December 2024 arXiv papers — page 70

Elies Gil-Fuster, Jonas R. Naujoks, Grégoire Montavon, Thomas Wiegand

A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active field of research in the last years, comparably little is known for quantum machine learning models. Despite a few recent works analyzing some

Marco Drewes, Jan Heisig, Valentin Weber

We search for an excess of electrons and positrons in the interplanetary space from the decays of heavy neutrinos produced in nuclear reactions in the Sun. Using measurements of the electron spectra in the MeV range from the Ulysses and SOHO satellites, we report the strongest direct upper bound to date on the mixing between heavy neutral leptons with MeV ma

Maolin He, Rena Gao, Mike Conway, Brian E. Chapman

Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external information and grounding responses in retrieved knowledge. However, query pipeline optimization for cancer patient question-answering (CPQA) systems requires separately optimizing multiple components with domain

Rahul Shah, Purba Mukherjee, Soumadeep Saha, Utpal Garain

Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch $r_d$ from early universe observations by assuming a cosmological model. We present a recalibration of two independent BAO datasets, SDSS and DESI, by employing deep learning techniques for model-independent estimation of $r_d$, and exp

Adel A. Rahman, Leonard Susskind

We consider a massive vector Boson in a static patch of $D$-dimensional de Sitter space (dS$_D$). We argue that this field is controlled by an effective physical (squared) mass $\mu_{\mathrm{v}}^2 = m_{\mathrm{v}}^2 + 2(D-1)\ell_{\mathrm{dS}}^{-2}$ which differs from the naive "Lagrangian" (squared) mass $m_{\mathrm{v}}^2$ that appears in the usual form of t

Ed Segal

These notes are a brief summary of the main results from the book `Discriminants, Resultants and Multidimensional Determinants' by Gelfand-Kapranov-Zelevinsky. We sketch the key ideas involved in the proofs, using as little technical background as possible.

Giovanni Montani, Giulia Maniccia, Elisa Fazzari, Alessandro Melchiorri

We propose a revised formulation of General Relativity for cosmological settings, in which the Einstein constant varies with the energy density of the Universe. We demonstrate that this modification has only phenomenological impact of providing an effective dark energy density expression. Assuming a state close to vacuum, here defined by the vanishing produc

Jiangong Pan, Wei Wan, Chenlong Bao, Zuoqiang Shi

In this paper, we solve unbalanced optimal transport (UOT) problem on surfaces represented by point clouds. Based on alternating direction method of multipliers algorithm, the original UOT problem can be solved by an iteration consists of three steps. The key ingredient is to solve a Poisson equation on point cloud which is solved by tangent radial basis fun

Hannah Blocher, Georg Schollmeyer

Non-standard data, which fall outside classical statistical data formats, challenge state-of-the-art analysis. Examples of non-standard data include partial orders and mixed categorical-numeric-spatial data. Most statistical methods required to represent them by classical statistical spaces. However, this representation can distort their inherent structure a

Davide Maran, Marcello Restelli

We address the problem of learning an unknown smooth function and its derivatives from noisy pointwise evaluations under the supremum norm. While classical nonparametric regression provides a strong theoretical foundation, traditional kernel-based estimators often incur high computational costs and memory requirements that scale with the sample size, limitin

Zheng Yu, Bingqiu Chen, Jianhui Lian, Chun Wang

Understanding the structure of the Galactic disk is crucial for understanding the formation and evolutionary history of the Milky Way. This study examines the structure of the Galactic disk by analyzing a sample of 138,667 primary red clump (RC) stars from the LAMOST and Gaia datasets. We have categorized these RC stars into mono-age populations and investig

Tomoya Kato, Akihiko Miyachi, Naoto Shida, Naohito Tomita

Bilinear Fourier multipliers of the form $e^{i (|\xi| + |\eta|+ |\xi + \eta|)} \sigma (\xi, \eta)$ are considered. It is proved that if $\sigma (\xi, \eta)$ is in the H\"ormander class $S^{m}_{1,0} (\mathbb{R}^{2n})$ with $m=-(n+1)/2$ then the corresponding bilinear operator is bounded in $L^{\infty} \times L^{\infty} \to bmo$, $h^{1} \times L^{\infty} \to L

