October 2024 arXiv papers — page 94

Xudong Wang, Wenxue Xu, Jiazu Zhou, Baocheng Zhu

In this paper, we study the non-degenerated $C$-pseudo-cones which can be uniquely decomposed into the sum of a $C$-asymptotic set and a $C$-starting point. Combining this with the novel work in \cite{Schneider-A_weighted_Minkowski_theorem}, we introduce the asymptotic weighted co-volume functional $T_\Theta(E)$ of the non-degenerated $C$-pseudo-cone $E$, wh

Tianqianjin Lin, Pengwei Yan, Kaisong Song, Zhuoren Jiang

Graph foundation models (GFMs) have recently gained significant attention. However, the unique data processing and evaluation setups employed by different studies hinder a deeper understanding of their progress. Additionally, current research tends to focus on specific subsets of graph learning tasks, such as structural tasks, node-level tasks, or classifica

Urvashi Kishnani, Sanchari Das

As e-commerce continues to expand, the urgency for stronger privacy and security measures becomes increasingly critical, particularly on platforms frequented by younger users who are often less aware of potential risks. In our analysis of 90 US-based e-commerce websites, we employed a dual-technique approach, combining automated tools with manual evaluations

Abdullah Al Helal

This work focuses on the degree bound of maps between balls with maximum geometric rank and minimum target dimension where this geometric rank occurs. Specifically, we show that rational proper maps between $\mathbb{B}_n$ and $\mathbb{B}_N$ with $n \geq 2$, $N = \frac{n(n+1)}{2}$, and geometric rank $n-1$ cannot have a degree of more than $n+1$.

Kazuhiro Ohta, Satoshi Ono

Neural Radiance Field (NeRF), capable of synthesizing high-quality novel viewpoint images, suffers from issues like artifact occurrence due to its fixed sampling points during rendering. This study proposes a method that optimizes sampling points to reduce artifacts and produce more detailed images.

Bryan Chan, Anson Leung, James Bergstra

Offline-to-online reinforcement learning (O2O RL) aims to obtain a continually improving policy as it interacts with the environment, while ensuring the initial policy behaviour is satisficing. This satisficing behaviour is necessary for robotic manipulation where random exploration can be costly due to catastrophic failures and time. O2O RL is especially co

Josef Ruzicka, Christian Asch, Esteban Meneses, Markus Rampp

The high-performance computing (HPC) community has recently seen a substantial diversification of hardware platforms and their associated programming models. From traditional multicore processors to highly specialized accelerators, vendors and tool developers back up the relentless progress of those architectures. In the context of scientific programming, it

Jingcheng Ma, Megan Laune, Pengju Li, Jing Lu

Unlike biomarkers in biofluids, airborne biomarkers are dilute and difficult to trace. Detecting diverse airborne biomarkers with sufficient sensitivity typically relies on bulky and expensive equipment like mass spectrometers that remain inaccessible to the general population. Here, we introduce Airborne Biomarker Localization Engine (ABLE), a simple, affor

Victor A. Penas, Marcelo Losada, Pedro W. Lamberti

In this work we apply a procedure based on the quantum imaginary time evolution method to solve the unit-disk maximum independent set problem. Numerical simulations were performed for instances of 6, 8 and 10-qubits graphs. We have found that the failure probability of the procedure is relatively small and rapidly decreases with the number of shots. In addit

Kimberly Liu, Ali Mani

Superhydrophobic surfaces (SHS) are textured hydrophobic surfaces which have the ability to trap air pockets when immersed in water. This can result in significant drag reduction, due to substantially lower viscosity of air resulting in substantial effective slip velocity at the interface. Past studies of both laminar and turbulent flows model this slip velo

Jiaqi Wang, Dongyan Fu, Yubing Dong

The electromagnetic and gravitational form factors of the nucleon are studied simultaneously using a covariant quark-diquark approach, and the pion cloud effect on the form factors is explicitly discussed. In this study, the electromagnetic form factors are calculated to determine the parameters of our approach. Then, the gravitational form factors of the nu

Zelin Xu, Jie Ren, Yupu Zhang, Jose Maria Gonzalez Ondina

Nearly 900 million people live in low-lying coastal zones around the world and bear the brunt of impacts from more frequent and severe hurricanes and storm surges. Oceanographers simulate ocean current circulation along the coasts to develop early warning systems that save lives and prevent loss and damage to property from coastal hazards. Traditionally, suc

