March 2025 arXiv papers — page 218

Dom Huh, Prasant Mohapatra

This paper proposes a diffusion-based auto-bidding framework that leverages graph representations to model large-scale auction environments. In such settings, agents must dynamically optimize bidding strategies under constraints defined by key performance indicator (KPI) metrics, all while operating in competitive environments characterized by uncertain, spa

Xiyu Zhang, Jiayi Ma, Jianwei Guo, Wei Hu

Geometric constraints between feature matches are critical in 3D point cloud registration problems. Existing approaches typically model unordered matches as a consistency graph and sample consistent matches to generate hypotheses. However, explicit graph construction introduces noise, posing great challenges for handcrafted geometric constraints to render co

Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim

Instruction tuning is crucial for adapting large language models (LLMs) to align with user intentions. Numerous studies emphasize the significance of the quality of instruction tuning (IT) data, revealing a strong correlation between IT data quality and the alignment performance of LLMs. In these studies, the quality of IT data is typically assessed by evalu

S M A Sharif, Rizwan Ali Naqvi, Farman Alic, Mithun Biswas

Single-shot image deblurring in a low-light condition is known to be a profoundly challenging image translation task. This study tackles the limitations of the low-light image deblurring with a learning-based approach and proposes a novel deep network named as DarkDeblurNet. The proposed DarkDeblur- Net comprises a dense-attention block and a contextual gati

Hugo Folonier, Sylvio Ferraz-Mello, Raphael Alves-Silva

This paper extends the creep tide theory to exoplanetary systems with significant obliquities. The extended theory allows us to obtain the stellar and planetary hydrodynamic equilibrium tides and the evolution of the rotational state of the bodies. The dynamic ellipsoidal figure of equilibrium of the body is calculated taking into account that its reaction t

Y. -T. Lai, T. Koga, Y. Iwasaki, Y. Ahn

The Belle~II experiment is designed to search for physics beyond the Standard Model by investigating rare decays at the SuperKEKB \(e^{+}e^{-}\) collider. Owing to the significant beam background at high luminosity, the data acquisition system employs a hardware-based Level-1~Trigger to reduce the readout data throughput by selecting collision events of inte

Zhongyi Shui, Honglin Li, Yunlong Zhang, Yuxuan Sun

Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. The gigapixel size of WSIs necessitates the use of sliding window methodology for nucleus detection. However, mainstream methods process each sliding window independently, which overlooks broader contextual information and easily leads to i

Mia Mohammad Imran, Robert Zita, Rebekah Copeland, Preetha Chatterjee

Software projects thrive on the involvement and contributions of individuals from different backgrounds. However, toxic language and negative interactions can hinder the participation and retention of contributors and alienate newcomers. Proactive moderation strategies aim to prevent toxicity from occurring by addressing conversations that have derailed from

Qinghao Yan, P. H. Diamond

We study the concentration field in a prescribed 2D Cahn-Hilliard Navier-Stokes (CHNS) system. We formulate a description for the target pattern formation and pattern merging processes, and compare this description with simulation results. Shear-augmented diffusion along streamlines causes a separation of time scales, thus 2D CHNS system can be simplified to

Dickness Kakitahi Kwesiga, Angshuman Guin, Michael Hunter

Previous studies that have formulated multi-agent reinforcement learning (RL) algorithms for adaptive traffic signal control have primarily used value-based RL methods. However, recent literature has shown that policy-based methods may perform better in partially observable environments. Additionally, RL methods remain largely untested for real-world normall

Hanjing Ye, Kuanqi Cai, Yu Zhan, Bingyi Xia

Autonomous robot person-following (RPF) systems are crucial for personal assistance and security but suffer from target loss due to occlusions in dynamic, unknown environments. Current methods rely on pre-built maps and assume static environments, limiting their effectiveness in real-world settings. There is a critical gap in re-finding targets under topogra

Toan Nguyen, Kien Do, Duc Kieu, Thin Nguyen

We introduce a theoretical framework for diffusion-based image editing by formulating it as a reverse-time bridge modeling problem. This approach modifies the backward process of a pretrained diffusion model to construct a bridge that converges to an implicit distribution associated with the editing target at time 0. Building on this framework, we propose h-

Eungwang Seo, Xikai Shan, Justin Janquart, Otto A. Hannuksela

When a gravitational wave signal encounters a massive object, such as a galaxy or galaxy cluster, it undergoes strong gravitational lensing, producing multiple copies of the original signal. These strongly lensed signals exhibit identical waveform morphology in the frequency domain, allowing analysis without the need for complex lens models. However, stellar

