May 2023 arXiv papers — page 139

Jason McCullough

Ananyan and Hochster proved the existence of a function $\Phi(m,d)$ such that any graded ideal $I$ generated by $m$ forms of degree at most $d$ in a standard graded polynomial ring satisfies $\mathrm{reg}(I) \le \Phi(m,d)$. Relatedly, Caviglia et. al. proved the existence of a function $\Psi(e)$ such that any nondegenerate prime ideal $P$ of degree $e$ in a

Meng Qin

In this study, we focus on the graph representation learning (a.k.a. network embedding) in attributed graphs. Different from existing embedding methods that treat the incorporation of graph structure and semantic as the simple combination of two optimization objectives, we propose a novel semantic graph representation (SGR) method to formulate the joint opti

Jessica Ojo, Kelechi Ogueji

Recent advancements in Natural Language Processing (NLP) has led to the proliferation of large pretrained language models. These models have been shown to yield good performance, using in-context learning, even on unseen tasks and languages. They have also been exposed as commercial APIs as a form of language-model-as-a-service, with great adoption. However,

Bo-Yan Chen, Geoffrey C. Bower, Jason Dexter, Sera Markoff

The variability of submillimeter emission provides a useful tool to probe the accretion physics in low-luminosity active galactic nuclei. We accumulate four years of observations using Submillimeter Array for Centaurus A, NGC 4374, NGC 4278, and NGC 5077 and one year of observations for NGC 4552 and NGC 4579. All sources are variable. We measure the characte

Md Asif-Ur-Rahman, Bayzid Ashik Hossain, Michael Bewong, Md Zahidul Islam

Integrating disparate and distributed vegetation data is critical for consistent and informed national policy development and management. Australia's National Vegetation Information System (NVIS) under the Department of Climate Change, Energy, the Environment and Water (DCCEEW) is the only nationally consistent vegetation database and hierarchical typology o

Hyungjin Huh, Kiyeon Lee

In this paper, we consider the Cauchy problem of Dirac equations with Chern-Simons-Proca (CSP) gauge field. We investigate global well-posedness and scattering theory for the solutions with small initial data. The main difficulties come from the fact that Strichartz estimate does not work and an absence of spinorial null structure which disturbs to show the

Sarthak Jain, Martina Cardone, Soheil Mohajer

The problem of distributed matrix-vector product is considered, where the server distributes the task of the computation among $n$ worker nodes, out of which $L$ are compromised (but non-colluding) and may return incorrect results. Specifically, it is assumed that the compromised workers are unreliable, that is, at any given time, each compromised worker may

Qing Zhang, Hao Jiang, Yongwei Nie, Wei-Shi Zheng

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsam

Zhihe Lu, Zeyu Xiao, Jiawang Bai, Zhiwei Xiong

The primary challenge in video super-resolution (VSR) is to handle large motions in the input frames, which makes it difficult to accurately aggregate information from multiple frames. Existing works either adopt deformable convolutions or estimate optical flow as a prior to establish correspondences between frames for the effective alignment and fusion. How

Umar Islambekov, Hasani Pathirana, Omid Khormali, Cuneyt Akcora

Large time-varying graphs are increasingly common in financial, social and biological settings. Feature extraction that efficiently encodes the complex structure of sparse, multi-layered, dynamic graphs presents computational and methodological challenges. In the past decade, a persistence diagram (PD) from topological data analysis (TDA) has become a popula

Yinchuan Li, Shuang Luo, Yunfeng Shao, Jianye Hao

We propose the GFlowNets with Human Feedback (GFlowHF) framework to improve the exploration ability when training AI models. For tasks where the reward is unknown, we fit the reward function through human evaluations on different trajectories. The goal of GFlowHF is to learn a policy that is strictly proportional to human ratings, instead of only focusing on

Han Cheol Moon, Shafiq Joty, Ruochen Zhao, Megh Thakkar

Large-scale pre-trained language models have shown outstanding performance in a variety of NLP tasks. However, they are also known to be significantly brittle against specifically crafted adversarial examples, leading to increasing interest in probing the adversarial robustness of NLP systems. We introduce RSMI, a novel two-stage framework that combines rand

