October 2024 arXiv papers — page 78

Zixuan Huang, Yinong He, Yating Lin, Dmitry Berenson

Long-horizon contact-rich manipulation has long been a challenging problem, as it requires reasoning over both discrete contact modes and continuous object motion. We introduce Implicit Contact Diffuser (ICD), a diffusion-based model that generates a sequence of neural descriptors that specify a series of contact relationships between the object and the envi

Marco A. Muñoz-Gutiérrez, Antonio Peimbert, Angeles Pérez-Villegas

The long-term evolution of the outer Solar System is subject to the influence of the giant planets, however, perturbations from other massive bodies located in the region imprint secular signatures, that are discernible in long-term simulations. In this work, we performed an in-depth analysis of the evolution of massive objects Eris, 2015 KH$_{162}$, Pluto,

Pawissanutt Lertpongrujikorn, Hai Duc Nguyen, Mohsen Amini Salehi

Current Serverless abstractions (e.g., FaaS) poorly support non-functional requirements (e.g., QoS and constraints), are provider-dependent, and are incompatible with other cloud abstractions (e.g., databases). As a result, application developers have to undergo numerous rounds of development and manual deployment refinements to finally achieve their desired

S. k. Tahmid Shahriar, Chris Feltman, Sean Machler, Nabila Tanjeem

We investigate two-dimensional crystal assemblies formed by a binary mixture of colloidal particles with a size ratio of 0.88 and driven by short-ranged depletion interactions. Our experiments show that the orientational order of the assembly decreases with an increasing fraction of impurity particles, reaching up to 18% reduction in a 1:1 binary mixture com

Seyed Navid Elyasi, Matteo A. C. Rossi, Marco G. Genoni

The possibility of extracting more work from a physical system thanks to the information obtained from measurements has been a topic of fundamental interest in the context of thermodynamics since the formulation of the Maxwell's demon thought experiment. We here consider this problem from the perspective of an open quantum battery interacting with an environ

Yukun Zhang, Qi Dong

This paper investigates the trade-off between short-term revenue generation and long-term social welfare optimization in online food delivery platforms. We first develop a static model that captures the equilibrium interactions among restaurants, consumers, and delivery workers, using Gross Merchandise Value (GMV) as a proxy for immediate performance. Buildi

Marcel Roth, Micha V. Nowak, Adrian Krenzer, Frank Puppe

Video capsule endoscopy has transformed gastrointestinal endoscopy (GIE) diagnostics by offering a non-invasive method for capturing detailed images of the gastrointestinal tract, enabling early disease detection. However, its potential is limited by the sheer volume of images generated during the imaging procedure, which can take anywhere from 6-8 hours and

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occ

Hiroshi Ishimoto

In an earlier paper of W. Casselman, the theory of local newforms and conductors was initiated. Later, Roberts and Schmidt studied local newforms for the metaplectic group of rank 1. In this paper we define and calculate conductors of irreducible genuine representations of the metaplectic group of rank 1 over non-archimedean local field of characteristic zer

Carl von Havighorst, Vincil Bishop

This research presents a novel approach to predicting option movements by analyzing residual transactions, which are trades that deviate from standard hedging activities. Unlike traditional methods that primarily focus on open interest and trading volume, this study argues that residuals can reveal nuanced insights into institutional sentiment and strategic

Wm. Matthew Kennedy, Daniel Vargas Campos

Operationalizing AI ethics and safety principles and frameworks is essential to realizing the potential benefits and mitigating potential harms caused by AI systems. To that end, actors across industry, academia, and regulatory bodies have created formal taxonomies of harm to support operationalization efforts. These include novel holistic methods that go be

Tao Sun, Xinwang Liu, Kun Yuan

Gradient clipping has long been considered essential for ensuring the convergence of Stochastic Gradient Descent (SGD) in the presence of heavy-tailed gradient noise. In this paper, we revisit this belief and explore whether gradient normalization can serve as an effective alternative or complement. We prove that, under individual smoothness assumptions, gra

Nikita Haduong, Noah A. Smith

Many domains now employ AI-based decision-making aids, and although the potential for AI systems to assist with decision making is much discussed, human-AI collaboration often underperforms due to factors such as (mis)trust in the AI system and beliefs about AI being incapable of completing subjective tasks. One potential tool for influencing human decision

