October 2024 arXiv papers — page 79

Xinyang Li, Sergei Tretiak, Yu Zhang

When the interaction between a molecular system and confined light modes in an optical or plasmonic cavity is strong enough to overcome the dissipative process, hybrid light-matter states (polaritons) emerge as the fundamental excitations in the system. Mixing the light and matter characters modifies molecules' photophysical and photochemical properties. It

Alireza Abdollahpoorrostam

\textit{Zero-shot} models like CLIP are often fine-tuned on a target dataset to improve its accuracy further, but this can compromise out-of-distribution (OOD) robustness. Robust Fine-Tuning (\texttt{RFT} )~\citep{wortsman2021robust}, which interpolates between the \textit{zero-shot} and \textit{fine-tuned} models, has been proposed to address this issue. Ho

Marie-Sophie Hartig, Joshua Marmor, Daniel George, Sarah Paczkowski

The coupling of the angular jitter of the spacecraft and their sub-assemblies with the optical bench and the telescope into the interferometric length readout will be a major noise source in the LISA mission. We refer to this noise as tilt-to-length (TTL) coupling. It will be reduced directly by realignments, and the residual noise will then be subtracted in

Wojtek Treyde, Seohyun Chris Kim, Nazim Bouatta, Mohammed AlQuraishi

Predicting a ligand's bound pose to a target protein is a key component of early-stage computational drug discovery. Recent developments in machine learning methods have focused on improving pose quality at the cost of model runtime. For high-throughput virtual screening applications, this exposes a capability gap that can be filled by moderately accurate bu

Kamal Al-Sabahi, Kang Yang, Wangwang Liu, Guanyu Jiang

To solve the Grammatical Error Correction (GEC) problem , a mapping between a source sequence and a target one is needed, where the two differ only on few spans. For this reason, the attention has been shifted to the non-autoregressive or sequence tagging models. In which, the GEC has been simplified from Seq2Seq to labeling the input tokens with edit comman

Manan Suri, Puneet Mathur, Franck Dernoncourt, Rajiv Jain

Document structure editing involves manipulating localized textual, visual, and layout components in document images based on the user's requests. Past works have shown that multimodal grounding of user requests in the document image and identifying the accurate structural components and their associated attributes remain key challenges for this task. To add

Gyu Whan Chang, Andreas Reinhart

An integral domain $D$ is a {\em valuation ideal factorization domain} (VIFD) if each nonzero principal ideal of $D$ can be written as a finite product of valuation ideals. Clearly, $\pi$-domains are VIFDs. We study the ring-theoretic properties of VIFDs and the $*$-operation analogs of VIFDs. Among them, we show that if $D$ is treed (resp., $*$-treed), then

Leo Liberti

The Buckminsterfullerene is an inorganic molecule consisting of 60 carbon atoms in the shape of a soccer ball. It was used in [Juhas et al., Nature 2006] to showcase algorithms that find the correct shape of a protein from limited data (length of inter-atomic distances) without any further chemical experiment: in that case, by means of a complicated construc

Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy, Banani Roy

Predicting software defects early in the development process not only enhances the quality and reliability of the software but also decreases the cost of development. A wide range of machine learning techniques can be employed to create software defect prediction models, but the effectiveness and accuracy of these models are often influenced by the choice of

Hedong Hou

We prove that any mild solution in the Koch--Tataru space to the incompressible Navier--Stokes equation with initial data in $\mathrm{BMO}^{-1}$ is weak*-continuous in time, valued in $\mathrm{BMO}^{-1}$. We also show that the global mild solution vanishes in $\mathrm{BMO}^{-1}$ at infinity in time.

Matheus C. Carvalho, Bavithra Naguleswaran, Pauline Barmby, Mark Gorski

UGC 2885 (z = 0.01935) is one of the largest and most massive galaxies in the local Universe, yet its undisturbed spiral structure is unexpected for such an object and unpredicted in cosmological simulations. Understanding the detailed properties of extreme systems such as UGC 2885 can provide insight on the limits of scaling relations and physical processes

Michael J. Facci, Ebrahim M. Kolahdouz, Boyce E. Griffith

The immersed interface method (IIM) for models of fluid flow and fluid-structure interaction imposes jump conditions that capture stress discontinuities generated by forces that are concentrated along immersed boundaries. Most prior work using the IIM for fluid dynamic applications has focused on smooth interfaces, but boundaries with sharp features such as

