October 2022 arXiv papers — page 69

Jackson C. Turner, Elena Cherkaev, Dong Wang

Eigendecomposition of the Laplace-Beltrami operator is instrumental for a variety of applications from physics to data science. We develop a numerical method of computation of the eigenvalues and eigenfunctions of the Laplace-Beltrami operator on a smooth bounded domain based on the relaxation to the Schr\"odinger operator with finite potential on a Riemanni

Liangrui Pan, Lian Wang, Zhichao Feng, Zhujun Xu

Colorectal cancer (CRC) is among the top three malignant tumor types in terms of morbidity and mortality. Histopathological images are the gold standard for diagnosing colon cancer. Cellular nuclei instance segmentation and classification, and nuclear component regression tasks can aid in the analysis of the tumor microenvironment in colon tissue. Traditiona

Buxin Su

Most prime gaps results have been proven using tools from analytic or algebraic number theory in the last few centuries. In this paper, we would like to present some probabilistic way of proving many essential results. A major component of the proof is a probabilistic approach to the sieve method. In addition, we discuss their connections with recent work by

Wenting Wang, Jinkang Lim, Abhinav Kumar Vinod, Mingbin Yu

Temporally stabilized optical pules, confined in microresonators driven by a continuous-wave laser, have attracted tremendous attention due to their fascinating features with many applications. Here we report the observations of mode-locked platicon frequency microcomb formation in normal dispersion dispersion-managed microresonators operating at microwave K

Yao Zhao, Youyang Qu, Yong Xiang, Md Palash Uddin

Recent advances in edge computing~(EC) have pushed cloud-based data caching services to edge, however, such emerging edge storage comes with numerous challenging and unique security issues. One of them is the problem of edge data integrity verification (EDIV) which coordinates multiple participants (e.g., data owners and edge nodes) to inspect whether data c

Weidong Tang

In most Bell tests, the measurement settings are specially chosen so that the maximal quantum violations of the Bell inequalities can be detected, or at least, the violations are strong enough to be observed. Such choices can usually associate the corresponding Bell operators to a kind of effective observables, called pseudo Pauli operators, providing us a m

Hajime Inoue

We study ejection mechanisms for two kinds of steady jets: one observed from black hole binaries in the low/hard state and the other from SS433. The specific energy of the ejected gas is required to be positive for the jets to get to infinity, while that of the accreted gas is naively considered to be negative at the outermost boundary of the accretion flow.

Rasheed Abid, S. Kaisar Alam

Automatically segmenting lesion area in breast ultrasound (BUS) images is a challenging one due to its noise, speckle and artifacts. Edge-map of BUS images also does not help because in most cases the edge-map gives no information whatsoever. Almost all segmentation technique takes the edge-map of the image as its first step, though there are a few algorithm

Henry Lam, Zhenyuan Liu

The bootstrap is a popular data-driven method to quantify statistical uncertainty, but for modern high-dimensional problems, it could suffer from huge computational costs due to the need to repeatedly generate resamples and refit models. We study the use of bootstraps in high-dimensional environments with a small number of resamples. In particular, we show t

Xinran Zhu, Leo Huang, Cameron Ibrahim, Eric Hans Lee

The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a joint prior over input warping and kernel hyperparameters. This fully Bayesian treatment of hyperparameters often provides more accurate regression estimates and superior uncertaint

Vijay John, Yasutomo Kawanishi

Utilizing the sensor characteristics of the audio, visible camera, and thermal camera, the robustness of person recognition can be enhanced. Existing multimodal person recognition frameworks are primarily formulated assuming that multimodal data is always available. In this paper, we propose a novel trimodal sensor fusion framework using the audio, visible,

Di Zhang, Youzhou Zhou

The goal of cryptocurrencies is decentralization. In principle, all currencies have equal status. Unlike traditional stock markets, there is no default currency of denomination (fiat), thus the trading pairs can be set freely. However, it is impractical to set up a trading market between every two currencies. In order to control management costs and ensure s

Min Fu, Yuanming Shi, Yong Zhou

To enable communication-efficient federated learning (FL), this paper studies an unmanned aerial vehicle (UAV)-enabled FL system, where the UAV coordinates distributed ground devices for a shared model training. Specifically, by exploiting the UAV's high altitude and mobility, the UAV can proactively establish short-distance line-of-sight links with devices