Roderick Murray-Smith, John H. Williamson, Sebastian Stein

Active Inference is a closed-loop computational theoretical basis for understanding behaviour, based on agents with internal probabilistic generative models that encode their beliefs about how hidden states in their environment cause their sensations. We review Active Inference and how it could be applied to model the human-computer interaction loop. Active

Alexander Van Werde, Jaron Sanders

We study the recovery of one-dimensional semipermeable barriers for a stochastic process in a planar domain. The considered process acts like Brownian motion when away from the barriers and is reflected upon contact until a sufficient but random amount of interaction has occurred, determined by the permeability, after which it passes through. Given a sequenc

Subroto Nag Pinku, Debajyoti Mondal, Chanchal K. Roy

Detecting and tracking code clones can ease various software development and maintenance tasks when changes in a code fragment should be propagated over all its copies. Several deep learning-based clone detection models have appeared in the literature for detecting syntactic and semantic clones, widely evaluated with the BigCloneBench dataset. However, class

Daniel Yang, Yao-Hung Hubert Tsai, Makoto Yamada

The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustworthiness. Uncertainty quantification for LLMs can establish more human trust into their responses, but also allows LLM agents to make more informed decisions based on each other's uncertainty. To estimate the unc

Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba

This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The revi

Maria Chatzieleftheriou, Alexander N. Rudenko, Yvan Sidis, Silke Biermann

The quasi-two-dimensional material Sr$_2$RuO$_4$ is a paradigmatic example of a correlated system that exhibits unconventional superconductivity and intriguing magnetic properties. The interplay between these two effects and the resulting strength and nature of spin fluctuations and their role for the properties of the compound have sparked significant debat

Itay Goldberg

This research explores the beta decay of the proton-rich nucleus 23Al. The nucleus was generated at the National Superconducting Cyclotron Laboratory (NSCL) through projectile fragmentation, utilizing a primary beam of 36Ar ions directed at a 9Be target. Simultaneous measurements of proton emission and gamma rays were conducted using the GADGET detection sys

He Zhang, Tong Liu, Zhongcheng Xiang, Kai Xu

Non-Hermitian systems exhibit a variety of unique features rooted in the presence of exceptional points (EP). The distinct topological structure in the proximity of an EP gives rise to counterintuitive behaviors absent in Hermitian systems, which emerge after encircling the EP either quasistatically or dynamically. However, experimental exploration of EP enc

James Prather, Juho Leinonen, Natalie Kiesler, Jamie Gorson Benario

Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programm

Santanu Acharjee, Sidhartha Medhi

Soft set theory, introduced by Molodtsov [Molodtsov, D. (1999). Soft set theory-first results. Comput. Math. Appl., 37(4-5), 19-31], provides a flexible framework for managing uncertainty and vagueness, addressing limitations in traditional approaches such as fuzzy set theory, rough set theory, and probability theory. Over time, fuzzy soft set theory has eme

Tian-Tian Li, Ze-Hong Guo, Xiao-Ning Wang, Qizhong Zhu

Recently, the simulation of moir\'e physics using cold atom platforms has gained significant attention. These platforms provide an opportunity to explore novel aspects of moir\'e physics that go beyond the limits of traditional condensed matter systems. Building on recent experimental advancements in creating twisted bilayer spin-dependent optical lattices f

Fabian Sven Karst, Sook-Yee Chong, Abigail A. Antenor, Enyu Lin

The banking sector faces challenges in using deep learning due to data sensitivity and regulatory constraints, but generative AI may offer a solution. Thus, this study identifies effective algorithms for generating synthetic financial transaction data and evaluates five leading models - Conditional Tabular Generative Adversarial Networks (CTGAN), DoppelGANge

Christian Hagemeier, Giuseppe de Giacomo, Moshe Y. Vardi

We study the problem of realizing strategies for an LTLf goal specification while ensuring that at least an LTLf backup specification is satisfied in case of unreliability of certain input variables. We formally define the problem and characterize its worst-case complexity as 2EXPTIME-complete, like standard LTLf synthesis. Then we devise three different sol

Ben S. Humphries, Dale Green, Garth A. Jones

In this work we develop a model for an undamped vibration in the presence of an overdamped bath. This two-bath model involves a new derivation of the hierarchical equations of motion (HEOM) for an overdamped Lorentz-Drude (LD) environment that is summed together with an undamped oscillator (UO) bath, termed LDUO-HEOM. We show that information transfer occurs

