Extensive quantitative and qualitative analysis suggests that our model
systematically and significantly outperforms state-of-art methods on VoxCeleb,
Taichi and TED-talk datasets w.r.t. generated quality.


title:
Convert, compress, correct: Three steps toward communication-efficient
  DNN training

abstract:
  In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, for
communication-efficiency distributed Deep Neural Network (DNN) training.
$\mathsf{CO}_3$ is a joint training/communication protocol, which encompasses
three processing steps for the network gradients: (i) quantization through
floating-point conversion, (ii) lossless compression, and (iii) error
correction. These three components are crucial in the implementation of
distributed DNN training over rate-constrained links. The interplay of these
three steps in processing the DNN gradients is carefully balanced to yield a
robust and high-performance scheme. The performance of the proposed scheme is
investigated through numerical evaluations over CIFAR-10.


title: Construction of nearly pseudocompactification

abstract:
  A space is nearly pseudocompact if and only if $\upsilon X\backslash X$ is
dense in $\beta X\backslash X$. If we denote $K=cl_{\beta X}(\upsilon
X\backslash X)$, then $\delta X=X\cup(\beta X\backslash K)$ is referred by
Henriksen and Rayburn \cite{hr80} as nearly pseudocompact extension of $X$.
Henriksen and Rayburn studied the nearly pseudocompact extension using
different properties of $\beta X$. In this paper our main motivation is to
construct nearly pseudocompact extension of $X$ independently and not using any
kind of extension property of $\beta X$. An alternative construction of $\beta
X$ is made by taking the family of all $z$-ultrafilters on $X$ and then
topologized in a suitable way. In this paper we also adopted the similar idea
of constructing the $\delta X$ from the scratch, taking the collection of all
$z$-ultrafilters on $X$ of some kind, called $hz$-ultrafilters, together with
fixed $z$-ultrafilter and then be topologized in the similar way what we do in
the construction of $\beta X.$ We have further shown that the extension $\delta
X$ is unique with respect to certain properties.


title: Memristive deep belief neural network by silicon synapses

abstract:
  Memristor-based neuromorphic computing systems address the memory-wall issue
in von Neumann architecture that prevents the efficient training of deep neural
networks (DNNs). Nevertheless, emerging memristor devices, with the existence
of several non-idealities, such as poor yield and limited number of reliable
conductance states, are still not mature for practical neuromorphic systems. At
the same time, the mainstream neuromorphic DNNs utilize the error
backpropagation-based gradient descent algorithm, requiring ideal synaptic
behavior of the memristive devices and complex neural circuits. To address this
challenge, we demonstrate the training of a memristive restricted Boltzmann
machine (memristive RBM) and deep belief neural network (memristive DBN) that
employ memristor devices fabricated in a commercial complementary
metal-oxide-semiconductor process as artificial synapses and utilize the
gradient descent algorithm based on contrastive divergence (CD). The memristor
devices are based on the floating gate (FG) principle, showing analog
tunability, high endurance, long retention time, predictable cycling
degradation, moderate device-to-device variations, high yield, and two orders
of magnitude higher energy efficiency for multiply-accumulate operations than
today's graphical processing units. The CD-based gradient descent algorithm
highly relaxes both the requirements of the synaptic behavior for the memristor
devices and the complexity of the neuron circuits. Two 12-by-8 arrays of FG
memristors are utilized to demonstrate the training of an RBM intended for
pattern recognition. We then benchmark an extrapolated memristive DBN
consisting of three memristive RBMs for the MNIST dataset, showing a
recognition accuracy up to 97.05%. The designed memristive RBM and DBN have
minimal neuron circuits where no digital-to-analog and analog-to-digital
converters are needed.

categories: physics.app-ph cond-mat.dis-nn cond-mat.mtrl-sci cs.ET


title:
Three-dimensional third-order gas-kinetic scheme on hybrid unstructured
  meshes for Euler and Navier-Stokes equations

abstract:
  In this paper, a third order gas kinetic scheme is developed on the three
dimensional hybrid unstructured meshes for the numerical simulation of
compressible inviscid and viscous flows. A third-order WENO reconstruction is
developed on the hybrid unstructured meshes, including tetrahedron, pyramid,
prism and hexahedron. A simple strategy is adopted for the selection of big
stencil and sub-stencils. Incorporate with the two-stage fourth-order temporal
discretization and lower-upper symmetric Gauss-Seidel methods, both explicit
and implicit high-order gas-kinetic schemes are developed. A variety of
numerical examples, from the subsonic to supersonic flows, are presented to
validate the accuracy and robustness for both inviscid and viscous flows.


title: Hand-held 3D Photoacoustic Imager with GPS

abstract:
  As an emerging medical diagnostic technology, photoacoustic imaging has been
implemented for both preclinical and clinical applications. For clinical
convenience, a handheld free scan photoacoustic tomography (PAT) system
providing 3D imaging capability is essentially needed, which has potential for
surgical navigation and disease diagnosis. In this paper, we proposed a free
scan 3D PAT (fsPAT) system based on a handheld linear array ultrasound probe. A
global positioning system (GPS) is applied for ultrasound probes coordinate
acquisition. The proposed fsPAT can simultaneously realize real time 2D
imaging, and large field of view 3D volumetric imaging, which is reconstructed
from the multiple 2D images with coordinate information acquired by the GPS. To
form a high quality 3D image, a dedicated space transformation method and
reconstruction algorithm are used and validated by the proposed system. Both
simulation and experimental studies have been performed to prove the
feasibility of the proposed fsPAT. To explore its clinical potential, in vivo
3D imaging of human wrist vessels is also conducted, showing clear subcutaneous
vessel network with high image contrast.


