November 2024 arXiv papers — page 28

Bhaktipriya Radharapu, Harish Krishna

The growing threat of deepfakes and manipulated media necessitates a radical rethinking of media authentication. Existing methods for watermarking synthetic data fall short, as they can be easily removed or altered, and current deepfake detection algorithms do not achieve perfect accuracy. Provenance techniques, which rely on metadata to verify content origi

Martin Kittel, Alexander Roth, Wolf-Peter Schill

Coping with prolonged periods of low availability of wind and solar power, also referred to as variable renewable energy droughts or "Dunkelflaute", emerges as a key challenge for realizing decarbonized energy systems based on renewable energy. Here we investigate the role of long-duration electricity storage and geographical balancing through transmission i

Robert Reischke, Michael Kovač, Andrina Nicola, Steffen Hagstotz

The dispersion measure (DM) of fast radio bursts (FRBs) is sensitive to the electron distribution in the Universe, making it a promising probe of cosmology and astrophysical processes such as baryonic feedback. However, cosmological analyses of FRBs require knowledge of the contribution to the observed DM coming from the FRB host. The size and distribution o

D. C. Rubie, K. I. Dale, G. Nathan, M. Nakajima

The Hf-W isotopic system is the reference chronometer for determining the chronology of Earth's accretion and differentiation. However, its results depend strongly on uncertain parameters, including the extent of metal-silicate equilibration and the siderophility of tungsten. Here we show that a multistage core-formation model based on N-body accretion simul

Tahia F. Dabash, Moataz H. Emam

In this paper, we continue previous work where one-brane spacetimes coupled to the N=2 ungauged five dimensional hypermultiplets were found. We explore their symmetries as well as study their full geodesic structure. The one-branes are characterized by a coupling constant that distinguishes the behavior of the geodesics from smooth and causally connected in

Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu

Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. This limitation hinders LLMs' ability to predict precise character positions, which is crucial in tasks like Chinese Spelling Correction (CSC) where identifying the positions of mis

Fredrick Almgren, William Browder, Gianmarco Caldini, Camillo De Lellis

In this article we prove that each integral cycle $T$ in an oriented Riemannian manifold $\mathcal{M}$ can be approximated in flat norm by an integral cycle in the same homology class which is a smooth submanifold $\Sigma$ of nearly the same area, up to a singular set of codimension 5. Moreover, if the homology class $\tau$ is representable by a smooth subma

Armin W. Thomas, Rom Parnichkun, Alexander Amini, Stefano Massaroli

Iterative improvement of model architectures is fundamental to deep learning: Transformers first enabled scaling, and recent advances in model hybridization have pushed the quality-efficiency frontier. However, optimizing architectures remains challenging and expensive. Current automated or manual approaches fall short, largely due to limited progress in the

Anthony Tang, Alexander Mamishev, Igor Novosselov

Dielectric barrier discharge (DBD) plasma actuators generate an electrohydrodynamic (EHD) force through the ionization and acceleration of charged species. Most active flow control DBD applications are only practical at lower Reynolds numbers, and increasing the momentum injection can extend the practical uses of the technology. Here, we experimentally demon

Jiazheng Li, Jundong Li, Chuxu Zhang

Graph neural networks stand as the predominant technique for graph representation learning owing to their strong expressive power, yet the performance highly depends on the availability of high-quality labels in an end-to-end manner. Thus the pretraining and fine-tuning paradigm has been proposed to mitigate the label cost issue. Subsequently, the gap betwee

Jacques Distler, Grant Elliot

In this paper, we investigate the twisted $A_{2n}$ sector of class-S theories. Heretofore, the Coulomb branches of such theories have been poorly understood. In this, and a companion paper, we make progress in our understanding of them. In particular, we find a formula for the dimension of the Coulomb branch of any twisted $A_{2n}$ class-S theory. Deferring

Han Zhang, Yu Lu, Liyun Zhang, Dian Ding

Understanding the emotions in a dialogue usually requires external knowledge to accurately understand the contents. As the LLMs become more and more powerful, we do not want to settle on the limited ability of the pre-trained language model. However, the LLMs either can only process text modality or are too expensive to process the multimedia information. We

Yael Vinker, Tamar Rott Shaham, Kristine Zheng, Alex Zhao

Sketching serves as a versatile tool for externalizing ideas, enabling rapid exploration and visual communication that spans various disciplines. While artificial systems have driven substantial advances in content creation and human-computer interaction, capturing the dynamic and abstract nature of human sketching remains challenging. In this work, we intro