Sean T. Vittadello, Léo Diaz, Yujing Liu, Adriana Zanca

An adult human body is made up of some 30 to 40 trillion cells, all of which stem from a single fertilized egg cell. The process by which the right cells appear to arrive in their right numbers at the right time at the right place -- development -- is only understood in the roughest of outlines. This process does not happen in isolation: the egg, the embryo,

Sandra Carillo, Claudio Giorgi

The term material with memory is generally used to indicate materials whose mechanical and/or thermodynamical behaviour depends not only on the process at the present time but also on the history of the process itself. Crucial in heat conductors with memory is the heat relaxation function which models the thermal response of the material. The present study i

Avyukta Manjunatha Vummintala, Shantanu Das, Sujit Gujar

This paper considers the problem of fair probabilistic binary classification with binary protected groups. The classifier assigns scores, and a practitioner predicts labels using a certain cut-off threshold based on the desired trade-off between false positives vs. false negatives. It derives these thresholds from the ROC of the classifier. The resultant cla

Ioannis Kleftogiannis, Ilias Amanatidis

The energy level statistics of uniform random graphs are studied, by treating the graphs as random tight-binding lattices. The inherent random geometry of the graphs and their dynamical spatial dimensionality, leads to various quantum chaotic and localized phases and transitions between them. Essentially the random geometry acts as disorder, whose strength i

Zijie Zhou, Zhaoqi Lu, Xuekai Wei, Rongqin Chen

Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes. Graph Transformers were introduced to address these issues,

M Hoppe, J Decker, U Sheikh, S Coda

We present a model for the particle balance in the post-disruption runaway electron plateau phase of a tokamak discharge. The model is constructed with the help of, and applied to, experimental data from TCV discharges investigating the so-called ``low-Z benign termination'' runaway electron mitigation scheme. In the benign termination scheme, the free elect

Siyuan Zhang, Linbo Xie

Output uncertainty indicates whether the probabilistic properties reflect objective characteristics of the model output. Unlike most loss functions and metrics in machine learning, uncertainty pertains to individual samples, but validating it on individual samples is unfeasible. When validated collectively, it cannot fully represent individual sample propert

Rolando Gonzales Martinez, Hinke Haisma

This document proposes an algorithm for a mobile application designed to monitor multidimensional child growth through digital phenotyping. Digital phenotyping offers a unique opportunity to collect and analyze high-frequency data in real time, capturing behavioral, psychological, and physiological states of children in naturalistic settings. Traditional mod

Kun Li, Dan Guo, Guoliang Chen, Chunxiao Fan

Micro-Action Recognition (MAR) has gained increasing attention due to its crucial role as a form of non-verbal communication in social interactions, with promising potential for applications in human communication and emotion analysis. However, current approaches often overlook the inherent ambiguity in micro-actions, which arises from the wide category rang

Salih Kibaroğlu

In this paper, we investigate anisotropic cosmological solutions within the framework of Born-Infeld-f(R) gravity, a modification of general relativity that incorporates higher-order curvature invariants. Specifically, we focus on the analysis of Bianchi type-I solutions, which provide a valuable framework for studying the evolution of spatial anisotropies i

Sangmin Lee, Woo-Jin Chung, Hong-Goo Kang

Building a universal multilingual automatic speech recognition (ASR) model that performs equitably across languages has long been a challenge due to its inherent difficulties. To address this task we introduce a Language-Agnostic Multilingual ASR pipeline through orthography Unification and language-specific Transliteration (LAMA-UT). LAMA-UT operates withou

Bhaskarjit Sarmah, Kriti Dutta, Anna Grigoryan, Sachin Tiwari

We argue that the Declarative Self-improving Python (DSPy) optimizers are a way to align the large language model (LLM) prompts and their evaluations to the human annotations. We present a comparative analysis of five teleprompter algorithms, namely, Cooperative Prompt Optimization (COPRO), Multi-Stage Instruction Prompt Optimization (MIPRO), BootstrapFewSho

J. Poyatos, O. Fors, J. M. Gómez Cama, I. Ribas

Stellar flares are powerful bursts of electromagnetic radiation triggered by magnetic reconnection in the chromosphere of stars, occurring frequently and intensely on active M dwarfs. While missions like TESS and Kepler have studied regular and super-flares, their detection of flares with energies below $10^{30}$ erg remains incomplete. Extending flare studi