Aparna Sasidharan

This article describes a geometric partitioning software that can be used for quick computation of data partitions on many-core HPC machines. It is most suited for dynamic applications with load distributions that vary with time. Partitioning costs were minimized with a lot of care, to tolerate frequent adjustments to the load distribution. The partitioning

Hsi-An Pan, Lihwai Lin, Sebastian F. Sanchez, Jorge K. Barrera-Ballesteros

Gas-phase metallicity in interacting and merging galaxies offers key insights into their star formation processes and evolutionary histories. This study investigates the spatial evolution of gas-phase metallicity (i.e, oxygen abundance, 12 $+$ log(O/H)) in these galaxies using integral field unit (IFU) data from the SDSS-IV MaNGA survey, focusing on changes

Riku Kobayashi, Yoshihiro Kawahara, Takuya Sasatani

The powering range of wireless power transfer (WPT) systems is typically confined to areas close to the transmitter. Shape-reconfigurable two-dimensional (2-D) relay resonator arrays have been developed to extend this range, offering greater deployment flexibility. However, these arrays encounter challenges due to cross-coupling among adjacent resonators, wh

Xue-Yun Zhao, Lei Guo, Xu-Chang Zheng, Huan-Yu Bi

This study forecasts the production of doubly heavy baryons, $\Xi_{cc}$, $\Xi_{bc}$, and $\Xi_{bb}$, within the nonrelativistic QCD framework at the Muon-Ion Collider (MuIC). It examines two production mechanisms: photon-gluon fusion ($\gamma + g \to (QQ')[n] +\bar{Q} +\bar{Q'}$) and extrinsic heavy quark channels ($\gamma + Q \to (QQ')[n] + \bar{Q'}$), wher

S. Mantovanini, N. Hurley-Walker, G. E. Anderson

Observations of Galactic supernova remnants (SNRs) are crucial to understanding supernova explosion mechanisms and their impact on our Galaxy's evolution. SNRs are usually identified by searching for extended, circular structures in all-sky surveys. However, the resolution and sensitivity of any given survey results in selection biases related to the brightn

David Rozado

Political biases in Large Language Model (LLM)-based artificial intelligence (AI) systems, such as OpenAI's ChatGPT or Google's Gemini, have been previously reported. While several prior studies have attempted to quantify these biases using political orientation tests, such approaches are limited by potential tests' calibration biases and constrained respons

Da Wang, Yu Zhang, Kai Zhang, Junqing Li

As the continuous deepening of low-carbon emission reduction policies, the manufacturing industries urgently need sensible energy-saving scheduling schemes to achieve the balance between improving production efficiency and reducing energy consumption. In energy-saving scheduling, reasonable machine states-switching is a key point to achieve expected goals, i

Mohsen Fathi, Yassine Sekhmani

Recent observations of the supermassive black holes M87* and Sgr A* by the Event Horizon Telescope (EHT) have opened new avenues for testing gravity theories through black hole shadow observables. These observations offer a means to distinguish between general relativity and modified gravity theories while providing insights into the astrophysical properties

María Alejandra Díaz Teodori, Jari J. E. Kajava, Celia Sánchez-Fernández, Andrea Sanna

Type-I X-ray bursts are thermonuclear explosions caused by the unstable burning of accreted material on the surface of neutron stars. We report the detection of seven type-I X-ray bursts from the ultracompact X-ray binary M15 X-2 observed by the Neutron Star Interior Composition Explorer (NICER) during its 2022 outburst. We found all the bursts occurred in t

Kaoru Teranishi, Takashi Tanaka

In this paper, we propose a secure two-party computation protocol for dynamic controllers using a secret sharing scheme. The proposed protocol realizes outsourcing of controller computation to two servers, while controller parameters, states, inputs, and outputs are kept secret against the servers. Unlike previous encrypted controls in a single-server settin

Saeed Ranjbar Alvar, Gursimran Singh, Mohammad Akbari, Yong Zhang

Large Multimodal Models (LMMs) have emerged as powerful models capable of understanding various data modalities, including text, images, and videos. LMMs encode both text and visual data into tokens that are then combined and processed by an integrated Large Language Model (LLM). Including visual tokens substantially increases the total token count, often by

