November 2024 arXiv papers — page 176

Rafid Mahmood

Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii) users interact with this model over a series of natural language prompts; and (iii) the model is ideally evaluated on binary user satisfaction with respect to model outputs. Given

Lucas Hedström, Seong-Gyu Yang, Ludvig Lizana

We present a novel framework for understanding node target search in systems organized as hierarchical networks-within-networks. Our work generalizes traditional search models on complex networks, where the mean-first passage time is typically inversely proportional to the node degree. However, real-world search processes often span multiple network layers,

Danilo S. Rando, Edson D. Leonel, Diego F. M. Oliveira

The kicked rotator model is an essential paradigm in nonlinear dynamics, helping us understand the emergence of chaos and bifurcations in dynamical systems. In this study, we analyze a two-dimensional kicked rotator model considering a homogeneous and generalized function approach to describe the convergence dynamics towards a stationary state. By examining

Tuukka Korhonen

We present $k^{O(k^2)} m$ time algorithms for various problems about decomposing a given undirected graph by edge cuts or vertex separators of size $<k$ into parts that are ``well-connected'' with respect to cuts or separators of size $<k$; here, $m$ is the total number of vertices and edges of the graph. As an application of our results, we obtain for every

Karthik Soman, Andrew Langdon, Catalina Villouta, Chinmay Agrawal

Rare diseases present unique challenges in healthcare, often suffering from delayed diagnosis and fragmented information landscapes. The scarcity of reliable knowledge in these conditions poses a distinct challenge for Large Language Models (LLMs) in supporting clinical management and delivering precise patient information underscoring the need for focused t

Ezequiel Albentosa-Ruiz, Nicola Marchili

Accurate time series analysis is essential for studying variable astronomical sources, where detecting periodicities and characterizing power spectral density (PSD) are crucial. The Lomb-Scargle periodogram, commonly used in astronomy for analyzing unevenly sampled time series data, often suffers from noise introduced by irregular sampling. This paper presen

Aafia Zainab, Artur Avakyan, Victor Doroshenko, Philipp Thalhammer

The eROSITA instrument on board Spectrum-Roentgen-Gamma has completed four scans of the X-ray sky, leading to the detection of almost one million X-ray sources in eRASS1 only, including multiple new X-ray binary candidates. We report on analysis of the X-ray binary 1eRASS J085039.9-421151, using a ~55\,ks long NuSTAR observation, following its detection in e

Elita Lobo, Justin Payan, Cyrus Cousins, Yair Zick

We study fair allocation of constrained resources, where a market designer optimizes overall welfare while maintaining group fairness. In many large-scale settings, utilities are not known in advance, but are instead observed after realizing the allocation. We therefore estimate agent utilities using machine learning. Optimizing over estimates requires tradi

Majbah Uddin, Robin Clark, Michael Hilliard, Joshua Thompson

To understand the system-level interactions between the entities in Carbon Capture, Utilization, and Storage (CCUS), an agent-based foundational modeling tool, CCUS-Agent, is developed for a large-scale study of transportation flows and infrastructure in the United States. Key features of the tool include (i) modular design, (ii) multiple transportation mode

Shunjing Zhao, Hanlun Lei, Xian Shi

Surface temperature distribution is crucial for thermal property-based studies about irregular asteroids in our Solar System. While direct numerical simulations could model surface temperatures with high fidelity, they often take a significant amount of computational time, especially for problems where temperature distributions are required to be repeatedly

Apurba Biswas, R. Rajesh

We explore the role of activity in the occurrence of the Mpemba effect within a system of an active colloid diffusing in a potential landscape devoid of metastable minimum. The Mpemba effect is characterized by a phenomenon where a hotter system reaches equilibrium quicker than a colder one when both are rapidly cooled to the same low temperature. While a mi

Angelina Tseng, Sean Kalaycioglu

This paper presents an innovative solution to the issue of infrastructure deterioration in the U.S., where a significant portion of facilities are in poor condition, and over 130,000 steel bridges have exceeded their lifespan. Aging steel structures face corrosion and hidden defects, posing major safety risks. The Silver Bridge collapse, resulting from an un

Peiyu Li, Omar Bahri, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of applications and tasks. The challenge of many state-of-the-art ML models is a lack of transparency and interpretab