Weiliang Yao, Supeng Liu, Zifan Xu, Daisuke Ishikawa

Kagome lattice $A$V$_3$Sb$_5$ ($A$ = K, Rb, and Cs) superconductors without magnetism from vanadium $d$-electrons are intriguing because they have a novel charge density wave (CDW) order around 90 K and display superconductivity at $\sim$3 K that competes with the CDW order. Recently, CsCr$_3$Sb$_5$, isostructural to $A$V$_3$Sb$_5$, was found to have concurr

Yueqi Song, Frank Xu, Shuyan Zhou, Graham Neubig

Web browsers are a portal to the internet, where much of human activity is undertaken. Thus, there has been significant research work in AI agents that interact with the internet through web browsing. However, there is also another interface designed specifically for machine interaction with online content: application programming interfaces (APIs). In this

Stefano Longhi

The Mpemba effect refers to the surprising observation where, under certain conditions, a far-from-equilibrium state can relax toward equilibrium faster than a state closer to equilibrium. A paradigmatic example is provided by the curious fact that hot water can sometimes freeze faster than cold water. The Mpemba effect has intrigued scientists since long ti

Yuqin Jiang, Zhenlong Li, Joon-Seok Kim, Huan Ning

Numerous researchers have utilized GPS-enabled vehicle data and SafeGraph mobility data to analyze human movements. However, the comparison of their ability to capture human mobility remains unexplored. This study investigates differences in human mobility using taxi trip records and the SafeGraph dataset in New York City neighborhoods. The analysis includes

Paria Khoshtab, Danial Namazifard, Mostafa Masoudi, Ali Akhgary

This study addresses the gap in the literature concerning the comparative performance of LLMs in interpreting different types of figurative language across multiple languages. By evaluating LLMs using two multilingual datasets on simile and idiom interpretation, we explore the effectiveness of various prompt engineering strategies, including chain-of-thought

Robert M. Quimby, Brian D. Metzger, Ken J. Shen, Allen W. Shafter

Observational constraints on classical novae are heavily biased to phases near optical peak and later because of the simple fact that novae are not typically discovered until they become bright. The earliest phases of brightening, coming before discovery, are typically missed, but this is changing with the proliferation of wide-field optical monitoring syste

Madhura Pathegama, Alexander Barg

Universal hash functions map the output of a source to random strings over a finite alphabet, aiming to approximate the uniform distribution on the set of strings. A classic result on these functions, called the Leftover Hash Lemma, gives an estimate of the distance from uniformity based on the assumptions about the min-entropy of the source. We prove severa

Yongheng Sun, Yueh Z. Lee, Genevieve A. Woodard, Hongtu Zhu

Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly utilizes Transformers to generateradiology reports, which can be computationally intensive,limiting their use in

Dong-Ho Lee, Adam Kraft, Long Jin, Nikhil Mehta

Recent progress in large language models (LLMs) offers promising new approaches for recommendation system tasks. While the current state-of-the-art methods rely on fine-tuning LLMs to achieve optimal results, this process is costly and introduces significant engineering complexities. Conversely, methods that directly use LLMs without additional fine-tuning r

Yi Han

We consider the convergence of the ESD for non-Hermitian random band matrices with independent entries to the circular law, which is the uniform measure on the unit disk in the center of the complex plane. We assume that the bandwidth of the matrix scales like $n^γ$ for some $γ\in(0,1]$, where $n$ is the matrix size, and the variance profile of the matrix is

Da JU, Song Jiang, Andrew Cohen, Aaron Foss

Travel planning is a challenging and time-consuming task that aims to find an itinerary which satisfies multiple, interdependent constraints regarding flights, accommodations, attractions, and other travel arrangements. In this paper, we propose To the Globe (TTG), a real-time demo system that takes natural language requests from users, translates it to symb

Lior Horesh, Vasileios Kalantzis, Yingdong Lu, Tomasz Nowicki

Monte Carlo matrix trace estimation is a popular randomized technique to estimate the trace of implicitly-defined matrices via averaging quadratic forms across several observations of a random vector. The most common approach to analyze the quality of such estimators is to consider the variance over the total number of observations. In this paper we present

Zhiwei Zhang, Fali Wang, Xiaomin Li, Zongyu Wu

Large language models (LLMs) have shown remarkable proficiency in generating text, benefiting from extensive training on vast textual corpora. However, LLMs may also acquire unwanted behaviors from the diverse and sensitive nature of their training data, which can include copyrighted and private content. Machine unlearning has been introduced as a viable sol