Renato Lui Geh, Zilei Shao, Guy Van den Broeck

Current LLM pipelines account for only one possible tokenization for a given string, ignoring exponentially many alternative tokenizations during training and inference. For example, the standard Llama3 tokenization of penguin is [p,enguin], yet [peng,uin] is another perfectly valid alternative. In this paper, we show that despite LLMs being trained solely o

Dimitris Bertsimas, Benjamin Boucher

Existing approaches of prescriptive analytics -- where inputs of an optimization model can be predicted by leveraging covariates in a machine learning model -- often attempt to optimize the mean value of an uncertain objective. However, when applied to uncertain constraints, these methods rarely work because satisfying a crucial constraint in expectation may

Pablo Arnault

We proceed to the canonical quantization of the complex scalar field without making use of its real and imaginary parts. Our motivation is to formally connect, as tightly as possible, the quantum-field notions of particle and antiparticle$\unicode{x2014}$most prominently represented, formally, by creation and annihilation operators$\unicode{x2014}$to the ini

Hongyu Lin, Haoran Luo, Hanghang Cao, Yang Liu

Complex Logical Query Answering (CLQA) involves intricate multi-hop logical reasoning over large-scale and potentially incomplete Knowledge Graphs (KGs). Although existing CLQA algorithms achieve high accuracy in answering such queries, their reasoning time and memory usage scale significantly with the number of First-Order Logic (FOL) operators involved, cr

Haoxiang You, Lekan Molu, Ian Abraham

The Bellman equation and its continuous-time counterpart, the Hamilton-Jacobi-Bellman (HJB) equation, serve as necessary conditions for optimality in reinforcement learning and optimal control. While the value function is known to be the unique solution to the Bellman equation in tabular settings, we demonstrate that this uniqueness fails to hold in continuo

Jiacheng Zhang, Benjamin I. P. Rubinstein, Jingfeng Zhang, Feng Liu

Statistical adversarial data detection (SADD) detects whether an upcoming batch contains adversarial examples (AEs) by measuring the distributional discrepancies between clean examples (CEs) and AEs. In this paper, we explore the strength of SADD-based methods by theoretically showing that minimizing distributional discrepancy can help reduce the expected lo

Scott Schmieding, Christopher-Lloyd Simon

This work is motivated by the study of continued fraction expansions of real numbers: we describe in dynamical terms their orbits under the action of $\mathrm{PGL}_2(\mathbb{Q})$. A real number gives rise to a Sturmian system encoding a rotation of the circle. It is well known that $\mathrm{PGL}_2(\mathbb{Z})$-equivalence of real numbers, characterized by th

Linyao Yang, Shi Luo, Xi Cheng, Lei Yu

Digital twin technology is a transformative innovation driving the digital transformation and intelligent optimization of manufacturing systems. By integrating real-time data with computational models, digital twins enable continuous monitoring, simulation, prediction, and optimization, effectively bridging the gap between the physical and digital worlds. Re

Matteo Di Giovanni, Davide Rozza, Rosario De Rosa, Enrico Calloni

We present an evaluation of how site dependent noise can affect the signal to noise ratio (SNR) of compact binary coalescence (CBC) signals in the future 3rd generation gravitational wave (GW) detector Einstein Telescope (ET). The design of ET is currently pushing the scientific community to study its scientific potential with respect to known, and possibly

Susan D. Benecchi, Simon B. Porter, Anne J. Verbiscer, David W. Gerdes

We propose a Roman Space Telescope survey to investigate fundamental properties of the distant solar system in the region of the Kuiper Belt where object characteristics and the size distribution are inaccessible from any other telescope. Our pointing is coincident with the search space accessible to NASA's New Horizons spacecraft meaning, that a discovered

Jiahao Zhang, Lu Zhang, Xiaodan Pang, Oskars Ozolins

Fiber-optic transmission systems are leveraged not only as high-speed communication channels but also as nonlinear kernel functions for machine learning computations, enabling the seamless integration of computational intelligence and communication.

