November 2024 arXiv papers — page 107

Wei Wang, Zhaowei Li, Qi Xu, Linfeng Li

Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks. However, they often encounter significant challenges due to inadequate alignment for fine-grained knowledge, which restricts their ability to accurately capture local details and attain a comprehensive global perception. Wh

Anne Inkenhaag, Peter G. Jonker, Andrew J. Levan, Morgan Fraser

Over time, core-collapse supernova (CCSN) spectra become redder due to dust formation and cooling of the SN ejecta. A UV detection of a CCSN at late times thus indicates an additional physical process such as interaction between the SN ejecta and the circumstellar material, or viewing down to the central engine of the explosion. Both these models have been p

Seongmin Lee, Hsiang Hsu, Chun-Fu Chen, Duen Horng Chau

LLM hallucination, where unfaithful text is generated, presents a critical challenge for LLMs' practical applications. Current detection methods often resort to external knowledge, LLM fine-tuning, or supervised training with large hallucination-labeled datasets. Moreover, these approaches do not distinguish between different types of hallucinations, which i

Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Monishwaran Maheswaran

Emerging Large Language Model (LLM) applications require long input context in order to perform complex tasks like document analysis and code generation. For these long context length applications, the length of the input prompt poses a significant challenge in terms of inference efficiency since the inference costs increase linearly with sequence length. Ho

Bertrand Lacroix-A-Chez-Toine, Yan V. Fyodorov

In this article, we introduce and analyse some statistical properties of a class of models of random landscapes of the form ${\cal H}({\bf x})=\frac{\mu}{2}{\bf x}^2+\sum_{l=1}^M \phi_l({\bf k}_l\cdot {\bf x}), \, \, {\bf x}\in \mathbb{R}^N,\,\, \mu>0 $ where both the functions $\phi_l(z)$ and vectors ${\bf k}_l$ are random. An important example of such land

Yantao Wu, Mauro Maggioni

Regressing a function $F$ on $\mathbb{R}^d$ without incurring the statistical and computational curse of dimensionality requires exploitable structure. Compositional models $F=f\circ g$ in which $g$ has a low-dimensional range include classical single- and multi-index models as well as certain neural networks; while the case of linear $g$ is well understood,

Michele Del Zotto, Matteo Dell'Acqua, Elias Riedel Gårding

Generalized symmetries of quantum field theories can be characterized by topological defects/operators organized into a higher category. In this paper we consider the Axion-Maxwell field theory in four dimensions and, building on the construction of its topological defects by Choi, Lam, Shao, Hidaka, Nitta and Yokokura, we discuss field theoretical methods t

Michał Czakon, Terry Generet, Alexander Mitov, Rene Poncelet

We report on a calculation of open heavy-flavor production at hadron colliders which extends to next-to-next-to-leading order (NNLO) accuracy the classic NLO-accurate formalism developed almost 30 years ago under the acronym FONLL. The approach retains the exact heavy-flavor mass dependence at low transverse momentum, $p_T$, and resums collinear logarithms t

Alexandra González, Xavier Franch, David Lo, Silverio Martínez-Fernández

Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. Aims: We apply an SE-oriented classification to PTMs and datasets on a popular open-source ML repository, Hugging Face (HF), and analyze the

Gilles Eerlings, Sebe Vanbrabant, Jori Liesenborgs, Gustavo Rovelo Ruiz

We present an approach, AI-Spectra, to leverage model multiplicity for interactive systems. Model multiplicity means using slightly different AI models yielding equally valid outcomes or predictions for the same task, thus relying on many simultaneous "expert advisors" that can have different opinions. Dealing with multiple AI models that generate potentiall

Chenglong Yu, Zhiwei Zheng

This paper gives the commensurability classification of Deligne--Mostow ball quotients and shows that the 104 Deligne--Mostow lattices form 38 commensurability classes. First, we find commensurability relations among Deligne--Mostow monodromy groups, which are not necessarily discrete. This generalizes previous work by Sauter and Deligne--Mostow in dimension

D. A. Langis, I. E. Papadakis, E. Kammoun, C. Panagiotou

Context: Extensive, multi-wavelength monitoring campaigns of nearby and higher redshift active galactic nuclei (AGN) have shown that the UV/optical variations are well correlated with time delays which increase with increasing wavelength. Such behaviour is expected in the context of the X-ray thermal reverberation of the accretion disc in AGN. Aims: Our main

