October 2024 arXiv papers — page 99

Gajraj Kuldeep, Qi Zhang

The Internet of Things (IoT) relies on resource-constrained devices for data acquisition, but the vast amount of data generated and security concerns present challenges for efficient data handling and confidentiality. Conventional techniques for data compression and secrecy often lack energy efficiency for these devices. Compressive sensing has the potential

Robert Godwin-Jones`

There are obvious benefits to integrating generative AI (artificial intelligence) into language learning and teaching. Those include using AI as a language tutor, creating learning materials, or assessing learner output. However, due to how AI systems under-stand human language, based on a mathematical model using statistical probability, they lack the lived

Norbert Mokrzański, Marcin Napiórkowski

We consider a variational approach to the Bose-Hubbard model based on Bogoliubov theory. We introduce the grand canonical and canonical free energy functionals for which we prove the existence of minimizers. By analyzing their structure we show the existence of a thermally driven phase transition by showing that the system is superfluid at sufficiently low t

Konstantin Grotov, Artem Borzilov, Maksim Krivobok, Timofey Bryksin

Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an increased potential for bugs. With the rise of code-fluent Large Language Models empowered with agentic techniques, smart bug-f

Bin Chen, Zhi-jun Yin

The black hole information paradox has been an important problem in quantum gravity. In the study of evaporating black hole, it has been proposed that the holographic map between the semi-classical effective description in bulk and the fundamental description in boundary cannot be isometric. In this work, we would like to study the reflected entropy in an ev

Wafaa Mohammed, Vlad Niculae

Large language models (LLMs) are increasingly strong contenders in machine translation. In this work, we focus on document-level translation, where some words cannot be translated without context from outside the sentence. Specifically, we investigate the ability of prominent LLMs to utilize the document context during translation through a perturbation anal

Boning Zhang, Dongzhu Liu, Osvaldo Simeone, Guanchu Wang

To support real-world decision-making, it is crucial for models to be well-calibrated, i.e., to assign reliable confidence estimates to their predictions. Uncertainty quantification is particularly important in personalized federated learning (PFL), as participating clients typically have small local datasets, making it difficult to unambiguously determine o

Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo

Model merging-based multitask learning (MTL) offers a promising approach for performing MTL by merging multiple expert models without requiring access to raw training data. However, in this paper, we examine the merged model's representation distribution and uncover a critical issue of "representation bias". This bias arises from a significant distribution g

Peter A. Wijeratne, Daniel C. Alexander

Disease progression models infer group-level temporal trajectories of change in patients' features as a chronic degenerative condition plays out. They provide unique insight into disease biology and staging systems with individual-level clinical utility. Discrete models consider disease progression as a latent permutation of events, where each event correspo

Constanza Fierro, Negar Foroutan, Desmond Elliott, Anders Søgaard

Large Language Models (LLMs) store and retrieve vast amounts of factual knowledge acquired during pre-training. Prior research has localized and identified mechanisms behind knowledge recall; however, it has only focused on English monolingual models. The question of how these mechanisms generalize to non-English languages and multilingual LLMs remains unexp

Iraklis Giannakis, Craig Warren, Antonios Giannopoulos, Georgios Leontidis

Ground-penetrating radar (GPR) is a mature geophysical method that has gained increasing popularity in planetary science over the past decade. GPR has been utilised both for Lunar and Martian missions providing pivotal information regarding the near surface geology of Terrestrial planets. Within that context, numerous processing pipelines have been suggested

C. Granados, B. Kumar Das, Christian Heide, Shambhu Ghimire

We present the first theoretical results on the generation of short-wavelength attosecond vortex beams in semiconductors through their interactions with an intense Laguerre-Gauss beam, in the limit where non-perturbative high-order harmonics are generated. We exploit the details of the novel microscopic mechanism for high-order harmonic generation (HHG) in c

Rishi Pokhrel, Tanay K. Dey

In this work, we holographically study the hydrodynamical properties of strongly coupled $\mathcal{N} = 4$ SYM baryon rich thermal plasma with large number of flavour quarks. Specifically, we study the drag force acting on the moving heavy probe quark and corresponding energy loss. We also study the jet quenching parameter, screening length and binding energ

