October 2024 arXiv papers — page 29

Yu-Hao Wan, Qing-Feng Sun

From the low-energy model, the topological field theory indicates that the surface magnetization can open a surface gap in 3D topological insulators (TIs), resulting in a half-quantized Hall conductance. Here by employing the realistic lattice model, we show the occurrence of { the surface phase transitions}, accompanied with the sharp changes of the surface

Zihan Pengmei, Zhengyuan Shen, Zichen Wang, Marcus Collins

Constructing transferable descriptors for conformation representation of molecular and biological systems finds numerous applications in drug discovery, learning-based molecular dynamics, and protein mechanism analysis. Geometric graph neural networks (Geom-GNNs) with all-atom information have transformed atomistic simulations by serving as a general learnab

Yi-Xin Dai, Yue Mao, Qing-Feng Sun

The combination of spin-orbit coupling and superconductivity induces unconventional spin-triplet correlation in Ising superconductors. We theoretically investigate the spin transport through a normal metal-Ising superconductor junction, showing that Ising superconductors also have the characteristic of spin superconductivity.Due to the existence of spin-trip

Wentao Liu, Cuihong Wen, Jieci Wang

Lorentz violation is a significant phenomenon in the framework of quantum physics, with implications for fundamental symmetries. In this paper, we explore the effects of Lorentz violation on quantum entanglement through a black hole spacetime that is coupled with a Lorentz-violating field. We establish the relationship between the Hartle-Hawking vacuum state

Apostolos Kokolis, Michael Kuchnik, John Hoffman, Adithya Kumar

Reliability is a fundamental challenge in operating large-scale machine learning (ML) infrastructures, particularly as the scale of ML models and training clusters continues to grow. Despite decades of research on infrastructure failures, the impact of job failures across different scales remains unclear. This paper presents a view of managing two large, mul

Jit Wu Yap

Let $X$ be a smooth projective variety defined over a number field $K$ and let $\varphi: X \to X$ a polarized endomorphism of degree $d \geq 2$. Let $\widehat{h}_{\varphi}$ be the canonical height associated to $\varphi$ on $X(\overline{K})$. Given a generic sequence of points $(x_n)$ with $\widehat{h}_{\varphi}(x_n) \to 0$ and a place $v \in M_K$, Yuan [Yua

Yong Hu, Congcong Le, Xianxin Wu, Ming Shi

The newly discovered family of titanium-based kagome metals, ATi$_{3}$Bi$_{5}$ (where A can be Rb or Cs), has been found to exhibit non-trivial band topology and fascinating electronic instabilities, including electronic nematicity and potential bulk superconductivity. Distinct from their vanadium-based counterparts (AV$_{3}$Sb$_{5}$), which display a charge

Yunpeng Jing, HaiLin Liu, Qunfeng Liu

Performance analysis is crucial in optimization research, especially when addressing black-box problems through nature-inspired algorithms. Current practices often rely heavily on statistical methods, which can lead to various logical paradoxes. To address this challenge, this paper introduces two criteria to ensure that performance analysis is unaffected by

Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu

Training large-scale models under given resources requires careful design of parallelism strategies. In particular, the efficiency notion of critical batch size (CBS), concerning the compromise between time and compute, marks the threshold beyond which greater data parallelism leads to diminishing returns. To operationalize it, we propose a measure of CBS an

Wenbo Liu, Handi Chen, Edith C. H. Ngai

The metaverse, emerging as a revolutionary platform for social and economic activities, provides various virtual services while posing security and privacy challenges. Wearable devices serve as bridges between the real world and the metaverse. To provide intelligent services without revealing users' privacy in the metaverse, leveraging federated learning (FL

Amartya Hatua, Trung T. Nguyen, Filip Cano, Andrew H. Sung

Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is sometimes desired for an ML model to forget part of the data it was trained on. In this paper, we introduce a novel unlearning approach based on Forgetting Neural Networks (FNNs), a neuroscience-inspired a

Jess Stephenson, William S. Stewart, Melissa Greeff

Landing a multirotor unmanned aerial vehicle (UAV) on an uncrewed surface vessel (USV) extends the operational range and offers recharging capabilities for maritime and limnology applications, such as search-and-rescue and environmental monitoring. However, autonomous UAV landings on USVs are challenging due to the unpredictable tilt and motion of the vessel

