arXiv papers from October 2024

Matías Bejas, Xianxin Wu, Debmalya Chakraborty, Andreas P. Schnyder

Almost four decades of intense research have been invested to study the physics of high-T$_c$ cuprate superconductors. The recent discovery of high-T$_c$ superconductivity in pressurized bilayer nickelates and its potential similarities with cuprate superconductors may open a new window to understand this long-standing problem. We have studied the proposed b

Jianwen Gan, Yan Chen, Peng Zhou, Liang Du

Most of the research on clustering ensemble focuses on designing practical consistency learning algorithms.To solve the problems that the quality of base clusters varies and the low-quality base clusters have an impact on the performance of the clustering ensemble, from the perspective of data mining, the intrinsic connections of data were mined based on the

Batoul Banihashemi, Ted Jacobson

The saddle point approximation to formal quantum gravitational partition functions has yielded plausible computations of horizon entropy in various settings, but it stands on shaky ground. In this paper we visit some of that shaky ground, address some foundational questions, and describe efforts toward a more solid footing. We focus on the case of de Sitter

Yiwei Wu, Leah Ajmani, Shayne Longpre, Hanlin Li

As new machine learning methods demand larger training datasets, researchers and developers face significant challenges in dataset management. Although ethics reviews, documentation, and checklists have been established, it remains uncertain whether consistent dataset management practices exist across the community. This lack of a comprehensive overview hind

Koffi Ismael Ouattara, Ioannis Krontiris, Theo Dimitrakos, Frank Kargl

Trustworthiness in neural networks is crucial for their deployment in critical applications, where reliability, confidence, and uncertainty play pivotal roles in decision-making. Traditional performance metrics such as accuracy and precision fail to capture these aspects, particularly in cases where models exhibit overconfidence. To address these limitations

Sina Rismanchian, Yasaman Razeghi, Sameer Singh, Shayan Doroudi

Humans have the ability to reason about geometric patterns in images and scenes from a young age. However, developing large multimodal models (LMMs) capable of similar reasoning remains a challenge, highlighting the need for robust evaluation methods to assess these capabilities. We introduce \Turtle, a benchmark designed to evaluate LMMs' capacity to interp

Grace Kim, Luca Powell, Filip Svoboda, Nicholas Lane

Space has emerged as an exciting new application area for machine learning, with several missions equipping deep learning capabilities on-board spacecraft. Pre-processing satellite data through on-board training is necessary to address the satellite downlink deficit, as not enough transmission opportunities are available to match the high rates of data gener

Mehmet Emre Akbulut, Yusuf Erdem Nacar

This paper investigates the relationships among key elements of the scientific research network, namely articles, researchers, and journals. We introduce a novel approach to use semantic information through the HITS algorithm-based propagation of topic information in the network. The topic information is derived by using the Named Entity Recognition and Enti

Allison Naaktgeboren, Sean Noble Anderson, Andrew Tolmach, Greg Sullivan

Fuzzing has proven to be very effective for discovering certain classes of software flaws, but less effective in helping developers process these discoveries. Conventional crash-based fuzzers lack enough information about failures to determine their root causes, or to differentiate between new or known crashes, forcing developers to manually process long, re

Murat Kurt, Ayda Kaltehei, Azmi Gençten, Selçuk Çakmak

In this research, we create a scalable version of the quantum Fourier transform-based arithmetic circuit to perform addition and subtraction operations on N n-bit unsigned integers encoded in quantum registers, and it is compatible with d-level quantum sources, called qudits. We present qubit- and ququart-based multi-input QFT adders, and we compare and disc

David Smerkous, Qinxun Bai, Fuxin Li

Particle-based Bayesian deep learning often requires a similarity metric to compare two networks. However, naive similarity metrics lack permutation invariance and are inappropriate for comparing networks. Centered Kernel Alignment (CKA) on feature kernels has been proposed to compare deep networks but has not been used as an optimization objective in Bayesi

Shiraz Khan, Gregory S. Chirikjian

The Fisher Information Metric (FIM) and the associated Cram\'er-Rao Bound (CRB) are fundamental tools in statistical signal processing, which inform the efficient design of experiments and algorithms for estimating the underlying parameters. In this article, we investigate these concepts for the case where the parameters lie on a homogeneous space. Unlike th