Toby Godfrey, William Hunt, Mohammad D. Soorati

Multi-agent reinforcement learning is a key method for training multi-robot systems. Through rewarding or punishing robots over a series of episodes according to their performance, they can be trained and then deployed in the real world. However, poorly trained policies can lead to unsafe behaviour during early training stages. We introduce Multi-Agent Reinf

Mahmoud Robaa, Mazen Balat, Rewaa Awaad, Esraa Omar

This study introduces an explainable AI (XAI) framework for the detection of dyslexia through handwriting analysis, achieving an impressive test precision of 99.65%. The framework integrates transfer learning and transformer-based models, identifying handwriting features associated with dyslexia while ensuring transparency in decision-making via Grad-CAM vis

Lovro Dulibić, Blaženka Melić, Alexey A. Petrov

A significant discrepancy, spanning multiple orders of magnitude, exists between the leading order contribution to the $D^0\overline{D^0}$ mixing parameters and experimental values. This is largely due to the Glashow-Iliopoulos-Maiani (GIM) mechanism, which results in substantial suppression of the theoretical predictions. To bridge this gap, various efforts

Enrico Bini

In many use cases the execution time of tasks is unknown and can be chosen by the designer to increase or decrease the application features depending on the availability of processing capacity. If the application has real-time constraints, such as deadlines, then the necessary and sufficient schedulability test must allow the execution times to be left unspe

Mingqian Li, Qiao Han, Ruifeng Li, Yao Yang

In multi-task learning, labels are often missing irregularly across samples, which can be fully labeled, partially labeled or unlabeled. The irregular label presence often appears in scientific studies due to experimental limitations. It triggers a demand for a new training and inference mechanism that could accommodate irregularly present labels and maximiz

Ziming Huang, Xurui Li, Haotian Liu, Feng Xue

Recently, multi-class anomaly classification has garnered increasing attention. Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior knowledge. Acquiring this knowledge faces two issues: the non-prominent and weak-semantics anomalies. In this paper, we propose AnomalyNCD, a multi-class anomaly classification network

Rosa M. Fernández-Alcalá, José D. Jiménez-López, Nicolas Le Bihan, Clive Cheong Took

This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimens

G. L. Klimchitskaya, A. S. Korotkov, V. V. Loboda, V. M. Mostepanenko

The cantilever tip of a nanoswitch in close proximity to the ground plate is considered with account of electrostatic, elastic, van der Waals (Casimir), and also contact repulsive forces. The van der Waals (Casimir) and contact repulsive forces are computed for a Si cantilever and either Au or Ni ground plates using the Lifshitz theory and the method of pair

Rongjun Huang, Aditi Vijayan, Mark R. Krumholz

Supernova-driven galactic outflows play a vital but still poorly-understood role in galactic chemical evolution, and one of the largest uncertainties about them is the extent to which they consist of supernova ejecta that are unmixed, or only poorly-mixed, with the remainder of the interstellar medium (ISM). Simulations of wind launching make a range of pred

Vasileios Vatellis

In an era increasingly focused on green computing and explainable AI, revisiting traditional approaches in theoretical and phenomenological particle physics is paramount. This project evaluates various machine learning (ML) algorithms-including Nearest Neighbors, Decision Trees, Random Forest, AdaBoost, Naive Bayes, Quadratic Discriminant Analysis (QDA), and

Harry Emerson, Liz Bates, Chris Hicks, Vasilios Mavroudis

CybORG++ is an advanced toolkit for reinforcement learning research focused on network defence. Building on the CAGE 2 CybORG environment, it introduces key improvements, including enhanced debugging capabilities, refined agent implementation support, and a streamlined environment that enables faster training and easier customisation. Along with addressing s

Marco Aruta, Vadim Malvone, Aniello Murano

In the last two decades, Alternating-time Temporal Logic (ATL) has been proved to be very useful in modeling strategic reasoning for Multi-Agent Systems (MAS). However, this logic struggles to capture the bounded rationality inherent in human decision-making processes. To overcome these limitations, Natural Alternating-time Temporal Logic (NatATL) has been r

J. Alonso-Santiago, A. Frasca, A. Bragaglia, G. Catanzaro

The Radcliffe Wave has only recently been recognised as a about 3 kpc long coherent gas structure encompassing most of the star forming regions in the solar vicinity. Since its discovery, it has been mainly studied from the perspective of dynamics, but a detailed chemical study is necessary to understand its nature and the composition of the natal clouds tha