Yijin Huang, Junyan Lyu, Pujin Cheng, Roger Tam

Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised image Transformer (SSiT) is proposed for Diabetic Retinopathy (DR) grading from fundus images. We novelly introduc

Hsien-Kuei Hwang, Svante Janson, Tsung-Hsi Tsai

We study divide-and-conquer recurrences of the form \begin{equation*} f(n) = \alpha f(\lfloor \tfrac n2\rfloor) + \beta f(\lceil \tfrac n2\rceil) + g(n) \qquad(n\ge2), \end{equation*} with $g(n)$ and $f(1)$ given, where $\alpha,\beta\ge0$ with $\alpha+\beta>0$; such recurrences appear often in analysis of computer algorithms, numeration systems, combinatoria

Yong-Shiuan Lee

Variable selection is crucial for sparse modeling in this age of big data. Missing values are common in data, and make variable selection more complicated. The approach of multiple imputation (MI) results in multiply imputed datasets for missing values, and has been widely applied in various variable selection procedures. However, directly performing variabl

T. Gomyou, S. Nayatani

Given a length function on the edge set of a finite graph, we define a vertex-weight and an edge-weight in terms of it and consider the corresponding graph Laplacian. In this paper, we consider the problem of maximizing the first nonzero eigenvalue of this Laplacian over all edge-length functions subject to a certain normalization. For an extremal solution o

Yilin Wang, Yiheng Feng

Model-based and learning-based methods are two major types of methodologies to model car following behaviors. Model-based methods describe the car-following behaviors with explicit mathematical equations, while learning-based methods focus on getting a mapping between inputs and outputs. Both types of methods have advantages and weaknesses. Meanwhile, most c

Zeel B Patel, Nipun Batra, Kevin Murphy

Gaussian processes are Bayesian non-parametric models used in many areas. In this work, we propose a Non-stationary Heteroscedastic Gaussian process model which can be learned with gradient-based techniques. We demonstrate the interpretability of the proposed model by separating the overall uncertainty into aleatoric (irreducible) and epistemic (model) uncer

Min Fu, Yong Zhou, Yuanming Shi, Chunxiao Jiang

In this paper, we study unmanned aerial vehicles (UAVs) assisted wireless data aggregation (WDA) in multicluster networks, where multiple UAVs simultaneously perform different WDA tasks via over-the-air computation (AirComp) without terrestrial base stations. This work focuses on maximizing the minimum amount of WDA tasks performed among all clusters by opti

Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang

This paper integrates manifold learning techniques within a \emph{Gaussian process upper confidence bound} algorithm to optimize an objective function on a manifold. Our approach is motivated by applications where a full representation of the manifold is not available and querying the objective is expensive. We rely on a point cloud of manifold samples to de

Andrew K. Yi, Saebyeok Ahn, Çağlar Kutlu, JinMyeong Kim

We report an axion dark matter search at Dine-Fischler-Srednicki-Zhitnitskii sensitivity with the CAPP-12TB haloscope, assuming axions contribute 100\% of the local dark matter density. The search excluded the axion--photon coupling $g_{a\gamma\gamma}$ down to about $6.2\times10^{-16}$ GeV$^{-1}$ over the axion mass range between 4.51 and 4.59 $\mu$eV at a 9

Mingi Kwon, Jaeseok Jeong, Youngjung Uh

Diffusion models achieve outstanding generative performance in various domains. Despite their great success, they lack semantic latent space which is essential for controlling the generative process. To address the problem, we propose asymmetric reverse process (Asyrp) which discovers the semantic latent space in frozen pretrained diffusion models. Our seman

Jianqiu Chen, Mingshan Sun, Ye Zheng, Tianpeng Bao

Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are implicitly influenced by their poses, the network allows inferring the pose by analyzing the differences in features in the visible region. However, due to the unpredictable and un

Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang

The increasing data privacy concerns in recommendation systems have made federated recommendations (FedRecs) attract more and more attention. Existing FedRecs mainly focus on how to effectively and securely learn personal interests and preferences from their on-device interaction data. Still, none of them considers how to efficiently erase a user's contribut