Grace Guo, Jenna Jiayi Kang, Raj Sanjay Shah, Hanspeter Pfister

Vision Language Models (VLMs) have been successful at many chart comprehension tasks that require attending to both the images of charts and their accompanying textual descriptions. However, it is not well established how VLM performance profiles map to human-like behaviors. If VLMs can be shown to have human-like chart comprehension abilities, they can then

Zihe Liu, Diptarka Saha, Feng Liang

This study introduces Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VARD distinguishes itself by its ability to independently assess the smoothness of each feature while enabling precise determination of whether a feature's contribution to the respon

Michael T. Goodrich, Ryuto Kitagawa, Vinesh Sridhar

Ateniese, Goodrich, Lekakis, Papamanthou, Paraskevas, and Tamassia introduced the Accountable Storage protocol, which is a way for a client to outsource their data to a cloud storage provider while allowing the client to periodically perform accountability challenges. An accountability challenge efficiently recovers any pieces of data the server has lost or

Lipismita Panigrahi, Prianka Rani Saha, Jurdana Masuma Iqrah, Sushil Prasad

Clinical diagnosis of breast malignancy (BM) is a challenging problem in the recent era. In particular, Deep learning (DL) models have continued to offer important solutions for early BM diagnosis but their performance experiences overfitting due to the limited volume of breast ultrasound (BUS) image data. Further, large BUS datasets are difficult to manage

Céline Duval, Taher Jalal, Ester Mariucci

In this paper, we study the nonparametric estimation of the density $f_\Delta$ of an increment of a L\'evy process $X$ based on $n$ observations with a sampling rate $\Delta$. The class of L\'evy processes considered is broad, including both processes with a Gaussian component and pure jump processes. A key focus is on processes where $f_\Delta$ is smooth fo

Maxwell Meyer, Jack Spruyt

Transformers and their derivatives have achieved state-of-the-art performance across text, vision, and speech recognition tasks. However, minimal effort has been made to train transformers capable of evaluating the output quality of other models. This paper examines SwinV2-based reward models, called the Input-Output Transformer (IO Transformer) and the Outp

Nhan Tri Tran, Lan Nguyen Tran

Intermolecular charge-transfer (xCT) excited states important for various practical applications are challenging for many standard computational methods. It is highly desirable to have an affordable method that can treat xCT states accurately. In the present work, we extend our self-consistent perturbation methods, named one-body second-order M{\o}ller-Pless

Shaun Fallat, Himanshu Gupta, Allen Herman, Johnna Parenteau

We study the minimum number of distinct eigenvalues over a collection of matrices associated with a graph. Lower bounds are derived based on the existence or non-existence of certain cycle(s) in a graph. A key result proves that every Johnson graph has a signed variant with exactly two distinct eigenvalues. We also explore applications to weighing matrices,

Muhieddine Shebaro, Lucas Rusnak, Martin Burtscher, Jelena Tešić

Spectral clustering methodologies, when extended to accommodate signed graphs, have encountered notable limitations in effectively encapsulating inherent grouping relationships. Recent findings underscore a substantial deterioration in the efficacy of spectral clustering methods when applied to expansive signed networks. We introduce a scalable hierarchical

Yubin Kim, Chanwoo Park, Hyewon Jeong, Cristina Grau-Vilchez

Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (LLMs) promise to streamline this process by synthesizing vast medical knowledge and multi-modal health data. However, single-agent are often ill-suited for nuanced medical contexts

Alan Jeffares, Alicia Curth, Mihaela van der Schaar

Deep learning sometimes appears to work in unexpected ways. In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network consisting of a sequence of first-order approximations telescoping out into a single empirically operational tool for practical analysis. Across thr

Lorenzo Basile, Valentino Maiorca, Luca Bortolussi, Emanuele Rodolà

When examined through the lens of their residual streams, a puzzling property emerges in transformer networks: residual contributions (e.g., attention heads) sometimes specialize in specific tasks or input attributes. In this paper, we analyze this phenomenon in vision transformers, focusing on the spectral geometry of residuals, and explore its implications

Jonathan Horner, Robert A. Wittenmyer, Stephen R. Kane, Timothy R. Holt

In this work, we examine seven systems discovered by TESS, to see whether there is any room in those systems for an additional planet (or several) to lurk unseen between the two planets already confirmed therein. In five of those systems (namely HD 15337; HD 21749; HD 63433; HD 73583 and LTT 3780) we find that there is ample room for an undiscovered planet t