Anthony Rhodes, Bin Jiang, Jenny Jiang

Non-negative Matrix Factorization (NMF) is an effective algorithm for multivariate data analysis, including applications to feature selection, pattern recognition, and computer vision. Its variant, Semi-Nonnegative Matrix Factorization (SNF), extends the ability of NMF to render parts-based data representations to include mixed-sign data. Graph Regularized S

Wenfeng Liu, Bernard Ennis, Corentin Coulais

Material nonlinearities such as hyperelasticity, viscoelasticity, and plasticity have recently emerged as design paradigms for metamaterials based on buckling. These metamaterials exhibit properties such as shape morphing, transition waves, and sequential deformation. In particular, plasticity has been used in the design of sequential metamaterials which com

Christabel Acquaye, Haozhe An, Rachel Rudinger

Recent work has highlighted the culturally-contingent nature of commonsense knowledge. We introduce AMAMMER${\epsilon}$, a test set of 525 multiple-choice questions designed to evaluate the commonsense knowledge of English LLMs, relative to the cultural contexts of Ghana and the United States. To create AMAMMER${\epsilon}$, we select a set of multiple-choice

Dong-han Yeom

In this article, we review the information loss paradox in the spirit of the Euclidean path integral approach. First, we argue that there is a long debate about the information loss paradox, and the non-perturbative quantum gravitational wave function must include the clue to the paradox. The Euclidean path integral approach provides the best way to describe

Alireza Mousavi-Hosseini, Adel Javanmard, Murat A. Erdogdu

Recently, there have been numerous studies on feature learning with neural networks, specifically on learning single- and multi-index models where the target is a function of a low-dimensional projection of the input. Prior works have shown that in high dimensions, the majority of the compute and data resources are spent on recovering the low-dimensional pro

Kayman J. Gonçalves, Giorgio Torrieri, Radoslaw Ryblewski

We estimate both the polarization of spin $1/2$ baryons and the spin density matrix coefficients of spin $1$ mesons using a thermal model that incorporates vorticity and polarization to describe quarks, along with a coalescence formalism where spin and vorticity are not in equilibrium. We find that while our model is not predictive due to its considerable nu

Manuel Uruena Palomo

Moderhai Milgrom's Modified Newtonian Dynamics (MOND) correction to Newtonian gravity or inertia is shown to be equivalent to a more fundamental formulation considering a non-inertial local reference frame and the fixed background of the observable universe, in the spirit of Mach's principle. Both Newton's gravitational constant $G\sim c^2/(M_u/R_u)$ and Mil

Kriss Gutierrez Anco, Tristan Nemoz, Peter Brown

How much cryptographically-secure randomness can be extracted from a quantum state? This fundamental question probes the absolute limits of quantum random number generation (QRNG) and yet, despite the technological maturity of QRNGs, it remains unsolved. In this work we consider a general adversarial model that allows for an adversary who has quantum side-in

L. J. Sun, J. Dopfer, A. Adams, C. Wrede

The particle x-ray coincidence technique (PXCT) was originally developed to measure average lifetimes in the $10^{-17}-10^{-15}$~s range for proton-unbound states populated by electron capture (EC). We have designed and built the Lifetimes and Branching Ratios Apparatus (LIBRA) to be used in the stopped-beam area at the Facility for Rare Isotope Beams that e

Jinbang Huang, Allen Tao, Rozilyn Marco, Miroslav Bogdanovic

Task and motion planning (TAMP) frameworks address long and complex planning problems by integrating high-level task planners with low-level motion planners. However, existing TAMP methods rely heavily on the manual design of planning domains that specify the preconditions and postconditions of all high-level actions. This paper proposes a method to automate

Ricardo Vega, Kevin Zhu, Connor Mattson, Daniel S. Brown

Despite significant research, robotic swarms have yet to be useful in solving real-world problems, largely due to the difficulty of creating and controlling swarming behaviors in multi-agent systems. Traditional top-down approaches in which a desired emergent behavior is produced often require complex, resource-heavy robots, limiting their practicality. This

Hao Bai, Yi Ma

Neurons in auto-regressive language models like GPT-2 can be interpreted by analyzing their activation patterns. Recent studies have shown that techniques such as dictionary learning, a form of post-hoc sparse coding, enhance this neuron-level interpretability. In our research, we are driven by the goal to fundamentally improve neural network interpretabilit

