April 2024 arXiv papers — page 173

Eyal Aharoni, Sharlene Fernandes, Daniel J. Brady, Caelan Alexander

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen and colleagues' (2000) proposal, by asking people to distinguish real human moral evaluations from those made by a pop

Yu Pan, Xiang Zhang, Yuguang Yang, Jixun Yao

Neural speech codecs have recently emerged as a focal point in the fields of speech compression and generation. Despite this progress, achieving high-quality speech reconstruction under low-bitrate scenarios remains a significant challenge. In this paper, we propose PSCodec, a series of neural speech codecs based on prompt encoders, comprising PSCodec-Base,

Dai-Neng Liu, Che Ming Ko, Yu-Gang Ma, Francesco Mazzaschi

Understanding the properties of hypernuclei helps to constrain the interaction between hyperon and nucleon, which is known to play an essential role in determining the properties of neutron stars. Experimental measurements have suggested that the hypertriton ($^3_\Lambda \text{H}$), the lightest hypernucleus, exhibits a halo structure with a deuteron core en

Jianhang Zhu, Jie Gong

The freshness of information in real-time monitoring systems has received increasing attention, with Age of Information (AoI) emerging as a novel metric for measuring information freshness. In many applications, update packets need to be computed before being delivered to a destination. Mobile edge computing (MEC) is a promising approach for efficiently acco

Zihan Yao, Yu He, Tianyu Qi, Ming Li

Addressing the issues of hallucinations and outdated knowledge in large language models is critical for their reliable application. Model Editing presents a promising avenue for mitigating these challenges in a cost-effective manner. However, existing methods often suffer from unsatisfactory generalization and unintended effects on non-edited samples. To ove

Rajneil Baruah, Subhadeep Mondal, Sunando Kumar Patra, Satyajit Roy

This article attempts to summarize the effort by the particle physics community in addressing the tedious work of determining the parameter spaces of beyond-the-standard-model (BSM) scenarios, allowed by data. These spaces, typically associated with a large number of dimensions, especially in the presence of nuisance parameters, suffer from the curse of dime

Fengyuan Liu, Haochen Luo, Yiming Li, Philip Torr

Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model that generates them. In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and t

Matthias Aschenbrenner, Lou van den Dries, Joris van der Hoeven

We show that every Hardy field extends to an $\omega$-free Hardy field. This result relates to classical oscillation criteria for second-order homogeneous linear differential equations. It is essential in [10], and here we apply it to answer questions of Boshernitzan, and to generalize a theorem of his.

Behrooz Razeghi, Parsa Rahimi, Sébastien Marcel

In this study, we apply the information-theoretic Privacy Funnel (PF) model to face recognition and develop a method for privacy-preserving representation learning within an end-to-end trainable framework. Our approach addresses the trade-off between utility and obfuscation of sensitive information under logarithmic loss. We study the integration of informat

F. Kruczkiewicz, F. Dulieu, A. V. Ivlev, P. Caselli

To explain grain growth and destruction in warm media, ice mantle formation and sublimation in cold media, and gas line emission spectroscopy, astrochemical models must mimic the gas--solid abundance ratio. Ice-sublimation mechanisms determine the position of snow lines and the nature of gas emitted by and locked inside planetary bodies in star-forming regio

Wolf-Jürgen Beyn, Thorsten Hüls

In this work we introduce the notion of an angular spectrum for a linear discrete time nonautonomous dynamical system. The angular spectrum comprises all accumulation points of longtime averages formed by maximal principal angles between successive subspaces generated by the dynamical system. The angular spectrum is bounded by angular values which have previ

Shatrughna Kumar, Wesley B. Cardoso, Boris A. Malomed

We introduce a model of a passive optical cavity based on a novel variety of the two-dimensional Lugiato-Lefever equation, with a localized pump carrying intrinsic vorticity S, and the cubic or cubic-quintic nonlinearity. Up to S = 5, stable confined vortex-ring states (vortex pixels) are produced by means of a variational approximation and in a numerical fo