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

Enhanced diffusion, which describes the accelerated spread of passive scalars due to the interaction between advection and molecular diffusion, has been extensively studied in simplified geometries, such as uniform shear and radial flows. However, many real-world applications occur in complex, anisotropic domains where standard assumptions do not hold. This

Alexei Bazavov, Brandon Henke, Leon Hostetler, Dean Lee

We present a comparison of different quantum state preparation algorithms and their overall efficiency for the Schwinger model with a theta term. While adiabatic state preparation (ASP) is proved to be effective, in practice it leads to large CNOT gate counts to prepare the ground state. The quantum approximate optimization algorithm (QAOA) provides excellen

Gustavo Arengas

Software bugs have caused enormous economic and human loss in recent years. Certified programming seeks to solve this problem by developing languages where we can make demonstrations that guarantee that our programs work properly. However, the rapid evolution of modern algorithms constantly forces us to develop new tools for this task. In this article we exp

Bill Fan, Jacob Roulier, Gina Olson

Soft robot arms have made significant progress towards completing human-scale tasks, but designing arms for tasks with specific load and workspace requirements remains difficult. A key challenge is the lack of model-based design tools, forcing advancement to occur through empirical iteration and observation. Existing models are focused on control and rely on

Ruiming Guo, Ayush Bhandari

In recent years, computational Time-of-Flight (ToF) imaging has emerged as an exciting and a novel imaging modality that offers new and powerful interpretations of natural scenes, with applications extending to 3D, light-in-flight, and non-line-of-sight imaging. Mathematically, ToF imaging relies on algorithmic super-resolution, as the back-scattered sparse

Kensuke Akita, Gideon Baur, Maksym Ovchynnikov, Thomas Schwetz

We investigate decays of hypothetical unstable new physics particles into metastable species such as muons, pions, or kaons in the Early Universe, when temperatures are in the MeV range, and study how they affect cosmic neutrinos. We demonstrate that the non-trivial dynamics of metastables in the plasma alters the impact of the new physics particles on the n

John P. Ralston

While ball lighting is known to exist from thousands of observations, its properties have never been explained by known physics. The combined order of magnitude of power, size, time scale, and characteristic behavior of ball lightning have defeated every model. The failure of standard physics does not hinge on fine details, but on the breadth of qualitative

Shaohua Liu, Junzhe Lu, Zuoya Gu, Jiajun Li

Representing underwater 3D scenes is a valuable yet complex task, as attenuation and scattering effects during underwater imaging significantly couple the information of the objects and the water. This coupling presents a significant challenge for existing methods in effectively representing both the objects and the water medium simultaneously. To address th

Declan Campbell, Sunayana Rane, Tyler Giallanza, Nicolò De Sabbata

Recent work has documented striking heterogeneity in the performance of state-of-the-art vision language models (VLMs), including both multimodal language models and text-to-image models. These models are able to describe and generate a diverse array of complex, naturalistic images, yet they exhibit surprising failures on basic multi-object reasoning tasks -

P. Tsintari, N. Dimitrakopoulos, R. Garg, K. Hermansen

The synthesis of heavy elements in supernovae is affected by low-energy (n,p) and (p,n) reactions on unstable nuclei, yet experimental data on such reaction rates are scarce. The SECAR (SEparator for CApture Reactions) recoil separator at FRIB (Facility for Rare Isotope Beams) was originally designed to measure astrophysical reactions that change the mass of

Mohamad Hossein Idjadi, Stefano Grillanda, Nicolas Fontaine, Mikael Mazur

We demonstrate dual-wavelength distributed acoustic sensing over 37 km of standard single-mode fiber using $\phi$-OFDR, utilizing a scalable hybrid-integrated dual-wavelength laser chip frequency-locked to a high-Q integrated SiN coil resonator.

