December 2024 arXiv papers — page 111

Chenyue Xie, An-Kang Gao, Xiyun Lu

Oceanic eddy kinetic energy (EKE) is a key quantity for measuring the intensity of mesoscale eddies and for parameterizing eddy effects in ocean climate models. Three decades of satellite altimetry observations allow a global assessment of sea surface information. However, the subsurface EKE with spatial filter has not been systematically studied due to the

Yang Hu, Jingxun Liang, Huacheng Yu, Junkai Zhang

In this paper, we design a new succinct static dictionary with worst-case constant query time. A dictionary data structure stores a set of key-value pairs with distinct keys in $[U]$ and values in $[\sigma]$, such that given a query $x\in [U]$, it quickly returns if $x$ is one of the input keys, and if so, also returns its associated value. The textbook solu

Xue Wu, Kostas Tsioutsiouliklis

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, they often struggle with complex reasoning tasks and are prone to hallucination. Recent research has shown promising results in leveraging knowledge graphs (KGs) to enhance LLM performance. KGs provide a structured representation

Jeremy L. Smallwood, Ya-Ping Li, Hongping Deng, Alessia Franchini

Binary star systems can accrete material originating from a circumbinary disc. Since it is common for the circumbinary disc to be tilted with respect to the binary orbital plane, we test whether the accretion dynamics can be a diagnostic for binary-disc misalignment. We present hydrodynamical simulations to model the accretion flow from a circumbinary disc a

Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu

With the growing deployment of pre-trained models like Transformers on cloud platforms, privacy concerns about model parameters and inference data are intensifying. Existing Privacy-Preserving Transformer Inference (PPTI) frameworks face the "impossible trinity" of balancing privacy, efficiency, and performance: Secure Multi-Party Computation (SMPC)-based ap

Changjin Kim, Tae Hyun Kim, Sungyong Baik

Removing noise from images, a.k.a image denoising, can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image denoising with the emergence of advanced deep learning architectures and real-world d

Yuhao Wang, Yang Liu, Aihua Zheng, Pingping Zhang

Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by combining complementary information from multiple modalities. Existing multi-modal object ReID methods primarily focus on the fusion of heterogeneous features. However, they often overlook the dynamic quality changes in multi-modal imaging. In addition, the shared information be

Christopher J. Tralie, Matt Amery, Benjamin Douglas, Ian Utz

As generative techniques pervade the audio domain, there has been increasing interest in tracing back through these complicated models to understand how they draw on their training data to synthesize new examples, both to ensure that they use properly licensed data and also to elucidate their black box behavior. In this paper, we show that if imperceptible e

Maria Cuellar

The diagnosis of Shaken Baby Syndrome/Abusive Head Trauma (SBS/AHT) is fraught with controversy due to critical statistical deficiencies in the data underpinning these diagnoses. This paper examines the reliability and scientific foundation of SBS/AHT through a statistical lens, highlighting the lack of independently verified ground truth, contextual biases,

Joonmo Ahna, Taehong Jang, Quan Fengnyu, Hyungil Lee

We implemented a high-performance optical character recognition model for classical handwritten documents using data augmentation with highly variable cropping within the document region. Optical character recognition in handwritten documents, especially classical documents, has been a challenging topic in many countries and research organizations due to its

Chan Yang, Zhujun Zhang

Is there a fixed dimension $n$ such that translational tiling of $\mathbb{Z}^n$ with a monotile is undecidable? Several recent results support a positive answer to this question. Greenfeld and Tao disprove the periodic tiling conjecture by showing that an aperiodic monotile exists in sufficiently high dimension $n$ [Ann. Math. 200(2024), 301-363]. In another

Ivan Marusic, Wagih Abu Rowin, Mitchell Lozier, Luka Lindić

We report two-dimensional particle image velocimetry experiments in high Reynolds number turbulent boundary layers imposed with a moderately strong streamwise pressure gradient. The unique aspect of these data are the highly resolved measurements across the outer region of a physically thick boundary layer, enabling accurate detection of turbulent/non-turbul

Shengheng Liu, Zihuan Mao, Xingkang Li, Mengguan Pan

Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wireless networks heavily rely on pure model-driven techniques to achieve positioning functionality, often succumbing to performance deterioration due to hardware impairments in practic