title:
Anticollinear order and degeneracy lifting in square lattice
  antiferromagnet LaSrCrO4

abstract:
  We report the static and dynamic magnetic properties of LaSrCrO$_4$, a
seemingly canonical spin-3/2 square-lattice antiferromagnet that exhibits
frustration between magnetic layers -- owing to their AB stacking -- and offers
a rare testbed to investigate accidental-degeneracy lifting in magnetism.
Neutron diffraction experiments on single-crystal samples uncover a remarkable
anticollinear magnetic order below $T_N$ = 170 K characterized by a N\'eel
arrangement of the spins within each layer and an orthogonal arrangement
between adjacent layers. To understand the origin of this unusual magnetic
structure, we analyze the spin-wave excitation spectrum by means of inelastic
neutron scattering and bulk measurements. A spectral gap of 0.5 meV, along with
a spin-flop transition at 3.2\, T, reflect the energy scale associated with the
degeneracy-lifting. A minimal model to explain these observations requires both
a positive biquadratic interlayer exchange and dipolar interactions, both of
which are on the order of 10$^{-4}$ meV, only a few parts per million of the
dominant exchange interaction $J_1 \approx 11$ meV. These results provide
direct evidence for the selection of a non-collinear magnetic structure by the
combined effect of two distinct degeneracy lifting interactions.


title: Insights for post-pandemic pedagogy across one CS department

abstract:
  Adaptive remote instruction has led to important lessons for the future,
including rediscovery of known pedagogical principles in new contexts and new
insights for supporting remote learning. Studying one computer science
department that serves residential and remote undergraduate and graduate
students, we conducted interviews with stakeholders in the department (n=26)
and ran a department-wide student survey (n=102) during the four academic
quarters from spring 2020 to spring 2021. Our case study outlines what the
instructors did, summarizes what instructors and students say about courses
during this period, and provides recommendations for CS departments with
similar scope going forward. Specific insights address: (1) how instructional
components are best structured for students; (2) how students are assessed for
their learning; and (3) how students are supported in student-initiated
components of learning. The institution is a large U.S. research university
that has a history of online programs including online enrollment in regular
on-campus courses and large-scale open enrollment courses. Our recommendations
to instructors across the scope of this department may also be applicable to
other institutions that provide technology-supported in-person instruction,
remote enrollment, and hybrid courses combining both modalities.


title: The Emergence of Bulges and Disks in the Universe

abstract:
  This thesis makes use of the imaging data from the Advanced Camera for
Surveys (ACS) of the Hubble Space Telescope (HST) in the Cosmic Evolution
Survey (COSMOS) and the Deep Extragalactic VIsible Legacy Survey (DEVILS)
field. We provide visual morphological classifications of 44,000 galaxies out
to redshift $z = 1$ and above a stellar mass of $10^{9.5} M_\odot$ (D10/ACS
sample). We perform a robust Bayesian bulge-disk decomposition analysis of the
D10/ACS sample. This study forms one of the largest morphological
classification and structural analyses catalogues in this field to date. Using
these catalogues, we explore the evolution of the stellar mass function (SMF)
and the stellar mass density (SMD) together with the stellar mass-size
relations ($M_*-R_e$) of galaxies as a function of morphological type as well
as for disks and bulges, separately. We quantify that one-third of the current
stellar mass of the Universe was formed during the last 8 Gyr. We find that the
moderate growth of the high-mass end of the SMF is dominated by the growth of
elliptical systems and that the vast majority of the stellar mass of the
Universe is locked up in disk+bulge systems at all epochs and that they
increase their contribution to the total SMD with time. The contribution of the
pure-disk morphology gradually decreases with time ($\sim40\%$), while
ellipticals increase their contribution by a factor of $1.7$ since $z = 1$. By
decomposing galaxies into disks and bulges we quantify that on average
$\sim50\%$ of the total stellar mass of the Universe at all epochs is in disk
structures with this contribution relatively unchanged since $z \sim 0.6$. With
this comes more rapid growth of pseudo-bulges and spheroids (bulges and
ellipticals) in mass. Furthermore, while the cosmic star-formation history is
declining the Universe is transitioning from a disk dominated era to ...


title:
DU-VLG: Unifying Vision-and-Language Generation via Dual
  Sequence-to-Sequence Pre-training

abstract:
  Due to the limitations of the model structure and pre-training objectives,
existing vision-and-language generation models cannot utilize pair-wise images
and text through bi-directional generation. In this paper, we propose DU-VLG, a
framework which unifies vision-and-language generation as sequence generation
problems. DU-VLG is trained with novel dual pre-training tasks: multi-modal
denoising autoencoder tasks and modality translation tasks. To bridge the gap
between image understanding and generation, we further design a novel
commitment loss. We compare pre-training objectives on image captioning and
text-to-image generation datasets. Results show that DU-VLG yields better
performance than variants trained with uni-directional generation objectives or
the variant without the commitment loss. We also obtain higher scores compared
to previous state-of-the-art systems on three vision-and-language generation
tasks. In addition, human judges further confirm that our model generates real
and relevant images as well as faithful and informative captions.