Ross J. Jennings, James M. Cordes, Shami Chatterjee

Time-of-arrival (TOA) measurements of pulses from pulsars are conventionally made by a template matching algorithm that compares a profile constructed by averaging a finite number of pulses to a long-term average pulse shape. However, the shapes of pulses can and do vary, leading to errors in TOA estimation. All pulsars show stochastic variations in shape, a

Jonathan Conrad, Jens Eisert, Steven T. Flammia

We consider the task of performing shadow tomography of a logical subsystem defined via the Gottesman-Kitaev-Preskill (GKP) error correcting code. Our protocol does not require the input state to be a code state but is implemented by appropriate twirling of the measurement channel, such that the encoded logical tomographic information becomes encoded in the

Mohamad Hossein Idjadi, Farshid Ashtiani, Kwangwoong Kim

We demonstrate a modulation-free laser stabilization system using a cavity-coupled MZI with aided acquisition on a low-loss SiN chip, achieving more than an order-of-magnitude improvement in locking range and over 36 dB noise suppression.

José Ignacio Olalde-Verano, Sascha Kirch, Clara Pérez-Molina, Sergio Martin

The state of health (SOH) of a Li-ion battery is a critical parameter that determines the remaining capacity and the remaining lifetime of the battery. In this paper, we propose SambaMixer a novel structured state space model (SSM) for predicting the state of health of Li-ion batteries. The proposed SSM is based on the MambaMixer architecture, which is desig

Gioacchino Antonelli, Robert Young

Let $\mathbb H$ denote the three-dimensional Heisenberg group. In this paper, we study vertical curves in $\mathbb H$ and fibers of maps $\mathbb H \to \mathbb R^2$ from a metric perspective. We say that a set in $\mathbb H$ is a vertical curve if it satisfies a cone condition with respect to a homogeneous cone with axis $\langle Z \rangle$, the center of $\

Aditya Konale, Vikas Srivastava

Soft polymers are ubiquitous materials in nature and as engineering materials with properties varying from rate-independent to rate-dependent. Current fracture toughness measures are non-unique for rate-dependent soft materials for varying loading profiles and specimen geometries. Works on modeling fracture in rate-dependent soft polymers are limited to spec

Akash Kundu, Leopoldo Sarra

Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large scale combinatorial problems, but its practical use is limited by device noise and constrained connectivity. Designing quantum circuits, which are fundamental to quantum algorithms, is therefore a central challenge in current quantum hardware. Existing

Kapilan Balagopalan, Kwang-Sung Jun

We propose a novel linear bandit algorithm called LinMED (Linear Minimum Empirical Divergence), which is a linear extension of the MED algorithm that was originally designed for multi-armed bandits. LinMED is a randomized algorithm that admits a closed-form computation of the arm sampling probabilities, unlike the popular randomized algorithm called linear T

Eyal Subag

We study and classify algebraic families of Harish-Chandra pairs over the complex affine line and over the complex projective line with generic fiber that is isomorphic to the Harish-Chandra pair of $SL_2(\mathbb{R})$.

Jonathan Zung

We present a combinatorial approach to the existence of foliations and contact structures transverse to a given pseudo-Anosov flow. Let $\varphi$ be a transitive pseudo-Anosov flow on a closed oriented 3-manifold. Our main technical result is that every codimension 1 foliation transverse to $\varphi$ is carried by a single branched surface coming from a veer

Brendan Hassett, Yuri Tschinkel

We provide new stable linearizability constructions for regular actions of finite groups on homogeneous spaces and low-dimensional quadrics.

Johanna Karras, Yingwei Li, Nan Liu, Luyang Zhu

We present Fashion-VDM, a video diffusion model (VDM) for generating virtual try-on videos. Given an input garment image and person video, our method aims to generate a high-quality try-on video of the person wearing the given garment, while preserving the person's identity and motion. Image-based virtual try-on has shown impressive results; however, existin

Gholamreza Davarpanah, Sajjad Mohammadi

A new three-phase hybrid-excited multi-tooth switched reluctance motor with embedded permanent magnets is proposed, capable of achieving higher torque density for transportation electrification applications. Operating principles and design considerations are discussed. A magnetic equivalent circuit is developed. Finite element method is employed in the field

Sara Honarvar, Yancy Diaz-Mercado

This paper presents a spatio-temporal inverse optimal control framework for understanding interactions in multi-agent systems (MAS). We employ a graph representation approach and model the dynamics of interactions between agents as state-dependent edge weights in a consensus algorithm, incorporating both spatial and temporal dynamics. Our method learns these