Christoph Fabianek, Stephan Krenn, Thomas Loruenser, Veronika Siska

This paper explores the integration of advanced cryptographic techniques for secure computation in data spaces to enable secure and trusted data sharing, which is essential for the evolving data economy. In addition, the paper examines the role of data intermediaries, as outlined in the EU Data Governance Act, in data spaces and specifically introduces the i

Xinjie Liu, Jingqi Li, Filippos Fotiadis, Mustafa O. Karabag

Feedback Nash equilibrium strategies in multi-agent dynamic games require availability of all players' state information to compute control actions. However, in real-world scenarios, sensing and communication limitations between agents make full state feedback expensive or impractical, and such strategies can become fragile when state information from other

M. E. Limes, N. Dural, M. V. Romalis, E. L. Foley

As the only stable spin-1/2 noble gas isotopes, $^{3}$He and $^{129}$Xe are promising systems for inertial rotation sensing and searches for exotic spin couplings. Spin-1/2 noble gases have intrinsic coherence times on the order of hours to days, which allows for incredibly low frequency error of free-precession measurements. However relaxation in miniature

Charles Bordenave, François Chapon, Mireille Capitaine

Toeplitz matrices form a rich class of possibly non-normal matrices whose asymptotic spectral analysis in high dimension is well-understood. The spectra of these matrices are notoriously highly sensitive to small perturbations. In this work, we analyze the spectrum of a banded Toeplitz matrix perturbed by a random matrix with iid entries of variance $\sigma_

Zehua Liu, Xiaolou Li, Chen Chen, Li Guo

Visual Speech Recognition (VSR) aims to recognize corresponding text by analyzing visual information from lip movements. Due to the high variability and weak information of lip movements, VSR tasks require effectively utilizing any information from any source and at any level. In this paper, we propose a VSR method based on audio-visual cross-modal alignment

Jason Hindes, Ira B. Schwartz

Random perturbations and noise can excite instabilities in population systems that result in large fluctuations. An interesting example involves class B lasers, where the dynamics is determined by the number of carriers and photons in a cavity with noise appearing in the electric-field dynamics. When such lasers are brought above threshold, the field intensi

Gonzalo Travieso, Alexandre Benatti, Luciano da F. Costa

The Jaccard similarity index has often been employed in science and technology as a means to quantify the similarity between two sets. When modified to operate on real-valued values, the Jaccard similarity index can be applied to compare vectors, an operation which plays a central role in visualization, classification, and modeling. The present work aims at

John A. D. Appleby, Emmet Lawless

This paper considers the asymptotic behaviour of deterministically and stochastically forced linear pantograph equations. The asymptotic behaviour is studied in the case when all solutions of the pantograph equation without forcing tend to a trivial equilibrium. In all cases, we give necessary and sufficient conditions on the forcing terms which enable all s

Jean Alexandre, Nick E. Mavromatos, Vasiliki A. Mitsou, Emanuela Musumeci

High-Electric-Charge compact Objects (HECOs) appear in several theoretical particle physics models beyond the Standard Model, and are actively searched for in current colliders, such as the Large Hadron Collider (LHC). In such searches, mass bounds of these objects have been placed, using Drell-Yan and Photon-Fusion processes at tree level so far. However, s

Viktor Eisler

We study the ground-state entanglement Hamiltonian of free nonrelativistic fermions for semi-infinite domains in one dimension. This is encoded in the two-point correlations projected onto the subsystem, an operator that commutes with the linear deformation of the physical Hamiltonian. The corresponding eigenfunctions are shown to possess the exact same stru

Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, AmirArsalan Rajabi, Aida Tayebi

The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks. Current methods for mitigating bias often result in information loss and an inadequate balance between accuracy and fairness. To address this, we propose a novel methodolog

Tian Yu Liu, Stefano Soatto

The semantic similarity between sample expressions measures the distance between their latent 'meaning'. These meanings are themselves typically represented by textual expressions. We propose a novel approach whereby the semantic similarity among textual expressions is based not on other expressions they can be rephrased as, but rather based on the imagery t

Zhiming Hu, Guanhua Zhang, Zheming Yin, Daniel Haeufle

Human hand and head movements are the most pervasive input modalities in extended reality (XR) and are significant for a wide range of applications. However, prior works on hand and head modelling in XR only explored a single modality or focused on specific applications. We present HaHeAE - a novel self-supervised method for learning generalisable joint repr