Zhijie Chen, Xinglin Zhang, Hongshu Guo, Yue-Jiao Gong

The landscape of Kolmogorov-Arnold Networks (KANs) is rapidly expanding, yet lacks a unified theoretical framework and a clear principle for efficient architecture design. This paper addresses these gaps with three core contributions. First, we introduce the Universal KAN (Uni-KAN) framework, a novel abstraction that formally unifies all KAN-style networks t

Achhiya Sultana, Dipto Das, Saadia Binte Alam, Mohammad Shidujaman

While content moderation in online platforms marginalizes users in the Global South at large, users of certain identities are further marginalized. Such users often come from Indigenous ethnic minority groups or identify as women. Through a qualitative study based on 18 semi-structured interviews, this paper explores how such users' experiences with hate spe

Junda Wang, Yujan Ting, Eric Z. Chen, Hieu Tran

Multimodal large language models (MLLMs) have made significant strides, yet they face challenges in the medical domain due to limited specialized knowledge. While recent medical MLLMs demonstrate strong performance in lab settings, they often struggle in real-world applications, highlighting a substantial gap between research and practice. In this paper, we

Marcus Gozon, Jingjin Yu

The Generalized Sliding-Tile Puzzle (GSTP), allowing many square tiles on a board to move in parallel while enforcing natural geometric collision constraints on the movement of neighboring tiles, provide a high-fidelity mathematical model for many high-utility existing and future multi-robot applications, e.g., at mobile robot-based warehouses or autonomous

Hanqun Cao, Mutian He, Ning Ma, Chang-yu Hsieh

DNA-encoded library (DEL) screening has revolutionized the detection of protein-ligand interactions through read counts, enabling rapid exploration of vast chemical spaces. However, noise in read counts, stemming from nonspecific interactions, can mislead this exploration process. We present DEL-Ranking, a novel distribution-correction denoising framework th

Mojtaba Heydari, Mehrez Souden, Bruno Conejo, Joshua Atkins

We introduce ImmerseDiffusion, an end-to-end generative audio model that produces 3D immersive soundscapes conditioned on the spatial, temporal, and environmental conditions of sound objects. ImmerseDiffusion is trained to generate first-order ambisonics (FOA) audio, which is a conventional spatial audio format comprising four channels that can be rendered t

Yi Liu, Chengxin Li, Shoukun Xu, Jungong Han

Multi-modal fusion has played a vital role in multi-modal scene understanding. Most existing methods focus on cross-modal fusion involving two modalities, often overlooking more complex multi-modal fusion, which is essential for real-world applications like autonomous driving, where visible, depth, event, LiDAR, etc., are used. Besides, few attempts for mult

Rafael Ferreira da Silva, Deborah Bard, Kyle Chard, Shaun de Witt

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale c

Francisco Márcio Barboza, Arthur Anthony da Cunha Romão E Silva, Bruno Motta de Carvalho

Basement relief gravimetry is crucial in geophysics, especially for oil exploration and mineral prospecting. It involves solving an inverse problem to infer geological model parameters from observed data. The model represents basement relief with constant-density prisms, and the data reflect gravitational anomalies from these prisms. Inverse problems are oft

Shijun Liao, Shijie Qin

Randomness is one of the most important characteristics of turbulence, but its origin remains an open question. By means of a ``thought experiment'' via several clean numerical experiments based on the Navier-Stokes equations for two-dimensional turbulent Kolmogorov flow, we reveal a new phenomenon, which we call the ``noise-expansion cascade'' whereby all m

Mingan Lin, Fan Yang, Yanjun Shen, Haoze Sun

We introduce Baichuan Alignment, a detailed analysis of the alignment techniques employed in the Baichuan series of models. This represents the industry's first comprehensive account of alignment methodologies, offering valuable insights for advancing AI research. We investigate the critical components that enhance model performance during the alignment proc

SangJong Lee, Jin-Kwang Kim, JunHo Kim, TaeHan Kim

In this study, we introduces a parameter-efficient model that outperforms traditional models in time series forecasting, by integrating High-order Polynomial Projection (HiPPO) theory into the Kolmogorov-Arnold network (KAN) framework. This HiPPO-KAN model achieves superior performance on long sequence data without increasing parameter count. Experimental re