Zhengchao Yang, Mithun Ghosh, Anish Saha, Dong Xu

Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles hierarchical time series forecasting in business settings. Though traditional hierarchical reconciliation methods ensu

Sarwan Ali, Haris Mansoor, Prakash Chourasia, Imdad Ullah Khan

In molecular structure data, SMILES (Simplified Molecular Input Line Entry System) strings are used to analyze molecular structure design. Numerical feature representation of SMILES strings is a challenging task. This work proposes a kernel-based approach for encoding and analyzing molecular structures from SMILES strings. The proposed approach involves comp

Eirini Chavli, Maud De Visscher, Alison Parker, Sarah Salmon

We show that the centre of the walled Brauer algebra $B_{r,1}(\delta)$ over the complex field $\mathbb{C}$, for any parameter $\delta\in \mathbb{C}$, is generated by the supersymmetric polynomials evaluated at the Jucys-Murphy elements. Moreover, we prove that its dimension is independent of the parameter $\delta$.

Taslim Murad, Prakash Chourasia, Sarwan Ali, Imdad Ullah Khan

The availability of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus data post-COVID has reached exponentially to an enormous magnitude, opening research doors to analyze its behavior. Various studies are conducted by researchers to gain a deeper understanding of the virus, like genomic surveillance, etc, so that efficient prevention mechan

Xenia de la Ossa, Magdalena Larfors, Matthew Magill, Eirik E. Svanes

Compactifications of the heterotic string, to first order in the $\alpha'$ expansion, on manifolds with integrable $G_2$ structure give rise to three-dimensional ${\cal N} = 1$ supergravity theories that admit Minkowski and AdS ground states. As shown in arXiv:1904.01027, such vacua correspond to critical loci of a real superpotential $W$. We perform a pertu

Qi Zhao, Christian Wressnegger

Neural networks can be drastically shrunk in size by removing redundant parameters. While crucial for the deployment on resource-constraint hardware, oftentimes, compression comes with a severe drop in accuracy and lack of adversarial robustness. Despite recent advances, counteracting both aspects has only succeeded for moderate compression rates so far. We

Alba Kalaja, Ian Harrison, William R Coulton

Measurements of weak gravitational lensing using the cosmic microwave background and the shapes of galaxies have refined our understanding of the late-time history of the Universe. While optical surveys have been the primary source for cosmic shear measurements, radio continuum surveys offer a promising avenue. Relevant radio sources, principally star-formin

Avetik Arakelyan, Rafayel Barkhudaryan

The present work is focused on exploring convergence of Physics-informed Neural Networks (PINNs) when applied to a specific class of second-order fully nonlinear Partial Differential Equations (PDEs). It is well-known that as the number of data grows, PINNs generate a sequence of minimizers which correspond to a sequence of neural networks. We show that such

Jiaming Chen

This paper describes directed polymer on general time-correlated random field. Law of large numbers, existence and smoothness of limiting free energies are proved at all temperature. We also display the delocalized-localized transition, via separating techniques for entanglement of the random field.

Ziteng Wang, Jun Zhu, Jianfei Chen

Sparsely activated Mixture-of-Experts (MoE) models are widely adopted to scale up model capacity without increasing the computation budget. However, vanilla TopK routers are trained in a discontinuous, non-differentiable way, limiting their performance and scalability. To address this issue, we propose ReMoE, a fully differentiable MoE architecture that offe

Alexander Hoen, Ambros Gleixner

Most state-of-the-art branch-and-bound solvers for mixed-integer linear programming rely on limited-precision floating-point arithmetic and use numerical tolerances when reasoning about feasibility and optimality during their search. While the practical success of floating-point MIP solvers bears witness to their overall numerical robustness, it is well-know

Byeong-Kweon Oh, Jongheun Yoon

Let $F$ be a local field and let $R$ be its ring of integers. For a positive integer $n$, an integral quadratic form defined over $R$ is called primitively $n$-universal if it primitively represents all quadratic forms of rank $n$. It was proved in arXiv:2005.11268 that the minimal rank of primitively $1$-universal quadratic forms over the $p$-adic integer r