Andrea Kang, Jun Yu Chen, Zoe Lee-Youngzie, Shuhao Fu

Automatic detection of depression is a rapidly growing field of research at the intersection of psychology and machine learning. However, with its exponential interest comes a growing concern for data privacy and scarcity due to the sensitivity of such a topic. In this paper, we propose a pipeline for Large Language Models (LLMs) to generate synthetic data t

Daan Camps, Thomas Mach, Raf Vandebril, David S. Watkins

Pole-swapping algorithms, generalizations of bulge-chasing algorithms, have been shown to be a viable alternative to the bulge-chasing QZ algorithm for solving the generalized eigenvalue problem for a matrix pencil A - {\lambda}B. It is natural to try to devise a pole-swapping algorithm that solves the standard eigenvalue problem for a single matrix A. This

V. E. Sándor Szabó

We simplify the proof of some widely used theoretical theorems, extending their applicability, while correcting some erroneous results. We also generalize key results and present new results that contribute to the development of the theory. Furthermore, we use the results obtained to investigate the monotonicity properties of some specific functions related

Burak Suyunu, Enes Taylan, Arzucan Özgür

Tokenization is a crucial step in processing protein sequences for machine learning models, as proteins are complex sequences of amino acids that require meaningful segmentation to capture their functional and structural properties. However, existing subword tokenization methods, developed primarily for human language, may be inadequate for protein sequences

Zihan Zhang, Jason D. Lee, Simon S. Du, Yuxin Chen

This work investigates stepsize-based acceleration of gradient descent with {\em anytime} convergence guarantees. For smooth (non-strongly) convex optimization, we propose a stepsize schedule that allows gradient descent to achieve convergence guarantees of $O(T^{-1.119})$ for any stopping time $T$, where the stepsize schedule is predetermined without prior

Curtis McDonald, Andrew R. Barron

This paper presents the study of a Bayesian estimation procedure for single-hidden-layer neural networks using $\ell_{1}$ controlled neuron weight vectors. We study the structure of the posterior density and provide a representation that makes it amenable to rapid sampling via Markov Chain Monte Carlo (MCMC). Let the neural network have $K$ neurons with inte

Hyunji Lee, Danni Liu, Supriti Sinhamahapatra, Jan Niehues

Multimodal foundation models aim to create a unified representation space that abstracts away from surface features like language syntax or modality differences. To investigate this, we study the internal representations of three recent models, analyzing the model activations from semantically equivalent sentences across languages in the text and speech moda

Tianqing Zhu

In this paper we prove the rationality of the capped vertex function with descendents for arbitrary Nakajima quiver varieties with generic stability conditions. We generalise the proof given by Smirnov to the general case, which requires to use techniques of tautological classes rather than the fixed-point basis. This result confirms that the "monodromy" of

Ronglai Zuo, Rolandos Alexandros Potamias, Evangelos Ververas, Jiankang Deng

Sign language is a visual language that encompasses all linguistic features of natural languages and serves as the primary communication method for the deaf and hard-of-hearing communities. Although many studies have successfully adapted pretrained language models (LMs) for sign language translation (sign-to-text), the reverse task-sign language generation (

Chafiq Benhida, Piotr Budzyński

Unbounded complex symmetric weighted shifts are studied. Complex symmetric unilateral weighted shifts whose $C^\infty$ vectors contain the image of the canonical orthonormal basis under the conjugation are shown to be decomposable into an orthogonal sum of infinitely many complex selfadjoint truncated weighted shifts, which generalizes a result of S. Zhu and

Metha Prathaban, Harry Bevins, Will Handley

There is an ever-growing need in the gravitational wave community for fast and reliable inference methods, accompanied by an informative error bar. Nested sampling satisfies the last two requirements, but its computational cost can become prohibitive when using the most accurate waveform models. In this paper, we demonstrate the acceleration of nested sampli

Raktim Gautam Goswami, Prashanth Krishnamurthy, Yann LeCun, Farshad Khorrami

Vision-based pose estimation of articulated robots with unknown joint angles has applications in collaborative robotics and human-robot interaction tasks. Current frameworks use neural network encoders to extract image features and downstream layers to predict joint angles and robot pose. While images of robots inherently contain rich information about the r

Abhay Shanbhag, Suramya Jadhav, Amogh Thakurdesai, Ridhima Sinare

Natural Language Processing (NLP) for low-resource languages, which lack large annotated datasets, faces significant challenges due to limited high-quality data and linguistic resources. The selection of embeddings plays a critical role in achieving strong performance in NLP tasks. While contextual BERT embeddings require a full forward pass, non-contextual