Hui Hu, Jia Wang, Han Pu, Xia-Ji Liu

In his seminal proposal of quantum droplets in binary Bose mixtures {[}Phys. Rev. Lett. \textbf{115}, 155302 (2015){]}, Dmitry Petrov suggested that the density ratio $n_{2}/n_{1}$ of the two bosonic components are locked to an optimal value, which is given by the square root of the ratio of the two intra-species scattering lengths, i.e., $\sqrt{a_{11}/a_{22

Daniel Gonzalez-Cuadra, Majd Hamdan, Torsten V. Zache, Boris Braverman

Lattice gauge theories (LGTs) describe a broad range of phenomena in condensed matter and particle physics. A prominent example is confinement, responsible for bounding quarks inside hadrons such as protons or neutrons. When quark-antiquark pairs are separated, the energy stored in the string of gluon fields connecting them grows linearly with their distance

Zineb Felfli, Zoe Landers, Alfred Z. Msezane

The structure and dynamics of the actinide atoms from U to Lr is probed through the electron elastic scattering total cross sections (TCSs) calculated using Regge pole analysis. The crucial Regge trajectories probe electron attachment at the fundamental level near threshold leading to stable ground, metastable and excited negative-ion formation as resonances

Edgar Dolores-Tesillos, Noelia Otero, Sam Allen

Renewable energy is becoming an increasingly important component of energy systems. However, renewable energy production is heavily dependent on the prevailing weather conditions, which are changing as a result of climate change. It is therefore necessary to build energy systems that are robust to energy shortages caused by weather-dependent changes to energ

Zihao Yang, Hui Tian, Steven Tomczyk, Xianyu Liu

The magnetic field in the Sun's corona stores energy that can be released to heat the coronal plasma and drive solar eruptions. Measurements of the global coronal magnetic field have been limited to a few snapshots. We present observations using the Upgraded Coronal Multi-channel Polarimeter, which provided 114 magnetograms of the global corona above the sol

Marco Avella-Medina, Alberto González-Sanz

Recent work has used optimal transport ideas to generalize the notion of (center-outward) quantiles to dimension $d\geq 2$. We study the robustness properties of these transport-based quantiles by deriving their breakdown point, roughly, the smallest amount of contamination required to make these quantiles take arbitrarily aberrant values. We prove that the

Arnur Nigmetov, Dmitriy Morozov

Persistent (co)homology is a central construction in topological data analysis, where it is used to quantify prominence of features in data to produce stable descriptors suitable for downstream analysis. Persistence is challenging to compute in parallel because it relies on global connectivity of the data. We propose a new algorithm to compute persistent coh

Anibal Velozo

In this article, we study the pressure at infinity of potentials defined over countable Markov shifts. We establish an upper semi-continuity result concerning the limiting behaviour of the pressure of invariant probability measures, where the escape of mass is controlled by the pressure at infinity. As a consequence, we establish criteria for the existence o

Charles Beichman, Geoffrey Bryden, Jorge Llop-Sayson, Marie Ygouf

The most prominent of the IRAS debris disk systems, $\alpha$ Lyrae (Vega), at a distance of 7.7 pc, has been observed by both the NIRCam and MIRI instruments on the James Webb Space Telescope (JWST). This paper describes NIRCam coronagraphic observations which have achieved F444W contrast levels of 3$\times10^{-7}$ at 1\arcsec\ (7.7 au), 1$\times10^{-7}$ at

Sudheesh Surendranath, Li-Cheng Tsai

We extend the construction of the semigroup of the two-dimensional delta-Bose gas in Gu, Quastel, and Tsai (2021) (based on Rajeev (1999) and Dimock and Rajeev (2004)) to a weighted $L^2$ space that allows exponentially growing functions. We further show that the semigroup of the mollified delta-Bose gas converges strongly to that of the delta-Bose gas.