Yichao Wang, Jie Yang

In this paper, we present, Wi-Mesh, a WiFi vision-based 3D human mesh construction system. Our system leverages the advances of WiFi to visualize the shape and deformations of the human body for 3D mesh construction. In particular, it leverages multiple transmitting and receiving antennas on WiFi devices to estimate the two-dimensional angle of arrival (2D A

Zefan Yang, Di Lin, Dong Ni, Yi Wang

Scribble-supervised medical image segmentation tackles the limitation of sparse masks. Conventional approaches alternate between: labeling pseudo-masks and optimizing network parameters. However, such iterative two-stage paradigm is unwieldy and could be trapped in poor local optima since the networks undesirably regress to the erroneous pseudo-masks. To add

Benjamin Elder, Jeremy Sakstein

We derive the weak field limit of scalar-Gauss-Bonnet theory and place novel bounds on the parameter space using terrestrial and space-based experiments. In order to analyze the theory in the context of a wide range of experiments, we compute the deviations from Einstein gravity around source masses with planar, cylindrical, and spherical symmetry. We find a

Kin Ming Hui, Kai-Seng Chou

It is shown every nonnegative solution of the heat equation in a bounded cylindrical domain has an integral representation in terms of a trace triple consisting of a bottom trace, a corner trace and a lateral trace on its parabolic boundary. Conversely this trace triple uniquely determines the solution.

Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner

Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions. While BO has been successfully applied to a wide range of scientific applications, traditional approaches to single-objective BO only seek to find a single best solution. This can be a significant limitation in situations where solutions may l

Joshua C. Chang, Carson C. Chow, Julia Porcino

The Work Disability Functional Assessment Battery (WD-FAB) is a multidimensional item response theory (IRT) instrument designed for assessing work-related mental and physical function based on responses to an item bank. In prior iterations it was developed using traditional means -- linear factorization and null hypothesis statistical testing for item partit

Yukun Feng, Patrick Xia, Benjamin Van Durme, João Sedoc

Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying smaller yet domain-representative subsets. We extend Cynical Data Selection, a statistical sentence scoring method that conditions on a

F. Bivort Haiek, A. M. P. Mendez, C. C. Montanari, D. M. Mitnik

The International Atomic Energy Agency (IAEA) stopping power database is a highly valued public resource compiling most of the experimental measurements published over nearly a century. The database-accessible to the global scientific community-is continuously updated and has been extensively employed in theoretical and experimental research for more than 30

Hongxing Li, Wei-Bing Zhang, Guanghui Zhou

The van der Waals (vdW) magnetic heterostructures provide flexible ways to realize particular magnetic properties that possess both scientific and practical significance. Here, by firstprinciples calculation, we predict strong Dzyaloshinskii-Moriya interactions (DMIs) by constructing CrI3/Metal vdW heterostructures. The underlaying mechanisms are ascribed th

Yiqian Chen, Peng Wang, Houwen Wu, Haitang Yang

It has been recently reported that, at late times, the total luminosity of a star freely falling in black holes decays exponentially with time, and one or two series of flashes with decreasing intensity are seen by a specific observer, depending on the number of photon spheres. In this paper, we examine observational appearances of an infalling star in a ref

Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian

Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems. Unfortunately, the majority of real-world data are unlabeled and can be highly heterogeneous across sources. In this work, we carefully study decentralized learning with unlabeled data thro

Alexander P. Wu, Thomas Markovich, Bonnie Berger, Nils Hammerla

Graph attention networks estimate the relational importance of node neighbors to aggregate relevant information over local neighborhoods for a prediction task. However, the inferred attentions are vulnerable to spurious correlations and connectivity in the training data, hampering the generalizability of the model. We introduce CAR, a general-purpose regular

Lu Wang, Saizhe Ding, Hang Yu, Xudong Li

The existence of bottlenecks often leads to the stagnation of pedestrian gatherings, which seriously affects the efficiency of traffic and reduces the flow of pedestrians. Some studies have shown that setting devices in front of bottlenecks can promote pedestrian evacuation under certain conditions. In this paper, the effect of setting diversion devices in f

Shuangshuang Xue, Xiang-Yang Li

In this work, we investigate online mechanisms for trading time-sensitive valued data. We adopt a continuous function $d(t)$ to represent the data value fluctuation over time $t$. Our objective is to design an \emph{online} mechanism achieving \emph{truthfulness} and \emph{revenue-competitiveness}. We first prove several lower bounds on the revenue competiti