Katsuaki Nakazawa, Kazutaka Mitsuishi, Iakoubovskii Konstantin, Shinji Kohara

Dynamic and structural heterogeneities play an important role in glass transition phenomena and in the formation of amorphous structures. Since structure and dynamics are mutually related, it is expected that there exists some relation between them; however, this relation has not been characterized by a direct experiment. Elucidation of this relation is the

Yi Zhang, Isao Yamada

Standard approaches to difference-of-convex (DC) programs require exact solution to a convex subproblem at each iteration, which generally requires noiseless computation and infinite iterations of an inner iterative algorithm. To tackle these difficulties, inexact DC algorithms have been proposed, mostly by relaxing the convex subproblem to an approximate mo

Rasool Ahmad, Mingliang Liu, Michael Ortiz, Tapan Mukerji

Determining effective elastic properties of rocks from their pore-scale digital images is a key goal of digital rock physics (DRP). Direct numerical simulation (DNS) of elastic behavior, however, incurs high computational cost; and surrogate machine learning (ML) model, particularly convolutional neural network (CNN), show promises to accelerate homogenizati

Yinbin Ma, Daniela Tuninetti

Coded caching, introduced by Maddah-Ali and Niesen (MAN), is a model where a server broadcasts multicast packets to users with a local cache that is leveraged so as to reduce the peak network communication load. The original MAN model does not consider missing demands (i.e., some users may not request a file) or privacy issues (i.e., decoding the multicast p

Hongbin Cui, Xiaoxiang Jiao, Xiaowei Xu

There are two significant families of minimal real matrix varieties: determinantal varieties and skew-symmetric determinantal varieties, the later ones are also known as Pfaffian varieties. In 1999, Kerckhove and Lawlor [Duke Math.J. 96(2),401--424,1999] proved that determinantal varieties are area-minimizing except for two families. In this paper we prove t

Lisa A. Upton, David H. Hathaway

Sunspot Cycle 25 over 3 years past the cycle minimum of December 2019. At this point, curve-fitting becomes reliable and consistently indicates a maximum sunspot number of 135+/-10 - slightly larger than Cycle 24's maximum of 116.4, but well below the Cycles 1-24 average of 179 (ranging from 81 for Cycle 6 to 285 for Cycle 19). A geomagnetic precursor, the m

Bo Li, Xiao-Bin Liang, Shao-Ming Fei

The superposition of states is one of the most fundamental issues in the quantum world. Generally there do not exist physical operations to superpose two unknown random states with nonzero probability. We investigate the superposition problem of unknown qubit states with respect to a known qubit state. It is shown that under trace-nonincreasing completely po

Qing-Bo Ma, Ling Peng

Due to the non-linear ionizing and heating processes, the 21-cm signals from epoch of reionization (EoR) are expected to have strong non-Gaussian fluctuations. In this paper, we use the semi-numerical simulations to study the non-Gaussian statistics i.e. skew spectrum and smoothed skewness of the 21-cm signals from EoR. We find the 21-cm skew spectrum and sm

David Albers, Melike Sirlanci, Matthew Levine, Jan Claassen

Prediction of physiologic states are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine because the generating system is complex and difficult to understand from data alone, and the data are sparse relative to the complexity of the generating processes due to human cos

L. Hernández-Sánchez, I. Ramos-Prieto, F. Soto-Eguibar, H. M. Moya-Cessa

This article investigates the effect of near non-resonant levels on the spectral lines of atoms interacting with an electromagnetic field. Specifically, we examine the AC Stark effect that occurs when the field frequency matches the transition frequency between two lower levels and the field has a small average number of photons ($|\alpha|^2 <4$). Our resear

Hongtao Kang, Die Luo, Li Chen, Junbo Hu

In practice, digital pathology images are often affected by various factors, resulting in very large differences in color and brightness. Stain normalization can effectively reduce the differences in color and brightness of digital pathology images, thus improving the performance of computer-aided diagnostic systems. Conventional stain normalization methods

Bixiang Wang

This paper is concerned with the large deviation principle of the stochastic reaction-diffusion lattice systems defined on the N-dimensional integer set, where the nonlinear drift term is locally Lipschitz continuous with polynomial growth of any degree and the nonlinear diffusion term is locally Lipschitz continuous with linear growth. We first prove the co