title:
Reducing Position Bias in Simultaneous Machine Translation with
  Length-Aware Framework

abstract:
  Simultaneous machine translation (SiMT) starts translating while receiving
the streaming source inputs, and hence the source sentence is always incomplete
during translating. Different from the full-sentence MT using the conventional
seq-to-seq architecture, SiMT often applies prefix-to-prefix architecture,
which forces each target word to only align with a partial source prefix to
adapt to the incomplete source in streaming inputs. However, the source words
in the front positions are always illusoryly considered more important since
they appear in more prefixes, resulting in position bias, which makes the model
pay more attention on the front source positions in testing. In this paper, we
first analyze the phenomenon of position bias in SiMT, and develop a
Length-Aware Framework to reduce the position bias by bridging the structural
gap between SiMT and full-sentence MT. Specifically, given the streaming
inputs, we first predict the full-sentence length and then fill the future
source position with positional encoding, thereby turning the streaming inputs
into a pseudo full-sentence. The proposed framework can be integrated into most
existing SiMT methods to further improve performance. Experiments on two
representative SiMT methods, including the state-of-the-art adaptive policy,
show that our method successfully reduces the position bias and thereby
achieves better SiMT performance.


title:
Revisiting constraints on asymmetric dark matter from collapse in white
  dwarf stars

abstract:
  The runaway collapse phase of a small dark matter cluster inside a white
dwarf star encompasses a reversible stage, where heat can be transferred back
and forth between nuclear and dark matter. Induced nuclear burning phases are
stable and early carbon depletion undermines previous claims of type Ia
supernova ignition. Instead, mini black holes are formed at the center of the
star that either evaporate or accrete stellar material until a macroscopic
sub-Chandrasekhar-mass black hole is formed. In the latter case, a 0.1 to 1
second lasting electromagnetic transient signal can be detected upon ejection
of the white dwarf's potential magnetic field. Binary systems that transmute to
black holes and subsequently merge emit gravitational waves. Advanced
LIGO/Virgo should detect one such sub-Chandrasekhar binary black hole inspiral
per year, while future Einstein telescope-like facilities will detect thousands
per year. The effective spin parameter distribution is peaked at 0.2 and
permits future studies to disentangle from primordial sub-Chandrasekhar black
holes. Such signatures are compatible with current direct detection
constraints, as well as with neutron star constraints in the case of bosonic
dark matter, even though they remain in conflict with the fermionic case for
part of the parameter space.


title: Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT

abstract:
  Transformer-based pre-trained models, such as BERT, have shown extraordinary
success in achieving state-of-the-art results in many natural language
processing applications. However, deploying these models can be prohibitively
costly, as the standard self-attention mechanism of the Transformer suffers
from quadratic computational cost in the input sequence length. To confront
this, we propose FCA, a fine- and coarse-granularity hybrid self-attention that
reduces the computation cost through progressively shortening the computational
sequence length in self-attention. Specifically, FCA conducts an
attention-based scoring strategy to determine the informativeness of tokens at
each layer. Then, the informative tokens serve as the fine-granularity
computing units in self-attention and the uninformative tokens are replaced
with one or several clusters as the coarse-granularity computing units in
self-attention. Experiments on GLUE and RACE datasets show that BERT with FCA
achieves 2x reduction in FLOPs over original BERT with <1% loss in accuracy. We
show that FCA offers a significantly better trade-off between accuracy and
FLOPs compared to prior methods.


title:
Robust Table Detection and Structure Recognition from Heterogeneous
  Document Images

abstract:
  We introduce a new table detection and structure recognition approach named
RobusTabNet to detect the boundaries of tables and reconstruct the cellular
structure of the table from heterogeneous document images. For table detection,
we propose to use CornerNet as a new region proposal network to generate higher
quality table proposals for Faster R-CNN, which has significantly improved the
localization accuracy of Faster R-CNN for table detection. Consequently, our
table detection approach achieves state-of-the-art performance on three public
table detection benchmarks, namely cTDaR TrackA, PubLayNet and IIIT-AR-13K, by
only using a lightweight ResNet-18 backbone network. Furthermore, we propose a
new split-and-merge based table structure recognition approach, in which a
novel spatial CNN based separation line prediction module is proposed to split
each detected table into a grid of cells, and a Grid CNN based cell merging
module is applied to recover the spanning cells. As the spatial CNN module can
effectively propagate contextual information across the whole table image, our
table structure recognizer can robustly recognize tables with large blank
spaces and geometrically distorted (even curved) tables. Thanks to these two
techniques, our table structure recognition approach achieves state-of-the-art
performance on three public benchmarks, including SciTSR, PubTabNet and cTDaR
TrackB. Moreover, we have further demonstrated the advantages of our approach
in recognizing tables with complex structures, large blank spaces, empty or
spanning cells as well as geometrically distorted or even curved tables on a
more challenging in-house dataset.