Sebastian Paeckel

The accurate computation of low-energy spectra of strongly correlated quantum many-body systems, typically accessed via Green's-functions, is a long-standing problem posing enormous challenges to numerical methods. When the spectral decomposition is obtained from Fourier transforming a time series, the Nyquist-Shannon theorem limits the frequency resolution

C Robin Graham, Tzu-Mo Kuo

We construct a version of geodesic normal coordinates adapted to a submanifold of a pseudo-Riemannian manifold and show that the Taylor coefficients of the metric in these coordinates can be expressed as universal polynomials in the components of the covariant derivatives of the background curvature tensor and the covariant derivatives of the second fundamen

Benedikt Alkin, Tobias Kronlachner, Samuele Papa, Stefan Pirker

Advancements in computing power have made it possible to numerically simulate large-scale fluid-mechanical and/or particulate systems, many of which are integral to core industrial processes. Among the different numerical methods available, the discrete element method (DEM) provides one of the most accurate representations of a wide range of physical systems

Animesh Chatterjee, Srubabati Goswami, Supriya Pan, Paras Thacker

In this article, we study the effect of invisible neutrino decay of the third neutrino state for accelerator neutrino experiments at two different baselines, 1300 km with a liquid argon time projection chamber (LArTPC) detector (similar to DUNE) and 2588 km with a water Cherenkov detector (similar to P2O). For such baselines, the matter effect starts to beco

Tong Pu, Yunran Wei, Yiying Zhang

We introduce a novel class of systemic risk measures, the Vulnerability Conditional risk measures, which try to capture the "tail risk" of a risky position in scenarios where one or more market participants is experiencing financial distress. Various theoretical properties of Vulnerability Conditional risk measures, along with a series of related contributio

Xiaohuan Xia, Mathieu Ouellet, Shubhankar P. Patankar, Diana I. Tamir

Modern science is formally structured around scholarly publication, where scientific knowledge is canonized through citation. Precisely how citations are given and accrued can provide information about the value of discovery, the history of scientific ideas, the structure of fields, and the space or scope of inquiry. Yet parsing this information has been cha

Fabio Apruzzi, Sakura Schafer-Nameki, Alison Warman

We construct non-invertible symmetries in 6d $\mathcal{N}=(2,0)$ superconformal field theories that arise from Green-Schwarz (GS) automorphisms, which form abelian or non-abelian groups. Applied to $\mathbb{Z}_2$, $\mathbb{Z}_3$ and $S_3$ GS automorphisms, gives rise to non-invertible duality, triality and $S_3$-ality defects, respectively, once combined wit

Luca Giangrande, Matthieu Heller, Yannick Favre, Teresa Montaruli

Silicon photo-multipliers (SiPM) have been replacing traditional photomultiplier tubes in most light sensing applications. However, when large detection surface coverage is needed, photomultipliers (PMTs) are still the preferred choice. The main reasons are the sensor thermal noise and the duration of the fast component of its signal, both increasing with th

Peter March

We prove an identity on a graph analogous to Bochner's identity on a Riemannian manifold. An auxiliary graph called the complete tangent graph intervenes in the term corresponding to Ricci curvature.

Peter Hintz, Antônio Sá Barreto, Gunther Uhlmann, Yang Zhang

We study the inverse problem of determining a time-dependent globally hyperbolic Lorentzian metric from the scattering operator for semilinear wave equations.

Saugata Basu, Deepam Patel

The concept of Vapnik-Chervonenkis (VC) density is pivotal across various mathematical fields, including discrete geometry, probability theory and model theory. In this paper, we introduce a topological generalization of VC-density. Let $Y$ be a topological space and $\mathcal{X}$ a family of closed subspaces of $Y$. For each $p \geq 0$, we define a number,

Trung-Phuc Vo, Olena Tkach, Sylvain Tricot, Didier Sebilleau

Photoelectron diffraction (PED) is a powerful and essential experimental technique for resolving the structure of surfaces with sub-angstrom resolution. In the high energy regime, researchers in angle-resolved photoemission spectroscopy (ARPES) observe modulating patterns attributed to X-ray-PED (XPD) effects. This is accompanied by other challenges such as

Yijia Xu, Yixu Wang, Christophe Vuillot, Victor V. Albert

Continuous-variable cat codes are encodings into a single photonic or phononic mode that offer a promising avenue for hardware-efficient fault-tolerant quantum computation. Protecting information in a cat code requires measuring the mode's occupation number modulo two, but this can be relaxed to a linear occupation-number constraint using the alternative two

