December 2024 arXiv papers — page 187

Md Ahasanuzzaman, Gustavo A. Oliva, Ahmed E. Hassan, Zhen Ming

Defect prediction plays a crucial role in software engineering, enabling developers to identify defect-prone code and improve software quality. While extensive research has focused on refining machine learning models for defect prediction, the exploration of new data sources for feature engineering remains limited. Defect prediction models primarily rely on

Derek Xu, Tong Xie, Botao Xia, Haoyu Li

Large language models (LLMs) have made significant strides at code generation through improved model design, training, and chain-of-thought. However, prompt-level optimizations remain an important yet under-explored aspect of LLMs for coding. This work focuses on the few-shot examples present in most code generation prompts, offering a systematic study on wh

Changjian Zhang, Parv Kapoor, Ian Dardik, Leyi Cui

Temporal logic specifications play an important role in a wide range of software analysis tasks, such as model checking, automated synthesis, program comprehension, and runtime monitoring. Given a set of positive and negative examples, specified as traces, LTL learning is the problem of synthesizing a specification, in linear temporal logic (LTL), that evalu

Ranganath Krishnan, Piyush Khanna, Omesh Tickoo

Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering capabilities. However, these models are sometimes prone to generating credible-sounding but incorrect information, a phenomenon known as LLM hallucinations. Reliable uncertainty estimation in LLMs is essential for f

Maria Escobar, Juanita Puentes, Cristhian Forigua, Jordi Pont-Tuset

Accurately estimating and forecasting human body pose is important for enhancing the user's sense of immersion in Augmented Reality. Addressing this need, our paper introduces EgoCast, a bimodal method for 3D human pose forecasting using egocentric videos and proprioceptive data. We study the task of human pose forecasting in a realistic setting, extending t

Maxwell Charles Siegel

What use can there be for a function from the $p$-adic numbers to the $q$-adic numbers, where $p$ and $q$ are distinct primes? The traditional answer, courtesy of the half-century old theory of non-archimedean functional analysis: not much. It turns out this judgment was premature. '$\left(p,q\right)$-adic analysis' of this sort appears to be naturally suite

Shibo Zhao, Honghao Zhu, Yuanjun Gao, Beomsoo Kim

Map-based LiDAR localization, while widely used in autonomous systems, faces significant challenges in degraded environments due to lacking distinct geometric features. This paper introduces SuperLoc, a robust LiDAR localization package that addresses key limitations in existing methods. SuperLoc features a novel predictive alignment risk assessment techniqu

Hanzhe Teng, Yipeng Wang, Dimitrios Chatziparaschis, Konstantinos Karydis

Unmanned and intelligent agricultural systems are crucial for enhancing agricultural efficiency and for helping mitigate the effect of labor shortage. However, unlike urban environments, agricultural fields impose distinct and unique challenges on autonomous robotic systems, such as the unstructured and dynamic nature of the environment, the rough and uneven

Johan Martens

We exhibit how the Hodge-Deligne moduli space of $\lambda$-connections over a smooth projective curve, for stable bundles with fixed determinant, can be understood as the dual of the Atiyah algebroid of the determinant of cohomology line bundle.

Jinming Zhang, Yunfei Long

Narrative understanding and story generation are critical challenges in natural language processing (NLP), with much of the existing research focused on summarization and question-answering tasks. While previous studies have explored predicting plot endings and generating extended narratives, they often neglect the logical coherence within stories, leaving a

Puxuan Yu, Luke Merrick, Gaurav Nuti, Daniel Campos

This paper presents the training methodology of Arctic-Embed 2.0, a set of open-source text embedding models built for accurate and efficient multilingual retrieval. While prior works have suffered from degraded English retrieval quality, Arctic-Embed 2.0 delivers competitive retrieval quality on multilingual and English-only benchmarks, and supports Matryos

Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh

Self-supervised learning (SSL) frameworks consist of pretext task, and loss function aiming to learn useful general features from unlabeled data. The basic idea of most SSL baselines revolves around enforcing the invariance to a variety of data augmentations via the loss function. However, one main issue is that, inattentive or deterministic enforcement of t

Thomas Baier, Michele Bolognesi, Johan Martens, Christian Pauly

We construct a "Hitchin-type" connection on bundles of non-abelian theta functions on higher-rank Prym varieties, for unramified double covers of curves. We formulate a version of level-rank duality in this Prym setting (building on work of Zelaci), show it holds for level one, and establish that the duality respects the flat connections at all levels.

