November 2024 arXiv papers — page 150

Sreyas Venkataraman, Yufei Wang, Ziyu Wang, Navin Sriram Ravie

Offline reinforcement learning can enable policy learning from pre-collected, sub-optimal datasets without online interactions. This makes it ideal for real-world robots and safety-critical scenarios, where collecting online data or expert demonstrations is slow, costly, and risky. However, most existing offline RL works assume the dataset is already labeled

Yasra Chandio, Victoria Interrante, Fatima Anwar

Enhancing presence in mixed reality (MR) relies on precise measurement and quantification. While presence has traditionally been measured through subjective questionnaires, recent research links presence with objective metrics like reaction time. Past studies examined this correlation with varying technical factors (object realism and behavior) and human con

Prasanna Pakkiam, N. Pradeep Kumar, Chun-Ching Chiu, David Sommers

We experimentally realise the theoretical proposal for in-situ tunable photonic edge states emerging from qubits coupled to a waveguide with a photonic bandgap. These edge states are directional, exhibiting theoretically zero population in the opposite direction. Our experiment implements a tunable Rice-Mele waveguide configuration, where the directionality

Alexander Thomas, Seth Rosen, Vishnu Vettrivel

This paper presents a comparative analysis of hallucination detection systems for AI, focusing on automatic summarization and question answering tasks for Large Language Models (LLMs). We evaluate different hallucination detection systems using the diagnostic odds ratio (DOR) and cost-effectiveness metrics. Our results indicate that although advanced models

Chi-en Amy Tai, Oustan Ding, Alexander Wong

In the United States, skin cancer ranks as the most commonly diagnosed cancer, presenting a significant public health issue due to its high rates of occurrence and the risk of serious complications if not caught early. Recent advancements in dataset curation and deep learning have shown promise in quick and accurate detection of skin cancer. However, current

Cheongho Han, Yoon-Hyun Ryu, Chung-Uk Lee, Andrew Gould

We analysed microlensing data to uncover the nature of the anomaly that appeared near the peak of the short-timescale microlensing event KMT-2024-BLG-1044. Despite the anomaly's brief duration of less than a day, it was densely observed through high-cadence monitoring conducted by the KMTNet survey. Detailed modelling of the light curve confirmed the planeta

Bai Zhiyue, Dai Minghui, Hou Fen, Shan hangguan

In this paper, we propose a novel integrated sensing and communication (ISAC)-enabled dual-scale channel estimation framework, where large-scale channel estimation benefits from sensing, and the temporal variation of small-scale channel state information is modeled via channel aging. By characterizing the impact of angular sensing error on the communication

Stephanie Young, Jerome Gilles

A 3D chaos game is shown to be a useful way for encoding DNA sequences. Since matching subsequences in DNA converge in space in 3D chaos game encoding, a DNA sequence's 3D chaos game representation can be used to compare DNA sequences without prior alignment and without truncating or padding any of the sequences. Two proposed methods inspired by shape-simila

Jerome Gilles

This paper describes the many image decomposition models that allow to separate structures and textures or structures, textures, and noise. These models combined a total variation approach with different adapted functional spaces such as Besov or Contourlet spaces or a special oscillating function space based on the work of Yves Meyer. We propose a method to

Mark G. Wallace

This paper investigates what properties a neighbourhood requires to support beneficial local search. We show that neighbourhood locality, and a reduction in cost probability towards the optimum, support a proof that search among neighbours is more likely to find an improving solution in a single search step than blind search. This is the first paper to intro

Timothy C Ralph, Matthew Winnel, S Nibedita Swain, Ryan J Marshman

The choice between the Schroedinger and Heisenberg pictures can significantly impact the computational resources needed to solve a problem, even though they are equivalent formulations of quantum mechanics. Here we present a method for analysing Bosonic quantum circuits based on the Heisenberg picture that allows, under certain conditions, a useful factoring

Yingying Fang, Zihao Jin, Shaojie Guo, Jinda Liu

Despite significant advancements in automated report generation, the opaqueness of text interpretability continues to cast doubt on the reliability of the content produced. This paper introduces a novel approach to identify specific image features in X-ray images that influence the outputs of report generation models. Specifically, we propose Cyclic Vision-L

Md Jueal Mia, M. Hadi Amini

Federated Learning has emerged as a leading approach for decentralized machine learning, enabling multiple clients to collaboratively train a shared model without exchanging private data. While FL enhances data privacy, it remains vulnerable to inference attacks, such as gradient inversion and membership inference, during both training and inference phases.

