November 2024 arXiv papers — page 137

Xiaowei Yu, Zhe Huang, Zao Zhang

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from labeled source domains to improve performance on the unlabeled target domains. While Convolutional Neural Networks (CNNs) have been dominant in previous UDA methods, recent research has shown promise in applying Vision Transformers (ViTs) to this task. In this study, we propose

Ayana Moshruba, Ihsen Alouani, Maryam Parsa

While machine learning (ML) models are becoming mainstream, especially in sensitive application areas, the risk of data leakage has become a growing concern. Attacks like membership inference (MIA) have shown that trained models can reveal sensitive data, jeopardizing confidentiality. While traditional Artificial Neural Networks (ANNs) dominate ML applicatio

Debojyoti Biswas, Eduardo D. Sontag, Noah J. Cowan

We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium point. To overcome this fundamental limitation, we propose a simple control scheme that includes a low-amplitude

Eva Zhang, Arka Pal, Akilesh Potti, Micah Goldblum

As fine-tuning large language models (LLMs) becomes increasingly prevalent, users often rely on third-party services with limited visibility into their fine-tuning processes. This lack of transparency raises the question: how do consumers verify that fine-tuning services are performed correctly? For instance, a service provider could claim to fine-tune a mod

Greg Martin, Chi Hoi Yip

Mossinghoff, Trudgian, and the first author~\cite{MMT23} recently introduced a family of arithmetic functions called ``fake $\mu$'s'', which are multiplicative functions for which there is a $\{-1,0,1\}$-valued sequence $(\varepsilon_j)_{j=1}^{\infty}$ such that $f(p^j) = \varepsilon_j$ for all primes $p$. They investigated comparative number-theoretic resul

Phuoc-Truong Huynh, Barbara Kaltenbacher

This work studies the inverse problem of photoacoustic tomography (more precisely, the acoustic subproblem) as the identification of a space-dependent source parameter. The model consists of a wave equation involving a time-fractional damping term to account for power law frequency dependence of the attenuation, as relevant in ultrasonics. We solve the inver

N. N. Bezuglov, I. I. Beterov, A. Cinins, K. Miculis

Precise individual addressing of single atoms in quantum registers formed by optical trap arrays is essential to achieve high-fidelity quantum gates in neutral-atom quantum computers and simulators. Two-qubit quantum gates are typically realized using coherent two-photon laser excitation of atoms to strongly interacting Rydberg states. However, two-photon ex

Akshar Prabhu Desai, Tejasvi Ravi, Mohammad Luqman, Mohit Sharma

Machine Learning and data mining techniques (i.e. supervised and unsupervised techniques) are used across domains to detect user safety violations. Examples include classifiers used to detect whether an email is spam or a web-page is requesting bank login information. However, existing ML/DM classifiers are limited in their ability to understand natural lang

Nitin Kumar, Jong E. Han, Karsten Beckmann, Nathaniel Cady

Negative differential resistance (NDR) behavior observed in several transition metal oxides is crucial for developing next-generation memory devices and neuromorphic computing systems. NbO2-based memristors exhibit two regions of NDR at room temperature, making them promising candidates for such applications. Despite this potential, the physical mechanisms b

Cem Gultekin, Adam Subel, Cheng Zhang, Matan Leibovich

Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the climate system. Parameterization is an approach to capture the effect of these processes, without resolving them explicitly. In recent years, data-driven parameterizations based on

J. Terzic, Bal K. Pokharel, Z. Z. Li, P. Senzier

The mechanism that controls the superconducting (SC) transition temperature $T_{\mathrm{c}}^{0}$ as a function of doping is one of the central questions in cuprate high-temperature superconductors. While it is generally accepted that $T_{\mathrm{c}}^{0}$ in underdoped cuprates is not determined by the scale of pairing but by the onset of global phase coheren

Decai Chen, Brianne Oberson, Ingo Feldmann, Oliver Schreer

3D Gaussian Splatting has recently achieved notable success in novel view synthesis for dynamic scenes and geometry reconstruction in static scenes. Building on these advancements, early methods have been developed for dynamic surface reconstruction by globally optimizing entire sequences. However, reconstructing dynamic scenes with significant topology chan