Omar Rodríguez-Abreo, José Luis Aragón, Mario Alan Quiroz-Juárez

Mathematical modeling is a powerful tool for describing, predicting, and understanding complex phenomena exhibited by real-world systems. However, identifying the equations that govern a system's dynamics from experimental data remains a significant challenge without a definitive solution. In this study, evolutionary computing techniques are presented to est

Milad Fotouhi, Mohammad Taha Bahadori, Oluwaseyi Feyisetan, Payman Arabshahi

The existing algorithms for identification of neurons responsible for undesired and harmful behaviors do not consider the effects of confounders such as topic of the conversation. In this work, we show that confounders can create spurious correlations and propose a new causal mediation approach that controls the impact of the topic. In experiments with two l

Mateusz Kandybo

Following the research from the paper "Triangles, squares and geodesics" (arXiv:0910.5688) of Rena Levitt and Jon McCammond we investigate the properties of groups acting on CAT(0) triangle-square complexes, focusing mostly on biautomaticity of such groups. In particular we show two examples of nonpositively curved triangle-square complexes $X_1$ and $X_2$,

Zekun Chang, Yixuan Gao, Yuta Noma, Shuo Feng

OriStitch is a computational fabrication workflow to turn existing flat fabrics into self-folding 3D structures. Users turn fabrics into self-folding sheets by machine embroidering functional threads in specific patterns on fabrics, and then apply heat to deform the structure into a target 3D structure. OriStitch is compatible with a range of existing materi

Dmitrii Torbunov, Yihui Ren, Animesh Ghose, Odera Dim

Event-based cameras (EBCs) have emerged as a bio-inspired alternative to traditional cameras, offering advantages in power efficiency, temporal resolution, and high dynamic range. However, the development of image analysis methods for EBCs is challenging due to the sparse and asynchronous nature of the data. This work addresses the problem of object detectio

Ajay N. Jain, Ann E. Cleves, W. Patrick Walters

The diffusion learning method, DiffDock, for docking small-molecule ligands into protein binding sites was recently introduced. Results included comparisons to more conventional docking approaches, with DiffDock showing superior performance. Here, we employ a fully automatic workflow using the Surflex-Dock methods to generate a fair baseline for conventional

Josh Fogg, Jaime Ortiz, Ivan Pocrnić, J. A. Julian Hall

Optimal contribution selection (OCS) is a selective breeding method that manages the conversion of genetic variation into genetic gain to facilitate short-term competitiveness and long-term sustainability in breeding programmes. Traditional approaches to OCS do not account for uncertainty in input data, which is always present and challenges optimization and

Alex Gu, Jamison Sloan, Charles Roques-Carmes, Seou Choi

Many quantum systems exhibit high sensitivity to their initial conditions, where microscopic quantum fluctuations can significantly influence macroscopic observables. Understanding how quantum states may influence the behavior of nonlinear dynamic systems may open new avenues in controlling light-matter interactions. To explore this issue, we analyze the sen

Roman Colman, Minh Vu, Manish Bhattarai, Martin Ma

For decades, corporations and governments have relied on scanned documents to record vast amounts of information. However, extracting this information is a slow and tedious process due to the sheer volume and complexity of these records. The rise of Vision Language Models (VLMs) presents a way to efficiently and accurately extract the information out of thes

Keyi Yin, Xiang Fang, Jixuan Ruan, Hezi Zhang

Quantum error correction (QEC) is critical for scalable and reliable quantum computing, but existing solutions, such as surface codes, incur significant qubit overhead. Quantum low-density parity check (qLDPC) codes have recently emerged as a promising alternative, requiring fewer qubits. However, the lack of efficient decoders remains a major barrier to the

Emin Kirimlioglu, Harrison Kung, Dominic Orlando

This project investigates factors that influence the perceived helpfulness of Amazon product reviews through machine learning techniques. After extensive feature analysis and correlation testing, we identified key metadata characteristics that serve as strong predictors of review helpfulness. While we initially explored natural language processing approaches

Toufique Ahmed, Martin Hirzel, Rangeet Pan, Avraham Shinnar

Test-driven development (TDD) is the practice of writing tests first and coding later, and the proponents of TDD expound its numerous benefits. For instance, given an issue on a source code repository, tests can clarify the desired behavior among stake-holders before anyone writes code for the agreed-upon fix. Although there has been a lot of work on automat