Lin Li, Xinchun Yu, Xinyu Chen, Peng Liang

Public Code Review (PCR) is developed in the Software Question Answering (SQA) community, assisting developers in exploring high-quality and efficient review services. Current methods on PCR mainly focus on the reviewer's perspective, including finding a capable reviewer, predicting comment quality, and recommending/generating review comments. However, it is

Akinori Asahara, Yoshihiro Osakabe, Yamamoto Mitsuya, Hidekazu Morita

A variational Bayesian inference for measured wave intensity, such as X-ray intensity, is proposed in this paper. The data is popular to obtain information about unobservable features of an object, such as a material sample and the components of it. The proposed method assumes particles represent the wave, and their behaviors are stochastically modeled. The

Gabriel Simmons

The paper "Is Complexity an Illusion?" (Bennett, 2024) provides a formalism for complexity, learning, inference, and generalization, and introduces a formal definition for a "policy". This reply shows that correct policies do not exist for a simple task of supervised multi-class classification, via mathematical proof and exhaustive search. Implications of th

Benjamin Eller, Charles W. Clark, YuHuang Wang

Quantum confinement significantly influences the excited states of sub-10 nm single-walled carbon nanotubes (SWCNTs), crucial for advancements in transistor technology and the development of novel opto-electronic materials such as fluorescent ultrashort nanotubes (FUNs). However, the length dependence of this effect in ultrashort SWCNTs is not yet fully unde

Jungmin Kim, Qingyi Zhou, Zongfu Yu

Systolic arrays have proven to be highly efficient for parallelized matrix-matrix multiplication (MMM), utilizing synchronized, heartbeat-like data flows across an array of processing elements. While optical structures such as waveguide crossbar arrays and Mach-Zehnder interferometer-based meshes serve as photonic equivalents to the systolic arrays, the disp

Rajeev Kohli, Kriste Krstovski, Hengyu Kuang, Hengxu Lin

Experience goods such as sporting and artistic events, songs, videos, news stories, podcasts, and television series, are often packaged and consumed in bundles. Many such bundles are ordered in the sense that the individual items are consumed sequentially, one at a time. We examine if an individual's decision to consume the next item in an ordered bundle can

Ya-Ting Yang, Tao Li, Quanyan Zhu

Social network platforms (SNP) rely heavily on user-generated content to attract users, yet they have limited control over content provision, which leads to misinformation. As countermeasures, SNPs have implemented policies to notify users by tagging the content and influencing users' responses to the tagged content. The population-level response creates a s

Chen Chen, Enhuai Liu, Daochang Liu, Mubarak Shah

Diffusion models, widely used for image and video generation, face a significant limitation: the risk of memorizing and reproducing training data during inference, potentially generating unauthorized copyrighted content. While prior research has focused on image diffusion models (IDMs), video diffusion models (VDMs) remain underexplored. To address this gap,

Edward Bormashenko, Shraga Shoval, Mark Frenkel, Michael Nosonovsky

The Voronoi Entropy (VE) and the continuous measure of symmetry (CSM) characterize the orderliness of a set of points on a 2D plane. The Voronoi entropy is the Shannon entropy of the Voronoi tessellation of the plane into polygons, quantifying the diversity of polygons. The VE is widely used to study the self-assembly of colloidal, supramolecular, and other

Cheng Qiu

Facial expressions are crucial to human communication, offering insights into emotional states. This study examines how specific facial features influence emotion classification, using facial perturbations on the Fer2013 dataset. As expected, models trained on data with the removal of some important facial feature experienced up to an 85% accuracy drop when

Yibo Dong, Yu Chen, Jianwen Li, Geguang Pu

Model checking is an automatic formal verification technique that is widely used in hardware verification. The state-of-the-art complete model-checking techniques, based on IC3/PDR and its general variant CAR, are based on computing symbolically sets of under - and over-approximating state sets (called frames) with multiple calls to a SAT solver. The perform

Zhigang Chang, Shibao Zheng

Vehicle re-identification (Vehicle ReID) aims at retrieving vehicle images across disjoint surveillance camera views. The majority of vehicle ReID research is heavily reliant upon supervisory labels from specific human-collected datasets for training. When applied to the large-scale real-world scenario, these models will experience dreadful performance decli

Jack Phelps

This study presents the methodology for creating a cost-efficient radio astronomy telescope that can be used to detect 21 cm emissions (1420.405 MHz) and determine the distribution and kinematics of neutral hydrogen specifically in the Milky Way. By measuring the Doppler shifts of the 21 cm emission, the velocities of hydrogen clouds relative to Earth can be