Nikesh Lilani

We holographically study quantum chaos in hyperscaling-violating Lifshitz (HVL) theories (with charge). Specifically, we present a detailed computation of the out-of-time ordered correlator (OTOC) via shockwave analysis in the bulk HVL geometry with a planar horizon topology. We also compute the butterfly velocity ($v_{B}$) using the entanglement wedge recon

Barrios-Munoz Ricardo, Bernabe Matteo, Lopez-Perez David, Gomez-Barquero David

Following the burgeoning interest in unmanned aerial vehicles (UAVs) utilization within human-inhabited spaces, critical challenges arise in ensuring reliable, low-latency communication-particularly important given the safety-critical nature of such operations in densely populated urban environments. Therefore, adequate cellular communication capabilities ar

Mauro L Mugnai, Rose Tchuenkam Batoum, Emanuela Del Gado

Natural and synthetic multi-component gels display emergent properties, which implies that they are more than just the sum of their components. This warrants the investigation of the role played by inter-species interactions in shaping gel architecture and rheology. Here, using computer simulations, we investigate the effect of changing the strength of the i

Ammar Jahin, Shi-Zeng Lin

We study the effect of the electron wavefunction on Kohn-Luttinger superconductivity. The role of the wavefunction is encoded in a complex form factor describing the topology and geometry of the bands. We show that the electron wavefunction significantly impacts the superconducting transition temperature and superconducting order parameter. We illustrate thi

Charlie Chen

The ATLAS collaboration at the LHC has published inclusive cross-section measurements for the single-top and $t\overline{t}$ production modes at center-of-mass energies of $\sqrt{s} = 5.02, 8.16$, $13$, and $13.6$ TeV. Single-top measurements are conducted in the $t$-channel and $tW$ channel. In addition to the nominal cross-section measurements, various mea

Freja Schou Guttesen, Per Hedegård

Van der Waals layered ferromagnetic compounds with high two-dimensional electronic conductivity holds strong potential for quantum computing, future unconventional superconductors, catalysts, batteries, and fuel cells. We suggest a minimal theoretical model to understand the magnetic properties of the metal-organic framework CrCl$_2$(pyz)$_2$ (pyz=pyrazine).

Shehzaad Dhuliawala, Ilia Kulikov, Ping Yu, Asli Celikyilmaz

During language model decoding, it is known that using higher temperature sampling gives more creative responses, while lower temperatures are more factually accurate. However, such models are commonly applied to general instruction following, which involves both creative and fact seeking tasks, using a single fixed temperature across all examples and tokens

David López-Pérez, Nicola Piovesan, Giovanni Geraci

This paper presents a new system model to evaluate the capacity and power consumption of multi-layer 6G networks utilising the upper mid-band (FR3). The model captures heterogeneous 4G, 5G, and 6G deployments, analyzing their performance under different deployment strategies. Our results show that strategic 6G deployments, non-co-located with existing 5G sit

Xianhua Peng, Xiang Zhou, Bo Xiao, Yi Wu

We propose a new risk sensitive reinforcement learning approach for the dynamic hedging of options. The approach focuses on the minimization of the tail risk of the final P&L of the seller of an option. Different from most existing reinforcement learning approaches that require a parametric model of the underlying asset, our approach can learn the optimal he

Yue Su, Xinyu Zhan, Hongjie Fang, Yong-Lu Li

Inferring object motion representations from observations enhances the performance of robotic manipulation tasks. This paper introduces a new paradigm for robot imitation learning that generates action sequences by reasoning about object motion from visual observations. We propose MBA (Motion Before Action), a novel module that employs two cascaded diffusion

Fan Yang, Yi Zhang

We study the tail asymptotics of the sum of two heavy-tailed random variables. The dependence structure is modeled by copulas with the so-called tail order property. Examples are presented to illustrate the approach. Further for each example we apply the main results to obtain the asymptotic expansions for Value-at-Risk of aggregate risk.