Yong Han

Structural geometries, formation enthalpies, and dissociation energies of all diatomic and triatomic molecules consisting of the four basic elements C, H, N, and/or O are calculated from the projector augmented wave (PAW) density functional theory (DFT) method with the PBE and optB88-vdW exchange-correlation functionals. The calculations are also extended to

Junjie Mao, G. Del Zanna, Liyi Gu, Chunyu Zhang

Plasma models built on extensive atomic data are essential to interpreting the observed cosmic spectra. H-like Lyman series and He-like triplets observable in the X-ray band are powerful diagnostic lines to measure the physical properties of various types of astrophysical plasmas. Electron-impact excitation is a fundamental atomic process for the formation o

Maxim Sukharev, Joseph Subotnik, Abraham Nitzan

We consider an ensemble of diatomic molecules resonantly coupled to an optical cavity under strong coupling conditions at normal incidence. Photodissociation dynamics is examined via direct numerical integration of the coupled Maxwell-Schrodinger equations with molecular ro-vibrational degrees of freedom explicitly taken into account. It is shown that the di

F. M. S. Lima

It is well-known that separation of variables in 2nd order partial differential equations (PDEs) for physical problems with spherical symmetry usually leads to Cauchy's differential equation for the radial coordinate and Legendre's differential equation for the polar angle $\theta$. For eigenvalues of the form $\,n\,(n+1)$, $n \ge 0\,$ being an integer, Lege

Tianhong Zhao

We give the definition of $p-adic$ Hermite operator and set up the $p-adic$ spectral measure. We compare the Archimedean case with non-Archimedean case. The structure of Hermite conjugate in $C^{*}$-Algebra corresponds to three canonical structures of $p-adic$ ultrametric Banach algebra: 1. mod $p$ reduction 2. Frobenius map 3. Teichm\"uller lift. There is a

Anna Ciurlo, Paolo Turri, Gunther Witzel, Jessica R. Lu

Images obtained with single-conjugate adaptive optics (AO) show spatial variation of the point spread function (PSF) due to both atmospheric anisoplanatism and instrumental aberrations. The poor knowledge of the PSF across the field of view strongly impacts the ability to take full advantage of AO capabilities. The AIROPA project aims to model these PSF vari

Wallace Sousa

In this paper we give a formal expression to the limits of dual plane curves by using the method introduced by Katz. As an application, we give a formula to compute the vertices of $C(0)$ a plane curve in $C(t)$ a one-parameter family of plane curves and we show that those vertices depend only on the first $\tau$ terms of that family, where $\tau$ is the gen

Benjamin Balzer, Antonio Rosato

We study reserve prices in auctions with independent private values when bidders are expectations-based loss averse. We find that the optimal public reserve price excludes fewer bidder types than under risk neutrality. Moreover, we show that public reserve prices are not optimal as the seller can earn a higher revenue with mechanisms that better leverage the

Paul E. Lammert, Vincent H. Crespi

The cluster expansion formalism used in materials science is reconstructed on an axiomatic basis with the aims of clarifying underlying concepts and improving computational procedures, and without using conventional cluster functions. Instead, cluster components of configuration functions are defined in an intrinsic manner, which can be viewed as Moebius inv

Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang, Neil Zhenqiang Gong

Federated learning is vulnerable to poisoning attacks in which malicious clients poison the global model via sending malicious model updates to the server. Existing defenses focus on preventing a small number of malicious clients from poisoning the global model via robust federated learning methods and detecting malicious clients when there are a large numbe

Bahman Moraffah, Antonia Papandreou-Suppappola

The problem of sequentially transferring from a source object track and a model to another Bayesian filter has become ubiquitous. Due to the lack of a structural model that can capture the dependence among different models, the transfer may not be fully specified. In this paper, we introduce a novel Bayesian model that accounts for the model-jump from which

Lucianno Defaveri, Eli Barkai, David A. Kessler

We study the motion of an overdamped particle connected to a thermal heat bath in the presence of an external periodic potential in one dimension. When we coarse-grain, i.e., bin the particle positions using bin sizes that are larger than the periodicity of the potential, the packet of spreading particles, all starting from a common origin, converges to a no