Manuel de la Cruz-López, Alfredo Herrera-Aguilar, Daniel Martínez-Carbajal, Sergio Patiño-López

We construct a noncommutative (NC) AdS$_4$-charged black hole with a planar horizon topology. The NC effects of this geometry are captured by a Gaussian distribution of black hole mass codified in a fluid-like energy-momentum tensor. A natural bound in radial coordinate is established, below which the scalar curvature changes its sign and defines a NC cutoff

Diana Romero, Yasra Chandio, Fatima Anwar, Salma Elmalaki

Understanding group behavior is crucial for enhancing collaboration and productivity in mixed reality (MR). This paper introduces a framework for group behavior analysis in MR, or GroupBeaMR for short for analyzing group behavior in MR. GroupBeaMR leverages MR headsets' sensors to analyze group behavior through conversation, shared attention, and proximity,

Hardik Routray, Bernhard Hientzsch

We propose a simple methodology to approximate functions with given asymptotic behavior by specifically constructed terms and an unconstrained deep neural network (DNN). The methodology we describe extends to various asymptotic behaviors and multiple dimensions and is easy to implement. In this work we demonstrate it for linear asymptotic behavior in one-dim

J. Kim, C. Ha, S. H. Kim, W. K. Kim

A new 2,400 L liquid scintillator has been produced for the COSINE-100 Upgrade, which is under construction at Yemilab for the next COSINE dark matter experiment phase. The linear-alkyl-benzene-based scintillator is designed to serve as a veto for NaI(Tl) crystal targets and a separate platform for rare event searches. We measured using a sample consisting o

Richard Bi, Karthekeyan Chandrasekaran, Soham Joshi

In submodular multiway partition (SUB-MP), the input is a non-negative submodular function $f:2^V \rightarrow \mathbb{R}_{\ge 0}$ given by an evaluation oracle along with $k$ terminals $t_1, t_2, \ldots, t_k\in V$. The goal is to find a partition $V_1, V_2, \ldots, V_k$ of $V$ with $t_i\in V_i$ for every $i\in [k]$ in order to minimize $\sum_{i=1}^k f(V_i)$.

Jaeyoo Park, Jin Young Choi, Jeonghyung Park, Bohyung Han

We present a novel OCR-free document understanding framework based on pretrained Multimodal Large Language Models (MLLMs). Our approach employs multi-scale visual features to effectively handle various font sizes within document images. To address the increasing costs of considering the multi-scale visual inputs for MLLMs, we propose the Hierarchical Visual

Lynnette Hui Xian Ng, Luo Qi Chan

Translation of code-mixed texts to formal English allow a wider audience to understand these code-mixed languages, and facilitate downstream analysis applications such as sentiment analysis. In this work, we look at translating Singlish, which is colloquial Singaporean English, to formal standard English. Singlish is formed through the code-mixing of multipl

Tong Chen, Hao Fang, Patrick Xia, Xiaodong Liu

Large language models (LMs) are typically adapted to improve performance on new contexts (\eg text prompts that define new tasks or domains) through fine-tuning or prompting. However, there is an accuracy compute tradeoff -- fine-tuning incurs significant training cost and prompting increases inference overhead. We introduce $GenerativeAdapter$, an effective

A. D. Bermúdez Manjarres

We study the eigenfunctions of the classical Liouville operator and investigate the conditions they must obey to be separable as a product state. We point out that the conditions for separability are equivalent to requirements of Liouville's integrability theorem, this is, the eigenfunctions are separable if and only if the system is integrable. On the other

Le Xuan Dung, Thanh Vu

We associate a sequence of positive integers, termed the type sequence, with a cochordal graph. Using this type sequence, we compute all graded Betti numbers of its edge ideal. We then classify all positive integer $n$ such that the zero divisor graph of $\mathbb{Z}/n \mathbb{Z}$ is cochordal and determine all the graded Betti numbers of its edge ideal.