Ayush Agarwal, Raghavendra Ramachandra, Sushma Venkatesh, S. R. Mahadeva Prasanna

In the domain of Extended Reality (XR), particularly Virtual Reality (VR), extensive research has been devoted to harnessing this transformative technology in various real-world applications. However, a critical challenge that must be addressed before unleashing the full potential of XR in practical scenarios is to ensure robust security and safeguard user p

Alexei Bazavov, David A. Clarke, Christine Davies, Carleton DeTar

We present complete results for the hadronic vacuum polarization (HVP) contribution to the muon anomalous magnetic moment $a_\mu$ in the short- and intermediate-distance window regions, which account for roughly 10% and 35% of the total HVP contribution to $a_\mu$, respectively. In particular, we perform lattice-QCD calculations for the isospin-symmetric con

Jonathan R. Cangelosi, Matthias Heinkenschloss

This work analyzes the sensitivities of the solution of a system of ordinary differential equations (ODEs) and a corresponding quantity of interest (QoI) to perturbations in a state-dependent component function that appears in the governing ODEs. This extends existing ODE sensitivity results, which consider the sensitivity of the ODE solution with respect to

Jianing Fang, Kevin Bowman, Wenli Zhao, Xu Lian

Do ecosystems primarily reflect evolutionary history or current environment? Predicting land-atmosphere exchange hinges on this unresolved question. Plant traits adapt to particular environments over evolutionary timescales, yet their individual relationships with current climate and soils are often obscured by limited sampling, plant-type effects, and multi

Amirhossein Taghvaei, Prashant G. Mehta

This chapter contains a self-contained introduction to the significance of Bayes' formula in the context of nonlinear filtering problems. Both discrete-time and continuous-time settings of the problem are considered in a unified manner. In control theory, the focus on optimization-based solution approaches is stressed together with a discussion of historical

Jiadong Liang, Adam Kabbara, Jiaying Liu, Ronaldo Luo

We show that human players' gameplay in the game of Wordle is influenced by the semantics, orthography, and phonology of the player's previous guesses. We compare actual human players' guesses with near-optimal guesses using NLP techniques. We study human language use in the constrained environment of Wordle, which is situated between natural language use an

Shaun Swain, Geraint Pratten, Patricia Schmidt

Recent developments in post-Minkowksian (PM) calculations have led to a fast-growing body of weak-field perturbative information. As such, there is major interest within the gravitational wave community as to how this information can be used to improve the accuracy of theoretical waveform models. In this work, we build on recent efforts to validate high-orde

Luca Fabbri

We consider the problem of having relativistic quantum mechanics re-formulated with hydrodynamic variables, and specifically the problem of deriving the Mathisson-Papapetrou-Dixon equations from the Dirac equation. The problem will be answered on a general manifold with torsion and gravity. We will demonstrate that when plane waves are considered the MPD equ

Kirtiman Ghosh, Katri Huitu, Rameswar Sahu

Supersymmetry (SUSY) addresses several problems of the Standard Model, such as the naturalness problem and gauge coupling unification, and can provide cosmologically viable dark matter candidates. SUSY must be broken at high energy scales with mechanisms like gravity, anomaly, gauge mediation, etc. This paper revisits the Gauge Mediated SUSY Breaking (GMSB)

Ahan Bhatt, Nandan Vaghela

This paper introduces Med-Bot, an AI-powered chatbot designed to provide users with accurate and reliable medical information. Utilizing advanced libraries and frameworks such as PyTorch, Chromadb, Langchain and Autogptq, Med-Bot is built to handle the complexities of natural language understanding in a healthcare context. The integration of llamaassisted da

Coleridge Faraday, W. A. Horowitz

We present quantitative predictions for the nuclear modification factor in both small and peripheral systems from a pQCD-based energy loss model that is constrained by light- and heavy-flavor suppression data from central heavy-ion collisions. We find nearly identical suppression for central $p / d + A$ collisions as for peripheral $A + A$ collisions, quanti

Manuel Bodirsky, Georg Loho, Mateusz Skomra

We present a polynomial-time reduction from max-plus-average constraints to the feasibility problem for semidefinite programs. This shows that Condon's simple stochastic games, stochastic mean payoff games, and in particular mean payoff games and parity games can all be reduced to semidefinite programming.