Christian Homeyer, Leon Begiristain, Christoph Schnörr

Recent progress in scene synthesis makes standalone SLAM systems purely based on optimizing hyperprimitives with a Rendering objective possible. However, the tracking performance still lacks behind traditional and end-to-end SLAM systems. An optimal trade-off between robustness, speed and accuracy has not yet been reached, especially for monocular video. In

James P. Perry, Kate Pattle, Doug Johnstone, Woojin Kwon

The relationship between B-field orientation and density structure in molecular clouds is often assessed using the Histogram of Relative Orientations (HRO). We perform a plane-of-the-sky geometrical analysis of projected B-fields, by interpreting HROs in dense, spheroidal, prestellar and protostellar cores. We use James Clerk Maxwell Telescope (JCMT) POL-2 8

Jie Jiang

\texttt{PSpectCosmo} is a high-performance \texttt{C++} program developed to investigate early-universe cosmological dynamics, with a specific emphasis on the inflationary epoch. Utilizing a Fourier-space pseudo-spectral method, \texttt{PSpectCosmo} enables the precise evolution of interacting scalar fields and gravitational waves, ensuring accurate represen

Juan C. Domínguez, Ismael de Farias, Jorge A. Morales

We present the first installment of the quantum computing (QC) formulation of the electron nuclear dynamics (END) method within the variational quantum simulator (VQS) scheme: END/QC/VQS. END is a time-dependent, variational, on-the-flight, and non-adiabatic method to simulate chemical reactions. END represents nuclei with frozen Gaussian wave packets and el

Fouad Battahi, Zaki Chbani, Simon K. Niederländer, Hassan Riahi

In a real Hilbert space setting, we investigate the asymptotic behavior of the solutions of the classical Arrow-Hurwicz differential system combined with Tikhonov regularizing terms. Under some newly proposed conditions on the Tikhonov terms involved, we show that the solutions of the regularized Arrow-Hurwicz differential system strongly converge toward the

F. Carreño-Navas, R. Alvarez-Nodarse, N. R. Quintero

We found two stationary solutions of the parametrically driven, damped nonlinear Schr\"odinger equation with nonlinear term proportional to $|\psi(x,t)|^{2 \kappa} \psi(x,t)$ for positive values of $\kappa$. By linearizing the equation around these exact solutions, we derive the corresponding Sturm-Liouville problem. Our analysis reveals that one of the stat

Timo S. Hänninen, Tuomas V. Oikari

Let $(F_i)$ be a sequence of sets in a Banach space $X$. For what sequences does the condition $$ \limsup_{i\to \infty} \sup_{f_i\in F_i} \|Tf_i\|_Y=0 $$ hold for every Banach space $Y$ and every compact operator $T:X\to Y$? We answer this question by giving sufficient (and necessary) criteria for such sequences. We illustrate the applicability of the criter

Claudio Landim, João Pedro Mangi, Beatriz Salvador

We consider a one-dimensional exclusion dynamics in mild contact with boundary reservoirs. In the diffusive scale, the particles' density evolves as the solution of the heat equation with non-linear Robin boundary conditions. For appropriate choices of the boundary rates, these partial differential equations have more than one stationary solution. We prove t

Michael William Healy-Kalesh

Nova super-remnants (NSRs) are substantially extended structures (up to ~130 parsecs across) encompassing recurrent novae. NSRs grow as a result of frequent nova eruptions transporting vast quantities of the locally surrounding interstellar medium away from the binary system over many millennia into a thin high-density shell, as the central white dwarf grows

Yi-Chien Lin, Woosuk Kwon, Ronald Pineda, Fanny Nina Paravecino

Efficiently serving Large Language Models (LLMs) requires selecting an optimal parallel execution plan, balancing computation, memory, and communication overhead. However, determining the best strategy is challenging due to varying parallelism techniques (data, pipeline, tensor) and workload characteristics (e.g., compute-intensive tasks with long prompts vs

Luca Motto Ros, Beatrice Pitton

Let $\lambda$ be an uncountable cardinal such that $2^{< \lambda } = \lambda$. Working in the setup of generalized descriptive set theory, we study the structure of $\lambda^+$-Borel measurable functions with respect to various kinds of limits, and isolate a suitable notion of $\lambda$-Baire class $\xi$ function. Among other results, we provide higher analo

