November 2024 arXiv papers — page 4

Nikhil Kumar Koditala, Chelsea Jui-Ting Ju, Ruirui Li, Minho Jin

A speaker verification (SV) system offers an authentication service designed to confirm whether a given speech sample originates from a specific speaker. This technology has paved the way for various personalized applications that cater to individual preferences. A noteworthy challenge faced by SV systems is their ability to perform consistently across a ran

Peixiang Wang, Binbin Li

In full-scale forced vibration tests, the demand often arises to capture high-spatial-resolution mode shapes with limited number of sensors and shakers. Multi-setup experimental modal analysis (EMA) addresses this challenge by roving sensors and shakers across multiple setups. To enable fast and accurate multi-setup EMA, this paper develops a Bayesian modal

Lin Chen, Ahmed Elzanaty, Mustafa A Kishk, Ying-Jun Angela Zhang

Reconfigurable intelligent surfaces (RISs) have shown the potential to improve signal-to-interference-plus-noise ratio (SINR) related coverage, especially at high-frequency communications. However, assessing electromagnetic field exposure (EMFE) and establishing EMFE regulations in RIS-assisted large-scale networks remain open issues. This paper proposes a s

Renzo Comolatti, Matteo Grasso, Giulio Tononi

Time flows, or at least the time of our experience does. Can we provide an objective account of why experience, confined to the short window of the conscious present, encompasses a succession of moments that slip away from now to then--an account of why time feels flowing? Integrated Information Theory (IIT) aims to account for both the presence and quality

Shaohan Yu, Pan Deng, Yu Zhao, Junting Liu

Achieving both accurate and consistent predictive performance across spatial nodes is crucial for ensuring the validity and reliability of outcomes in fair spatial-temporal forecasting tasks. However, existing training methods treat heterogeneous nodes with a fully averaged perspective, resulting in inherently biased prediction targets. Balancing accuracy an

Jingzhe Liu, Haitao Mao, Zhikai Chen, Bingheng Li

Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require careful domain-specific architecture designs and training from scratch on each dataset, leading to an expertise-intensive process with difficulty in generalizing across graphs from diff

Fangzhou Xu, Sai Zhang, Zhenchang Xing, Xiaowang Zhang

Code quality evaluation involves scoring generated code quality based on a reference code for a specific problem statement. Currently, there are two main forms of evaluating code quality: match-based evaluation and execution-based evaluation. The former requires the collection of a large number of test cases, making a huge cost. The latter relies on superfic

Olivier Nourry, Masanari Kondo, Shinobu Saito, Yukako Iimura

Throughout their lifetime, open-source software systems will naturally attract new contributors and lose existing contributors. Not all OSS contributors are equal, however, as some contributors within a project possess significant knowledge and expertise of the codebase (i.e., core developers). When investigating the ability of projects to attract new contri

Kazi Nazmul Haque, Rajib Rana, Tasnim Jarin, Bjorn W. Schuller

This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised features in the pre-processing step. Unlike recent trends in literature, which often focuses on designing frontends or filters for only the initial layers of CNNs, our research introd

Dafne Zorzetto, Paolo Dalla Torre, Sonia Petrone, Francesca Dominici

Understanding the causal effects of air pollution exposures on social mobility is attracting increasing attention. At the same time, education is widely recognized as a key driver of social mobility. However, the causal pathways linking fine particulate matter (PM2.5) exposure, educational attainment, and social mobility remain largely unexplored. To address

Yanbin He, Geethu Joseph

This work studies the problem of jointly estimating unknown parameters from Kronecker-structured multidimensional signals, which arises in applications like intelligent reflecting surface (IRS)-aided channel estimation. Exploiting the Kronecker structure, we decompose the estimation problem into smaller, independent subproblems across each dimension. Each su

Feiyang Liu, Dan Guo, Jingyuan Xu, Zihao He

Following the gaze of other people and analyzing the target they are looking at can help us understand what they are thinking, and doing, and predict the actions that may follow. Existing methods for gaze following struggle to perform well in natural scenes with diverse objects, and focus on gaze points rather than objects, making it difficult to deliver cle

Karine Karine, Susan A. Murphy, Benjamin M. Marlin

In settings where the application of reinforcement learning (RL) requires running real-world trials, including the optimization of adaptive health interventions, the number of episodes available for learning can be severely limited due to cost or time constraints. In this setting, the bias-variance trade-off of contextual bandit methods can be significantly