E. Gosset, Y. Damerdji, T. Morel, L. Delchambre

Gaia is an astrometric space experiment that is measuring positions, proper motions as well as parallaxes for a huge number of stars. It operates a medium-dispersion spectrometer, the RVS, that provides spectra and thus radial velocity time-series. The paper is centred on the analysis of the RV time-series. We try to fit orbital and trend models and restrict

Nils Grandien, Quentin Delfosse, Kristian Kersting

Deep reinforcement learning (RL) agents rely on shortcut learning, preventing them from generalizing to slightly different environments. To address this problem, symbolic method, that use object-centric states, have been developed. However, comparing these methods to deep agents is not fair, as these last operate from raw pixel-based states. In this work, we

Róbert Erdélyi, György Csaba, Levente Maucha, Felix Kohl

We present a computational framework for the design of magnonic transducers, where waveguide antennas generate and pick up spin-wave signals. Our method relies on the combination of circuit-level models with micromagnetic simulations and allows simulation of complex geometries in the magnonic domain. We validated our model with experimental measurements, whi

Radu Ioan Boţ, Enis Chenchene

In this paper, we introduce a novel Extra-Gradient method with anchor term governed by general parameters. Our method is derived from an explicit discretization of a Tikhonov-regularized monotone flow in Hilbert space, which provides a theoretical foundation for analyzing its convergence properties. We establish strong convergence to specific points within t

Huaiyuan Yao, Longchao Da, Vishnu Nandam, Justin Turnau

The integration of autonomous vehicles into urban traffic has great potential to improve efficiency by reducing congestion and optimizing traffic flow systematically. In this paper, we introduce CoMAL (Collaborative Multi-Agent LLMs), a framework designed to address the mixed-autonomy traffic problem by collaboration among autonomous vehicles to optimize tra

Midhun E K, Ashwini Ratnoo

This paper focuses on developing a bearings-only measurement-based three-dimensional window traversal guidance method for quadrotor Uninhabitated Aerial Vehicles (UAVs). The desired flight path and heading angles of the quadrotor are proposed as functions of the bearing angle information of the four vertices of the window. These angular guidance inputs emplo

Nhat A. Nghiem

We describe a simple quantum algorithm to simulate time-dependent Hamiltonian, extending the methodology of quantum signal processing. The framework achieves optimal scaling up to some factor with respect to other parameters, and nearly optimal in inverse of error tolerance, which could be improved to optimal scaling under certain input models. As applicatio

Laura Gálvez Jiménez, Christine Decaestecker

Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to the complexity of the annotation process. In this work, we investigate the impact of noisy annotations on the tr

Dwijay Bane, Anurag Gupta, Manan Suri

This technical report investigates the application of event-based vision sensors in non-invasive qualitative vibration analysis, with a particular focus on frequency measurement and motion magnification. Event cameras, with their high temporal resolution and dynamic range, offer promising capabilities for real-time structural assessment and subtle motion ana

Tathagata Banerjee, Anushka De, Subhamoy Maitra, Diganta Mukherjee

This paper presents a data-driven statistical framework to quantify the role of skill in games, addressing the long-standing question of whether success in a game is predominantly driven by skill or chance. We analyze player level data from four popular games Chess, Rummy, Ludo, and Teen Patti, using empirical win statistics across varying levels of experien

Sarah Taylor

This paper examines a government's strategic resource allocation choices when facing an opposing group whose military power is uncertain. We investigate how this uncertainty affects the government's decision to divide resources in a way that either guarantees peace, despite unresolved uncertainty, or risks conflict. We find that under low uncertainty, the go

Tianyu Liu, Kevin Du, Mrinmaya Sachan, Ryan Cotterell

One strength of modern language models is their ability to incorporate information from a user-input context when answering queries. However, they are not equally sensitive to the subtle changes to that context. To quantify this, Du et al. (2024) gives an information-theoretic metric to measure such sensitivity. Their metric, susceptibility, is defined as th

Kohei Suzuki

We study spin-$S$ Ising models with $p$-spin interactions on the one-dimensional chain and the two-dimensional square lattice. Here, $S$ denotes the magnitude of the spin and $p$ represents the number of spins involved in each interaction. The analysis is performed for $S=1/2,1,3/2,2$ and $p=3,4,5$. For the one-dimensional model, we formulate transfer matric

Monroe Eskew, Yair Hayut

In this paper, we obtain the consistency, relative to large cardinals, of the existence of dense ideals on every successor of a regular cardinal simultaneously. Using a consequent transfer principle, we show that in this model there is a $\sigma$-complete, $\aleph_1$-dense ideal on $\aleph_{n+1}$ for every $n < \omega$, answering a question of Foreman. Using

Mathieu Dedenon, Karsten Kruse

Tissues of living cells are a prime example of active fluids. There is experimental evidence that tissues generate extensile active stress even though their constituting cells are contractile. Fluctuating forces that could result from cell-substrate interactions have been proposed to be able to induce a transition from contractile to extensile active stress.