Joel Castaño, Rafael Cabañas, Antonio Salmerón, David Lo

The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Face (HF) enable this evolving ecosystem, yet a large-scale longitudinal study of how these models change is lacking. This study addresses this gap by analyzing over 680,000 commits

Guillermo Alvarez, Ibrahim Ekren, Anastasis Kratsios, Xuwei Yang

Dynamic Stackelberg games are a broad class of two-player games in which the leader acts first, and the follower chooses a response strategy to the leader's strategy. Unfortunately, only stylized Stackelberg games are explicitly solvable since the follower's best-response operator (as a function of the control of the leader) is typically analytically intract

Stefan Orf, Sven Ochs, Jens Doll, Albert Schotschneider

Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous components motivate a fault diagnosis of the system and overall system integrity. AD systems are composed of many heterogeneous components, each with different functionality and p

Alkis Kalavasis, Anay Mehrotra, Grigoris Velegkas

Specifying all desirable properties of a language model is challenging, but certain requirements seem essential. Given samples from an unknown language, the trained model should produce valid strings not seen in training and be expressive enough to capture the language's full richness. Otherwise, outputting invalid strings constitutes "hallucination," and fa

Shankhadeep Chakrabortty, Subramanya Hegde, Arpit Maurya

The differential representation of AdS correlators offers a framework to express exchange Witten diagrams as functions of non-local differential operators applied to contact Witten diagrams. In this paper, we develop the differential representation for scalar Carrollian correlators. We first construct this representation using the recently formulated Carroll

Senem Işık, Jinyoung Park

Benjamini, Yadin, and Yehudayoff (2007) showed that if the maximum degree of a graph $G$ is 'sub-logarithmic,' then the typical range of random $\mathbb Z$-homomorphisms is super-constant. Furthermore, they showed that there is a sharp transition on the range of random $\mathbb Z$-homomorphisms on the graph $C_{n,k}$, the tensor product of the $n$-cycle and

Jifan Gao, Guanhua Chen

Causal concept effect estimation is gaining increasing interest in the field of interpretable machine learning. This general approach explains the behaviors of machine learning models by estimating the causal effect of human-understandable concepts, which represent high-level knowledge more comprehensibly than raw inputs like tokens. However, existing causal

Yongbo Deng, Jan G. Korvink

This paper presents fiber bundle topology optimization for mass and heat transfer in surface and volume flow in the laminar region, to optimize the matching between the pattern of a surface structure and the implicit 2-manifold on which the pattern is defined. The fiber bundle concept is used to describe the pattern of the surface structure together with the

Debjyoti Biswas, Shrikant Utagi, Prabha Mandayam

We consider the problem of quantum error correction (QEC) for non-Markovian noise. Using the well known Petz recovery map, we first show that conditions for approximate QEC can be easily generalized for the case of non-Markovian noise, in the strong coupling regime where the noise map becomes non-completely-positive at intermediate times. While certain appro

Georgios Pantazis, Reza Rahimi Baghbadorani, Sergio Grammatico

We consider a class of Wasserstein distributionally robust Nash equilibrium problems, where agents construct heterogeneous data-driven Wasserstein ambiguity sets using private samples and radii, in line with their individual risk-averse behaviour. By leveraging relevant properties of this class of games, we show that equilibria of the original seemingly infi

Xingya Wang, Yang Han, Yushi Liu, Szu-Yu Tang

This article quantifies the uncertainty reduction achievable for \textit{counterfactual} estimand, and cautions against potential bias when the estimand uses Digital Twins. Posed by Neyman (1923a) who showed unbiased \textit{point estimation} from designed \textit{factual} experiments is possible, \textit{counterfactual} uncertainty quantification (CUQ) rema

Xilin Zhang, Jason Detwiler, Clint Wiseman

We study neutrino, muon, and gamma-ray fluxes in extraterrestrial environments in our Solar System via semi-analytical estimates and Monte Carlo simulations. In sites with negligible atmosphere, we find a strong reduction in the cosmic-ray-induced neutrino and muon fluxes relative to their intensities on Earth. Neutrinos with energies between 50 MeV and 100

Saeed Shaabanian

Hitting rate and escape rate are two examples of recurrence laws for a dynamical system, and a general limit connects them. We show that for both Gibbs-Markov systems or any systems with the $\phi$-mixing measure, for a sequence of nested sets whose intersection is a measure zero set, this general limit equals one in the absence of short returns and less tha

Ahmad Rahmatpour, Allison Shields, Parmita Mondal, Parisa Naghdi

This study leverages convolutional neural networks to enhance the temporal resolution of 3D angiography in intracranial aneurysms focusing on the reconstruction of volumetric contrast data from sparse and limited projections. Three patient-specific IA geometries were segmented and converted into stereolithography files to facilitate computational fluid dynam