Vijayanta Jain, Sanonda Datta Gupta, Sepideh Ghanavati, Sai Teja Peddinti

Mobile applications are required to give privacy notices to the users when they collect or share personal information. Creating consistent and concise privacy notices can be a challenging task for developers. Previous work has attempted to help developers create privacy notices through a questionnaire or predefined templates. In this paper, we propose a nove

Hanglong Zhang, Xiwang Cao

In this paper, we investigate the first few largest coset leaders modulo $\frac{q^m+1}{\lambda}$ where $\lambda\mid q+1$ and $q$ is an odd prime power, and give the dimensions of some LCD BCH codes of length $\frac{q^m+1}{\lambda}$ with large designed distances.We also determine the dimensions of some LCD BCH codes of length $n=\frac{(q^m+1)}{\lambda}$ with

Benjamin T. Hendel, Rafael Ruggiero

In this paper, we present a new method for the analysis of piecewise dynamical systems that are similar to the Collatz conjecture in regard to certain properties of the commutator of their sub-functions. We use the fact that the commutator of polynomials $E(n)=n/2$ and $O(n)=(3n+1)/2$ is constant to study rearrangements of compositions of $E(n)$ and $O(n)$.

Sean Ovens

Nearly thirty years ago, it was shown that $\Omega(\sqrt{n})$ registers are needed to solve obstruction-free consensus among $n$ processes. This lower bound was improved to $n$ registers in 2018, which exactly matches the best upper bound. The $\Omega(\sqrt{n})$ space complexity lower bound actually applies to a class of objects called historyless objects, w

Nan Li, Wei-Min Wang

The two-color laser field scheme for broad-band terahertz (THz) radiation from air has been intensively and broadly investigated due to the simplicity in technology and relative high yield efficiency. Experiments showed that the detected THz angular distribution is usually conical with a dip in the laser propagation axis which is unfavorable for its applicat

Hanglong Zhang, Xiwang Cao

For a simple-root $\lambda$-constacyclic code $\mathcal{C}$ over $\mathbb{F}_q$, let $\langle\rho\rangle$ and $\langle\rho,M\rangle$ be the subgroups of the automorphism group of $\mathcal{C}$ generated by the cyclic shift $\rho$, and by the cyclic shift $\rho$ and the scalar multiplication $M$, respectively. Let $N_G(\mathcal{C}^\ast)$ be the number of orbi

Denitsa Staicova

Putting constraints on a possible Lorentz Invariance Violation (LIV) from astrophysical sources such as gamma-ray bursts (GRBs) is essential for finding evidences of new theories of quantum gravity (QG) that predict an energy-dependent speed of light. This search has its own difficulties, so usually, the effect of the cosmological model is understudied, with

Jing Guo, Hailun Wu, Heping Zhang

A graph $G$ with four or more vertices is called bicritical if the removal of any pair of distinct vertices of $G$ results in a graph with a perfect matching. A bicritical graph is minimal if the deletion of each edge results in a non-bicritical graph. Recently, Y. Zhang et al. and F. Lin et al. respectively showed that bicritical graphs without removable ed

Adrian Ka-Wai Chung, Nicolas Yunes

We develop a method to perform an untargeted Bayesian search for anisotropic gravitational-wave backgrounds that can efficiently and accurately reconstruct the background intensity map. Our method employs an analytic marginalization of the posterior of the spherical-harmonic components of the intensity map, without assuming the background possesses any speci

Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong

Large-scale pre-training and instruction tuning have been successful at creating general-purpose language models with broad competence. However, building general-purpose vision-language models is challenging due to the rich input distributions and task diversity resulting from the additional visual input. Although vision-language pretraining has been widely

Bolun Dai, Prashanth Krishnamurthy, Andrew Papanicolaou, Farshad Khorrami

We develop a computationally efficient learning-based forward-backward stochastic differential equations (FBSDE) controller for both continuous and hybrid dynamical (HD) systems subject to stochastic noise and state constraints. Solutions to stochastic optimal control (SOC) problems satisfy the Hamilton-Jacobi-Bellman (HJB) equation. Using current FBSDE-base

Annalisa Conversano, Nicolas Monod

Ulam asked whether all Lie groups can be represented faithfully on a countable set. We establish a reduction of Ulam's problem to the case of simple Lie groups. In particular, we solve the problem for all solvable Lie groups and more generally Lie groups with a linear Levi component. It follows that every amenable locally compact second countable group acts