Leni Aniva, Chuyue Sun, Brando Miranda, Clark Barrett

Machine-assisted theorem proving refers to the process of conducting structured reasoning to automatically generate proofs for mathematical theorems. Recently, there has been a surge of interest in using machine learning models in conjunction with proof assistants to perform this task. In this paper, we introduce Pantograph, a tool that provides a versatile

Wan Lin, Junhui Chen, Tianhao Wang, Zhenyu Zhou

Modern speaker verification systems primarily rely on speaker embeddings, followed by verification based on cosine similarity between the embedding vectors of the enrollment and test utterances. While effective, these methods struggle with multi-talker speech due to the unidentifiability of embedding vectors. In this paper, we propose Neural Scoring (NS), a

Athira Menon, Michal Pawlak, Daniel J. Lennon, Koushik Sen

Massive contact binaries (CBs) are key to understanding close-binary evolution and stellar mergers, yet their study has been limited by the scarcity of observed systems, particularly of B-type binaries expected to dominate this class. We bridge this gap by mining a large sample of massive CB candidates from the OGLE-IV database, increasing their known number

Emanuele Galiffi, Anthony C. Harwood, Stefano Vezzoli, Romain Tirole

Time-invariant photonic structures amplify or absorb light based on their intrinsic material gain or loss. The coherent interference of multiple beams in space, e.g., in a resonator, can be exploited to tailor the wave interaction with material gain or loss, respectively maximizing lasing or coherent perfect absorption. By contrast, a time-varying system is

Mehrshad Shokati, Parisa Mohammadi, Atoosa Amirinian

In response to global warming and energy shortages, there has been a significant shift towards integrating renewable energy sources, energy storage systems, and electric vehicles. Deploying electric vehicles within smart grids offers a promising solution to reduce carbon emissions. However, managing the charging and discharging processes of them as distribut

Ching Fang, Christopher Sandino, Behrooz Mahasseni, Juri Minxha

Many healthcare applications are inherently multimodal, involving several physiological signals. As sensors for these signals become more common, improving machine learning methods for multimodal healthcare data is crucial. Pretraining foundation models is a promising avenue for success. However, methods for developing foundation models in healthcare are sti

Amol Khanna, Adam McCormick, Andre Nguyen, Chris Aguirre

In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researchers, privacy lawyers, and data scientists working closely with the U.S. government. After introducing differential privacy, we highlight three significant challenges which current

Marcos Barrios, Marcelo Lanzilotta, Gustavo Mata

In [8] V. G\'elinas introduced a homological invariant, called {\it delooping level} (dell), that bounds the finitistic dimension. In this article, we introduce another homological invariant (Dell) related to the delooping level for an Artin algebra. We compare this new tool with other dimensions as the finitistic dimension or the $\phi$-dimension (where $\p

John A. D. Appleby, Emmet Lawless

This is the first of a two-part paper which determines necessary and sufficient conditions on the asymptotic behaviour of forcing functions so that the solutions of additively pertubed linear differential equations obey certain growth or decay estimates. Part I considers deterministic equations, and part II It\^o-type stochastic differential equations. Resul

Xu-Hui Zhou, Zhuo-Ran Liu, Heng Xiao

Bayesian inference offers a robust framework for updating prior beliefs based on new data using Bayes' theorem, but exact inference is often computationally infeasible, necessitating approximate methods. Though widely used, these methods struggle to estimate marginal likelihoods accurately, particularly due to the rigid functional structures of deterministic

Douglas Baptista de Souza, Bruno Paes Leao

This work presents a novel data augmentation solution for non-stationary multivariate time series and its application to failure prognostics. The method extends previous work from the authors which is based on time-varying autoregressive processes. It can be employed to extract key information from a limited number of samples and generate new synthetic sampl

Yizhe Tang, Yue Wang, Teng Hu, Ran Yi

Stroke-based Rendering (SBR) aims to decompose an input image into a sequence of parameterized strokes, which can be rendered into a painting that resembles the input image. Recently, Neural Painting methods that utilize deep learning and reinforcement learning models to predict the stroke sequences have been developed, but suffer from longer inference time

Zewei Zhang, Guillaume Bellegarda, Milad Shafiee, Auke Ijspeert

Typical legged locomotion controllers are designed or trained offline. This is in contrast to many animals, which are able to locomote at birth, and rapidly improve their locomotion skills with few real-world interactions. Such motor control is possible through oscillatory neural networks located in the spinal cord of vertebrates, known as Central Pattern Ge