Carmelo Rosales-Guzmán, Valeria Rodriguez-Fajardo

For the past few decades, structured light has been gaining popularity across various research fields. Its fascinating properties have been exploited for both previously unforeseen and established applications from new perspectives. Crucial to this is the several techniques that have been proposed for both their generation and characterisation. On one hand,

James P. Bailey

We provide a complete characterization for uniqueness of equilibria in unconstrained polymatrix games. We show that while uniqueness is natural for coordination and general polymatrix games, zero-sum games require that the dimension of the combined strategy space is even. Therefore, non-uniqueness is common in zero-sum polymatrix games. In addition, we study

Mohi Reza, Ioannis Anastasopoulos, Shreya Bhandari, Zachary A. Pardos

Involving subject matter experts in prompt engineering can guide LLM outputs toward more helpful, accurate, and tailored content that meets the diverse needs of different domains. However, iterating towards effective prompts can be challenging without adequate interface support for systematic experimentation within specific task contexts. In this work, we in

Usman Akram, Haris Vikalo

Predicting the behavior of a dynamical system from noisy observations of its past outputs is a classical problem encountered across engineering and science. For linear systems with Gaussian inputs, the Kalman filter -- the best linear minimum mean-square error estimator of the state trajectory -- is optimal in the Bayesian sense. For nonlinear systems, Bayes

Zhongchen Deng, Zhechen Yang, Chi Chen, Cheng Zeng

Plane instance segmentation from RGB-D data is a crucial research topic for many downstream tasks. However, most existing deep-learning-based methods utilize only information within the RGB bands, neglecting the important role of the depth band in plane instance segmentation. Based on EfficientSAM, a fast version of SAM, we propose a plane instance segmentat

Warren L. Davis, Max Carlson, Irina Tezaur, Diana Bull

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure

Jingwei Huang, Kuroush Nezafati, Ismael Villanueva-Miranda, Zifan Gu

This study introduces a LLMs powered multiagent ensemble method to address challenges in hallucination and data labeling, particularly in large-scale EHR datasets. Manual labeling of such datasets requires domain expertise and is labor-intensive, time-consuming, expensive, and error-prone. To overcome this bottleneck, we developed an ensemble LLMs method and

Jiachen Yao, Mayank Goswami, Chao Chen

A prevalent assumption regarding real-world data is that it lies on or close to a low-dimensional manifold. When deploying a neural network on data manifolds, the required size, i.e., the number of neurons of the network, heavily depends on the intricacy of the underlying latent manifold. While significant advancements have been made in understanding the geo

Ignatios Antoniadis, Dimitri V. Nanopoulos, Keith A. Olive

Based on a previously derived superstring model possessing a cosmological sector that mimics Starobinsky inflation, we analyze several questions addressed in the recent literature: the generation of an effective $R^2$-term, the stability of the sgoldstino , the modular symmetry of the inflaton potential and the large distance swampland conjecture. We first s

Yingqian Cui, Pengfei He, Xianfeng Tang, Qi He

Few-shot Chain-of-Thought (CoT) prompting has demonstrated strong performance in improving the reasoning capabilities of large language models (LLMs). While theoretical investigations have been conducted to understand CoT, the underlying transformer used in these studies isolates the CoT reasoning process into separated in-context learning steps (Stepwise IC

M. I. Desai, J. F. Drake, T. Phan, Z. Yin

We report observations of direct evidence of energetic protons being accelerated above ~400 keV within the reconnection exhaust of a heliospheric current sheet (HCS) crossing by NASA's Parker Solar Probe (PSP) at a distance of ~16.25 solar radii (Rs) from the Sun. Inside the extended exhaust, both the reconnection-generated plasma jets and the accelerated pr

Joshua T. S. Hewson

As artificial intelligence (AI) becomes deeply integrated into critical infrastructures and everyday life, ensuring its safe deployment is one of humanity's most urgent challenges. Current AI models prioritize task optimization over safety, leading to risks of unintended harm. These risks are difficult to address due to the competing interests of governments

R. J. Bueno Rogerio, G. B. de Gracia

In this work, we analyze the possibilities of certain gauge transformations regarding some specific spinorial dual structures. To this end, we define a general structure, which can be expressed in terms of discrete symmetry operators associated with parity, charge conjugation, and time reversal. To this end, we consider the demand of tensor covariance of the

Katie Fankhauser, Evan Thomas, Zia Mehrabi

High-resolution satellite-based crop yield mapping offers enormous promise for monitoring progress towards the SDGs. Across 15,000 villages in Rwanda we uncover areas that are on and off track to double productivity by 2030. This machine learning enabled analysis is used to design spatially explicit productivity targets that, if met, would simultaneously ens

John M. Willis

The impressive performance of deep learning models, particularly Convolutional Neural Networks (CNNs), is often hindered by their lack of interpretability, rendering them "black boxes." This opacity raises concerns in critical areas like healthcare, finance, and autonomous systems, where trust and accountability are crucial. This paper introduces the QIXAI F