M. Reza Ebrahimi, Jun Chen, Ashish Khisti

This paper investigates a novel lossy compression framework operating under logarithmic loss, designed to handle situations where the reconstruction distribution diverges from the source distribution. This framework is especially relevant for applications that require joint compression and retrieval, and in scenarios involving distributional shifts due to pr

Chen Chen, Daochang Liu, Mubarak Shah, Chang Xu

Text-to-image diffusion models have achieved unprecedented proficiency in generating realistic images. However, their inherent tendency to memorize and replicate training data during inference raises significant concerns, including potential copyright infringement. In response, various methods have been proposed to evaluate, detect, and mitigate memorization

John R. Lawson

Communicating forecast uncertainty effectively is a persistent challenge in predictive endeavours such as weather forecasting. This paper explores the application of possibility theory as a complementary approach to traditional probability in risk communication. Unlike probability, possibility theory allows for the representation of uncertain events as range

Yongkang Ding, Rui Mao, Hanyue Zhu, Anqi Wang

In public safety and social life, the task of Clothes-Changing Person Re-Identification (CC-ReID) has become increasingly significant. However, this task faces considerable challenges due to appearance changes caused by clothing alterations. Addressing this issue, this paper proposes an innovative method for disentangled feature extraction, effectively extra

Jiaqi Han, Mingjian Jiang, Yuxuan Song, Stefano Ermon

Preference optimization has made significant progress recently, with numerous methods developed to align language models with human preferences. This paper introduces $f$-divergence Preference Optimization ($f$-PO), a novel framework that generalizes and extends existing approaches. $f$-PO minimizes $f$-divergences between the optimized policy and the optima

Zhichao Liu, Liuquan Yao, Yuan Li, Huazi Zhang

In this paper, we establish the partial order (POs) for both the binary erasure channel (BEC) and the binary memoryless symmetric channel (BMSC) under any block rate-matched polar codes. Firstly, we define the POs in the sense of rate-matched polar codes as a sequential block version. Furthermore, we demonstrate the persistence of POs after block rate matchi

Yuki K. Wakabayashia, Yoshiharu Krockenberger, Kosuke Takiguchi, Hideki Yamamoto

SrTiO3 (STO), a promising wide-bandgap semiconductor for high-k capacitors and photocatalysis, requires precise surface control for device fabrication. This study investigates the impact of ion milling on STO's surface conductivity. We find that ion milling at incident angles below 10 degree preserves the insulating state, while ion milling at larger angles

Letian Gong, Yan Lin, Xinyue Zhang, Yiwen Lu

Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in these sequences, which closely reflect human visiting intent

Jazmin Collins, Crescentia Jung, Yeonju Jang, Danielle Montour

As social VR applications grow in popularity, blind and low vision users encounter continued accessibility barriers. Yet social VR, which enables multiple people to engage in the same virtual space, presents a unique opportunity to allow other people to support a user's access needs. To explore this opportunity, we designed a framework based on physical sigh

Chenlan Lin, Xiaoming Chen, Zhaoyang Zhang

In high-dynamic low earth orbit (LEO) satellite communication (SATCOM) systems, frequent channel state information (CSI) acquisition consumes a large number of pilots, which is intolerable in resource-limited SATCOM systems. To tackle this problem, we propose to track the state-dependent parameters including Doppler shift and channel angles, by exploiting th

Hira Saleem, Flora Salim, Cormac Purcell

Physics-based Earth system models (ESMs) are essential for attributing climate change and generating scenario projections, yet their reliance on high-resolution numerical integration makes multi-decadal experiments expensive. In parallel, deep learning has delivered strong gains in short-range weather forecasting; however, auto-regressive roll-outs can accum

Kazuki Uematsu, Kosuke Haruki, Taiji Suzuki, Mitsuhiro Kimura

Out-of-distribution (OOD) detection is a critical issue for the stable and reliable operation of systems using a deep neural network (DNN). Although many OOD detection methods have been proposed, it remains unclear how the differences between in-distribution (ID) and OOD samples are generated by each processing step inside DNNs. We experimentally clarify thi

Sakshi Malhotra, Yang Jiao, Oleg Makarenkov

We consider a simple lattice spring model in which every spring is elastoplastic and is capable to conduct current. The elasticity bounds of spring $i$ are taken as $[-c_i,c_i]$ and the resistance of spring $i$ is taken as $1/c_i$, which allows us to compute the resistance of the system. The model is further subjected to a gradual stretching and, due to plas