Zohim Chandani, Kazuki Ikeda, Zhong-Bo Kang, Dmitri E. Kharzeev

Determining the spectrum and wave functions of excited states of a system is crucial in quantum physics and chemistry. Low-depth quantum algorithms, such as the Variational Quantum Eigensolver (VQE) and its variants, can be used to determine the ground-state energy. However, current approaches to computing excited states require numerous controlled unitaries

Mojtaba Alaei, Artem R. Oganov

In this paper, we introduce an efficient, linear algebra-based method for optimizing supercell selection to determine Heisenberg exchange parameters from DFT calculations. A widely used approach for deriving these parameters involves mapping DFT energies from various magnetic configurations within a supercell to the Heisenberg Hamiltonian. However, periodic

Juan José Galano-Frutos, Luca Bergamasco, Paolo Vigo, Matteo Morciano

Aquaporins (AQPs) and aquaglyceroporins (AQGPs) play a crucial role in regulating water transport and solute selectivity across biological membranes. Besides their biological relevance, AQPs have at-tracted growing interest as models for the design of next-generation biomimetic membranes for water filtration. In this work, we present a pore-level Quantitativ

Nikolaos Karnesis, Argyro Sasli, Riccardo Buscicchio, Nikolaos Stergioulas

Future Gravitational Wave observatories will give us the opportunity to search for stochastic signals of astrophysical, or even cosmological origins. However, parameter estimation and search will be challenging, mostly due to the overlap of multiple signal components, as well as the potentially partially unknown properties of the instrumental noise. In this

Natabara Máté Gyöngyössy, Bernát Török, Csilla Farkas, Laura Lucaj

Regulatory frameworks for the use of AI are emerging. However, they trail behind the fast-evolving malicious AI technologies that can quickly cause lasting societal damage. In response, we introduce a pioneering Assistive AI framework designed to enhance human decision-making capabilities. This framework aims to establish a trust network across various field

Betony Adams, Abbas Hassasfar, Ilya Sinayskiy, Alistair Nunn

For the first time in history, humankind might conceivably begin to imagine itself as a multi-planetary species. This goal will entail technical innovation in a number of contexts, including that of healthcare. All life on Earth shares an evolution that is coupled to specific environmental conditions, including gravitational and magnetic fields. While the hu

Mohammad Reza Alipour, Mohammad Ali S. Afshar, Saeed Noori Gashti, Jafar Sadeghi

In this study, we investigate the Weak Gravity Conjecture (WGC) in the context of quantum-corrected AdS-Reissner-Nordstrom (AdS-RN) black holes within Kiselev spacetime. Our focus is on photon spheres, which serve as markers for stable and unstable photon spheres. We confirm the validity of the WGC by demonstrating that quantum corrections do not alter the e

D. I. Novikov, K. O. Parfenov

We present a detailed test for Gaussianity of Planck polarization data using statistics of unpolarized points on the sky, i.e. such points where the linear polarization vanishes. The algorithm we propose for finding such points is stable and guarantees their 100% detection. Our approach allows us to analyze the data for Gaussianity of the signal at different

Jihoon Ok, Giovanni Scilla, Bianca Stroffolini

We study partial regularity for degenerate elliptic systems of double-phase type, where the growth function is given by $H(x,t)=t^p+a(x)t^q$ with $1<p\leq q$ and $a(x)$ a nonnegative $C^{0,\alpha}$-continuous function. Our main result proves that if $\frac{q}{p}\leq 1+\frac{\alpha}{n}$, the gradient of any weak solution is locally H\"older continuous, except

Mariángeles Gómez-Molleda, Joan-C. Lario

We present several ruler and compass practical geometric constructions that can be performed in the lemniscate curve. To be precise, we provide recipes for halving, doubling, adding, subtracting, and transferring lemniscate arcs with ruler and compass. This note complements the instructions for the lemnatomic equilateral triangle and pentagon discussed in \c