Giulio Benedetti, Ely Seraidarian, Theotime Pralas, Akewak Jeba

$\textbf{Motivation:}$ Hierarchical data structures are prevalent across several fields of research, as they represent an organised and efficient approach to study complex interconnected systems. Their significance is particularly evident in microbiome analysis, where microbial communities are classified at various taxonomic levels along the phylogenetic tre

Thulio Amorim, Tiago Nascimento, Akash Chaudhary, Eliseo Ferrante

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic sensory require

Kwangyul Hu, Denis R. Candido, Michael E. Flatté

The presence or absence of topologically-produced edge states of a crystal are robust to disorder; their stability in the presence of decay is less clear. For topologically nontrivial bosonic systems with finite particle lifetimes, such as photonic, phononic, or magnonic structures, a natural hypothesis suggests that if the linewidth from particle decay exce

Maria Cardei, Sabit Ahmed, Gretchen Chapman, Afsaneh Doryab

Spatiotemporal pairwise movement analysis involves identifying shared geographic-based behaviors between individuals within specific time frames. Traditionally, this task relies on sequence modeling and behavior analysis techniques applied to tabular or video-based data, but these methods often lack interpretability and struggle to capture partial matching.

Yizuo Chen, Amit Bhatia

We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. We then present sound and complete algorithms for discovering direct ca

Alin Marin Elena, Prathami Divakar Kamath, Théo Jaffrelot Inizan, Andrew S. Rosen

Metal-organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Funct

Weiguang Jiang, Tim Egert, Sonia Bacca, Francesca Bonaiti

We present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model and predict nuclear dipole responses. We train the network on a dataset of experimentally measured dipole strength functions for 216 different nuclei. To assess its predictive capability, we test the trained model on an add

Ankita Samaddar, Nicholas Potteiger, Xenofon Koutsoukos

Autonomous agents for cyber applications take advantage of modern defense techniques by adopting intelligent agents with conventional and learning-enabled components. These intelligent agents are trained via reinforcement learning (RL) algorithms, and can learn, adapt to, reason about and deploy security rules to defend networked computer systems while maint

Davi Santos, Martin Saska, Tiago Nascimento

This paper addresses the problem of thrust estimation and control for the rotors of small-sized multirotors Uncrewed Aerial Vehicles (UAVs). Accurate control of the thrust generated by each rotor during flight is one of the main challenges for robust control of quadrotors. The most common approach is to approximate the mapping of rotor speed to thrust with a

Maxim Sukharev, Joseph E. Subotnik, Abraham Nitzan

Understanding the quantum dynamics of strongly coupled molecule-cavity systems remains a significant challenge in molecular polaritonics. This work develops a comprehensive self-consistent model simulating electromagnetic interactions of diatomic molecules with quantum ro-vibrational degrees of freedom in resonant optical cavities. The approach employs an ef

Duc Toan Nguyen

Geodesic nets are types of graphs in Riemannian manifolds where each edge is a geodesic segment. One important object used in the construction of geodesic nets is a balanced vertex, where the sum of unit tangent vectors along adjacent edges is zero. We prove the existence of a balanced vertex of a triangle (with three unbalanced vertices) on a general two-di

Alin Dondera, Anuj Singh, Hadi Jamali-Rad

Masked Autoencoders (MAEs) are an important divide in self-supervised learning (SSL) due to their independence from augmentation techniques for generating positive (and/or negative) pairs as in contrastive frameworks. Their masking and reconstruction strategy also nicely aligns with SSL approaches in natural language processing. Most MAEs are built upon Tran

Xiaoyu Yin, Elisabetta Peri, Eduard Pelssers, Jaap den Toonder

Background and objective: Diabetes is one of the four leading causes of death worldwide, necessitating daily blood glucose monitoring. While sweat offers a promising non-invasive alternative for glucose monitoring, its application remains limited due to the low to moderate correlation between sweat and blood glucose concentrations, which has been obtained un

Yizuo Chen, Adnan Darwiche

We study the identification of causal effects in the presence of different types of constraints (e.g., logical constraints) in addition to the causal graph. These constraints impose restrictions on the models (parameterizations) induced by the causal graph, reducing the set of models considered by the identifiability problem. We formalize the notion of const