Ke Xu, Ting Liang, Nan Xu, Penghua Ying

Water's unique hydrogen-bonding network and anomalous properties pose significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across varied conditions. Although machine-learned potentials have advanced the prediction of individual properties, a unified computational framework capable of simultaneously capturing wa

Maxime Ligonnière

In this paper, we study a Galton-Watson process $(Z_n)$ with infinitely many types in a random ergodic environment $\bar{\xi}=(\xi_n)_{n\geq 0}$. We focus on the supercritical regime of the process, where the quenched average of the size of the population grows exponentially fast to infinity. We work under Doeblin-type assumptions coming from a previous pape

Papia Panda, Priya Mishra, Samiran Roy, Monojit Ghosh

In this study, we explore two intriguing new physics scenarios: the theory of Large Extra Dimensions (LED) and the theory of neutrino decay. We analyze the impact of LED on neutrino oscillations in the contexts of Protvino to Super-ORCA (P2SO), DUNE, and T2HK, with a particular emphasis on P2SO. In contrast, the effects of neutrino decay are examined exclusi

Yuyao Liu, Jiayuan Mao, Joshua Tenenbaum, Tomás Lozano-Pérez

We present a novel approach, MAGIC (manipulation analogies for generalizable intelligent contacts), for one-shot learning of manipulation strategies with fast and extensive generalization to novel objects. By leveraging a reference action trajectory, MAGIC effectively identifies similar contact points and sequences of actions on novel objects to replicate a

S. M. Yousuf Iqbal Tomal, Debojit Bhattacharjee

Quantum mechanics has revolutionized our understanding of information transmission, leading to the development of quantum communication protocols that promise unprecedented security in data transfer. Quantum teleportation, in particular, has emerged as a cornerstone protocol for quantum communication, operating within the constraints of noisy intermediate-sc

Mikhail Khodak, Lester Mackey, Alexandra Chouldechova, Miroslav Dudík

Disaggregated evaluation -- estimation of performance of a machine learning model on different subpopulations -- is a core task when assessing performance and group-fairness of AI systems. A key challenge is that evaluation data is scarce, and subpopulations arising from intersections of attributes (e.g., race, sex, age) are often tiny. Today, it is common f

Xun Zhou, Charlie Ruan, Zihe Zhao, Tianqi Chen

We present the MIDInfinite, a web application capable of generating symbolic music using a large-scale generative AI model locally on commodity hardware. Creating this demo involved porting the Anticipatory Music Transformer, a large language model (LLM) pre-trained on the Lakh MIDI dataset, to the Machine Learning Compilation (MLC) framework. Once the model

Nicholas Cox, Joseph Murray, Joseph Hart, Brandon Redding

In this work, we introduce and experimentally demonstrate a photonic frequency-multiplexed next generation reservoir computer (FM-NGRC) capable of performing real-time inference at GHz speed. NGRCs apply a feed-forward architecture to produce a feature vector directly from the input data over a fixed number of time steps. This feature vector, analogous to th

F. Adetunji, A. Karukayil, P. Samant, S. Shabana

This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of

Pablo L. Saldanha, Herman Batelaan

We consider different schemes for the electrodynamic Aharonov-Bohm (AB) effect introduced in Ref. [Phys. Rev. A $\mathbf{108}$, 062218 (2023)], exploring the phenomenon to enhance the understanding of its topological nature in spacetime. In the treated examples, the electric current in a solenoid varies in time, changing its internal magnetic field and produ

Airam Blancas, María Clara Fittipaldi, Saraí Hernández-Torres

In this survey, we explore the connections between two areas of probability: percolation theory and population genetic models. Our first goal is to highlight a construction on Galton-Watson trees, which has been described in two different ways: Bernoulli bond percolation and neutral mutations. Next, we introduce a novel connection between the Divide-and-Colo

Anthony N. Ciavarella, Christian W. Bauer

A Hamiltonian lattice formulation of lattice gauge theories opens the possibility for quantum simulations of the non-perturbative dynamics of QCD. By parametrizing the gauge invariant Hilbert space in terms of plaquette degrees of freedom, we show how the Hilbert space and interactions can be expanded in inverse powers of $N_c$. At leading order in this expa

Sebastian F. Sanchez, Alfredo Mejía-Narvaez, Oleg V. Egorov, Kathryn Kreckel

We introduce the Data Analysis Pipeline (DAP) for the Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM) project, referred to as the LVM-DAP. We outline our methods for recovering both stellar and emission line components from the optical integral field spectroscopy, highlighting the developments and changes implemented to address specific challen