Hanti Lin

The debate between scientific realism and anti-realism remains at a stalemate, making reconciliation seem hopeless. Yet, important work remains: exploring a common ground, even if only to uncover deeper points of disagreement and, ideally, to benefit both sides of the debate. I propose such a common ground. Specifically, many anti-realists, such as instrumen

R. Porto, R. C. de Lamare

An iterative detection and decoding (IDD) scheme is proposed for multiuser multiple-antenna systems assisted by an active or a passive Reconfigurable Intelligent Surface (RIS). The proposed approach features an IDD strategy that incorporates Low-Density Parity-Check (LDPC) codes, RIS processing with refinements of soft information in the form of log likeliho

Chaitanya Morey, Sundararajan Natarajan, Chandramouli Padmanabhan

This paper demonstrates numerically and experimentally that it is possible to tailor flexural band gaps in the low-frequency regime by appropriate choice of cutout characteristics. The finite element method is used to obtain the numerical dispersion relation and band gaps. The influence of the cutout's shape, size, and location on the band gap is systematica

Amis Sharma, Chun-Chia Chen, Jordan McCourt, Mingi Kim

We perform transport measurements on proximitized, ballistic, bilayer graphene Josephson junctions (BGJJs) in the intermediate-to-long junction regime ($L>\xi$). We measure the device's differential resistance as a function of bias current and gate voltage for a range of different temperatures. The extracted critical current $I_{C}$ follows an exponential tr

Xiaoran Ma, Wensheng Guo, Peter Kotanko, Yuedong Wang

Clinical measurements, such as body temperature, are often collected over time to monitor an individual's underlying health condition. These measurements exhibit complex temporal dynamics, necessitating sophisticated statistical models to capture patterns and detect deviations. We propose a novel multiprocess state space model with feedback and switching mec

Bishnu Paudel, Chris Pinner

We obtain a complete description of the integer group determinants for $\mathbb Z_{18}$ (these are the $18\times18$ circulant determinants with integer entries) and $\mathbb Z_3 \times \mathbb Z_6$, the two abelian groups of order 18. This completes the groups of order less than 20.

Jinzhao Liu, Tianyou Wang, Zhizhao Che

Cavitation is a ubiquitous phenomenon in nature and bubble dynamics in open spaces have been widely studied, but the effects of the wall on the dynamics of cavitation bubbles in confined spaces are still unclear. Here, the dynamics of cavitation bubbles in small corners is studied experimentally, focusing on the interaction of the bubble with the wall. High-

Yixuan Even Xu, Zhe Feng, Fei Fang

We consider the Coalition Structure Learning (CSL) problem in multi-agent systems, motivated by the existence of coalitions in many real-world systems, e.g., trading platforms and auction systems. In this problem, there is a hidden coalition structure within a set of $n$ agents, which affects the behavior of the agents in games. Our goal is to actively desig

Nick Huntington-Klein, Eleanor J. Murray

Large language models (LLMs) offer the potential to automate a large number of tasks that previously have not been possible to automate, including some in science. There is considerable interest in whether LLMs can automate the process of causal inference by providing the information about causal links necessary to build a structural model. We use the case o

Georgios Chouliaras, V. Archontis

We have performed 3D MHD simulations to study the effect of partial ionization in the process of magnetic flux emergence in the Sun. In fact, we continue previous work and we now focus: 1) on the emergence of the magnetic fields above the solar photosphere and 2) on the eruptive activity, which follows the emergence into the corona. We find that in the simul

Chenyu Zhou, Yuzhou Fang, Jingbo Wang, Chao Wang

We propose a method for conducting algebraic program analysis (APA) incrementally in response to changes of the program under analysis. APA is a program analysis paradigm that consists of two distinct steps: computing a path expression that succinctly summarizes the set of program paths of interest, and interpreting the path expression using a properly-defin

Nataliya Nechyporenko, Ryan Hoque, Christopher Webb, Mouli Sivapurapu

Teleoperation for robot imitation learning is bottlenecked by hardware availability. Can high-quality robot data be collected without a physical robot? We present a system for augmenting Apple Vision Pro with real-time virtual robot feedback. By providing users with an intuitive understanding of how their actions translate to robot motions, we enable the col