Rızacan Çiloğlu

We provide a description of Iwahori-Whittaker equivariant perverse sheaves on affine flag varieties associated to tamely ramified reductive groups, in terms of Langlands dual data. This extends the work of Arkhipov-Bezrukavnikov from the case of split reductive groups. To achieve this, we first extend the theory of Wakimoto sheaves to our context and prove c

A. J. Nayana, Raffaella Margutti, Eli Wiston, Ryan Chornock

We present the results from our extensive hard-to-soft X-ray (NuSTAR, Swift-XRT, XMM-Newton, Chandra) and meter-to-mm wave radio (GMRT, VLA, NOEMA) monitoring campaign of the very nearby (d $=6.9$ Mpc) Type II SN2023ixf spanning $\approx$ 4--165 d post-explosion. This unprecedented dataset enables inferences on the explosion's circumstellar medium (CSM) dens

Rami Masri, Keegan L. A. Kirk, Eirill Hauge, Miroslav Kuchta

We formulate and analyze interior penalty discontinuous Galerkin methods for coupled elliptic PDEs modeling excitable tissue, represented by intracellular and extracellular domains sharing a common interface. The PDEs are coupled through a dynamic boundary condition, posed on the interface, that relates the normal gradients of the solutions to the time deriv

Birkett Huber, Casper Neo, Keiran Sampson, Alex Kantchelian

We present a method to detect departures from business-justified workflows among support agents. Our goal is to assist auditors in identifying agent actions that cannot be explained by the activity within their surrounding context, where normal activity patterns are established from historical data. We apply our method to help audit millions of actions of ov

Alec MacIver, Hina Shaheen

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the progressive accumulation of misfolded proteins, leading to cognitive decline. This study presents a novel stochastic modelling approach to simulate the propagation of these proteins within the brain. We employ a network diffusion model utilizing the Laplacian matrix derived

Stephen McAleese, Mark Keane

Counterfactual explanations can be used to interpret and debug text classifiers by producing minimally altered text inputs that change a classifier's output. In this work, we evaluate five methods for generating counterfactual explanations for a BERT text classifier on two datasets using three evaluation metrics. The results of our experiments suggest that e

Frederick U. Nitta, Koosha Nassiri Nazif, Eric Pop

With the rapid expansion of the Internet of Things (IoT), efficient and durable energy harvesters for powering IoT devices operating indoors and outdoors are imperative. Promising materials for indoor photovoltaic (PV) technologies include transition metal dichalcogenides (TMDs) such as MoS2, MoSe2, WS2, and WSe2, mainly due to their high absorption coeffici

Sajjad Bakrani

We consider a $\mathbb{Z}_{2}$-equivariant 4-dimensional system of ODEs with a smooth first integral $H$ and a saddle equilibrium state $O$. We assume that there exists a transverse homoclinic orbit $\Gamma$ to $O$ that approaches $O$ along the nonleading directions. Suppose $H(O) = c$. In \cite{Bakrani2022JDE}, the dynamics near $\Gamma$ in the level set $H

Bogdan A. Danciu, Christos E. Frouzakis

We present KinetiX, a software toolkit to generate computationally efficient fuel-specific routines for the chemical source term, thermodynamic and mixture-averaged transport properties for use in combustion simulation codes. The C++ routines are designed for high-performance execution on both CPU and GPU architectures. On CPUs, chemical kinetics computation

Abhiram Kandiyana, Peter R. Mouton, Yaroslav Kolinko, Lawrence O. Hall

Traditional deep learning-based methods for classifying cellular features in microscopy images require time- and labor-intensive processes for training models. Among the current limitations are major time commitments from domain experts for accurate ground truth preparation; and the need for a large amount of input image data. We previously proposed a soluti

Bidisha Chakraborty, Shree Mitra

In order to improve model accuracy, generalization, and class imbalance issues, this work offers a strong methodology for classifying endoscopic images. We suggest a hybrid feature extraction method that combines convolutional neural networks (CNNs), multi-layer perceptrons (MLPs), and radiomics. Rich, multi-scale feature extraction is made possible by this

Supriyo Bandyopadhyay

The straintronic magnetic tunnel junction (s-MTJ) is an MTJ whose resistance state can be changed continuously or gradually from high to low with a gate voltage that generates strain the magnetostrictive soft layer. This unusual feature, not usually available in MTJs that are switched abruptly with spin transfer torque, spin-orbit torque or voltage-controlle