Zhiyu Wang, Mohammad Goudarzi, Rajkumar Buyya

With the continuous increase of IoT applications, their effective scheduling in edge and cloud computing has become a critical challenge. The inherent dynamism and stochastic characteristics of edge and cloud computing, along with IoT applications, necessitate solutions that are highly adaptive. Currently, several centralized Deep Reinforcement Learning (DRL

Sharv Murgai, Hrishikesh Bhagwat, Raj Abhijit Dandekar, Rajat Dandekar

Carbon emissions are rising at an alarming rate, posing a significant threat to global efforts to mitigate climate change. Electric vehicles have emerged as a promising solution, but their reliance on lithium-ion batteries introduces the critical challenge of battery degradation. Accurate prediction and forecasting of battery degradation over both short and

Fei Tao, Huaying Wei, Yaosong Yang

A simple arc $\Gamma = \gamma(0, T]$, growing into the unit disk $\mathbb D$ from its boundary, generates a driving term $\xi$ and a conformal welding $\phi$ through the Loewner differential equation. When $\Gamma$ is the slit of a Weil--Petersson quasislit-disk $\mathbb D\setminus\Gamma$, the Loewner transform and its inverse $\Gamma \leftrightarrow \xi$ ha

Semih Filiz, Klaus Werner, Thomas Rauch, Nicole Reindl

Hydrogen-rich white dwarfs (WDs) comprise the majority of the WD population, but are only rarely found at the very hot end of the WD cooling sequence. A small subgroup that exhibits both hydrogen and helium lines in their spectra, the so-called hybrid (or DAO) WDs, represents the majority of hydrogen-rich WDs at effective temperatures $T_{eff}$ $\approx$ 100

D. García-Pons, J. Pérez-Bailón, A. Méndiz, V. Júlvez

Magnetic nanoparticles play a crucial role in different fields such as biomedicine or information and quantum technologies. These applications require nanoparticles with a single, well-defined energy minimum, free of metastable states, and characterized by narrow switching field distributions. Here, we demonstrate that high-transition-temperature nanoSQUIDs

Ariel Neufeld, Philipp Schmocker

This paper extends the universal approximation property of single-hidden-layer feedforward neural networks beyond compact domains, which is of particular interest for the approximation within weighted $C^k$-spaces and weighted Sobolev spaces over unbounded domains. More precisely, by assuming that the activation function is non-polynomial, we establish unive

Junan Chen, Matteo Ronchetti, Verena Stehl, Van Nguyen

Recent developments in the registration of histology and micro-computed tomography ({\mu}CT) have broadened the perspective of pathological applications such as virtual histology based on {\mu}CT. This topic remains challenging because of the low image quality of soft tissue CT. Additionally, soft tissue samples usually deform during the histology slide prep

Ajendra Singh, Souvik Chakraborty, Rajib Chowdhury

We propose a novel dual physics-informed neural network for topology optimization (DPNN-TO), which merges physics-informed neural networks (PINNs) with the traditional SIMP-based topology optimization (TO) algorithm. This approach leverages two interlinked neural networks-a displacement network and an implicit density network-connected through an energy-mini

D. Sanchez-Manzano, V. Humbert, D. Zhang, A. Gutiérrez-Llorente

Characterizing the dimensionality of the superconducting state in infinite-layer (IL) nickelates is essential for understanding its nature. Most studies have addressed this by examining the anisotropy of the upper critical fields. However, the dominance of Pauli paramagnetic effects over orbital effects complicates the interpretation of these experiments in

Josiah Aklilu, Xiaohan Wang, Serena Yeung-Levy

Precise action localization in untrimmed video is vital for fields such as professional sports and minimally invasive surgery, where the delineation of particular motions in recordings can dramatically enhance analysis. But in many cases, large scale datasets with video-label pairs for localization are unavailable, limiting the opportunity to fine-tune video

Emil Engström

The Dirichlet-Neumann method is a common domain decomposition method for nonoverlapping domain decomposition and the method has been studied extensively for linear elliptic equations. However, for nonlinear elliptic equations, there are only convergence results for some specific cases in one spatial dimension. The aim of this manuscript is therefore to prove