Brian C. Kiedrowski

The Monte Carlo differential operator sampling method is applied to the computation of sensitivity coefficients of unresolved resonance probability table cross sections. Three new analytical benchmarks for verifying unresolved resonance treatments and sensitivity coefficient computations are developed. The method and its research-code implementation are veri

Yizhi Song, Liu He, Zhifei Zhang, Soo Ye Kim

Personalized image generation has emerged from the recent advancements in generative models. However, these generated personalized images often suffer from localized artifacts such as incorrect logos, reducing fidelity and fine-grained identity details of the generated results. Furthermore, there is little prior work tackling this problem. To help improve th

Ying-Chi Hu, Chin-Fei Lee, Zhe-Yu Daniel Lin, Zhi-Yun Li

Grain growth in disks around young stars plays a crucial role in the formation of planets. Early grain growth has been suggested in the HH 212 protostellar disk by previous polarization observations. To confirm it and to determine the grain size, we analyze high-resolution multi-band observations of the disk obtained with Atacama Large Millimeter/submillimet

Yingjie Huang, Dafne Zorzetto, Roberta De Vito

Bayesian factor models are widely used for dimensionality reduction and pattern discovery in high-dimensional datasets across diverse fields. These models typically focus on imposing priors on factor loading to induce sparsity and improve interpretability. However, factor scores, which play a critical role in individual-level associations with factors, have

Reggie C. Pantig, Gaetano Lambiase, Ali Övgün, Nikko John Leo S. Lobos

This paper links the advanced formulation of the Generalized Uncertainty Principle, termed the Asymptotic Generalized Extended Uncertainty Principle (AGEUP), to the corpuscular framework to derive the AGEUP-inspired black hole metric. The former incorporates spacetime curvature effects to explore black hole dynamics under quantum gravitational corrections, w

Judy X Yang, Jing Wang, Chen Hong Sui, Zekun Long

The integration of hyperspectral imaging (HSI) and LiDAR data within new linear feature spaces offers a promising solution to the challenges posed by the high-dimensionality and redundancy inherent in HSIs. This study introduces a dual linear fused space framework that capitalizes on bidirectional reversed convolutional neural network (CNN) pathways, coupled

Hadi Hosseini, Duohan Zhang

Two-sided matching markets have demonstrated significant impact in many real-world applications, including school choice, medical residency placement, electric vehicle charging, ride sharing, and recommender systems. However, traditional models often assume that preferences are known, which is not always the case in modern markets, where preferences are unkn

Owen Burns, Dana Hughes, Katia Sycara

Automated planning using a symbolic planning language, such as PDDL, is a general approach to producing optimal plans to achieve a stated goal. However, creating suitable machine understandable descriptions of the planning domain, problem, and goal requires expertise in the planning language, limiting the utility of these tools for non-expert humans. Recent

Paul A. Bosomworth, Rui Zhang, Lawrence M. Anovitz

Young's modulus is an important parameter for characterizing the strength of, and wave propagation through, a given material. This study improves the estimation of Young's modulus using the impulse excitation (IE) technique based on an experimental analysis of 19 borosilicate glass bars. Analysis of the frequency equations relating Young's modulus to the out

Jason Crann, Monica Jinwoo Kang

Motivated by the theory of holographic quantum error correction in the anti-de Sitter/conformal field theory (AdS/CFT) correspondence, together with the kink transform conjecture on the bulk AdS description of boundary cocycle flow, we characterize (approximate) complementary recovery in terms of (approximate) intertwining of bulk and boundary cocycle deriva

Michael V. Klibanov, Trung Truong

It is proposed to monitor spatial and temporal spreads of epidemics via solution of a Coefficient Inverse Problem for a system of three coupled nonlinear parabolic equations. To solve this problem numerically, a version of the so-called Carleman contraction mapping method is developed for this problem. On each iteration, a linear problem with the incomplete

Danqing He, Kangwei Li, Jiqiang Zheng

We improve the range of indices when the multilinear Bochner-Riesz means converges pointwisely. We obtain this result by establishing the $L^p$ estimates and weighted estimates of $k$-linear maximal Bochner-Riesz operators inductively, which is new when $p<2/k$ in higher dimensions. To prove these estimates, we make use of a variant of Stein's square functio