Andrew Hannum, Max Conway, Mario Lopez, André Harrison

This paper presents a novel data-driven hierarchical approach to open set recognition (OSR) for robust perception in robotics and computer vision, utilizing constrained agglomerative clustering to automatically build a hierarchy of known classes in embedding space without requiring manual relational information. The method, demonstrated on the Animals with A

Ryan J. French, Laura A. Hayes, Maria D. Kazachenko, Katharine K. Reeves

We present simultaneous X-ray and spectral ultraviolet (UV) observations of strikingly-coherent oscillations in emission from a coronal looptop and fan structure, during the impulsive phase of a long-duration M-class solar flare. The 50 s oscillations are observed near in-phase by Solar Orbiter/STIX, GOES, and IRIS Fe XXI intensity, Doppler and non-thermal v

Li Guo, Richard Gustavson, Yunnan Li

This paper introduces algebraic structures for Volterra integral operators with separable kernels, in the style of differential algebra for derivations and Rota-Baxter algebra for operators with kernels dependent solely on a dummy variable. We demonstrate that these operators satisfy a generalization of the algebraic identity defining the classical Reynolds

Youssef Elmir, Hayet Touati, Ouassila Melizou

Surveillance systems often struggle with managing vast amounts of footage, much of which is irrelevant, leading to inefficient storage and challenges in event retrieval. This paper addresses these issues by proposing an optimized video recording solution focused on activity detection. The proposed approach utilizes a hybrid method that combines motion detect

Atakan Seyitoğlu, Aleksei Kuvshinov, Leo Schwinn, Stephan Günnemann

An unintended consequence of the vast pretraining of Large Language Models (LLMs) is the verbatim memorization of fragments of their training data, which may contain sensitive or copyrighted information. In recent years, unlearning has emerged as a solution to effectively remove sensitive knowledge from models after training. Yet, recent work has shown that

Vaibhav Sharma, Erich J Mueller

We develop a method for visualizing the internal structure of multipartite entanglement in pure stabilizer states. Our algorithm graphically organizes the many-body correlations in a hierarchical structure. This provides a rich taxonomy from which one can simultaneously extract many quantitative features of a state including some traditional quantities such

Joep Geuskens, Nishank Gite, Michael Krämer, Vinicius Mikuni

Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced c

E. Ulises Moya-Sánchez, Yazid S. Mikail, Daisy Nyang'anyi, Michael J. Smith

Machine learning has great potential to increase crop production and resilience to climate change. Accurate maps of where crops are grown are a key input to a number of downstream policy and research applications. In this proposal, we present preliminary work showing that it is possible to accurately classify crops from time series derived from Sentinel 1 an

Lorenzo Pettinari

Berezin and Weyl quantization are renown procedures for mapping, commutative Poisson algebras of observables to their non-commutative, quantum counterparts. The latter is famous for its use on Weyl algebras, while the former is more appropriate for continuous functions decaying at infinity. In this work, we define a variant of the Berezin quantization map, w

Deok-Hyeon Cho, Hyung-Seok Oh, Seung-Bin Kim, Seong-Whan Lee

Emotional text-to-speech (TTS) technology has achieved significant progress in recent years; however, challenges remain owing to the inherent complexity of emotions and limitations of the available emotional speech datasets and models. Previous studies typically relied on limited emotional speech datasets or required extensive manual annotations, restricting

Nathan B. Clayburn, Andrew Glassford, Andrew Leiker, Thomas Uelmen

Terrestrial experiments that use electrons in Earth as a spin-polarized source have been demonstrated to provide strong bounds on exotic long-range spin-spin and spin-velocity interactions. These bounds constrain the coupling strength of many proposed ultralight bosonic dark-matter candidates. Recently, it was pointed out that a monopole-dipole coupling betw

Minghao Ning, Yaodong Cui, Yufeng Yang, Shucheng Huang

This paper presents a novel real-time, delay-aware cooperative perception system designed for intelligent mobility platforms operating in dynamic indoor environments. The system contains a network of multi-modal sensor nodes and a central node that collectively provide perception services to mobility platforms. The proposed Hierarchical Clustering Considerin

Vivek Myers, Evan Ellis, Sergey Levine, Benjamin Eysenbach

Assistive agents should make humans' lives easier. Classically, such assistance is studied through the lens of inverse reinforcement learning, where an assistive agent (e.g., a chatbot, a robot) infers a human's intention and then selects actions to help the human reach that goal. This approach requires inferring intentions, which can be difficult in high-di

