November 2024 arXiv papers — page 95

David Jewitt

Non-gravitational forces play surprising and, sometimes, centrally important roles in shaping the motions and properties of small planetary bodies. In the solar system, the morphologies of comets, the delivery of meteorites and the shapes and dynamics of asteroids are all affected by non-gravitational forces. In exoplanetary systems and debris disks, non-gra

Wentao Bao, Kai Li, Yuxiao Chen, Deep Patel

Action detection aims to detect (recognize and localize) human actions spatially and temporally in videos. Existing approaches focus on the closed-set setting where an action detector is trained and tested on videos from a fixed set of action categories. However, this constrained setting is not viable in an open world where test videos inevitably come beyond

Divya Chalise, Sean D. Lubner, Sumanjeet Kaur, Venkat Srinivasan

Understanding the performance of electrochemical energy storage systems requires probing the electrochemical properties at each layer and interface during cell operation. While traditional onboard and operando methods can measure impedance, voltage, or capacity, they lack spatial resolution to pinpoint the properties to specific layers and interfaces. In thi

Arushi Gupta, Rafal Kocielnik, Jiayun Wang, Firdavs Nasriddinov

During surgical training, real-time feedback from trainers to trainees is important for preventing errors and enhancing long-term skill acquisition. Accurately predicting the effectiveness of this feedback, specifically whether it leads to a change in trainee behavior, is crucial for developing methods for improving surgical training and education. However,

Danial Abshari, Peiran Shi, Chenglong Fu, Meera Sridhar

Cyber-Physical Systems (CPS) are vulnerable to cyber-physical attacks that violate physical laws. While invariant-based anomaly detection is effective, existing methods are limited: data-driven approaches lack semantic context, and physics-based models require extensive manual work. We propose INVARLLM, a hybrid framework that uses large language models (LLM

Mukhit Kulmaganbetov, Taranjit Singh, Dmitry Pushin, Pinki Chahal

Psychophysical discrimination of structured light (SL) stimuli may be useful in screening for various macular disorders, including degenerative macular diseases. The circularly-oriented macular pigment optical density (coMPOD), calculated from the discrimination performance of SL-induced entoptic phenomena, may reveal a novel functional biomarker of macular

Gabriella Bretti, Cristina M. Belfiore

In this paper we propose a mathematical model of the capillary and permeability properties of lime-based mortars from the historic built heritage of Catania (Sicily, Italy) produced by using two different types of volcanic aggregate, i.e. ghiara and azolo. In order to find a formulation for the capillary pressure and the permeability as functions of the satu

A. D. White, S. Popa, J. Mellado-Munoz, N. J. Fitch

We study the properties of a cryogenic buffer gas source that uses a low temperature two-stage buffer gas cell to produce very slow beams of ytterbium monofluoride molecules. The molecules are produced by laser ablation inside the cell and extracted into a beam by a flow of cold helium. We measure the flux and velocity distribution of the beam as a function

Wolfgang Hoegele, Sarah Brockhaus

We introduce a general framework for regression in the errors-in-variables regime, allowing for full flexibility about the dimensionality of the data, observational error probability density types, the (nonlinear) model type and the avoidance of ad-hoc definitions of loss functions. In this framework, we introduce model fitting for partially unpaired data, i

Jing Zhao, Guangru Bai, Qian Zhang, Bin Zhang

Attosecond transient absorption spectroscopy (ATAS) is becoming an indispensable and powerful tool in the emerging field of attochemistry, while the interpretation of measurements often requires full considerations of the coupling among various freedoms of motion. Here we develop the ionization-coupling model to incorporate the transient absorption and explo

Dieter Bothe, Jun Liu, Pierre-Etienne Druet, Tomislav Maric

An analytical derivation of the buoyancy-induced initial acceleration of a spherical gas bubble in a host liquid is presented. The theory makes no assumptions further than applying the two-phase incompressible Navier-Stokes equations, showing that neither the classical approach using potential theory nor other simplifying assumptions are needed. The result f

Yufei Guo, Muzhe Guo, Juntao Su, Zhou Yang

Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various NLP tasks

Sizhe Wang, Yongqi Tong, Hengyuan Zhang, Dawei Li

Reinforcement Learning with Human Feedback (RLHF) is the key to the success of large language models (LLMs) in recent years. In this work, we first introduce the concepts of knowledge breadth and knowledge depth, which measure the comprehensiveness and depth of an LLM or knowledge source respectively. We reveal that the imbalance in the number of prompts and