Ilham El Atmani

The experimental and theoretical research on the physics of massive neutrinos is based on the standard paradigm of three-neutrino mixing, which describes the oscillations of neutrino flavors measured in solar, atmospheric, and long-baseline experiments. However, several anomalies, corresponding to an $L/E$ of 1m/MeV, could be interpreted by involving sterile

Mukesh Karunanethy, Raghunathan Rengaswamy, Mahesh V Panchagnula

We test the hypothesis that the microscopic temporal structure of near-field turbulence downstream of a sudden contraction contains geometry-identifiable information pertaining to the shape of the upstream obstruction. We measure a set of spatially sparse velocity time-series data downstream of differently-shaped orifices. We then train random forest multicl

Di Xu, Xin Miao, Hengjie Liu, Jessica E. Scholey

Abstract Purpose: High-quality 4D MRI requires an impractically long scanning time for dense k-space signal acquisition covering all respiratory phases. Accelerated sparse sampling followed by reconstruction enhancement is desired but often results in degraded image quality and long reconstruction time. We hereby propose the chained iterative reconstruction

Tomson Qu, Dichen Li, Avideh Zakhor, Wenhao Yu

Hexapod robots are potentially suitable for carrying out tasks in cluttered environments since they are stable, compact, and light weight. They also have multi-joint legs and variable height bodies that make them good candidates for tasks such as stairs climbing and squeezing under objects in a typical home environment or an attic. Expanding on our previous

Aneesh Raghavan, Karl H Johansson

We consider the problem of identification of safe regions in the environment of an autonomous system. The environment is divided into a finite collections of Voronoi cells, with each cell having a representative, the Voronoi center. The extent to which each region is considered to be safe by an oracle is captured through a trust distribution. The trust place

Alireza Zaeemzadeh, Giulio Tononi

Information theory, introduced by Shannon, has been extremely successful and influential as a mathematical theory of communication. Shannon's notion of information does not consider the meaning of the messages being communicated but only their probability. Even so, computational approaches regularly appeal to "information processing" to study how meaning is

Changrui Liu, Shengling Shi, Bart De Schutter

Handling model mismatch is a common challenge in model predictive control (MPC). While robust MPC is effective, its conservatism often makes it less desirable. Certainty-equivalence MPC (CE-MPC), which uses a nominal model, offers an appealing alternative due to its design simplicity and low computational costs. This paper investigates CE-MPC for uncertain n

Julian D. Santamaria, Claudia Isaza, Jhony H. Giraldo

Foundation Models (FMs) have been successful in various computer vision tasks like image classification, object detection and image segmentation. However, these tasks remain challenging when these models are tested on datasets with different distributions from the training dataset, a problem known as domain shift. This is especially problematic for recognizi

Dongfang Zhao

The increasing prevalence of high-dimensional data demands efficient and scalable compression methods to support modern applications. However, existing techniques like PCA and Autoencoders often rely on auxiliary metadata or intricate architectures, limiting their practicality for streaming or infinite datasets. In this paper, we introduce a stateless compre

Peilong Wang, Jason Holmes, Zhengliang Liu, Dequan Chen

Purpose: We present an updated study evaluating the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released models. Methods: A set of 100 multiple-choice radiation oncology physics questions, previously created by a well-experienced physicist, was used for this study. The answer options

Arash Hajisafi, Maria Despoina Siampou, Bita Azarijoo, Zhen Xiong

Clinical time series are often irregularly sampled, with varying sensor frequencies, missing observations, and misaligned timestamps. Prior approaches typically address these irregularities by interpolating data into regular sequences, thereby introducing bias, or by generating inconsistent and uninterpretable relationships across sensor measurements, compli

Jia-Lin Hsu, Mao-Pei Tsui

In this paper, inspired by the work Lee-Wan, we researched the rigidity of contracting maps between closed manifolds with positive curvature. We focused on the relation between curvature pinching and contracting conditions involving arbitrary $k$ singular values.

Hui Wu, Jia-Jie Yang, Chao-Qun Li, Jin-Hua Ran

Elliptic Fourier analysis (EFA) is a powerful tool for shape analysis, which is often employed in geometric morphometrics. However, the normalization of elliptic Fourier descriptors has persistently posed challenges in obtaining unique results in basic contour transformations, requiring extensive manual alignment. Additionally, contemporary contour/outline e

Albert Schwarz

The main goal of present paper is to analyze the adiabatic definition of scattering matrix in the formalism of L-functionals. This definition leads to the notion of inclusive scattering matrix closely related to inclusive cross sections. We discuss this notion and the relation of our techniques to adiabatic quantum computing.