Henry Adams, Armando Albornoz, Glenn Bruda, Jianda Du

The Hausdorff distance measures how far apart two sets are in a common metric space. By contrast, the Gromov-Hausdorff distance provides a notion of distance between two abstract metric spaces. How do these distances behave for quotients of spaces under group actions? Suppose a group $G$ acts by isometries on two metric spaces $X$ and $Y$. In this article, w

O. Verhamme, J. Sundqvist, A. de Koter, H. Sana

Context. Current implementations of mass loss for hot, massive stars in stellar evolution models include a sharp increase in mass loss when blue supergiants become cooler than Teff 20-22kK. This drastic mass-loss jump has been motivated by the potential presence of a so-called bistability ionisation effect, which may occur for line-driven winds in this tempe

Adam Lechowicz, Joshua Comden, Andrey Bernstein

As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid operators lack direct control over end-users' grid interactions, such as energy usage, but incentives can influence behavior

Danhua Song

We consider extended Cartan homotopy formula (ECHF) for higher gauge theory. Firstly, we construct an oriented simplex based on 2-connections and present differential and integral forms of the higher ECHF. Then, we study the higher Chern-Simons-Antoniadis-Savvidy (ChSAS) theory and prove that the higher ECHF can reproduce the higher Chern-Weil theorem and gi

Tianyi Xiang, Borui Li, Xin Pan, Quan Zhang

This paper has proposed an easily replicable and novel approach for developing a Digital Twin (DT) system for industrial robots in intelligent manufacturing applications. Our framework enables effective communication via Robot Web Service (RWS), while a real-time simulation is implemented in Unity 3D and Web-based Platform without any other 3rd party tools.

Y. Qiang Sun, Pedram Hassanzadeh, Mohsen Zand, Ashesh Chattopadhyay

Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather models and long-term climate emulators. An important open question is whether AI models can extrapolate from weaker weather events present in the training set to stronger, unseen weather extremes. To test thi

Shuning Zhang, Lyumanshan Ye, Xin Yi, Jingyu Tang

Memories, encompassing past inputs in context window and retrieval-augmented generation (RAG), frequently surface during human-LLM interactions, yet users are often unaware of their presence and the associated privacy risks. To address this, we propose MemoAnalyzer, a system for identifying, visualizing, and managing private information within memories. A se

Chaoyang Gong, Yu-Cheng Chen

Cellular membrane dynamics play an important role in a variety of physiological processes. However, due to the stringent light-coupling conditions required for exciting evanescent waves, label-free mapping of cellular membrane dynamics on curved substrates remains challenging. Here, we report surface-emitting resonator interference microscopy (SERIM), which

I. Luviano Soto, Y. Concha Sánchez, A. Raya

This study investigates the application of an artificial neural network framework for analysing water pollution caused by solids. Water pollution by suspended solids poses significant environmental and health risks. Traditional methods for assessing and predicting pollution levels are often time-consuming and resource-intensive. To address these challenges,

Tianyi Xiang, Borui Li, Quan Zhang, Mark Leach

This paper has proposed a Digital Twin (DT) framework for real-time motion and pose control of soft robotic grippers. The developed DT is based on an industrial robot workstation, integrated with our newly proposed approach for soft gripper control, primarily based on computer vision, for setting the driving pressure for desired gripper status in real-time.

Zijie Zhao, Roy E. Welsch

Automated equity trading requires converting noisy market and news signals into executable portfolio decisions under risk, turnover, and transaction costs. We propose Hierarchical Reinforced Trader (HRT), a bi-level reinforcement learning framework for text-aware portfolio management in multi-asset equity markets. HRT separates trading into two coordinated d

Zijie Zhao, Roy E. Welsch

Financial sentiment analysis is crucial for trading and investment decision-making. This study introduces an adaptive retrieval augmented framework for Large Language Models (LLMs) that aligns with human instructions through Instruction Tuning and incorporates market feedback to dynamically adjust weights across various knowledge sources within the Retrieval

Shuning Zhang, Shixuan Li

The rise of Generative Artificial Intelligence (G-AI) has transformed the creative arts landscape by producing novel artwork, whereas in the same time raising ethical concerns. While previous studies have addressed these concerns from technical and societal viewpoints, there is a lack of discussion from an HCI perspective, especially considering the communit