Takeshi Fukuyama

The formation of supermassive black holes (SMBHs) at high red shift $z$ by ultralight axion dark matter (DM) is discussed in the general relativistic framework. The critical condition of collapsing of self-attracting DM in non-relativistic treatment corresponds to dust particle, which allows to estimate the time scale of the SMBH formation and the its length

Alvin Y. Sukmadji, Frank R. Kschischang

Performance-complexity-latency trade-off curves for rate-0.88 concatenated outer Reed--Solomon codes and inner Chase-algorithm-based soft-decision Bose--Ray-Chaudhuri--Hocquenghem codes with PAM4 constellation using bit-interleaved coded modulation and multilevel coding coded modulation schemes over the AWGN channel are presented.

Giulia DeSalvo, Jean-Fracois Kagy, Lazaros Karydas, Afshin Rostamizadeh

We present a novel framework, SoftSRV, that is used to generate targeted synthetic fine-tuning data for improving task-specific model performance. Given a sample from a target distribution, our proposed framework uses a data-driven loss minimization approach to steer a frozen large language model (LLM) to generate synthetic sequences that are similar to thos

Saif Punjwani, Larry Heck

As virtual agents become increasingly prevalent in human-computer interaction, generating realistic and contextually appropriate gestures in real-time remains a significant challenge. While neural rendering techniques have made substantial progress with static scripts, their applicability to human-computer interactions remains limited. To address this, we in

Álvaro Perales-Eceiza, Toby Cubitt, Mile Gu, David Pérez-García

The study of undecidability in problems arising from physics has experienced a renewed interest, mainly in connection with quantum information problems. The goal of this review is to survey this recent development. After a historical introduction, we first explain the necessary results about undecidability in mathematics and computer science. Then we briefly

Aryaman Arora, Dan Jurafsky, Christopher Potts, Noah D. Goodman

In-context learning (ICL) is a powerful technique for getting language models to perform complex tasks with no training updates. Prior work has established strong correlations between the number of in-context examples provided and the accuracy of the model's predictions. In this paper, we seek to explain this correlation by showing that ICL approximates a Ba

Luis Chacon, Guangye Chen

We consider the issue of strict, fully discrete \emph{local} energy conservation for a whole class of fully implicit local-charge- and global-energy-conserving particle-in-cell (PIC) algorithms. Earlier studies demonstrated these algorithms feature strict global energy conservation. However, whether a local energy conservation theorem exists (in which the lo

Joshua T. S. Hewson

Artificial Intelligence systems are rapidly evolving, integrating extrinsic and intrinsic motivations. While these frameworks offer benefits, they risk misalignment at the algorithmic level while appearing superficially aligned with human values. In this paper, we argue that an intrinsic motivation for kindness is crucial for making sure these models are int

Hariprasauth Ramamoorthy, Shubhankar Gupta, Suresh Sundaram

Trust and Reputation Assessment of service providers in citizen-focused environments like e-commerce is vital to maintain the integrity of the interactions among agents. The goals and objectives of both the service provider and service consumer agents are relevant to the goals of the respective citizens (end users). The provider agents often pursue selfish g

Siva Viknesh, Younes Tatari, Chase Christenson, Amirhossein Arzani

Identifying dynamical systems characterized by nonlinear parameters presents significant challenges in deriving mathematical models that enhance understanding of physics. Traditional methods, such as Sparse Identification of Nonlinear Dynamics (SINDy) and symbolic regression, can extract governing equations from observational data; however, they also come wi

Jonathan Brokman, Omer Hofman, Oren Rachmil, Inderjeet Singh

This report presents a comparative analysis of open-source vulnerability scanners for conversational large language models (LLMs). As LLMs become integral to various applications, they also present potential attack surfaces, exposed to security risks such as information leakage and jailbreak attacks. Our study evaluates prominent scanners - Garak, Giskard, P

Osman Doğan, Raffaele Mattera, Philipp Otto, Süleyman Taşpınar

We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility-specific observed and latent factors. The model offers a more general network interpretation, making it applicable for studying various types of network spillovers. The prim

Atsuro Arai, Seiru Miyagawa, Koretaka Yuge

For classical discrete systems under constant composition, canonical average provides equilibrium configuration from a set of many-body interactions, which typically acts as nonlinear map. The nonlinearity has recently been investigated in terms of configurational geometry, where two measures for the nonlinearity as vector field on configuration space and di