Alessandro Fontanella, Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang

Text-to-image diffusion generative models can generate high quality images at the cost of tedious prompt engineering. Controllability can be improved by introducing layout conditioning, however existing methods lack layout editing ability and fine-grained control over object attributes. The concept of multi-layer generation holds great potential to address t

Quan Ze Chen, K. J. Kevin Feng, Chan Young Park, Amy X. Zhang

When different groups' values differ, one approach to model alignment is to steer models at inference time towards each group's preferences. However, techniques like in-context learning only consider similarity when drawing few-shot examples and not cross-group differences in values. We propose SPICA, a framework that accounts for group-level differences dur

Junlan Liu, Qian Yin, Mengshu He, Jun Zhou

The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations, using the moment tensor potential (MTP) as a reference. The low root mean square

Athanasios P. lliopoulos, Evelyn Lunasin, John G. Michopoulos, Steven N. Rodriguez

This study presents a method, along with its algorithmic and computational framework implementation, and performance verification for dynamical system identification. The approach incorporates insights from phase space structures, such as attractors and their basins. By understanding these structures, we have improved training and testing strategies for oper

Lucas A. Oliveira, Wei Chen

By applying the projector to the filled lattice eigenstates on a specific position, or applying the local electron annihilation operator on the many-body ground state, one can construct a quantum state localized around a specific position in a solid. The overlap of two such local states at slightly different positions defines a quantum metric in real space,

Vardis Kandiros, Charilaos Pipis, Constantinos Daskalakis, Christopher Harshaw

A fundamental problem in network experiments is selecting an appropriate experimental design in order to precisely estimate a given causal effect of interest. In this work, we propose the Conflict Graph Design, a general approach for constructing experiment designs under network interference with the goal of precisely estimating a pre-specified causal effect

Matthew J. Dula, Naim Shandi, Jeffrey A. Nanzer

Distributed phased arrays have recently garnered interest in applications such as satellite communications and high-resolution remote sensing. High-performance coherent distributed operations such as distributed beamforming are dependent on the ability to synchronize the spatio-electrical states of the elements in the array to the order of the operational wa

Philips George John, Arnab Bhattacharyya, Silviu Maniu, Dimitrios Myrisiotis

Reinforcement learning algorithms are usually stated without theoretical guarantees regarding their performance. Recently, Jin, Yang, Wang, and Jordan (COLT 2020) showed a polynomial-time reinforcement learning algorithm (namely, LSVI-UCB) for the setting of linear Markov decision processes, and provided theoretical guarantees regarding its running time and

Samyadip Sarkar, Qi Huang, Sarthak Antal, Mayukh Nath

Seamless interaction between Humans and AI-empowered battery-operated miniaturized electronic devices, exponentially transforming the wearable technology industry while forming an anthropomorphic artificial nervous system for distributed computing around the human body, demands high-speed low-power connectivity. If interconnected via radio frequency (RF) bas

John M. Neuberger, Nándor Sieben, James W. Swift

In a polydiagonal subspace of the Euclidean space, certain components of the vectors are equal (synchrony) or opposite (anti-synchrony). Polydiagonal subspaces invariant under a matrix have many applications in graph theory and dynamical systems, especially coupled cell networks. We describe invariant polydiagonal subspaces in terms of coloring vectors. This

Isaac A. Harris, Sajid Husain, Peter Meisenheimer, Maya Ramesh

Magnons carriers of spin information can be controlled by electric fields in the multiferroic BiFeO$_3$ (BFO), a milestone that brings magnons closer to application in future devices. The origin of magnon-spin currents in BFO, however, is not fully understood due to BFO's complicated magnetic texture. In this letter, we present a phenomenological model to el

Mohammadhamed Tangestanizadeh, Mohammad Dehghani Tezerjani, Saba Yousefian Jazi

Lane detection involves identifying lanes on the road and accurately determining their location and shape. This is a crucial technique for modern assisted and autonomous driving systems. However, several unique properties of lanes pose challenges for detection methods. The lack of distinctive features can cause lane detection algorithms to be confused by oth