Andrea Appel, Bart Vlaar

We introduce a universal framework for boundary transfer matrices, inspired by Sklyanin's two-row transfer matrix approach for quantum integrable systems with boundary conditions. The main examples arise from quantum symmetric pairs of finite and affine type. As a special case we recover a construction by Kolb in finite type. We review recent work on univers

Jeremy Vonderfecht, Feng Liu

Several recent studies have demonstrated that deep-learning based image generation models, such as GANs, can be uniquely identified, and possibly even reverse-engineered, by the fingerprints they leave on their output images. We extend this research to single image super-resolution (SISR) networks. Compared to previously studied models, SISR networks are a u

Crescentia Jung, Jazmin Collins, Ricardo E. Gonzalez Penuela, Jonathan Isaac Segal

Social VR has increased in popularity due to its affordances for rich, embodied, and nonverbal communication. However, nonverbal communication remains inaccessible for blind and low vision people in social VR. We designed accessible cues with audio and haptics to represent three nonverbal behaviors: eye contact, head shaking, and head nodding. We evaluated t

Jesús A. De Loera, Denae Ventura, Liuyue Wang, William J. Wesley

A famous result in arithmetic Ramsey theory says that for many linear homogeneous equations $E$ there is a threshold value $R_k(E)$ (the Rado number of $E$) such that for any $k$-coloring of the integers in the interval $[1,n]$, with $n \ge R_k(E)$, there exists at least one monochromatic solution. But one can further ask, how many monochromatic solutions is

Zhenghua Xu, Irene Sabadini

The concept of generalized partial-slice monogenic functions has been recently introduced to include the two theories of monogenic functions and of slice monogenic functions over Clifford algebras. The main purpose of this article is to develop the Segal-Bargmann transform and give a Schr\"{o}dinger representation in the setting of generalized partial-slice

Kang Yin, Hye-Bin Shin, Dan Li, Seong-Whan Lee

Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availability. In this paper, we introduce a novel multimodal framework that accommodates not only conventional modalities such as video, images, and audio, but also incorporates EEG data.

Ruili Zhao, Jun Cai, Jiangtao Luo, Junpeng Gao

Low-Earth orbit (LEO) satellites utilizing beam hopping (BH) technology offer extensive coverage, low latency, high bandwidth, and significant flexibility. However, the uneven geographical distribution and temporal variability of ground traffic demands, combined with the high mobility of LEO satellites, present significant challenges for efficient beam resou

Alexandra C. Rosenthal, Scott M. Ransom, Kyle A. Corcoran, Megan E. DeCesar

We present a 34-year timing solution of the redback pulsar system Terzan 5A (Ter5A). Ter5A, also known as B1744$-$24A or J1748$-$2446A, has a 11.56 ms pulse period, a $\sim$0.1 solar mass dwarf companion star, and an orbital period of 1.82 hours. Ter5A displays highly variable eclipses and orbital perturbations. Using new timing techniques, we have determine

Yufei Zhang, Yicheng Xu, Hongxin Wei, Zhiping Lin

Test-Time Adaptation (TTA) enables pre-trained models to bridge the gap between source and target datasets using unlabeled test data, addressing domain shifts caused by corruptions like weather changes, noise, or sensor malfunctions in test time. Multi-Modal Continual Test-Time Adaptation (MM-CTTA), as an extension of standard TTA, further allows models to h

Shanchao Liang, Yiran Hu, Nan Jiang, Lin Tan

Recently, a number of repository-level code generation benchmarks-such as CoderEval, DevEval, RepoEval, RepoBench, and LongCodeArena-have emerged to evaluate the capabilities of large language models (LLMs) beyond standalone benchmarks like HumanEval and MBPP. Thus, a natural question is, would LLMs have similar performance in real world coding tasks as thei

Matias C. Relyea

The solvability of the cubic congruence $x^{3}\equiv 2\pmod{p}$ is referred to as the $\textit{cubic character of 2}$. In evaluating the cubic character of 2, we introduce the Eisenstein integers, Gauss and Jacobi sums, and the law of cubic reciprocity. We motivate this proof by giving ample historical information surrounding the early development of higher

Jeremy Vonderfecht, Feng Liu

Implicit Neural Representations (INRs), which encode signals such as images, videos, and 3D shapes in the weights of neural networks, are becoming increasingly popular. Among their many applications is signal compression, for which there is great interest in achieving the highest possible fidelity to the original signal subject to constraints such as neural