Jakob L. Andersen, Akbar Davoodi, Rolf Fagerberg, Christoph Flamm

The explosion of data available in life sciences is fueling an increasing demand for expressive models and computational methods. Graph transformation is a model for dynamic systems with a large variety of applications. We introduce a novel method of the graph transformation model construction, combining generative and dynamical viewpoints to give a fully au

Juho Pelto, Kari Auranen, Janne Kujala, Leo Lahti

Differential abundance analysis is a key component of microbiome studies. Although dozens of methods exist there is currently no consensus on the preferred methods. While the correctness of results in differential abundance analysis is an ambiguous concept and cannot be fully evaluated without setting the ground truth and employing simulated data, we argue t

Yichuan Deng, Zhao Song, Jing Xiong, Chiwun Yang

Sparse Attention is a technique that approximates standard attention computation with sub-quadratic complexity. This is achieved by selectively ignoring smaller entries in the attention matrix during the softmax function computation. Variations of this technique, such as pruning KV cache, sparsity-based fast attention, and Sparse Transformer, have been exten

Niels Van Santen, Jan Ryckebusch, Luis E. C. Rocha

Public opinion is subject to peer interaction via social networks and external pressure from the media, advertising, and other actors. In this paper, we study the interaction between external and peer influence on the stochastic opinion dynamics of a majority vote model. We introduce a model where agents update their opinions based on the combined influence

Jules Hedges, Riu Rodríguez Sakamoto

We show that several major algorithms of reinforcement learning (RL) fit into the framework of categorical cybernetics, that is to say, parametrised bidirectional processes. We build on our previous work in which we show that value iteration can be represented by precomposition with a certain optic. The outline of the main construction in this paper is: (1)

Jiali Zheng, Rolandos Alexandros Potamias, Stefanos Zafeiriou

In recent years, there has been a significant shift in the field of digital avatar research, towards modeling, animating and reconstructing clothed human representations, as a key step towards creating realistic avatars. However, current 3D cloth generation methods are garment specific or trained completely on synthetic data, hence lacking fine details and r

Hongfei Wang, Binghui Liu, Long Feng

In this paper, we address the problem of testing independence between two high-dimensional random vectors. Our approach involves a series of max-sum tests based on three well-known classes of rank-based correlations. These correlation classes encompass several popular rank measures, including Spearman's $\rho$, Kendall's $\tau$, Hoeffding's D, Blum-Kiefer-Ro

Sehyun Choi

Recently, multiple architectures has been proposed to improve the efficiency of the Transformer Language Models through changing the design of the self-attention block to have a linear-cost inference (LCI). A notable approach in this realm is the State-Space Machines (SSMs) architecture, which showed on-par performance on language modeling tasks with the sel

Frank Wolter, Michael Zakharyaschev

The interpolant existence problem (IEP) for a logic L is to decide, given formulas P and Q, whether there exists a formula I, built from the shared symbols of P and Q, such that P entails I and I entails Q in L. If L enjoys the Craig interpolation property (CIP), then the IEP reduces to validity in L. Recently, the IEP has been studied for logics without the

Arianna Muti, Federico Ruggeri, Cagri Toraman, Lorenzo Musetti

Misogyny is often expressed through figurative language. Some neutral words can assume a negative connotation when functioning as pejorative epithets. Disambiguating the meaning of such terms might help the detection of misogyny. In order to address such task, we present PejorativITy, a novel corpus of 1,200 manually annotated Italian tweets for pejorative l

Son Ho, Aymeric Fromherz, Jonathan Protzenko

The Rust programming language continues to rise in popularity, and as such, warrants the close attention of the programming languages community. In this work, we present a new foundational contribution towards the theoretical understanding of Rust's semantics. We prove that LLBC, a high-level, borrow-centric model previously proposed for Rust's semantics and

Celia Rubio-Madrigal, Jules Hedges

String diagrams are a graphical language used to represent processes that can be composed sequentially or in parallel, which correspond graphically to horizontal or vertical juxtaposition. In this paper we demonstrate how to compute the layout of a string diagram by folding over its algebraic representation in terms of sequential and parallel composition ope

Hailong Jin, Huiying Li

Semantic correspondence remains a challenging task for establishing correspondences between a pair of images with the same category or similar scenes due to the large intra-class appearance. In this paper, we introduce a novel problem called 'Small Object Semantic Correspondence (SOSC).' This problem is challenging due to the close proximity of keypoints ass

Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion, Sarina Meyer

The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states. The organizers provide development and evaluation datasets and evaluation scripts, as well as baseline anonymization systems and a list of training resources formed on the b

Lois Smith, H. Ali Karimi-Varzaneh, Sebastian Finger, Giuliana Giunta

Polymer composite materials require softening to reduce their glass transition temperature and improve processability. To this end, plasticizers, which are small organic molecules, are added to the polymer matrix. The miscibility of these plasticizers has a large impact on their effectiveness and therefore their interactions with the polymer matrix must be c

Noam Kolt, Markus Anderljung, Joslyn Barnhart, Asher Brass

Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new

Priyanka Sharma, Aviral K. Pandey, Gaurav Shukla, Devendra Kumar Mishra

A coherent seeded SU(1,1) interferometer provides a prominent technique in the field of precision measurement. We theoretically study the phase sensitivity of SU(1,1) interferometer with Kerr state seeding under single intensity and homodyne detection schemes. To find the lower bound in this case we calculate the quantum Cram\'er-Rao bound using the quantum

Giovanni Viglietta

In the theoretical study of distributed communication networks, "history trees" are a discrete structure that naturally models the concept that anonymous agents become distinguishable upon receiving different sets of messages from neighboring agents. By conveniently organizing temporal information in a systematic manner, history trees have been instrumental

Martin Raum

While examples of Ramanujan-type congruences are amply available via their relation to Hecke operators, it remains unclear which of them should be considered of combinatorial origin and which of them are mere artifacts of the connection with modular forms. Ranks and generalized ranks have been proposed as a tool to discern this question. We formalize this id

Matteo Mogliani, Anna Simoni

We propose a Machine Learning approach for optimal macroeconomic density forecasting in a high-dimensional setting where the underlying model exhibits a known group structure. Our approach is general enough to encompass specific forecasting models featuring either many covariates, or unknown nonlinearities, or series sampled at different frequencies. By rely

Nicolas Gilliers

We study $\mathcal{O}$-operators and post-Lie products over the same Lie algebra compatible in a certain sense. We prove that the group product corresponding to the formal integration of the Lie algebra, which is adjacent to the sum of two compatible post-Lie products, can be factorized in a way reminiscent of the classical Semenov-Tian-Shanskii factorizatio

Germain Poullot

In this paper, we compute a triangulation of certain faces of the submodular cone. More precisely, graphical zonotopes are generalized permutahedra, and hence their deformation cones are faces of the submodular cone. We give a triangulation of these faces for graphs without induced complete sub-graph on 4 vertices. We deduce the rays of these faces: Minkowsk

Sijie Zhao, Hao Chen, Xueliang Zhang, Pengfeng Xiao

Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in effectively modeling context. While transformer-based models possess global modeling capabilities, they encounter computational challenges when applied to large VHR images due to thei

Mayank Sharma, A. Bhattacharyay

Biological environments at micrometer scales and below are often crowded, and experience incessant stochastic thermal fluctuations. The presence of membranes/pores, and multiple biological entities in a constricted space can make the damping/diffusion inhomogeneous. This effect of inhomogeneity is presented by the diffusion becoming coordinate-dependent. In

Marco Buratti, Donald L. Kreher, Douglas R. Stinson

In a nesting of a balanced incomplete block design (or BIBD), we wish to add a point (the \emph{nested point}) to every block of a $(v,k,\lambda)$-BIBD in such a way that we end up with a partial $(v,k+1,\lambda+1)$-BIBD. In the case where the partial $(v,k+1,\lambda+1)$-BIBD is in fact a $(v,k+1,\lambda+1)$-BIBD, we have a \emph{perfect nesting}. We show th

Isiaka Aremua, Laure Gouba

In this paper, we study the exotic Landau problem at the classical level where two conserved quantities are derived. At the quantum level, the corresponding quantum operators of the conserved quantities provide two oscillator representations from which we derive two Boson Fock spaces. Using the normalized coherent states which are the minimum uncertainty sta