Jaime Merino, Arnaud Ralko

Motivated by the three-dimensional structure of Kitaev materials we explore multilayer Kitaev models. The magnetic properties of a multilayer of an arbitrary number of Kitaev honeycomb layers stacked on top of each other coupled through a Heisenberg interaction, J, is analyzed through Abrikosov fermion mean-field theory. The system sustains quantum spin liqu

Chinmaya Mishra, Gabriel Skantze, Peter Hagoort, Rinus Verdonschot

The increased interest in developing next-gen social robots has raised questions about the factors affecting the perception of robot emotions. This study investigates the impact of robot appearances (humanlike, mechanical) and face regions (full-face, eye-region) on human perception of robot emotions. A between-subjects user study (N = 305) was conducted whe

Minsheng Huang, Pan Wang, Chengbao Yao, Lidong Cheng

We establish a high-resolution, high-performance, and high-confidence compressible multiphysics system in a Cartesian grid with irregular boundary topologies to simulate intensive blast waves propagating in large-scale and extremely complex environments. The multiphysics system is modeled by a multi-component model solved using a generalized Godunov method a

Marcin Wieśniak

This essay reviews a modern understanding of a quantum measurement. Rather than reducing the picture to the observer's experience with quantum system, we try to put it in the context of a broader physical picture. We also attempt to distinguish some basic components and stages of a generic measurement. In this way, we want to understand which aspects of quan

Blanca Calvo Figueras, Rodrigo Agerri

The development of Large Language Models (LLMs) has brought impressive performances on mitigation strategies against misinformation, such as counterargument generation. However, LLMs are still seriously hindered by outdated knowledge and by their tendency to generate hallucinated content. In order to circumvent these issues, we propose a new task, namely, Cr

Taras Kucherenko, Derek Peristy, Judith Bütepage

Animation data is often obtained through optical motion capture systems, which utilize a multitude of cameras to establish the position of optical markers. However, system errors or occlusions can result in missing markers, the manual cleaning of which can be time-consuming. This has sparked interest in machine learning-based solutions for missing marker rec

Florian D. van Leeuwen, Shubhayu Bhattacharyay, Alex Carriero, Ethan Jacob Moyer

Patients with traumatic brain injury (TBI) often experience pathological increases in intracranial pressure (ICP), leading to intracranial hypertension (tIH), a common and serious complication. Early warning of an impending rise in ICP could potentially improve patient outcomes by enabling preemptive clinical intervention. However, the limited availability o

Yin Xie, Kaicheng Yang, Peirou Liang, Xiang An

Large Multimodal Models (LMMs) often face a modality representation gap during pretraining: while language embeddings remain stable, visual representations are highly sensitive to contextual noise (e.g., background clutter). To address this issue, we introduce a visual comprehension stage, which we call ViCToR (Visual Comprehension via Token Reconstruction),

Songheng Zhang, Lei Wang, Toby Jia-Jun Li, Qiaomu Shen

Text documents with numerical values involved are widely used in various applications such as scientific research, economy, public health and journalism. However, it is difficult for readers to quickly interpret such data-involved texts and gain deep insights. To fill this research gap, this work aims to automatically generate charts to accurately convey the

Andrea Malara

The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration, employing innovative approaches including advanced neural network architectures, attention-based mechanisms, and advers

Burak Özer, Kyrylo Ochkan, Raghav Chaturvedi, Evgenii Maltsev

Quantum Hall phases have recently emerged as a platform to investigate non-Hermitian topology in condensed-matter systems. This platform is particularly interesting due to its tunability, which allows to modify the properties and topology of the investigated non-Hermitian phases by tuning external parameters of the system such as the magnetic field. Here, we

Gi-Chol Cho, Chikako Idegawa

We study the two Higgs doublet model with a complex singlet scalar whose imaginary part acts as dark matter (DM). The scattering of DM and quarks, mediated by three CP-even scalars in this model, is suppressed when masses of CP-even scalars are degenerate; that is called the ``degenerate scalar scenario''. Based on this scenario, we show that the strong firs

Nalini Joshi, Frank W. Nijhoff, Allan Steel

The family of mappings of the plane possessing a biquadratic invariant, which is known collectively as QRT maps, is composed of two involutions, one preserving a vertical shift and the other preserving a horizontal shift in the plane. In this paper, we extend the map by replacing each shift by the group operation on each of two families of elliptic curves, w