Robert Simon Fong, Boyu Li, Peter Tino

This paper explores the representational structure of linear Simple Cycle Reservoirs (SCR) operating at the edge of stability. We view SCR as providing in their state space feature representations of the input-driving time series. By endowing the state space with the canonical dot-product, we ``reverse engineer" the corresponding kernel (inner product) opera

Ryley Hill, Maria del Carmen Polletta, Matthieu Bethermin, Herve Dole

Planck's High-Frequency Instrument observed the whole sky between 350um and 3mm, discovering thousands of unresolved peaks in the cosmic infrared background. The nature of these peaks is still poorly understood - while some are strong gravitational lenses, the majority are overdensities of star-forming galaxies but with almost no redshift constraints. PLCK G

Akash Karthikeyan, Yash Vardhan Pant

Despite recent advances in learning-based behavioral planning for autonomous systems, decision-making in multi-task missions remains a challenging problem. For instance, a mission might require a robot to explore an unknown environment, locate the goals, and navigate to them, even if there are obstacles along the way. Such problems are difficult to solve due

Michael Jünger, Paul J. Jünger, Petra Mutzel, Gerhard Reinelt

This is a short description of our solver OSCM submitted by our team MPPEG to the PACE 2024 challenge both for the exact track and the parameterized track, available at https://github.com/pauljngr/PACE2024 and https://doi.org/10.5281/zenodo.11546972.

Tianyang Zhong, Zhenyuan Yang, Zhengliang Liu, Ruidong Zhang

Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite their significance, these languages face critical challenges, including data scarcity and technological limitations, which hinder their comprehensive study and preservation. Recent advancements in large language models (L

Jianhao Jiao, Ruoyu Geng, Yuanhang Li, Ren Xin

The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and semantic information consistency. In this p

Avirath Sundaresan, Jason R. Parham, Jonathan Crall, Rosemary Warungu

In both 2016 and 2018, a census of the highly-endangered Grevy's zebra population was enabled by the Great Grevy's Rally (GGR), a citizen science event that produces population estimates via expert and algorithmic curation of volunteer-captured images. A complementary, scalable, and long-term Grevy's population monitoring approach involves deploying camera t

Indrajeet Dhananjay Prasad, Sumitra Shit, Yunus Waheed, Jithin Thoppil Surendran

Bilayers of transition-metal dichalcogenides show many exciting features, including long-lived interlayer excitons and wide bandgap tunability using strain. Not many investigations on experimental determinations of deformation potentials relating changes in optoelectronic properties of bilayer WSe$_2$ with the strain are present in the literature. Our experi

Matthew Gerry, Jonathan J. Wang, Joanna Li, Ofir Shein-Lumbroso

Delta-T shot noise is activated in temperature-biased electronic junctions, down to the atomic scale. It is characterized by a quadratic dependence on the temperature difference and a nonlinear relationship with the transmission coefficients of partially opened conduction channels. In this work, we demonstrate that delta-T noise, measured across an ensemble

Kelly Roberta Mazzutti Lübeck, Valério Ramos Batista

In 1989 H.Karcher rewrote the theory of elliptic functions through an approach that is much more geometrical than analytical. Therewith he obtained an optimal control over the behaviour and image values of these functions, which allowed for their broad application in minimal surfaces. Our work is devoted to presenting the theory of elliptic functions accordi

Hong Jian Zhao, Yanchao Wang, Laurent Bellaiche, Yanming Ma

The anisotropic propagation of particles is a fundamental transport phenomenon in solid state physics. As for crystalline semiconductors, the anisotropic charge transport opens novel designing routes for electronic devices, where the electrical or magnetic manipulation of anisotropic resistance provides essential guarantees. Motivated by the concept of aniso

Koustubh Phalak, Archisman Ghosh, Swaroop Ghosh

Quantum Embeddings (QE) are essential for loading classical data into quantum systems for Quantum Machine Learning (QML). The performance of QML algorithms depends on the type of QE and how features are mapped to qubits. Traditionally, the optimal embedding is found through optimization, but we propose framing it as a search problem instead. In this work, we

Yusuke Yamada

We discuss the scattering process of a scalar field having a time-dependent mass with another scalar field having a constant mass as a toy model of the scattering problems during preheating after inflation. Despite a general difficulty of analytically solving such models, in our previous work [1], we considered an exactly calculable model of such scattering

Yuta Mizuno, Mohammad Ali, Tamiki Komatsuzaki

Combinatorial problems such as combinatorial optimization and constraint satisfaction problems arise in decision-making across various fields of science and technology. In real-world applications, when multiple optimal or constraint-satisfying solutions exist, enumerating all these solutions is often desirable, as it provides flexibility in decision-making.