S. Estrada, X. H. Fu, I. Herzog, S. Odabaşı

A theory of ordinal powers of the ideal $\mathfrak{g}_{\mathcal{S}}$ of $\mathcal{S}$-ghost morphisms is developed by introducing for every ordinal $\lambda$, the $\lambda$-th inductive power $\mathcal{J}^{(\lambda)}$ of an ideal $\mathcal{J}.$ The Generalized $\lambda$-Generating Hypothesis ($\lambda$-GGH) for an ideal $\mathcal J$ of an exact category $\ma

Rômulo Damasclin Chaves dos Santos

This study presents an extension of the corrected Smagorinsky model, incorporating advanced techniques for error estimation and regularity analysis of far-from-equilibrium turbulent flows. A new formulation that increases the model's ability to explain complex dissipative processes in turbulence is presented, using higher-order Sobolev spaces to address inco

Robert L. Grossman, Ceilyn Boyd, Nhan Do, Danne C. Elbers

Over the past few years, a growing number of data platforms have emerged, including data commons, data repositories, and databases containing biomedical, environmental, social determinants of health and other data relevant to improving health outcomes. With the growing number of data platforms, interoperating multiple data platforms to form data meshes, data

Gautam A. Kavuri, Jasper Palfree, Dileep V. Reddy, Yanbao Zhang

The unpredictability of random numbers is fundamental to both digital security and applications that fairly distribute resources. However, existing random number generators have limitations-the generation processes cannot be fully traced, audited, and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators are auditable, b

Jonathan Che, Xiang Meng, Luke Miratrix

Matching promises transparent causal inferences for observational data, making it an intuitive approach for many applications. In practice, however, standard matching methods often perform poorly compared to modern approaches such as response-surface modeling and optimizing balancing weights. We propose Caliper Synthetic Matching (CSM) to address these chall

Ko Aoki

We construct a locally profinite set of cardinality $\aleph_ω$ with infinitely many first cohomology classes of which any distinct finite product does not vanish. Building on this, we construct the first example of a nondescendable faithfully flat map between commutative rings of cardinality $\aleph_ω$ within Zermelo--Fraenkel set theory.

Luis E. Sánchez-González, Miguel A. Mojarro, Jeús A. Maytorena, Ramon Carrillo-Bastos

We study the electronic band structure and optical response of a hybrid model, a $α-\mathcal{T}_3$ model featuring a $\sqrt{3}\times\sqrt{3}$ Kekulé pattern modulation. Such a hybrid system may result from the depositing of adatoms in a hexagonal lattice, where the two sublattices are displaced in the perpendicular direction, like in germanene and silicene.

Manas Mejari, Valentina Breschi, Simone Formentin, Dario Piga

Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI) systems using noisy state-input data, employing novel noise mitigation techniques. Specifically, we demonstrate that using data-based covariance parameterization of the controller

Ilia Mahrooghi, Mahshad Moradi, Sina Akbari, Negar Kiyavash

While significant progress has been made in designing algorithms that minimize regret in online decision-making, real-world scenarios often introduce additional complexities, perhaps the most challenging of which is missing outcomes. Overlooking this aspect or simply assuming random missingness invariably leads to biased estimates of the rewards and may resu

Boyang Chen, Jue Xu, Qi Zhao, Xiao Yuan

Understanding algorithmic error accumulation in quantum simulation is crucial due to its fundamental significance and practical applications in simulating quantum many-body system dynamics. Conventional theories typically apply the triangle inequality to provide an upper bound for the error. However, these often yield overly conservative and inaccurate estim

Manuel Ladra, Bernardo Leite da Cunha, Samuel A. Lopes

Working over an arbitrary field of characteristic different from $2$, we extend the Skjelbred-Sund method to compatible Lie algebras and give a full classification of nilpotent compatible Lie algebras up to dimension $4$. In case the base field is cubically closed, we find that there are three isomorphism classes and a one-parameter family in dimension $3$,

Shuxiang Zhou, Zilong Hua, Kaustubh K. Bawane, Hao Zhou

Cubic boron arsenide (BAs) stands out as a promising material for advanced electronics, thanks to its exceptional thermal conductivity and ambipolar mobility. However, effective control of p- and n-type doping in BAs poses a significant challenge, mostly as a result of the influence of defects. In the present study, we employed density functional theory (DFT

Chuyu Zhou

Fixing two positive integers $d$ and $k$, a positive number $v$, and a positive integer $I$, we prove that the K-semistable domain of the log pair $(X, \sum_{j=1}^kD_j)$ is a rational polytope lying in the $k$-dimensional simplex $\overline{Δ^k}$, where $X$ is a Fano variety of dimension $d$, $D_j\sim_\mathbb{Q} -K_X$, $(-K_X)^d=v$, $I(K_X+D_j)\sim 0$, and $