Urvashi Kishnani, Sanchari Das

The surge in website attacks, including Denial of Service (DoS), Cross-Site Scripting (XSS), and Clickjacking, underscores the critical need for robust HTTPS implementation-a practice that, alarmingly, remains inadequately adopted. Regarding this, we analyzed HTTP security headers across N=3,195 globally popular websites. Initially, we employed automated cat

Xiaohan Fu, Shuheng Li, Zihan Wang, Yihao Liu

Large Language Model (LLM) Agents are an emerging computing paradigm that blends generative machine learning with tools such as code interpreters, web browsing, email, and more generally, external resources. These agent-based systems represent an emerging shift in personal computing. We contribute to the security foundations of agent-based systems and surfac

Florian Saling, Andrea Emanuele Maria Casini, Andreas Treuer, Martial Costantini

The use of eXtended Reality (XR) technologies in the space domain has increased significantly over the past few years as it can offer many advantages when simulating complex and challenging environments. Space agencies are currently using these disruptive tools to train astronauts for Extravehicular Activities (EVAs), to test equipment and procedures, and to

V. A. Ivanov, V. I. Galkin, E. A. Bonvech, T. M. Roganova

The study of cosmic rays in the energy range from 1 to 1000 PeV is crucial for understanding their origins and propagation paths. As part of this research, a new SPHERE-3 installation is being developed, featuring enhanced light sensitivity and optical resolution, based on the experience gained with the balloon-borne SPHERE-2 installation. This report descri

Declan Kopper, Marina S. Leite

Thermophotovoltaics (TPVs) have the potential to exhibit higher power conversion efficiencies than traditional photovoltaics (PVs), with a broad range of applicability from waste recovery systems to aerospace solutions. They operate by preferentially radiating above bandgap photons via a high temperature optical emitter, whose spectrum is tuned through choic

Abhishek Yadav, David Wolpert

Thermodynamic cost of communication is a major factor in the thermodynamic cost of real-world computers, both biological and digital. Despite its importance, the fundamental principles underlying this cost remain poorly understood. This paper makes two major contributions to addressing this gap. First, we derive a universal relationship between information t

Mingyuan Zhou, Huangjie Zheng, Yi Gu, Zhendong Wang

Score identity Distillation (SiD) is a data-free method that has achieved SOTA performance in image generation by leveraging only a pretrained diffusion model, without requiring any training data. However, its ultimate performance is constrained by how accurate the pretrained model captures the true data scores at different stages of the diffusion process. I

Tucker Hartland, Cosmin G. Petra, Noemi Petra, Jingyi Wang

We present a scalable approach to solve a class of elliptic partial differential equation (PDE)-constrained optimization problems with bound constraints. This approach utilizes a robust full-space interior-point (IP)-Gauss-Newton optimization method. To cope with the poorly-conditioned IP-Gauss-Newton saddle-point linear systems that need to be solved, once

Tanya Tafolla, Stéphane Gaudreault, Mayya Tokman

High order exponential integrators require computing linear combination of exponential like $\varphi$-functions of large matrices $A$ times a vector $v$. Krylov projection methods are the most general and remain an efficient choice for computing the matrix-function-vector-product evaluation when the matrix is $A$ is large and unable to be explicitly stored,

Jasmine Jerry Aloor, Siddharth Nayak, Sydney Dolan, Hamsa Balakrishnan

Multi-agent systems are trained to maximize shared cost objectives, which typically reflect system-level efficiency. However, in the resource-constrained environments of mobility and transportation systems, efficiency may be achieved at the expense of fairness -- certain agents may incur significantly greater costs or lower rewards compared to others. Tasks

Chloe E. Shiff, Noah A. Rosenberg

Rooted binary perfect phylogenies provide a generalization of rooted binary unlabeled trees in which each leaf is assigned a positive integer value that corresponds in a biological setting to the count of the number of indistinguishable lineages associated with the leaf. For the rooted binary unlabeled trees, these integers equal 1. We address a variety of e

Qi Zhou

Dark states, which are incapable of absorbing and emitting light, have been widely applied in multiple disciplines of physics. However, the existence of dark states relies on certain strict constraints on the system. For instance, in the fundamental {\Lambda} system, a perturbation breaking the degeneracy between two energy levels may destroy the destructive