Yun-Jing Huang, Chad Hanna, Becca Ewing, Patrick Godwin

As gravitational-wave observations expand in scope and detection rate, the data analysis infrastructure must be modernized to accommodate rising computational demands and ensure sustainability. We present a scalable gravitational-wave search pipeline which modernizes the GstLAL pipeline by adapting the core filtering engine to the PyTorch framework, enabling

Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris

Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as data assimilation. However, most previous numerical and machine learning approaches that target forecasting cannot be applied out-of-the-box for data assimilation. Recently, diffusion

Doga Murat Kürkçüoglu, Henry Lamm, Andrea Maestri

In this work, we investigate the effect of decomposition basis on primitive qudit gates on superconducting radio-frequency cavity-based quantum computers with applications to lattice gauge theory. Three approaches are tested: SNAP & Displacement gates, ECD & single-qubit rotations $R(\theta,\phi)$, and optimal pulse control. For all three decompositions, imp

M. V. Dubinin, A. A. Fomin, G. G. Kozlov, M. Yu. Petrov

We propose a simple method of measuring the autocorrelation function of a spin noise based on multiplication and averaging two digitized signal traces, with one of them being a time-reversed copy of the other. This procedure allows one to obtain, with lower computational expenses, all the information usually derived in the Fourier transform spin-noise spectr

M. Shumko, D. L. Turner, A. Y. Ukhorskiy, I. J. Cohen

Earth's magnetotail, and the plasma sheet embedded in it, is a highly dynamic region that is coupled to both the solar wind and to the inner magnetosphere. As a consequence of this coupling, the plasma sheet undergoes explosive energy releases in the form of substorms. One consequence of this energy release is heating of thermal electrons and acceleration of

Angelo Moroncelli, Vishal Soni, Marco Forgione, Dario Piga

Recently, generative AI and reinforcement learning (RL) have been redefining what is possible for AI agents that take information flows as input and produce intelligent behavior. As a result, we are seeing similar advancements in embodied AI and robotics for control policy generation. Our review paper examines the integration of generative AI models with RL

Mengjiao Zhang, Jia Xu

While NLP models significantly impact our lives, there are rising concerns about privacy invasion. Although federated learning enhances privacy, attackers may recover private training data by exploiting model parameters and gradients. Therefore, protecting against such embedding attacks remains an open challenge. To address this, we propose Subword Embedding

Matija Medvidović, Jaylyn C. Umana, Iman Ahmadabadi, Domenico Di Sante

Density functional theory (DFT) offers a desirable balance between quantitative accuracy and computational efficiency in practical many-electron calculations. Its central component, the exchange-correlation energy functional, has been approximated with increasing levels of complexity ranging from strictly local approximations to nonlocal and orbital-dependen

Zhaoyuan Deng, Amith Ananthram, Kathleen McKeown

Live comments, also known as Danmaku, are user-generated messages that are synchronized with video content. These comments overlay directly onto streaming videos, capturing viewer emotions and reactions in real-time. While prior work has leveraged live comments in affective analysis, its use has been limited due to the relative rarity of live comments across

Md Asifuzzaman Jishan, Vikas Singh, Ayan Kumar Ghosh, Md Shahabub Alam

This study applies Bayesian models to predict hotel booking cancellations, a key challenge affecting resource allocation, revenue, and customer satisfaction in the hospitality industry. Using a Kaggle dataset with 36,285 observations and 17 features, Bayesian Logistic Regression and Beta-Binomial models were implemented. The logistic model, applied to 12 fea

Giovanni Pittiglio, Fabio Leuenberger, Margherita Mencattelli, Max McCandless

This paper introduces a novel magnetic navigation system for cardiac ablation. The system is formed from two key elements: a magnetic ablation catheter consisting of a chain of spherical permanent magnets; and an actuation system comprised of two cart-mounted permanent magnets undergoing pure rotation. The catheter design enables a large magnetic content wit

Vihang Mehta, Marc Rafelski, Ben Sunnquist, Harry I. Teplitz

The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep HST F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and EGS). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust ph

Debasish Borah, Nayan Das

We study the possibility of producing the observed baryon asymmetry of the Universe (BAU) and dark matter (DM) from evaporating primordial black holes (PBH) beyond the semi-classical regime incorporating the impact of memory burden. In the simplest scenario of baryogenesis via vanilla leptogenesis with hierarchical right handed neutrino (RHN), it is possible