C. Beck, A. Prasad, Q. Hu, M. S. Yalim

Full-disk measurements of the solar magnetic field by the Helioseismic and Magnetic Imager (HMI) are often used for magnetic field extrapolations, but its limited spatial and spectral resolution can lead to significant errors. We compare HMI data with observations of NOAA 12104 by the Hinode Spectropolarimeter (SP) to derive a scaling curve for the magnetic

Romy Marie Merkel

We study special Lagrangian submanifolds in the Calabi-Yau manifold $T^*S^n$ with the Stenzel metric, as well as calibrated submanifolds in the $\text{G}_2$-manifold $\Lambda^2_-(T^*X)$ $(X^4 = S^4, \mathbb{CP}^2)$ and the $\text{Spin}(7)$-manifold $\$_{\!-}(S^4)$, both equipped with the Bryant-Salamon metrics. We twist naturally defined calibrated subbundle

Ao Xu, C. Reichhardt, C. J. O. Reichhardt, Yan Feng

Langevin dynamical simulations are performed to investigate the formation of clusters and voids of a two-dimensional-periodic-substrate (2DPS) modulated two-dimensional dusty plasma (2DDP) driven by an oscillatory force. It is discovered that, as the frequency of the oscillatory force decreases gradually, the substrate-modulated 2DDP undergoes the cyclic tra

Claudia Cuttano, Gabriele Trivigno, Gabriele Rosi, Carlo Masone

Referring Video Object Segmentation (RVOS) relies on natural language expressions to segment an object in a video clip. Existing methods restrict reasoning either to independent short clips, losing global context, or process the entire video offline, impairing their application in a streaming fashion. In this work, we aim to surpass these limitations and des

Yujie Dai, Brian Sullivan, Axel Montout, Amy Dillon

The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled outcomes. In this context, we leverage a linked EHR dataset of approximately one million de-identified individuals from

Till Korten, Vladimir Rybnikov, Peter Steinbach, Najmeh Mirian

Accurate characterization of radiation pulse profiles is crucial for optimizing beam quality and enhancing experimental outcomes in Free Electron Laser (FEL) research. In this paper, we present a novel approach that employs machine learning techniques for real-time virtual diagnostics of FEL radiation pulses. Our advanced artificial intelligence (AI)-based d

Yehia Lalili, Toufik Bouden, Morad Grimes, Abderrazek Lachouri

In this work, a new 4-D hyperchaotic system for image encryption is proposed and its effectiveness is demonstrated by incorporating it into an existing Elliptic Curve Cryptography (ECC) mapping scheme. The proposed system is considered simple because it consists of eight terms with two nonlinearities. The system exhibits high sensitivity to initial condition

Maria Rita Vasconcelos Brandão Souza, Luís Henrique de Freitas, Glaucia de Souza Silva, Felipe Xavier de Carvalho

This work explores the use of Role Playing Games (RPG) as an active methodology in teaching Modern Physics, focusing on a game called Newton's Revenge. The game was developed with the aim of engaging students in collaborative and investigative learning processes, using gamification elements to increase motivation and involvement. Based on the constructivist

Cristóbal Melo, Matías Reyes. F., Diego Arroyo, Esteban S. Gómez

The use of multicore optical fibers is now recognized as one of the most promising methods to implement the space-division multiplexing techniques required to overcome the impending capacity limit of conventional single-mode optical fibers. Nonetheless, new devices for networking operations compatible with these fibers will be required in order to implement

Jiangbin Zheng, Qianhui Xu, Ruichen Xia, Stan Z. Li

Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. The emergent deep learning methods excel at learning antigen binding patterns from known TCRs but struggle with novel or sparsely represented antigens. However, binding specificity for unseen antigens or exogenou

Dillon Bass, Daniel Fabrycky

When an exoplanet passes in front of its host star, the resulting eclipse causes an observable decrease in stellar flux, and when multiple such transits are detected, the orbital period of the exoplanet can be determined. Over the past six years, NASA's Transiting Exoplanet Survey Satellite (TESS) has discovered thousands of potential planets by this method,

Leonardo A. M. Souza

This paper explores as didactically as possible the fundamental principles of both classical and quantum metrology, focusing on the Cram\'er-Rao Bound and how it defines the maximum precision in parameter estimation, taking into account noise and the information extracted from the data. We also conduct a detailed study of Fisher Information (both classical a

Peter Barnett, Lisa Thiergart

AI evaluations are an important component of the AI governance toolkit, underlying current approaches to safety cases for preventing catastrophic risks. Our paper examines what these evaluations can and cannot tell us. Evaluations can establish lower bounds on AI capabilities and assess certain misuse risks given sufficient effort from evaluators. Unfortunat