Rohit Bokade, Xiaoning Jin

Efficient traffic control (TSC) is essential for urban mobility, but traditional systems struggle to handle the complexity of real-world traffic. Multi-agent Reinforcement Learning (MARL) offers adaptive solutions, but online MARL requires extensive interactions with the environment, making it costly and impractical. Offline MARL mitigates these challenges b

Leonardo Banchi, Jason Pereira, Marco Zamboni

The ability to extract general laws from a few known examples depends on the complexity of the problem and on the amount of training data. In the quantum setting, the learner's generalization performance is further challenged by the destructive nature of quantum measurements that, together with the no-cloning theorem, limits the amount of information that ca

Kaixuan Ye, Hanke Feng, Randy te Morsche, Akhileshwar Mishra

Stimulated Brillouin scattering (SBS) is revolutionizing low-noise lasers and microwave photonic systems. However, despite extensive explorations of a low-loss and versatile integrated platform for Brillouin photonic circuits, current options fall short due to limited technological scalability or inadequate SBS gain. Here we introduce the thin-film lithium n

Roman Vasyltsiv, Joseph Harms, Megan Clark, David J. Gladstone

Background: Ultra-high dose rate proton therapy shows promise in tissue sparing by enhancing therapeutic ratio through the FLASH effect. In radiotherapy, accurate in vivo dosimetry is crucial for quality assurance, but remains challenging for UHDR as existing systems lack spatial and temporal resolution to verify dose and dose rate in complex anatomical regi

André Coelho, José Ruela, Gonçalo Queirós, Ricardo Trancoso

This paper proposes the Mobile Cell (MC) concept for on-demand 5G private networks. The MC is designed to extend, restore, and reinforce 5G wireless coverage and network capacity on-demand, especially in areas with temporary communications needs or where it is costly or not possible to deploy a permanent fixed infrastructure. The design of the MC as well as

Natalia Sergienko, Elisabeth Cuervo Lumbaque, Jelena Radjenovic

MnxOy coated over TiO2 nanotube array substrate was doped with Mo and polyaniline (PANI) and applied for electrochemical desulfurization of concentrated sulfide (HS) solutions at basic pH, typical of biogas scrubbing solutions and industrial wastewater. Mo and PANI co-dopants significantly enhanced the anode activity towards sulfide oxidation and ensured its

Hadeel Awwad, Eloy García, Robert Martí

Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improved analysis. Although Finite Element Analysis (FEA) is reliable for approximating soft tissue deformation, it struggles w

Michael Dumbser, Alessia Lucca, Ilya Peshkov, Olindo Zanotti

We present a novel variational derivation of the Maxwell-GLM system, which augments the original vacuum Maxwell equations via a generalized Lagrangian multiplier approach (GLM) by adding two supplementary acoustic subsystems and which was originally introduced by Munz et al. for purely numerical purposes in order to treat the divergence constraints of the ma

Bruce G. Elmegreen, Angela Adamo, Varun Bajaj, Ana Duarte-Cabral

JWST/MIRI images have been used to study the Fourier transform power spectra (PS) of two spiral galaxies, NGC 628 and NGC 5236, and two dwarfs, NGC 4449 and NGC 5068, at distances ranging from 4 to 10 Mpc. The PS slopes on scales larger than 200 pc range from -0.6 at 21 microns to -1.2 at 5.6 microns. These slopes for one-dimensional PS are consistent with t

Letian Yang, Dennis Shen

Modern deep learning has revealed a surprising statistical phenomenon known as benign overfitting, with high-dimensional linear regression being a prominent example. This paper contributes to ongoing research on the ordinary least squares (OLS) interpolator, focusing on the partial regression setting, where only a subset of coefficients is implicitly regular

Wolfgang Altmannshofer, Shibasis Roy

In light of the recent branching fraction measurement of the $B^{+}\to K^{+} \nu\bar{\nu}$ decay by Belle II and its poor agreement with the SM expectation, we analyze the effects of an axion-like particle (ALP) in $B$ meson decays. We assume a long-lived ALP with a mass of the order of the pion mass that decays to two photons. We focus on a scenario where t

Durbadal Ghosh, Debajyoti Sinha, Antonio R. Linero, George Rust

Usual parametric and semi-parametric regression methods are inappropriate and inadequate for large clustered survival studies when the appropriate functional forms of the covariates and their interactions in hazard functions are unknown, and random cluster effects and cluster-level covariates are spatially correlated. We present a general nonparametric metho