Xilong Xu, Haonan Wang, Li Yang

The recent discovery of topological flat bands in twisted transition metal dichalcogenide homobilayers and multilayer graphene has sparked significant research interest. We propose a new platform for realizing tunable topological moire flat bands: twisted type-II Rashba homobilayers. The interplay between Rashba spin-orbit coupling and interlayer interaction

Amit Jain, Puneet Singla, Roshan Eapen

In this work, we leverage the Hamiltonian kind structure for accurate uncertainty propagation through a nonlinear dynamical system. The developed approach utilizes the fact that the stationary probability density function is purely a function of the Hamiltonian of the system. This fact is exploited to define the basis functions for approximating the solution

Jiaming Hu, Jan Szczekulski, Sudhansh Peddabomma, Henrik I. Christensen

Finding an high-quality solution for the tabletop object rearrangement planning is a challenging problem. Compared to determining a goal arrangement, rearrangement planning is challenging due to the dependencies between objects and the buffer capacity available to hold objects. Although orla* has proposed an A* based searching strategy with lazy evaluation f

Bochao Gu, Hengzhi He, Guang Cheng

In this paper, we propose a novel statistical framework for watermarking generative categorical data. Our method systematically embeds pre-agreed secret signals by splitting the data distribution into two components and modifying one distribution based on a deterministic relationship with the other, ensuring the watermark is embedded at the distribution-leve

Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib

The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictions are not forgotten when the training set is increased in between rounds. The former is measured by the accuracy of the model and the latter i

Khushbu Mehboob Shaikh, Georgios Giannakopoulos

Interactive Voice Response (IVR) systems have undergone significant transformation in recent years, moving from traditional code-based development to more user-friendly approaches leveraging widgets and, most recently, harnessing the power of Artificial Intelligence (AI) for automated IVR flow creation. This paper explores the evolution of IVR building techn

Supravat Sarkar

Assuming Hartshorne's conjecture on complete intersections, we classify projective bundles over projective spaces which has a smooth blow up structure over another projective space. Under some assumptions, we also classify projective bundles over projective spaces which has a smooth blow up structure over some arbitrary smooth projective variety, not necessa

Gil Ben-Artzi, Feras Daragma, Shahar Mahpod

While state-of-the-art models for breast cancer detection leverage multi-view mammograms for enhanced diagnostic accuracy, they often focus solely on visual mammography data. However, radiologists document valuable lesion descriptors that contain additional information that can enhance mammography-based breast cancer screening. A key question is whether deep

Mirza Wasif Baig, Marek Pederzoli, Mojmír Kývala, Jiří Pittner

A computational study of I-BODIPY (2-ethyl-4,4-difluoro-6,7-diiodo-1,3-dimethyl-4-bora-3a,4a-diaza-s-indacene) was conducted to investigate its photophysical properties as a potential triplet photosensitizer for singlet oxygen generation. Multireference CASPT2 and CASSCF methods were used to calculate vertical excitation energies and spin-orbit couplings (SO

Tomer Ezra, Tamar Garbuz

We study the classic single-choice prophet secretary problem through a resource augmentation lens. Our goal is to bound the $(1-\epsilon)$-competition complexity for different classes of online algorithms. This metric asks for the smallest $k$ such that the expected value of the online algorithm on $k$ copies of the original instance, is at least a $(1 - \ep

Evgeny Hershkovitch Neiterman, Gil Ben-Artzi

Incorporating stochasticity into the training process of deep convolutional networks is a widely used technique to reduce overfitting and improve regularization. Existing techniques often require modifying the architecture of the network by adding specialized layers, are effective only to specific network topologies or types of layers - linear or convolution

Khairul Alam, Banani Roy, Chanchal K. Roy, Kartik Mittal

Scientific Workflow Systems (SWSs) are advanced software frameworks that drive modern research by orchestrating complex computational tasks and managing extensive data pipelines. These systems offer a range of essential features, including modularity, abstraction, interoperability, workflow composition tools, resource management, error handling, and comprehe

Chengyuan Deng, Jie Gao, Kevin Lu, Feng Luo

We introduce Non-Euclidean-MDS (Neuc-MDS), an extension of classical Multidimensional Scaling (MDS) that accommodates non-Euclidean and non-metric inputs. The main idea is to generalize the standard inner product to symmetric bilinear forms to utilize the negative eigenvalues of dissimilarity Gram matrices. Neuc-MDS efficiently optimizes the choice of (both