Yixiang Qu, Yifan Dai, Shilin Yu, Pradham Tanikella

Large Language Models (LLMs) have demonstrated remarkable proficiency in automated text annotation within natural language processing. However, their deployment in clinical settings is severely constrained by strict privacy regulations and the prohibitive computational cost of processing voluminous unstructured Electronic Health Records (EHRs). In this study

Cynthia Marcelino, Jack Shahhoud, Stefan Nastic

Serverless Computing is a computing paradigm that provides efficient infrastructure management and elastic scalability. Serverless functions scale up or down based on demand, which means that functions are not directly addressable and rely on platform-managed invocation. Serverless stateless nature requires functions to leverage external services, such as ob

Andrew Suk, Ethan Patrick White

For fixed $d\geq 3$, we construct subsets of the $d$-dimensional lattice cube $[n]^d$ of size $n^{\frac{3}{d + 1} - o(1)}$ with no $d+2$ points on a sphere or a hyperplane. This improves the previously best known bound of $\Omega(n^{\frac{1}{d-1}})$ due to Thiele from 1995.

Trung-Anh Dang, Vincent Nguyen, Ngoc-Son Vu, Christel Vrain

Contrastive learning has significantly improved representation quality, enhancing knowledge transfer across tasks in continual learning (CL). However, catastrophic forgetting remains a key challenge, as contrastive based methods primarily focus on "soft relationships" or "softness" between samples, which shift with changing data distributions and lead to rep

Andrew Akerson, Aakila Rajan, Kaushik Bhattacharya

We propose a method to accurately and efficiently identify the constitutive behavior of complex materials through full-field observations. We formulate the problem of inferring constitutive relations from experiments as an indirect inverse problem that is constrained by the balance laws. Specifically, we seek to find a constitutive behavior that minimizes th

Lucas Nogueira Nobrega, Ewerton de Oliveira, Martin Saska, Tiago Nascimento

The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a major issue in HRI, especially when using

Andrey Vayner, Tanio Díaz-Santos, Peter R. M. Eisenhardt, Daniel Stern

We report integral field spectroscopy observations with the Near-Infrared Spectrograph on board JWST targeting the 60 kpc environment surrounding the most luminous obscured quasar known at $z=4.6$. We detect ionized gas filaments on 40 kpc scales connecting a network of merging galaxies likely to form a cluster. We find regions of low ionization consistent w

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, Mikael Skoglund

We study the performance of the Thompson Sampling algorithm for logistic bandit problems. In this setting, an agent receives binary rewards with probabilities determined by a logistic function, $\exp(\beta \langle a, \theta \rangle)/(1+\exp(\beta \langle a, \theta \rangle))$, with slope parameter $\beta>0$, and where both the action $a\in \mathcal{A}$ and pa

M. F. Sousa, R. Jr. Costa, Jaziel G. Coelho, R. C. Dos Anjos

Recent multi-wavelength observations have highlighted magnetars as significant sources of cosmic rays, particularly through their gamma-ray emissions. This study examines three magnetar regions - CXOU J171405.7-31031, Swift J1834-0846, and SGR 1806-20 - known for emitting detectable electromagnetic signals. We assess the detectability of these regions using

Haohe Liu, Gael Le Lan, Xinhao Mei, Zhaoheng Ni

Video and audio are closely correlated modalities that humans naturally perceive together. While recent advancements have enabled the generation of audio or video from text, producing both modalities simultaneously still typically relies on either a cascaded process or multi-modal contrastive encoders. These approaches, however, often lead to suboptimal resu

Arthur Boschet, Armand Collin, Nishka Katoch, Julien Cohen-Adad

The segmentation of histological images is critical for various biomedical applications, yet the lack of annotated data presents a significant challenge. We propose a microscopy pseudo labeling pipeline utilizing unsupervised image translation to address this issue. Our method generates pseudo labels by translating between labeled and unlabeled domains witho

Youssef Mansour, Reinhard Heckel

We investigate fingerprints in pretraining datasets for large language models (LLMs) through dataset classification experiments. Building on prior work demonstrating the existence of fingerprints or biases in popular computer vision datasets, we analyze popular open-source pretraining datasets for LLMs derived from CommonCrawl including C4, RefinedWeb, Dolma

Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis, Bryan A. Plummer

Multi-Source Domain Generalization (DG) is the task of training on multiple source domains and achieving high classification performance on unseen target domains. Recent methods combine robust features from web-scale pretrained backbones with new features learned from source data, and this has dramatically improved benchmark results. However, it remains uncl

Tianyi Lyu, Dian Gu, Peiyuan Chen, Yaoting Jiang

This study introduces a method for efficiently detecting objects within 3D point clouds using convolutional neural networks (CNNs). Our approach adopts a unique feature-centric voting mechanism to construct convolutional layers that capitalize on the typical sparsity observed in input data. We explore the trade-off between accuracy and speed across diverse n

M. E. Cates, C. Nardini

In active systems, whose constituents have non-equilibrium dynamics at local level, fluid-fluid phase separation is widely observed. Examples include the formation of membraneless organelles within cells; the clustering of self-propelled colloidal particles in the absence of attractive forces, and some types of ecological segregation. A schematic understandi

Hanbing Wang, Junyan Du, Yue Li, Lie Zhang

Cultural heritage, a testament to human history and civilization, has gained increasing recognition for its significance in preservation and dissemination. The integration of immersive technologies has transformed how cultural heritage is presented, enabling audiences to engage with it in more vivid, intuitive, and interactive ways. However, the adoption of

Yang Zhang, Er Jin, Wenzhong Liang, Yanfei Dong

Diffusion models have achieved impressive advancements in various vision tasks. However, these gains often rely on increasing model size, which escalates computational complexity and memory demands, complicating deployment, raising inference costs, and causing environmental impact. While some studies have explored pruning techniques to improve the memory eff

Oliver Simonoski, Dijana Capeska Bogatinoska

This research explores the integration of blockchain technology in healthcare, focusing on enhancing the security and efficiency of Electronic Health Record (EHR) management. We propose a novel Ethereum-based system that empowers patients with secure control over their medical data. Our approach addresses key challenges in healthcare blockchain implementatio

Alexander Saffer, Emmanuel Fonseca, Scott Ransom, Ingrid Stairs

The binary pulsar J0348+0432 was previously shown to have a mass of approximately 2\,${\rm M_\odot}$, based on the combination of radial-velocity and model-dependent mass parameters derived from high-resolution optical spectroscopy of its white-dwarf companion. We present follow-up timing observations that combine archival observations with data acquired by

L. Barrault, L. Bugnet, S. Mathis

The presence of dips in the gravity modes period spacing versus period diagram of gamma Doradus stars is now well established by recent asteroseismic studies. Such Lorentzian-shaped inertial dips arise from the interaction of gravito-inertial modes in the radiative envelope of intermediate-mass main sequence stars with pure inertial modes in their convective

Iryna Yehorchenko

We find implicit general solutions for modified eikonal equations $u_a u_a=F(u_t)$, where lower indices at dependent variables designate derivatives, $a=1,2,..,n$, and summation is implied over the repeated indices. We will consider the general solutions and symmetries of these equations.

Sylvester Eriksson-Bique, Josh Kline

In this paper, we prove a self-improvement result for $(\theta,p)$-fractional Hardy inequalities, in both the exponent $1<p<\infty$ and the regularity parameter $0<\theta<1$, for bounded domains in doubling metric measure spaces. The key conceptual tool is a Caffarelli-Silvestre-type argument, which relates fractional Sobolev spaces on $Z$ to Newton-Sobolev

Ben R Gordon, Helena Buschermöhle, Wata Tubthong, David V. Martin

Confirmation of the first exomoon remains elusive. Although several exomoon candidates exist around single stars, there are currently no candidates around circumbinary planets (CBPs). Most circumbinary planets are thought to form far from the host binary and migrate through the protoplanetary disc. Therefore, an exomoon of a CBP represents a fascinating yet

Jorryt Matthee, Rohan P. Naidu, Gauri Kotiwale, Lukas J. Furtak

JWST observations have unveiled faint active galactic nuclei (AGN) at high-redshift that provide insights on the formation of supermassive black holes (SMBHs) and their coevolution with galaxies. However, disentangling stellar from AGN light in these sources is challenging. Here, we use an empirical approach to infer the average stellar mass of 6 faint broad

Muhammad Zawad Mahmud, Samiha Islam, Shahran Rahman Alve, Al Jubayer Pial

An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic response to threats are a few of the problems that intrusion identification is used to solve. The biological system known as