Zihao Zhao, Yuchen Yang, Anjalie Field, Yinzhi Cao

Machine unlearning, enabling a trained model to forget specific data, is crucial for addressing erroneous data and adhering to privacy regulations like the General Data Protection Regulation (GDPR)'s "right to be forgotten". Despite recent progress, existing methods face two key challenges: residual information may persist in the model even after unlearning,

R. Piron, M. Tacu

We propose an explicit numerical method to solve Milne's phase-amplitude equations. Previously proposed methods solve directly Milne's nonlinear equation for the amplitude. For that reason, they exhibit high sensitivity to errors and are prone to instability through the growth of a spurious, rapidly varying component of the amplitude. This makes the systemat

Faezeh Shirmohammadi, Deyan Draganov, Ranajit Ghose, Eric Verschuur

High-resolution seismic reflections are essential for imaging and monitoring applications. In seismic land surveys using sources and receivers at the surface, surface waves often dominate, masking the reflections. In this study, we demonstrate the efficacy of a two-step procedure to suppress surface waves in an active-source reflection seismic dataset. First

Pit Henrich, Jiawei Liu, Jiawei Ge, Samuel Schmidgall

To track tumors during surgery, information from preoperative CT scans is used to determine their position. However, as the surgeon operates, the tumor may be deformed which presents a major hurdle for accurately resecting the tumor, and can lead to surgical inaccuracy, increased operation time, and excessive margins. This issue is particularly pronounced in

Rianna Parla

The Exploit Prediction Scoring System (EPSS) is designed to assess the probability of a vulnerability being exploited in the next 30 days relative to other vulnerabilities. The latest version, based on a research paper published in arXiv, assists defenders in deciding which vulnerabilities to prioritize for remediation. This study evaluates EPSS's ability to

Nouf Alabbasi, Omar Erak, Omar Alhussein, Ismail Lotfi

The telecommunications industry's rapid evolution demands intelligent systems capable of managing complex networks and adapting to emerging technologies. While large language models (LLMs) show promise in addressing these challenges, their deployment in telecom environments faces significant constraints due to edge device limitations and inconsistent documen

Carlos A. Sánchez-Villalobos, Bertrand Delamotte, Nicolás Wschebor

The study of phase transitions in frustrated magnetic systems with $O(N)\times O(2)$ symmetry has been the subject of controversy for more than twenty years, with theoretical, numerical and experimental results in disagreement. Even theoretical studies lead to different results, with some predicting a first-order phase transition while others find it to be s

Donia Ben Amor, Michael Joham, Wolfgang Utschick

In this work, we propose an approach to robust precoder design based on a minorization maximization technique that optimizes a surrogate function of the achievable spectral efficiency. The presented method accounts for channel estimation errors during the optimization process and is, hence, robust in the case of imperfect channel state information (CSI). Add

Sharon Chokuwa, Muhammad Haris Khan

Diabetic Retinopathy (DR) constitutes 5% of global blindness cases. While numerous deep learning approaches have sought to enhance traditional DR grading methods, they often falter when confronted with new out-of-distribution data thereby impeding their widespread application. In this study, we introduce a novel deep learning method for achieving domain gene

Tuomas Hytönen

We characterise the Schatten class $S^p$ properties of commutators $[b,T]$ of singular integrals and pointwise multipliers in a general framework of (quasi-)metric measure spaces. This covers, unifies, and extends a range of previous results in different special cases. As in the classical results on $\mathbb R^d$, the characterisation has three parts: (1) Fo

Mohsen Annabestani, Sandhya Sriram, S. Chiu Wong, Alexandros Sigaras

Extended Reality (XR) technologies are gaining traction as effective tools for medical training and procedural guidance, particularly in complex cardiac interventions. This paper presents a novel system for real-time 3D tracking and visualization of intracardiac echocardiography (ICE) catheters, with precise measurement of the roll angle. A custom 3D-printed

Wei He, Tiago Kramer Vieira, Marcos Garcia, Carolina Scarton

Idiomatic expressions are an integral part of human languages, often used to express complex ideas in compressed or conventional ways (e.g. eager beaver as a keen and enthusiastic person). However, their interpretations may not be straightforwardly linked to the meanings of their individual components in isolation and this may have an impact for compositiona