Michael Y. Li, Vivek Vajipey, Noah D. Goodman, Emily B. Fox

Understanding the world through models is a fundamental goal of scientific research. While large language model (LLM) based approaches show promise in automating scientific discovery, they often overlook the importance of criticizing scientific models. Criticizing models deepens scientific understanding and drives the development of more accurate models. Aut

Inhwi Hwang

This paper proposes a method to achieve zero voltage switching (ZVS) across the full duty cycle range in hybrid flying capacitor multilevel (FCML) converters, eliminating the need for dynamic level changing and active re-balancing. Utilizing skipped adjacency pulse width modulation (SAPWM), this approach avoids the nearest pole voltage level, thereby increas

Nived J M

We demonstrate that when a graph exhibits a specific type of symmetry, it satisfies the Union Closed Conjecture(UCC). Additionally, we show that certain graph classes, such as Cylindrical Grid Graphs and Torus Grid Graphs also satisfy the conjecture. We prove the known result that the union closed family generated by cyclic translates of a fixed set satisfie

Mohammad Sadegh Eshaghi, Cosmin Anitescu, Manish Thombre, Yizheng Wang

Solving partial differential equations (PDEs) is a required step in the simulation of natural and engineering systems. The associated computational costs significantly increase when exploring various scenarios, such as changes in initial or boundary conditions or different input configurations. This study proposes the Variational Physics-Informed Neural Oper

Arman Sykot, Mohammad Hasibur Rahman, Rifat Tasnim Anannya, Khan Shariya Hasan Upoma

This paper presents a novel hybrid Quantum Key Distribution ,QKD, protocol that combines entanglement based and non entanglement based approaches to optimize security and the number of generated keys. We introduce a dynamic system that integrates a three particle GHZ state method with the two state B92 protocol, using a quantum superposition state to probabi

Oluwatobi Adeniji, Charles Henry, Stephen Thomas, Robert Colson Sapp

We present a proof-of-concept design for an atomtronic rotation sensor consisting of an array of ``double-target'' Bose-Einstein condensates (BECs). A ``target'' BEC is a disk-shaped condensate surrounded by a concentric ring-shaped condensate. A ``double-target'' BEC is two adjacent target BECs whose ring condensates partially overlap. The sensor consists o

Reymond Akpanya, Adi Rivkin, Frederick Stock

In this work we study inside-out dissections of polygons and polyhedra. We first show that an arbitrary polygon can be inside-out dissected with $2n+1$ pieces, thereby improving the best previous upper bound of $4(n-2)$ pieces. Additionally, we establish that a regular polygon can be inside-out dissected with at most $6$ pieces. Lastly, we prove that any pol

Elad Yoshai, Gil Goldinger, Miki Haifler, Natan T. Shaked

In histological pathology, frozen sections are often used for rapid diagnosis during surgeries, as they can be produced within minutes. However, they suffer from artifacts and often lack crucial diagnostic details, particularly within the cell nuclei region. Permanent sections, on the other hand, contain more diagnostic detail but require a time-intensive pr

Mikołaj Rosman, Michał Palczewski, Paweł Pilarczyk, Agnieszka Bartłomiejczyk

We conduct numerical analysis of the 2-dimensional discrete-time gene expression model originally introduced by Andrecut and Kauffman (Phys. Lett. A 367: 281-287, 2007). In contrast to the previous studies, we analyze the dynamics with different reaction rates $\alpha_1$ and $\alpha_2$ for each of the two genes under consideration. We explore bifurcation dia

Joan Solà Peracaula, Cristian Moreno-Pulido

The cosmological constant term (CC), $\Lambda$, is a pivotal ingredient in the standard model of cosmology or $\Lambda$CDM, but it is a rigid quantity for the entire cosmic history. This is unnatural and inconsistent. Different theoretical and phenomenological conundrums suggest that the $\Lambda$CDM necessitates further theoretical underpinning to cope with

Yang Su, Na Yan, Yansha Deng, Mischa Dohler

Federated fine-tuning of pre-trained Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving privacy. However, challenges such as high computational and memory demands, heterogeneous client resources, bandwidth constraints, and ineffective global aggregation hinder its efficiency. To address these issues, we pro