Xu Cao, Wenqian Ye, Kenny Moise, Megan Coffee

In the aftermath of the COVID-19 pandemic and amid accelerating climate change, emerging infectious diseases, particularly those arising from zoonotic spillover, remain a global threat. Mpox (caused by the monkeypox virus) is a notable example of a zoonotic infection that often goes undiagnosed, especially as its rash progresses through stages, complicating

Amirhossein Jamarani, Yazhou Tu, Xiali Hei

The widespread accessibility and ease of use of additive manufacturing (AM), widely recognized as 3D printing, has put Intellectual Property (IP) at great risk of theft. As 3D printers emit acoustic and magnetic signals while printing, the signals can be captured and analyzed using a smartphone for the purpose of IP attack. This is an instance of physical-to

Ansh Shah, K Madhava Krishna

Recovering metric depth from a single image remains a fundamental challenge in computer vision, requiring both scene understanding and accurate scaling. While deep learning has advanced monocular depth estimation, current models often struggle with unfamiliar scenes and layouts, particularly in zero-shot scenarios and when predicting scale-ergodic metric dep

R. Andrew Hicks

The eigenmirror problem asks: ``When does the reflection of a surface in a curved mirror appear undistorted to an observer?'' We call such a surface an {\em eigensurface} and the corresponding mirror an {\em eigenmirror}. The data for an eigenmirror problem consists of a homogeneous transformation ${\bf H}:\mathbb{R}^3 \to \mathbb{R}^3$ that encodes what it

Mahdi Rezapour

Recommendation systems have become the fundamental services to facilitate users information access. Generally, recommendation system works by filtering historical behaviors to understand and learn users preferences. With the growth of online information, recommendations have become of crucial importance in information filtering to prevent the information ove

Cheng Gu, Yicheng Zhang, Nael Abu-Ghazaleh

Operating Systems enforce logical isolation using abstractions such as processes, containers, and isolation technologies to protect a system from malicious or buggy code. In this paper, we show new types of side channels through the file system that break this logical isolation. The file system plays a critical role in the operating system, managing all I/O

Abuzar B. M. Adam, Elhadj Moustapha Diallo, Mohammed A. M. Elhassan

In this work, we explore UAV-assisted reconfigurable intelligent surface (RIS) technology to enhance downlink communications in wireless networks. By integrating RIS on both UAVs and ground infrastructure, we aim to boost network coverage, fairness, and resilience against challenges such as UAV jitter. To maximize the minimum achievable user rate, we formula

Xiwei Liu, Min Xu, Qirong Ho

With the growing application of transformer in computer vision, hybrid architecture that combine convolutional neural networks (CNNs) and transformers demonstrates competitive ability in medical image segmentation. However, direct fusion of features from CNNs and transformers often leads to feature imbalance and redundant information. To address these issues

Suddhasattwa Brahma, Robert Brandenberger, Keshav Dasgupta, Yue Lei

We study a two-matrix toy model with a BFSS-like interaction term using the collective field formalism. The main technical simplification is obtained by gauge-fixing first, and integrating out the off-diagonal elements, before changing to the collective field variable. We show that the resulting $(2+1)$-dimensional collective field action has novel features

Md. Nazmus Sadat Samin, Jawad Ibn Ahad, Tanjila Ahmed Medha, Fuad Rahman

This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional languages, are distinctive variations of a language spoken in a particular location and are identified by their phonetics, pronunciations, and lexicon. Subtle changes in pronunciation a

Jawad Ibn Ahad, Rafeed Mohammad Sultan, Abraham Kaikobad, Fuad Rahman

This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies support articles to provide a comprehensive understanding. We know that a meta-article provides a structured analysis of several articles. However, c

Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro, Laura A. Heymann

Despite the utility that Generative AI (GenAI) tools provide for tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. As lawmakers and regulators engage with those questions, the views of users can provide relevant perspectives. In this paper, we provide: (1

A. I. Kuiroukidis, D. A. Kaltsas, G. N. Throumoulopoulos

We construct analytic solutions to the generalized Grad-Shafranov equation, which incorporates both toroidal and poloidal flows. This is achieved by adopting a general linearizing ansatz for the free-function terms of the equation and expanding the generalized Solov'ev solution [Ch. Simintzis, G. N. Throumoulopoulos, G. Pantis and H. Tasso, Phys. Plasmas {\b

Marcielis Espitia, Santiago Martinchich, Rafael Potrie

We study transitive partially hyperbolic diffeomorphisms in dimension 3 preserving a center foliation on which they act quasi-isometrically. We show that the diffeomorphism is up to finite lift and iterate, either a skew-product or a discretised Anosov flow.