Chandrakanth Gudavalli, Bowen Zhang, Connor Levenson, Kin Gwn Lore

In this paper, we present ReeFRAME, a scalable Reeb graph-based framework designed to analyze vast volumes of GPS-enabled human trajectory data generated at 1Hz frequency. ReeFRAME models Patterns-of-life (PoL) at both the population and individual levels, utilizing Multi-Agent Reeb Graphs (MARGs) for population-level patterns and Temporal Reeb Graphs (TERGs

Abdellah Touati

In this paper, we investigate the elastic scattering of an electron by a Yukawa potential within the framework of non-commutative (NC) geometry. We first derive the NC correction to the Yukawa potential at leading order in the NC parameter, resulting in a modified potential resembling a screened Kratzer potential. This potential reduces to the standard Kratz

Yusuke Sakai, Shinya Yamada, Toshiki Sato, Ryota Hayakawa

Decadal changes in a nearby supernova remnant (SNR) were analyzed using a multiepoch maximum likelihood estimation (MLE) approach. To achieve greater accuracy in capturing the dynamics of SNRs, kinematic features and point-spread function effects were integrated into the MLE framework. Using Cassiopeia A as a representative example, data obtained by the Chan

Sunwoo Kim, Joungho Park, Jay H. Lee

In the global pursuit of sustainable energy solutions, mitigating carbon dioxide (CO2) emissions stands as a pivotal challenge. With escalating atmospheric CO2 levels, the imperative of direct air capture (DAC) systems becomes evident. Simultaneously, green hydrogen (GH) emerges as a pivotal medium for renewable energy. Nevertheless, the substantial expenses

Minhua Cheng, Carlos Ospina, Kurt Vinhage, Yibo Zhai

We define variational properties for dynamical systems with subexponential complexity, and study these properties in certain specific examples. By computing the value of slow entropy directly, we show that some subshifts are not variational, while a class of interval exchange transformations are variational.

Curtis T. Asplund, Elisa Panciu

We define predictive states and predictive complexity for quantum systems composed of distinct subsystems. This complexity is a generalization of entanglement entropy. It is inspired by the statistical or forecasting complexity of predictive state analysis of stochastic and complex systems theory, but is intrinsically quantum. Predictive states of a subsyste

Xiuqiang He, Josué Duarte, Verena Häberle, Florian Dörfler

This article explores a flexible and coordinated control design for an aggregation of heterogeneous distributed energy resources (DERs) in a dynamic virtual power plant (DVPP). The control design aims to provide a desired aggregate grid-forming (GFM) response based on the coordination of power contributions between different DERs. Compared to existing DVPP d

Yuhan Liang, Yijun Li, Yumeng Niu, Qianhe Shen

The robustness of Vision-Language Models (VLMs) such as CLIP is critical for their deployment in safety-critical applications like autonomous driving, healthcare diagnostics, and security systems, where accurate interpretation of visual and textual data is essential. However, these models are highly susceptible to adversarial attacks, which can severely comp

Carlos Carvalho, Alberto Abad

Self-supervised learning (SSL) leverages large amounts of unlabelled data to learn rich speech representations, fostering improvements in automatic speech recognition (ASR), even when only a small amount of labelled data is available for fine-tuning. Despite the advances in SSL, a significant challenge remains when the data used for pre-training (source doma

Suyoung Kim, Obinna P. Uzoh, Eundeok Mun

We report thermodynamic and transport properties of LaCu$_{x}$Sb$_{2}$ ($0.92 \leq x \leq 1.12$), synthesized by controlling the initial loading composition and investigated by magnetization, electrical resistivity, and specific heat measurements. The physical properties of this system are highly dependent on Cu-site occupancy $x$, where residual resistivity

Florin Panaite

Given two associative algebras A, C and a linear space V together with some linear maps R_1, R_2, R_3, E satisfying some conditions, we define an associative algebra structure on A\otimes V\otimes C called a two-sided crossed product. Particular cases of this construction are the iterated twisted tensor product of algebras and the two-sided crossed product o

Patrick Salter, Celina Wilkerson, Qiuhua Huang, Paulo Cesar Tabares-Velasco

High penetration of distributed energy resources in distribution systems, such as rooftop solar PVs, has caused voltage fluctuations which are much faster than typical voltage control devices can react to, leading to increased operation cost and reduced equipment life. Residential buildings consume about 35% of the electricity in U.S. and are co-located with