Agung Budiyono

Measurement in quantum mechanics is notoriously unpredictable. The uncertainty in quantum measurement can arise from the noncommutativity between the state and the measurement basis which is intrinsically quantum, but it may also be of classical origin due to the agent's ignorance. It is of fundamental as well as practical importance to cleanly separate the

Zhiyu Xue, Guangliang Liu, Bocheng Chen, Kristen Marie Johnson

The security of Large Language Models (LLMs) has become an important research topic since the emergence of ChatGPT. Though there have been various effective methods to defend against jailbreak attacks, prefilling attacks remain an unsolved and popular threat against open-sourced LLMs. In-Context Learning (ICL) offers a computationally efficient defense again

Jackson R. Fliss, Ben Freivogel, Eleni-Alexandra Kontou, Diego Pardo Santos

Smeared null energy has been shown to be bounded from below for free minimally coupled quantum field theories. This is not the case for conformally coupled free bosonic theories where states of unbounded null energy can be constructed by increasing the particle number. Little is known for interacting conformal field theories (CFTs) in dimensions larger than

Jiaqing Zhang, Miguel Contreras, Sabyasachi Bandyopadhyay, Andrea Davidson

Estimation of patient acuity in the Intensive Care Unit (ICU) is vital to ensure timely and appropriate interventions. Advances in artificial intelligence (AI) technologies have significantly improved the accuracy of acuity predictions. However, prior studies using machine learning for acuity prediction have predominantly relied on electronic health records

Shatarupa Ghosh, Jonathan Rusert

Adversarial text attack research is useful for testing the robustness of NLP models, however, the rise of transformers has greatly increased the time required to test attacks. Especially when researchers do not have access to adequate resources (e.g. GPUs). This can hinder attack research, as modifying one example for an attack can require hundreds of querie

Avinandan Bose, Zhihan Xiong, Aadirupa Saha, Simon Shaolei Du

Reinforcement Learning from Human Feedback (RLHF) is currently the leading approach for aligning large language models with human preferences. Typically, these models rely on extensive offline preference datasets for training. However, offline algorithms impose strict concentrability requirements, which are often difficult to satisfy. On the other hand, whil

Maryann Rui, Munther Dahleh

We study the problem of learning mixtures of linear dynamical systems (MLDS) from input-output data. The mixture setting allows us to leverage observations from related dynamical systems to improve the estimation of individual models. Building on spectral methods for mixtures of linear regressions, we propose a moment-based estimator that uses tensor decompo

Phil Howlett, Anatoli Torokhti

We propose and justify a matrix reduction method for calculating the optimal approximation of an observed matrix $A \in {\mathbb C}^{m \times n}$ by a sum $\sum_{i=1}^p \sum_{j=1}^q B_iX_{ij}C_j$ of matrix products where each $B_i \in {\mathbb C}^{m \times g_i}$ and $C_j \in {\mathbb C}^{h_j \times n}$ is known and where the unknown matrix kernels $X_{ij}$ a

Shuang Li

X-ray free-electron lasers (XFELs) are renowned for their high brightness, significantly impacting biology, chemistry, and nonlinear X-ray optics. However, current XFELs are large, expensive, and exhibit significant shot-to-shot instability. Here, we propose a novel compact apparatus for generating X-ray lasers. The setup integrates an ion source to produce

Bo Jiang, Wanrong Zhang, Donghang Lu, Jian Du

Data engineering often requires accuracy (utility) constraints on results, posing significant challenges in designing differentially private (DP) mechanisms, particularly under stringent privacy parameter $\epsilon$. In this paper, we propose a privacy-boosting framework that is compatible with most noise-adding DP mechanisms. Our framework enhances the like

Stanisław Barański, Ben Biedermann, Joshua Ellul

Voting is a cornerstone of collective participatory decision-making in contexts ranging from political elections to decentralized autonomous organizations (DAOs). Despite the proliferation of internet voting protocols promising enhanced accessibility and efficiency, their evaluation and comparison are complicated by a lack of standardized criteria and unifie