Sabin Viorel Anton, Bernardo Sousa Alves, Christian Siemes, Jose van den IJssel

The increasing density of space objects in low-Earth orbit highlights the critical need for accurate orbit predictions to minimise operational disruptions. One significant challenge lies in accurately modelling the interaction of gas particles with the surfaces of these objects, as errors in aerodynamic coefficient modelling directly impact orbit prediction

Khairul Alam, Kartik Mittal, Banani Roy, Chanchal Roy

Large Language Models (LLMs) have gained widespread popularity due to their exceptional capabilities across various domains, including chatbots, healthcare, education, content generation, and automated support systems. However, developers encounter numerous challenges when implementing, fine-tuning, and integrating these models into real-world applications.

Alessandro T. Gifford, Maya A. Jastrzębowska, Johannes J. D. Singer, Radoslaw M. Cichy

Now published in Nature Human Behavior doi: https://doi.org/10.1038/s41562-025-02252-z Human vision is mediated by a complex interconnected network of cortical brain areas that jointly represent visual information. While these areas are increasingly understood in isolation, their representational relationships remain elusive. Here we developed relational neu

Thomas H. T. Wong, George M. Fuller

Tidal disruption events (TDEs) result from stars being gravitationally-scattered into low angular momentum orbits around massive black holes. We show that the short lifetimes of massive Population III stars at high redshifts could significantly suppress the volumetric TDE rate because they are too short-lived to reach disruption-fated orbits. However, this s

Zhiyang Xun, David Zuckerman

We present the first efficient averaging sampler that achieves asymptotically optimal randomness complexity and near-optimal sample complexity. For any $\delta < \varepsilon$ and any constant $\alpha > 0$, our sampler uses $m + O(\log (1 / \delta))$ random bits to output $t = O((\frac{1}{\varepsilon^2} \log \frac{1}{\delta})^{1 + \alpha})$ samples $Z_1, \dot

Ashwin Poduval, Hayden Coffey, Michael Swift

Memory performance is often the main bottleneck in modern computing systems. In recent years, researchers have attempted to scale the memory wall by leveraging new technology such as CXL, HBM, and in- and near-memory processing. Developers optimizing for such hardware need to understand how target applications perform to fully take advantage of these systems

Sari Masri, Huthaifa I. Ashqar, Mohammed Elhenawy

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traf

Lane H. Rogers, Emma J. Reid, Robert A. Bridges

Social influence plays a significant role in shaping individual sentiments and actions, particularly in a world of ubiquitous digital interconnection. The rapid development of generative AI has engendered well-founded concerns regarding the potential scalable implementation of radicalization techniques in social media. Motivated by these developments, we pre

Vipula Rawte, Sarthak Jain, Aarush Sinha, Garv Kaushik

Recent advances in Large Multimodal Models (LMMs) have expanded their capabilities to video understanding, with Text-to-Video (T2V) models excelling in generating videos from textual prompts. However, they still frequently produce hallucinated content, revealing AI-generated inconsistencies. We introduce ViBe (https://vibe-t2v-bench.github.io/): a large-scal

Rafal Filipow, Adam Kwela, Paolo Leonetti

Let $X$ be an uncountable Polish space and let $\mathcal{I}$ be an ideal on $\omega$. A point $\eta \in X$ is an $\mathcal{I}$-limit point of a sequence $(x_n)$ taking values in $X$ if there exists a subsequence $(x_{k_n})$ convergent to $\eta$ such that the set of indexes $\{k_n: n \in \omega\}\notin \mathcal{I}$. Denote by $\mathscr{L}(\mathcal{I})$ the fa

Chris Davis Jaldi, Eleni Ilkou, Noah Schroeder, Cogan Shimizu

Education is poised for a transformative shift with the advent of neurosymbolic artificial intelligence (NAI), which will redefine how we support deeply adaptive and personalized learning experiences. NAI-powered education systems will be capable of interpreting complex human concepts and contexts while employing advanced problem-solving strategies, all grou