Steven L. Kleiman, Jan O. Kleppe

Macaulay Duality, between quotients of a polynomial ring over a field, annihilated by powers of the variables, and finitely generated submodules of the ring's graded dual, is generalized over any Noetherian ring, and used to provide isomorphisms between the subschemes of the Hilbert scheme parameterizing various sorts of these quotients, and the correspondin

Andres Alvarez, Nico Denner, Zhe Feng, David Fischer

This report describes our approach to design and evaluate a software stack for a race car capable of achieving competitive driving performance in the different disciplines of the Formula Student Driverless. By using a 360{\deg} LiDAR and optionally three cameras, we reliably recognize the plastic cones that mark the track boundaries at distances of around 35

Bohan Zhou

In this note, we provide with a simple example to show a defect in the definition of the geometric mixing scale, and then introduce an improved scale, called as the strong geometric mixing scale. The main theorem in this note is the equivalence between geometric mixing scale, strong geometric mixing scale and functional mixing scale, in the sense of weak con

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, R. Abbott

Gravitational waves are expected to be produced from neutron star oscillations associated with magnetar giant flares and short bursts. We present the results of a search for short-duration (milliseconds to seconds) and long-duration ($\sim$ 100 s) transient gravitational waves from 13 magnetar short bursts observed during Advanced LIGO, Advanced Virgo and KA

Filippo Fagioli

Given a Hermitian holomorphic vector bundle over a complex manifold, consider its flag bundles with the associated universal vector bundles endowed with the induced metrics. We prove that the universal formula for the push-forward of a polynomial in the Chern classes of all the possible universal vector bundles also holds pointwise at the level of Chern form

Benjamín Madariaga, Susana Mondschein, Soledad Torres

Introduction: The goal of this paper is to study inequities in breast cancer (BC) health care outcomes for Chilean women, including case fatality (FR) and survival rates (SR), stratified by type of health care provider and geographical area. A secondary goal is to estimate BC incidence (IR) and mortality (MR) rates by health care providers and region. Method

A. K. M. Bahalul Haque, B. M. Arifuzzaman, Sayed Abu Noman Siddik, Abul Kalam

Today, healthcare has become one of the largest and most fast-paced industries due to the rapid development of digital healthcare technologies. The fundamental thing to enhance healthcare services is communicating and linking massive volumes of available healthcare data. However, the key challenge in reaching this ambitious goal is letting the information ex

Jack Valmadre

Many classification problems consider classes that form a hierarchy. Classifiers that are aware of this hierarchy may be able to make confident predictions at a coarse level despite being uncertain at the fine-grained level. While it is generally possible to vary the granularity of predictions using a threshold at inference time, most contemporary work consi

Mark Bowron

The Kuratowski monoid $\mathbf{K}$ is generated under operator composition by closure and complement in a nonempty topological space. It satisfies $2\leq|\mathbf{K}|\leq14$. The Gaida-Eremenko (or GE) monoid $\mathbf{KF}$ extends $\mathbf{K}$ by adding the boundary operator. It satisfies $4\leq|\mathbf{KF}|\leq34$. We show that when $|\mathbf{K}|<14$ the GE

Marvin Jesse, Dawei Sun, Inseok Hwang

Motivated by the increasing need to monitor safety-critical systems subject to uncertainties, a novel set-membership approach is proposed to estimate the state of a dynamical system with unknown-but-bounded exogenous inputs. The proposed method decomposes the system into the strongly observable and weakly unobservable subsystem in which an unknown input obse

Zain H. Saleem, Anil Shaji, Stephen K. Gray

We study the time-dependent quantum Fisher information (QFI) in an open quantum system satisfying the Gorini-Kossakowski-Sudarshan-Lindblad master equation. We also study the dynamics of the system from an effective non-Hermitian dynamics standpoint and use it to understand the scaling of the QFI when multiple probes are used. A focus of our work is how the

Matthias Gerstgrasser, David C. Parkes

Stackelberg equilibria arise naturally in a range of popular learning problems, such as in security games or indirect mechanism design, and have received increasing attention in the reinforcement learning literature. We present a general framework for implementing Stackelberg equilibria search as a multi-agent RL problem, allowing a wide range of algorithmic