title: A Real-Time Millimeter Wave V2V Channel Sounder

abstract:
  Wireless communication in millimeter wave spectrum is poised to provide the
latency and bandwidth needed for advanced use cases unfeasible at lower
frequencies. Despite the market potential of vehicular communication networks,
investigations into the millimeter wave vehicular channel are lacking. In this
paper, we present a detailed overview of a novel 1 GHz wide, multi-antenna
vehicle to vehicle directional channel sounding and measurement platform
operating at 28 GHz. The channel sounder uses two 256-element phased arrays at
the transmitter vehicle and four 64-element arrays at the receiver vehicle,
with the receiver measuring 116 different directional beams in less than 1
millisecond. By measuring the full multi-beam channel impulse response at large
bandwidths, our system provides unprecedented insight in instantaneous mobile
vehicle to vehicle channels. The system also uses centimeter-level global
position tracking and 360 degree video capture to provide additional contextual
information for joint communication and sensing applications. An initial
measurement campaign was conducted on highway and surface streets in Austin,
Texas. We show example data that highlights the sensing capability of the
system. Preliminary results from the measurement campaign show that bumper
mounted mmWave arrays provide rich scattering in traffic as well a provide
significant directional diversity aiding towards high reliability vehicular
communication. Additionally, potential waveguide effects from high traffic in
lanes can also extend the range of mmWave signals significantly.


title: On the $L^2$ boundedness of pseudo-multipliers for Hermite expansions

abstract:
  We give various conditions for Hermite pseudo-multipliers to be bounded on
$L^2(\mathbb{R}^n)$. As a by-product we also give results on
$L^p(\mathbb{R}^n)$, as well as new results for pseudo-multipliers for the
Gaussian measure setting. One of our key tools is a new integration by-parts
formula for Hermite expansions.


title:
TMR transition and highly sensitive pressure sensors based on magnetic
  tunnel junctions with black phosphorus barrier

abstract:
  Black phosphorus is a promising material to serve as the barrier of magnetic
tunnel junctions (MTJs) due to the weak van der Waals interlayer interactions.
In particular, the special band features of black phosphorus may bring
intriguing physical characteristics. Here, we study theoretically the effect of
band gap tunability of black phosphorus on the MTJs with black phosphorus
barrier. It is found that, the tunneling magnetoresistance (TMR) may achieve a
transition from finite value to infinity owing to the variation of the band gap
of black phosphorus. Combining with the latest experimental results of the
pressure-induced band gap tunability, we further investigate the pressure
effect of TMR in the MTJs with black phosphorus barrier. The calculations show
that the pressure sensitivity can be quite high under appropriate parameters.
Physically, the high sensitivity originates from the TMR transition phenomenon.
To take advantage of the high pressure sensitivity, we propose and design a
detailed structure of highly sensitive pressure sensors based on MTJs with
black phosphorus barrier, whose working mechanism is basically different from
the convential pressure sensors. The present pressure sensors possess four
advantages and benifits: (1) high sensitivity, (2) well anti-interference, (3)
high spatial resolution, and (4) fast response speed. Our study may advance new
research area for both the MTJs and pressure sensors.


title:
Chiral Majorana Fermions in two dimensional square lattice
  antiferromagnet with proximity-induced superconductivity

abstract:
  Proximity-induced superconductivity in two dimensional square lattice
antiferromagnet with spin-orbit coupling and nonsymmorphic symmetry could
induce topological superconductor phase with chiral Majorana edge states. The
tight binding model of Bogolyubov-de Gennes (BdG) Hamiltonian is applied to
study the phase diagram of bulks, and the chiral Majorana edge states in
nanoribbons. Numerical results show that difference between the superconducting
pairing parameters at two sublattices is necessary to induce topological
superconductor phase. The additional proximity to ferromagnetic substrate
modifies the phase diagram, in which uniform superconducting pairing parameters
could induce topological superconductor phase. The BdG Chern number of certain
topological superconductor phases is $\pm1$, so that the corresponding
nanoribbons have only one pair of chiral Majorana edge states.


title: Event-triggered boundary control of 2x2 semilinear hyperbolic systems

abstract:
  We present an event-triggered boundary control scheme for hyperbolic systems.
The trigger condition is based on predictions of the state on determinate sets,
where the control input is only updated if the predictions deviate from the
reference by a given margin. Closed-loop stability and absence of Zeno
behaviour is established analytically. For the special case of linear systems,
the trigger condition can be expressed in closed-form as an $L_2$-scalar
product of kernels with the distributed state. The presented controller can
also be combined with existing observers to solve the event-triggered
output-feedback control problem. A numerical simulation demonstrates the
effectiveness of the proposed approach.


title:
Hyperuniformity of the determinantal point processes associated with the
  Heisenberg group

abstract:
  The Ginibre point process is given by the eigenvalue distribution of a
non-hermitian complex Gaussian matrix in the infinite matrix-size limit. This
is a determinantal point process (DPP) on the complex plane ${\mathbb{C}}$ in
the sense that all correlation functions are given by determinants specified by
an integral kernel called the correlation kernel. Shirai introduced the
one-parameter ($m \in {\mathbb{N}}_0$) extensions of the Ginibre DPP and called
them the Ginibre-type point processes. In the present paper we consider a
generalization of the Ginibre and the Ginibre-type point processes on
${\mathbb{C}}$ to the DPPs in the higher-dimensional spaces, ${\mathbb{C}}^D,
D=2,3, \dots$, in which they are parameterized by a multivariate level $m \in
{\mathbb{N}}_0^D$. We call the obtained point processes the extended Heisenberg
family of DPPs, since the correlation kernels are generally identified with the
correlations of two points in the space of Heisenberg group expressed by the
Schr\"{o}dinger representations. We prove that all DPPs in this large family
are in Class I of hyperuniformity.