Aygün Varol, Naser Hossein Motlagh, Mirka Leino, Sasu Tarkoma

Smart spaces are ubiquitous computing environments that integrate diverse sensing and communication technologies to enhance space functionality, optimize energy utilization, and improve user comfort and well-being. The integration of emerging AI methodologies into these environments facilitates the formation of AI-driven smart spaces, which further enhance f

L. Hermosa Muñoz, A. Alonso-Herrero, A. Labiano, P. Guillard

As part of the guaranteed time observations program Mid-Infrared Characterization Of Nearby Iconic galaxy Centers (MICONIC), we used the medium-resolution spectrometer (MRS) of the Mid-Infrared Instrument (MIRI) on board of the JWST to study the nearby merger NGC6240. We aim to characterise the dual active galactic nuclei (AGN), the ionised gas outflows and

Jianrong Zhang, Hehe Fan, Yi Yang

Diffusion models, particularly latent diffusion models, have demonstrated remarkable success in text-driven human motion generation. However, it remains challenging for latent diffusion models to effectively compose multiple semantic concepts into a single, coherent motion sequence. To address this issue, we propose EnergyMoGen, which includes two spectrums

Shi Guo, Zixuan Chen, Ziran Zhang, Yutian Chen

High dynamic range (HDR) imaging is a crucial task in computational photography, which captures details across diverse lighting conditions. Traditional HDR fusion methods face limitations in dynamic scenes with extreme exposure differences, as aligning low dynamic range (LDR) frames becomes challenging due to motion and brightness variation. In this work, we

Shiming Gu, Ludovic van Waerbeke, Francis Bernardeau, Roohi Dalal

Weak lensing surveys, along with most other late-Universe probes, have consistently measured a lower amplitude of the matter fluctuation spectrum, denoted by the parameter $S_8$, compared to predictions from early-Universe measurements in cosmic microwave background data. Improper modelling of nonlinear scales may partially explain these discrepancies in len

Christoph Stockinger, Jörg S. Eismann, Natale Pruiti, Marc Sorel

Phase is an intrinsic property of light, and thus a crucial parameter across numerous applications in modern optics. Various methods exist for measuring the phase of light, each presenting challenges and limitations-from the mechanical stability requirements of free-space interferometers to the computational complexity usually associated with methods based o

Jaideep Ray

Kubernetes offers a powerful orchestration platform for machine learning training, but memory management can be challenging due to specialized needs and resource constraints. This paper outlines how Kubernetes handles memory requests, limits, Quality of Service classes, and eviction policies for ML workloads, with special focus on GPU memory and ephemeral st

Vincent Caudrelier, Derek Harland

Lagrangian multiform theory is a variational framework for integrable systems. In this article we introduce a new formulation which is based on symplectic geometry and which treats position, momentum and time coordinates of a finite-dimensional integrable hierarchy on an equal footing. This formulation allows a streamlined one-step derivation of both the mul

K. Murari, S. Sundar

Radiation heat transfer in a graded-index medium often suffers accuracy problems due to the gradual changes in the refractive index. The finite element method, meshfree, and other numerical methods often struggle to maintain accuracy when applied to this medium. To address this issue, we apply physics-informed neural networks (PINNs)-based machine learning a

Yudi Pawitan, Chris Holmes

There is a growing literature on reasoning by large language models (LLMs), but the discussion on the uncertainty in their responses is still lacking. Our aim is to assess the extent of confidence that LLMs have in their answers and how it correlates with accuracy. Confidence is measured (i) qualitatively in terms of persistence in keeping their answer when

Giovanni Covi, Maarten de Hoop, Mikko Salo

In this paper we construct a parametrix for the fractional Helmholtz equation $((-\Delta)^s - \tau^{2s} r(x)^{2s} + q(x))u=0$ making use of geometrical optics solutions. We show that the associated eikonal equation is the same as in the classical case, while in the first transport equation the effect of nonlocality is only visible in the zero-th order term,

Wei Yue, Xiaohu Zheng, Chongli Yang, Kun Peng

The Mott insulator a-RuCl3, featuring the intertwined interplay of spin-orbit coupling (SOC) and Kitaev spin correlations, provides an unparalleled platform for probing quantum many-body physics. Using scanning tunneling microscopy/spectroscopy (STM/STS), we compare temperature-dependent dI/dV spectra between in-situ grown monolayers and exfoliated bulk samp