E. C. Cortés García, I. Agapov, W. Hillert

In this contribution we present the design of a resonant slow extraction based on the radio frequency knock-out (RF-KO) scheme, where we make use of the transverse resonance islands bucket (TRIB) optics. The generation of the TRIB optics is presented in two example lattices, that are considered for the potential upgrade of the current booster at DESY. The sl

Jorge Pedraza Arpasi

This article proposes a new approach in the treatment of the Hilbert transform and some cases of the Fourier transform whose improper integrals are principal values. This approach may be useful for teaching these issues to undergraduate engineering students. Traditional literature of Complex Analysis deals with these transformation integrals with the Cauchy-

Da Li, Peian Li, Jiabiao Zhao, Jianjian Liang

Unmanned Aerial Vehicle (UAV) assisted terahertz (THz) wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analys

Brian Allen

Although scalar curvature is the simplest curvature invariant, our understanding of scalar curvature has not matured to the same level as Ricci or sectional curvature. Despite this fact, many rigidity phenomenon have been established which give some of the strongest insights into scalar curvature. Important examples include Geroch's conjecture, the positive

Jiayuan Cui, Da Li, Jiabiao Zhao, Jiacheng Liu

The dielectric properties of environmental surfaces, including walls, floors and the ground, etc., play a crucial role in shaping the accuracy of terahertz (THz) channel modeling, thereby directly impacting the effectiveness of communication systems. Traditionally, acquiring these properties has relied on methods such as terahertz time-domain spectroscopy (T

Nandish Chattopadhyay, Atreya Goswami, Anupam Chattopadhyay

Adversarial attacks on machine learning algorithms have been a key deterrent to the adoption of AI in many real-world use cases. They significantly undermine the ability of high-performance neural networks by forcing misclassifications. These attacks introduce minute and structured perturbations or alterations in the test samples, imperceptible to human anno

Eduardo Neto, Fabio A. Faria, Amanda A. S. de Oliveira, Álvaro L. Fazenda

The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem. Unfortunately, deforestation and degradation impact millions of hectares annually, necessitating government or private initiatives for effective forest monitoring. This study introduces a novel framework that emplo

A. G. de Oliveira, E. Diamond-Hitchcock, D. M. Walker, M. T. Wells-Pestell

Neutral atom arrays have emerged as a versatile platform towards scalable quantum computation and optimization. In this paper we present demonstrations of solving maximum weighted independent set problems on a Rydberg atom array using annealing with local light-shifts. We verify the ability to prepare weighted graphs in 1D and 2D arrays, including embedding

Taiqiang Wu, Chaofan Tao, Jiahao Wang, Runming Yang

Kullback-Leiber divergence has been widely used in Knowledge Distillation (KD) to compress Large Language Models (LLMs). Contrary to prior assertions that reverse Kullback-Leibler (RKL) divergence is mode-seeking and thus preferable over the mean-seeking forward Kullback-Leibler (FKL) divergence, this study empirically and theoretically demonstrates that nei

Keqiang Fan, Xiaohao Cai, Mahesan Niranjan

Unlike typical visual scene recognition domains, in which massive datasets are accessible to deep neural networks, medical image interpretations are often obstructed by the paucity of data. In this paper, we investigate the effectiveness of data-based few-shot learning in medical imaging by exploring different data attribute representations in a low-dimensio

Florian Geissler, Karsten Roscher, Mario Trapp

Generative AI is increasingly important in software engineering, including safety engineering, where its use ensures that software does not cause harm to people. This also leads to high quality requirements for generative AI. Therefore, the simplistic use of Large Language Models (LLMs) alone will not meet these quality demands. It is crucial to develop more

Mozhi Zhang, Mianqiu Huang, Rundong Shi, Linsen Guo

Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. In this paper, we decompose the language model confidence into the \textit{

Renzo Cavalieri, Steffen Marcus, Jonathan Wise

We study the tropical version of the contraction morphism $\mathcal{T}$ between moduli spaces of stable and pseudostable curves. By promoting $\mathcal{T}$ to a logarithmic morphism, we obtain a piecewise linear function between the generalized cone complexes parameterizing tropical stable and pseudostable curves. The ray corresponding to the contracted divi