Vinicius S. de Angelis, Ahmed H. Dorrah, Leonardo A. Ambrosio, David A. B. Miller

Achieving precise control of light intensity in 3D volumes is highly in demand in many applications in optics. Various wavefront shaping techniques have been utilized to reconstruct a target amplitude profile within a 3D space. However, these techniques are intrinsically limited by cross-talk and often rely on optimization methods to improve the quality of t

Gourab Giri, Joydeep Bagchi, Kshitij Thorat, Roger P. Deane

Giant radio galaxies (GRGs), a minority among the extended-jetted population, form in a wide range of jet and environmental configurations, complicating the identification of the growth factors that facilitate their attainment of megaparsec scales. This study aims to numerically investigate the hypothesized formation mechanisms of GRGs extending $\gtrsim 1$

Arnab Kumar Roy, Hemant Kumar Kathania, Adhitiya Sharma

Facial Emotion Recognition (FER) plays a crucial role in computer vision, with significant applications in human-computer interaction, affective computing, and areas such as mental health monitoring and personalized learning environments. However, a major challenge in FER task is the class imbalance commonly found in available datasets, which can hinder both

Emery Doucet, Sebastian Deffner

The study of measurements in quantum mechanics exposes many of the ways in which the quantum world is different. For example, one of the hallmarks of quantum mechanics is that observables may be incompatible, implying among other things that it is not always possible to find joint probability distributions which fully capture the joint statistics of multiple

Ruslan Idelfonso Magaña Vsevolodovna

The generation of complex, large-scale code projects using generative AI models presents challenges due to token limitations, dependency management, and iterative refinement requirements. This paper introduces the See-Saw generative mechanism, a novel methodology for dynamic and recursive code generation. The proposed approach alternates between main code up

Manuel Bodirsky, Santiago Guzmán-Pro

Many computational problems can be modelled as the class of all finite structures $\mathbb A$ that satisfy a fixed first-order sentence $\phi$ hereditarily, i.e., we require that every (induced) substructure of $\mathbb A$ satisfies $\phi$. We call the corresponding computational problem the hereditary model checking problem for $\phi$, and denote it by Her$

Benjamin V. Holt

Natural numbers which are nontrivial multiples of some permutation of their base-$b$ digit representations are called permutiples. Specific cases include numbers which are multiples of cyclic permutations (cyclic numbers) and reversals of their digits (palintiples). Previous efforts have produced methods which construct new examples of permutiples with the s

Nathalia Nascimento, Everton Guimaraes, Sai Sanjna Chintakunta, Santhosh Anitha Boominathan

The adoption of Large Language Models (LLMs) for code generation in data science offers substantial potential for enhancing tasks such as data manipulation, statistical analysis, and visualization. However, the effectiveness of these models in the data science domain remains underexplored. This paper presents a controlled experiment that empirically assesses

Aaron Sonabend, Jiangshan Zhang, Edgar Castro, Joel Schwartz

Humans are exposed to complex mixtures of environmental pollutants rather than single chemicals, necessitating methods to quantify the health effects of such mixtures. Research on environmental mixtures provides insights into realistic exposure scenarios, informing regulatory policies that better protect public health. However, statistical challenges, includ

Surasakdi Siripong, Apirak Chaiyapan, Thanakorn Phonchai

Remote Sensing Visual Question Answering (RSVQA) is a challenging task that involves interpreting complex satellite imagery to answer natural language questions. Traditional approaches often rely on separate visual feature extractors and language processing models, which can be computationally intensive and limited in their ability to handle open-ended quest

Colomba Brancaccio

I discuss the status of the computation of the two-loop QCD corrections to top-quark pair production associated with a jet at hadron colliders. This amplitude is a missing ingredient for next-to-next-to-leading order (NNLO) QCD predictions. I briefly present computational techniques to tackle the algebraic and analytic complexities of two-loop multi-scale am

Adam Caulfield, Norrathep Rattanavipanon, Ivan De Oliveira Nunes

Remote run-time attestation methods, including Control Flow Attestation (CFA) and Data Flow Attestation (DFA), have been proposed to generate precise evidence of execution's control flow path (in CFA) and optionally execution data inputs (in DFA) on a remote and potentially compromised embedded device, hereby referred to as a Prover (Prv). Recent advances in