Mohamed Tahar Kadaoui Abbassi, Abderrahim Mekrami

In this paper, we introduce a broad class of metrics on the slit tangent bundle of Finsler manifolds, termed \emph{$F$-natural metrics}. These metrics parallel the well-established $g$-natural metrics on the tangent bundles of Riemannian manifolds and are constructed using six real functions defined over the domain of positive real numbers. We provide an in-

Tianqi Wang, Andrew Zimmer

In this paper we investigate the Gromov hyperbolicity of the classical Kobayashi and Hilbert metrics, and the recently introduced minimal metric. Using the linear isoperimetric inequality characterization of Gromov hyperbolicity, we show if these metrics have an "expanding property" near the boundary, then they are Gromov hyperbolic. This provides a new char

Umut Demirhan, Ahmed Alkhateeb

Leveraging perception from radar data can assist multiple communication tasks, especially in highly-mobile and large-scale MIMO systems. One particular challenge, however, is how to distinguish the communication user (object) from the other mobile objects in the sensing scene. This paper formulates this \textit{user identification} problem and develops two s

Felix Frohnert, Xuemei Gu, Mario Krenn, Evert van Nieuwenburg

As the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchange of ideas across structurally similar problems in different subfields. To encourage cross-talk among these different specialized areas, data-driven approaches using machine learni

Soumya Sai Vanka, Lennart Hannink, Jean-Baptiste Rolland, George Fazekas

In our demo, participants are invited to explore the Diff-MSTC prototype, which integrates the Diff-MST model into Steinberg's digital audio workstation (DAW), Cubase. Diff-MST, a deep learning model for mixing style transfer, forecasts mixing console parameters for tracks using a reference song. The system processes up to 20 raw tracks along with a referenc

Kathrin Krieger, David P. Leins, Thorben Markmann, Robert Haschke

Most commercially available haptic gloves compromise the accuracy of hand-posture measurements in favor of a simpler design with fewer sensors. While inaccurate posture data is often sufficient for the task at hand in biomedical settings such as VR-therapy-aided rehabilitation, measurements should be as precise as possible to digitally recreate hand postures

Ment Reeze, Menno A. Veerman, Chiel C. van Heerwaarden

Simulating radiative transfer in the atmosphere with Monte Carlo ray tracing provides realistic surface irradiance in cloud-resolving models. However, Monte Carlo methods are computationally expensive because large sampling budgets are required to obtain sufficient convergence. Here, we explore the use of machine learning for denoising direct and diffuse sur

Jiahao Zhang, Christian Moya, Guang Lin

Optimizing the learning rate remains a critical challenge in machine learning, essential for achieving model stability and efficient convergence. The Vector Auxiliary Variable (VAV) algorithm introduces a novel energy-based self-adjustable learning rate optimization method designed for unconstrained optimization problems. It incorporates an auxiliary variabl

Mehmet Efe Lorasdagi, Ahmet Berker Koc, Ali Taha Koc, Suleyman Serdar Kozat

Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a clustering framework that eliminates this assumption by grouping the data according to the relations between the fea

Shalin Parekh

A recent paper of Tsai shows how the first few moments of a stochastic flow in the space of measures can completely determine its law. Here we give another proof of this result for the particular case of the one-dimensional multiplicative stochastic heat equation (mSHE), and then we investigate two corollaries. The first one recovers a recent result of Haire

Alessandro D'Angelo

In this paper, we extend the Virtual Localization Formula of Levine to a wide class of motivic ring spectra, obtaining in particular a localization formula for virtual fundamental classes in Witt theory $ \mathrm{KW} $. Applying standard tools of $\mathbb A^1$-intersection theory to any $ SL_{\eta} $-oriented spectra $ \mathrm A $, we obtain an additive pres

G V Sumukha Bharadwaj, S Raja

We address the black-box polynomial identity testing (PIT) problem for non-commutative polynomials computed by $+$-regular circuits, a class of homogeneous circuits introduced by [AJMR](STOC 2017, Theory of Computing 2019). These circuits can compute polynomials with a number of monomials that are doubly exponential in the circuit size. They gave an efficien

Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster, Patrick Rebeschini

Evaluating preference optimization (PO) algorithms on LLM alignment is a challenging task that presents prohibitive costs, noise, and several variables like model size and hyper-parameters. In this work, we show that it is possible to gain insights on the efficacy of PO algorithm on simpler benchmarks. We design a diagnostic suite of MuJoCo tasks and dataset

Salish Maharjan, Cong Bai, Han Wang, Yiyun Yao

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids, sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolate

James S. Cummins, Natalia G. Berloff

Portfolio optimization is a ubiquitous problem in financial mathematics that relies on accurate estimates of covariance matrices for asset returns. However, estimates of pairwise covariance could be better and calculating time-sensitive optimal portfolios is energy-intensive for digital computers. We present an energy-efficient, fast, and fully analog pipeli

Ting-Ju Wei, Chuin-Shan Chen

We present the Material Masked Autoencoder (MMAE), a self-supervised Vision Transformer pretrained on a large corpus of short-fiber composite images via masked image reconstruction. The pretrained MMAE learns latent representations that capture essential microstructural features and are broadly transferable across tasks. We demonstrate two key applications:

Shixiong Wang, Wei Dai, Geoffrey Ye Li

As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest (SoI) while mitigating interference plus noise (IPN). When uncertainties exist in the signal model or the data size of snapshots is limited, the performance of beamformers significantly degrades. In this article, we comprehensively study

Ashwani Punia, Rajendra K. Ray

This work introduces a new higher-order super-compact (HOSC) implicit finite difference scheme for analyzing three-dimensional (3D) natural convection and entropy generation in non-Newtonian fluids. The proposed scheme achieves fourth-order accuracy in space and second-order accuracy in time while utilizing only seven directly adjacent grid points of the com

Christian Banse, Angelika Schneider, Immanuel Kunz

The usefulness of semantic technologies in the context of security has been demonstrated many times, e.g., for processing certification evidence, log files, and creating security policies. Integrating semantic technologies, like ontologies, in an automated workflow, however, is cumbersome since they introduce disruptions between the different technologies an

Emmanuele Cappelluti, Habib Rostami, Federico Cilento

Single-layer semiconducting transition-metal dichalcogenides, lacking point inversion symmetry, provide an efficient platform for valleytronics, where the electronic, magnetic, valley and lattice degrees of freedom can be selectively manipulated by using polarized light. This task is however thought to be limited in parent bulk compounds where the point inve

Joe Gorka, Noah Rhodes, Line Roald

An increasing number of individuals, companies and organizations are interested in computing and minimizing the carbon emissions associated with their real-time electricity consumption. To achieve this, they require a carbon signal, i.e. a metric that defines the real-time carbon intensity of their electricity supply. Unfortunately, in a grid with multiple g

Yu Gu, Kai Zhang, Yuting Ning, Boyuan Zheng

Language agents based on large language models (LLMs) have demonstrated great promise in automating web-based tasks. Recent work has shown that incorporating advanced planning algorithms, e.g., tree search, is advantageous over reactive planning for web agents. However, unlike simulated sandbox environments, real-world environments such as the web are rife w

Jeffrey Considine

We recast move generators for solving board games as operations on compressed sets of strings. We aim for compressed representations with space sublinear in the number of game positions for interesting sets of positions, move generation in time roughly linear in the compressed size and membership tests in constant time. To the extent that we achieve these tr

Zhennan Chen, Yajie Li, Haofan Wang, Zhibo Chen

Regional prompting, or compositional generation, which enables fine-grained spatial control, has gained increasing attention for its practicality in real-world applications. However, previous methods either introduce additional trainable modules, thus only applicable to specific models, or manipulate on score maps within cross-attention layers using attentio

Demir Arikan, Peiyao Zhang, Michael Sommersperger, Shervin Dehghani

Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space.