Alejandro Aponte, Arthur Caetano, Yunhao Luo, Misha Sra

Everyday objects, like remote controls or electric toothbrushes, are crafted with hand-accessible interfaces. Expanding on this design principle, extended reality (XR) interfaces for physical tasks could facilitate interaction without necessitating the release of grasped tools, ensuring seamless workflow integration. While established data, such as hand anth

Min Gao, Yukun Guo, Tristan T. Hormel, Jie Wang

To develop a new method to quantify nonperfused retinal capillaries (NPCs) by using co-registered optical coherence tomography (OCT) and OCT angiography (OCTA), and to evaluate NPCs in eyes with age-related macular degeneration (AMD) and diabetic retinopathy (DR). Multiple consecutive 3x3-mm OCT/OCTA scans were obtained using a commercial device (Solix; Visi

Reet Barik, Marco Minutoli, Mahantesh Halappanavar, Ananth Kalyanaraman

Owing to the ongoing COVID-19 pandemic and other recent global epidemics, epidemic simulation frameworks are gaining rapid significance. In this work, we present a workflow that will allow researchers to simulate the spread of an infectious disease under different intervention schemes. Our workflow is built using the Covasim simulator for COVID-19 alongside

I. P-Castro, J. L. Díaz-Cruz, A. Pérez-Lorenzana

In this paper, we review the fundamental aspects of ${\cal N} = 1$ supergravity (SUGRA) and employ the chiral formalism to describe spin-$\frac{3}{2}$ fermions. Armed with this, we analyze the discrete symmetries of the graviton in the context of linearized gravity (LG) and Rarita-Schwinger (RS) fields, which include the description of the gravitino. Additio

Leandro Aurichi, Gabriel Fernandes, Paulo Magalhães Júnior

We prove the followings result for ray inflations of sparse graphs on $ω_1$-trees. First, let $T$ and $S$ be pruned $ω_1$-trees, let $G_T$ be a sparse $T$-graph, and let $G_S$ be a sparse $S$-graph with uniformly finite adhesion. If $T$ is not special, then no subdivision of $G_S\# \mathbb{N}$ is isomorphic to $G_T\# \mathbb{N}$. Second, if $T$ is almost-Sus

Kyongmin Yeo, Małgorzata J. Zimoń, Mykhaylo Zayats, Sergiy Zhuk

A super-resolution (SR) method for the reconstruction of Navier-Stokes (NS) flows from noisy observations is presented. In the SR method, first the observation data is averaged over a coarse grid to reduce the noise at the expense of losing resolution and, then, a dynamic observer is employed to reconstruct the flow field by reversing back the lost informati

Mahtab Faraji, Homa Rashidisabet, George R. Nahass, RV Paul Chan

Here, we examine the latest advances in glaucoma detection through Deep Learning (DL) algorithms using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study focuses on three aspects of DL-based glaucoma detection frameworks: input data modalities, processing strategies, and model architectures and applications. Moreover, we

Moshik Hershcovitch, Andrew Wood, Leshem Choshen, Guy Girmonsky

With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type of compression - one that represents the

Simon Wagner, Leif Seute, Vsevolod Viliuga, Nicolas Wolf

We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA), an extension of the invariant point attention (IPA) architecture from AlphaFold2, in which the backbone residue frames and geometric features are represented in the projective geo

Inko Bovenzi, Adi Carmel, Michael Hu, Rebecca M. Hurwitz

In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning (IRL) that identifies suboptimal clinician actions based on the actions of their peers. This approach centers two stages of IRL with an intermediate step to prune trajectories dis

Taha Sajjad, Andrew W. Eckford

Biological systems transduce signals from their surroundings in numerous ways. This paper introduces a communication system using the light-gated ion channel Channelrhodopsin-2 (ChR2), which causes an ion current to flow in response to light. Our design includes a ChR2-based receiver along with encoding, modulation techniques and detection. Analyzing the res

Sebastian Builes, Jhoana P Romero-Leiton, Leon A. Valencia

In this work, we study the qualitative properties of a simple mathematical model inspired by antimicrobial resistance (AMR), focusing on the reversal of resistance. In particular, we analyze the model from three perspectives: ordinary differential equations (ODEs), stochastic differential equations (SDEs) driven by Brownian motion, and fractional differentia