Toros Arikan, Luca M. Chackalackal, Fatima Ahsan, Konrad Tittel

Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution to the fragile echo labeling problem to be effective. In previous publications, we proposed a general deep learning-based method for two-dimensional environment estimation which ou

Shakiba Davari, Doug A. Bowman

By converting the entire 3D space around the user into a screen, Extended Reality (XR) can ameliorate traditional displays' space limitations and facilitate the consumption of multiple pieces of information at a time. However, if designed inappropriately, these XR interfaces can overwhelm the user and complicate information access. In this work, we explored

Tamer Elkhatib

I report on the experimental observation of DC instability and self-amplification through stimulated emission of 0.2 and 1.63 THz radiation using InGaAs/GaAs HEMT operating in the deep saturation regime at room temperature. I demonstrate both theoretically and experimentally, that the Sub-THz and THz response of FETs are attributable to the rectification of

Timothy A. Burt

In this study, we computed three critical exponents ($\alpha, \beta, \gamma$) for the 3D Ising model with Metropolis Algorithm using Finite-Size Scaling Analysis on six cube length scales (L=20,30,40,60,80,90), and performed a supervised Deep Learning (DL) approach (3D Convolutional Neural Network or CNN) to train a neural network on specific conformations o

Fan Nie, Xiaotian Hou, Shuhang Lin, James Zou

The propensity of Large Language Models (LLMs) to generate hallucinations and non-factual content undermines their reliability in high-stakes domains, where rigorous control over Type I errors (the conditional probability of incorrectly classifying hallucinations as truthful content) is essential. Despite its importance, formal verification of LLM factuality

Mason L. Rhodes, Sam Slezak, Anirban Chowdhury, Yiğit Subaşı

Quantum counting is the task of determining the dimension of the subspace of states that are accepted by a quantum verifier circuit. It is the quantum analog of counting the number of valid solutions to NP problems -- a problem well-studied in theoretical computer science with far-reaching implications in computational complexity. The complexity of solving t

Amna Liaqat, Fannie Liu, Brian Berengard, Jiaxun Cao

Young people are major consumers of Augmented Reality (AR) tools like Pok\'emon GO, but they rarely engage in creating these experiences. Creating with technology gives young people a platform for expressing themselves and making social connections. However, we do not know what young people want to create with AR, as existing AR authoring tools are largely d

Sonali Hedditch, Dhaval Vyas

While HCI scholars have examined how e-textiles serve to bridge the gender divide, there is little research into refugee, asylum seeker and low socioeconomic migrant women (WRAMs) and e-textiles. This paper presents the results of a series of two community-led participatory design workshops to study the factors that enable these women, who face intersecting

Jennifer Grannen, Siddharth Karamcheti, Suvir Mirchandani, Percy Liang

We introduce Vocal Sandbox, a framework for enabling seamless human-robot collaboration in situated environments. Systems in our framework are characterized by their ability to adapt and continually learn at multiple levels of abstraction from diverse teaching modalities such as spoken dialogue, object keypoints, and kinesthetic demonstrations. To enable suc

Alessandro Manta, Harold C. Steinacker, Tung Tran

We elaborate further on the one-loop effective action of the IKKT model on 3 + 1 dimensional covariant quantum spacetime in the presence of fuzzy extra dimensions. In particular, we describe the one-loop effective action in terms of a remarkable $SO(1, 9)$ character, which allows to evaluate the pertinent traces over the internal modes explicitly. This also

Nodirbek Korchiev, Akash Pateria, Vodelina Samatova, Sogolsadat Mansouri

Blockchains are being positioned as the "technology of trust" that can be used to mediate transactions between non-trusting parties without the need for a central authority. They support transaction types that are native to the blockchain platform or user-defined via user programs called smart contracts. Despite the significant flexibility in transaction pro

Mushal Zia, Benjamin Jones, Hongsong Feng, Guo-Wei Wei

Directionality in molecular and biomolecular networks plays a significant role in the accurate represention of the complex, dynamic, and asymmetrical nature of interactions present in protein-ligand binding, signal transduction, and biological pathways. Most traditional techniques of topological data analysis (TDA), such as persistent homology (PH) and persi

Anh Le, Amirreza Hashemi, Mark P. Ottensmeyer, Hamid Sabet

The design of nuclear imaging scanners is crucial for optimizing detection and imaging processes. While advancements have been made in simplistic, symmetrical modalities, current research is progressing towards more intricate structures, however, the widespread adoption of computer-aided design (CAD) tools for modeling and simulation is still limited. This p