Judy X Yang, Jing Wang, Zekun Long, Chenhong Sui

Classifying hyperspectral images (HSIs) is a complex task in remote sensing due to the high-dimensional nature and volume of data involved. To address these challenges, we propose the Spectral-Spatial non-Linear Model, a novel framework that significantly reduces data volume while enhancing classification accuracy. Our model employs a bidirectional reversed

Saurya Das, Mitja Fridman, Sourav Sur

We present a classical theory of gravity, which is singularity free at short distances and reduces to General Relativity at large distances. We discuss its implications.

Oscar Díaz, Xabier Garmendia, Juanan Pereira

The increasing volume of research paper submissions poses a significant challenge to the traditional academic peer-review system, leading to an overwhelming workload for reviewers. This study explores the potential of integrating Large Language Models (LLMs) into the peer-review process to enhance efficiency without compromising effectiveness. We focus on ma

Etienne Peyrot, Raphaël Porcher, Francois Petit

Background: Inverse probability of treatment weighting (IPTW) is used for confounding adjustment in observational studies. Newer weighting methods include energy balancing (EB), kernel optimal matching (KOM), and tailored-loss covariate balancing propensity scores (TLF), but practical guidance remains limited. We evaluate their performance when implemented a

Matias Carrasco, Andres Ferragut, Fernando Paganini

In this paper, we analyze the optimal management of local memory systems, using the tools of stationary point processes. We provide a rigorous setting of the problem, building upon recent work, and characterize the optimal causal policy that maximizes the hit probability. We specialize the result for the case of renewal request processes and derive a suitabl

Tao Sun, Sander Bohté

Uncertainty estimation is a standard tool to quantify the reliability of modern deep learning models, and crucial for many real-world applications. However, efficient uncertainty estimation methods for spiking neural networks, particularly for regression models, have been lacking. Here, we introduce two methods that adapt the Average-Over-Time Spiking Neural

Feng Xu, David Ahmedt-Aristizabal, Lars Petersson, Dadong Wang

Facial expression recognition (FER) systems raise significant privacy concerns due to the potential exposure of sensitive identity information. This paper presents a study on removing identity information while preserving FER capabilities. Drawing on the observation that low-frequency components predominantly contain identity information and high-frequency c

Euntak Lee, Rim Slama, Ludovic Leclercq

The global ride-hailing (RH) industry plays an essential role in multi-modal transportation systems by improving user mobility, particularly as first- and last-mile solutions. However, the flexibility of on-demand mobility services can lead to local supply-demand imbalances. While many RH rebalancing studies focus on nominal scenarios with regular demand pat

Lorenzo Gavassino

We compute the linearised dispersion relations of shear waves, heat waves, and sound waves in relativistic ''matter+radiation'' fluids with grey absorption opacities. This is done by solving radiation hydrodynamics perturbatively in the ratio ''radiation stress-energy''/''matter stress-energy''. The resulting expressions $\omega \, {=} \, \omega(k)$ accurate

Tom Bisson, Isil Dogan O, Iris Piwonski, Tim-Rasmus Kiehl

Surgical treatment for prostate cancer often involves organ removal, i.e., prostatectomy. Pathology reports on these specimens convey treatment-relevant information. Beyond these reports, the diagnostic process generates extensive and complex information that is difficult to represent in reports, although it is of significant interest to the other medical sp

Noemí DeCastro-García, Miguel V. Carriegos, Ángel Luis Muñoz Castañeda

This paper addresses the construction of observable convolutional codes that exhibit good performance with the available decoding algorithms for erasure channels. Our construction is based on the use of input/state/output (I/S/O) representations and the invariance of certain properties of linear systems under various group actions.

