November 2024 arXiv papers — page 134

Zhe Li, Feng Xue, Xin-Yi Tang, Xiyu Hong

Optimizing the magnetic Zeeman-splitting term, specifically the magnetic gap of the topological surface states (TSSs), is a crucial issue and central challenge in advancing higher-temperature quantum anomalous Hall (QAH) states. In this work, we demonstrate a counterintuitive, nonmonotonic relationship between the magnetic gap and the hybridization strength

Bruno Viti, Franz Thaler, Kathrin Lisa Kapper, Martin Urschler

Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular diseases. Most recent techniques rely on deep learning and usually require an extensive amount of labeled data. To overcome this problem, few-shot learning has the capability of reducing

Nicholas Pischke

We prove the convergence of the proximal point algorithm for finding the unique minimizer of a strongly quasiconvex function in general nonlinear Hadamard spaces, generalizing a recent result due to F. Lara. Our argument is rather elementary and brief and relies only on a few properties of strongly quasiconvex functions and their proximal operators which are

Patrick Bastian

We consider the problem of detecting deviations from a white noise assumption in time series. Our approach differs from the numerous methods proposed for this purpose with respect to two aspects. First, we allow for non-stationary time series. Second, we address the problem that a white noise test, for example checking the residuals of a model fit, is usuall

Hao Liang, Zirong Chen, Hejun Dong, Wentao Zhang

Video question-answering (QA) is a core task in video understanding. Evaluating the quality of video QA and video caption data quality for training video large language models (VideoLLMs) is an essential challenge. Although various methods have been proposed for assessing video caption quality, there remains a lack of dedicated evaluation methods for Video Q

Jorge García-Beni, Iris Paparelle, Valentina Parigi, Gian Luca Giorgi

A new approach suitable for distributed quantum machine learning and exhibiting memory is proposed for a photonic platform. This measurement-based quantum reservoir computing takes advantage of continuous variable cluster states as the main quantum resource. Cluster states are key to several photonic quantum technologies, enabling universal quantum computing

Saurabh K. Shukla

This article revisits the validity of tree-level statements regarding the Yukawa sector of various minimal-renormalisable \(SU(5)\) frameworks at the loop level. It is well-known that an \(SU(5)\) model with only the \(45_{\mathrm{H}}\) dimensional irreducible representation~(irrep) contributing to the Yukawa sector is highly incompatible in yielding the low

Yiyuan Pan, Zhaojian Wang

Modern discrete manufacturing requires real-time energy and production co-scheduling to reduce business costs. In discrete manufacturing, production lines and equipment are complex and numerous, which introduces significant uncertainty during the production process. Among these uncertainties, decision-dependent uncertainties (DDUs) pose additional challenges

Dirk Nowotka, Max Wiedenhöft

Patterns are words with terminals and variables. The language of a pattern is the set of words obtained by uniformly substituting all variables with words that contain only terminals. Length constraints restrict valid substitutions of variables by associating the variables of a pattern with a system (or disjunction of systems) of linear diophantine inequalit

Romanos Diogenes Malikiosis, Francisco Santos, Matthias Schymura

Tao (2018) showed that in order to prove the Lonely Runner Conjecture (LRC) up to $n+1$ runners it suffices to consider positive integer velocities in the order of $n^{O(n^2)}$. Using the zonotopal reinterpretation of the conjecture due to the first and third authors (2017) we here drastically improve this result, showing that velocities up to $\binom{n+1}{2

Di Luo, Ning Jiang, Xin Liu

Periodic variability in active galactic nuclei (AGNs) is a promising method for studying sub-parsec supermassive black hole binaries (SMBHBs), which are a challenging detection target. While extensive searches have been made in the optical, X-ray and gamma-ray bands, systematic infrared (IR) studies remain limited. Using data from the Wide-field Infrared Sur

Taisei Takabayashi, Takeru Goto, Masayuki Ohzeki

Quantum annealing is a generic solver for combinatorial optimization problems that utilizes quantum fluctuations. Recently, there has been extensive research applying quantum annealers, which are hardware implementations of quantum annealing. Since quantum annealers can only handle quadratic unconstrained binary optimization problems, to solve constrained co