Masahiro Sato

The development of laser science and technology have stimulated the study of condensed matter physics, especially, dynamical or non-equilibrium nature in solids. The laser technique in terahertz (THz) regime, whose photon energy is comparable to those of typical collective modes in solids such as magnetic excitations, phonons, etc., has remarkably proceeded

Neil He, Menglin Yang, Rex Ying

Hyperbolic neural networks have emerged as a powerful tool for modeling hierarchical data structures prevalent in real-world datasets. Notably, residual connections, which facilitate the direct flow of information across layers, have been instrumental in the success of deep neural networks. However, current methods for constructing hyperbolic residual networ

Hermann König, Vitali Milman

We show rigidity results for the operator equations T(f.g) = Tf.Tg, T(f*g) = Tf.Tg and T(f.g) = Tf*Tg for bijective operators T acting on sufficently large spaces of smooth functions. Typically a condition like |T(f.g) - Tf.Tg| < a for all f, g with a fixed function a will imply T(f.g) = Tf.Tg. Theorems of Alesker, Artstein-Avidan, Faifman and Milman then yi

Daniel Loughran, Judith Ortmann

Serre famously showed that almost all plane conics over $\mathbb{Q}$ have no rational point. We investigate versions of this over global function fields, focusing on a specific family of conics over $\mathbb{F}_2(t)$ which illustrates new behaviour. We obtain an asymptotic formula using harmonic analysis, which requires a Tauberian theorem over function fiel

Yuchen Su, Zhineng Chen, Yongkun Du, Zhilong Ji

Connected component (CC) is a proper text shape representation that aligns with human reading intuition. However, CC-based text detection methods have recently faced a developmental bottleneck that their time-consuming post-processing is difficult to eliminate. To address this issue, we introduce an explicit relational reasoning network (ERRNet) to elegantly

Jean-Guy Caputo, Nathan Rouxelin

Type II superconductors can trap a transient magnetic field and become "cryomagnets" that are very useful for applications. During this process, flux jumps i.e. sudden jumps of the total magnetization occur and hinder the properties of these magnets. To understand the electrodynamics of these systems and in particular flux jumps, we analyzed mathematically a

Yuriy V. Pershin, Dyk Chung Nguyen

Recently, there has been an increasing interest in employing dynamical systems as solvers of NP-complete problems. In this paper, we present accurate implementations of two continuous-time dynamical solvers, known in the literature as analog SAT and digital memcomputing, using advanced numerical integration algorithms of SPICE circuit simulators. For this pu

Xuekai Zhu, Daixuan Cheng, Hengli Li, Kaiyan Zhang

Model collapse in synthetic data indicates that iterative training on self-generated data leads to a gradual decline in performance. With the proliferation of AI models, synthetic data will fundamentally reshape the web data ecosystem. Future GPT-$\{n\}$ models will inevitably be trained on a blend of synthetic and human-produced data. In this paper, we focu

Peixin Huang, Xiang Zhao, Minghao Hu, Zhen Tan

To understand a document with multiple events, event-event relation extraction (ERE) emerges as a crucial task, aiming to discern how natural events temporally or structurally associate with each other. To achieve this goal, our work addresses the problems of temporal event relation extraction (TRE) and subevent relation extraction (SRE). The latest methods

Hang Zhou, Kexin Sun, Zhenna Lu, Hao Li

The proposed Super Tau-Charm Facility (STCF) is an electron-positron collider designed to operate in a center-of-mass energy range from 2 to 7 GeV. It provides a unique platform for physics research in the tau-charm energy region. To fulfill the physics goals of STCF, high tracking efficiency and good momentum resolution is required for charged particles wit

Jordi Montes Sanabria, Pol Alvarez Vecino

The emergence of Large Language Models has fundamentally transformed the capabilities of AI agents, enabling a new class of autonomous agents capable of interacting with their environment through dynamic code generation and execution. These agents possess the theoretical capacity to operate as independent economic actors within digital markets, offering unpr