Md Lal Mamud, Maruti K. Mudunuru, Satish Karra, Bulbul Ahmmed

Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics laws such as mass balance are enforced softly in PINN. This paper investigates how mass balance constraints are satisfied when PINN is used to solve the resulting

F. Fambri, E. Zampa, S. Busto, L. Río-Martín

We present a new divergence-free and well-balanced hybrid FV/FE scheme for the incompressible viscous and resistive MHD equations on unstructured mixed-element meshes in 2 and 3 space dimensions. The equations are split into subsystems. The pressure is defined on the vertices of the primary mesh, while the velocity field and the normal components of the magn

Richa P. Madhogaria, Shirin Mozaffari, Heda Zhang, William R. Meier

Thermal and thermoelectric measurements are known as powerful tools to uncover the physical properties of quantum materials due to their sensitivity towards the scattering and chirality of heat carriers. We use these techniques to confirm the presence of momentum and real-space topology in ScMn$_6$Sn$_6$. There is an unconventional dramatic increase in the S

Janice Nguyen, Y. Curtis Wang

The deaf and hard of hearing community relies on American Sign Language (ASL) as their primary mode of communication, but communication with others who do not know ASL can be difficult, especially during emergencies where no interpreter is available. As an effort to alleviate this problem, research in computer vision based real time ASL interpreting models i

B. Dubetsky

It is proposed to use rectangular Raman pulses for the technique of sequantial large momentum transfer. It is shown that the small parameters that make it possible to use this technology for precision atom interferometry can be 40--200 times smaller than in the case of the Bragg regime. It is predicted that in the case of a non-equidistant timing of auxiliar

Gael PILLONNET, Mahmoud Hmada, Patrick P. Mercier

Hybrid switched-capacitor converters (HSCC) offer great potential for high efficiency and power density compared to purely capacitor- or inductor-based converters. However, the recent proliferation of HSCC topologies has made it difficult to choose the best one for a particular application. This paper presents a benchmarking framework that allows for direct

Jihao Liu, Fanjun Meng, Lingyao Xie

We present an extension of several results on pairs and varieties to foliated surface pairs. We prove the boundedness of local complements, the local index theorem, and the uniform boundedness of minimal log discrepancies (mlds), as well as establishing the existence of uniform rational lc polytopes. Furthermore, we address two questions posed by P. Cascini

Hadjer Benmeziane, Halima Bouzidi, Hamza Ouarnoughi, Ozcan Ozturk

Deep learning has enabled various Internet of Things (IoT) applications. Still, designing models with high accuracy and computational efficiency remains a significant challenge, especially in real-time video processing applications. Such applications exhibit high inter- and intra-frame redundancy, allowing further improvement. This paper proposes a similarit

Michael A. McGuire, Eleanor M. Clements, Qiang Zhang, Satoshi Okamoto

The connectivity and inherent frustration of the kagome lattice can produce interesting electronic structures and behaviors in compounds containing this structural motif. Here we report the properties of Pt3X2 (X = In and Tl) that adopt a double-layer kagome net structure related to that of the topologically nontrivial high temperature ferromagnet Fe3Sn2 and

J. J. Voermans, J. Rabault, A. Marchenko, T. Nose

The Marginal Ice Zone is a highly dynamic region where the atmosphere, ocean, waves and sea ice meet. Waves play a fundamental role in this coupled system, but progress in our understanding of wave-ice interactions is currently hindered by the lack of observations of sea ice properties in-situ. In this study we aim to estimate the ice thickness and effective

Feifei Xiang, Neeta Bisht, Binbin Da, Mohammed S. G. Mohammed

Patterning and defect engineering are key methods to tune 2D materials' properties. However, generating 2D periodic patterns of point defects in 2D materials has been elusive until now, despite the well-established methods for creating isolated point defects and defect lines. Herein, we report on intrinsically patterned 2D transition metal dihalides on metal

Sebastian Lobentanzer, Shaohong Feng, The BioChatter Consortium, Andreas Maier

Current-generation Large Language Models (LLMs) have stirred enormous interest in recent months, yielding great potential for accessibility and automation, while simultaneously posing significant challenges and risk of misuse. To facilitate interfacing with LLMs in the biomedical space, while at the same time safeguarding their functionalities through sensib