S. Prabhu, A. K. Arulmozhi, M. Arulperumjothi

Networks are designed to communicate, operate and allocate the tasks to the respective commodities. Operating the supercomputers became challenging, and it was handled by the network design commonly known as hypercube, denoted by $Q^n$. In a recent study, the hypercube networks were not enough to hold the parallel processors in the supercomputers. Thus, vari

Mianqiu Huang, Xiaoran Liu, Shaojun Zhou, Mozhi Zhang

Recent advancements in model architectures and length extrapolation techniques have significantly extended the context length of large language models (LLMs), paving the way for their application in increasingly complex tasks. However, despite the growing capabilities of long-context LLMs, the safety issues in long-context scenarios remain underexplored. Whi

Yutaro Tanaka, Daichi Nakamura, Ryo Okugawa, Kohei Kawabata

Point-gap topological phases of non-Hermitian systems exhibit exotic boundary states that have no counterparts in Hermitian systems. Here, we develop classification of second-order point-gap topological phases protected by reflection symmetry. Based on this classification, we propose exceptional second-order topological insulators, exhibiting second-order bo

Bojan Novakovic, Marco Fenucci

Asteroid Didymos, recently targeted by the NASA DART mission, is also planned to be visited by the ESA Hera mission. The main goal of the DART mission was to impact Dimorphos, the small satellite of Didymos, which was accomplished in September 2022. This collision altered the Didymos-Dimorphos system, generating a notable quantity of ejecta that turned Dimor

Hemal Naik, Junran Yang, Dipin Das, Margaret C Crofoot

Understanding animal behaviour is central to predicting, understanding, and mitigating impacts of natural and anthropogenic changes on animal populations and ecosystems. However, the challenges of acquiring and processing long-term, ecologically relevant data in wild settings have constrained the scope of behavioural research. The increasing availability of

Ao Liu, Jing Chen, Kun He, Ruiying Du

Blockchain sharding has emerged as a promising solution to the scalability challenges in traditional blockchain systems by partitioning the network into smaller, manageable subsets called shards. Despite its potential, existing sharding solutions face significant limitations in handling dynamic workloads, ensuring secure cross-shard transactions, and maintai

Yury Panov

The features of the response of frustrated states to the external field are considered on the example of a diluted Ising chain. In the ferromagnetic case, partial ordering occurs, which leads to a decrease in entropy. In the antiferromagnetic case, the switching on of the field leads to the appearance of a long-range order in the system, although the state r

Yawen Xiang, Heng Zhou, Chengyang Li, Zhongbo Li

Image deblurring is an essential image preprocessing technique, aiming to recover clear and detailed images form blurry ones. However, existing algorithms often fail to effectively integrate multi-scale feature extraction with frequency enhancement, limiting their ability to reconstruct fine textures. Additionally, non-uniform blur in images also restricts t

Esa Räsänen, Niko Gullsten, Otto Pulkkinen, Tuomas Virtanen

The Rosanna shuffle, the drum pattern from Toto's 1982 hit "Rosanna", is one of the most recognized drum beats in popular music. Recorded by Jeff Porcaro, this drum beat features a half-time shuffle with rapid triplets on the hi-hat and snare drum. In this analysis, we examine the timing and dynamics of the original drum track, focusing on rhythmic variation

Fan Yang, Wataru Idehara, Kenya Tanaka, Keisuke Shinokita

Simple mechanical exfoliation of layered materials is the most frequently employed method for producing high-quality monolayers of two-dimensional semiconducting materials. However, the mechanical exfoliation by human hands is a microscopically sophisticated process with a large number of microscopic parameters, which requires significant operator efforts an

Anson Lei, Bernhard Schölkopf, Ingmar Posner

Capturing the interactions between entities in a structured way plays a central role in world models that flexibly adapt to changes in the environment. Recent works motivate the benefits of models that explicitly represent the structure of interactions and formulate the problem as discovering local causal structures. In this work, we demonstrate that reliabl

Benjamin Weder, Johanna Barzen, Martin Beisel, Fabian Bühler

Quantum computers are available via a variety of different quantum cloud offerings. These offerings are heterogeneous and differ in features, such as pricing models or types of access to quantum computers. Furthermore, quantum circuits can be implemented using different quantum programming languages, which are typically only supported by a small subset of qu