Wei Xie

This study introduces a novel supervised learning approach for spiking neural networks that does not rely on traditional backpropagation. Instead, it employs spike-timing-dependent plasticity (STDP) within a supervised framework for image recognition tasks. The effectiveness of this method is demonstrated using the MNIST dataset. The model achieves approxima

Jan Spörer, Bernhard Bermeitinger, Tomas Hrycej, Niklas Limacher

In training neural networks, it is common practice to use partial gradients computed over batches, mostly very small subsets of the training set. This approach is motivated by the argument that such a partial gradient is close to the true one, with precision growing only with the square root of the batch size. A theoretical justification is with the help of

Jooyun Woo, Kaya Mori, Charles J. Hailey, Elizabeth Spira-Savett

Young supernova remnants (SNRs) are believed to be the origin of energetic cosmic rays (CRs) below the "knee" of their spectrum at $\sim3$ petaelectronvolt (PeV, $10^{15}$ eV). Nevertheless, the precise location, duration, and operation of CR acceleration in young SNRs are open questions. Here, we report on multi-epoch X-ray observations of Cassiopeia A (Cas

Jordan Benson

We determine the $\tau^n$-torsion in the first 5-lines of the $E_2$ page of the $\mathbb{C}$-motivic Adams spectral sequence using the techniques of Burklund-Xu. In particular, every element in this range is either $\tau^1$-torsion or $\tau$-free. We also show that $\tau^n$-torsion elements can appear only in Adams filtration at least $2n+2$ and give further

Naba Rizvi, Harper Strickland, Daniel Gitelman, Tristan Cooper

As our understanding of autism and ableism continues to increase, so does our understanding of ableist language towards autistic people. Such language poses a significant challenge in NLP research due to its subtle and context-dependent nature. Yet, detecting anti-autistic ableist language remains underexplored, with existing NLP tools often failing to captu

Praneeth Kumar T, Nidhi Srivastava, Rakshith Mahishi, Chayadevi M L

In the current technological era, the medical profession has emerged as one of the researchers' favorite subject areas, and cancer is one of them. Because there is now no effective treatment for this illness, it is a matter of concern. Only if this disease is discovered early may patients be rescued (stage I and stage II). The likelihood of survival is quite

Davide Tebaldi, Roberto Zanasi

The applications of the partial fraction decomposition in control and systems engineering are several. In this letter, we propose a new interpretation of residues in the partial fraction decomposition, which is employed for the following purposes: to address the pole sensitivity problem, namely to study the speed of variation of the system poles when the con

Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-Wong

Deep Reinforcement Learning (DRL) algorithms have achieved great success in solving many challenging tasks while their black-box nature hinders interpretability and real-world applicability, making it difficult for human experts to interpret and understand DRL policies. Existing works on interpretable reinforcement learning have shown promise in extracting d

Kairan Zhao, Peter Triantafillou

Machine unlearning (MUL) focuses on removing the influence of specific subsets of data (such as noisy, poisoned, or privacy-sensitive data) from pretrained models. MUL methods typically rely on specialized forms of fine-tuning. Recent research has shown that data memorization is a key characteristic defining the difficulty of MUL. As a result, novel memoriza

Barry McKernan, K. E. Saavik Ford, Harrison E. Cook, Vera Delfavero

Active galactic nuclei (AGN) are a promising source of the binary black hole (BBH) mergers observed in gravitational waves with LIGO-Virgo-Kagra (LVK). Constraining the AGN channel allows us to limit AGN parameter space (disk density, size, average lifetime) and nuclear star cluster (NSC) parameter space. Constraints on AGN and NSCs have implications for $\L

M. Cristina Câmara, Gabriel Lopes Cardoso

We consider the canonical Wiener-Hopf factorisation of $2 \times 2$ symmetric matrices $\mathcal M$ with respect to a contour $\Gamma$. For the case that the quotient $q$ of the two diagonal elements of $\mathcal M$ is a rational function, we show that due to the symmetric nature of the matrix $\mathcal M$, the second column in each of the two matrix factors

Xiaohang Tang, Sam Wong, Marcus Huynh, Zicheng He

Effective personalized feedback is crucial for learning programming. However, providing personalized, real-time feedback in large programming classrooms poses significant challenges for instructors. This paper introduces SPHERE, an interactive system that leverages Large Language Models (LLMs) and structured LLM output review to scale personalized feedback f