Zongyuan Shen, James P. Wilson, Shalabh Gupta, Ryan Harvey

The paper presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast replanning in dynamic environments. SMART performs risk based tree-pruning if the current path is obstructed by nearby moving obstacle(s), resulting in multiple disjoint subtrees. Then, for speedy recovery, it exploits these subtrees and performs inf

Régis de la Bretèche, Gérald Tenenbaum

We evaluate friable averages of arithmetic functions whose Dirichlet series is analytically close to some complex power of the Riemann zeta function. We obtain asymptotic expansions resembling those provided by the Selberg-Delange method in the non-friable case. Some application are provided to the friable distribution of the additive function counting the t

Mert İnan, Aishwarya Padmakumar, Spandana Gella, Patrick Lange

Task planning is an important component of traditional robotics systems enabling robots to compose fine grained skills to perform more complex tasks. Recent work building systems for translating natural language to executable actions for task completion in simulated embodied agents is focused on directly predicting low level action sequences that would be ex

Micael Andrade Dias, Francisco Marcos de Assis

Consider the problem of using a finite set of coherent states to distribute secret keys over a quantum channel. It is known that computing the exact secret key rate in this scenario is intractable due to the infinite dimensionality of the Hilbert spaces and usually one computes a lower bound using a Gaussian equivalent bipartite state in the entangled based

Jannes S. Magnusson, Anna Hilsmann, Peter Eisert

This work proposes a novel concept for tree and plant reconstruction by directly inferring a Lindenmayer-System (L-System) word representation from image data in an image captioning approach. We train a model end-to-end which is able to translate given images into L-System words as a description of the displayed tree. To prove this concept, we demonstrate th

Julio A. Oscanoa, Frank Ong, Siddharth S. Iyer, Zhitao Li

Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for 3D non-Cartesian acquisitions. One common approach is to reduce the number of coil channels actively used during reconstruction as in coil compression. While effective for Cart

Murat Kuscu

Molecular Communications (MC) underpins signaling in biological systems, enabling information transfer through biochemical molecules. The prospect of engineering this natural communication mechanism has inspired the Internet of Bio-Nano Things (IoBNT) applications, which rely on heterogeneous collaborative networks of natural and engineered biological device

Pavel Naumov, Oliver Orejola

An operation is called covert if it conceals the identity of the actor; it is called clandestine if the very fact that the operation is conducted is concealed. The paper proposes a formal semantics of clandestine operations and introduces a sound and complete logical system that describes the interplay between the distributed knowledge modality and a modalit

Zepu Wang, Dingyi Zhuang, Yankai Li, Jinhua Zhao

Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from loop detectors or similar sources, often contains missing values (MVs), which can adversely impact associated applications and research. Instead of discarding this incomplete data,

Susana Furtado, Charles Johnson

In prioritization schemes, based on pairwise comparisons, such as the Analytical Hierarchy Process, it is important to extract a cardinal ranking vector from a reciprocal matrix that is unlikely to be consistent. It is natural to choose such a vector only from efficient ones. Recently a method to generate inductively all efficient vectors for any reciprocal

Adam Schmidt, Omid Mohareri, Simon DiMaio, Septimiu E. Salcudean

Deformable tracking and real-time estimation of 3D tissue motion is essential to enable automation and image guidance applications in robotically assisted surgery. Our model, Sparse Efficient Neural Depth and Deformation (SENDD), extends prior 2D tracking work to estimate flow in 3D space. SENDD introduces novel contributions of learned detection, and sparse

Ehsan Tohidi, Robert Stoecker, Julia-Marie Koeszegi, Slawomir Stanczak

Reflecting intelligent surface (RIS) has emerged as a promising technology for enhancing wireless communication performance and enabling new applications in 6G networks with potentially low energy consumption and hardware complexity thanks to their passive nature. Despite the significant growth of the literature on RIS in recent years, covering various aspec

Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy

Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their previous behavior remains an emerging and still unclear research question. Traditionally, Collaborative Filtering (CF) has been the most effect

Wenqi Wei, Ling Liu, Jingya Zhou, Ka-Ho Chow

This paper presents a holistic approach to gradient leakage resilient distributed Stochastic Gradient Descent (SGD). First, we analyze two types of strategies for privacy-enhanced federated learning: (i) gradient pruning with random selection or low-rank filtering and (ii) gradient perturbation with additive random noise or differential privacy noise. We ana