Sebastiaan Fauquenot, Aritra Sarkar, Sebastian Feld

This research investigates the possibility of using quantum optimal control techniques to co-optimize the energetic cost and the process fidelity of a quantum unitary gate. The energetic cost is theoretically defined, and thereby, the gradient of the energetic cost for pulse engineering is derived. We empirically demonstrate the Pareto optimality in the trad

The Anh Bui, Linfei Zheng

Let $L$ be a closed, densely defined operator on $L^2(\mathbb{R}^n)$ satisfying suitable $L^p-L^q$ off-diagonal estimates of order $\kappa > 0$. This paper aims to investigate the two-weight estimate and the Bloom weighted estimate for the fractional operator $L^{-\alpha/\kappa}$ with $0 < \alpha < n$ through the method of sparse domination. Our assumptions

Ludovica Pannitto, Caterina Mauri

The paper presents an overview of initial design choices discussed towards the creation of a treebank for the Italian KIParla corpus

Faisal Mehmood, Xin Guo, Enqing Chen, Muhammad Azeem Akbar

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data.

Aycan Deniz Vit, Ujal Rzayev, Bahrem Serhat Danis, Ali Najjar Amiri

We propose a novel design paradigm for arbitrarily capable deep photonic networks of cascaded Mach-Zehnder Interferometers (MZIs) for on-chip universal polarization handling. Using a device architecture made of cascaded Mach-Zehnder interferometers, we modify and train the phase difference between interferometer arms for both polarizations through wide opera

Weixuan Chen, Qianqian Yang

Diffusion-based semantic communication methods have shown significant advantages in image transmission by harnessing the generative power of diffusion models. However, they still face challenges, including generation randomness that leads to distorted reconstructions and high computational costs. To address these issues, we propose CASC, a condition-aware se

J. Banasiak, Bime M. Ghakanyuy, Gideon A. Ngwa

We consider a recently introduced model of mosquito dynamics that includes mating and progression through breeding, questing and egg-laying stages of mosquitoes using human and other vertebrate sources for blood meals. By exploiting a multiscale character of the model and recent results on their uniform-in-time asymptotics, we derive a simplified monotone mo

Chaymae El Jabri, Marc Frappier, Pierre-Martin Tardif

This paper investigates the use of the ASTD language for ensemble anomaly detection in data logs. It uses a sliding window technique for continuous learning in data streams, coupled with updating learning models upon the completion of each window to maintain accurate detection and align with current data trends. It proposes ASTD patterns for combining learni

Recep Vural, Aymen Khaleel, Ertugrul Basar

Reconfigurable intelligent surface (RIS)-assisted communication is a key enabling technology for next-generation wireless communication networks, allowing for the reshaping of wireless channels without requiring traditional radio frequency (RF) active components. While their passive nature makes RISs highly attractive, it also presents a challenge: RISs cann

Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Sarah Masud Preum

Since the COVID-19 pandemic, clinicians have seen a large and sustained influx in patient portal messages, significantly contributing to clinician burnout. To the best of our knowledge, there are no large-scale public patient portal messages corpora researchers can use to build tools to optimize clinician portal workflows. Informed by our ongoing work with a

Usafa Akther Rifa, Pronay Debnath, Busra Kamal Rafa, Shamaun Safa Hridi

In recent years, YouTube has become the leading platform for Bangla movies and dramas, where viewers express their opinions in comments that convey their sentiments about the content. However, not all comments are relevant for sentiment analysis, necessitating a filtering mechanism. We propose a system that first assesses the relevance of comments and then a

Roopendra Singh Rajawat, Tianhong Wang, V. Khudik, G. Shvets

We present the process of ion acceleration using ultra-thin foils irradiated by elliptically polarized, high-intensity laser pulses. Recently, efficient generation of monoenergetic ion beams was introduced using the concept of laser-ion lensing and acceleration (LILA). LILA is an innovative technique where the target's radially varying thickness enables simu

Sunny Atalig, Alexander Hickerson, Arrdya Srivastav, Tingting Zheng

We consider the classical single-source shortest path problem in directed weighted graphs. D.~Eppstein proved recently an $\Omega(n^3)$ lower bound for oblivious algorithms that use relaxation operations to update the tentative distances from the source vertex. We generalize this result by extending this $\Omega(n^3)$ lower bound to \emph{adaptive} algorithm

Chao Huang

We study the problem of multilateral collaboration among agents with transferable utilities. Any group of agents can sign a contract consisting of a primitive contract and monetary transfers among the signatories. We propose a dynamic auction that finds a stable outcome when primitive contracts are gross complements for all participants.