Ehsan Ganjidoost, Jeff Orchard

An adversarial example is a modified input image designed to cause a Machine Learning (ML) model to make a mistake; these perturbations are often invisible or subtle to human observers and highlight vulnerabilities in a model's ability to generalize from its training data. Several adversarial attacks can create such examples, each with a different perspectiv

Zachary Tam, Karthik Dharmarajan, Tianshuang Qiu, Yahav Avigal

In logistics, the ability to quickly compute and execute pick-and-place motions from bins is critical to increasing productivity. We present Bin-Optimized Motion Planning (BOMP), a motion planning framework that plans arm motions for a six-axis industrial robot with a long-nosed suction tool to remove boxes from deep bins. BOMP considers robot arm kinematics

Nephtalí Eliceo Martínez Pérez, Cupatitzio Ramírez Romero

Continuous symmetries of spacetime such as spatial homogeneity and isotropy are rigorously defined using the concept of isometries and Killing vectors. In supergravity, the metric, or rather the tetrad, is not a standalone entity, but is part of a multiplet containing also the Rarita-Schwinger spinor-vector. Thus, we pursue a generalization of the Killing eq

Jessica Renz, Kazeem A. Dauda, Olav N. L. Aga, Ramon Diaz-Uriarte

Can we understand and predict the evolutionary pathways by which bacteria acquire multi-drug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of antimicrobial resistance (AMR). Here, we review how a class of machine learning approaches called evolutionary acc

Fabian Gonzalez, O. Deniz Akyildiz, Dan Crisan, Joaquin Miguez

Nudging is a popular algorithmic strategy in numerical filtering to deal with the problem of inference in high-dimensional dynamical systems. We demonstrate in this paper that general nudging techniques can also tackle another crucial statistical problem in filtering, namely the misspecification of the transition kernel. Specifically, we rely on the formulat

Haozhe Lei, Yunfei Ge, Quanyan Zhu

The integration of AI into modern critical infrastructure systems, such as healthcare, has introduced new vulnerabilities that can significantly impact workflow, efficiency, and safety. Additionally, the increased connectivity has made traditional human-driven penetration testing insufficient for assessing risks and developing remediation strategies. Consequ

Hsien-Chih Chang, Vincent Cohen-Addad, Jonathan Conroy, Hung Le

Cohen-Addad, Le, Pilipczuk, and Pilipczuk [CLPP23] recently constructed a stochastic embedding with expected $1+\varepsilon$ distortion of $n$-vertex planar graphs (with polynomial aspect ratio) into graphs of treewidth $O(\varepsilon^{-1}\log^{13} n)$. Their embedding is the first to achieve polylogarithmic treewidth. However, there remains a large gap betw

Linyang Zou, Hao Hu, Haotian Wu, Yang Long

Flat bands typically describe energy bands whose energy dispersion is entirely or almost entirely degenerate. One effective method to form flat bands is by constructing Moir\'e superlattices. Recently, there has been a shift in perspective regarding the roles of space (momentum) and time (energy) in a lattice, with the concept of photonic time crystals that

Arianna Bunnell, Dustin Valdez, Thomas K. Wolfgruber, Brandon Quon

Background: Breast density, as derived from mammographic images and defined by the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound (BUS) is an alternative breast cancer screening modality, particularly useful for early detection in low-resource, rura

Abhinav Kumar, Kirankumar Shiragur, Caroline Uhler

The ability to conduct interventions plays a pivotal role in learning causal relationships among variables, thus facilitating applications across diverse scientific disciplines such as genomics, economics, and machine learning. However, in many instances within these applications, the process of generating interventional data is subject to noise: rather than

D. Karlovets, A. Chaikovskaia, D. Grosman, D. Kargina

Cherenkov radiation of charged particles moving with superluminal velocities in transparent media is a well-studied phenomenon with a plethora of applications. Its microscopic origins can be traced to the polarization of atomic shells, characterized by time scales in the subfemtosecond range - dynamics that eludes conventional macroscopic treatment. Here we

Lucas N. S. Martins

In this work, we investigate off-shell supersymmetric string configurations in Euclidean Ad$\mathbb{S}_5\times \mathbb{S}^5$ background. By extending the Green-Schwarz supersymmetric equations using the pure spinor formalism, we demonstrate that while the solutions to the Green-Schwarz equations are necessarily minimal surfaces, the solutions to the extended