Dong Li, Hanyang Dong, Wei Chen, Yang Su

The Neupert effect refers to the strong correlation between the soft X-ray (SXR) light curve and the time-integrated hard X-rays (HXR) or microwave flux, which is frequently observed in solar flares. In this article, we therefore utilized the newly launched Hard X-ray Imager (HXI) on board the Advanced Space-based Solar Observatory to investigate the Neupert

Evgueni Doubtsov

We prove analogs of Peyri\`ere's mutual singularity theorem for standard and generalized Riesz products on the unit sphere of $\mathbb{C}^n$, $n\ge 2$. As a corollary, we obtain an analog of Zygmund's dichotomy for the Riesz products under consideration.

Mengke Zhao, Jianjun Zhou, Willem A. Baan, Yue Hu

We report the detection of the ordered alignment between the magnetic field and kpc-scale bubbles in the nearby spiral galaxy, NGC\,628. Applying the Velocity Gradient Technique (VGT) on CO spectroscopic data from the ALMA-PHANGS, the magnetic field of NGC\,628 is measured at the scale of 191\,pc ($\sim$ 4\,$''$). The large-scale magnetic field is oriented p

Anna Kruspe

Over the last year, Large Language Models (LLMs) like ChatGPT have become widely available and have exhibited fairness issues similar to those in previous machine learning systems. Current research is primarily focused on analyzing and quantifying these biases in training data and their impact on the decisions of these models, alongside developing mitigation

Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek

We investigate the role of uncertainty in decision-making problems with natural language as input. For such tasks, using Large Language Models as agents has become the norm. However, none of the recent approaches employ any additional phase for estimating the uncertainty the agent has about the world during the decision-making task. We focus on a fundamental

Khalid Albagami, Nguyen Van Huynh, Geoffrey Ye Li

Recently, deep learning (DL) has been emerging as a promising approach for channel estimation and signal detection in wireless communications. The majority of the existing studies investigating the use of DL techniques in this domain focus on analysing channel impulse responses that are generated from only one channel distribution such as additive white Gaus

Qing-Hua Zhu

Recent evidence for stochastic gravitational waves reported by pulsar timing array (PTA) collaborations might open a new window for studying cosmology and astrophysical phenomena. In addition to signals from gravitational waves, there is motivation to explore residual signals from oscillating dark matter, which might partially comprise the galactic halo. We

Sven Ochs, Jens Doll, Daniel Grimm, Tobias Fleck

Most automated driving functions are designed for a specific task or vehicle. Most often, the underlying architecture is fixed to specific algorithms to increase performance. Therefore, it is not possible to deploy new modules and algorithms easily. In this paper, we present our automated driving stack which combines both scalability and adaptability. Due to

Sven Ochs, Jens Doll, Marc Heinrich, Philip Schörner

Motion planning is an essential part of autonomous mobile platforms. A good pipeline should be modular enough to handle different vehicles, environments, and perception modules. The planning process has to cope with all the different modalities and has to have a modular and flexible design. But most importantly, it has to be safe and robust. In this paper, w

Vaidotas Characiejus, Piotr Kokoszka, Xiangdong Meng

For a broad class of nonlinear time series known as Bernoulli shifts, we establish the asymptotic normality of the smoothed periodogram estimator of the long-run variance. This estimator uses only a narrow band of Fourier frequencies around the origin and so has been extensively used in local Whittle estimation. Existing asymptotic normality results apply on

Fabiano L. Ribeiro, Vinicius M. Netto

Understanding how size influences the internal characteristics of a system is a crucial concern across various fields. Concepts like scale invariance, universalities, and fractals are fundamental to this inquiry and find application in biology, physics, and particularly urbanism. Size profoundly impacts how cities develop and function economically and social

Andreas Bartel, Manuel Schaller

Port-Hamiltonian systems provide an energy-based modeling paradigm for dynamical input-state-output systems. At their core, they fulfill an energy balance relating stored, dissipated and supplied energy. To accurately resolve this energy balance in time discretizations, we propose an adaptive grid refinement technique based on a posteriori error estimation.