Samuel Baldwin, Cole Hausman, Mohamed Bakr, Edward Talmage

We explore the problem of efficiently implementing shared data structures in an asynchronous computing environment. We start with a traditional FIFO queue, showing that full replication is possible with a delay of only a single round-trip message between invocation and response of each operation. This is optimal, or near-optimal, runtime for the Dequeue oper

Devjani Basu

Clifford theory establishes a relation between the representation theory of a finite group and its normal subgroups. In this paper, we establish the Clifford theory for the modular representations of finite groups. The proofs are based on an explicit analysis of the representation spaces and their decompositions. We also analyze the relation between the modu

Jianzhong You, Yuan Gao, Sangwook Kim, Chris Mcintosh

Computed tomography (CT) is a key imaging modality for diagnosis, yet its clinical utility is marred by high radiation exposure and long turnaround times, restricting its use for larger-scale screening. Although chest radiography (CXR) is more accessible and safer, existing CXR foundation models focus primarily on detecting diseases that are readily visible

Meike Hatzel, Michał Pilipczuk

Dumas, Foucaud, Perez, and Todinca [SIAM J. Disc. Math., 2024] proved that if the vertex set of a graph $G$ can be covered by $k$ shortest paths, then the pathwidth of $G$ is bounded by $\mathcal{O}(k \cdot 3^k)$. We prove a coarse variant of this theorem: if in a graph $G$ one can find~$k$ shortest paths such that every vertex is at distance at most $\rho$

Xiaoqin Guo, Hung Vinh Tran, Yuming Paul Zhang

We study the convergence rates of policy iteration (PI) for nonconvex viscous Hamilton--Jacobi equations using a discrete space-time scheme, where both space and time variables are discretized. We analyze the case with an uncontrolled diffusion term, which corresponds to a possibly degenerate viscous Hamilton--Jacobi equation. We first obtain an exponential

Ziqing Ma, Ewoud J. J. Smeur, Guido C. H. E. de Croon

Tailsitter aircraft attract considerable interest due to their capabilities of both agile hover and high speed forward flight. However, traditional tailsitters that use aerodynamic control surfaces face the challenge of limited control effectiveness and associated actuator saturation during vertical flight and transitions. Conversely, tailsitters relying sol

Aofei Chang, Le Huang, Parminder Bhatia, Taha Kass-Hout

Large Vision Language Models (LVLMs) are becoming increasingly important in the medical domain, yet Medical LVLMs (Med-LVLMs) frequently generate hallucinations due to limited expertise and the complexity of medical applications. Existing benchmarks fail to effectively evaluate hallucinations based on their underlying causes and lack assessments of mitigatio

Stepan Mazokha, Fanchen Bao, George Sklivanitis, Jason O. Hallstrom

WiFi-based mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movements. However, MAC address randomization introduces a significant obstacle in accurately estimating congestion levels and path trajectories. To this end, we consider radio frequency fingerprinting and re-identification for attributing WiFi traf

Kevin Buck, Jessica Babyak, Paolo Piersanti, Kevin Zumbrun

We review convergence and behavior of stochastic gradient descent for convex and nonconvex optimization, establishing various conditions for convergence to zero of the variance of the gradient of the objective function, and presenting a number of simple examples demonstrating the approximate evolution of the probability density under iteration, including app

Sheng Yue, Zerui Qin, Yongheng Deng, Ju Ren

Federated Learning (FL) has garnered widespread interest in recent years. However, owing to strict privacy policies or limited storage capacities of training participants such as IoT devices, its effective deployment is often impeded by the scarcity of training data in practical decentralized learning environments. In this paper, we study enhancing FL with t

Christian Remling

We consider entire matrix functions $A(z)$ taking values in $\operatorname{SL}(2,\mathbb C)$. These map pairs of Herglotz functions by acting pointwise as linear fractional transformations. The main examples of such Toda maps are provided by transfer matrices of differential and difference operators and by the cocycles associated with the classical integrabl

Sonia Cromp, Satya Sai Srinath Namburi GNVV, Mohammed Alkhudhayri, Catherine Cao

While advances in large language models (LLMs) have greatly improved the quality of synthetic text data in recent years, synthesizing tabular data has received relatively less attention. We address this disparity with Tabby, a simple but powerful post-training modification to the standard Transformer language model architecture, enabling its use for tabular

Wenxin Zhao, Fangyu Yu, Peng Zhang, Hansu Gu

To mitigate the negative impacts of online videos on teenagers, existing research and platforms have implemented various parental mediation mechanisms, such as Parent-Child Joint Media Engagement (JME). However, JME generally relies heavily on parents' time, knowledge, and experience. To fill this gap, we aim to design an automatic tool to help parents/child