Rebeckah K. Fussell, Megan Flynn, Anil Damle, Michael F. J. Fox

Recent advancements in large language models (LLMs) hold significant promise in improving physics education research that uses machine learning. In this study, we compare the application of various models to perform large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in students' typed lab

Carmengelys Cordova, Joaquin Taverner, Elena Del Val, Estefania Argente

Multi-agent systems (MAS) have gained relevance in the field of artificial intelligence by offering tools for modelling complex environments where autonomous agents interact to achieve common or individual goals. In these systems, norms emerge as a fundamental component to regulate the behaviour of agents, promoting cooperation, coordination and conflict res

Amin Haghnejad, Mahboobeh Farahati

Meta-analysis employs statistical techniques to synthesize the results of individual studies, providing an estimate of the overall effect size for a specific outcome of interest. The direction and magnitude of this estimate, along with its confidence interval, offer valuable insights into the underlying phenomenon or relationship. As an extension of standard

Viktor T. Toth

The usual derivation of Einstein's field equations from the Einstein--Hilbert action is performed by silently assuming the metric tensor's symmetric character. If this symmetry is not assumed, the result is a new theory, such as Einstein's attempted Unified Field Theory or Moffat's Nonsymmetric Gravitational Theory. Explicitly enforcing the constraint by mea

Orcun Yildiz, Tom Peterka

With the advent of large language models (LLMs), there is a growing interest in applying LLMs to scientific tasks. In this work, we conduct an experimental study to explore applicability of LLMs for configuring, annotating, translating, explaining, and generating scientific workflows. We use 5 different workflow specific experiments and evaluate several open

Borja Molina-Coronado

Federated learning is a versatile framework for training models in decentralized environments. However, the trust placed in clients makes federated learning vulnerable to backdoor attacks launched by malicious participants. While many defenses have been proposed, they often fail short when facing heterogeneous data distributions among participating clients.

Melissa Hall, Oscar Mañas, Reyhane Askari-Hemmat, Mark Ibrahim

As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound, there are few unified benchmarking libraries that provide a framework for performing evaluations across many datasets and metrics. Furthermore, the rapid introduction of increasi

Kyu-Young Kim, Chang-Min Lee, Amirehsan Boreiri, Purbita Purkayastha

Silicon G centers show significant promise as single photon sources in a scalable silicon platform. But these color centers have large non-radiative decay and a low Debye-Waller factor, limiting their usability in quantum applications. In this work, we demonstrate bright Purcell-enhanced emission from a silicon G center by coupling it to a nanophotonic cavit

Marianne Akian, Stephane Gaubert, Dariush Kiani, Hanieh Tavakolipour

We investigate the properties of positive definite and positive semi-definite symmetric matrices within the framework of symmetrized tropical algebra, an extension of tropical algebra adapted to ordered valued fields. We focus on the eigenvalues and eigenvectors of these matrices. We prove that the eigenvalues of a positive (semi)-definite matrix in the trop

Jingtao Min, Alexander Grayver

The time-varying geomagnetic field is a superposition of contributions from multiple internal and external current systems. A major source of geomagnetic variations at periods less than a few years are current systems external to the solid Earth, namely the ionospheric and magnetospheric currents, as well as associated induced currents. The separation of the

Karthik Sivakoti

Traditional automated toll collection systems depend on complex hardware configurations, that require huge investments in installation and maintenance. This research paper presents an innovative approach to revolutionize automated toll collection by using a single camera per plaza with the YOLOv11 computer vision architecture combined with an ensemble OCR te

Nikunj Sanghai, Nik Bear Brown

The introduction of Transformers architecture has brought about significant breakthroughs in Deep Learning (DL), particularly within Natural Language Processing (NLP). Since their inception, Transformers have outperformed many traditional neural network architectures due to their "self-attention" mechanism and their scalability across various applications. I

Peng Cheng, Zhao-Qian Yao, Daniele Binosi, Craig D. Roberts

Working with the $29$ available data on the ratio of proton electric and magnetic form factors, $\mu_p G_E^p(Q^2)/ G_M^p(Q^2)$, and independent of any model or theory of strong interactions, we use the Schlessinger point method to objectively address the question of whether the ratio possesses a zero and, if so, its location. Our analysis predicts that, with