Ruhanshi Barad, Qicheng Tang, Wei Zhu, Xueda Wen

The global symmetry, either invertible or non-invertible, has been extensively studied in two dimensional conformal field theories in recent years. When the theory is defined on a manifold with open boundaries, however, many interesting conformal boundary conditions will fully or partially break such global symmetry. In this work, we study the effect of symm

Khashayar Gatmiry, Zhiyuan Li, Sashank J. Reddi, Stefanie Jegelka

The remarkable generalization ability of neural networks is usually attributed to the implicit bias of SGD, which often yields models with lower complexity using simpler (e.g. linear) and low-rank features. Recent works have provided empirical and theoretical evidence for the bias of particular variants of SGD (such as label noise SGD) toward flatter regions

Zhehao Zhang, Ryan Rossi, Tong Yu, Franck Dernoncourt

While vision-language models (VLMs) have demonstrated remarkable performance across various tasks combining textual and visual information, they continue to struggle with fine-grained visual perception tasks that require detailed pixel-level analysis. Effectively eliciting comprehensive reasoning from VLMs on such intricate visual elements remains an open ch

Man-Ho Ho

For any given submersion $\pi:X\to B$ with closed, oriented and spin$^c$ fibers of even dimension, equipped with a Riemannian and differential spin$^c$ structure, we apply the Atiyah-Singer-Gorokhovsky-Lott approach to the local family index theorem without the kernel bundle assumption to construct an analytic index $\textrm{ind}^a_k$ in odd $\mathbb{Z}/k\ma

Baris Askin, Pranay Sharma, Gauri Joshi, Carlee Joe-Wong

We study a federated version of multi-objective optimization (MOO), where a single model is trained to optimize multiple objective functions. MOO has been extensively studied in the centralized setting but is less explored in federated or distributed settings. We propose FedCMOO, a novel communication-efficient federated multi-objective optimization (FMOO) a

Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling

As humanity prepares for new missions to the Moon and Mars, astronauts will need to operate with greater autonomy, given the communication delays that make real-time support from Earth difficult. For instance, messages between Mars and Earth can take up to 24 minutes, making quick responses impossible. This limitation poses a challenge for astronauts who mus

Adam Szabelski

A phenomenological analysis based on the published branching fractions and $CP$ asymmetry observables of the $B\to K\pi$, $B\to\pi\pi$ and $B\to KK$ dataset is performed. The amplitude decomposition by the topological diagrams and the $SU(3)$ irreducible representation is used. The result of the global fit can be interpreted as a solution to the ``$B\to K\pi

Malte Prinzler, Egor Zakharov, Vanessa Sklyarova, Berna Kabadayi

We introduce Joker, a new method for the conditional synthesis of 3D human heads with extreme expressions. Given a single reference image of a person, we synthesize a volumetric human head with the reference identity and a new expression. We offer control over the expression via a 3D morphable model (3DMM) and textual inputs. This multi-modal conditioning si

Fathima Shifa M, Shantanu Desai

We search for a spatial coincidence between high energy neutrinos detected by the IceCube neutrino detector and ten red dwarfs which have been observed in gamma-rays. For our analysis, we use the unbinned maximum likelihood method to look for a statistically significant excess. We do not find any such spatial association between any of the red dwarfs and Ice

Maude Gull, Daniel R. Weisz, Kareem El-Badry, Jan Henneco

We present archival HST and JWST ultraviolet through near infrared time series photometric observations of a massive minimal-contact binary candidate in the metal-poor galaxy WLM ($Z = 0.14 Z_{\odot}$). This discovery marks the lowest metallicity contact binary candidate observed to date. We determine the nature of the two stars in the binary by using the ec

Matthieu Lin, Jenny Sheng, Andrew Zhao, Shenzhi Wang

This survey organizes the intricate literature on the design and optimization of emerging structures around post-trained LMs. We refer to this overarching structure as scaffolded LMs and focus on LMs that are integrated into multi-step processes with tools. We view scaffolded LMs as semi-parametric models wherein we train non-parametric variables, including

Zou Yang, Seung Hee Lee, Julia R. Köhler, AmirEmad Ghassami

Traditional panel-data causal inference frameworks, such as difference-in-differences and synthetic control methods, rely on pre-intervention data to estimate counterfactual means. However, such data may be unavailable in real-world settings when interventions are implemented in response to sudden events, such as public health crises or epidemiological shock