A. I. Breev, S. P. Gavrilov, D. M. Gitman

We present a new exactly solvable case in strong-field QED with one-dimensional step potential (x-step). The corresponding x-step is given by an analytic asymmetric with respect to the axis x reflection function. The step can be considered as a certain analytic "deformation" of the symmetric Sauter field. Moreover, it can be treated as a new regularization o

Kharanshu N. Solanki, Karim Mosani, Omkar Deshpande, Pankaj S. Joshi

A spacetime singularity, identified by the existence of incomplete causal geodesics in the spacetime, is called a (Tipler) strong curvature singularity if the volume form acting on independent Jacobi fields along causal geodesics vanishes in the approach of the singularity. It is called naked if at least one of these causal geodesics is past incomplete. Here

Jakub Rydzewski

Constructing reduced representations of high-dimensional systems is a fundamental problem in physical chemistry. Many unsupervised machine learning methods can automatically find such low-dimensional representations. However, an often overlooked problem is what high-dimensional representation should be used to describe systems before dimensionality reduction

Junyan Ye, Qiyan Luo, Jinhua Yu, Huaping Zhong

This paper aims at achieving fine-grained building attribute segmentation in a cross-view scenario, i.e., using satellite and street-view image pairs. The main challenge lies in overcoming the significant perspective differences between street views and satellite views. In this work, we introduce SG-BEV, a novel approach for satellite-guided BEV fusion for c

Patrick Levi, Christoph P. Neumann

The fast advancements in Large Language Models (LLMs) are driving an increasing number of applications. Together with the growing number of users, we also see an increasing number of attackers who try to outsmart these systems. They want the model to reveal confidential information, specific false information, or offensive behavior. To this end, they manipul

Felix Riehn, Anatoli Fedynitch, Ralph Engel

In the last decade, an increasing number of datasets have revealed a consistent discrepancy between the number of muons measured in ultra-high-energy extensive air showers (EAS) and the numbers predicted by simulations. This gap persists despite incorporating Large Hadron Collider (LHC) data into the tuning of current hadronic interaction models, leading to

Simeon vom Dahl, Christian Kanzow

In this paper, we introduce an inexact regularized proximal Newton method (IRPNM) that does not require any line search. The method is designed to minimize the sum of a twice continuously differentiable function $f$ and a convex (possibly non-smooth and extended-valued) function $\varphi$. Instead of controlling a step size by a line search procedure, we upd

SeungJeh Chung, JooHyun Park, HyeongYeop Kang

3D stylization, the application of specific styles to three-dimensional objects, offers substantial commercial potential by enabling the creation of uniquely styled 3D objects tailored to diverse scenes. Recent advancements in artificial intelligence and text-driven manipulation methods have made the stylization process increasingly intuitive and automated.

Taiki Matsushita, Takeshi Mizushima, Yusuke Masaki, Satoshi Fujimoto

Superconducting spintronics explores the interplay between superconductivity and magnetism, sparking significant interest in nonunitary superconductors as a platform for novel magneto-superconducting phenomena. However, identifying nonunitary superconductors remains challenging. We demonstrate that spin current driven by thermal gradients sensitively probes

Peter beim Graben, Thomas Noll

We adopt some basic ideas on quantum-theoretical modeling of tonal attraction and develop them further in an alternative direction. Fitting Gaussian Mixture Models (GMM) to the Krumhansl-Kessler (KK) probe tone profiles for static attraction opens the possibility to investigate the underlying wave function as the stationary ground state of an anharmonic quan

Tuo Wu, Cunhua Pan, Kangda Zhi, Junteng Yao

This paper introduces a novel mobile tracking framework leveraging the high-dimensional signal received from extremely large-scale (XL) reconfigurable intelligent surfaces (RIS). This received signal, named XL-RIS information, has a much larger data dimension and therefore offers a richer feature set compared to the traditional base station (BS) received sig

Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah

Seismic data often contain gaps due to various obstacles in the investigated area and recording instrument failures. Deep learning techniques offer promising solutions for reconstructing missing data parts by leveraging existing information. However, self-supervised methods frequently struggle with capturing under-represented features such as weaker events,