Edoardo Bellincioni, Detlef Lohse, Sander G. Huisman

We experimentally investigated the melting of floating ice cylinders. Experiments were carried out in a tank, with ice cylinders with radii between 5 cm and 12 cm, floating horizontally with their axis perpendicular to gravity. The water in the tank was at room temperature, with salinities ranging from 0 g/L to 35 g/L. These conditions correspond to Rayleigh

Tejas Nareddy, Abhishek Mishra

We employ techniques from group theory to show that, in many cases, counting problems on graphs are almost as hard to solve in a small number of instances as they are in all instances. Specifically, we show the following results. 1. Goldreich (2020) asks if, for every constant $\delta < 1 / 2$, there is an $\tilde{O} \left( n^2 \right)$-time randomized reduc

Nancy R. Newlin, Kurt Schilling, Serge Koudoro, Bramsh Qamar Chandio

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from va

R. Altmann, M. Hermann, D. Peterseim, T. Stykel

This paper addresses the computation of ground states of multicomponent Bose-Einstein condensates, defined as the global minimiser of an energy functional on an infinite-dimensional generalised oblique manifold. We establish the existence of the ground state, prove its uniqueness up to scaling, and characterise it as the solution to a coupled nonlinear eigen

Souvik Banerjee, Johanna Erdmenger, Jonathan Karl

In holography, two manifestations of the black hole information paradox are given by the non-isometric nature of the bulk-boundary map and by the factorisation puzzle. By considering time-shifted microstates of the eternal black hole, we demonstrate that both these puzzles may be simultaneously resolved by taking into account non-local quantum corrections th

Sang Hyun Choi, Zhi-Feng Huang, Nigel Goldenfeld

Chiral active matter is predicted to exhibit odd elasticity, with nontraditional elastic response arising from a combination of chirality, being out of equilibrium, and the presence of nonreciprocal interactions. One of the resulting phenomena is the possible occurrence of odd elastic waves in overdamped systems, although its experimental realization still r

Fabrice Baudoin, Hongyi Chen, Cheng Ouyang

We study the effect of curvature on the Parabolic Anderson model by posing it over a Cartan-Hadamard manifold. We first construct a family of noises white in time and colored in space parameterized by a regularity parameter $\alpha$, which we use to explore regularity requirements for well-posedness. Then, we show that conditions on the heat kernel imply an

Hang Gao, Yongfeng Zhang

By augmenting Large Language Models (LLMs) with external tools, their capacity to solve complex problems has been significantly enhanced. However, despite ongoing advancements in the parsing capabilities of LLMs, incorporating all available tools simultaneously in the prompt remains impractical due to the vast number of external tools. Consequently, it is es

Renjie Cao, Miaoyan Hu, Jiahan Wei, Baha Ihnaini

Moral sentiments expressed in natural language significantly influence both online and offline environments, shaping behavioral styles and interaction patterns, including social media selfpresentation, cyberbullying, adherence to social norms, and ethical decision-making. To effectively measure moral sentiments in natural language processing texts, it is cru

Oleksandr Melnychuk, Bianca Giaccone, Nicholas Bornman, Raphael Cervantes

There are strong arguments that quantum mechanics may be nonlinear in its dynamics. A discovery of nonlinearity would hint at a novel understanding of the interplay between gravity and quantum field theory, for example. As such, experiments searching for potential nonlinear effects in the electromagnetic sector are important. Here we outline such an experime

R. Molina, Wei-Hong Liang, Chu-Wen Xiao, Zhi-Feng Sun

In this talk, we present a new interpretation for the recently observed $\Xi(1820)$ resonance. We recall that the chiral unitary approach for the interaction of pseudoscalar mesons with the baryons of the decuplet predicts two states for the $\Xi(1820)$ resonance, one with a narrow width and the other one with a large width. We contrast this fact with the re

Sreelakshmi P Chakyar, Sarath Prabhavu J, Lekshmi Resmi

The empirical classification of Gamma-Ray Bursts (GRBs) is based on their distribution in the plane of burst duration and spectral hardness. Two distinct distributions, long-soft and short-hard bursts, are observed in this plane, forming the basis for the long and short classification scheme. Traditionally, this scheme was mapped to two different GRB progeni