Gaurav Hirani, Waleed Abdulla

The deep convolutional neural network (DCNN) in computer vision has given promising results. It is widely applied in many areas, from medicine, agriculture, self-driving car, biometric system, and almost all computer vision-based applications. Filters or weights are the critical elements responsible for learning in DCNN. Backpropagation has been the primary

Yuxu Wu, Yiren Xing

With the rapid development of Natural Language Processing (NLP) technology, the accuracy and efficiency of machine translation have become hot topics of research. This paper proposes a novel Seq2Seq model aimed at improving translation quality while reducing the storage space required by the model. The model employs a Bidirectional Long Short-Term Memory net

Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Sameera Ramasinghe, Stephen Gould

Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. These INRs typically learn the implicit field from sampled input points. This is often done using a single network for the entire domain, imposing many global constraints on a single function. In this paper, we propose a mixt

I. K. Kozlov

We prove that any bi-Hamiltonian system $v = \left(\mathcal{A} + \lambda \mathcal{B}\right)dH_{\lambda}$ on a real smooth manifold that is Hamiltonian with respect all Poisson brackets $\left(\mathcal{A} + \lambda \mathcal{B}\right)$ is locally bi-integrable. We construct a complete set of functions $\mathcal{G}$ in bi-involution by extending the set of stan

Kehan Sui, Jinxu Xiang, Fang Jin

Singing voice synthesis (SVS) aims to produce high-fidelity singing audio from music scores, requiring a detailed understanding of notes, pitch, and duration, unlike text-to-speech tasks. Although diffusion models have shown exceptional performance in various generative tasks like image and video creation, their application in SVS is hindered by time complex

Si-Ioi Ng, Lingfeng Xu, Ingo Siegert, Nicholas Cummins

There has been a surge of interest in leveraging speech as a marker of health for a wide spectrum of conditions. The underlying premise is that any neurological, mental, or physical deficits that impact speech production can be objectively assessed via automated analysis of speech. Recent advances in speech-based Artificial Intelligence (AI) models for diagn

Nicholas Ferrante, Jerome Gilles, Shibin Parameswaran

In this work, we extract the optical flow field corresponding to moving objects from an image sequence of a scene impacted by atmospheric turbulence \emph{and} captured from a moving camera. Our procedure first computes the optical flow field and creates a motion model to compensate for the flow field induced by camera motion. After subtracting the motion mo

Deepak Sridhar, Abhishek Peri, Rohith Rachala, Nuno Vasconcelos

Recent advances in generative modeling with diffusion processes (DPs) enabled breakthroughs in image synthesis. Despite impressive image quality, these models have various prompt compliance problems, including low recall in generating multiple objects, difficulty in generating text in images, and meeting constraints like object locations and pose. For fine-g

Rafael Rivera Soto, Barry Chen, Nicholas Andrews

High-quality paraphrases are easy to produce using instruction-tuned language models or specialized paraphrasing models. Although this capability has a variety of benign applications, paraphrasing attacks$\unicode{x2013}$paraphrases applied to machine-generated texts$\unicode{x2013}$are known to significantly degrade the performance of machine-text detectors

Ioannis Anagnostides, Tuomas Sandholm

Gradient-based algorithms have shown great promise in solving large (two-player) zero-sum games. However, their success has been mostly confined to the low-precision regime since the number of iterations grows polynomially in $1/\epsilon$, where $\epsilon > 0$ is the duality gap. While it has been well-documented that linear convergence -- an iteration compl

Andrew Zhao

We study the problem of learning an unknown quantum many-body Hamiltonian $H$ from black-box queries to its time evolution $e^{-\mathrm{i} H t}$. Prior proposals for solving this task either impose some assumptions on $H$, such as its interaction structure or locality, or otherwise use an exponential amount of computational postprocessing. In this paper, we

Jiahe Bai, Baojian Zhou, Deqing Yang, Yanghua Xiao

Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank, Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks, and many other graph-related problems. Standard iterative methods require accessing the whole graph per iteration, making them time-consuming for large-scale graphs. While existin

Nathan Herring, Shuyang Cao, Daniel Boyanovsky

We study the applicability of the finite temperature effective potential in the equation of motion of a homogeneous "misaligned" scalar condensate $\varphi$, and find important caveats that severely restrict its domain of validity: i:) the assumption of local thermodynamic equilibrium (LTE) is in general not warranted, ii:) we show a direct relation between