Debmalya Mandal, Goran Radanovic

We study the setting of \emph{performative reinforcement learning} where the deployed policy affects both the reward, and the transition of the underlying Markov decision process. Prior work~\parencite{MTR23} has addressed this problem under the tabular setting and established last-iterate convergence of repeated retraining with iteration complexity explicit

Zhuotong Chen, Fang Liu, Jennifer Zhu, Wanyu Du

Direct Preference Optimization (DPO) and its variants have become the de facto standards for aligning large language models (LLMs) with human preferences or specific goals. However, DPO requires high-quality preference data and suffers from unstable preference optimization. In this work, we aim to improve the preference optimization pipeline by taking a clos

Luiza Chiarelli Conte, Débora Missio Bayer, Fábio M. Bayer

The Pettitt test has been widely used in climate change and hydrological analyzes. However, studies evidence difficulties of this test in detecting change points, especially in small samples. This study presents a bootstrap application of the Pettitt test, which is numerically compared with the classical Pettitt test by an extensive Monte Carlo simulation st

Shengzhi Li, Kittipat Kampa, Rongyu Lin, Bohang Li

Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the effectiveness of leveraging high-quality academic peer review data for fine-tuning LLMs to enhance their long-context capabilities. We compare the Direct Preference Optimization (DP

Adriana Caraeni, Alexander Scarlatos, Andrew Lan

Recent advances in generative artificial intelligence (AI) have shown promise in accurately grading open-ended student responses. However, few prior works have explored grading handwritten responses due to a lack of data and the challenge of combining visual and textual information. In this work, we leverage state-of-the-art multi-modal AI models, in particu

Susmita Haldar, Luiz Fernando Capretz

Software defect prediction models can assist software testing initiatives by prioritizing testing error-prone modules. In recent years, in addition to the traditional defect prediction model approach of predicting defects from class, modules, etc., Just-In-Time defect prediction research, which focuses on the change history of software products is getting pr

Luis M. Lopez-Ramos, Florian Leiser, Aditya Rastogi, Steven Hicks

The joint implementation of federated learning (FL) and explainable artificial intelligence (XAI) could allow training models from distributed data and explaining their inner workings while preserving essential aspects of privacy. Toward establishing the benefits and tensions associated with their interplay, this scoping review maps the publications that joi

Renato Cortinovis, Tamer Mohamed Abdellatif, Devender Goyal, Luiz Fernando Capretz

This paper discusses further evaluations of the educational effectiveness of an existing CPU visual simulator (CPUVSIM). The CPUVSIM, as an Open Educational Resource, has been iteratively improved over a number of years following an Open Pedagogy approach, and was designed to enhance novices understanding of computer operation and mapping from high-level cod

Yequan Zhao, Hai Li, Ian Young, Zheng Zhang

Back propagation (BP) is the default solution for gradient computation in neural network training. However, implementing BP-based training on various edge devices such as FPGA, microcontrollers (MCUs), and analog computing platforms face multiple major challenges, such as the lack of hardware resources, long time-to-market, and dramatic errors in a low-preci

Ryan D'Orazio, Danilo Vucetic, Zichu Liu, Junhyung Lyle Kim

Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minimizing projected Bellman error and min-max optimization, cannot be modelled as minimizing a scalar loss function but instead correspond to solving a variational inequality (VI) problem. This difference in setting has caus

Sabyasachee Baruah, Shrikanth Narayanan

Computational narrative understanding studies the identification, description, and interaction of the elements of a narrative: characters, attributes, events, and relations. Narrative research has given considerable attention to defining and classifying character types. However, these character-type taxonomies do not generalize well because they are small, t

Mingming Lu, Ziwen Wang, Li Lin Yang

We present a novel approach for loop integral reduction in the Feynman parametrization using intersection theory and relative cohomology. In this framework, Feynman integrals correspond to boundary-supported differential forms in the language of relative cohomology. The integral reduction can then be achieved by computing intersection numbers. We apply our m

Corwin Grant Jeon MacMillan, K. Andrea Scott, Matthew Garvin, Zhao Pan

Rapid ice recession in the Arctic Ocean, with predictions of ice-free summers by 2060, opens new maritime routes but requires reliable navigation solutions. Current approaches rely heavily on subjective expert judgment, underscoring the need for automated, data-driven solutions. This study leverages machine learning to assess ice conditions using ship-borne

Varvara Arzt, Allan Hanbury

This paper investigates the transparency in the creation of benchmarks and the use of leaderboards for measuring progress in NLP, with a focus on the relation extraction (RE) task. Existing RE benchmarks often suffer from insufficient documentation, lacking crucial details such as data sources, inter-annotator agreement, the algorithms used for the selection