Shreelekha Revankar, Cheng Perng Phoo, Utkarsh Mall, Bharath Hariharan

Recognition of features in satellite imagery (forests, swimming pools, etc.) depends strongly on the spatial scale of the concept and therefore the resolution of the images. This poses two challenges: Which resolution is best suited for recognizing a given concept, and where and when should the costlier higher-resolution (HR) imagery be acquired? We present

Thanh-Dung Le, Vu Nguyen Ha, Ti Ti Nguyen, Geoffrey Eappen

This study presents an innovative dynamic weighting knowledge distillation (KD) framework tailored for efficient Earth observation (EO) image classification (IC) in resource-constrained settings. Utilizing EfficientViT and MobileViT as teacher models, this framework enables lightweight student models, particularly ResNet8 and ResNet16, to surpass 90% in accu

Luc Jonveaux

This paper presents a new approach to urban sustainability assessment through the use of Large Language Models (LLMs) to streamline the use of the ISO 37101 framework to automate and standardise the assessment of urban initiatives against the six "sustainability purposes" and twelve "issues" outlined in the standard. The methodology includes the development

Benjamin Jones

We introduce a class of left cancellative categories we call ordinal graphs for which there is a functor $d:\Lambda\rightarrow\mathrm{Ord}$ by which morphisms of $\Lambda$ factor. We use generators and relations to study the Cuntz-Krieger algebra $\mathcal{O}\left(\Lambda\right)$ defined by Spielberg. In particular, we construct a $\mathrm{C}^{*}$-correspond

Anastasia Siapka

The debate surrounding the 'future of work' is saturated with alarmist warnings about the loss of work as an intrinsically valuable activity. Instead, the present doctoral research approaches this debate from the perspective of human flourishing (eudaimonia). It articulates a neo-Aristotelian interpretation according to which the prospect of mass AI-driven a

Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte, Sanjit A. Seshia

Goal-conditioned reinforcement learning is a powerful way to control an AI agent's behavior at runtime. That said, popular goal representations, e.g., target states or natural language, are either limited to Markovian tasks or rely on ambiguous task semantics. We propose representing temporal goals using compositions of deterministic finite automata (cDFAs)

Keivan Rezaei, Khyathi Chandu, Soheil Feizi, Yejin Choi

Large language models trained on web-scale corpora can memorize undesirable data containing misinformation, copyrighted material, or private or sensitive information. Recently, several machine unlearning algorithms have been proposed to eliminate the effect of such datapoints from trained models -- that is, to approximate a model that had never been trained

Dairong Chen, Augustin Couton Wyporek, Pierre Chailloleau, Ahmed Sidi El Valli

Magnetic Tunnel Junctions (MTJs) are of great interest for non-conventional computing applications. The Toffoli gate is a universal reversible logic gate, enabling the construction of arbitrary boolean circuits. Here, we present a proof-of-concept construction of a gadget which encodes the Toffoli gate's truth table into the ground state of coupled uniaxial

CMS Collaboration

A search is presented for a heavy resonance decaying into a Z boson and a Higgs (H) boson. The analysis is based on data from proton-proton collisions at a centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of 138 fb$^{-1}$, recorded with the CMS experiment in the years 2016-2018. Resonance masses between 1.4 and 5 TeV are considered,

Nidhal Jegham, Chan Young Koh, Marwan Abdelatti, Abdeltawab Hendawi

This study presents a comprehensive benchmark analysis of various YOLO (You Only Look Once) algorithms. It represents the first comprehensive experimental evaluation of YOLOv3 to the latest version, YOLOv12, on various object detection challenges. The challenges considered include varying object sizes, diverse aspect ratios, and small-sized objects of a sing

Nassim Oufattole, Teya Bergamaschi, Aleksia Kolo, Hyewon Jeong

Effective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-quality baseline models for diverse supervised learning tasks in an efficient and performant manner. Historically, producing such baseline models has been a largely manual effort--i

Yongxuan Yan, Fumitake Fujii, Takehiro Shiinoki

This study investigates the noise characteristics of intraoperative X-ray fluoroscopic images acquired during real-time image-guided radiotherapy (IGRT), and presents a novel noise image generation method based on the identified noise amplitude and spatial probability patterns. Initially, noise-free digitally reconstructed radiographs (DRRs) were generated u