Christopher J. McDevitt, Xian-Zhu Tang, Christopher J. Fontes, Prashant Sharma

In a post-thermal-quench plasma, mitigated or unmitigated, the plasma power balance is mostly between collisional or Ohmic heating and plasma radiative cooling. In a plasma of atomic mixture $\{n_\alpha\}$ with $\alpha$ labeling the atomic species, the power balance sets the plasma temperature, ion charge state distribution $\{n_\alpha^i\}$ with $i$ the char

Bao-Jun Cai, Bao-An Li

Nucleon-nucleon short-range correlations (SRCs) induce a high momentum tail (HMT) in the single-nucleon momentum distribution function $n_{\v{k}}^J(\rho,\delta)$ in cold neutron-rich matter. While there are clear experimental evidences that the SRC/HMT effects are different for neutrons and protons and their strengths depend strongly on the isospin asymmetry

Evgenii Reznichenko

Let $X$ and $Y$ be pseudocompact spaces and let the function $\Phi: X\times Y\to \mathbb R$ be separately continuous. The following conditions are equivalent: (1) there is a dense $G_\delta$ subset of $D\subset Y$ so that $\Phi$ is continuous at every point of $X\times D$ (Namioka property); (2) $\Phi$ is quasicontinuous; (3) $\Phi$ extends to a separately c

Ashwin Bhat, Adou Sangbone Assoa, Arijit Raychowdhury

There has been a recent surge in the field of Explainable AI (XAI) which tackles the problem of providing insights into the behavior of black-box machine learning models. Within this field, \textit{feature attribution} encompasses methods which assign relevance scores to input features and visualize them as a heatmap. Designing flexible accelerators for mult

Kaitlin N. Smith, Gokul Subramanian Ravi, Jonathan M. Baker, Frederic T. Chong

Fixed-frequency transmon quantum computers (QCs) have advanced in coherence times, addressability, and gate fidelities. Unfortunately, these devices are restricted by the number of on-chip qubits, capping processing power and slowing progress toward fault-tolerance. Although emerging transmon devices feature over 100 qubits, building QCs large enough for mea

Vaishnavi Patil, Matthew Evanusa, Joseph JaJa

As we enter the era of machine learning characterized by an overabundance of data, discovery, organization, and interpretation of the data in an unsupervised manner becomes a critical need. One promising approach to this endeavour is the problem of Disentanglement, which aims at learning the underlying generative latent factors, called the factors of variati

Julian Léonard, Sooshin Kim, Joyce Kwan, Perrin Segura

Strongly interacting topological matter exhibits fundamentally new phenomena with potential applications in quantum information technology. Emblematic instances are fractional quantum Hall states, where the interplay of magnetic fields and strong interactions gives rise to fractionally charged quasi-particles, long-ranged entanglement, and anyonic exchange s

Mohammad Reza Shabanniya, Ali Naji

We investigate Brownian noise effects on magnetic focusing of prolate and oblate spheroids carrying permanent magnetic dipoles in channel (Poiseuille) flow subject to a uniform magnetic field. The focusing is effected by the low-Reynolds-number wall-induced hydrodynamic lift which can be tuned via tilt angle of the field relative to the flow direction. This

Kayvon Mazooji, Ilan Shomorony

In the trace reconstruction problem, one seeks to reconstruct a binary string $s$ from a collection of traces, each of which is obtained by passing $s$ through a deletion channel. It is known that $\exp(\tilde O(n^{1/5}))$ traces suffice to reconstruct any length-$n$ string with high probability. We consider a variant of the trace reconstruction problem wher

Jack B. Collings, Ricardo Rama-Eiroa, Rubén M. Otxoa, Richard F. L. Evans

We present a general approach to the derivation of the effective anisotropy field which determines the dynamical behaviour of magnetic spins according to the Landau-Lifshitz-Gilbert equation. The approach is based on the gradient in spherical polar coordinates with the final results being expressed in Cartesian coordinates as usually applied in atomistic and

Dillon F. Hanlon, G. Todd Andrews, Roger A. Mason

Brillouin light scattering spectroscopy was used along with detailed composition information obtained from electron probe microanalysis to study the influence of octahedral site chemistry on the elastic properties of biotite crystals. Elastic wave velocities for a range of directions in the AC and BC crystallographic planes were obtained for each crystal by

Fenglin Liu, Xuancheng Ren, Xian Wu, Wei Fan

Recently, attention based models have been used extensively in many sequence-to-sequence learning systems. Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words. However, for each time step in the decoding process, the attention based models usually use the hidden state of the cur

Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh

Large and diverse datasets have been the cornerstones of many impressive advancements in artificial intelligence. Intelligent creatures, however, learn by interacting with the environment, which changes the input sensory signals and the state of the environment. In this work, we aim to bring the best of both worlds and propose an algorithm that exhibits an e

Katrina Morgan, Jared Wunsch

A rotating cosmic string spacetime has a singularity along a timelike curve corresponding to a one-dimensional source of angular momentum. Such spacetimes are not globally hyperbolic: they admit closed timelike curves near the string. This presents challenges to studying the existence of solutions to the wave equation via conventional energy methods. In this

Jaime Cofre

Cytasters have been underestimated in terms of their potential relevance to embryonic development and evolution. From the perspective discussed herein, structures such as the multiciliated cells of comb rows and balancers supporting mineralized statoliths and macrocilia in Beroe ovata point to a past event of multiflagellate fusion in the origin of metazoans

Qin Wang, Guangsheng Yu, Shange Fu, Shiping Chen

Existing NFTs confront restrictions of \textit{one-time incentive} and \textit{product isolation}. Creators cannot obtain benefits once having sold their NFT products due to the lack of relationships across different NFTs, which results in controversial possible profit sharing. This work proposes a referable NFT scheme to extend the incentive sustainability

J. M. Almenara, X. Bonfils, T. Forveille, N. Astudillo-Defru

The Transiting Exoplanet Survey Satellite mission identified a deep and asymmetric transit-like signal with a periodicity of 4.5 days orbiting the M4 dwarf star TOI-3884. The signal has been confirmed by follow-up observations collected by the ExTrA facility and Las Cumbres Observatory Global Telescope, which reveal that the transit is chromatic. The light c

Hiroki Sukeno, Takuya Okuda

Numerical simulation of lattice gauge theories is an indispensable tool in high energy physics, and their quantum simulation is expected to become a major application of quantum computers in the future. In this work, for an Abelian lattice gauge theory in $d$ spacetime dimensions, we define an entangled resource state (generalized cluster state) that reflect

Song Liu, Xin Hao, Shaofan Liu, Liu Zhao

We find a near detailed balance solution to the relativistic Boltzmann equation under the relaxation time approximation with a collision term which differs from the Anderson-Witting model and is dependent on the stationary observer. Using this new solution, we construct an explicit covariant transport equation for the particle flux in response to the general

Sean H. Merritt, Alexander P. Christensen

Developing interpretable machine learning models has become an increasingly important issue. One way in which data scientists have been able to develop interpretable models has been to use dimension reduction techniques. In this paper, we examine several dimension reduction techniques including two recent approaches developed in the network psychometrics lit

Suvodeep Majumder, Stanislas Lauly, Maria Nadejde, Marcello Federico

This paper addresses the task of contextual translation using multi-segment models. Specifically we show that increasing model capacity further pushes the limits of this approach and that deeper models are more suited to capture context dependencies. Furthermore, improvements observed with larger models can be transferred to smaller models using knowledge di

Ian Scott Knight, Slava Naprienko, John J. Irwin

The standard quantitative metric for evaluating enrichment capacity known as $\textit{LogAUC}$ depends on a cutoff parameter that controls what the minimum value of the log-scaled x-axis is. Unless this parameter is chosen carefully for a given ROC curve, one of the two following problems occurs: either (1) some fraction of the first inter-decoy intervals of

Ziang Liu, Sundar Aditya, Hongyu Li, Bruno Clerckx

Integrated sensing and communication (ISAC) has been envisioned as a solution to realize the sensing capability required for emerging applications in wireless networks, while efficiently utilizing the available spectral, hardware and energy resources. A commonly studied scenario in the ISAC literature involves a transceiver (e.g., a base station) serving a c

Istiak Ahmad, Fahad AlQurashi, Rashid Mehmood

Research in Natural Language Processing (NLP) has increasingly become important due to applications such as text classification, text mining, sentiment analysis, POS tagging, named entity recognition, textual entailment, and many others. This paper introduces several machine and deep learning methods with manual and automatic labelling for news classificatio

Hanieh Naderi, Chinthaka Dinesh, Ivan V. Bajic, Shohreh Kasaei

Adversarial attacks pose serious challenges for deep neural network (DNN)-based analysis of various input signals. In the case of three-dimensional point clouds, methods have been developed to identify points that play a key role in network decision, and these become crucial in generating existing adversarial attacks. For example, a saliency map approach is