Boqi Chen, Yuanzhi Zhu, Yunke Ao, Sebastiano Caprara

Single-source domain generalization (SDG) aims to learn a model from a single source domain that can generalize well on unseen target domains. This is an important task in computer vision, particularly relevant to medical imaging where domain shifts are common. In this work, we consider a challenging yet practical setting: SDG for cross-modality medical imag

Rohan Choudhury, Guanglei Zhu, Sihan Liu, Koichiro Niinuma

Transformers are slow to train on videos due to extremely large numbers of input tokens, even though many video tokens are repeated over time. Existing methods to remove such uninformative tokens either have significant overhead, negating any speedup, or require tuning for different datasets and examples. We present Run-Length Tokenization (RLT), a simple ap

Kyle Pratt

We study rational points on the Erd\H{o}s-Selfridge curves \begin{align*} y^\ell = x(x+1)\cdots (x+k-1), \end{align*} where $k,\ell\geq 2$ are integers. These curves contain "trivial" rational points $(x,y)$ with $y=0$, and a conjecture of Sander predicts for which pairs $(k,\ell)$ the curve contains "nontrivial" rational points where $y\neq 0$. Suppose $\el

Yuehao Bai, Shunzhuang Huang, Sarah Moon, Andres Santos

We propose a general approach for inference for a broad class of treatment effect parameters in a setting of a discrete valued treatment and instrument with a general outcome variable. The class of parameters considered are those that can be expressed as the expectation of a function of the response type conditional on a generalized principal stratum. Here,

David Willmes, Nick Krall, James Tanis, Zachary Terner

With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and c

Arthur Caetano, Misha Sra

Virtual content placement in physical scenes is a crucial aspect of augmented reality (AR). This task is particularly challenging when the virtual elements must adapt to multiple target physical environments that are unknown during development. AR authors use strategies such as manual placement performed by end-users, automated placement powered by author-de

Amirbek Djanibekov, Hawau Olamide Toyin, Raghad Alshalan, Abdullah Alitr

Developing robust automatic speech recognition (ASR) systems for Arabic requires effective strategies to manage its diversity. Existing ASR systems mainly cover the modern standard Arabic (MSA) variety and few high-resource dialects, but fall short in coverage and generalization across the multitude of spoken variants. Code-switching with English and French

Yu Wang, Guodong Li, Zhijie Xiao, Lihu Xu

We study in this paper the problem of least absolute deviation (LAD) regression for high-dimensional heavy-tailed time series which have finite $\alpha$-th moment with $\alpha \in (1,2]$. To handle the heavy-tailed dependent data, we propose a Catoni type truncated minimization problem framework and obtain an $\mathcal{O}\big( \big( (d_1+d_2) (d_1\land d_2)

Quinn Langfitt, Reuben Tate, Stephan Eidenbenz

The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm that can be used to approximately solve combinatorial optimization problems. However, a major limitation of QAOA is that it is a "local" algorithm for finite circuit depths, meaning it can only optimize over local properties of the graph. In this paper, we present Phanto

Adam Kaplan, Collin Calvert, Bridget C. Griffith, Daniel Bertenthal

Misclassification in binary outcomes is not uncommon and statistical methods to investigate its impact on policy-driving study results are lacking. While misclassifying binary outcomes is a statistically ubiquitous phenomena, we focus on misclassification in a public health application: vaccinations. One such study design in public health that addresses poli

Minjia Wang, Pingping Lin, Siqi Cai, Shengnan An

Content moderation, the process of reviewing and monitoring the safety of generated content, is important for development of welcoming online platforms and responsible large language models. Content moderation contains various tasks, each with its unique requirements tailored to specific scenarios. Therefore, it is crucial to develop a model that can be easi

Xiaochen Duan, Sergei S. Pilyugin

In many real life applications, a continuous culture bioreactor may cease to function properly due to bioclogging which is typically caused by the microbial overgrowth. This is a problem that has been largely overlooked in the chemostat modeling literature, despite the fact that a number of models explicitly accounted for biofilm development inside the biore

Jinxuan Xu, Shiyu Jin, Yutian Lei, Yuqian Zhang

Recent advances in Large Language Models (LLMs) have showcased their remarkable reasoning capabilities, making them influential across various fields. However, in robotics, their use has primarily been limited to manipulation planning tasks due to their inherent textual output. This paper addresses this limitation by investigating the potential of adopting t