Kan Li, José C. Príncipe

Motivated by the surge of interest in Koopman operator theory, we propose a machine-learning alternative based on a functional Bayesian perspective for operator-theoretic modeling of unknown, data-driven, nonlinear dynamical systems. This formulation is directly done in an infinite-dimensional space of linear operators or Hilbert space with universal approxi

James Li, Noam Zilberstein, Alexandra Silva

While there is a long tradition of reasoning about (non)termination in program analysis, specialized logics are typically needed to give different termination criteria. This includes partial correctness, where termination is not guaranteed, and total correctness, where it is guaranteed. We present Total Outcome Logic (TOL), a single logic which can express t

Brody McNutt, Libby Zhang, Angus Carey-Douglas, Fritz Vollrath

This research represents a pioneering application of automated pose estimation from drone data to study elephant behavior in the wild, utilizing video footage captured from Samburu National Reserve, Kenya. The study evaluates two pose estimation workflows: DeepLabCut, known for its application in laboratory settings and emerging wildlife fieldwork, and YOLO-

Marcello Carammia, Stefano Maria Iacus, Giuseppe Porro

Large Language Models (LLMs) are distinguished by their architecture, which dictates their parameter size and performance capabilities. Social scientists have increasingly adopted LLMs for text classification tasks, which are difficult to scale with human coders. While very large, closed-source models often deliver superior performance, their use presents si

Aris J. Aristorenas

This study presents a machine learning framework for assessing similarity between audio content and predicting sentiment score. We construct a dataset containing audio samples from music covers on YouTube along with the audio of the original song, and sentiment scores derived from user comments, serving as proxy labels for content quality. Our approach invol

Linjin Zheng, M. T. Kotschenreuther, F. L. Waelbroeck, M. E. Austin

The flux coordinates with dual-region safety factor (q) in the poloidal direction are developed in this work. The X-point effects on the ideal MHD modes in tokamaks are then analyzed using this coordinate system. Since the X-point effects mainly affect the edge region, the modes localized at the tokamak edge are particularly examined. Two types of modes are

Marek Kirejczyk, Maciej Kalka, Leonid Logvinov

This paper presents a comprehensive analysis of storage proofs in the Ethereum ecosystem, examining their role in addressing historical and cross-chain state access challenges. We systematically review existing approaches to historical state verification, comparing Merkle Mountain Range (MMR) and Merkle-Patricia trie (MPT) architectures. An analysis involves

Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang

Monocular Depth Estimation (MDE) is a pivotal component of vision-based Autonomous Driving (AD) systems, enabling vehicles to estimate the depth of surrounding objects using a single camera image. This estimation guides essential driving decisions, such as braking before an obstacle or changing lanes to avoid collisions. In this paper, we explore vulnerabili

David Breitenmoser

This monograph presents advancements in Airborne Gamma-Ray Spectrometry (AGRS), a critical tool for emergency response to radiological incidents such as severe nuclear accidents or nuclear weapon detonations. Current AGRS calibration and data evaluation methods struggle to accurately quantify many radioactive materials expected in radiological emergencies, l

Jonas M. Mikhaeil, Donald P. Green

A growing statistical literature focuses on causal inference in the context of experiments where the target of inference is the average treatment effect in a finite population and random assignment determines which subjects are allocated to one of the experimental conditions. In this framework, variances of average treatment effect estimators remain unidenti

Tempest A. van Schaik, Xinggang Liu, Louis Atallah, Omar Badawi

This work proposes a fairness monitoring approach for machine learning models that predict patient mortality in the ICU. We investigate how well models perform for patient groups with different race, sex and medical diagnoses. We investigate Documentation bias in clinical measurement, showing how fairness analysis provides a more detailed and insightful comp

Bahadır Utku Kesgin, Uğur Teğin

Optical computing has gained significant attention as a potential solution to the growing computational demands of machine learning, particularly for tasks requiring large-scale data processing and high energy efficiency. Optical systems offer promising alternatives to digital neural networks by exploiting light's parallelism. This study explores a photonic

Yu Pan, Jianxin Sun, Hongfeng Yu, Joe Luck

Current agricultural data management and analysis paradigms are to large extent traditional, in which data collecting, curating, integration, loading, storing, sharing and analyzing still involve too much human effort and know-how. The experts, researchers and the farm operators need to understand the data and the whole process of data management pipeline to

Carlo Bellavita, Vassilis Daskalogiannis, Georgios Stylogiannis

This article aims to explore the most recent developments in the study of the Hilbert matrix, acting as an operator on spaces of analytic functions and sequence spaces. We present the latest advances in this area, aiming to provide a concise overview for researchers interested in delving into the captivating theory of operator matrices.