Promit Chakroborty, Michael D. Shields

In engineering examples, one often encounters the need to sample from unnormalized distributions with complex shapes that may also be implicitly defined through a physical or numerical simulation model, making it computationally expensive to evaluate the associated density function. For such cases, MCMC has proven to be an invaluable tool. Random-walk Metrop

Steven Charlton, Lukas Mauth, Anna Medvedovsky

We consider a special subsequence of the Fourier coefficients of powers of the Dedekind $\eta$-function, analogous to the sequence $\delta_\ell := 24^{-1} \pmod{\ell}$ on which exceptional congruences of the partition function are supported. Therefrom we define a notion of density $D(r)$ for a normalized eta-power $\eta^r$ measuring the proportion of primes

Suramya Jadhav, Abhay Shanbhag, Amogh Thakurdesai, Ridhima Sinare

Low-resource languages face significant challenges due to the lack of sufficient linguistic data, resources, and tools for tasks such as supervised learning, annotation, and classification. This shortage hinders the development of accurate models and datasets, making it difficult to perform critical NLP tasks like sentiment analysis or hate speech detection.

Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz, Elton Yechao Zhu

Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certain limit. We present a neural network pruning technique that builds upon the Combinatorial Brain Surgeon, but solves an optimization problem over a subset of the network weights in

Harsh Singh, Rocktim Jyoti Das, Mingfei Han, Preslav Nakov

Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation. While recent efforts in robotics have leveraged LLMs both for high-level and low-level planning, these approaches often face significant challenges, such as hallucinations in long-horizon tasks and limited adapt

Callum E. Flowerday, Ryan Thalman, Jaron C. Hansen

Air quality is a prevalent concern due to its imposing health risks. The state of Utah, USA, has, at times over the last 20 years, experienced some of the worst air quality in the nation. The propensity for Utah to experience elevated concentrations of particulate matter ($\mathrm{PM_{2.5}}$) and ozone ($\mathrm{O_3}$) can, in part, be attributed to its uniq

Amit Acharya, Janusz Ginster, Ambar N. Sengupta

A scheme for generating weakly lower semi-continuous action functionals corresponding to the Euler-Lagrange equations of Chern-Simons theory is described. Coercivity is deduced for such a functional in appropriate function spaces to prove the existence of a minimizer, which constitutes a solution to the Euler-Lagrange equations of Chern-Simons theory in a re

Jiangbin Zheng, Ge Wang, Han Zhang, Stan Z. Li

Computational protein design (CPD) offers transformative potential for bioengineering, but current deep CPD models, focused on universal domains, struggle with function-specific designs. This work introduces a novel CPD paradigm tailored for functional design tasks, particularly for enzymes-a key protein class often lacking specific application efficiency. T

June-Young Kim, Christian Weiss

We study the nonperturbative properties of the nucleon's chiral-odd generalized parton distributions (transversity GPDs) in the large-$N_c$ limit of QCD. This includes the parametric ordering of the spin-flavor components, the polynomiality property of the moments, and the sum rules connecting the GPDs with the tensor form factors. A multipole expansion in t

Matteo Perugini

Let $n\geq 1$, and let $\Omega\subset \mathbb{R}^n$ be an open and connected set with finite Lebesgue measure. Among functions of bounded variation in $\Omega$ we introduce the class of \emph{minimally singular} functions. Inspired by the original theory of Vol'pert of one-dimensional restrictions of $BV$ functions, we provide a geometric characterization fo

Jiaxuan Li, Junwen Mo, MinhDuc Vo, Akihiro Sugimoto

Multimodal Large Language Models (MLLMs) have made notable advances in visual understanding, yet their abilities to recognize objects modified by specific attributes remain an open question. To address this, we explore MLLMs' reasoning capabilities in object recognition, ranging from commonsense to beyond-commonsense scenarios. We introduce a novel benchmark

Qi-Rui Yang, Ruo-Yu Liu, Xiang-Yu Wang

High-energy neutrinos from the blazar TXS 0506+056 are usually thought to arise from the relativistic jet pointing to us. However, the composition of jets of active galactic nuclei (AGNs), whether they are baryon dominated or Poynting flux dominated, is largely unknown. In the latter case, no comic rays and neutrinos are expected from the AGN jets. In this w

Michael J. Quin, Antonino Di Piazza, Çağrı Erciyes, Christoph H. Keitel

Attosecond pulses of coherent extreme ultraviolet (XUV) light are instrumental for investigating subatomic dynamics and can be produced using a free-electron laser (FEL). It has been suggested that an optical FEL, which employs a laser pulse in place of a conventional undulator, could enable a dramatically more compact implementation of such a light source.