Sohyeon Hwang, Priyanka Nanayakkara, Yan Shvartzshnaider

Decentralized social media protocols enable users in independent, user-hosted servers (i.e., instances) to interact with each other while they self-govern. This community-based model of social media governance opens up new opportunities for tailored decision-making about information flows -- i.e., what user data is shared to whom and when -- and in turn, for

Chavalchart Herabut, Bryan Rangel Valle, Vikram D. Kodibagkar, Sung-Min Sohn

While positive-intrinsic-negative (PIN) diodes are commonly used in radio frequency (RF) circuits, their use often degrades the signal-to-noise ratio (SNR) due to high insertion loss and interference from additional biasing circuit components, which is critical for SNR-prioritized applications. This work presents the design of a novel pneumatically controlle

Zachary Mesyan

Given a semigroup $S$ and $s,t \in S$, write $s \sim_p^1 t$ if $s=pr$ and $t=rp$, for some $p,r \in S \cup \{1\}$. This relation, known as "primary conjugacy", along with its transitive closure $\sim_p$, has been extensively used and studied in many fields of algebra. This paper is devoted to a natural generalization, defined by $s \sim_s^1 t$ whenever $s=p_

Chia-Wei Hsu, Nien-Ti Tsou, Yu-Cheng Chen, Yang Jeong Park

Gradient-based optimization drives the unprecedented performance of modern deep neural network models across diverse applications. Adaptive algorithms have accelerated neural network training due to their rapid convergence rates; however, they struggle to find ``flat minima" reliably, resulting in suboptimal generalization compared to stochastic gradient des

Elisa Baldazzi, Pietro Biroli, Marina Della Giusta, Florent Dubois

Reliance on stereotypes is a persistent feature of human decision-making and has been extensively documented in educational settings, where it can shape students' confidence, performance, and long-term human capital accumulation. While effective techniques exist to mitigate these negative effects, a crucial first step is to establish whether teachers can rec

L. L. Lage, S. Bravo, O. Arroyo-Gascón, Leonor Chico

Sliding-induced topological transitions in biphenylene bilayers are investigated, considering various stacking configurations which are analyzed from a symmetry perspective and described in detail,highlighting the intricate patterns of type-II Dirac cone crossings. Topological changes in the Fermi surface are assessed via the Euler characteristic, linking ea

Areej Dweib, Montaser Tanina, Shehab Alawi, Mohammad Dyab

This study investigates the performance of various classification models for a malware classification task using different feature sets and data configurations. Six models-Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, Random Forest (RF), and Extreme Gradient Boosting (XGB)-were evaluated alongside two deep lea

Jordan Peper, Zhenjiang Mao, Yuang Geng, Siyuan Pan

As autonomous systems are increasingly deployed in open and uncertain settings, there is a growing need for trustworthy world models that can reliably predict future high-dimensional observations. The learned latent representations in world models lack direct mapping to meaningful physical quantities and dynamics, limiting their utility and interpretability

Takuya Kataiwa, Cho Hakaze, Tetsushi Ohki

In this study, we measure the Intrinsic Dimension (ID) of token embedding to estimate the intrinsic dimensions of the manifolds spanned by the representations, so as to evaluate their redundancy quantitatively compared to their extrinsic dimensionality. In detail, (1) we estimate the ID of token embeddings in small-scale language models and also modern large

Ahmad Antari, Yazan Abo-Aisheh, Jehad Shamasneh, Huthaifa I. Ashqar

This study uses various models to address network traffic classification, categorizing traffic into web, browsing, IPSec, backup, and email. We collected a comprehensive dataset from Arbor Edge Defender (AED) devices, comprising of 30,959 observations and 19 features. Multiple models were evaluated, including Naive Bayes, Decision Tree, Random Forest, Gradie

Janis Smits, Yaser Silani, Zaili Peng, Bryan A. Richards

Non-inductive magnetometers based on solid-state spins offer a promising solution for small-volume nuclear magnetic resonance (NMR) detection. A remaining challenge is to operate at a sufficiently high magnetic field to resolve chemical shifts at the part-per-billion level. Here, we demonstrate a Ramsey-M_z protocol that uses Ramsey interferometry to convert

CMS Collaboration

Bound states of charm and anticharm quarks, known as charmonia, have a rich spectroscopic structure that can be used to probe the dynamics of hadron production in high-energy hadron collisions. Here, the cross section ratio of excited ($\psi$(2S)) and ground state (J/$\psi$) vector mesons is measured as a function of the charged-particle multiplicity in prot