Claudio Muñoz

In this short note, we review recent results concerning the long time dynamics of large data solutions to several dispersive models. Starting with the KdV case and ending with the KP models, we review the literature and present new results where virial estimates allow one to prove local energy decay in different regions of space where solitons, lumps or soli

Albertoo Coccarelli, Michael D. Nelson

Reactive hyperemia is a well-established technique for the non-invasive evaluation of the peripheral microcirculatory function, measured as the magnitude of limb re-perfusion after a brief period of ischemia. Despite widespread adoption by researchers and clinicians alike, many uncertainties remain surrounding interpretation, compounded by patient-specific c

Ángel J. Omella, David Pardo

We introduce an $r-$adaptive algorithm to solve Partial Differential Equations using a Deep Neural Network. The proposed method restricts to tensor product meshes and optimizes the boundary node locations in one dimension, from which we build two- or three-dimensional meshes. The method allows the definition of fixed interfaces to design conforming meshes, a

Erdem Bıyık

When robots enter everyday human environments, they need to understand their tasks and how they should perform those tasks. To encode these, reward functions, which specify the objective of a robot, are employed. However, designing reward functions can be extremely challenging for complex tasks and environments. A promising approach is to learn reward functi

W. G. Parrott, C. Bouchard, C. T. H. Davies

We present HPQCD's improved scalar, vector and tensor form factors for $B \to K$ semileptonic decays, using the heavy-HISQ formalism for more accurate normalisation of the weak currents. Working with masses close to the physical $b$ on the finest ensemble and including three ensembles with physical light quarks, we cover the full physical $q^2$ range with go

Guy Bar-Shalom, Yonatan Geifman, Ran El-Yaniv

To deploy and operate deep neural models in production, the quality of their predictions, which might be contaminated benignly or manipulated maliciously by input distributional deviations, must be monitored and assessed. Specifically, we study the case of monitoring the healthy operation of a deep neural network (DNN) receiving a stream of data, with the ai

Joanna Rankin, Haley Wahl, Arun Venkataraman, Timothy Olszanski

This paper provides analyses of the emission beam structure of 76 ``B''-named pulsars within the Arecibo sky. Most of these objects are included in both the Gould & Lyne and LOFAR High Band surveys and thus complement our other works treating various parts of these populations. These comprise a further group of mostly well studied pulsars within the Arecibo

Ross Dempsey, Igor R. Klebanov, Loki L. Lin, Silviu S. Pufu

The mass spectrum of $1+1$-dimensional $\mathrm{SU}(N)$ gauge theory coupled to a Majorana fermion in the adjoint representation has been studied in the large $N$ limit using Light-Cone Quantization. Here we extend this approach to theories with small values of $N$, exhibiting explicit results for $N=2, 3$, and $4$. In the context of Discretized Light-Cone Q

Arkava Ganguly, Ankur Gupta

We study the two-dimensional motion of a self-propelling asymmetric bent rod. By employing slender body theory and the Lorentz reciprocal theorem, we determine particle trajectories for different geometric configurations and arbitrary surface activities. Our analysis reveals that all particle trajectories can be mathematically expressed through the equation

Federico Bianchini, Marius Millea

We develop an optimal Bayesian solution for jointly inferring secondary signals in the Cosmic Microwave Background (CMB) originating from gravitational lensing and from patchy screening during the epoch of reionization. This method is able to extract full information content from the data, improving upon previously considered quadratic estimators for lensing

Navodini Wijethilake, Mithunjha Anandakumar, Cheng Zheng, Peter T. C. So

Limited throughput is a key challenge in in-vivo deep-tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the wide-field imaging modalities used for optically cleared or thin specimens. We recently introduced 'De-scattering with Excitation Patterning or DEEP', as

Enrico Andriolo, Vasilis Niarchos, Constantinos Papageorgakis, Elli Pomoni

Operators with integer scaling dimensions in even-dimensional conformal field theories exhibit well-known type-B Weyl anomalies. In general, these anomalies depend non-trivially on exactly marginal couplings. We study the corresponding fully covariantised anomaly functional on conformal manifolds in several examples. We show that a natural consequence of the