Shrajal Bajpai, Lakshmi Kanta Patra

A doubly type-II censored scheme is an important sampling scheme in the life testing experiment and reliability engineering. In the present commutation, we have considered estimating ordered scale parameters of two exponential distributions based on doubly type-II censored samples with respect to a general scale invariant loss function. We have obtained seve

Hamed Taghavian, Jens Sjölund

Transforming an asymmetric system into a symmetric system makes it possible to exploit the simplifying properties of symmetry in control problems. We define and characterize the family of symmetrizable systems, which can be transformed into symmetric systems by a linear transformation of their inputs and outputs. In the special case of complete symmetry, the

Emilio A. Lauret, Juan Sebastián Rodríguez

The action of the subgroup $\operatorname{G}_2$ of $\operatorname{SO}(7)$ (resp.\ $\operatorname{Spin}(7)$ of $\operatorname{SO}(8)$) on the Grassmannian space $M=\frac{\operatorname{SO}(7)}{\operatorname{SO}(5)\times\operatorname{SO}(2)}$ (resp.\ $M=\frac{\operatorname{SO}(8)}{\operatorname{SO}(5)\times\operatorname{SO}(3)}$) is still transitive. We prove t

Heedong Do, Namyoon Lee, Angel Lozano

An estimation method is presented for polynomial phase signals, i.e., those adopting the form of a complex exponential whose phase is polynomial in its indices. Transcending the scope of existing techniques, the proposed estimator can handle an arbitrary number of dimensions and an arbitrary set of polynomial degrees along each dimension; the only requiremen

Frank Backs, S. A. Brands, A. de Koter, L. Kaper

Mass loss through a stellar wind is an important physical process that steers the evolution of massive stars and controls the properties of their end-of-life products, such as the supernova type and the mass of compact remnants. For an accurate mass loss determination, the inhomogeneities in the wind, known as clumping, needs to be taking into account. We ai

Shenyu Liu

In this work, we propose a novel discrete-time distributed algorithm for finding least-squares solutions of linear algebraic equations with a scheduling protocol to further enhance its scalability. Each agent in the network is assumed to know some rows of the coefficient matrix and the corresponding entries in the observation vector. Unlike typical distribut

Thomas Rivinius, Robert Klement

Classical Be stars, the "e" standing for the presence of spectroscopic line emission, are main sequence stars of spectral type B that are able to form a gaseous disk in Keplerian motion from star-ejected matter. The main driver of this capability is the rapid surface rotation, which might be acquired via binary interaction or through internal stellar evoluti

Zhongxuan Han, Li Zhang, Chaochao Chen, Xiaolin Zheng

Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training data in FL is inherently geographically distributed among diverse user groups. Existing research on fairness predominantly assumes access to the entire training data, making direct t

Guilherme Ramos, Diogo Poças, Sérgio Pequito

In multi-agent systems, strong connectivity of the communication network is often crucial for establishing consensus protocols, which underpin numerous applications in decision-making and distributed optimization. However, this connectivity requirement may not be inherently satisfied in geographically distributed settings. Consequently, we need to find the m

Jai G Singla

Accurate classification of buildings into residential and non-residential categories is crucial for urban planning, infrastructure development, population estimation and resource allocation. It is a complex job to carry out automatic classification of residential and nonresidential buildings manually using satellite data. In this paper, we are proposing a no

Sheng Tian, Xintan Zeng, Yifei Hu, Baokun Wang

Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological relationships among users, thereby offering enhanced interpretability in comparison to black-box models commonly utilized for classification and recognition tasks. For instance, wi

Rikiya Takehi, Ellen M. Voorhees, Tetsuya Sakai, Ian Soboroff

Test collections are information-retrieval tools that allow researchers to quickly and easily evaluate ranking algorithms. While test collections have become an integral part of IR research, the process of data creation involves significant manual-annotation effort, which often makes it very expensive and time-consuming. Consequently, test collections can be

Luca Ferrari, Francesco Verciani

Naples parking functions were introduced as a generalization of classical parking functions, in which cars are allowed to park backwards, by checking up to a fixed number of previous slots, before proceedings forward as usual. In our previous work (arXiv:2405.07522, 2024) we have provided a characterization of Naples parking functions in terms of the new not

Marco Ceglie, Nicola Menga, Giuseppe Carbone

Peeling is one of the most common detachment mechanisms adopted in industrial applications. However, although several experimental investigations have proven the possible occurrence of relative sliding at the interface close to the peeling front, a comprehensive model considering the effect of both the tape viscoelasticity and frictional interfacial dissipat