A. D. Levin, G. M. Gusev, V. A. Chitta, A. S. Jaroshevich

We investigated magnetotransport in mesoscopic samples containing electrons from three different subbands in GaAs triple wells. At high temperatures, we observed positive magnetoresistance, which we attribute to the imbalance between different types of particles that are sensitive to bulk viscosities. At low temperatures, we found negative magnetoresistance,

Jiawei Zhou, Amy Z. Chen, Darshi Shah, Laura M. Schwab Reese

Recent breakthroughs in large language models (LLMs) have generated both interest and concern about their potential adoption as information sources or communication tools across different domains. In public health, where stakes are high and impacts extend across diverse populations, adopting LLMs poses unique challenges that require thorough evaluation. Howe

Masoud Khalkhali, Damien Tageddine

We construct several $C^*$-algebras and spectral triples associated to the Berkovich projective line $\mathbb{P}^1_{\mathrm{Berk}}({\mathbb{C}_p})$. In the commutative setting, we construct a spectral triple as a direct limit over finite $\mathbb{R}$-trees. More general $C^*$-algebras generated by partial isometries are also presented. We use their represent

Harshavardhana T. Gowda, Zachary D. McNaughton, Lee M. Miller

Objective. In this article, we present data and methods for decoding speech articulations using surface electromyogram (EMG) signals. EMG-based speech neuroprostheses offer a promising approach for restoring audible speech in individuals who have lost the ability to speak intelligibly due to laryngectomy, neuromuscular diseases, stroke, or trauma-induced dam

Hakima Bessaih, Annie Millet

We prove that a semi-implicit time Euler scheme for the two-dimensional B\'enard-Boussinesq model on the torus D converges. The rate of convergence in probability is almost 1/2 for a multiplicative noise; this relies on moment estimates in various norms for the processes and the scheme. In case of an additive noise, due to the coupling of the equations, prov

Philip Lippmann, Konrad Skublicki, Joshua Tanner, Shonosuke Ishiwatari

Due to the significant time and effort required for handcrafting translations, most manga never leave the domestic Japanese market. Automatic manga translation is a promising potential solution. However, it is a budding and underdeveloped field and presents complexities even greater than those found in standard translation due to the need to effectively inco

Mohammad Mahdavian, Mo Chen, Yu Zhang

In this paper, we propose a tracking-based HD mapping algorithm for top-down road images, referred to as tile images. While HD maps traditionally rely on perspective camera images, our approach shows that tile images can also be effectively utilized, offering valuable contributions to this research area as it can be start of a new path in HD mapping algorith

Philipp M. Srivastava, Ugur Demir, Aggelos Katsaggelos, Vicky Kalogera

Modeling of large populations of binary stellar systems is an intergral part of a many areas of astrophysics, from radio pulsars and supernovae to X-ray binaries, gamma-ray bursts, and gravitational-wave mergers. Binary population synthesis codes that employ self-consistently the most advanced physics treatment available for stellar interiors and their evolu

Tobias Mömke, Hang Zhou

The Traveling Salesman Problem (TSP) in the $d$-dimensional Euclidean space is among the oldest and most famous NP-hard optimization problems. In breakthrough works, Arora [J. ACM 1998] and Mitchell [SICOMP 1999] gave the first polynomial time approximation schemes. To improve the running time, Rao and Smith [STOC 1998] gave a randomized $(1/\varepsilon)^{O(

Xian Yeow Lee, Haiyan Wang, Daisuke Katsumata, Takaharu Matsui

Dynamic dispatching rules that allocate resources to tasks in real-time play a critical role in ensuring efficient operations of many automated material handling systems across industries. Traditionally, the dispatching rules deployed are typically the result of manually crafted heuristics based on domain experts' knowledge. Generating these rules is time-co

Connor Pryce, Lanyu Li, Zhao Pan

In this work, we demonstrate the equivalency of the Rotating Parallel Ray Omnidirectional Integration (RPR-ODI) and the Pressure Poisson Equation (PPE) for pressure field reconstruction from corrupted image velocimetry data (dubbed 'ODI $\equiv$ PPE'). Building on the work by Zigunov and Charonko (2024b), we show that performing the ODI is equivalent to purs

Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar

MPI_Alltoallv generalizes the uniform all-to-all communication (MPI_Alltoall) by enabling the exchange of data blocks of varied sizes among processes. This function plays a crucial role in many applications, such as FFT computation and relational algebra operations. Popular MPI libraries, such as MPICH and OpenMPI, implement MPI_Alltoall using a combination

Zahra Ahani, Moein Shahiki Tash, Fazlourrahman Balouchzahi, Luis Ramos

Social support, conveyed through a multitude of interactions and platforms such as social media, plays a pivotal role in fostering a sense of belonging, aiding resilience in the face of challenges, and enhancing overall well-being. This paper introduces Social Support Detection (SSD) as a Natural language processing (NLP) task aimed at identifying supportive

Shuo Tao, Li Zhu

Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian

Joao Basso, Chi-Fang Chen, Alexander M. Dalzell

A central challenge in quantum simulation is to prepare low-energy states of strongly interacting many-body systems. In this work, we study the problem of preparing a quantum state that optimizes a random all-to-all, sparse or dense, spin or fermionic $k$-local Hamiltonian. We prove that a simplified quantum Gibbs sampling algorithm achieves a $\Omega(\frac{

Jill Burstein, Geoffrey T. LaFlair

Validity, reliability, and fairness are core ethical principles embedded in classical argument-based assessment validation theory. These principles are also central to the Standards for Educational and Psychological Testing (2014) which recommended best practices for early applications of artificial intelligence (AI) in high-stakes assessments for automated

Ruishi Zou, Siyi Wu, Racquel Fygenson, Bingsheng Yao

Decision-makers consult multiple forecasts to account for uncertainties when forming judgments about future events. While prior works have compared unaggregated and highly-aggregated designs for displaying multiple forecasts (e.g., Multiple Forecast Visualizations versus confidence interval plots), it remains unclear how partial aggregation impacts judgment.

Carlos Alberto da Silva Junior, Marconi de Arruda Pereira, Angelo Passaro

Complex engineering problems can be modelled as optimisation problems. For instance, optimising engines, materials, components, structure, aerodynamics, navigation, control, logistics, and planning is essential in aerospace. Metaheuristics are applied to solve these optimisation problems. The present paper presents a systematic study on applying metaheuristi

Stephen P. Boyd, Tetiana Parshakova, Ernest K. Ryu, Jaewook J. Suh

We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circuit whose continuous-time dynamics converge to the solution of the optimization problem at hand. Then, the second stage is an automated, comp

Kian Kenyon-Dean, Zitong Jerry Wang, John Urbanik, Konstantin Donhauser

Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To use these images in downstream analysis, we need models that can map each image into a feature space that represents diverse biological phenotypes consistently, in the sense that per

J. M. Sánchez Cerritos, J. A. Martínez-Cadena, A. Marín-López, J. Delgado-Fernández

Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first without adding temporal features and intera

Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoeybi, Jimmy Lin

State-of-the-art retrieval models typically address a straightforward search scenario, in which retrieval tasks are fixed (e.g., finding a passage to answer a specific question) and only a single modality is supported for both queries and retrieved results. This paper introduces techniques for advancing information retrieval with multimodal large language mo

Leonardo Plini, Luca Scofano, Edoardo De Matteis, Guido Maria D'Amely di Melendugno

Identifying procedural errors online from egocentric videos is a critical yet challenging task across various domains, including manufacturing, healthcare, and skill-based training. The nature of such mistakes is inherently open-set, as unforeseen or novel errors may occur, necessitating robust detection systems that do not rely on prior examples of failure.

Johannes Himmelreich, Arbie Hsu, Kristian Lum, Ellen Veomett

A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups -- and verifying that such fairness has been attained. Because intersectional groups tend to be small, verifying whether a model is fair raises statistical as well as moral-methodological challenges. This p

Darko Mitrovic

We consider Leray solutions of the three--dimensional incompressible Navier--Stokes equations on $\R^3$ with smooth, rapidly decaying initial data. The analysis is based on a frequency decomposition into low and high modes via the cutoffs $\A_R=\phi(|D|/R)$ and $\A^R=I-\A_R$. Combining the energy inequality with Bernstein estimates yields uniform control of

Cristian Ciulică

We study the stability at blow-up and deformations of a class of Hermitian metrics whose fundamental two-form $\omega$ satisfies the condition $\partial \bar \partial \omega^k=0$, for any $k$ between 1 and $n-1$ (where $n$ is the complex dimension of the manifold). We are motivated by the existence of compact complex manifold supporting such metrics.