Meng Xiong, Yi Yu, Yury Berdnikov, Simon Klinck Borregaard

The interaction between light and matter can be enhanced by spatially concentrating the light field to boost the photon energy density and increasing the photon dwell time to prolong energy transfer between light and matter. Traditionally, strong spatial light localization has been achieved using plasmonics, which, despite its effectiveness, entails ohmic lo

Ido Nachum, Marco Bondaschi, Michael Gastpar, Anatoly Khina

\emph{Batch normalization} is a successful building block of neural network architectures. Yet, it is not well understood. A neural network layer with batch normalization comprises three components that affect the representation induced by the network: \emph{recentering} the mean of the representation to zero, \emph{rescaling} the variance of the representat

Iryna Yehorchenko

A general solution for a coupled system of eikonal equations $u_\mu u_\mu =0$, $v_\mu v_\mu =0$, $u_\mu v_\mu =1$ is presented, where lower indices designate derivatives, $\mu=0,1,2,3$, and summation is implied over the repeated indices. This solution is of interest by itself due to wide applications of the eikonal equations, but the system considered also a

Hocine Djellout

This work focuses on the incoherent and coherent combination of pulsed laser beams, building on previous research [1] that addressed the combination of continuous-wave laser beams. For the pulsed combination, we have focused on the temporal evolution of the combined intensity at the image plane. Simulation results show that at the pulse peak, the combined in

Joost van Dijk, Nico Westerbeck, Lars Schewe, Andrea Benigni

Distribution factors are indispensable tools in the design and analysis of power transmission grids. Recently, they received a renewed interest in the field of topology optimization, leading to the definition of bus merge and bus split distribution factors. In this article, we introduce a unified derivation of the most relevant distribution factors based on

Monika Henzinger, Jalaj Upadhyay

Differentially private weighted prefix sum under continual observation is a crucial component in the production-level deployment of private next-word prediction for Gboard, which, according to Google, has over a billion users. More specifically, Google uses a differentially private mechanism to sum weighted gradients in its \emph{private follow-the-regulariz

Li Zhou, Changsheng Fang, Bahareh Morovati, Yongtong Liu

This paper introduces $\rho$-NeRF, a self-supervised approach that sets a new standard in novel view synthesis (NVS) and computed tomography (CT) reconstruction by modeling a continuous volumetric radiance field enriched with physics-based attenuation priors. The $\rho$-NeRF represents a three-dimensional (3D) volume through a fully-connected neural network

Xiangyu Jiang, Xiwen Chen, Hao Wang, Abolfazl Razi

Traffic accident prediction is crucial for enhancing road safety and mitigating congestion, and recent Graph Neural Networks (GNNs) have shown promise in modeling the inherent graph-based traffic data. However, existing GNN- based approaches often overlook or do not explicitly exploit geographic position information, which often plays a critical role in unde

Anil Kurt, Gokhan M. Guvensen

This paper proposes the joint use of digital self-interference cancellation (DSIC) and spatial suppression to mitigate far-field self-interference (SI) in full-duplex multiple-input multiple-output (MIMO) systems. Far-field SI, caused by echoes from environmental scatterers, is modeled based on the scatterers' angle and delay parameters, stored in a scattere

Sarthak Kumar Maharana, Baoming Zhang, Leonid Karlinsky, Rogerio Feris

Although open-vocabulary classification models like Contrastive Language Image Pretraining (CLIP) have demonstrated strong zero-shot learning capabilities, their robustness to common image corruptions remains poorly understood. Through extensive experiments, we show that zero-shot CLIP lacks robustness to common image corruptions during test-time, necessitat

M. Enciu, A. Obertelli, P. Doornenbal, M. Heinz

The first spectroscopy of $^{52}$K was investigated via in-beam $\gamma$-ray spectroscopy at the RIKEN Radioactive Isotope Beam Factory after one-proton and one-neutron knockout from $^{53}$Ca and $^{53}$K beams impinging on a 15-cm liquid hydrogen target at $\approx$ 230~MeV/nucleon. The energy level scheme of $^{52}$K was built using single $\gamma$ and $\

Miranda Caputo, Rupali Chandar, Angus Mok, Sean Linden

Observations of young star clusters in a variety of galaxies have been used to constrain basic properties related to star-formation, such as the fraction of stars found in clusters (Gam) and the shape of the cluster mass function. However, the results can depend heavily on the reliability of the cluster age-dating process and other assumptions. One of the bi