Eleftherios Ioannidis, Yannick Zakowski, Steve Zdancewic, Sebastian Angel

Mechanized verification of liveness properties for infinite programs with effects and nondeterminism is challenging. Existing temporal reasoning frameworks operate at the level of models such as traces and automata. Reasoning happens at a very low-level, requiring complex nested (co-)inductive proof techniques and familiarity with proof assistant mechanics (

Jonah Blasiak, Henry Cohn, Joshua A. Grochow, Kevin Pratt

The Cohn-Umans (FOCS '03) group-theoretic framework for matrix multiplication produces fast matrix multiplication algorithms from three subsets of a finite group $G$ satisfying a simple combinatorial condition (the Triple Product Property). The complexity of such an algorithm then depends on the representation theory of $G$. In this paper we extend the group

Yifan Wu, Ramesh Johari, Vasilis Syrgkanis, Gabriel Y. Weintraub

We consider a retailer running a switchback experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price $p$ from a set of predefined prices that consist of a reference price and a few discounted price levels. The goal is to estimate the demand gradient at the reference price point, with the goal of adjusting

Alexei A. Mailybaev

In this paper, we study the stability and bifurcations of spontaneous stochasticity using an approach reminiscent of the Feigenbaum renormalization group (RG). We consider dynamical models on a self-similar space-time lattice as toy models for multiscale motion in hydrodynamic turbulence. Here an ill-posed ideal system is regularized at small scales and the

Dong-Hyun Jung, Hongjae Nam, Junil Choi, David J. Love

As the number of low Earth orbit (LEO) satellites rapidly increases, the consideration of frequency sharing or cooperation between geosynchronous Earth orbit (GEO) and LEO satellites is gaining attention. In this paper, we consider a hybrid GEO-LEO satellite network where GEO and LEO satellites are distributed according to independent Poisson point processes

Joakim Blikstad, Ta-Wei Tu

Given two matroids $\mathcal{M}_1$ and $\mathcal{M}_2$ over the same $n$-element ground set, the matroid intersection problem is to find a largest common independent set, whose size we denote by $r$. We present a simple and generic auction algorithm that reduces $(1-\varepsilon)$-approximate matroid intersection to roughly $1/\varepsilon^2$ rounds of the eas

Chengze Ye, Linda-Sophie Schneider, Yipeng Sun, Mareike Thies

This paper introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits using a differentiable shift-variant filtered backprojection (FBP) neural network. Traditional CBCT reconstruction methods for arbitrary orbits, like iterative reconstruction algorithms, are computationally expensive and memory-intensive.

Zhongze Cai, Hansheng Jiang, Xiaocheng Li

In this paper, we consider the contextual robust optimization problem under an out-of-distribution setting. The contextual robust optimization problem considers a risk-sensitive objective function for an optimization problem with the presence of a context vector (also known as covariates or side information) capturing related information. While the existing

Devlina Chakravarty, Myeongsang Lee, Lauren L. Porter

In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predic

Xinli Guo

In this study, we leverage state-of-the-art Natural Language Processing (NLP) techniques to perform sentiment analysis on Amazon product reviews. By employing transformer-based models, RoBERTa, we analyze a vast dataset to derive sentiment scores that accurately reflect the emotional tones of the reviews. We provide an in-depth explanation of the underlying

Melissa Roemmele, Andrew S. Gordon

LLMs can now perform a variety of complex writing tasks. They also excel in answering questions pertaining to natural language inference and commonsense reasoning. Composing these questions is itself a skilled writing task, so in this paper we consider LLMs as authors of commonsense assessment items. We prompt LLMs to generate items in the style of a promine

Sabina Sagynbayeva, Rixin Li, Aleksandra Kuznetsova, Zhaohuan Zhu

We investigate how the formation and structure of circumplanetary disks (CPDs) varies with planet mass and protoplanetary disk aspect ratio. Using static mesh refinement and a near-isothermal equation of state, we perform a small parameter survey of hydrodynamic simulations with parameters appropriate for disk-embedded protoplanets at moderate to large orbit

Sangyun Lee, Yilun Xu, Tomas Geffner, Giulia Fanti

Consistency models have recently been introduced to accelerate sampling from diffusion models by directly predicting the solution (i.e., data) of the probability flow ODE (PF ODE) from initial noise. However, the training of consistency models requires learning to map all intermediate points along PF ODE trajectories to their corresponding endpoints. This ta