Ali Hasan, Haoming Yang, Yuting Ng, Vahid Tarokh

Regularizing neural networks is important for anticipating model behavior in regions of the data space that are not well represented. In this work, we propose a regularization technique for enforcing a level of smoothness in the mapping between the data input space and the loss value. We specify the level of regularity by requiring that the loss of the netwo

Nahom Seyoum, Haoxiang You

This work explores a novel perspective on solving nonconvex and nonsmooth optimization problems by leveraging sampling based methods. Instead of treating the objective function purely through traditional (often deterministic) optimization approaches, we view it as inducing a target distribution.We then draw samples from this distribution using Markov Chain M

Jiahao Cao, Qingpo Cai, Lance A. Waller, DeMarc A. Hickson

Basketball shot charts provide valuable information regarding local patterns of in-game performance to coaches, players, sports analysts, and statisticians. The spatial patterns of where shots were attempted and whether the shots were successful suggest options for offensive and defensive strategies as well as historical summaries of performance against part

Yarui Zhang, Shanshan Wang, Joe Wiart

The prediction of the electric field (E-field) plays a crucial role in monitoring radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks. In this paper, a deep learning framework is proposed to predict E-field levels in complex urban environments. First, the measurement campaign and publicly accessible databases used to construct

M. Aehle, J. Alme, G. G. Barnaföldi, G. Bíró

Proton computed tomography (pCT) aims to facilitate precise dose planning for hadron therapy, a promising and effective method for cancer treatment. Hadron therapy utilizes protons and heavy ions to deliver well focused doses of radiation, leveraging the Bragg peak phenomenon to target tumors while sparing healthy tissues. The Bergen pCT Collaboration aims t

Z. L. Li, R. Z. Jiang, Y. J. Li

The nonadiabatic quantum kinetic equations and Dirac-Heisenberg-Wigner formalism for Schwinger pair production in a spatially uniform and time-varying electric field with multiple components are derived and proven to be equivalent. The relation between nonadiabatic and adiabatic quantum kinetic equations is also established. By analyzing the time evolution o

Cristóbal López, Eduardo H. Colombo, Emilio Hernández-García, Ricardo Martinez-Garcia

This chapter investigates some mechanisms behind pattern formation driven by competitive-only or repelling interactions, and explores how these patterns are influenced by different types of particle movement. Despite competition and repulsion are both anti-crowding interactions, collective effects may lead to clusters of individuals, which can arrange period

Max Landauer, Leonhard Alton, Martina Lindorfer, Florian Skopik

Kernel rootkits provide adversaries with permanent high-privileged access to compromised systems and are often a key element of sophisticated attack chains. At the same time, they enable stealthy operation and are thus difficult to detect. Thereby, they inject code into kernel functions to appear invisible to users, for example, by manipulating file enumerat

Runjie Hu

Although the local information of the $L$-spectra is well understood, the problem of whether this local information can be identified with the geometric data for bundles remains open for decades, which was originally raised in the 1960s and 1970s by Sullivan, Brumfiel, Taylor-Williams and others independently. In this paper, we provide an affirmative answer

Fred Rowley

This paper characterises the structure of every maximal weak or strong Gallai-Schur partition. The results confirm the exact values of Gallai-Schur numbers provided by Budden (2020) in the strong case, and provide corresponding values for weak Gallai-Schur numbers. The proofs are elementary and standalone.

Nisha Devasia, Runhua Zhao, Jin Ha Lee

Rapid spread of harmful misinformation has led to a dire need for effective media literacy interventions, to which educational games have been suggested as a possible solution. Researchers and educators have created several games that increase media literacy and resilience to misinformation. However, the existing body of misinformation education games rarely

Yanxiang Chen, Pablo de Oliveira Castro, Paolo Bientinesi, Niclas Jansson

Mixed-precision computing has the potential to significantly reduce the cost of exascale computations, but determining when and how to implement it in programs can be challenging. In this article, we propose a methodology for enabling mixed-precision with the help of computer arithmetic tools, roofline model, and computer arithmetic techniques. As case studi

Damien Masson, Zhe Liu, Charles Xu

Given the ubiquity of SmartTVs and head-mounted-display-based virtual environments, recent research has explored techniques to support eyes-free text entry using touchscreen devices. However, proposed techniques, leveraging lexicons, limit the user's ability to enter out-of-vocabulary words. In this paper, we investigate how to enter text while relying on un

Allassan Tchangmena A Nken, Susan Mckeever, Peter Corcoran, Ihsan Ullah

Considerable effort has been made in privacy-preserving video human activity recognition (HAR). Two primary approaches to ensure privacy preservation in Video HAR are differential privacy (DP) and visual privacy. Techniques enforcing DP during training provide strong theoretical privacy guarantees but offer limited capabilities for visual privacy assessment.