Yuki Yasuda, Ryo Onishi

A two-stage super-resolution simulation method is proposed for street-scale air temperature and wind velocity, which considerably reduces computation time while maintaining accuracy. The first stage employs a convolutional neural network (CNN) to correct large-scale flows above buildings in the input low-resolution simulation results. The second stage uses a

Ana Lucía Báez-Camargo, Daniel Hartley, Christian Käding, Ivette Fuentes-Guridi

Understanding the nature of dark energy and dark matter is one of modern physics' greatest open problems. Scalar-tensor theories with screened scalar fields like the chameleon model are among the most popular proposed solutions. In this article, we present the first analysis of the impact of a chameleon field on the dynamical Casimir effect, whose main featu

Vasilis Gkolemis, Christos Diou, Dimitris Kyriakopoulos, Konstantinos Tsopelas

Effector is a Python package for interpreting machine learning (ML) models that are trained on tabular data through global and regional feature effects. Global effects, like Partial Dependence Plot (PDP) and Accumulated Local Effects (ALE), are widely used for explaining tabular ML models due to their simplicity -- each feature's average influence on the pre

Yunuo Xiong

Path integral Monte Carlo (PIMC) and path integral molecular dynamics (PIMD) provide the golden standard for the ab initio simulations of identical particles. In this work, we achieved significant GPU acceleration based on PIMD, which is equivalent to PIMC in the ab initio simulations, and developed an open-source PIMD code repository that does not rely on a

Naoki Ogino, Makoto Arimoto, Hamid Hamidani, Takanori Sakamoto

The detection of the short gamma-ray burst (SGRB) 050709 by the HETE-2 satellite opened a new window into understanding the nature of SGRBs, offering clues about their emission mechanism and progenitors, with the crucial aid of optical follow-up observations. Here, we revisit the prompt emission of GRB 050709. Our analysis reveals an initial hard spike ~200

Marthe Bideault, Jérôme Creuze, Ryoji Asahi, Erich Wimmer

We present the development and applications of a quadratic Spectral Neighbor Analysis Potential (q-SNAP) for ferromagnetic cobalt. Trained on Density Functional Theory calculations using the Perdew-Burke-Ernzerhof (DFT-PBE) functional, this machine-learned potential enables simulations of large systems over extended time scales across a wide range of tempera

Mokanarangan Thayaparan, Marco Valentino, André Freitas

Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However, traditional ILP frameworks are non-differentiable, posing critical challenges for the integration of continuous language representations based on deep learning. In this paper, we introduce a

Ikuo Nakamura

Skeleton-based gesture recognition methods have achieved high success using Graph Convolutional Network (GCN). In addition, context-dependent adaptive topology as a neighborhood vertex information and attention mechanism leverages a model to better represent actions. In this paper, we propose self-attention GCN hybrid model, Multi-Scale Spatial-Temporal self

Sebastian Munoz

We study the intermediate asymptotic behavior of solutions to the first-order mean field games system with a local coupling, when the initial density is a compactly supported function on the real line, and the coupling is of power type. Addressing a question that was left open in arXiv:2308.00314, we prove that the solutions converge to the self-similar prof

Julia Rozanova, Marco Valentino, André Freitas

Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems. However, this is such a desirable form of analysis from both an interpretability and model evaluation perspective, that it is valuable to investigate specific patterns of reasoning with enough structure

Andrei Buciulea, Jiaxi Ying, Antonio G. Marques, Daniel P. Palomar

This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on the graph, enabling the development of a graph-learning formulation that combines the strengths of graphical lasso with a more encompassing model. Specifically,

Florian Herold, Christoph Kuzmics

In a finite two player game consider the matrix of one player's payoff difference between any two consecutive pure strategies. Define the half space induced by a column vector of this matrix as the set of vectors that form an obtuse angle with this column vector. We use Farkas' lemma to show that this player can be made indifferent between all pure strategie

Katrin Erk, Marianna Apidianaki

Embedding spaces contain interpretable dimensions indicating gender, formality in style, or even object properties. This has been observed multiple times. Such interpretable dimensions are becoming valuable tools in different areas of study, from social science to neuroscience. The standard way to compute these dimensions uses contrasting seed words and comp