Zelei Cheng, Xian Wu, Jiahao Yu, Shuo Han

Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations for training an effective toxicity classifier. Additionally, the standard approach to training a classifier u

Remus Floricel, Peter Wadel

This paper investigates the structure of product systems of Hilbert spaces derived from Banach space-valued L\'evy processes. We establish conditions under which these product systems are completely spatial and show that Gaussian L\'evy processes with non-degenerate covariance always give rise to product systems of type I. Furthermore, we construct a continu

Kaustav Chatterjee, Ramij Raja Hossain, Sai Pushpak Nandanoori, Soumya Kundu

The paper discusses fast frequency control in bulk power systems using embedded networks of grid-forming energy storage resources. Differing from their traditional roles of regulating reserves, the storage resources in this work operate as fast-acting grid assets shaping transient dynamics. The storage resources in the network are autonomously controlled usi

Dripto M. Debroy, Matt McEwen, Craig Gidney, Noah Shutty

Recently, usage of detecting regions facilitated the discovery of new circuits for fault-tolerantly implementing the surface code. Building on these ideas, we present LUCI, a framework for constructing fault-tolerant circuits flexible enough to construct aperiodic and anisotropic circuits, making it a clear step towards quantum error correction beyond static

Yunhao Yang, Leonard Berthellemy, Ufuk Topcu

We develop a method that integrates the tree of thoughts and multi-agent framework to enhance the capability of pre-trained language models in solving complex, unfamiliar games. The method decomposes game-solving into four incremental tasks -- game summarization, area selection, action extraction, and action validation -- each assigned to a specific language

Kartik G. Waghmare, Victor M. Panaretos

We consider the problem of characterizing extreme points of the convex set of positive linear operators on a possibly infinite-dimensional Hilbert space under linear constraints. We show that even perturbations of points in such sets admit what resembles a Douglas factorization. Using this result, we prove that an operator is extreme iff a corresponding set

Christopher R. Serrano, Jonathan Gallagher, Kenji Yamada, Alexei Kopylov

The boolean satisfiability (SAT) problem asks whether there exists an assignment of boolean values to the variables of an arbitrary boolean formula making the formula evaluate to True. It is well-known that all NP-problems can be coded as SAT problems and therefore SAT is important both practically and theoretically. From both of these perspectives, better u

Javier Feijóo, Aníbal Iucci, Alejandro M. Lobos

We theoretically propose a one-dimensional electronic nanodevice inspired in recently fabricated semiconductor-superconductor-ferromagnetic insulator (SE-SC-FMI) hybrid heterostructures, and investigate its zero-temperature transport properties. While previous related studies have primarily focused on the potential for generating topological superconductors

Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen

Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However, existing GAD methods are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This largely limits their applicability in real-world scenari

Qiran Dong, Paul Grigas, Vishal Gupta

Many applications require minimizing a family of optimization problems indexed by some hyperparameter $\lambda \in \Lambda$ to obtain an entire solution path. Traditional approaches proceed by discretizing $\Lambda$ and solving a series of optimization problems. We propose an alternative approach that parameterizes the solution path with a set of basis funct

Vitalijs Brejevs, Feride Ceren Kose

We define the symmetric braid index $b_s(K)$ of a ribbon knot $K$ to be the smallest index of a braid whose closure yields a symmetric union diagram of $K$, and derive a Khovanov-homological characterisation of knots with $b_s(K)$ at most three. As applications, we show that there exist knots whose symmetric braid index is strictly greater than the braid ind

María Jesús Lopez-Herrero, Diana Taipe

This paper deals with the long-term behaviour and incidence of a vaccine-preventable contact disease, under the assumption that both vaccine protection and immunity after recovery are not lifelong. The mathematical model is developed in a stochastic markovian framework. The evolution of the disease in a finite population is thus represented by a three-dimens

Zihan Wang, Daniel W. Yang, Zerui Liu, Evan Yan

This study presents the first implementation of multilayer neural networks on a memristor/CMOS integrated system on chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative AI techniques are used to enhance the dataset, improving the classifier's robustness and diversity. The system achieves notable performan