Haitian Wang, Yu Chen, Xiaoling Cui

We establish an exact mapping between identical particles in one dimension with arbitrary exchange statistics, including bosons, anyons and fermions, provided they share the same scattering length. This boson-anyon-fermion mapping facilitates the construction of anyons from a linear superposition of spatially symmetric and anti-symmetric states. This scheme

CMS Collaboration

A measurement of the top quark pair ($\mathrm{t\bar{t}}$) production cross section in proton-proton collisions at a centre-of-mass energy of 5.02 TeV is presented. The data were collected at the LHC in autumn 2017, in dedicated runs with low-energy and low-intensity conditions with respect to the default configuration, and correspond to an integrated luminos

Sambhav Khurana, Xiner Li, Shurui Gui, Shuiwang Ji

Large language models (LLMs) are being increasingly explored for graph tasks. Despite their remarkable success in text-based tasks, LLMs' capabilities in understanding explicit graph structures remain limited, particularly with large graphs. In this work, we introduce Hierarchical Language Model for Graph (HLM-G), which employs a two-block architecture to ca

Akshaya Agrawal, Parker Mayer, Zachary Kingston, Geoffrey A. Hollinger

Cooperative manipulation tasks impose various structure-, task-, and robot-specific constraints on mobile manipulators. However, current methods struggle to model and solve these myriad constraints simultaneously. We propose a twofold solution: first, we model constraints as a family of manifolds amenable to simultaneous solving. Second, we introduce the con

Pratheba Selvaraju, Victoria Fernandez Abrevaya, Timo Bolkart, Rick Akkerman

Reconstructing 3D face models from a single image is an inherently ill-posed problem, which becomes even more challenging in the presence of occlusions. In addition to fewer available observations, occlusions introduce an extra source of ambiguity where multiple reconstructions can be equally valid. Despite the ubiquity of the problem, very few methods addre

M. De Sanctis

The QCD running coupling costant is studied in the perturbative region, considering the existing experimental data, and also in the nonperurbative region, at low momentum transfer. A continous phenomenological function is determined by means of three different models also calculating the corresponding finite value of the vector quark self-energy. These two q

Seyed Hossein Alavi, Sudha Rao, Ashutosh Adhikari, Gabriel A DesGarennes

We propose a novel approach that uses large language models (LLMs) to generate persona-driven conversations between Players and Non-Player Characters (NPC) in games. Showcasing the application of our methodology, we introduce the Minecraft Persona-driven Dialogue dataset (MCPDial). Starting with a small seed of expert-written conversations, we employ our met

Sha Wu, Yingqing Xiao

Let $\left\{b_{k}\right\}_{k=1}^{\infty}$ be a sequence of integers with $|b_{k}|\geq2$ and $\left\{D_{k}\right\}_{k=1}^{\infty} $ be a sequence of equidifferent digit sets with $D_{k}=\left\{0,1, \cdots, N-1\right\}t_{k},$ where $N\geq2$ is a prime number and $\{t_{k}\}_{k=1}^{\infty}$ is bounded. In this paper, we study the existence of the Cantor-Moran me

Jonathan Nino-Cortes, Cynthia Vinzant

The higher-rank numerical range is a convex compact set generalizing the classical numerical range of a square complex matrix, first appearing in the study of quantum error correction. We will discuss some of the real algebraic and convex geometry of these sets, including a generalization of Kippenhahn's theorem, and describe an algorithm to explicitly calcu

Sam Wilken, Gabrielle R. Abraham, Omar A. Saleh

Cells operate in part by compartmentalizing chemical reactions. For example, recent work has shown that chromatin, the material that contains the cell's genome, can auto-regulate its structure by utilizing reaction products (proteins, RNA) to compartmentalize biomolecules via liquid-liquid phase separation (LLPS). Here, we develop a model biomolecular system

Swayam Kesari, Alka B. Garg, Nilesh P. Salke, Rekha Rao

Most reported negative linear compressibility (NLC) materials exhibit either a small NLC over a large pressure range or a high NLC over a very small pressure range. Here, we report the remarkable discovery of giant NLC in the low-temperature form of CuCN (LT-CuCN) over an unusually large pressure range. High-pressure XRD studies on LT-CuCN observed the NLC o