Julian Siegl, Anton Bleibaum, Wen Wan, Marcin Kurpas

In 1965 Kohn and Luttinger proposed a genuine electronic mechanism for superconductivity. Despite the bare electrostatic interaction between two electrons being repulsive, in a metal electron-hole fluctuations can give rise to Friedel oscillations of the screened Coulomb potential. Cooper pairing among the electrons then emerges when taking advantage of the

E. Dmitriev, Ph. Korneev

A structure of a laser pulse may significantly influence the dynamics of interacting particles. In the case of dilute plasma the particle dynamics may be considered in the single particle approximation. In this paper the problem of the angular momentum gain of a single particle in a focused structured pulse is considered for some certain cases, including rad

A. Shehata Abdelatief, A. J. Renders, M. Alqedra, J. J. Hansen

We report on experimental investigation of potential high-performance cavity length stabilization using odd-indexed higher-order spatial modes. Schemes based on higher-order modes are particularly useful for micro-cavities that are used for enhanced fluorescence detection of a few emitters, which need to minimize photons leaking from a stabilization beam. We

Giacomo Bastianel, Marta Vanin, Dirk Van Hertem, Hakan Ergun

Driven by global climate goals, an increasing amount of Renewable Energy Sources (RES) is currently being installed worldwide. Especially in the context of offshore wind integration, hybrid AC/DC grids are considered to be the most effective technology to transmit this RES power over long distances. As hybrid AC/DC systems develop, they are expected to becom

Henry Crumley

This paper studies the energy decoherence of an interacting quantum system. It first reviews the experiments that motivated the postulates of quantum mechanics. It then discusses a decoherence that occurs dynamically in a closed system. This effect is studied in interacting quantum systems consisting of an oscillator and spins using analytical and numerical

Gengzhi He, Curtis Sparks, Nicholas Gravish

Rigid multi-link robotic arms face a tradeoff between their overall reach distance (the workspace), and how compactly they can be collapsed (the storage volume). Increasing the workspace of a robot arm requires longer links, which adds weight to the system and requires a larger storage volume. However, the tradeoff between workspace and storage volume can be

Zezhi Deng, Ruoxing Yang

Transit networks often have existing infrastructure that cannot be modified when designing new lines for the network. This paper provides an algorithm to generate a line within a transit network without changing any existing lines or connections between stations. Additionally, a method of analyzing the efficiency of a transit line and network is provided, an

K. C. Hammond

Designing magnets for three-dimensional plasma confinement is a key task for advancing the stellarator as a fusion reactor concept. Stellarator magnets must produce an accurate field while leaving adequate room for other components and being reasonably simple to construct and assemble. In this paper, a framework for coil design and optimization is introduced

Jialong Li, Federico De Marchi, Yiming Lei, Raj Joshi

Optical data center networks (DCNs) are emerging as a promising solution for cloud infrastructure in the post-Moore's Law era, particularly with the advent of 'fast-switched' optical architectures capable of circuit reconfiguration at microsecond or even nanosecond scales. However, frequent reconfiguration of optical circuits introduces a unique challenge: i

Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk

Speech is a hierarchical collection of text, prosody, emotions, dysfluencies, etc. Automatic transcription of speech that goes beyond text (words) is an underexplored problem. We focus on transcribing speech along with non-fluencies (dysfluencies). The current state-of-the-art pipeline SSDM suffers from complex architecture design, training complexity, and s

Javier Quintero Arenas, Claudia Quinteros Cartaya, Andrea Padilla Lafarga, Carlos Moraila

Earthquake early warning systems are crucial for protecting areas that are subject to these natural disasters. An essential part of these systems is the detection procedure. Traditionally these systems work with seismograph data, but high rate GNSS data has become a promising alternative for the usage in large earthquake early warning systems. Besides tradit

Patrick Sattler, Matthias Kirstein, Lars Wüstrich, Johannes Zirngibl

Happy Eyeballs (HE) started out by describing a mechanism that prefers IPv6 connections while ensuring a fast fallback to IPv4 when IPv6 fails. The IETF is currently working on the third version of HE. While the standards include recommendations for HE parameters choices, it is up to the client and OS to implement HE. In this paper we investigate the state o

Tewodros Amdeberhan, Rupam Barman, Ajit Singh

In the present work, we extend current research in a nearly-forgotten but newly revived topic, initiated by P. A. MacMahon, on a generalized notion which relates the divisor sums to the theory of integer partitions and two infinite families of $q$-series by Ramanujan. Our main emphasis will be on explicit representations for a variety of $q$-series, studied