Yan-Fu Li, Huan Wang, Muxia Sun

Prognostics and health management (PHM) technology plays a critical role in industrial production and equipment maintenance by identifying and predicting possible equipment failures and damages, thereby allowing necessary maintenance measures to be taken to enhance equipment service life and reliability while reducing production costs and downtime. In recent

Jake Fillman, Wencai Liu, Rodrigo Matos

We present a method to estimate the number of irreducible components of the Fermi varieties of periodic Schr\"odinger operators on graphs in terms of suitable asymptotics. Our main theorem is an abstract bound for the number of irreducible components of Laurent polynomials in terms of such asymptotics. We then show how the abstract bound implies irreducibili

Cosimo Flavi

We establish an upper bound for the rank of every power of an arbitrary quadratic form. Specifically, for any $s\in\mathbb{N}$, we prove that the $s$-th power of a quadratic form of rank $n$ grows as $n^s$. Furthermore, we demonstrate that its rank is subgeneric for all $n>(2s-1)^2$.

Osama Hanna, Xinlin Li, Suhas Diggavi, Christina Fragouli

The exact common information between a set of random variables $X_1,...,X_n$ is defined as the minimum entropy of a shared random variable that allows for the exact distributive simulation of $X_1,...,X_n$. It has been established that, in certain instances, infinite entropy is required to achieve distributive simulation, suggesting that continuous random va

Myrto Arapinis, Ábel Kocsis, Nikolaos Lamprou, Liam Medley

Simultaneous broadcast (SBC) protocols [Chor et al., FOCS 1985] constitute a special class of broadcast channels which have proved extremely useful in the design of various distributed computing constructions (e.g., multiparty computation, coin flipping, e-voting, fair bidding). As with any communication channel, it is crucial that SBC security is composable

Jernej Činč, Piotr Oprocha

In this paper we construct a paramaterized family of annular homeomorphisms with Birkhoff-like rotational attractors that vary continuously with the parameter, are all homeomorphic to the pseudo-circle, display interesting boundary dynamics and furthermore preserve the induced Lebesgue measure from the circle. Namely, in the constructed family of attractors

Ryan Giordano, Tamara Broderick

The frequentist variability of Bayesian posterior expectations can provide meaningful measures of uncertainty even when models are misspecified. Classical methods to asymptotically approximate the frequentist covariance of Bayesian estimators such as the Laplace approximation and the nonparametric bootstrap can be practically inconvenient, since the Laplace

Tianyu Wang, Zachary M. Pisano, Carey E. Priebe

We investigate the evidence/flexibility (i.e., "Occam") paradigm and demonstrate the theoretical and empirical consistency of Bayesian evidence for the task of determining an appropriate generative model for network data. This model selection framework involves determining a collection of candidate models, equipping each of these models' parameters with prio

Daniel Krashen, Max Lieblich, Minseon Shin

We prove a local-to-global principle for Brauer classes: for any finite collection of non-trivial Brauer classes on a variety over a field of transcendence degree at least 3, there are infinitely many specializations where each class stays non-trivial. This is deduced from a Grothendieck--Lefschetz-type theorem for Brauer groups of certain smooth stacks. Thi

Kelsey Merrill, Zachary Newman, Santiago Torres-Arias, Karen Sollins

Software repositories, used for wide-scale open software distribution, are a significant vector for security attacks. Software signing provides authenticity, mitigating many such attacks. Developer-managed signing keys pose usability challenges, but certificate-based systems introduce privacy problems. This work, Speranza, uses certificates to verify softwar

Alexander Perepechko

Generic flexibility of affine cones over Fano varieties is a subject of active study recently. For del Pezzo surfaces the question is completely studied in degree at least 3, and partially in degree 2. We present a Sagemath module that facilitates most operations for verifying the generic flexibility of affine cones over del Pezzo surfaces and weak del Pezzo

Yi-Kai Li, Athina Petropulu

This short tutorial presents several ideas for designing dual function radar communication (DFRC) systems aided by intelligent reflecting surfaces (IRS). These problems are highly nonlinear in the IRS parameter matrix, and further, the IRS parameters are subject to non-convex unit modulus constraints. We present classical semidefinite relaxation based method