Michal Araszkiewicz

We introduce a novel conceptual Case Frame model that represents the content of cases involving statutory interpretation within civil law frameworks, accompanied by an associated argument scheme enriched with critical questions. By validating our approach with a modest dataset, we demonstrate its robustness and practical applicability. Our model not only pro

Antoine Hanna-Asaad, Decky Aspandi, Titus Zaharia

Video captioning aims to describe video contents using natural language format that involves understanding and interpreting scenes, actions and events that occurs simultaneously on the view. Current approaches have mainly concentrated on visual cues, often neglecting the rich information available from other important modality of audio information, including

Yasuto Watanabe, Eiichi Tajika, Arihiro Kamada

The present Martian climate is characterized by a cold and dry environment with a thin atmosphere of carbon dioxides (CO2). In such conditions, the planetary climate and habitability are determined by the distribution of CO2 between exchangeable reservoirs, that is the atmosphere, ice caps, and regolith. This produces unique responses of the Martian CO2-driv

Konstantinos Katsaros, Ioannis Mavromatis, Kostantinos Antonakoglou, Saptarshi Ghosh

The development of the sixth generation of communication networks (6G) has been gaining momentum over the past years, with a target of being introduced by 2030. Several initiatives worldwide are developing innovative solutions and setting the direction for the key features of these networks. Some common emerging themes are the tight integration of AI, the co

Junho Kim, Hyungjin Chung, Byung-Hoon Kim

Category-agnostic pose estimation (CAPE) has traditionally relied on support images with annotated keypoints, a process that is often cumbersome and may fail to fully capture the necessary correspondences across diverse object categories. Recent efforts have explored the use of text queries, leveraging their enhanced stability and generalization capabilities

Nicolas Masino, Antonio Quintero-Rincon

Breast cancer detection is still an open research field, despite a tremendous effort devoted to work in this area. Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale. Feature selection is widely used to reduce the dimensionality of data by selecting only a subset of predictor variables

Adam Klukowski

We prove the Congruence Subgroup Property for two families of subgroups of Mapping Class Groups of finite-type surfaces. The first one is related to nilpotent quotients of the fundamental group and Johnson filtration, and along the way we give an elementary proof of a theorem of Ben-Ezra-Lubotzky. The second family consists of certain geometric subgroups obt

Boci Peng, Yongchao Liu, Xiaohe Bo, Sheng Tian

Commonsense question answering is a crucial task that requires machines to employ reasoning according to commonsense. Previous studies predominantly employ an extracting-and-modeling paradigm to harness the information in KG, which first extracts relevant subgraphs based on pre-defined rules and then proceeds to design various strategies aiming to improve th

B. L. S. Prakasa Rao

We discuss nonparametric estimation of the trend coefficient in models governed by a stochastic differential equation driven by a multiplicative stochastic volatility.

Andrés Muñoz, Nancy Thomas, Annita Vapsi, Daniel Borrajo

Many industrial and service sectors require tools to extract vehicle characteristics from images. This is a complex task not only by the variety of noise, and large number of classes, but also by the constant introduction of new vehicle models to the market. In this paper, we present Veri-Car, an information retrieval integrated approach designed to help on

Bacui Li, Tansu Alpcan, Chandra Thapa, Udaya Parampalli

By leveraging the principles of quantum mechanics, QML opens doors to novel approaches in machine learning and offers potential speedup. However, machine learning models are well-documented to be vulnerable to malicious manipulations, and this susceptibility extends to the models of QML. This situation necessitates a thorough understanding of QML's resilienc

Richard Vale

This note observes that the Cobb-Douglas function is uniquely characterized by the property that, if the labour share of cost for a constant-returns-to-scale firm remains constant when the firm minimizes its cost for any given output level, then the firm's production function must be Cobb-Douglas.