Jiahuei Lin, Dayi Lin, Sky Zhang, Ahmed E. Hassan

AI judge systems are designed to automatically evaluate Foundation Model-powered software (i.e., FMware). Due to the intrinsic dynamic and stochastic nature of FMware, the development of AI judge systems requires a unique engineering life cycle and presents new challenges. In this paper, we discuss the challenges based on our industrial experiences in develo

Cyrill Bösch, Malte Schade, Giacomo Aloisi, Scott D. Keating

We present a quantum algorithmic framework for simulating linear, anti-Hermitian (lossless) wave equations in heterogeneous, anisotropic, and time-independent media. This framework encompasses a broad class of wave equations, including the acoustic wave equation, Maxwell$'$s equations and the elastic wave equation. Our formulation is compatible with standard

Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang, Sihao Hu

The alignment of pre-trained LLMs continues to draw significant attention from both industry and academia, aiming to ensure responses that are helpful, harmless, and honest. However, identifying a point in the model's representation subspace that simultaneously satisfies all these properties remains challenging. H3Fusion addresses this challenge by introduci

Jean-Luc Baril, Nathanaël Hassler

We focus on a family of subsets $(\F^p_n)_{p\geq 2}$ of Dyck paths of semilength $n$ that avoid the patterns $DUU$ and $D^{p+1}$, which are enumerated by the generalized Fibonacci numbers. We endow them with the partial order relation induced by the well-known Stanley lattice, and we prove that all these posets are sublattices of the Stanley lattice. We prov

Callum E. Flowerday, Philip Lundrigan, Christopher Kitras, Tu Nguyen

Air quality has important climate and health effects. There is a need, therefore, to monitor air quality both indoors and outdoors. Methods of measuring air quality should be cost-effective if they are to be used widely, and one such method is low-cost sensors (LCS). This study reports on the use of LCSs in Ulaanbaatar, Mongolia, to measure $\mathrm{PM_{2.5}

Pranava Seth, Mamta Gulati

The rapid increase in the deployment of Low Earth Orbit (LEO) satellites, catering to diverse applications such as communication, Earth observation, environmental monitoring, and scientific research, has significantly amplified the complexity of trajectory management. The current work focuses on calculating and analyzing perturbation effects on a satellite's

Jaewoong Lee, Junhee Woo, Sejin Kim, Cinthya Paulina

Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce a novel multi modal data-driven approach employing an Automatic Battery data Collector (ABC) that integrates a large language model (LLM) with an automatic graph mining tool, Material G

Autumn Nguyen, Sulagna Saha

Quantifying forest aboveground biomass (AGB) is crucial for informing decisions and policies that will protect the planet. Machine learning (ML) and remote sensing (RS) techniques have been used to do this task more effectively, yet there lacks a systematic review on the most recent working combinations of ML methods and multiple RS sources, especially with

E. Chaussidon, C. Yèche, A. de Mattia, C. Payerne

We analyse the large-scale clustering of the Luminous Red Galaxy (LRG) and Quasar (QSO) sample from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). In particular, we constrain the primordial non-Gaussianity (PNG) parameter $f_{\rm NL}^{\rm loc}$ via the large-scale scale-dependent bias in the power spectrum using $1,631,716$

Souvik Dey, Dipankar Ghosh, Aniruddha Saha

The complexity and curvature of a module, introduced by Avramov, measure the growth of Betti and Bass numbers of a module, and distinguish the modules of infinite homological dimension. The notion of complexity was extended by Avramov-Buchweitz to pairs of modules that measure the growth of Ext modules. The related notion of Tor complexity was first studied

Md. Fahim Sultan, Tasmin Karim, Md. Shazzad Hossain Shaon, Mohammad Wardat

In software, a vulnerability is a defect in a program that attackers might utilize to acquire unauthorized access, alter system functions, and acquire information. These vulnerabilities arise from programming faults, design flaws, incorrect setups, and a lack of security protective measures. To mitigate these vulnerabilities, regular software upgrades, code

Arsen Khvedelidze, Dimitar Mladenov, Astghik Torosyan

A method allowing to increase a computational efficiency of evaluation of non-local characteristics of a pair of qubits is described. The method is based on the construction of coordinates on a generic section of 2-qubit's entanglement space $\mathcal{E}_{2\times2}$ represented as the direct product of an ordered 3-dimensional simplex and the double coset $\