Blaine Hoak, Ryan Sheatsley, Patrick McDaniel

Bias significantly undermines both the accuracy and trustworthiness of machine learning models. To date, one of the strongest biases observed in image classification models is texture bias-where models overly rely on texture information rather than shape information. Yet, existing approaches for measuring and mitigating texture bias have not been able to cap

Son Minh Nguyen, Linh Duy Tran, Duc Viet Le, Paul J. M Havinga

We propose iMoT, an innovative Transformer-based inertial odometry method that retrieves cross-modal information from motion and rotation modalities for accurate positional estimation. Unlike prior work, during the encoding of the motion context, we introduce Progressive Series Decoupler at the beginning of each encoder layer to stand out critical motion eve

Thorsten Neuschel, Martin Venker

We study transitions between the three universal limiting kernels Airy, Pearcey and sine kernel, arising in Random Matrix Theory at edge, cusp and bulk points of the spectrum. Under appropriate rescalings, we provide complete asymptotic expansions of the extended Airy kernel and the extended Pearcey kernel approaching the extended sine kernel, expliciting th

Md Sanzeed Anwar, Paramveer S. Dhillon, Grant Schoenebeck

Traditional recommender systems based on revealed preferences often fail to capture the fundamental duality in user behavior, where consumption choices are driven by both inherent value (enrichment) and instant appeal (temptation). Consequently, these systems may generate recommendations that prioritize short-term engagement over long-lasting user satisfacti

Sara Ghazanfari, Siddharth Garg, Nicolas Flammarion, Prashanth Krishnamurthy

Human perception of similarity across uni- and multimodal inputs is highly complex, making it challenging to develop automated metrics that accurately mimic it. General purpose vision-language models, such as CLIP and large multi-modal models (LMMs), can be applied as zero-shot perceptual metrics, and several recent works have developed models specialized in

M. Kamradek, I. Kasik, J. Aubrecht, P. Varak

Highly-doped holmium fibers have been prepared using modified chemical vapor deposition in combination with nanoparticle-doping method. Within a series of various Al2O3 and Ho3+ concentrations, relations between fibers composition and their fluorescence and laser parameters have been studied. Al/Ho molar ratio equal to at least 50 was found to be the key fac

Anatoliy Swishchuk

This paper is devoted to the study of a new class of random evolutions (RE), so-called self-exciting random evolutions (SEREs), and their applications. We also introduce a new random process $x(t)$ such that it is based on a superposition of a Markov chain $x_n$ and a Hawkes process $N(t),$ i.e., $x(t):=x_{N(t)}.$ We call this process self-walking imbedded s

Chengzhang Jiang, Robert Antonucci, Jose A. Acosta-Pulido, Patrick Ogle

We present the polarization spectra of the nucleus of 3C~270. We confirm that the polarization angle of both the continuum and the emission lines are close to perpendicular to the jet direction after careful correction of interstellar polarization, which indicates polar scattering. The Stokes flux spectrum resembles the total flux spectrum, with no need for

Alexander Strachan, Nigel Topham

Current methods of implementing wireless radio typically take one of two forms; either dedicated fixed-function hardware, or pure Software Defined Radio (SDR). Fixed function hardware is efficient, but being specific to each radio standard it lacks flexibility, whereas Software Defined Radio is highly flexible but requires powerful processors to meet real-ti

Lojze Žust, Matej Kristan

Panoptic segmentation is a fundamental task in computer vision and a crucial component for perception in autonomous vehicles. Recent mask-transformer-based methods achieve impressive performance on standard benchmarks but face significant challenges with small objects, crowded scenes and scenes exhibiting a wide range of object scales. We identify several fu

Walter Carnielli, Lorenzzo Frade, Abilio Rodrigues

This paper presents a sound, complete, and decidable analytic tableau system for the logic of evidence and truth \letf, introduced in Rodrigues, Bueno-Soler \& Carnielli (Synthese, DOI: 10.1007/s11229-020-02571-w, 2020). \letf\ is an extension of the logic of first-degree entailment (\fde), also known as Belnap-Dunn logic. \fde\ is a widely studied four-valu

Hari Mohan Pandey

Artificial Intelligence (AI) is a broad field that is upturning mental health care in many ways, from addressing anxiety, depression, and stress to increasing access, personalization of treatment, and real-time monitoring that enhances patient outcomes. The current paper discusses the evolution, present application, and future challenges in the field of AI f