Arthur Caetano, Alyssa Lawson, Yimeng Liu, Misha Sra

With recent computer vision techniques and user-generated content, we can augment the physical world with metadata that describes attributes, such as names, geo-locations, and visual features of physical objects. To assess the benefits of these potentially ubiquitous labels for foreign vocabulary learning, we built a proof-of-concept system that displays bil

Ge Wang, Roy Pea

In societies increasingly entangled with algorithms, our choices are constantly influenced and shaped by automated systems. This convergence highlights significant concerns for individual autonomy in the age of data-driven AI. It leads to pressing issues such as data-driven segregation, gaps in accountability for algorithmic decisions, and the infringement o

Yide Ran, Zhaozhuo Xu, Yuhang Yao, Zijian Hu

The rapid advancement of Large Language Models (LLMs) has led to their increased integration into mobile devices for personalized assistance, which enables LLMs to call external API functions to enhance their performance. However, challenges such as data scarcity, ineffective question formatting, and catastrophic forgetting hinder the development of on-devic

Subhayan Roy Moulik, Sergii Strelchuk

Out-of-Time-Order Correlation function measures transport properties of dynamical systems. They are ubiquitously used to measure quantum mechanical quantities, such as scrambling times, criticality in phase transitions, and detect onset of thermalisation. We characterise the computational complexity of estimating OTOCs over all eigenstates and show it is Com

Sonal Prabhune, Donald J. Berndt

Knowing that the generative capabilities of large language models (LLM) are sometimes hampered by tendencies to hallucinate or create non-factual responses, researchers have increasingly focused on methods to ground generated outputs in factual data. Retrieval Augmented Generation (RAG) has emerged as a key approach for integrating knowledge from data source

Bruno Gudac, Petar Sačer, Filip Orbanić, Ivan Kokanović

Among various topological insulators, Sn-doped Bi$_{1.1}$Sb$_{0.9}$Te$_{2}$S stands out for its exceptional properties. It has a wide energy gap and typically exhibits a well-isolated Dirac point and a Fermi level positioned within the gap. The samples we present display metallic-like low-temperature resistivity attributed to surface states, pronounced quant

Manuel Rocamora, Anita Reimer, Guillem Martí-Devesa, Ralf Kissmann

Context. Star-forming regions are gaining considerable interest in the high-energy astrophysics community as possible Galactic particle accelerators. In general, the role of electrons has not been fully considered in this kind of cosmic-ray source. However, the intense radiation fields inside these regions might make electrons significant gamma-ray contribut

Bowei Li, Yang Xu, Ran Zhang, Jiang

Deep reinforcement learning (DRL) has been extensively applied to Multi-Unmanned Aerial Vehicle (UAV) network (MUN) to effectively enable real-time adaptation to complex, time-varying environments. Nevertheless, most of the existing works assume a stationary user distribution (UD) or a dynamic one with predicted patterns. Such considerations may make the UD-

Alexander Schied, Zhenyuan Zhang

Fractional Wiener--Weierstrass bridges are a class of Gaussian processes that arise from replacing the trigonometric function in the construction of classical Weierstrass functions by a fractional Brownian bridge. We investigate the sample path properties of such processes, including local and uniform moduli of continuity, $\Phi$-variation, Hausdorff dimensi

Taylor Kutra, Lisa Prato, Benjamin M Tofflemire, Rachel Akeson

This article presents the latest results of our ALMA program to study circumstellar disk characteristics as a function of orbital and stellar properties in a sample of young binary star systems known to host at least one disk. Optical and infrared observations of the eccentric, ~48-year period binary DF Tau indicated the presence of only one disk around the

Yinghe Qi, Wei Guo

Understanding boundary layer flows in high Reynolds number (Re) turbulence is crucial for advancing fluid dynamics in a wide range of applications, from improving aerodynamic efficiency in aviation to optimizing energy systems in industrial processes. However, generating such flows requires complex, power-intensive large-scale facilities. Furthermore, the us

I. A. Qattan, J. Arrington, K. Aniol, O. K. Baker

The advent of high-intensity, high-polarization electron beams led to significantly improved measurements of the ratio of the proton's charge to electric form factors, GEp/GMp. However, high-$Q^2$ measurements yielded significant disagreement with extractions based on unpolarized scattering, raising questions about the reliability of the measurements and con