Matteo Pennisi, Giovanni Bellitto, Simone Palazzo, Mubarak Shah

We present DiffExplainer, a novel framework that, leveraging language-vision models, enables multimodal global explainability. DiffExplainer employs diffusion models conditioned on optimized text prompts, synthesizing images that maximize class outputs and hidden features of a classifier, thus providing a visual tool for explaining decisions. Moreover, the a

Bingnan Ni, Huanyu Wang, Dongfeng Bai, Minghe Weng

Neural Radiance Fields (NeRF) give rise to learning-based 3D reconstruction methods widely used in industrial applications. Although prevalent methods achieve considerable improvements in small-scale scenes, accomplishing reconstruction in complex and large-scale scenes is still challenging. First, the background in complex scenes shows a large variance amon

Yizhu Liu, Ran Tao, Shengyu Guo, Yifan Yang

Topic relevance between query and document is a very important part of social search, which can evaluate the degree of matching between document and user's requirement. In most social search scenarios such as Dianping, modeling search relevance always faces two challenges. One is that many documents in social search are very long and have much redundant info

Anton A. Nazarenko, A. V. Nazarenko

Guided by physical needs, we deal with the rotationally isotropic Poincar\'e ball, when considering the complement of Borromean rings embedded in it. We consistently describe the geometry of the complement and realize the fundamental group as isometry subgroup in three dimensions. Applying this realization, we reveal normal stochastization and multifractal b

Yunjie Chen, Jelmer M. Wolterink, Olaf M. Neve, Stephan R. Romeijn

Vestibular schwannomas (VS) are benign tumors that are generally managed by active surveillance with MRI examination. To further assist clinical decision-making and avoid overtreatment, an accurate prediction of tumor growth based on longitudinal imaging is highly desirable. In this paper, we introduce DeepGrowth, a deep learning method that incorporates neu

Ruobing Jiang, Chuqiao Jiang, Alim Ruzi, Tianyi Yang

Multi-boson productions can be exploited as novel probes either for standard model precision tests or new physics searches, and have become one of those popular topics in the ongoing LHC experiments, and in future collider studies, including those for electron-positron and muon-muon colliders. Here we focus on two examples, i.e., ZZZ direct productions throu

Anastasia Cosma

The overconsumption of consumers under today's increasingly scarce natural resources has overwhelmed the textile industry in middle-income countries, such as Romania. It is becoming more and more essential to encourage sustainable clothing consumption behaviors, such as purchasing recyclable clothes. Notwithstanding there is a limited number of studies tryin

Iván Sevillano-García, Julián Luengo, Francisco Herrera

As artificial intelligence systems become integral across domains, the demand for explainability grows, the called eXplainable artificial intelligence (XAI). Existing efforts primarily focus on generating and evaluating explanations for black-box models while a critical gap in directly enhancing models remains through these evaluations. It is important to co

HaoJie Huang

The prime number problem falls within the realm of number theory, specifically elementary number theory. Current research approaches have unnecessarily complicated this matter. In contrast to more advanced mathematical tools, the methods of elementary number theory can effectively address the twin prime problem. The primary contribution of this article lies

Matteo Ferrari

Recently, Steinbach et al. introduced a novel operator $\mathcal{H}_T: L^2(0,T) \to L^2(0,T)$, known as the modified Hilbert transform. This operator has shown its significance in space-time formulations related to the heat and wave equations. In this paper, we establish a direct connection between the modified Hilbert transform $\mathcal{H}_T$ and the canon

Jeferson Gonzalez-Gomez, Hassan Nassar, Lars Bauer, Jorg Henkel

With the continuous evolution of computational devices, more and more applications are being executed remotely. The applications operate on a wide spectrum of devices, ranging from IoT nodes with low computational capabilities to large cloud providers with high capabilities. Remote execution often deals with sensitive data or executes proprietary software. H

G. Angloher, S. Banik, G. Benato, A. Bento

In recent times, the sensitivity of low-mass direct dark matter searches has been limited by unknown low energy backgrounds close to the energy threshold of the experiments known as the low energy excess (LEE). The CRESST experiment utilises advanced cryogenic detectors constructed with different types of crystals equipped with Transition Edge Sensors (TESs)