Igor Halperin

We present CAISSON, a novel hierarchical approach to Retrieval-Augmented Generation (RAG) that transforms traditional single-vector search into a multi-view clustering framework. At its core, CAISSON leverages dual Self-Organizing Maps (SOMs) to create complementary organizational views of the document space, where each view captures different aspects of doc

William Franz Lamberti

Artificial intelligence (AI) has transformed various sectors and institutions, including education and healthcare. Although AI offers immense potential for innovation and problem solving, its integration also raises significant ethical concerns, such as privacy and bias. This paper delves into key considerations for developing AI policies within institutions

Mohammad Yousuf Mehmood, Syed Junaid Haqqani, Faraz Zaidi, Celine Rozenblat

Cities are widely considered the lifeblood of a nations economy housing the bulk of industries, commercial and trade activities, and employment opportunities. Within this economic context, multinational corporations play an important role in this economic development of cities in particular, and subsequently the countries and regions they belong to, in gener

S. I. Chastain, A. J. van der Horst, A. Horesh, A. Rowlinson

The sensitivity and field of view of the MeerKAT radio telescope provides excellent opportunities for commensal transient searches. We carry out a commensal transient search in supernova and short gamma-ray burst fields using methodologies established in~\citet{commensal1}. We search for transients in MeerKAT L-band images with 30 minute integration times, f

Hieu Tran, Zonghai Yao, Junda Wang, Yifan Zhang

This work introduces RARE (Retrieval-Augmented Reasoning Enhancement), a versatile extension to the mutual reasoning framework (rStar), aimed at enhancing reasoning accuracy and factual integrity across large language models (LLMs) for complex, knowledge-intensive tasks such as commonsense and medical reasoning. RARE incorporates two innovative actions withi

Patrick Daley, Kevin J. Resch, Robert W. Spekkens

Our article described an experiment that adjudicates between different causal accounts of Bell inequality violations by a comparison of their predictive power, finding that certain types of models that are structurally radical but parametrically conservative, of which a class of superdeterministic models are an example, overfit the data relative to models th

Ran Hu, Divy H. Kanani, Jingru Zhang

In this paper, we consider the (weighted) one-center problem of uncertain points on a cactus graph. Given are a cactus graph $G$ and a set of $n$ uncertain points. Each uncertain point has $m$ possible locations on $G$ with probabilities and a non-negative weight. The (weighted) one-center problem aims to compute a point (the center) $x^*$ on $G$ to minimize

Rostyslav O. Serha, Andrey A. Voronov, David Schmoll, Rebecca Klingbeil

Quantum magnonics aims to exploit the quantum mechanical properties of magnons for nanoscale quantum information technologies. Ferrimagnetic yttrium iron garnet (YIG), which offers the longest magnon lifetimes, is a key material typically grown on gadolinium gallium garnet (GGG) substrates for structural compatibility. However, the increased magnetic damping

Markus P. Mueller

I argue for an approach to the Foundations of Physics that puts the question in the title center stage, rather than asking "what is the case in the world?". This approach, algorithmic idealism, attempts to give a mathematically rigorous in-principle-answer to this question both in the usual empirical regime of physics and in some more exotic regimes within c

Hao Wang, Wenhui Zhu, Xuanzhao Dong, Yanxi Chen

In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challenges such as overfitting and limited dataset variability by training multiple models with distinct data augmentation strategies and different model complexities. Through this fusion

Gilderlan T de Araujo, Andre L. F. de Almeida

The channel estimation problem has been widely discussed in traditional reconfigurable intelligent surface assisted multiple-input multiple-output. However, solutions for channel estimation adapted to beyond diagonal RIS need further study, and few recent works have been proposed to tackle this problem. Moreover, methods that avoid or minimize the use of pil

Leroy Z. Wang, R. Thomas McCoy, Shane Steinert-Threlkeld

What factors contribute to the relative success and corresponding difficulties of in-context learning for Large Language Models (LLMs)? Drawing on insights from the literature on human concept learning, we test LLMs on carefully designed concept learning tasks, and show that task performance highly correlates with the Boolean complexity of the concept. This

Nina Girotto Erhardt, Jan Berges, Samuel Poncé, Dino Novko

We investigate the superconducting properties of molybdenum disulphide (MoS$_2$) monolayer across a broad doping range, successfully recreating the so far unresolved superconducting dome. Our first-principles findings reveal several dynamically stable phases across the doping-dependent phase diagram. We observe a doping-induced increase in the superconductin