B. O'Connor, D. Pasham, I. Andreoni, J. Hare

We present the results of our multi-wavelength (X-ray to radio) follow-up campaign of the Einstein Probe transient EP240408a. The initial 10 s trigger displayed bright soft X-ray (0.5-4 keV) radiation with peak luminosity $L_\textrm{X} \gtrsim 10^{49}$ ($10^{50}$) erg s$^{-1}$ for an assumed redshift z>0.5 (2.0). The Neil Gehrels Swift Observatory and Neutro

Jian Qian, Alexander Rakhlin, Nikita Zhivotovskiy

We revisit the sequential variants of linear regression with the squared loss, classification problems with hinge loss, and logistic regression, all characterized by unbounded losses in the setup where no assumptions are made on the magnitude of design vectors and the norm of the optimal vector of parameters. The key distinction from existing results lies in

Ali Pourramezan Fard, Mohammad Mehdi Hosseini, Timothy D. Sweeny, Mohammad H. Mahoor

Automated Facial Expression Recognition (FER) is challenging due to intra-class variations and inter-class similarities. FER can be especially difficult when facial expressions reflect a mixture of various emotions (aka compound expressions). Existing FER datasets, such as AffectNet, provide discrete emotion labels (hard-labels), where a single category of e

Matías Díaz-Vera, Carlos Román

We consider extreme type-II superconductors modeled by the Ginzburg--Landau energy with a pinning term $a_\varepsilon(x)$, which we assume to be a bounded measurable function such that $b\leq a_\varepsilon(x)\leq 1$ for some constant $b>0$. A crucial feature of this type of superconductors is the occurrence of vortices, which appear above the so-called first

Urmila Chadayammuri, Lukas Eisert, Annalisa Pillepich, Katrin Lehle

The physical properties of the intracluster medium (ICM) reflect signatures of the underlying gravitational potential, mergers and strong interactions with other halos and satellite galaxies, as well as galactic feedback from supernovae and supermassive black holes (SMBHs). Traditionally, clusters have been characterized in terms of summary statistics, such

Ryszard Gieniusz, Pawel Gruszecki, Jan Kisielewski, Anuj Kumar Dhiman

Results of extensive combined experimental and theoretical investigations on static and dynamic properties of Ir/Co/Pt multilayer with low uniaxial anisotropy and asymmetric Ir/Co and Co/Pt interfaces responsible for large interfacial Dzyaloshinskii-Moriya interaction (IDMI) are presented. Within longitudinal magneto-optical Kerr effect-based microscopy and

Łukasz Baran, Pablo Llombart, Luis G. MacDowell

In this work, we perform a systematic computer simulation study of ice premelting, and explore the thickness and structure of quasi-liquid layers formed at the interface of ice with substrates of different hydrophilicity. Our study shows that interfacial premelting occurs on neutral substrates of whatever hydrophilicity, forming films of limited thickness fo

Antonio A. Ginart, Naveen Kodali, Jason Lee, Caiming Xiong

While frontier large language models (LLMs) are capable tool-using agents, current AI systems still operate in a strict turn-based fashion, oblivious to passage of time. This synchronous design forces user queries and tool-use to occur sequentially, preventing the systems from multitasking and reducing interactivity. To address this limitation, we introduce

Chance Sanford

Using Abel's five-term relation, we derive a new two-parameter series identity for the Rogers dilogarithm. By specializing this identity, we obtain dilogarithm series involving Lucas sequences. These results generalize certain series identities of Bridgeman related to solutions of Pell's equations, which were obtained via a completely different approach.

Yuntian He, Pranav Maneriker, Anutam Srinivasan, Aditya T. Vadlamani

Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. Although recent studies have applied conformal prediction to graph neural networks, they have largely emphasized post-hoc prediction set generation. Improving conformal prediction during the training stage remains unaddressed. In this work, we tackle this

Wenda Zhang, Weimin Yuan, Zhixing Ling, Yong Chen

We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare las

Yiwen Qiu, Yujia Zheng, Kun Zhang

When solving long-horizon tasks, it is intriguing to decompose the high-level task into subtasks. Decomposing experiences into reusable subtasks can improve data efficiency, accelerate policy generalization, and in general provide promising solutions to multi-task reinforcement learning and imitation learning problems. However, the concept of subtasks is not

Taiyi Pan, Junyang He, Chao Chen, Yiming Li

Visual place recognition (VPR) enables autonomous robots to identify previously visited locations, which contributes to tasks like simultaneous localization and mapping (SLAM). VPR faces challenges such as accurate image neighbor retrieval and appearance change in scenery. Event cameras, also known as dynamic vision sensors, are a new sensor modality for VPR