Frank Nielsen

The symmetric Kullback-Leibler centroid also called the Jeffreys centroid of a set of mutually absolutely continuous probability distributions on a measure space provides a notion of centrality which has proven useful in many tasks including information retrieval, information fusion, and clustering in image, video and sound processing. However, the Jeffreys

Lukas Tatzel, Bálint Mucsányi, Osane Hackel, Philipp Hennig

Quadratic approximations form a fundamental building block of machine learning methods. E.g., second-order optimizers try to find the Newton step into the minimum of a local quadratic proxy to the objective function; and the second-order approximation of a network's loss function can be used to quantify the uncertainty of its outputs via the Laplace approxim

Bo Cheng, Yuhang Ma, Liebucha Wu, Shanyuan Liu

The task of layout-to-image generation involves synthesizing images based on the captions of objects and their spatial positions. Existing methods still struggle in complex layout generation, where common bad cases include object missing, inconsistent lighting, conflicting view angles, etc. To effectively address these issues, we propose a \textbf{Hi}erarchi

Swagatam Haldar, Christoph Stein-Thoeringer, Vadim Borisov

Understanding the complex interactions within the microbiome is crucial for developing effective diagnostic and therapeutic strategies. Traditional machine learning models often lack interpretability, which is essential for clinical and biological insights. This paper explores the application of symbolic regression (SR) to microbiome relative abundance data,

Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov

We present several generative and predictive algorithms based on the RKHS (reproducing kernel Hilbert spaces) methodology, which, most importantly, are scale up efficiently with large datasets or high-dimensional data. It is well recognized that the RKHS methodology leads one to efficient and robust algorithms for numerous tasks in data science, statistics,

Henry G. McGhee, Henry J. Thompson, James Thompson, Yu Zhang

The presence of two chlorine atoms in 1,2-dichloroethene allows for isomerisation around the double bond. This isomerisation can lead to rich photochemistry. We present a time-resolved pump-probe photoelectron spectroscopy measurement on both the cis- and trans- isomers of 1,2-dichloroethene. A universal XUV probe of 22.3 eV is used allowing observation of p

Shigang Liu, Bushra Sabir, Seung Ick Jang, Yuval Kansal

Large Language Models (LLMs) have shown remarkable potential in code generation, making them increasingly important in the field. However, the security issues of generated code have not been fully addressed, and the usability of LLMs in code generation still requires further exploration. This work introduces SecCode, a framework that leverages an innovative

Giampaolo Bella, Gianpietro Castiglione, Sergio Esposito, Mario Raciti

Data and derived information about target victims has always been key for successful attacks, both during historical wars and modern cyber wars. Ours turns out to be an era in which modern cars generate a plethora of data about their drivers, and such data could be extremely attractive for offenders. This paper seeks to assess how well modern cars protect th

Samuel Poncé, Jae-Mo Lihm, Cheol-Hwan Park

Verification and validation of methods and first-principles software are at the core of computational solid-state physics but are too rarely addressed. We compare four first-principles codes: Abinit, Quantum ESPRESSO, EPW, ZG, and three methods: (i) the Allen-Heine-Cardona theory using density functional perturbation theory (DFPT), (ii) the Allen-Heine-Cardo

Zheng Wei, Xiaodong Wei

In engineering design, one of the most daunting problems in the design-through-analysis workflow is to deal with trimmed NURBS (Non-Uniform Rational B-Splines), which often involve topological/geometric issues and lead to inevitable gaps and overlaps in the model. Given the dominance of the trimming technology in CAD systems, reconstructing such a model as a

Catherine Lorenz, Alena Otto, Michel Gendreau

Major players in e-commerce process dynamically incoming orders in real-time and already use advanced anticipation techniques, like AI, to predict characteristics of future orders. However, at the warehousing level, there are still no unambiguous recommendations on integrating anticipation with intelligent online optimization algorithms, nor an unbiased benc

Floris Holstege, Bram Wouters, Noud van Giersbergen, Cees Diks

A distribution shift between the training and test data can severely harm performance of machine learning models. Importance weighting addresses this issue by assigning different weights to data points during training. We argue that existing heuristics for determining the weights are suboptimal, as they neglect the increase of the variance of the estimated m