Deven B. Dangi, Beni B. Dangi, Oliver Steinbock

When microliter drops of salt solutions dry on non-porous surfaces, they form erratic yet characteristic deposit patterns influenced by complex crystallization dynamics and fluid motion. Using OpenAI's image-enabled language models, we analyzed deposits from 12 salts with 200 images per salt and per model. GPT-4o classified 57% of the salts accurately, signi

Sagar Pokhrel, Julio Gea-Banacloche

We consider a model for a quantum battery consisting of a collection of $N$ two-level atoms driven by a classical field and decaying to a common reservoir. In the extensive regime, where the energy $E$ scales as $N$ and the fluctuations $\Delta E/E \to 0$, our dissipative charging protocol yields a power proportional to $N^2$, a scaling that cannot be achiev

Abilio Rodrigues

The aim of this paper is to show that Frege's argument which concluded that the reference of a sentence is its truth-value, presented in 'On Sense and Reference' (1892), can be reconstructed taking into account the problems of the notion of conceptual content presented in the 'Begriffsschrift' (1879) and also other passages from a letter to Russell (1902) an

Son Minh Nguyen, Linh Duy Tran, Duc Viet Le, Paul J. M Havinga

Despite remarkable progress in knowledge transfer across visual and textual domains, extending these achievements to indoor localization, particularly for learning transferable representations among Received Signal Strength (RSS) fingerprint datasets, remains a challenge. This is due to inherent discrepancies among these RSS datasets, largely including varia

Shiyang Chen, Rain Jiang, Dezhi Yu, Jinlai Xu

Large Language Models (LLMs) have become the new foundation for many applications, reshaping human society like a storm. Disaggregated inference, which separates prefill and decode stages, is a promising approach to improving hardware utilization and service quality. However, due to inefficient inter-node communication, existing systems restrict disaggregate

Angiolo Huaman, Salvador Barraza-Lopez

Nonlinear Hall-like currents can be generated by a time-periodic alternating bias on two-dimensional (2D) materials lacking inversion symmetry. To hint that the moir\'e between graphene and its supporting substrate contributes to the homogeneity of nonlinear currents, the change in the local potential $\Delta V(r)$ around horizontally strained graphene due t

Alejandra Castillo, Jamie Haddock, Iryna Hartsock, Paulina Hoyos

Randomized iterative algorithms, such as the randomized Kaczmarz method, have gained considerable popularity due to their efficacy in solving matrix-vector and matrix-matrix regression problems. Our present work leverages the insights gained from studying such algorithms to develop regression methods for tensors, which are the natural setting for many applic

Runsheng "Anson" Huang, Lara J. Martin, Chris Callison-Burch

WHAT-IF -- Writing a Hero's Alternate Timeline through Interactive Fiction -- is a system that uses zero-shot meta-prompting to create branching narratives from a prewritten story. Played as an interactive fiction (IF) game, WHAT-IF lets the player choose between decisions that the large language model (LLM) GPT-4 generates as possible branches in the story.

Kyeongsu Choi, Robert Haslhofer

In this paper, we classify all noncollapsed singularities of the mean curvature flow in $\mathbb{R}^4$. Specifically, we prove that any ancient noncollapsed solution either is one of the classical historical examples (namely $\mathbb{R}^j\times S^{3-j}$, $\mathbb{R}\times $2d-bowl, $\mathbb{R}\times $2d-oval, the rotationally symmetric 3d-bowl, or a cohomoge

Belén Costanza, Claudia G. Scóccola, Matías Zaldarriaga

To study the early Universe, it is essential to estimate cosmological parameters with high accuracy, which depends on the optimal reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of their power spectrum. In this paper, we generalize the neural network developed for applying the Wiener Filter, initially presented for temperature ma

Hsiao-Wen Chen, Fakhri S. Zahedy

Galaxies are part of a vast cosmic ecosystem, embedded in an extensive gaseous reservoir that regulates their growth by providing the necessary fuel for star formation while preserving a fossil record of past interactions, outflows, and feedback processes. The circumgalactic medium (CGM) contains multiphase gas spanning a broad dynamic range in spatial scale