J. M. Losada, O. E. Garcia

This study investigates a novel method for estimating two-dimensional velocities using coarse-grained imaging data, which is particularly relevant for applications in plasma diagnostics. The method utilizes measurements from three non-collinear points and is derived from a stochastic model that describes the propagation of uncorrelated pulses through two-dim

Mrunmayee Deshpande, Manoranjan Majji, J. Humberto Ramos

This paper presents a novel approach for vehicle localization by leveraging the ambient magnetic field within a given environment. Our approach involves introducing a global mathematical function for magnetic field mapping, combined with Euclidean distance-based matching technique for accurately estimating vehicle position in suburban settings. The mathemati

Yuki Shirai, Tong Zhao, H. J. Terry Suh, Huaijiang Zhu

Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effectively. However, applying classical contro

Kuikui Liu, Nitya Mani, Francisco Pernice

In a seminal paper, Weitz showed that for two-state spin systems, such as the Ising and hardcore models from statistical physics, correlation decay on trees implies correlation decay on arbitrary graphs. The key gadget in Weitz's reduction has been instrumental in recent advances in approximate counting and sampling, from analysis of local Markov chains like

Yiqiao Huang, Yuancheng Wang, Jiaqi Li, Haotian Guo

In debating, rebuttal is one of the most critical stages, where a speaker addresses the arguments presented by the opposing side. During this process, the speaker synthesizes their own persuasive articulation given the context from the opposing side. This work proposes a novel zero-shot text-to-speech synthesis system for rebuttal, namely Debatts. Debatts ta

Daniel Sebastia-Saez, Jinyuan Luo, Mengqi Qin, Tao Chen

This review examines the emerging role of mechanistic mathematical models based on continuum mechanics to address current challenges in glaucoma research. At present, the advent of Artificial Intelligence and data-based models have resulted in significant progress in drug candidate screening, target identification and delivery optimization for glaucoma treat

Biman Barua, M. Shamim Kaiser

This research proposes the development of a next generation airline reservation system that incorporates the Cloud microservices, distributed artificial intelligence modules and the blockchain technology to improve on the efficiency, safety and customer satisfaction. The traditional reservation systems encounter issues related to the expansion of the systems

Griffin Heier, Sergey Y. Savrasov

Spin fluctuations have been proposed as a key mechanism for mediating superconductivity, particularly in high-temperature superconducting cuprates, where conventional electron-phonon interactions alone cannot account for the observed critical temperatures. Traditionally, their role has been analyzed through tight-binding based model Hamiltonians. In this wor

Jared C. Stimac, Nir Goldman

CeO$_2$ (ceria) is an attractive material for heterogeneous catalysis applications involving hydrogen due to its favorable redox activity combined with its relative impermeability to hydrogen ions and molecules. However, to date, many bulk ceria/hydrogen properties remain unresolved in part due to a scarcity of experimental data combined with quantum calcula

Ninad Naik

Large Language Models (LLMs) have shown significant advances in text generation but often lack the reliability needed for autonomous deployment in high-stakes domains like healthcare, law, and finance. Existing approaches rely on external knowledge or human oversight, limiting scalability. We introduce a novel framework that repurposes ensemble methods for c

Chuxiao Liu, Qingtao Pu

Geodesic equations are solved when at least two of $\tau$, $\theta$, $\varphi$ are constant on metrics of self-dual Taub-NUT type. They can also be solved also on self-dual Taub-NUT metrics if only $r$, $\theta$ or $\varphi$ is constant. However, the explicit solution of the geodesic equations is not available yet if only $\tau$ is constant.

Yi Wang, Li Li, Zaihong Jiang

In this paper, we study the Dirichlet boundary value problem of steady-state relativistic Boltzmann equation in half-line with hard potential model, given the data for the outgoing particles at the boundary and a relativistic global Maxwellian with nonzero macroscopic velocities at the far field. We first explicitly address the sound speed for the relativist

Félix Montjovet-Basset, Jayash Panigrahi, Diana Serrano, Alban Ferrier

Quantum state lifetimes $T_2$, or equivalently homogeneous linewidths $\Gamma_h = 1/\pi T_2$, are a key parameter for understanding decoherence processes in quantum systems and assessing their potential for applications in quantum technologies. The most common tool for measuring narrow optical homogeneous linewidths, i.e. long $T_2$, is the measurement of co