Radu Cimpeanu, Alfonso A. Castrejon-Pita, Lee Nai Lim, Manu Vatish

Oocyte retrieval forms a crucial part of in vitro fertilisation treatment and its ultimate outcome. Standard double-lumen needles, which include a sequence of aspiration and flushing steps, are characterised by a similar success rate to single-lumen needles, despite their increased cost. A novel hydrodynamics-based needle called the OxIVF needle is proposed

Loraine Franke, Tae Young Park, Jie Luo, Yogesh Rathi

We present a real-time visualization system for Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique for treating various brain disorders and mental health diseases. Our solution targets the current challenges of slow and labor-intensive practices in treatment planning. Integrating Deep Learning (DL), our system rapidly predicts

Bhupen Dabholkar, Xiaoxue Ran, Junchen Rong, Zheng Yan

We study a classical model of fully-packed loops on the square lattice, which interact through attractive loop segment interactions between opposite sides of plaquettes. This study is motivated by effective models of interacting quantum matter arising in frustrated magnets or Rydberg atom arrays, for which loop degrees of freedom appear at low energy. Throug

Stephen H. Yuwono, A. Eugene DePrince

One-electron reduced density matrices (1RDMs) from equation-of-motion (EOM) coupled-cluster with single and double excitations (CCSD) calculations are analyzed to assess their N-representability ({\em i.e.}, whether they are derivable from an physical N-electron state). We identify EOM-CCSD stationary states whose 1RDMs violate either ensemble-state N-repres

Zhengyi Luo, Jinkun Cao, Alexander Winkler, Kris Kitani

We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e.g. pose estimates from video or generated from language) and unexpected falls. Our controller scales up to learning ten thousand motion clips without using any external stabilizing forces and learns to natu

Robin Bartlett

For a split reductive group $G$ we realise identities in the Grothendieck group of $\widehat{G}$-representation in terms of cycle relations between certain closed subschemes inside the affine grassmannian. These closed subschemes are obtained as a degeneration of $e$-fold products of flag varieties and, under a bound on the Hodge type, we relate the geometry

Satori Tsuzuki, Daichi Yanagisawa, Eri Itoh, Katsuhiro Nishinari

We analyzed agent behavior in complex networks: Barab\'asi-Albert (BA), Erdos-R\'enyi (ER), and Watts-Strogatz (WS) models under the following rules: agents (a) randomly select a destination among adjacent nodes; (b) exclude the most congested adjacent node as a potential destination and randomly select a destination among the remaining nodes; or (c) select

Zhenlong Li, Huan Ning

Large Language Models (LLMs), such as ChatGPT, demonstrate a strong understanding of human natural language and have been explored and applied in various fields, including reasoning, creative writing, code generation, translation, and information retrieval. By adopting LLM as the reasoning core, we introduce Autonomous GIS as an AI-powered geographic informa

Mohsen Ghaffari, Anton Trygub

We provide the first deterministic distributed synchronizer with near-optimal time complexity and message complexity overheads. Concretely, given any distributed algorithm $\mathcal{A}$ that has time complexity $T$ and message complexity $M$ in the synchronous message-passing model (subject to some care in defining the model), the synchronizer provides a dis

Emanuele Grossi, Luca Venturino

We consider a radar architecture where an illuminator composed of few sources is used as a feeder for a (passive) reconfigurable intelligent surface (RIS), so as to mimic the behavior of a multiple-input multiple-output (MIMO) radar composed of as many active elements as the RIS. In this framework, we study the problem of beampattern design in the space-freq

Tilman Michaeli, Stefan Seegerer, Ralf Romeike

Technological advances in the context of digital transformation are the basis for rapid developments in the field of artificial intelligence (AI). Although AI is not a new topic in computer science (CS), recent developments are having an immense impact on everyday life and society. In consequence, everyone needs competencies to be able to adequately and comp

Giacomo Del Nin, Lucia De Luca

We prove finite crystallization for particles in the plane interacting through a soft disc potential, as originally shown by C. Radin \cite{Radin_soft}. We give an alternative proof that relies on the geometric decomposition of the energy proved in \cite{DLF1}, and that is based on showing that any minimizer has at least as many boundary points as the canoni

Nikhil Churamani, Tolga Dimlioglu, German I. Parisi, Hatice Gunes

Understanding human affective behaviour, especially in the dynamics of real-world settings, requires Facial Expression Recognition (FER) models to continuously adapt to individual differences in user expression, contextual attributions, and the environment. Current (deep) Machine Learning (ML)-based FER approaches pre-trained in isolation on benchmark datase