Konstantin S. Denisov, Igor V. Rozhansky, Sergio O. Valenzuela, Igor Žutić

The two-dimensional (2D) materials are highly susceptible to the influence of their neighbors, thereby enabling the design by proximity phenomena. We reveal a remarkable terahertz (THz) spin-light interaction in 2D Dirac materials that arises from magnetic and spin-orbital proximity effects. The dynamical realization of the spin-charge conversion, the electr

Joao Prazeres, Rafael Rodrigues, Manuela Pereira, Antonio M. G. Pinheiro

Typically, point cloud encoders allocate a similar bitrate for geometry and attributes (usually RGB color components) information coding. This paper reports a quality study considering different coding bitrate tradeoff between geometry and attributes. A set of five point clouds, representing different characteristics and types of content was encoded with the

S. Srimathy

Given a discrete valued field $K$ of positive characteristic, we study the cyclic lifting problem of purely inseparable extensions of the residue field. We prove that unlike the mixed characteristic case, cyclic lifts of any finite purely inseparable modular extension exist and show how to explicitly construct them. Moreover, given such a residual extension,

Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architec

E. Chacón, H. García-Compeán, G. Robles

The self-dual double copy is further explored. In previous work, it has been shown that hyper-Hermitian manifolds also have associated the self-dual gauge theories via Kerr-Schild double copy. The self-dual double copy is generalized in the structure of the kinematic algebra by replacing the area-preserving diffeomorphisms algebra with the diffeomorphisms on

Chun-Wei Kong, Luca Laurenti, Jay McMahon, Morteza Lahijanian

Stochastic differential equations are commonly used to describe the evolution of stochastic processes. The state uncertainty of such processes is best represented by the probability density function (PDF), whose evolution is governed by the Fokker-Planck partial differential equation (FP-PDE). However, it is generally infeasible to solve the FP-PDE in closed

Diego A. Rojas

Prokhorov's Theorem in probability theory states that a family $\Gamma$ of probability measures on a Polish space is tight if and only if every sequence in $\Gamma$ has a weakly convergent subsequence. Due to the highly non-constructive nature of (relative) sequential compactness, however, the effective content of this theorem has not been studied. To this e

Konstantin S. Denisov, Igor Žutić

Antiferromagnets (AFM) with a nonrelativistic spin splitting (NSS) of electronic bands expand the range of spin-dependent phenomena and their applications. A crucial understanding for both of them pertains to the inherent spin dynamics. We demonstrate that the $d$-wave NSS gives rise to extremely anisotropic spin dynamics of carriers driven by the relaxation

Yassine Tissaoui, Stephen R. Guimond, Francis X. Giraldo, Simone Marras

Tropical cyclones (TCs) are powerful, natural phenomena that can severely impact populations and infrastructure. Enhancing our understanding of the mechanisms driving their intensification is crucial for mitigating these impacts. To this end, researchers are pushing the boundaries of TC simulation resolution down to scales of just a few meters. However, high

Anupam Datta

We use homotopy theoretic ideas to study the $K$-theory of (graded, Real) $C^*$-algebras in detail. After laying the foundations, and deriving the formal properties, the comparison of the model with the Kasparov picture of $K$-theory has been made, and Bott periodicity has been proven using a Dirac-dual Dirac method.

Şeyma Selcan Mağara, Noah Dietrich, Ali Burak Ünal, Mete Akgün

Record linkage is a crucial concept for integrating data from multiple sources, particularly when datasets lack exact identifiers, and it has diverse applications in real-world data analysis. Privacy-Preserving Record Linkage (PPRL) ensures this integration occurs securely, protecting sensitive information from unauthorized access. This is especially importa

Osamah Alkasassbeh, Abha Rajan, Michael Engelhardt, Simonetta Liuti

Using the equations of motion of QCD and Lorentz invariance relations, we show a new, more direct way of obtaining the decomposition of the proton's quark transverse angular momentum into its spin and orbital components. The new decomposition can be understood as a twist-three relation, with the quark transverse spin described by the parton distribution $g_T

Clara Grazian

Model selection in the presence of intractable likelihoods remains a central challenge in Bayesian inference. Approximate Bayesian computation (ABC) provides a flexible likelihood-free framework, but its use for model choice is known to be sensitive to the choice of summary statistics, often leading to poorly calibrated posterior model probabilities. Recent