Matthew Bonas, Paolo Giani, Paola Crippa, Stefano Castruccio

An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo State Autoenco

P. Bisht

We investigate a simple Vicsek-type rule-based model for self-propelled particles, where each particle orients itself antiparallel to the average orientation of particles within a defined neighborhood of radius $R$. The particle orientation is updated asynchronously and randomly across the system. In steady state, particles self-organize into clusters-despit

Laura Maxim, Julien Rabatel, Jean-Marc Douguet, Natalia Grabar

To address the current crises (climatic, social, economic), the self-sufficiency -- a set of practices that combine energy sobriety, self-production of food and energy, and self-construction - arouses an increasing interest. The CNRS STAY project (Savoirs Techniques pour l'Auto-suffisance, sur YouTube) explores this topic by analyzing techniques shared on Yo

Karina Halevy, Karly Hou, Charumathi Badrinath

Data augmentation methods, especially SoTA interpolation-based methods such as Fair Mixup, have been widely shown to increase model fairness. However, this fairness is evaluated on metrics that do not capture model uncertainty and on datasets with only one, relatively large, minority group. As a remedy, multicalibration has been introduced to measure fairnes

T. Heinzl, B. King, D. Liu

We introduce form factors that relate the amplitude of a QED process in vacuum to its corresponding background-field process. The latter is characterised by a reduced S-matrix element where one or more photon field operators are replaced by classical background fields. In the associated Feynman diagram, external photon lines are supplanted with lines represe

Yiwen Gu, Mahir Patel, Margrit Betke

In this paper, we present a contrastive learning based framework, ExeChecker, for the interpretation of rehabilitation exercises. Our work builds upon state-of-the-art advances in the area of human pose estimation, graph-attention neural networks, and transformer interpretablity. The downstream task is to assist rehabilitation by providing informative feedba

Brandon B. Le

Although neuron models have been well studied for their rich dynamics and biological properties, limited research has been done on the complex geometries that emerge from the basins of attraction and basin boundaries of multistable neuron systems. In this paper, we investigate the geometrical properties of the strange attractors, four-dimensional basins, and

John Irving, Mohamed Omar

We revisit the R\'{e}dei-Berge symmetric function $\mathcal{U}_D$ for digraphs $D$, a specialization of Chow's path-cycle symmetric function. Through the lens of matrix algebra, we consolidate and expand on the work of Chow, Grinberg and Stanley, and Lass concerning the resolution of $\mathcal{U}_D$ in the power sum and Schur bases. Along the way we also rev

Rishiraj Saha Roy, Joel Schlotthauer, Chris Hinze, Andreas Foltyn

Retrieval Augmented Generation (RAG) works as a backbone for interacting with an enterprise's own data via Conversational Question Answering (ConvQA). In a RAG system, a retriever fetches passages from a collection in response to a question, which are then included in the prompt of a large language model (LLM) for generating a natural language (NL) answer. H

Giorgio Morales, John Sheppard

Obtaining high certainty in predictive models is crucial for making informed and trustworthy decisions in many scientific and engineering domains. However, extensive experimentation required for model accuracy can be both costly and time-consuming. This paper presents an adaptive sampling approach designed to reduce epistemic uncertainty in predictive models

Dong Hoon Lee, Seunghoon Hong

Recent token reduction methods for Vision Transformers (ViTs) incorporate token merging, which measures the similarities between token embeddings and combines the most similar pairs. However, their merging policies are directly dependent on intermediate features in ViTs, which prevents exploiting features tailored for merging and requires end-to-end training

Conrad Borchers, Jeroen Ooge, Cindy Peng, Vincent Aleven

Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined with showin

Shaohan Chen, Zheyan Liu, Huili Zheng, Qimin Zhang

Cardiovascular disease (CVD) persists as a primary cause of death on a global scale, which requires more effective and timely detection methods. Traditional supervised learning approaches for CVD detection rely heavily on large-labeled datasets, which are often difficult to obtain. This paper employs semi-supervised learning models to boost efficiency and ac

Umar Khalid, Kashif Munir, Hasan Iqbal, Azib Farooq

Editing complex visual content from ambiguous or partially specified instructions remains a core challenge in vision-language modeling. Existing models can contextualize content but often fail to infer the underlying intent within a reference image or scene, leading to inconsistent or misaligned edits. We introduce the Editing Vision-Language Model (EVLM), a