Andreas Bauer, William Bosl, Oliver Aalami, Paul Schmiedmayer

Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in many regions. A scalable solution integrating standardized assessments with physiological data collection, such as electroencephalogram (EEG) recordings, could enable early detection in routine settings by non-spec

Arya Akhavan, Alexandre B. Tsybakov

We address the problem of zero-order optimization from noisy observations for an objective function satisfying the Polyak-{\L}ojasiewicz or the strong convexity condition. Additionally, we assume that the objective function has an additive structure and satisfies a higher-order smoothness property, characterized by the H\"older family of functions. The addit

Zhixuan Lin, Evgenii Nikishin, Xu Owen He, Aaron Courville

An essential component of modern recurrent sequence models is the forget gate. While Transformers do not have an explicit recurrent form, we show that a forget gate can be naturally incorporated into Transformers by down-weighting the unnormalized attention scores in a data-dependent way. We name this attention mechanism Forgetting Attention and the resultin

Hao Yu

Path regularization has shown to be a very effective regularization to train neural networks, leading to a better generalization property than common regularizations i.e. weight decay, etc. We propose a first near-complete (as will be made explicit in the main text) nonasymptotic generalization theory for multilayer neural networks with path regularizations

Isaac Corley, Conor Wallace, Sourav Agrawal, Burton Putrah

Solar photovoltaic (PV) farms represent a major source of global renewable energy generation, yet their true operational efficiency often remains unknown at scale. In this paper, we present a comprehensive, data-driven framework for large-scale airborne infrared inspection of North American solar installations. Leveraging high-resolution thermal imagery, we

Qifan Fu, Xu Chen, Muhammad Asad, Shanxin Yuan

Although diffusion methods excel in text-to-image generation, generating accurate hand gestures remains a major challenge, resulting in severe artifacts, such as incorrect number of fingers or unnatural gestures. To enable the diffusion model to learn spatial information to improve the quality of the hands generated, we propose HanDrawer, a module to conditi

Zhe Liu

Recent advances in large language models (LLMs) offer unprecedented opportunities to enhance human-AI collaboration in qualitative research methods, including interviews. While interviews are highly valued for gathering deep, contextualized insights, interviewers often face significant cognitive challenges, such as real-time information processing, question

Enrico Amico, Lorenzo Matteucci, Gioacchino Cafiero

In this study, eXplainable Artificial Intelligence (XAI) methods are applied to analyze flow fields obtained through PIV measurements of an axisymmetric turbulent jet. A convolutional neural network (U-Net) was trained to predict velocity fields at subsequent time steps. Three XAI methods: SHapley Additive explanations (SHAP), Gradient-SHAP, and Grad-CAM wer

Fengdi Che, Bryan Chan, Chen Ma, A. Rupam Mahmood

Off-policy policy evaluation (OPE), an essential component of reinforcement learning, has long suffered from stationary state distribution mismatch, undermining both stability and accuracy of OPE estimates. While existing methods correct distribution shifts by estimating density ratios, they often rely on expensive optimization or backward Bellman-based upda

Ran Hao, Yanlin Xiang, Junliang Du, Qingyuan He

This study proposed a hybrid model of a convolutional neural network (CNN) and a Transformer to predict and diagnose heart disease. Based on CNN's strength in detecting local features and the Transformer's high capacity in sensing global relations, the model is able to successfully detect risk factors of heart disease from high-dimensional life history data.

Emmanuel A. Olowe, Danial Chitnis

The Test and Measurement domain, known for its strict requirements for accuracy and efficiency, is increasingly adopting Generative AI technologies to enhance the performance of data analysis, automation, and decision-making processes. Among these, Large Language Models (LLMs) show significant promise for advancing automation and precision in testing. Howeve

Perrine Jouteur

We generalize in two steps the quantized action of the modular group on $q$-deformed real numbers introduced by Morier-Genoud and Ovsienko. First, we let the projective general linear group $PGL_2(\mathbb{Z})$ act on $q$-real numbers via a $q$-deformed action. The quantized matrices we get have combinatorial interpretations. Then we consider an extension of

Zahra Mohammadi Khangheshlaghi, Katrin Tent

We axiomatize the theory of the Farey graph and prove that it is $\omega$-stable of Morley rank $\omega$.