Klaus M. Miller, Karlo Lukic, Bernd Skiera

This study explores the impact of the General Data Protection Regulation (GDPR), introduced on May 25th, 2018, on online trackers, vital elements in the online advertising ecosystem. Using a difference-in-differences approach with a balanced panel of 294 publishers, we compare publishers subject to the GDPR with those unaffected (the control group). Drawing

Jean-Dominique Deuschel, Martin Slowik, Weile Weng

We study a class of non-reversible, continuous-time random walks in random environments on $\mathbb{Z}^d$ that admit a cycle representation with finite cycle length. The law of the transition rates, taking values in $[0, \infty)$, is assumed to be stationary and ergodic with respect to space shifts. Moreover, the transition rate from $x$ to $y$, denoted by $

Abdullah Fajar, Setiadi Yazid, Indra Budi

Phishing attacks remain a persistent threat to online security, demanding robust detection methods. This study investigates the use of machine learning to identify phishing URLs, emphasizing the crucial role of feature selection and model interpretability for improved performance. Employing Recursive Feature Elimination, the research pinpointed key features

Grace S. Garden, Stephan Tillmann

In the seminal work of Culler and Shalen from 1983, essential surfaces in 3-manifolds are associated to ideal points of their $\text{SL}_2(\mathbb{C})$-character varieties, and connections between the algebraic geometry of the character variety and the topology of the 3-manifold are established via group actions on trees. Here, we lay a general foundation fo

Ranabir Devgupta, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat

In this study, we apply two pillars of Scientific Machine Learning: Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs) to the Lotka Volterra Predator Prey Model, a fundamental ecological model describing the dynamic interactions between predator and prey populations. The Lotka-Volterra model is critical for under

Jingcheng Liu, Chunyang Wang, Yitong Yin, Yixiao Yu

We study algebraic properties of partition functions, particularly the location of zeros, through the lens of rapidly mixing Markov chains. The classical Lee-Yang program initiated the study of phase transitions via locating complex zeros of partition functions. Markov chains, besides serving as algorithms, have also been used to model physical processes ten

Jonas Marko, Gerd Wachsmuth

We investigate a broad class of integer optimal control problems with vector-valued controls and switching regularization using a total variation functional involving the p-norm, which influences the structure of a solution. We derive optimality conditions of first and second order for the integer optimal control problem via a switching-point reformulation.

Prashant Kapil, Asif Ekbal

Hate speech, offensive language, aggression, racism, sexism, and other abusive language are common phenomena in social media. There is a need for Artificial Intelligence(AI)based intervention which can filter hate content at scale. Most existing hate speech detection solutions have utilized the features by treating each post as an isolated input instance for

Qi Song, Jie Lou, Yan Chen

We reveal a quantum coherent state characterized by composite bosonic trions, wherein paired fermions further bind with bosons, in one-dimensional Bose-Fermi mixtures.This phase emerges in two separate models, both featuring onsite boson-fermion attraction that induces negative binding energy for the composite trions. The first is the pair-hopping model, in

M. Facão, D. Malheiro, M. I. Carvalho

We studied the characteristics, regions of existence and stability of different types of solitons for a distributed model of a mode-locked laser whose dispersion is purely quartic and normal. Among the different types of solitons, we identified three main branches that are named according to their different amplitude: low, medium and high amplitude solitons.

Xinqi Yang, Scott Zang, Yong Ren, Dingjie Peng

In recent years, Large Language Models (LLMs) have demonstrated remarkable versatility across various applications, including natural language understanding, domain-specific knowledge tasks, etc. However, applying LLMs to complex, high-stakes domains like finance requires rigorous evaluation to ensure reliability, accuracy, and compliance with industry stand

Urtzi Otamendi, Mikel Maiza, Igor G. Olaizola, Basilio Sierra

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The propose

Miguel Antunes-García, Luis M. Bergasa, Santiago Montiel-Marín, Rafael Barea

Accurate object detection and prediction are critical to ensure the safety and efficiency of self-driving architectures. Predicting object trajectories and occupancy enables autonomous vehicles to anticipate movements and make decisions with future information, increasing their adaptability and reducing the risk of accidents. Current State-Of-The-Art (SOTA)

Jebish Purbey, Siddartha Pullakhandam, Kanwal Mehreen, Muhammad Arham

This paper presents a detailed system description of our entry for the CHiPSAL 2025 shared task, focusing on language detection, hate speech identification, and target detection in Devanagari script languages. We experimented with a combination of large language models and their ensembles, including MuRIL, IndicBERT, and Gemma-2, and leveraged unique techniq