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


title:
Hierarchical Intention Tracking for Robust Human-Robot Collaboration in
  Industrial Assembly Tasks

abstract:
  Collaborative robots require effective intention estimation to safely and
smoothly work with humans in less structured tasks such as industrial assembly.
During these tasks, human intention continuously changes across multiple steps,
and is composed of a hierarchy including high-level interactive intention and
low-level task intention. Thus, we propose the concept of intention tracking
and introduce a collaborative robot system with a hierarchical framework that
concurrently tracks intentions at both levels by observing force/torque
measurements, robot state sequences, and tracked human trajectories. The
high-level intention estimate enables the robot to both (1) safely avoid
collision with the human to minimize interruption and (2) cooperatively
approach the human and help recover from an assembly failure through admittance
control. The low-level intention estimate provides the robot with task-specific
information (e.g., which part the human is working on) for concurrent task
execution. We implement the system on a UR5e robot, and demonstrate robust,
seamless and ergonomic collaboration between the human and the robot in an
assembly use case through an ablative pilot study.


title:
Attribute Surrogates Learning and Spectral Tokens Pooling in
  Transformers for Few-shot Learning

abstract:
  This paper presents new hierarchically cascaded transformers that can improve
data efficiency through attribute surrogates learning and spectral tokens
pooling. Vision transformers have recently been thought of as a promising
alternative to convolutional neural networks for visual recognition. But when
there is no sufficient data, it gets stuck in overfitting and shows inferior
performance. To improve data efficiency, we propose hierarchically cascaded
transformers that exploit intrinsic image structures through spectral tokens
pooling and optimize the learnable parameters through latent attribute
surrogates. The intrinsic image structure is utilized to reduce the ambiguity
between foreground content and background noise by spectral tokens pooling. And
the attribute surrogate learning scheme is designed to benefit from the rich
visual information in image-label pairs instead of simple visual concepts
assigned by their labels. Our Hierarchically Cascaded Transformers, called
HCTransformers, is built upon a self-supervised learning framework DINO and is
tested on several popular few-shot learning benchmarks.
  In the inductive setting, HCTransformers surpass the DINO baseline by a large
margin of 9.7% 5-way 1-shot accuracy and 9.17% 5-way 5-shot accuracy on
miniImageNet, which demonstrates HCTransformers are efficient to extract
discriminative features. Also, HCTransformers show clear advantages over SOTA
few-shot classification methods in both 5-way 1-shot and 5-way 5-shot settings
on four popular benchmark datasets, including miniImageNet, tieredImageNet,
FC100, and CIFAR-FS. The trained weights and codes are available at
https://github.com/StomachCold/HCTransformers.


title:
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point
  Cloud Dataset

abstract:
  Although various 3D datasets with different functions and scales have been
proposed recently, it remains challenging for individuals to complete the whole
pipeline of large-scale data collection, sanitization, and annotation.
Moreover, the created datasets usually suffer from extremely imbalanced class
distribution or partial low-quality data samples. Motivated by this, we explore
the procedurally synthetic 3D data generation paradigm to equip individuals
with the full capability of creating large-scale annotated photogrammetry point
clouds. Specifically, we introduce a synthetic aerial photogrammetry point
clouds generation pipeline that takes full advantage of open geospatial data
sources and off-the-shelf commercial packages. Unlike generating synthetic data
in virtual games, where the simulated data usually have limited gaming
environments created by artists, the proposed pipeline simulates the
reconstruction process of the real environment by following the same UAV flight
pattern on different synthetic terrain shapes and building densities, which
ensure similar quality, noise pattern, and diversity with real data. In
addition, the precise semantic and instance annotations can be generated fully
automatically, avoiding the expensive and time-consuming manual annotation.
Based on the proposed pipeline, we present a richly-annotated synthetic 3D
aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16
$km^2$ of landscapes and up to 18 fine-grained semantic categories. For
verification purposes, we also provide a parallel dataset collected from four
areas in the real environment. Extensive experiments conducted on our datasets
demonstrate the effectiveness and quality of the proposed synthetic dataset.


title: X-ray Polarization at the Crossroads

abstract:
  We argue that measurements of X-ray polarization using the recently launched
Imaging X-ray Polarimetry Explorer will answer many open questions about
magnetars in particular the physical state of their surfaces, whether vacuum
birefringence exists, and the nature of the hard X-ray emission from these
objects. We outline the capabilities of the instrument, specific models and the
results of simulations for the magnetar 4U~0142+61.


title: UNIMO-2: End-to-End Unified Vision-Language Grounded Learning

abstract:
  Vision-Language Pre-training (VLP) has achieved impressive performance on
various cross-modal downstream tasks. However, most existing methods can only
learn from aligned image-caption data and rely heavily on expensive regional
features, which greatly limits their scalability and performance. In this
paper, we propose an end-to-end unified-modal pre-training framework, namely
UNIMO-2, for joint learning on both aligned image-caption data and unaligned
image-only and text-only corpus. We build a unified Transformer model to
jointly learn visual representations, textual representations and semantic
alignment between images and texts. In particular, we propose to conduct
grounded learning on both images and texts via a sharing grounded space, which
helps bridge unaligned images and texts, and align the visual and textual
semantic spaces on different types of corpora. The experiments show that our
grounded learning method can improve textual and visual semantic alignment for
improving performance on various cross-modal tasks. Moreover, benefiting from
effective joint modeling of different types of corpora, our model also achieves
impressive performance on single-modal visual and textual tasks. Our code and
models are public at the UNIMO project page https://unimo-ptm.github.io/.