Macarena Arenas

We show that, under suitable hypotheses, the coned-off spaces associated to $C(9)$ cubical small-cancellation presentations are aspherical, and use this to provide classifying spaces, or classifying spaces for proper actions, for their fundamental groups. Along the way, we show that the Cohen--Lyndon property holds for the subgroups of the fundamental group

Pierdomenico Duttilo

This PhD Thesis presents an investigation into the analysis of financial returns using mixture models, focusing on mixtures of generalized normal distributions (MGND) and their extensions. The study addresses several critical issues encountered in the estimation process and proposes innovative solutions to enhance accuracy and efficiency. In Chapter 2, the f

Jeremy J. Webb, Marta Reina-Campos, J. M. Diederik Kruijssen

Most of the dynamical mass loss from star clusters is thought to be caused by the time-variability of the tidal field (``tidal shocks''). Systematic studies of tidal shocks have been hampered by the fact that each tidal history is unique, implying both a reproducibility and a generalisation problem. Here we address these issues by investigating how star clus

Soumen Roy, Rodrigo Vicente

The gravitational-wave events observed by the LIGO-Virgo-KAGRA collaboration are attributed to compact binary coalescences happening in vacuum. However, several studies suggest that gaseous environments may play a significant role in the formation and evolution of compact binaries. Why have we not seen environmental effects in LVK signals? While matched-filt

Luke Staszewski, Alexander Wietek

We investigate the fundamental dynamical process of an initial quench of the chemical potential of the two-dimensional $t$-$J$ model. Depending on the ground state phase, sharply different dynamical behavior of the charge distribution and entanglement properties are observed. In the stripe phase, the intertwining of the spin and charge density waves remains

Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu

How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to address this challenge by training models that can accurately classify known, in-distribution (ID) classes while identifying and handling previously unseen classes during inference. It

Sahil Kumar, Deepa Paikar, Kiran Sai Vutukuri, Haider Ali

Effective communication within universities is crucial for addressing the diverse information needs of students, alumni, and external stakeholders. However, existing chatbot systems often fail to deliver accurate, context-specific responses, resulting in poor user experiences. In this paper, we present KatzBot, an innovative chatbot powered by KatzGPT, a cus

BaoZhi Chu, YanYan Li, Zongyuan Li

The $\sigma_k(A_g)$ curvature and the boundary $\mathcal{B}^k_g$ curvature arise naturally from the Chern--Gauss--Bonnet formula for manifolds with boundary. In this paper, we prove a Liouville theorem for the equation $\sigma_k(A_g)=1$ in $\overline{\mathbb{R}^n_+}$ with the boundary condition $\mathcal{B}^k_g=c$ on $\partial\mathbb{R}^n_+$, where $g=e^{2v}

Nicholas Potteiger, Ankita Samaddar, Hunter Bergstrom, Xenofon Koutsoukos

Modern network defense can benefit from the use of autonomous systems, offloading tedious and time-consuming work to agents with standard and learning-enabled components. These agents, operating on critical network infrastructure, need to be robust and trustworthy to ensure defense against adaptive cyber-attackers and, simultaneously, provide explanations fo

Federico Domínguez, Michael Fellner, Berend Klaver, Stefan Rombouts

Using the Parity Flow formalism, we show that physical SWAP gates can be eliminated in linear hardware architectures, without increasing the total number of two-qubit operations. This has a significant impact on the execution time of quantum circuits in linear Quantum Charge-Coupled Devices (QCCDs), where SWAP gates are implemented by physically changing the

Basabendu Barman, Arghyajit Datta, Md Riajul Haque

We examine the impact of charged lepton Yukawa equilibration on leptogenesis during gravitational reheating. During the post-inflationary era, the inflaton field is assumed to oscillate around the minimum of a monomial potential, leading to the gravitational production of Standard Model (SM) particles, constituting the radiation bath. The heavy right-handed

Nico Hauser, Matthias J. Bayerbach, Simone E. D'Aurelio, Raphael Weber

Fault-tolerant fusion-based photonic quantum computing (FBQC) greatly relies on entangling two-photon measurements, called fusions. These fusions can be realized using linear-optical projective Bell-state measurements (BSMs). These linear-optical BSMs are limited to a success probability of 50%, greatly reducing the performance of FBQC schemes. To improve th