Kevis-Kokitsi Maninis, Kaifeng Chen, Soham Ghosh, Arjun Karpur

While image-text representation learning has become very popular in recent years, existing models tend to lack spatial awareness and have limited direct applicability for dense understanding tasks. For this reason, self-supervised image-only pretraining is still the go-to method for many dense vision applications (e.g. depth estimation, semantic segmentation

Mārtiņš Klevs, Andrejs Tatuļčenkovs, Lāsma Puķina-Slava, Guntars Kitenbergs

Complex fluid flows are important in many real-life problems. For an in-depth understanding, new and more elaborate methods of flow description are necessary. Often experimental and numerical data are accumulated in large quantities however only simple flow properties are extracted. Here we show an approach that allows quantitative extraction of the initial

Robin Anthony-Petersen, Clarence L. Chang, Yen-Yung Chang, Luke Chaplinsky

We describe observations of low energy excess (LEE) events, background events observed in all light dark matter direct detection calorimeters, and noise in a Transition Edge Sensor based two-channel silicon athermal phonon detector with 375 meV baseline energy resolution. We measure two distinct LEE populations: ``shared'' multichannel events with a pulse sh

Lily H. Zhang, Hamid Dadkhahi, Mara Finkelstein, Firas Trabelsi

Despite growing interest in incorporating feedback to improve language models, most efforts focus only on sequence-level annotations. In this work, we explore the potential of utilizing fine-grained span-level annotations from offline datasets to improve model quality. We develop a simple finetuning algorithm, called Training with Annotations (TWA), to direc

René Pfitscher

On a generalized flag variety of rank one, we count rational approximations to a real point chosen randomly according to the Riemannian volume. In particular, our results apply to Grassmann varieties and quadric hypersurfaces. The proof uses exponential mixing in the space of lattices and tools from geometry of numbers.

Ozioma C. Oguine, Jinkyung Katie Park, Mamtaj Akter, Johanna Olesk

Youth implicated in the child welfare and juvenile justice systems, as well as those with an incarcerated parent, are considered the most vulnerable Children in Need of Services (CHINS). We identified 1,160 of these at-risk youth (ages 13-17) who sought support via an online peer support platform to understand their adverse childhood experiences and explore

Zhiqiang Cai, Junpyo Choi, Min Liu

This paper studies the approximation property of ReLU neural networks (NNs) to piecewise constant functions with unknown interfaces in bounded regions in $\mathbb{R}^d$. Under the assumption that the discontinuity interface $\Gamma$ may be approximated by a connected series of hyperplanes with a prescribed accuracy $\varepsilon >0$, we show that a three-laye

Ramaneswaran Selvakumar, Sonal Kumar, Hemant Kumar Giri, Nishit Anand

Open-vocabulary audio language models (ALMs), like Contrastive Language Audio Pretraining (CLAP), represent a promising new paradigm for audio-text retrieval using natural language queries. In this paper, for the first time, we perform controlled experiments on various benchmarks to show that existing ALMs struggle to generalize to linguistic variations in t

Mohannad Shehadeh, Frank R. Kschischang

We introduce a unified generalization of several well-established high-throughput coding techniques including staircase codes, tiled diagonal zipper codes, continuously interleaved codes, open forward error correction (OFEC) codes, and Robinson-Bernstein convolutional codes as special cases. This generalization which we term "higher-order staircase codes" ar

Saif Punjwani, Larry Heck

The scarcity of high-quality, multimodal training data severely hinders the creation of lifelike avatar animations for conversational AI in virtual environments. Existing datasets often lack the intricate synchronization between speech, facial expressions, and body movements that characterize natural human communication. To address this critical gap, we intr

Jason Chan, Robert Gaizauskas, Zhixue Zhao

Formal logic enables computers to reason in natural language by representing sentences in symbolic forms and applying rules to derive conclusions. However, in what our study characterizes as "rulebreaker" scenarios, this method can lead to conclusions that are typically not inferred or accepted by humans given their common sense and factual knowledge. Inspir

Yuxi Liu, Fangzhu Shen, Kushagra Ghosh, Amir Gilad

Datasets may include errors, and specifically violations of integrity constraints, for various reasons. Standard techniques for ``minimal-cost'' database repairing resolve these violations by aiming for minimum change in the data, and in the process, may sway representations of different sub-populations. For instance, the repair may end up deleting more fema