title:
Grating-based microcavity with independent control of resonance energy
  and linewidth for non-Hermitian polariton system

abstract:
  Exciton-polaritons have become an emerging platform for implementing
non-Hermitian physics. The implementation commonly requires control of both the
real and imaginary parts of the eigenmodes of the system. We present an
experimental method to achieve this purpose using microcavities with
sub-wavelength gratings as reflectors. The reflectivity and reflection phase of
the grating can be changed by its geometric parameters and they determine the
energy and linewidth of the polariton modes. We demonstrate that this method
allows a wide range of possible polariton energy and linewidth, suitable for
implementing non-Hermitian polariton systems with coupled modes.


title: A Rudin--de Leeuw type theorem for functions with spectral gaps

abstract:
  Our starting point is a theorem of de Leeuw and Rudin that describes the
extreme points of the unit ball in the Hardy space $H^1$. We extend this result
to subspaces of $H^1$ formed by functions with smaller spectra. More precisely,
given a finite set $\mathcal K$ of positive integers, we prove a Rudin--de
Leeuw type theorem for the unit ball of $H^1_{\mathcal K}$, the space of
functions $f\in H^1$ whose Fourier coefficients $\widehat f(k)$ vanish for all
$k\in\mathcal K$.


title: Proactive Posturing of Large Power Grid for Mitigating Hurricane Impacts

abstract:
  In the past decade, natural disasters such as hurricanes have challenged the
operation and control of U.S. power grid. It is crucial to develop proactive
strategies to assist grid operators for better emergency response and minimized
electricity service interruptions; the better the grid may be preserved, the
faster the grid can be restored. In this paper, we propose a proactive
posturing of power system elements, and formulate a Security-Constrained
Optimal Power Flow (SCOPF) informed by cross-domain hurricane modeling as well
as its potential impacts on grid elements. Simulation results based on
real-world power grid and historical hurricane event have verified the
applicability of the proposed optimization formulation, which shows potential
to enable grid operators and planners with interactive cross-domain data
analytics for mitigating hurricane impacts.


title:
Homotopy transfer for QFT on non-compact manifold with boundary: a case
  study

abstract:
  In this work we report a homological perturbation calculation to construct
effective theories of topological quantum mechanics on $\mathbb{R}_{\geqslant
0}$. Such calculation can be regarded as a generalization of Feynman graph
computation. The resulting effective theories fit into derived BV algebra
structure, which generalizes BV quantization. Besides, our construction may
serve as the simplest example of a process called "boundary transfer", which
may help study bulk-boundary correspondence.


title: Gaussian Multi-head Attention for Simultaneous Machine Translation

abstract:
  Simultaneous machine translation (SiMT) outputs translation while receiving
the streaming source inputs, and hence needs a policy to determine where to
start translating. The alignment between target and source words often implies
the most informative source word for each target word, and hence provides the
unified control over translation quality and latency, but unfortunately the
existing SiMT methods do not explicitly model the alignment to perform the
control. In this paper, we propose Gaussian Multi-head Attention (GMA) to
develop a new SiMT policy by modeling alignment and translation in a unified
manner. For SiMT policy, GMA models the aligned source position of each target
word, and accordingly waits until its aligned position to start translating. To
integrate the learning of alignment into the translation model, a Gaussian
distribution centered on predicted aligned position is introduced as an
alignment-related prior, which cooperates with translation-related soft
attention to determine the final attention. Experiments on En-Vi and De-En
tasks show that our method outperforms strong baselines on the trade-off
between translation and latency.


title: Ask to Understand: Question Generation for Multi-hop Question Answering

abstract:
  Multi-hop Question Answering (QA) requires the machine to answer complex
questions by finding scattering clues and reasoning from multiple documents.
Graph Network (GN) and Question Decomposition (QD) are two common approaches at
present. The former uses the "black-box" reasoning process to capture the
potential relationship between entities and sentences, thus achieving good
performance. At the same time, the latter provides a clear reasoning logical
route by decomposing multi-hop questions into simple single-hop sub-questions.
In this paper, we propose a novel method to complete multi-hop QA from the
perspective of Question Generation (QG). Specifically, we carefully design an
end-to-end QG module on the basis of a classical QA module, which could help
the model understand the context by asking inherently logical sub-questions,
thus inheriting interpretability from the QD-based method and showing superior
performance. Experiments on the HotpotQA dataset demonstrate that the
effectiveness of our proposed QG module, human evaluation further clarifies its
interpretability quantitatively, and thorough analysis shows that the QG module
could generate better sub-questions than QD methods in terms of fluency,
consistency, and diversity.


title:
Numerical simulations of semi-linear Klein-Gordon equations in the de
  Sitter spacetime with structure preserving scheme

abstract:
  We perform some simulations of the Klein-Gordon equation in the de Sitter
spacetime. We reported the stable and high-precision numerical results of the
equation using the structure preserving scheme (SPS) in an earlier publication
(Tsuchiya and Nakamura in J. Comput. Appl. Math. 361: 396--412, 2019). To
investigate the factors of the stability and accuracy of the simulations with
SPS, we perform some simulations with three discretized formulations. The first
formulation is the discretized equations with SPS, the second is with SPS which
replaced the second derivative term as the standard difference, and the third
is with SPS which replaced the non-linear term as the standard expression. As a
result, the above two replacements in SPS are found to be effective for stable
and high-precision simulations.