Hao Guo, Zihan Ma, Zhi Zeng, Minnan Luo

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake

Matteo Ceci, Giovanni Cresci, Santiago Arribas, Torsten Böker

Merger events are thought to be an important phase in the assembly of massive galaxies. At the same time, Active Galactic Nuclei (AGN) play a fundamental role in the evolution of their star formation histories. Both phenomena can be observed at work in NGC 6240, a local prototypical merger, classified as an UltraLuminous InfraRed Galaxy (ULIRG) thanks to its

Yunsu Kim, AhmedElmogtaba Abdelaziz, Thiago Castro Ferreira, Mohamed Al-Badrashiny

As the demand for artificial intelligence (AI) grows to address complex real-world tasks, single models are often insufficient, requiring the integration of multiple models into pipelines. This paper introduces Bel Esprit, a conversational agent designed to construct AI model pipelines based on user-defined requirements. Bel Esprit employs a multi-agent fram

Alexander Jesser, Kai Krycki, Ryan G. McClarren, Martin Frank

We investigate the numerical solution of multiscale transport equations using Physics Informed Neural Networks (PINNs) with ReLU activation functions. Therefore, we study the analogy between PINNs and Least-Squares Finite Elements (LSFE) which lies in the shared approach to reformulate the PDE solution as a minimization of a quadratic functional. We prove th

David Bevan, Julien Condé

We extend the scope of analytic combinatorics to classes containing objects that have irrational sizes. The generating function for such a class is a power series that admits irrational exponents (which we call a Ribenboim series). A transformation then yields a generalised Dirichlet series from which the asymptotics of the coefficients can be extracted by s

Noah Roux, M. Cristina Volpe

Neutrinos being massive could undergo non-radiative decay, a property for which the diffuse supernova neutrino background has a unique sensitivity. We extend previous analyses to explore our ability to disentangle predictions for the diffuse supernova neutrino background in presence or absence of neutrino non-radiative two-body decay. In a three-neutrino fra

Yonghao He, Hu Su, Haiyong Yu, Cong Yang

Open-set object detection (OSOD) is highly desirable for robotic manipulation in unstructured environments. However, existing OSOD methods often fail to meet the requirements of robotic applications due to their high computational burden and complex deployment. To address this issue, this paper proposes a light-weight framework called Decoupled OSOD (DOSOD),

Christian Mancas

This paper rigorously and concisely defines, in the context of our (Elementary) Mathematical Data Model ((E)MDM), the mathematical concepts of self-map, composite mapping, totality, one-to-oneness, non-primeness, ontoness, bijectivity, default value, (null-)reflexivity, irreflexivity, (null-)symmetry, asymmetry, (null-)idempotency, anti-idempotency, (null-)e

Youngmin Oh, Hyunju Lee, Bumsub Ham

Neural architecture search (NAS) enables finding the best-performing architecture from a search space automatically. Most NAS methods exploit an over-parameterized network (i.e., a supernet) containing all possible architectures (i.e., subnets) in the search space. However, the subnets that share the same set of parameters are likely to have different charac

A. Acus, A. Dargys

The modern algebra concepts are used to construct tables of algebraic spinors related to Clifford algebra multivectors with real and complex coefficients. The following data computed by Mathematica are presented in form of tables for individual Clifford geometric algebras: 1. Initial idempotent; 2. Two-sided ideal; 3. Left ideal basis (otherwise projector, o

Takehiro Yoshioka, Nobunari Kashikawa, Yoshihiro Takeda, Kei Ito

The Ly$\alpha$ emission line is a characteristic feature found in high-$z$ galaxies, serving as a probe of cosmic reionization. While previous works present various correlations between Ly$\alpha$ emission and physical properties of host galaxies, it is still unclear which characteristics predominantly determine the Ly$\alpha$ emission. In this study, we int

Özgecan Koçak, Phanish Puranam, Afşar Yegin

Prior research indicates that to be able to mediate conflict, observers of disagreements between parties must be able to reliably distinguish the sources of their disagreement as stemming from differences in beliefs about what is true (causality) vs. differences in what they value (morality). In this paper, we test if OpenAI's Large Language Models GPT 3.5 a

Wei Wang, Yingjie Liang, Aleksei V. Chechkin, Ralf Metzler

We study fractional Laplace motion (FLM) obtained from subordination of fractional Brownian motion to a gamma process, in the presence of an external drift that acts on the composite process or of an internal drift acting solely on the parental process. We derive the statistical properties of this FLM process and find that the external drift does not influen