Aaron D. Mullen, Daniel R. Harris, Peter Rock, Katherine Thompson

We present efforts in the fields of machine learning and time series forecasting to accurately predict counts of future suspected opioid overdoses recorded by Emergency Medical Services (EMS) in the state of Kentucky. Forecasts help government agencies properly prepare and distribute resources related to opioid overdoses. Our approach uses county and distric

Jiayi Liu, Denys Iliash, Angel X. Chang, Manolis Savva

We address the challenge of creating 3D assets for household articulated objects from a single image. Prior work on articulated object creation either requires multi-view multi-state input, or only allows coarse control over the generation process. These limitations hinder the scalability and practicality for articulated object modeling. In this work, we pro

Naoki Otani, Nikita Bhutani, Estevam Hruschka

Advances in Natural Language Processing (NLP) have the potential to transform HR processes, from recruitment to employee management. While recent breakthroughs in NLP have generated significant interest in its industrial applications, a comprehensive overview of how NLP can be applied across HR activities is still lacking. This paper discovers opportunities

Jayeeta Chaudhuri, Hassan Nassar, Dennis R. E. Gnad, Jorg Henkel

FPGAs are now ubiquitous in cloud computing infrastructures and reconfigurable system-on-chip, particularly for AI acceleration. Major cloud service providers such as Amazon and Microsoft are increasingly incorporating FPGAs for specialized compute-intensive tasks within their data centers. The availability of FPGAs in cloud data centers has opened up new op

Chris Fields, James F. Glazebrook, Antonino Marciano, Emanuele Zappala

We show that in the operational setting of a two-agent, local operations, classical communication (LOCC) protocol, Alice and Bob cannot operationally distinguish monogamous entanglement from a topological identification of points in their respective local spacetimes, i.e. that ER = EPR can be recovered as an operational theorem. Our construction immediately

Maria Chudnovsky, Sepehr Hajebi, Sophie Spirkl

We prove that for every graph $G$ with a sufficiently large complete bipartite induced minor, either $G$ has an induced minor isomorphic to a large wall, or $G$ contains a large constellation; that is, a complete bipartite induced minor model such that on one side of the bipartition, each branch set is a singleton, and on the other side, each branch set indu

Dheer Noal Desai, Runze Wang

In a graph, we assign distinct integers to the vertices, and take the sum of two integers if they are on two adjacent vertices. The minimum possible number of different sums is the \emph{sum index} of this graph. In this paper, we present some new developments on graph sum index. First, we explain the connections between graph sum index and results in additi

Lucas Clarté, Lenka Zdeborová

Conformal prediction has emerged as a powerful tool for building prediction intervals that are valid in a distribution-free way. However, its evaluation may be computationally costly, especially in the high-dimensional setting where the dimensionality and sample sizes are both large and of comparable magnitudes. To address this challenge in the context of ge

Bahar Ali, Anwar Shah, Malik Niaz, Musadaq Mansoord

Advanced automated AI techniques allow us to classify protein sequences and discern their biological families and functions. Conventional approaches for classifying these protein families often focus on extracting N-Gram features from the sequences while overlooking crucial motif information and the interplay between motifs and neighboring amino acids. Recen

Rhonda Morgan, Dmitry Savransky, Michael Turmon, Mario Damiano

Habitable Worlds Observatory (HWO) will search for biosignatures from Earth-size exoplanets in the habitable zones of nearby stars. The wavelength range for biosignatures used by the HabEx and LUVOIR mission concept studies was 200 nm to 2 microns and, as such, this is a candidate wavelength range for HWO. The visible wavelength range (500-1000 nm) provides

Wenkai Li, Jiarui Liu, Andy Liu, Xuhui Zhou

In this work, we tackle the challenge of embedding realistic human personality traits into LLMs. Previous approaches have primarily focused on prompt-based methods that describe the behavior associated with the desired personality traits, suffering from realism and validity issues. To address these limitations, we introduce BIG5-CHAT, a large-scale dataset c

Niels Lundtorp Olsen

The Cholesky decomposition is a popular way of decomposing positive definite matrices; in particular it leads to a simple formula for computing the determinant. We present and proof an equivalent formula for computing the Fredholm determinant of a positive definite integral operator on $L^2 [0, 1]$.