title:
Weaker yet again: mass spectrum-consistent cosmological constraints on
  the neutrino lifetime

abstract:
  We consider invisible neutrino decay $\nu_H \to \nu_l + \phi$ in the
ultra-relativistic limit and compute the neutrino anisotropy loss rate relevant
for the cosmic microwave background (CMB) anisotropies. Improving on our
previous work which assumed massless $\nu_l$ and $\phi$, we reinstate in this
work the daughter neutrino mass $m_{\nu l}$ in a manner consistent with the
experimentally determined neutrino mass splittings. We find that a nonzero
$m_{\nu l}$ introduces a new phase space factor in the loss rate $\Gamma_{\rm
T}$ proportional to $(\Delta m_\nu^2/m_{\nu_H}^2)^2$ in the limit of a small
squared mass gap between the parent and daughter neutrinos, i.e., $\Gamma_{\rm
T} \sim (\Delta m_\nu^2/m_{\nu H}^2)^2 (m_{\nu H}/E_\nu )^5 (1/\tau_0)$, where
$\tau_0$ is the $\nu_H$ rest-frame lifetime. Using a general form of this
result, we update the limit on $\tau_0$ using the Planck 2018 CMB data. We find
that for a parent neutrino of mass $m_{\nu H} \lesssim 0.1 {\rm eV}$, the new
phase space factor weakens the constraint on its lifetime by up to a factor of
50 if $\Delta m_\nu^2$ corresponds to the atmospheric mass gap and up to
$10^{5}$ if the solar mass gap, in comparison with naive estimates that assume
$m_{\nu l}=0$. The revised constraints are (i) $\tau^0 \gtrsim (6 \to 10)
\times 10^5~{\rm s}$ and $\tau^0 \gtrsim (400 \to 500)~{\rm s}$ if only one
neutrino decays to a daughter neutrino separated by, respectively, the
atmospheric and the solar mass gap, and (ii) $\tau^0 \gtrsim (2 \to 3) \times
10^7~{\rm s}$ in the case of two decay channels with one near-common
atmospheric mass gap. In contrast to previous, naive limits which scale as
$m_{\nu H}^5$, these mass spectrum-consistent $\tau_0$ constraints are
remarkably independent of the parent mass and open up a swath of parameter
space within the projected reach of IceCube and other neutrino telescopes in
the next two decades.


title: C$^3$ Demonstration Research and Development Plan

abstract:
  C$^3$ is an opportunity to realize an e$^+$e$^-$ collider for the study of
the Higgs boson at $\sqrt{s} = 250$ GeV, with a well defined upgrade path to
550 GeV while staying on the same short facility footprint. C$^3$ is based on a
fundamentally new approach to normal conducting linear accelerators that
achieves both high gradient and high efficiency at relatively low cost. Given
the advanced state of linear collider designs, the key system that requires
technical maturation for C$^3$ is the main linac. This white paper presents the
staged approach towards a facility to demonstrate C$^3$ technology with both
Direct (source and main linac) and Parallel (beam delivery, damping ring,
ancillary component) R&D. The white paper also includes discussion on the
approach for technology industrialization, related HEP R&D activities that are
enabled by C$^3$ R&D, infrastructure requirements and siting options.


title: Evaluating Posterior Distributions by Selectively Breeding Prior Samples

abstract:
  Using Markov chain Monte Carlo to sample from posterior distributions was the
key innovation which made Bayesian data analysis practical. Notoriously,
however, MCMC is hard to tune, hard to diagnose, and hard to parallelize. This
pedagogical note explores variants on a universal {\em non}-Markov-chain Monte
Carlo scheme for sampling from posterior distributions. The basic idea is to
draw parameter values from the prior distributions, evaluate the likelihood of
each draw, and then copy that draw a number of times proportional to its
likelihood. The distribution after copying is an approximation to the posterior
which becomes exact as the number of initial samples goes to infinity; the
convergence of the approximation is easily analyzed, and is uniform over
Glivenko-Cantelli classes. While not {\em entirely} practical, the schemes are
straightforward to implement (a few lines of R), easily parallelized, and
require no rejection, burn-in, convergence diagnostics, or tuning of any
control settings. I provide references to the prior art which deals with some
of the practical obstacles, at some cost in computational and analytical
simplicity.


title:
Study of spectrum of certain subrings of a commutative ring with
  identity

abstract:
  By a ring we always mean a commutative ring with identity. It is well known
that maximal spectrum of $C(X)$, $C^*(X)$ and any intermediate subrings between
$C(X)$ and $C^* (X)$ are homeomorphic and homeomorphic with $\beta X$, the
Stone-$\check{C}$ech compactification of $X$. In this paper we generalized
these results to an arbitrary ring by introducing a notion of dense subring. We
proved that if $A$ is completely normal and dense subring of $B$, then maximal
spectrum of $A$ and $B$ are homeomorphic and hence maximal spectrum of all
intermediate subrings between $A$ and $B$ where $A$ is dense, are homeomorphic.
We also proved that $A$ is dense subring of $B$ if and only if spectrum of $B$
is densely embedded in spectrum of $A$ and have further shown that if $A$ is
dense subring of $B$, any minimal prime ideal of $A$ is precisely of the form
$Q\cap A$ for some unique minimal prime ideal $Q$ of $B$. As a consequence, we
concluded that if $A$ is dense in $B$, minimal spectrum of $A$ and that of $B$
are homeomorphic. We also studied different properties of dense subrings of a
ring.