Nabil Mohammed, Shehab Ahmed, Charalambos Konstantinou

Regulating the voltage of the common DC bus, also referred to as the load bus, in DC microgrids is crucial for ensuring reliability and maintaining the nominal load voltage, which is essential for protecting sensitive loads from voltage variations. Stability and reliability are thereby enhanced, preventing malfunctions and extending the lifespan of sensitive

Moritz Heep, Eduard Zell

Image segmentation in RGB space is a notoriously difficult task where state-of-the-art methods are trained on thousands or even millions of annotated images. While the performance is impressive, it is still not perfect. We propose a novel image segmentation method, achieving similar segmentation quality but without training. Instead, we require an image sequ

Conlain Kelly, Surya R. Kalidindi

Engineering problems frequently require solution of governing equations with spatially-varying discontinuous coefficients. Even for linear elliptic problems, mapping large ensembles of coefficient fields to solutions can become a major computational bottleneck using traditional numerical solvers. Furthermore, machine learning methods such as neural operators

Bijean Ghafouri, Shahrad Mohammadzadeh, James Zhou, Pratheeksha Nair

Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration $\unicode{x2013}$ where a model's linguistic assertiveness fails to reflect its tru

Faiq Raees, Weiren Zhao

In this paper, we prove the local well-posedness of a scaled anisotropic Navier-Stokes-Maxwell system in a two-dimensional striped domain with a transverse magnetic field around $ (0,0,1)$ in Gevrey-2 class. We also justify the limit from the scaled anisotropic equations to the associated hydrostatic system and obtain the precise convergence rate. Then, we p

Ephrem Fola, Yang Luo, Chunbo Luo

Deep learning (DL)-based methods have demonstrated remarkable achievements in addressing orthogonal frequency division multiplexing (OFDM) channel estimation challenges. However, existing DL-based methods mainly rely on separate real and imaginary inputs while ignoring the inherent correlation between the two streams, such as amplitude and phase information

Wanquan Feng, Jiawei Liu, Pengqi Tu, Tianhao Qi

Video generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality videos that accurately meet user expectations. However, existing camera control methods still suffer from several limitations, including control precision and the neglect of the co

Cesare Spinoso-Di Piano, Jackie Chi Kit Cheung

Query-focused summarization (QFS) is the task of generating a summary in response to a user-written query. Despite its user-oriented nature, there has been limited work in QFS in explicitly considering a user's understanding of a generated summary, potentially causing QFS systems to underperform at inference time. In this paper, we adapt the Rational Speech

Shayla Sharmin, Md Fahim Abrar, Roghayeh Leila Barmaki

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical technique that measures brain activity by estimating blood oxygenation using near-infrared light. Traditionally, PsychoPy is used in many studies to send task-specific markers, requiring a separate device to interface with the fNIRS data collection system. In this work, we present a Pyth

Siyu Lv, Zhen Wu, Jie Xiong, Xin Zhang

In this paper, we study an optimal stopping problem in the presence of model uncertainty and regime switching. The max-min formulation for robust control and the dynamic programming approach are adopted to establish a general theoretical framework for such kind of problem. First, based on the dynamic programming principle, the value function of the optimal s

Louis Jose, James C. Welch, Timothy D. Tharp, Scott D. Baalrud

Strongly magnetized plasmas, characterized by having a gyrofrequency larger than the plasma frequency ($\beta = \omega_c/\omega_p \gg 1$), are known to exhibit novel transport properties. Previous works studying pure electron plasmas have shown that strong magnetization significantly inhibits energy exchange between parallel and perpendicular directions, lea

Amar Kumar, Sujeet Chaudhary, Sharat Chandra

Spin gapless semiconductors (SGSs), novel quantum materials, are notable for their tunable spin-transport properties. Considering that the SGS materials might have an invariably deformed lattice upon integration into devices, and given that the SGS nature is highly sensitive to external factors, the impact of lattice distortions on the different physical pro

Eric Rouviere, Olivier Rivoire, Rama Ranganathan

Allostery is a fundamental property of proteins that represents the functional coupling between distantly located sites. In different manifestations, this property underlies signal transduction, gene expression, and regulation -- elementary reactions in networks comprising cellular information and metabolic processing systems. In this work, we present a redu