Yifan Tan, Cheng Tan, Zeyu Mi, Haibo Chen

Confidential computing on GPUs, like NVIDIA H100, mitigates the security risks of outsourced Large Language Models (LLMs) by implementing strong isolation and data encryption. Nonetheless, this encryption incurs a significant performance overhead, reaching up to 52.8 percent and 88.2 percent throughput drop when serving OPT-30B and OPT-66B, respectively. To

V. Blavatska, Yu. Holovatch

We consider the model of complex hyperbranched polymer structures formed on the basis of scale-free graphs, where functionalities (degrees) $k$ of nodes obey a power law decaying probability $p(k)\sim{k^{-\alpha}}$. Such polymer topologies can be considered as generalization of regular hierarchical dendrimer structures with fixed functionalities. The conform

Ryan P. Fitzgerald, Bradley Alpert, Denis E. Bergeron, Max Carlson

We demonstrate a method for radionuclide assay that is spectroscopic with 100 % counting efficiency for alpha decay. Advancing both cryogenic decay energy spectrometry (DES) and drop-on-demand inkjet metrology, a solution of Am-241 was assayed for massic activity (of order 100 kBq/g) with a relative combined standard uncertainty less than 1 %. We implement l

Meng Cao, Yuyang Liu, Yingfei Liu, Tiancai Wang

Instruction tuning constitutes a prevalent technique for tailoring Large Vision Language Models (LVLMs) to meet individual task requirements. To date, most of the existing approaches are confined to single-task adaptation, whereas the requirements in real-world scenarios are inherently varied and continually evolving. Thus an ideal LVLM should sustain contin

Petra Tang, Renate Meyer, Jan Eldridge

We analyse the energy spectral density properties of Gravitational waves from Galactic binary populations in the~\text{mHz} band targeted by the Laser Interferometer Space Antenna mission. Our analysis is based on combining BPASS with a Milky Way analogue galaxy from the Feedback In Realistic Environment (FIRE) simulations and the GWs these populations emit.

Zewen Zhuo, Ilya Belevich, Ville Leinonen, Eija Jokitalo

Segmentation of cellular structures in electron microscopy (EM) images is fundamental to analyzing the morphology of neurons and glial cells in the healthy and diseased brain tissue. Current neuronal segmentation applications are based on convolutional neural networks (CNNs) and do not effectively capture global relationships within images. Here, we present

Dayu Yang, Natawut Monaikul, Amanda Ding, Bozhao Tan

In the era of data-driven decision-making, accurate table-level representations and efficient table recommendation systems are becoming increasingly crucial for improving table management, discovery, and analysis. However, existing approaches to tabular data representation often face limitations, primarily due to their focus on cell-level tasks and the lack

Paul Badu Yakubu, Evans Owusu, Lesther Santana, Mohamed Rahouti

Denial of Service (DoS) attacks pose a significant threat in the realm of AI systems security, causing substantial financial losses and downtime. However, AI systems' high computational demands, dynamic behavior, and data variability make monitoring and detecting DoS attacks challenging. Nowadays, statistical and machine learning (ML)-based DoS classificatio

Thilo Krill

We investigate which classes of infinite graphs have the Erd\H{o}s-P\'osa property (EPP). In addition to the usual EPP, we also consider the following infinite variant of the EPP: a class $\mathcal{G}$ of graphs has the $\kappa$-EPP, where $\kappa$ is an infinite cardinal, if for any graph $\Gamma$ there are either $\kappa$ disjoint graphs from $\mathcal{G}$

Niccolò D'Archivio, Amos Korman, Emanuele Natale, Robin Vacus

Understanding how information can efficiently spread in distributed systems under noisy communications is a fundamental question in both biological research and artificial system design. When agents are able to control whom they interact with, noise can often be mitigated through redundancy or other coding techniques, but it may have fundamentally different

Xinhao Zhang, Jinghan Zhang, Wujun Si, Kunpeng Liu

Deep Reinforcement Learning has shown excellent performance in generating efficient solutions for complex tasks. However, its efficacy is often limited by static training modes and heavy reliance on vast data from stable environments. To address these shortcomings, this study explores integrating dynamic weight adjustments into Deep Q-Networks (DQN) to enhan