title:
On the convergence of decentralized gradient descent with diminishing
  stepsize, revisited

abstract:
  Distributed optimization has received a lot of interest in recent years due
to its wide applications in various fields. In this work, we revisit the
convergence property of the decentralized gradient descent [A.
Nedi{\'c}-A.Ozdaglar (2009)] on the whole space given by $$ x_i(t+1) =
\sum^m_{j=1}w_{ij}x_j(t) - \alpha(t) \nabla f_i(x_i(t)), $$ where the stepsize
is given as $\alpha (t) = \frac{a}{(t+w)^p}$ with $0< p\leq 1$. Under the
strongly convexity assumption on the total cost function $f$ with local cost
functions $f_i$ not necessarily being convex, we show that the sequence
converges to the optimizer with rate $O(t^{-p})$ when the values of $a>0$ and
$w>0$ are suitably chosen.


title: Experimental Quantum End-to-End Learning on a Superconducting Processor

abstract:
  Machine learning can be substantially powered by a quantum computer owing to
its huge Hilbert space and inherent quantum parallelism. In the pursuit of
quantum advantages for machine learning with noisy intermediate-scale quantum
devices, it was proposed that the learning model can be designed in an
end-to-end fashion, i.e., the quantum ansatz is parameterized by directly
manipulable control pulses without circuit design and compilation. Such
gate-free models are hardware friendly and can fully exploit limited quantum
resources. Here, we report the first experimental realization of quantum
end-to-end machine learning on a superconducting processor. The trained model
can achieve 98% recognition accuracy for two handwritten digits (via two
qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National
Institute of Standards and Technology) database. The experimental results
exhibit the great potential of quantum end-to-end learning for resolving
complex real-world tasks when more qubits are available.


title:
Do We Really Need a Learnable Classifier at the End of Deep Neural
  Network?

abstract:
  Modern deep neural networks for classification usually jointly learn a
backbone for representation and a linear classifier to output the logit of each
class. A recent study has shown a phenomenon called neural collapse that the
within-class means of features and the classifier vectors converge to the
vertices of a simplex equiangular tight frame (ETF) at the terminal phase of
training on a balanced dataset. Since the ETF geometric structure maximally
separates the pair-wise angles of all classes in the classifier, it is natural
to raise the question, why do we spend an effort to learn a classifier when we
know its optimal geometric structure? In this paper, we study the potential of
learning a neural network for classification with the classifier randomly
initialized as an ETF and fixed during training. Our analytical work based on
the layer-peeled model indicates that the feature learning with a fixed ETF
classifier naturally leads to the neural collapse state even when the dataset
is imbalanced among classes. We further show that in this case the cross
entropy (CE) loss is not necessary and can be replaced by a simple squared loss
that shares the same global optimality but enjoys a more accurate gradient and
better convergence property. Our experimental results show that our method is
able to achieve similar performances on image classification for balanced
datasets, and bring significant improvements in the long-tailed and
fine-grained classification tasks.


title:
Confidence Dimension for Deep Learning based on Hoeffding Inequality and
  Relative Evaluation

abstract:
  Research on the generalization ability of deep neural networks (DNNs) has
recently attracted a great deal of attention. However, due to their complex
architectures and large numbers of parameters, measuring the generalization
ability of specific DNN models remains an open challenge. In this paper, we
propose to use multiple factors to measure and rank the relative generalization
of DNNs based on a new concept of confidence dimension (CD). Furthermore, we
provide a feasible framework in our CD to theoretically calculate the upper
bound of generalization based on the conventional Vapnik-Chervonenk dimension
(VC-dimension) and Hoeffding's inequality. Experimental results on image
classification and object detection demonstrate that our CD can reflect the
relative generalization ability for different DNNs. In addition to
full-precision DNNs, we also analyze the generalization ability of binary
neural networks (BNNs), whose generalization ability remains an unsolved
problem. Our CD yields a consistent and reliable measure and ranking for both
full-precision DNNs and BNNs on all the tasks.


title:
Functional Renormalization Group Study of Superconductivity in
  Rhombohedral Trilayer Graphene

abstract:
  We employ a functional renormalization group approach to ascertain the
pairing mechanism and symmetry in the recently observed superconducting phase
of rhombohedral trilayer graphene. Superconductivity in this system occurs in a
regime of carrier density and displacement field with a weakly distorted
annular Fermi sea. We find that repulsive Coulomb interactions can induce
electron pairing on the Fermi surface by taking advantage of momentum-space
structure associated with its finite width. The degeneracy between spin-singlet
and spin-triplet pairing is lifted by valley-exchange interactions that
strengthen under the RG flow and develop nontrivial momentum-space structures.
In our calculation, the inner and outer Fermi surfaces prefer $d$-wave
spin-singlet and $p$-wave spin-triplet pairings, respectively. We predict that
a transition from the spin-singlet pairing observed at present to spin-triplet
pairing will occur when the electrical gates of the device are positioned
further from the dielectric encapsulated trilayer.