Can Chen, Gabriel Oliveira, Hossein Sharifi Noghabi, Tristan Sylvain

Time series~(TS) modeling is essential in dynamic systems like weather prediction and anomaly detection. Recent studies utilize Large Language Models (LLMs) for TS modeling, leveraging their powerful pattern recognition capabilities. These methods primarily position LLMs as the predictive backbone, often omitting the mathematical modeling within traditional

Yongheng Sun, Mingxia Liu, Chunfeng Lian

Brain tumor segmentation is crucial for accurate diagnosisand treatment planning, but the small size and irregular shapeof tumors pose significant challenges. Existing methods of-ten fail to effectively incorporate medical domain knowledgesuch as tumor grade, which correlates with tumor aggres-siveness and morphology, providing critical insights for moreaccu

Bikash Ghosh, Maria Labendik, Liliia Musina, Vladimir Umansky

Fractional quantum statistics are the defining characteristic of anyons. Measuring the phase generated by an exchange of anyons is challenging, as standard interferometry setups -- such as the Fabry-P\'erot interferometer -- suffer from charging effects that obscure the interference signal. Here, we present the observation of anyonic interference and exchang

J. Alex Hurt, Grant J. Scott, Derek Weitzel, Huijun Zhu

The National Science Foundation (NSF) funded National Research Platform (NRP) is a hyper-converged cluster of nationally and globally interconnected heterogeneous computing resources. The dominant computing environment of the NRP is the x86 64 instruction set architecture (ISA), often with graphics processing units (GPUs). Researchers across the nation lever

Ruoqi Liu, Yuelin Bai, Xiang Yue, Ping Zhang

The electrocardiogram (ECG) is an essential non-invasive diagnostic tool for assessing cardiac conditions. Existing automatic interpretation methods suffer from limited generalizability, focusing on a narrow range of cardiac conditions, and typically depend on raw physiological signals, which may not be readily available in resource-limited settings where on

Peter Veroutis, Frédéric Godin

The Multiarmed Bandits (MAB) problem has been extensively studied and has seen many practical applications in a variety of fields. The Survival Multiarmed Bandits (S-MAB) open problem is an extension which constrains an agent to a budget that is directly related to observed rewards. As budget depletion leads to ruin, an agent's objective is to both maximize

Nazanin Moradinasab, Hassan Jafarzadeh, Donald E. Brown

Domain adaptive semantic segmentation is the task of generating precise and dense predictions for an unlabeled target domain using a model trained on a labeled source domain. While significant efforts have been devoted to improving unsupervised domain adaptation for this task, it is crucial to note that many models rely on a strong assumption that the source

Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Dennis Wei

Providing human-understandable insights into the inner workings of neural networks is an important step toward achieving more explainable and trustworthy AI. Existing approaches to such mechanistic interpretability typically require substantial prior knowledge and manual effort, with strategies tailored to specific tasks. In this work, we take a step toward

Juan Pablo Paz, Augusto J. Roncaglia

This is a pedagogical paper where we present a physically motivated approach to introduce the coherent states of a harmonic oscillator from which it is simple to rigorously derive their mathematical definition. We do this in two different ways that turn out to be equivalent but emphasize two related but different aspects of classicality. First, we analyze wh

Samuel K. Grunblatt

Despite the recent discoveries of planets orbiting stars at all evolutionary stages, the evolution of planetary systems remains poorly understood. Studying planetary systems around red giant branch stars can reveal how main sequence planetary systems can change and evolve into white dwarf systems over time. Decades of radial velocity and transit surveys have

Gaotian Wang, Kejia Ren, Andrew S. Morgan, Kaiyu Hang

Real-world object manipulation has been commonly challenged by physical uncertainties and perception limitations. Being an effective strategy, while caging configuration-based manipulation frameworks have successfully provided robust solutions, they are not broadly applicable due to their strict requirements on the availability of multiple robots, widely dis

Ben Hayes

We consider inclusions $\mathcal{S}\leq \mathcal{R}$ of discrete, probability measure-preserving orbit equivalence relations. In previous work with Ab\'{e}rt-Fra\c{c}zyk, we established the pointwise almost sure existence of the cospectral radius of a random walk on the $\mathcal{R}$-classes. In this paper, we investigate the connections of this cospectral r

Bakhao Dioum, Virginia D'Auria, Giuseppe Patera

In multimode optical systems, the spectral covariance matrix encodes all the information about quantum correlations between the quadratures of Gaussian states. Recent research has revealed that, in scenarios that are more common than previously thought, part of these correlations remain inaccessible to standard homodyne detection scheme. Formally, this effec