Crescenzo Tortora, Rossella Ragusa, Massimiliano Gatto, Marilena Spavone

The VLT Survey Telescope Survey of Mass Assembly and Structural Hierarchy (VST-SMASH) aims to detect tidal features and remnants around very nearby galaxies, a unique and essential diagnostic of the hierarchical nature of galaxy formation. Leveraging optimal sky conditions at ESO's Paranal Observatory, combined with the VST's multi-band optical filters, VST-

Ronak Pradeep, Nandan Thakur, Shivani Upadhyay, Daniel Campos

This report provides an initial look at partial results from the TREC 2024 Retrieval-Augmented Generation (RAG) Track. We have identified RAG evaluation as a barrier to continued progress in information access (and more broadly, natural language processing and artificial intelligence), and it is our hope that we can contribute to tackling the many challenges

Tom Richtler

In the year 1924, a paper by Carl Wirtz appeared in Astronomische Nachrichten, entitled 'De Sitter's cosmology and the radial motion of spiral galaxies'. This paper and its author remained largely unnoticed by the community, but it seems to be the first cosmological interpretation of the redshift of galaxies as a time dilation effect and the expansion of the

Lee F. Ricketson, Jingwei Hu

We present an explicit temporal discretization of particle-in-cell schemes for the Vlasov equation that results in exact energy conservation when combined with an appropriate spatial discretization. The scheme is inspired by a simple, second-order explicit scheme that conserves energy exactly in the Eulerian context. We show that direct translation to partic

Yifan Shao

In recent years, attention mechanisms have significantly enhanced the performance of object detection by focusing on key feature information. However, prevalent methods still encounter difficulties in effectively balancing local and global features. This imbalance hampers their ability to capture both fine-grained details and broader contextual information-t

Matteo Forlini, Marianna Ciccarelli, Luca Carbonari, Alessandra Papetti

The polishing of luxury leather shoes is a delicate, labor intensive process traditionally performed by skilled craftsmen. Footwear companies aim to automate parts of this process to enhance quality, productivity, and operator well-being, but the unique nature of luxury shoe production presents challenges. This paper introduces a solution involving a collabo

Carla Pracias, Maycol Falla Luza

This paper investigates flat webs on the projective plane. We present two methods for constructing such webs: the first involves taking the product of finitely many convex reduced foliations and invariant lines, while the second consists of taking the product of finitely many convex homogeneous foliations and invariant lines. In both cases, we demonstrate th

Cogan Shimizu, Pascal Hitzler

Large Language Models bear the promise of significant acceleration of key Knowledge Graph and Ontology Engineering tasks, including ontology modeling, extension, modification, population, alignment, as well as entity disambiguation. We lay out LLM-based Knowledge Graph and Ontology Engineering as a new and coming area of research, and argue that modular appr

Qianqian Zhang, Ye Hu, Minchae Jung

The rapid advancement of low Earth orbit (LEO) satellite communication systems has significantly enhanced global connectivity, offering high-capacity, low-latency services crucial for next-generation applications. However, the dense configuration of LEO constellations poses challenges in resource allocation optimization and interference management, complicat

Nicolas R. Mangine, Devin W. Laurence, Patricia M. Sabin, Wensi Wu

Many approaches have been used to model chordae tendineae geometries in finite element simulations of atrioventricular heart valves. Unfortunately, current "functional" chordae tendineae geometries lack fidelity that would be helpful when informing clinical decisions. The objectives of this work are (i) to improve synthetic chordae tendineae geometry fidelit

Bipasha Kundu, Bidur Khanal, Richard Simon, Cristian A. Linte

Accurate left atrium (LA) segmentation from pre-operative scans is crucial for diagnosing atrial fibrillation, treatment planning, and supporting surgical interventions. While deep learning models are key in medical image segmentation, they often require extensive manually annotated data. Foundation models trained on larger datasets have reduced this depende

Tejas Nareddy, Abhishek Mishra

We say that a function is rare-case hard against a given class of algorithms (the adversary) if all algorithms in the class can compute the function only on an $o(1)$-fraction of instances of size $n$ for large enough $n$. Starting from any NP-complete language, for each $\alpha > 0$, we construct a function that cannot be computed correctly even on a $1/n^\

Ivan Agullo, Adrià Delhom, Álvaro Parra-López

Pair creation is a fundamental prediction of quantum field theory in curved spacetimes. While classical aspects of this phenomenon have been observed, the experimental confirmation of its quantum origin remains elusive. In this article, we quantify the entanglement produced by pair creation in two dimensional Bose-Einstein Condensate (BEC) analogues of expan