Adi Cohen, Daniel Wong, Jung-Suk Lee, Sharon Gannot

This paper introduces an explainable DNN-based beamformer with a postfilter (ExNet-BF+PF) for multichannel signal processing. Our approach combines the U-Net network with a beamformer structure to address this problem. The method involves a two-stage processing pipeline. In the first stage, time-invariant weights are applied to construct a multichannel spati

Shaochen Xu, Yifan Zhou, Zhengliang Liu, Zihao Wu

Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making. Traditional model-based approaches, including those leveraging in-context demonstrations and those with specialized medical fine-tuning, have demonstrated strong performance in medical language pro

Evelia R. García Barroso, Marcelo E. Hernandes, M. Fernando Hernández Iglesias

We study, for plane complex branches of genus one, the topological type of its generic polar curve, as a function of the semigroup of values and the Zariski invariant of the branch. We improve some results given by Casas-Alvero in 2023, since we filter the topological type fixed for the branch by the possible values of Zariski invariants.

Kota Katsumi, Jiahao Liang, Ralph Romero, Ke Chen

We have investigated the terahertz (THz) nonlinear response of the multigap superconductor MgB$_2$, using THz two-dimensional coherent spectroscopy (THz 2DCS). With broadband THz drive fields, we identified a nonlinear response at twice the lower superconducting gap energy $2\Delta_{\pi}$ at the lowest temperatures. Using narrow-band THz driving pulses, we o

Yijie Mo, Xiao-Jiao Wang, Zheng-Yang Zhuang, Zhongbo Yan

Dirac semimetals are renowned for the host of singular symmetry-protected band degeneracies which can give rise to other exotic phases. In this work, we consider a two-dimensional Dirac semimetal stabilized by PT symmetry and nonsymmorphic symmetries. We find that an out-of-plane Zeeman field can lift the Dirac points and transform the system into a Chern in

Masaya Kitajima

Let $p$ and $r$ be positive real numbers. Then, we consider the lattice point problem of the closed curve $p$-circle $\{x\in\mathbb{R}^{2}|\ |x_{1}|^{p}+|x_{2}|^{p}=r^{p}\}$ which is a generalization of the circle ($p=2$). Following the harmonic analytic approach of S. Kuratsubo and E. Nakai for the case of a circle, we need to investigate properties of appr

V Shokeen, M Pavelka, R Chulkov, A Yaroslavtsev

Manipulating magnetism at the THz timescale in atomically thin ferromagnets by exploiting the interactions of spins with optical phonon modes presents an innovative idea for THz spintronics and magnonics. Utilizing the coupling of phonon modes to the magnetization could lead to new ways of generating and controlling spin wave excitations in future applicatio

Jiajie Fan, Babak Gholami, Thomas Bäck, Hao Wang

Boundary Representation (B-Rep) is the de facto representation of 3D solids in Computer-Aided Design (CAD). B-Rep solids are defined with a set of NURBS (Non-Uniform Rational B-Splines) surfaces forming a closed volume. To represent a surface, current works often employ the UV-grid approximation, i.e., sample points uniformly on the surface. However, the UV-

Merab Gogberashvili, Alexander S. Sakharov

We propose that modifications to the Higgs potential within a narrow atmospheric layer near the event horizon of an astrophysical black hole could significantly enhance the rate of sphaleron transitions, as well as transform the Chern-Simons number into a dynamic variable. As a result, sphaleron transitions in this region occur without suppression, in contra

S. E. Ocal, O. Yilmaz, S. Ayik, A. S. Umar

Background: Multi-nucleon transfer (MNT) reactions in actinide systems are a promising method to synthesize transuranium neutron-rich elements. Appropriate theoretical approaches are needed to understand the mechanism behind MNT. Purpose: This work aims to produce neutron-rich isotopes in the super-heavy region through the ${}^{238} \mathrm{U}+{ }^{248} \mat

Jaisidh Singh, Sonam Singh, Amit Arvind Kale, Harsh K Gandhi

This paper presents a novel method for discovering systematic errors in segmentation models. For instance, a systematic error in the segmentation model can be a sufficiently large number of misclassifications from the model as a parking meter for a target class of pedestrians. With the rapid deployment of these models in critical applications such as autonom

Andreea I. Bordianu, Mircea Cimpoeas

We study the Stanley depth and the Hilbert depth of the edge ideals of path graphs, cycle graphs, generalized star graphs and double broom graphs.