Claudio Iuliano, Stefan Hollands, Stephen R. Green, Peter Zimmerman

We consider weakly non-linear gravitational perturbations of a near-extremal Kerr black hole governed by the second order vacuum Einstein equation. Using the GHZ formalism [Green et al., Class. Quant. Grav. 7(7):075001, 2020], these are parameterized by a Hertz potential. We make an ansatz for the Hertz potential as a series of zero-damped quasinormal modes

Bhimsen Shivamoggi, Nicole Tuovila

The purpose of this paper is to consider the application of the direct interaction approximation (DIA) developed by Kraichnan to generalized stochastic models in the turbulence problem. Previous developments were based on the Boltzmann-Gibbs prescription for the underlying entropy measure, which exhibits the extensivity property and is suited for ergodic sys

Lingxiao Wei, He Yan, Xiangju Lu, Junmin Zhu

Large language models (LLMs) have been well-researched in various long-context tasks. However, the scarcity of long-context summarization datasets hinders progress in this area. To address this, we introduce CNNSum, a multi-scale long-context summarization benchmark based on Chinese novels, featuring human-driven annotations across four subsets totaling 695

Renzo Cavalieri, Andreas Gross

Under suitable conditions on a family of logarithmic curves, we endow the tropicalization of the family with an affine structure in a neighborhood of the sections in such a way that the tropical $\psi$ classes from \cite{psi-classes} arise as tropicalizations of algebraic $\psi$ classes.

Md Omar Faruque, Peter Jamieson, Ahmad Patooghy, Abdel-Hameed A. Badawy

Traditionally, inserting realistic Hardware Trojans (HTs) into complex hardware systems has been a time-consuming and manual process, requiring comprehensive knowledge of the design and navigating intricate Hardware Description Language (HDL) codebases. Machine Learning (ML)-based approaches have attempted to automate this process but often face challenges s

Ali Rasteh, Raghavendra Palayam Hari, Hao Guo, Marco Mezzavilla

The upper mid-band (or FR3, spanning 6-24 GHz) is a crucial frequency range for next-generation mobile networks, offering a favorable balance between coverage and spectrum efficiency. From another perspective, the systems operating in the near-field in both indoor environment and outdoor environments can support line-of-sight multiple input multiple output (

Alessandro Monteverdi, Elizabeth Winstanley

We study the canonical quantization of a massive scalar field on a five dimensional, rotating black hole space-time. We focus on the case where the space-time is asymptotically anti-de Sitter and the black hole's two angular momentum parameters are equal. In this situation the geometry possesses additional symmetries which simplify both the mode solutions of

Madisen Holbrook, Julian Ingham, Daniel Kaplan, Luke Holtzman

The topological properties of Bloch bands are intimately tied to the structure of their electronic wavefunctions within the unit cell of a crystal. Here, we show that scanning tunneling microscopy (STM) measurements on the prototypical transition metal dichalcogenide (TMD) semiconductor WSe$_2$ can be used to unambiguously fix the location of the Wannier cen

S. F. Peterson, Y. U. Idzerda

Thin-films of bct Co$_{1-x}$Mn$_x$ grown by molecular beam epitaxy on MgO(001) were measured to have an enhanced atomic magnetic moment of $2.52 \pm 0.07$ $\mu_\text{B}/\text{atom}$ beyond the pinnacle of the Slater-Pauling curve for Fe$_{1-x}$Co$_{x}$ with a moment of $2.42$ $\mu_\text{B}/\text{atom}$. The compositional variation of the average total moment

Lea Bold, Friedrich M. Philipp, Manuel Schaller, Karl Worthmann

Data-driven techniques for analysis, modeling, and control of complex dynamical systems are on the uptake. Koopman theory provides the theoretical foundation for the popular kernel extended dynamic mode decomposition (kEDMD). In this work, we propose a novel kEDMD scheme to approximate nonlinear control systems accompanied by an in-depth error analysis. Key

Steve Hanneke, Mingyue Xu

The well-known empirical risk minimization (ERM) principle is the basis of many widely used machine learning algorithms, and plays an essential role in the classical PAC theory. A common description of a learning algorithm's performance is its so-called "learning curve", that is, the decay of the expected error as a function of the input sample size. As the