Santiago Estupiñán-Salamanca, Oliver Pechenik

The plactic monoid $\mathbf{P}$ of Lascoux and Sch\"{u}tzenberger (1981) plays an important role in proofs of the Littlewood-Richardson rule for computing multiplicities in the linear representation theory of the symmetric group $\mathfrak{S}_n$ and the cohomology of Grassmannians. Commonly, $\mathbf{P}$ is defined as a quotient of a free monoid by relations

Abhishek Ojha, Naveen N. Narisetty

We address the challenge of conducting inference for a categorical treatment effect related to a binary outcome variable while taking into account high-dimensional baseline covariates. The conventional technique used to establish orthogonality for the treatment effect from nuisance variables in continuous cases is inapplicable in the context of binary treatm

Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren, Hitender Oswal

The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate

Guanjie Chen, Xinyu Zhao, Yucheng Zhou, Xiaoye Qu

Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability. However, their practical application suffers from inherent dynamic feature instability, leading to error amplification during cached inference. Through systematic analysis, we identify the absence of long-range featu

Shoya Motonaga

We study optimization problems in ergodic theory from the view point of minimax problems. We give minimax characterizations of maximum ergodic averages involving time averages. Our approach works for the abstract variational principle of generalized pressure functions which is proved by Bi\'{s} et al. (2022). We also describe the relationship between our min

Bipasha Banerjee, William A. Ingram, Edward A. Fox

Traditional archival practices for describing electronic theses and dissertations (ETDs) rely on broad, high-level metadata schemes that fail to capture the depth, complexity, and interdisciplinary nature of these long scholarly works. The lack of detailed, chapter-level content descriptions impedes researchers' ability to locate specific sections or themes,

Laurent Bartholdi, Persi Diaconis

P\'olya trees are rooted, unlabeled trees on $n$ vertices. This paper gives an efficient, new way to generate P\'olya trees. This allows comparing typical unlabeled and labeled tree statistics and comparing asymptotic theorems with `reality'. Along the way, we give a product formula for the number of rooted labeled trees preserved by a given automorphism; th

Matthias R. Gaberdiel, Felix Lichtner, Beat Nairz

Recently, the anomalous conformal dimensions of the symmetric orbifold under the $2$-cycle twisted sector deformation were calculated using the perturbed action of the supercharges. In particular, explicit and simple formulae for the dispersion relations of the torus magnons in the $w$-cycle twisted sector were derived for large $w$. In this paper we reprodu

Pulak Banerjee, Chinmoy Dey, M. C. Kumar, V. Ravindran

We present for the first time the second-order corrections of pseudo-scalar($A$) Higgs decay to three parton to higher orders in the dimensional regulator. We compute the one and two-loop amplitudes for processes, $A\to ggg$ and $A\to q\bar{q}g$ in the effective theory framework. With suitable crossing of the external momenta, these calculations are well-sui

Niharika Hegde, Shishir Muralidhara, René Schuster, Didier Stricker

In autonomous driving, environment perception has significantly advanced with the utilization of deep learning techniques for diverse sensors such as cameras, depth sensors, or infrared sensors. The diversity in the sensor stack increases the safety and contributes to robustness against adverse weather and lighting conditions. However, the variance in data a

Kiara Chantel Ruffin, Gray Rybka

The axion were proposed as a result to a solution to the Strong CP Problem in quantum chromodynamics (QCD) and is now considered a leading candidate for dark matter. Direct axion dark matter detection experiments are challenging due to the axion's weak interaction with electromagnetism. Recent work has suggested the possibility of an enhancement of astrophys

Gino Kwun, Bingzhi Zhang, Quntao Zhuang

Generative quantum machine learning has gained significant attention for its ability to produce quantum states with desired distributions. Among various quantum generative models, quantum denoising diffusion probabilistic models (QuDDPMs) [Phys. Rev. Lett. 132, 100602 (2024)] provide a promising approach with stepwise learning that resolves the training issu

Aohan Zeng, Zhengxiao Du, Mingdao Liu, Lei Zhang

Speech language models (SpeechLMs) accept speech input and produce speech output, allowing for more natural human-computer interaction compared to text-based large language models (LLMs). Traditional approaches for developing SpeechLMs are constrained by the limited availability of unsupervised speech data and parallel speech-text data, which are significant