Graham M. Jones, Shai Satran, Arvind Satyanarayan

This article proposes a new integration of linguistic anthropology and machine learning (ML) around convergent interests in both the underpinnings of language and making language technologies more socially responsible. While linguistic anthropology focuses on interpreting the cultural basis for human language use, the ML field of interpretability is concerne

Saheed Popoola, Vineela Kunapareddi, Hazem Said

This paper presents an experience report on the establishment and sustenance of a student-driven software solutions center named Information Technology Solutions Center (ITSC), a unit within the School of Information Technology at the University of Cincinnati. A student-driven solution center empowers students to drive the design, development, execution, and

Leitian Tao, Xiang Chen, Tong Yu, Tung Mai

Large Language Models (LLMs) have revolutionized code generation but require significant resources and often over-generalize, limiting their task-specific efficiency. Fine-tuning smaller, open-source LLMs provides a cost-effective alternative. However, standard supervised approaches rely only on correct examples, missing valuable insights from failures. We i

Raef Bassily, Cristóbal Guzmán, Michael Menart

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of $(\epsilon,\delta)$-differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the $\ell_p/\ell_q$ setup, $p,q\in[1,2]$. Here, we obtain a bou

Cheng Zhang, Hanna Foerster, Robert D. Mullins, Yiren Zhao

It is now a common business practice to buy access to large language model (LLM) inference rather than self-host, because of significant upfront hardware infrastructure and energy costs. However, as a buyer, there is no mechanism to verify the authenticity of the advertised service including the serving hardware platform, e.g. that it is actually being serve

Sourabh Sangle, Sa'ed Alajlouni, Pablo A. Tarazaga

Over several decades, electromechanical impedance (EMI) measurements have been employed as a basis for structural health monitoring and damage detection. Traditionally, Root-mean-squared-deviation (RMSD) and Cross-correlation (XCORR) based metrics have been used to interpret EMI measurements for damage assessment. These tools, although helpful and widely use

Siting Li, Pang Wei Koh, Simon Shaolei Du

Recent research has shown that CLIP models struggle with visual reasoning tasks that require grounding compositionality, understanding spatial relationships, or capturing fine-grained details. One natural hypothesis is that the CLIP vision encoder does not embed essential information for these tasks. However, we find that this is not always the case: The enc

Joey Hong, Jessica Lin, Anca Dragan, Sergey Levine

Recent progress on large language models (LLMs) has enabled dialogue agents to generate highly naturalistic and plausible text. However, current LLM language generation focuses on responding accurately to questions and requests with a single effective response. In reality, many real dialogues are interactive, meaning an agent's utterances will influence thei

Joey Hong, Anca Dragan, Sergey Levine

Value-based reinforcement learning (RL) can in principle learn effective policies for a wide range of multi-turn problems, from games to dialogue to robotic control, including via offline RL from static previously collected datasets. However, despite the widespread use of policy gradient methods to train large language models for single turn tasks (e.g., que

Alexander Spangher, James Youn, Matt DeButts, Nanyun Peng

Human writers plan, then write. For large language models (LLMs) to play a role in longer-form article generation, we must understand the planning steps humans make before writing. We explore one kind of planning, source-selection in news, as a case-study for evaluating plans in long-form generation. We ask: why do specific stories call for specific kinds of

G. Bautista, R. de A. Capistrano--Filho, B. Chentouf, O. Sierra Fonseca

A higher-order nonlinear Boussinesq system with a time-dependent boundary delay is considered. Sufficient conditions are presented to ensure the well-posedness of the problem by utilizing Kato's variable norm technique and the Fixed-Point Theorem. More significantly, the energy decay for the linearized problem is demonstrated using the energy method.

Oumayma El Bir, Abderrahim Lakhfif, Abdallah Slaoui

Entanglement serves as a core resource for quantum information technologies, including applications in quantum cryptography, quantum metrology, and quantum communication. In this study, we give a unifying description of the stationary bipartite and tripartite entanglement in a coupled optomechanical ring cavity comprising photon and phonon modes. We numerica

Usman Anwar, Johannes Von Oswald, Louis Kirsch, David Krueger

In this work, we make two contributions towards understanding of in-context learning of linear models by transformers. First, we investigate the adversarial robustness of in-context learning in transformers to hijacking attacks -- a type of adversarial attacks in which the adversary's goal is to manipulate the prompt to force the transformer to generate a sp