João Mattos, Zexi Huang, Mert Kosan, Ambuj Singh

Link prediction is a fundamental problem in graph data. In its most realistic setting, the problem consists of predicting missing or future links between random pairs of nodes from the set of disconnected pairs. Graph Neural Networks (GNNs) have become the predominant framework for link prediction. GNN-based methods treat link prediction as a binary classifi

Gordon Lim, Stefan Larson, Yu Huang, Kevin Leach

Crowdsourcing is a common approach to rapidly annotate large volumes of data in machine learning applications. Typically, crowd workers are compensated with a flat rate based on an estimated completion time to meet a target hourly wage. Unfortunately, prior work has shown that variability in completion times among crowd workers led to overpayment by 168% in

Yifan Zhu, Tianyi Xiang, Aaron Dollar, Zherong Pan

Identifying predictive world models for robots in novel environments from sparse online observations is essential for robot task planning and execution in novel environments. However, existing methods that leverage differentiable programming to identify world models are incapable of jointly optimizing the geometry, appearance, and physical properties of the

J. Barbish, M. R. Paul

The stochastic dynamics of small elastic objects in fluid are central to many important and emerging technologies. It is now possible to measure and use the higher modes of motion of elastic structures when driven by Brownian motion alone. Although theoretical descriptions exist for idealized conditions, computing the stochastic multimodal dynamics for the c

Janardhanraj Subburaj, Miguel Figueroa-Labastida, Aamir Farooq

Shock tubes are instrumental in studying high-temperature kinetics and simulating high-speed flows. They swiftly elevate the thermodynamic conditions of test gases, making them ideal for examining rapid chemical reactions and generating high-enthalpy flows for aerodynamic research. However, non-ideal effects, stemming from factors like diaphragm opening proc

Simon Mielke, Anthony Stein

Animal excretions in form of urine puddles and feces are a significant source of emissions in livestock farming. Automated detection of soiled floor in barns can contribute to improved management processes but also the derived information can be used to model emission dynamics. Previous research approaches to determine the puddle area require manual detectio

Arash Ashuri, Vasilis Gkatzelis, Alkmini Sgouritsa

We study the fair allocation of indivisible goods among a group of agents, aiming to limit the envy between any two agents. The central open problem in this literature, which has proven to be extremely challenging, is regarding the existence of an EFX allocation, i.e., an allocation such that any envy from some agent i toward another agent j would vanish if

Donald Silberger

We treat the functions $\star^k:{\mathbf N}\rightarrow{\mathbf N}$ where $\star:x\mapsto \star x := x(x+1)$. The set $\{\star^k x+1: \{x,k\}\subseteq{\mathbf N}\}$ is pairwise coprime; so, the set ${\mathbf P}$ of primes is infinite. Our Theorem 4 resorts to the mother sequence, M, that is obtained by factoring the infinite sequence $2,3,4,5,\ldots$ into pri

Poorva Shukla, Bassam Bamieh

We study phenomena where some eigenvectors of a graph Laplacian are largely confined in small subsets of the graph. These localization phenomena are similar to those generally termed Anderson Localization in the Physics literature, and are related to the complexity of the structure of large graphs in still unexplored ways. Using spectral perturbation theory

Talha Tahir

Cognitive Behavioral Therapy (CBT) is a well-established, evidence-based treatment for Major Depressive Disorder. Unfortunately, there exist significant barriers to individuals accessing CBT, including cost, scarcity of therapists and stigma. This study explores the feasibility of fine-tuning small open weight large language models (LLMs) to deliver CBT for

Izumi Hachisu, Mariko Kato, Frederick M. Walter

KT Eri is a classical nova which went into outburst in 2009. Recent photometric analysis in quiescence indicates a relatively longer orbital period of 2.6 days, so that KT Eri could host a very bright accretion disk during the outburst like in the recurrent nova U Sco, the orbital period of which is 1.23 days. We reproduced the optical $V$ light curve as wel

Simon Caron-Huot, Frank Coronado, Zahra Zahraee

We study four-point correlation functions of the stress-tensor multiplet in $\mathcal{N} = 4$ super Yang-Mills (sYM) theory by leveraging integrability and localization techniques. We combine dispersive sum rules and spectral information from integrability, used previously, with integrated constraints from supersymmetric localization. We obtain two-sided bou