André-Nicolas Chené, Georgios Vasilopoulos, Lidia M. Oskinova, Clara Martínez-Vázquez

The end points of massive star evolution are poorly known, especially those in interacting binary systems containing compact objects, such as neutron stars or black holes. Such systems are bright in X-rays, and the most luminous among them are called ultra-luminous X-ray sources (ULXs). In this paper, we address the enigmatic NGC 300 ULX-1. It's X-ray activi

Siddartha Devic, Nurendra Choudhary, Anirudh Srinivasan, Sahika Genc

Many machine learning algorithms and classifiers are available only via API queries as a ``black-box'' -- that is, the downstream user has no ability to change, re-train, or fine-tune the model on a particular target distribution. Indeed, the downstream user may not even have knowledge of the \emph{original} training distribution or performance metric used t

Cédric Solenthaler, Joshua Smailes, Martin Strohmeier

An increase in availability of Software Defined Radios (SDRs) has caused a dramatic shift in the threat landscape of legacy satellite systems, opening them up to easy spoofing attacks by low-budget adversaries. Physical-layer authentication methods can help improve the security of these systems by providing additional validation without modifying the space s

Haoming Yang, Ali Hasan, Vahid Tarokh

Regularizing continual learning techniques is important for anticipating algorithmic behavior under new realizations of data. We introduce a new approach to continual learning by imposing the properties of a parabolic partial differential equation (PDE) to regularize the expected behavior of the loss over time. This class of parabolic PDEs has a number of fa

Ashwin Verma, Soheil Mohajer, Behrouz Touri

We formulate the problem of fake news detection using distributed fact-checkers (agents) with unknown reliability. The stream of news/statements is modeled as an independent and identically distributed binary source (to represent true and false statements). Upon observing a news, agent $i$ labels the news as true or false which reflects the true validity of

Jimmy K. Yu, Marcos Martínez-Romero, Matthew Horridge, Mete U. Akdogan

In the age of big data, it is important for primary research data to follow the FAIR principles of findability, accessibility, interoperability, and reusability. Data harmonization enhances interoperability and reusability by aligning heterogeneous data under standardized representations, benefiting both repository curators responsible for upholding data qua

Bartlomiej Surma, Michael Backes, Yang Zhang

Graph Neural Networks (GNNs) have shown remarkable success in various graph-based learning tasks. However, recent studies have raised concerns about fairness and privacy issues in GNNs, highlighting the potential for biased or discriminatory outcomes and the vulnerability of sensitive information. This paper presents a comprehensive investigation of fairness

Andrew Gordon Wilson

Deep neural networks are often seen as different from other model classes by defying conventional notions of generalization. Popular examples of anomalous generalization behaviour include benign overfitting, double descent, and the success of overparametrization. We argue that these phenomena are not distinct to neural networks, or particularly mysterious. M

Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova, Wahid Bhimji

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The

Qianyi Zhang, Wentao Luo, Boyi Liu, Ziyang Zhang

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points--a novel directional cue within a reinforcement learning-based framework--is introduced. A structured method for identifying guidance points is developed, consisting of

Xiaohan Zhu, Nathan Srebro

We provide a complete characterization of the entire regularization curve of a modified two-part-code Minimum Description Length (MDL) learning rule for binary classification, based on an arbitrary prior or description language. Grunwald and Langford [2004] previously established the lack of asymptotic consistency, from an agnostic PAC (frequentist worst cas

Boris V. Pashinsky, Alexander Kato, Boris B. Blinov

We investigate the structural properties and melting behavior of two-dimensional ion crystals in an RF trap, focusing on the effects of ion temperature and trap potential symmetry. We identify distinct crystal structures that form under varying trapping conditions and temperatures through experimental observations and theoretical analyses. As the temperature

Elham Afzali, Saman Muthukumarana, Liqun Wang

Generalized Bayesian Inference (GBI) provides a flexible framework for updating prior distributions using various loss functions instead of the traditional likelihoods, thereby enhancing the model robustness to model misspecification. However, GBI often suffers the problem associated with intractable likelihoods. Kernelized Stein Discrepancy (KSD), as utiliz