Cong Wei, Yujie Zhong, Haoxian Tan, Yong Liu

This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite significant progress in current unified segmentation methods, limitations in adaptation to both image and video scenarios, as well as the complex reasoning segmentation, make it difficul

Yanqi Bao, Jing Liao, Jing Huo, Yang Gao

We present DGGS, a novel framework that addresses the previously unexplored challenge: $\textbf{Distractor-free Generalizable 3D Gaussian Splatting}$ (3DGS). It mitigates 3D inconsistency and training instability caused by distractor data in the cross-scenes generalizable train setting while enabling feedforward inference for 3DGS and distractor masks from r

Daniel Huybrechts

We conjecture that every unramified Brauer class $\alpha\in \text{Br}(X)$ on a projective hyperk\"ahler manifold $X$ satisfies $\text{ind}(\alpha)\mid\text{per}(\alpha)^{\dim(X)/2}$. We provide evidence for this conjecture by proving it for two large classes of projective hyperk\"ahler manifolds: For projective hyperk\"ahler manifolds admitting a Lagrangian

Neha Makhija, Wolfgang Gatterbauer

Deletion Propagation (DP) refers to a family of database problems rooted in the classical view-update problem: how to propagate intended deletions in a view (query output) back to the source database while satisfying constraints and minimizing side effects. Although studied for over 40 years, DP variants, their complexities, and practical algorithms have bee

Archana S Morye, Sreenanda S B, Prakash Saivasan

In this article, we investigate the cardinality of Groebner bases under various monomial orderings. We identify a family of polynomials F and a criterion such that the reduced Groebner basis of F is double exponential in cardinality with respect to any monomial ordering which satisfies this criterion. We also show that the said criterion is satisfied by orde

Seung-Jo Jung, In-Kyun Kim, Morihiko Saito, Youngho Yoon

For a hypersurface isolated singularity defined by a convergent power series $f$, the Steenbrink spectrum can be defined as the Poincar\'e polynomial of the graded quotients of the $V$-filtration on the Jacobian ring of $f$. The Tjurina subspectrum is defined by replacing the Jacobian ring with its quotient by the image of the multiplication by $f$. We prove

Bipasha Banerjee, Jennifer Goyne, William A. Ingram

The Virginia Tech University Libraries (VTUL) Digital Library Platform (DLP) hosts digital collections that offer our users access to a wide variety of documents of historical and cultural importance. These collections are not only of academic importance but also provide our users with a glance at local historical events. Our DLP contains collections compris

Bora Çalım, Ioannis Iakovakis, Sophie Long, Jack Moffatt

We prove that $\mathop{\mathbb{E}}_{m \leq M} \mathop{\mathbb{E}}_{n \leq N} \Lambda(n) \Lambda\bigl(n + \lfloor m^c \rfloor\bigr) = 1 + \rm{O}(\log^{2 - Bc} N)$, where $c > 2$ is a non-integer, $B \geq 3/c$, and $M$ is of order $N^{1/c} \log^{-B} N$. As a combinatorial consequence, we obtain that the primes contain infinitely many pairs whose difference bel

William A. Ingram, Bipasha Banerjee, Edward A. Fox

As research institutions increasingly commit to supporting the United Nations' Sustainable Development Goals (SDGs), there is a pressing need to accurately assess their research output against these goals. Current approaches, primarily reliant on keyword-based Boolean search queries, conflate incidental keyword matches with genuine contributions, reducing re

Federico Vaccari

This paper presents a model of costly information acquisition where decision-makers can choose whether to elaborate information superficially or precisely. The former action is costless, while the latter entails a processing cost. Within this framework, decision-makers' beliefs may polarize even after they have access to the same evidence. From the perspecti

Kimon Boehmer, Lukas Lee George, Fanny Hauser, Jesse Palarus

The 2024 PACE Challenge focused on the One-Sided Crossing Minimization (OCM) problem, which aims to minimize edge crossings in a bipartite graph with a fixed order in one partition and a free order in the other. We describe our OCM solver submission that utilizes various reduction rules for OCM and, for the heuristic track, employs local search approaches as

Shuyi Jin, Lu Chen, Hongru Ding, Meijie Wang

This study evaluates the performance of large language models (LLMs) and the HINT model in predicting clinical trial outcomes, focusing on metrics including Balanced Accuracy, Matthews Correlation Coefficient (MCC), Recall, and Specificity. Results show that GPT-4o achieves superior overall performance among LLMs but, like its counterparts (GPT-3.5, GPT-4min