Oscar Plaisant, Max Lemoine

There are a lot of different programming paradigms. Since all Turing-complete programming languages are formally equivalent (they have the same ability to express any computable problem), the existence of so many different paradigms may seem surprising, even pointless. In this article, we will try to understand why there are so many different paradigms. We w

Devin Murphy, Junyi Zhu, Paul Pu Liang, Wojciech Matusik

Past research has widely explored the design and fabrication of resistive matrix-based tactile sensors as a means of creating touch-sensitive devices. However, developing portable, adaptive, and long-lasting tactile sensing systems that incorporate these sensors remains challenging for individuals having limited prior experience with them. To address this, w

Adnan Chakra, Christina Puijk, Goran T. Vladisavljević, Cécile Cottin-Bizonne

The separation of colloidal particles based solely on their surface properties is a highly challenging task. This study demonstrates that diffusiophoresis and diffusioosmosis enable the continuous separation of carboxylate polystyrene particles with similar sizes and zeta potentials but distinct surface concentrations of carboxyl groups. The particles are ex

Tianqi Shang, Weiqing He, Tianlong Chen, Ying Ding

Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG. We introduce a novel fairness formulation for graph embeddings, focusing on i

Gordon Lim, Stefan Larson, Kevin Leach

In deep learning (DL) systems, label noise in training datasets often degrades model performance, as models may learn incorrect patterns from mislabeled data. The area of Learning with Noisy Labels (LNL) has introduced methods to effectively train DL models in the presence of noisily-labeled datasets. Traditionally, these methods are tested using synthetic l

Qiujing Lu, Meng Ma, Ximiao Dai, Xuanhan Wang

To guarantee the safety and reliability of autonomous vehicle (AV) systems, corner cases play a crucial role in exploring the system's behavior under rare and challenging conditions within simulation environments. However, current approaches often fall short in meeting diverse testing needs and struggle to generalize to novel, high-risk scenarios that closel

Shaoxiang Wang, Yaxu Xie, Chun-Peng Chang, Christen Millerdurai

Neural implicit fields have recently emerged as a powerful representation method for multi-view surface reconstruction due to their simplicity and state-of-the-art performance. However, reconstructing thin structures of indoor scenes while ensuring real-time performance remains a challenge for dense visual SLAM systems. Previous methods do not consider varyi

H. Çağrı Bilgi, Lydia Y. Chen, Kubilay Atasu

Graph Neural Networks (GNNs) have seen significant advances in recent years, yet their application to multigraphs, where parallel edges exist between the same pair of nodes, remains under-explored. Standard GNNs, designed for simple graphs, compute node representations by combining all connected edges at once, without distinguishing between edges from differ

Abimbola Abolarinwa, Yisa O Anthony

By a systematic development of fundamental concepts of conformable calculus we establish conformable divergence theorem and Green's identities which we combine with some new anisotropic Picone type identities to derive a generalized anisotropic Hardy type inequality with weights and conformable fractional differential operators. As a consequence, several Har

Orlando Marquez Ayala, Patrice Béchard

AI technologies are moving rapidly from research to production. With the popularity of Foundation Models (FMs) that generate text, images, and video, AI-based systems are increasing their complexity. Compared to traditional AI-based software, systems employing FMs, or GenAI-based systems, are more difficult to design due to their scale and versatility. This

Junbo Jacob Lian, Haoran Chen, Kaichen Ouyang, Yujun Zhang

Twisted Convolutional Networks (TCNs) are proposed as a novel deep learning architecture for classifying one-dimensional data with arbitrary feature order and minimal spatial relationships. Unlike conventional Convolutional Neural Networks (CNNs) that rely on structured feature sequences, TCNs explicitly combine subsets of input features through theoreticall

Aaron Bateni

In recent years, Spiking Neural Networks (SNNs) have gathered significant interest due to their temporal understanding capabilities. This work introduces, to the best of our knowledge, the first Cortical Column like hybrid architecture for the Time-Series Data Classification Task that leverages SNNs and is inspired by the brain structure, inspired from the p