Santhosh Malarvannan, Pandiyaraju V, Shravan Venkatraman, Abeshek A

Diabetic retinopathy is a leading cause of blindness around the world and demands precise AI-based diagnostic tools. Traditional loss functions in multi-class classification, such as Categorical Cross-Entropy (CCE), are very common but break down with class imbalance, especially in cases with inherently challenging or overlapping classes, which leads to bias

Jialun Cao, Songqiang Chen, Wuqi Zhang, Hau Ching Lo

Data contamination presents a critical barrier preventing widespread industrial adoption of advanced software engineering techniques that leverage code language models (CLMs). This phenomenon occurs when evaluation data inadvertently overlaps with the public code repositories used to train CLMs, severely undermining the credibility of performance evaluations

Akash Agrawal, Christopher McComb

Multi-fidelity Reinforcement Learning (RL) frameworks significantly enhance the efficiency of engineering design by leveraging analysis models with varying levels of accuracy and computational costs. The prevailing methodologies, characterized by transfer learning, human-inspired strategies, control variate techniques, and adaptive sampling, predominantly de

Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski

This paper addresses the optimal control of quantum coherence in multi-level systems, modeled by the Lindblad master equation, which captures both unitary evolution and environmental dissipation. We develop an energy minimization framework to control the evolution of a qutrit (three-level) quantum system while preserving coherence between states. The control

Atharv Mittal

Due to increasing privacy regulations and regulatory compliance, Machine Unlearning (MU) has become essential. The goal of unlearning is to remove information related to a specific class from a model. Traditional approaches achieve exact unlearning by retraining the model on the remaining dataset, but incur high computational costs. This has driven the devel

Minghao Zhang, Karnpiwat Tantratian, So-Yeon Ham, Zhuo Wang

Soft metals like lithium and sodium play a critical role in battery technology owing to their high energy density. Texture formation by grain selection growth of soft metals during electrochemical processes is a crucial factor affecting power and safety. Developing a framework to understand and control grain growth is a multifaceted challenge. Here, a genera

Animesh Sah, Mohamed Rameez, Subir Sarkar, Christos Tsagas

We employ Maximum Likelihood Estimators to examine the Pantheon+ catalogue of Type Ia supernovae for large scale anisotropies in the expansion rate of the Universe. The analyses are carried out in the heliocentric frame, the CMB frame, as well as the Local Group frame. In all frames, the Hubble expansion rate in the redshift range 0.023 < z < 0.15 is found t

Yousef Abuseta

The complexity of IoT, owing to the inherent distributed and dynamic nature of such systems, brings more challenges to the software development process. A vast number of devices with different communication protocols and data formats is involved and needs to be connected and exchange data with each other in a seamless manner. Traditional software architectur

Guojun Lei, Chi Wang, Hong Li, Rong Zhang

We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations. Specifically, we carefully design a multi-scale control feature fusion network to construct a common motion representation f

Kengo Hirata, Chris Heunen

Uncomputation is a feature in quantum programming that allows the programmer to discard a value without losing quantum information, and that allows the compiler to reuse resources. Whereas quantum information has to be treated linearly by the type system, automatic uncomputation enables the programmer to treat it affinely to some extent. Automatic uncomputat

Edmundo J. Huertas, Manuel Mañas

Mixed orthogonal Laurent polynomials on the unit circle of CMV type are constructed utilizing a matrix of moments and its Gauss--Borel factorization and employing a multiple extension of the CMV ordering. A systematic analysis of the associated multiple orthogonality and biorthogonality relations, and an examination of the degrees of the Laurent polynomials

Félix del Teso, Łukasz Płociniczak

We establish uniform error bounds of the L1 discretization of the Caputo derivative of H\"older continuous functions. The result can be understood as: error = (degree of smoothness - order of the derivative). We present an elementary proof and illustrate its optimality with numerical examples.

Jakob Niehues, Robin Delabays, Anna Büttner, Frank Hellmann

The stability of inverter-dominated power grids remains an active area of research. This paper presents novel sufficient conditions for ensuring small-signal stability in lossless and constant $R/X$ grids with highly heterogeneous mixes of grid-forming inverters that implement an adapted $V$-$q$ droop control. The proposed conditions can be evaluated in the