Ryan Zhang, Herbert Woisetschläger, Shiqiang Wang, Hans Arno Jacobsen

Open-weight large language model (LLM) zoos allow users to quickly integrate state-of-the-art models into systems. Despite increasing availability, selecting the most appropriate model for a given task still largely relies on public benchmark leaderboards and educated guesses. This can be unsatisfactory for both inference service providers and end users, whe

Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar

Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their reason-agnostic nature. By choosing from a pre-defined set of actions, such methods implicitly assume t

Ilia Mikhailov, Anton Domnin, Robert Evarestov

This work sheds light on the electronic band properties of chiral WSSe Janus nanotubes from the quantum mechanical perspective. Line groups theory developed by Damnjanovich was used to model mechanical torsion of chiral nanotubes with different symmetries. Known natural torsion in chiral nanotubes was observed. It was shown that mechanical stress can be used

Amirhossein Yazdkhasti, Hendrik De Klerk, Andreea Renata Lucaciu, Rana Moeinzad

The current methods of assessing tendon health such as clinical examination, imaging techniques, and implanted pressure sensors, are often based on a subjective assessment or are not accurate enough, are extremely expensive, or are limited to relatively large damage such as partial or gross tear of the tendon and cannot accurately assess and monitor smaller

Livia Deme, Krisztián Sárneczky, Antal Igaz, Balázs Csák

We present statistical analysis of video meteor observations for the Perseid and Geminid showers taken with two camera systems operating in Hungary from the end of 2019 through 2023. Zenithal hourly rates (ZHR) and meteor fluxes, determined by MetRec-based analog video cameras HUKON, HUPIS and HUHOD, are inferred and compared with detections of slow fireball

Moulay-Tahar Benameur, Victor Moulard

Given a finitely generated discrete group {\Gamma}, we construct for any admissible crossed product completion and for any metrizable finite dimensional compact {\Gamma}-space X, a universal Higson-Roe six-term exact sequence for the transformation groupoid X\rtimes {\Gamma}. In particular, we generalize the maximal Higson- Roe sequence to such groupoids. In

Curtis Bechtel, Shaddin Dughmi

Consider a principal who wants to search through a space of stochastic solutions for one maximizing their utility. If the principal cannot conduct this search on their own, they may instead delegate this problem to an agent with distinct and potentially misaligned utilities. This is called delegated search, and the principal in such problems faces a mechanis

Felix Koehler, Simon Niedermayr, Rüdiger Westermann, Nils Thuerey

We introduce the Autoregressive PDE Emulator Benchmark (APEBench), a comprehensive benchmark suite to evaluate autoregressive neural emulators for solving partial differential equations. APEBench is based on JAX and provides a seamlessly integrated differentiable simulation framework employing efficient pseudo-spectral methods, enabling 46 distinct PDEs acro

Mark Díaz, Angela DR Smith

Human experts are often engaged in the development of machine learning systems to collect and validate data, consult on algorithm development, and evaluate system performance. At the same time, who counts as an 'expert' and what constitutes 'expertise' is not always explicitly defined. In this work, we review 112 academic publications that explicitly referen

Panagiota Gatoula, Dimitrios E. Diamantis, Anastasios Koulaouzidis, Cristina Carretero

Synthetic Data Generation (SDG) based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered by overcoming privacy barriers that currently render clinical data sharing difficult. This is the key to accelerating the development of digital tools contributing to enhanced patient safety. Such tools include robust data-driven clinica

Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers, Adji Bousso Dieng

Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based materials property prediction, which hinders progress. We present LLM4Mat-Bench, the largest benchmark to date for evaluating the performance of LLMs in predicting the properties of

Wencai Liu, Matthew Powell, Xueyin Wang

We employ Weyl's method and Vinogradov's method to analyze skew-shift dynamics on semi-algebraic sets. Consequently, we improve the quantum dynamical upper bounds of Jitomirskaya-Powell, Liu, and Shamis-Sodin for long-range operators with skew-shift potentials.