Edison Cuba

In this paper, we investigate the existence of a finite number of vortex patches for the generalized surface quasi-geostrophic (gSQG) equations with $\alpha \in [1,2)$, focusing on configurations that may rotate uniformly, translate, or remain stationary. Using a desingularization technique, we reformulate the problem to resolve singularities arising in the

Giovanna Carnovale, Francesco Esposito, Lleonard Rubio y Degrassi

Motivated by an equivalence of categories established by Kapranov and Schechtman, we introduce, for each non-negative integer d, the category of connected bialgebras modulo d+1. We show that these categories fit into an inverse system of categories whose inverse limit category is equivalent to the category of connected bialgebras. In addition, we extend the

Hambur Wang

This study employs a Bayesian Probit model to empirically analyze peer effects and herd behavior among consumers during the "Double 11" shopping festival, using data collected through a questionnaire survey. The results demonstrate that peer effects significantly influence consumer decision-making, with the probability of participation in the shopping event

Aishwarya Kumar, Fereshteh Rajabi, Martin Houde

We present a mathematical analysis of propagation-induced distortions in the spectro-temporal properties of Fast Radio Bursts (FRBs). Within the Triggered Relativistic Dynamical Model, we derive a centroid-based formulation of the sub-burst slope law, which is an inverse relation between frequency-drift rate and temporal width of sub-bursts. We extend our an

Dipendra Gupta, David Brandes, Michael J Lanzone, Tricia Miller

Turbulence grounds aircraft and combating it in flight requires energy, yet volant wildlife fly effortlessly even on windy days. The nature of the interactions between soaring birds and transient turbulent gusts is not clear, especially when compared with our understanding of flight in larger and steadier airflows during thermal or dynamic soaring. We show t

Udo Hahn

We survey clinical document corpora, with focus on German textual data. Due to rigid data privacy legislation in Germany these resources, with only few exceptions, are stored in safe clinical data spaces and locked against clinic-external researchers. This situation stands in stark contrast with established workflows in the field of natural language processi

Héctor del Castillo, Guy Henniart, Luis Lomelí

Let $F$ be a locally compact non-Archimedean field, and $\bf G$ a connected quasi-split reductive group over $F$. We are interested in complex irreducible smooth generic representations $\pi$ of ${\bf G}(F)$. When $F$ has positive characteristic, we prove important properties which previously were only available for $F$ of characteristic 0. The first one is

Ritoban Kundu, Xu Shi, Michael Kleinsasser, Lars G. Fritsche

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inver

Gautier Ponsinet

We relate the structure of the Bloch-Kato groups associated with a de Rham Galois representation over a perfectoid field to the Galois theory of the ring $\mathbf{B}_\mathrm{dR}^+$ of $p$-adic periods. As an application, we answer the question raised by Coates and Greenberg and motivated by Iwasawa theory to compute the Bloch-Kato groups over perfectoid fiel

Mehdi Elahi, Mohamed R. Elshamy, Abdel-Hameed Badawy, Mahdi Fazeli

As mobile systems become more advanced, the security of System-on-Chips (SoCs) is increasingly threatened by thermal attacks. This research introduces a new attack method called the Multi-stage Adaptive Thermal Trojan for Efficiency and Resilience Degradation (MATTER). MATTER takes advantage of weaknesses in Dynamic Thermal Management (DTM) systems by manipu

Michail Koumpanakis, Ricardo Vilalta

This paper proposes a new way to learn Physics-Informed Neural Network loss functions using Generalized Additive Models. We apply our method by meta-learning parametric partial differential equations, PDEs, on Burger's and 2D Heat Equations. The goal is to learn a new loss function for each parametric PDE using meta-learning. The derived loss function replac

Saurabh Mishra, Mahendra Shinde, Aniket Yadav, Bilal Ayyub

Infrastructure construction, often dubbed an "industry of industries," is closely linked with government spending and public procurement, offering significant opportunities for improved efficiency and productivity through better transparency and information access. By leveraging these opportunities, we can achieve notable gains in productivity, cost savings,

V. G. Syromyatnikov

The prospects of using last version of a compact supermirror transmission polarizer TRUNPOSS on silicon in modern neutron research facilities of the instrumental base being created for the PIK reactor (PNPI, Gatchina, Russia) will discuss in the paper. The results of calculations of the parameters and main characteristics of this polarizer for using it in IN