Jianfa Chen, Emily Shen, Trupti Bavalatti, Xiaowen Lin

Robust content moderation classifiers are essential for the safety of Generative AI systems. In this task, differences between safe and unsafe inputs are often extremely subtle, making it difficult for classifiers (and indeed, even humans) to properly distinguish violating vs. benign samples without context or explanation. Scaling risk discovery and mitigati

Elise Askelsen, Ryan Kinser

We classify the (filtered) Hopf actions of Hopf-Ore extensions of group algebras on path algebras of quivers, extending results in several other works from special cases to this general setting. Having done this for general Hopf-Ore extensions of group algebras, we demonstrate application by specializing our main result to certain Hopf-Ore extensions includi

Jiachen Li, Xiwen Li, Justin Steinberg, Akshat Choube

Passive tracking methods, such as phone and wearable sensing, have become dominant in monitoring human behaviors in modern ubiquitous computing studies. While there have been significant advances in machine-learning approaches to translate periods of raw sensor data to model momentary behaviors, (e.g., physical activity recognition), there still remains a si

Annika Mütze, Natalie Grabowsky, Edgar Heinert, Matthias Rottmann

Recent research has investigated the shape and texture biases of pre-trained deep neural networks (DNNs) in image classification. Those works test how much a trained DNN relies on specific image cues like texture. The present study shifts the focus to understanding the cue influence during training, analyzing what DNNs can learn from shape, texture, and colo

Ramij Raja Hossain, Kaustav Chatterjee, Sai Pushpak Nandanoori, Soumya Kundu

Inverter-based storages are poised to play a prominent role in future power grids with massive renewable generation. Grid-forming inverters (GFMs) are emerging as a dominant technology with synchronous generators (SG)-like characteristics through primary control loops. Advanced secondary control schemes, e.g., consensus algorithms, allow GFM-interfaced stora

Nikita Polyanskii, Sebastian Muller, Mayank Raikwar

This paper introduces Slipstream, a Byzantine Fault Tolerance (BFT) protocol where nodes concurrently propose blocks to be added to a Directed Acyclic Graph (DAG) and aim to agree on block ordering. Slipstream offers two types of block orderings: an optimistic ordering, which is live and secure in a sleepy model under up to 50% Byzantine nodes, and a final o

Shantanu Thorat, Tianbao Yang

As LLMs increase in accessibility, LLM-generated texts have proliferated across several fields, such as scientific, academic, and creative writing. However, LLMs are not created equally; they may have different architectures and training datasets. Thus, some LLMs may be more challenging to detect than others. Using two datasets spanning four total writing do

Tianxiao Zhang, Bo Luo, Guanghui Wang

Multi-Head Self-Attention (MHSA) is the cornerstone of Vision Transformers, allowing models to capture diverse feature representations by projecting tokens into independent subspaces. However, the standard MHSA strictly isolates these heads, preventing any information exchange during the attention computation itself. In this paper, we propose Multi-Overlappe

Alexander J. Pattison, Stephanie M. Ribet, Marcus M. Noack, Georgios Varnavides

Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy. Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments. Here, we present an automated method of correcting first- and second-order

Evan Frick, Tianle Li, Connor Chen, Wei-Lin Chiang

We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback). The gold-standard approach is to run a full RLHF training pipeline and directly probe downstream LLM performance. However, this process is prohibitively expensive. To address this, we build

Sung Jae Jun, Sokbae Lee

We develop a difference-in-differences framework to measure the persuasive impact of informational treatments on behavior in staggered treatment settings. We introduce two causal parameters, the forward and backward average persuasion rates on the treated, which refine the average treatment effect on the treated. The forward rate excludes cases of "preac

Elliot Blackstone, Louise Gassot, Patrick Gérard, Peter D. Miller

We show that the initial-value problem for the Benjamin-Ono equation on $\mathbb{R}$ with $L^2(\mathbb{R})$ rational initial data with only simple poles can be solved in closed form via a determinant formula involving contour integrals. The dimension of the determinant depends on the number of simple poles of the rational initial data only and the matrix ele

Emre Işık, Sami K. Solanki, Robert H. Cameron, Alexander I. Shapiro

Besides a dense coverage of their high latitudes by starspots, rapidly rotating cool stars also display low-latitude spots in Doppler images, although generally with a lower coverage. In contrast, flux emergence models of fast-rotating stars predict strong poleward deflection of radially rising magnetic flux as the Coriolis effect dominates over buoyancy, le