Ai-Te Kuo, Haiquan Chen, Yu-Hsuan Kuo, Wei-Shinn Ku

Early detection of mental disorder is crucial as it enables prompt intervention and treatment, which can greatly improve outcomes for individuals suffering from debilitating mental affliction. The recent proliferation of mental health discussions on social media platforms presents research opportunities to investigate mental health and potentially detect ins

Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu

We study multi-agent reinforcement learning in the setting of episodic Markov decision processes, where multiple agents cooperate via communication through a central server. We propose a provably efficient algorithm based on value iteration that enable asynchronous communication while ensuring the advantage of cooperation with low communication overhead. Wit

Alessandro Ferreri, Vincenzo Macrì, Frank K. Wilhelm, Franco Nori

We present a quantum heat engine based on a cavity with two oscillating mirrors that confine a quantum field as the working substance. The engine performs an Otto cycle during which the walls and a field mode interact via a nonlinear Hamiltonian. Resonances between the frequencies of the cavity mode and the walls allow to transfer heat from the hot and the c

Ivan Rosas-Soto

By using the triangulated category of \'etale motives over a field $k$, for a smooth projective variety $X$ over $k$, we define the group $\text{CH}^\text{\'et}_0(X)$ as an \'etale analogue of 0-cycles. We study the properties of $\text{CH}^\text{\'et}_0(X)$, giving a description about the birational invariance of such group. We define and present the \'etal

Shengling Gao, Leyang Xue, Bnaya Gross, Zhikun She

The models of $k$-core percolation and interdependent networks (IN) have been extensively studied in their respective fields. A recent study has revealed that they share several common critical exponents. However, several newly discovered exponents in IN have not been explored in $k$-core percolation, and the origin of the similarity still remains unclear. H

Tilman Michaeli, Stefan Seegerer, Lennard Kerber, Ralf Romeike

As a consequence of the increasing influence of machine learning on our lives, everyone needs competencies to understand corresponding phenomena, but also to get involved in shaping our world and making informed decisions regarding the influences on our society. Therefore, in K-12 education, students need to learn about core ideas and principles of machine l

Tony Gherghetta, Andrey Shkerin

Dark matter particles can form halos gravitationally bound to massive astrophysical objects. The Earth could have such a halo where depending on the particle mass, the halo either extends beyond the surface or is confined to the Earth's interior. We consider the possibility that if dark matter particles are coupled to neutrinos, then neutrino oscillations ca

Tal Adi, Sarah Libanore, Hector Afonso G. Cruz, Ely D. Kovetz

Primordial magnetic fields (PMFs) offer a compelling explanation for the origin of observed magnetic fields, especially on extragalactic scales. Such PMFs give rise to excess of power in small scale matter perturbations that could strongly influence structure formation. We study the impact of the magnetically enhanced matter power spectrum on the signal that

Daniel Erhabor, Sreeharsha Udayashankar, Meiyappan Nagappan, Samer Al-Kiswany

GitHub Copilot is an artificially intelligent programming assistant used by many developers. While a few studies have evaluated the security risks of using Copilot, there has not been any study to show if it aids developers in producing code with better runtime performance. We evaluate the runtime performance of C++ code produced when developers use GitHub C

Ibrahim Isik, Hamidreza Arjmandi, Christophe Corre, Adam Noel

Using agar plates hosting a 2D cell population stimulated with signaling molecules is crucial for experiments such as gene regulation and drug discovery in a wide range of biological studies. In this paper, a biophysical model is proposed that incorporates droplet soaking, diffusion of molecules within agar, cell growth over an agar surface, and absorption o

Di Yang, Yaohui Wang, Quan Kong, Antitza Dantcheva

Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time. This leads to loss of pertinent information related to temporal relationships, rendering actions such as `enter' and `leave' to be indistinguishable. To mitigate this limitation, we

Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis

With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throughput by optimizing the warehouse layout

Raffaele Marino, Federico Ricci-Tersenghi

The use of mini-batches of data in training artificial neural networks is nowadays very common. Despite its broad usage, theories explaining quantitatively how large or small the optimal mini-batch size should be are missing. This work presents a systematic attempt at understanding the role of the mini-batch size in training two-layer neural networks. Workin