Changwu Liu, Yuan Shen

Many navigation problems can be formulated as observer design on linear observed systems with a two-frame group structure, on which an invariant filter can be implemented with guaranteed consistency and stability. It's still unclear how this could be generalized to simultaneous estimation of the poses of multiple frames and the general forms of the linear ob

Estelle Aflalo, Gabriela Ben Melech Stan, Tiep Le, Man Luo

Large Vision Language Models (LVLMs) have achieved significant progress in integrating visual and textual inputs for multimodal reasoning. However, a recurring challenge is ensuring these models utilize visual information as effectively as linguistic content when both modalities are necessary to formulate an accurate answer. We hypothesize that hallucination

Edvard O. S. Grødem, Donatas Sederevičius, Esten H. Leonardsen, Bradley J. MacIntosh

Longitudinal imaging allows for the study of structural changes over time. One approach to detecting such changes is by non-linear image registration. This study introduces Multi-Session Temporal Registration (MUSTER), a novel method that facilitates longitudinal analysis of changes in extended series of medical images. MUSTER improves upon conventional pair

Hassane Kissane, Achim Schilling, Patrick Krauss

This study investigates the internal representations of verb-particle combinations within transformer-based large language models (LLMs), specifically examining how these models capture lexical and syntactic nuances at different neural network layers. Employing the BERT architecture, we analyse the representational efficacy of its layers for various verb-par

Ziyi Liu, Dengpan Ye, Changsong Yang, Yong Ding

Industrial control network (ICN) is characterized by real-time responsiveness and reliability, which plays a key role in increasing production speed, rational and efficient processing, and managing the production process. Despite tremendous advantages, ICN inevitably struggles with some challenges, such as malicious user intrusion and hacker attack. To detec

Jierui Zhang, Jianhao Huang, Kaibin Huang

Federated learning (FL) has emerged as a widely adopted paradigm for enabling edge learning with distributed data while ensuring data privacy. However, the traditional FL with deep neural networks trained via backpropagation can hardly meet the low-latency learning requirements in the sixth generation (6G) mobile networks. This challenge mainly arises from t

Dorin Popescu

This is mainly a small exposition on extensions of valuation rings

Jesús Dueñas, Carmen Núñez, Rafael Obaya

A mathematical modeling process for phenomena with a single state variable that attempts to be realistic must be given by a scalar nonautonomous differential equation $x'=f(t,x)$ that is concave with respect to the state variable $x$ in some regions of its domain and convex in the complementary zones. This article takes the first step towards developing a th

Sacha Guerrini, Martin Kilbinger, Hubert Leterme, Axel Guinot

Weak gravitational lensing requires precise measurements of galaxy shapes and therefore an accurate knowledge of the PSF model. The latter can be a source of systematics that affect the shear two-point correlation function. A key stake of weak lensing analysis is to forecast the systematics due to the PSF. Correlation functions of galaxies and the PSF, the s

Foivos Alimisis, Daniel Kressner, Nian Shao, Bart Vandereycken

Preconditioned eigenvalue solvers offer the possibility to incorporate preconditioners for the solution of large-scale eigenvalue problems, as they arise from the discretization of partial differential equations. The convergence analysis of such methods is intricate. Even for the relatively simple preconditioned inverse iteration (PINVIT), which targets the

Jason L. Evans

Axion-like particles are a well-motivated candidate for ultralight dark matter. Because dark matter must be non-relativistic, the effects of its scattering with Standard Model particles are negligible and generally go unnoticed. However, due to the large occupation number of ultralight dark matter, the sum of all scatterings leads to a classical field-like i

Marco Minici, Luca Luceri, Francesco Fabbri, Emilio Ferrara

Social media platforms have become vital spaces for public discourse, serving as modern agor\`as where a wide range of voices influence societal narratives. However, their open nature also makes them vulnerable to exploitation by malicious actors, including state-sponsored entities, who can conduct information operations (IOs) to manipulate public opinion. T

Quoc Hung Pham, Mathias Noe, Rainer Nast

Dynamic resistance occurs in a superconducting tape carrying a dc transport current while being exposed to an alternating magnetic field. This effect is caused by flux movements interacting with the transport current. The dynamic resistance is already applied in many superconducting applications, for example superconducting flux pumps or persistent current s