M. Assié, H. Jacob, Y. Blumenfeld, V. Girard-Alcindor

Pair transfer is a unique tool to study pairing correlations in nuclei. Neutron-proton pairing is investigated in the N=Z nuclei of the f-shell, through the reaction (p,3He) in inverse kinematics, that allows to populate at the same time the lowest J=0+, T=1 (isovector pairing) state and J=1+, T=0 (isoscalar pairing) state. Radioactive beams of 56Ni and 52Fe

Giovanni Di Meglio, Dariusz Chruściński, Koenraad Audenaert, Martin B. Plenio

We investigate the long-time behavior of quantum Markovian dynamics generated by time-dependent Gorini-Kossakowski-Lindblad-Sudarshan (GKLS) master equations. We introduce a notion of weak relaxation and derive sufficient conditions guaranteeing asymptotic independence from the initial state. Our results provide a quantitative extension of the Spohn-Frigerio

Ankita Dutta, Nabendu Chaki, Rajat K. De

DNN training is time-consuming and requires efficient multi-accelerator parallelization, where a single training iteration is split over available accelerators. Current approaches often parallelize training using intra-batch parallelization. Combining inter-batch and intra-batch pipeline parallelism is common to further improve training throughput. In this a

Vojtech Kovarik, Nathaniel Sauerberg, Lewis Hammond, Vincent Conitzer

AI agents will be predictable in certain ways that traditional agents are not. Where and how can we leverage this predictability in order to improve social welfare? We study this question in a game-theoretic setting where one agent can pay a fixed cost to simulate the other in order to learn its mixed strategy. As a negative result, we prove that, in contras

Wadhah Zai El Amri, Malte Kuhlmann, Nicolás Navarro-Guerrero

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing datase

Ruihan Yang, Caiqi Zhang, Zhisong Zhang, Xinting Huang

While Large Language Models (LLMs) demonstrate impressive capabilities, they still struggle with generating factually incorrect content (i.e., hallucinations). A promising approach to mitigate this issue is enabling models to express uncertainty when unsure. Previous research on uncertainty modeling has primarily focused on short-form QA, but realworld appli

Huifang Ma, Long Feng, Zhaojun Wang

In this study, we focus on applying L-statistics to the high-dimensional one-sample location test problem. Intuitively, an L-statistic with $k$ parameters tends to perform optimally when the sparsity level of the alternative hypothesis matches $k$. We begin by deriving the limiting distributions for both L-statistics with fixed parameters and those with dive

Francesco Sylos Labini

The circular velocity curve traced by stars provides a direct means of investigating the potential and mass distribution of the Milky Way. Recent measurements of the Galaxy's rotation curve have revealed a significant decrease in velocity for galactic radii larger than approximately 15 kpc. While these determinations have primarily focused on the Galactic pl

Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata

By embedding discrete representations into a continuous latent space, we can leverage continuous-space latent diffusion models to handle generative modeling of discrete data. However, despite their initial success, most latent diffusion methods rely on fixed pretrained embeddings, limiting the benefits of joint training with the diffusion model. While jointl

Sneha Latha Kommuguri, Smrutishree Pratihary, Thangjam Rishikanta Singh, Suraj Kumar Sinha

Unlike junctions in solid-state devices, a plasma-metal junction (pm-junction) is a junction of classical and quantum electrons. The plasma electrons are Maxwellain in nature, while metal electrons obey the Fermi-Dirac distribution. In this experiment, the current-voltage characteristics of solid-state devices that form homo or hetero-junction are compared t

Po-Yu Hsieh, June-Hao Hou

Continuum robots, known for their high flexibility and adaptability, offer immense potential for applications such as medical surgery, confined-space inspections, and wearable devices. However, their non-linear elastic nature and complex kinematics present significant challenges in digital modeling and visualization. Identifying the modal shape coefficients

Victor G. Zakharov

The generalization, similarly to exponential multivariate bases in the Fourier transform, of the Bessel functions to many dimensions is offered. Analogously to the Fourier transform property under the differentiation, the similar Hankel transform property is extended to many dimensions.

Vicente J. Bolos, Rafael Benitez, Vicente Coll-Serrano

In the framework of data envelopment analysis (DEA), Tone (2001) introduced the slacks-based measure (SBM) of efficiency, which is a nonradial model that incorporates all the slacks of the evaluated decision-making units (DMUs) into their efficiency scores, unlike classical radial efficiency models. Next, Tone (2002) developed the SBM super-efficiency model