Swapnil Barman, Pratap Kumar Pal, Rajib Kumar Mitra

The field of hybrid magnonics has gained significant momentum in recent years, driven by its potential to enable coherent information transfer and quantum transduction. This study delves into the tunable magnon-magnon coupling within a two-dimensional array of Ni80Fe20 (Permalloy) square nanomagnets by modulating its internal magnetic field configuration. Us

Johannes Hertrich, Sebastian Neumayer

We consider the approximation of functions by 2-layer neural networks with a small number of hidden weights based on the squared loss and small datasets. Due to the highly non-convex energy landscape, gradient-based training often suffers from local minima. As a remedy, we initialize the hidden weights with samples from a learned proposal distribution, which

Wang Zhijian, Shan Lixia, Yao Qinmei, Wang Yijia

We conducted a laboratory experiment involving human subjects to test the theoretical hypothesis that equilibrium selection can be impacted by manipulating the games dynamics process, by using modern control theory. Our findings indicate that human behavior consists with the predictions derived from evolutionary game theory paradigm. The consistency is suppo

Saeed Seyedfaraji, Severin Jager, Salar Shakibhamedan, Asad Aftab

In-SRAM computing promises energy efficiency, but circuit nonlinearities and PVT variations pose major challenges in designing robust accelerators. To address this, we introduce OPTIMA, a modeling framework that aids in analyzing bit-line discharge and power consumption in 6T-SRAM-based accelerators. It provides insights into limiting factors and enables fas

Hai-Tao Hu, Xiaoshui Lin, Ai-Min Guo, Guangcan Guo

In one-dimensional quasiperiodic systems, only a few models with exact mobility edges (MEs) have been constructed using generalized self-duality theory, Avila's global theory, or the renormalization group method. This raises an intriguing question that whether we can realize more physical models with exact solvable MEs. In this work, we uncover the hidden se

A. A. Araújo Filho

In this work, we examine particle creation and the evaporation process in the context of Kalb-Ramond gravity. Specifically, we build upon two existing solutions from the literature [1] (Model I) and [2] (Model II), both addressing a static, spherically symmetric configuration. For this study, we focus on the scenario in which the cosmological constant vanish

Suvankar Purkait, Tanmay Maiti, Pooja Agarwal, Suparna Sahoo

Edge reconstruction of gate-tunable compressible quantum Hall fluids in the filling fraction range 1/3 to 2/3 is studied by measuring transmitted conductance of two individually excited fractional $e^2/3h$ edge modes of bulk 2/3 fractional quantum Hall fluid. Our findings reveal that the measured transmitted conductance deviates from the fully equilibrated v

Runming Yang, Taiqiang Wu, Jiahao Wang, Pengfei Hu

Knowledge distillation (KD) has been a predominant method for compressing Large Language Models (LLMs). In this paper, we first revisit KD and Low-Rank Adaption (LoRA) and demonstrate that they follow the same paradigm. Inspired by this observation, we propose a parameter-efficient knowledge distillation method, LLM-NEO, which integrates LoRA into KD to impr

Seanghort Born, Madeth May, Claudine Piau-Toffolon, Sébastien Iksal

Homophones present a significant challenge to authors in any languages due to their similarities of pronunciations but different meanings and spellings. This issue is particularly pronounced in the Khmer language, rich in homophones due to its complex structure and extensive character set. This research aims to address the difficulties faced by Khmer authors

Francesco Tornabene, Marco Veneroni, Giuseppe Savaré

We study the existence and uniqueness of the barycenter of a signed distribution of probability measures on a Hilbert space. The barycenter is found, as usual, as a minimum of a functional. In the case where the positive part of the signed measure is atomic, we can show also uniqueness. In the one-dimensional case, we characterize the quantile function of th

Sander Noels, Alexander Rogiers, Maarten Buyl, Tijl De Bie

The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, enabling fully-automated, personalized, and interactive content generation at an unprecedented scale. In this paper, we survey the emerging field of LLM-based persuasion, reviewing empirical studies that measure the influence of LLM Systems o

Ashutosh Sao, Simon Gottschalk

Accurate spatio-temporal prediction is crucial for the sustainable development of smart cities. However, current approaches often struggle to capture important spatio-temporal relationships, particularly overlooking global relations among distant city regions. Most existing techniques predominantly rely on Convolutional Neural Networks (CNNs) to capture glob

Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis

With the introduction of the transformers architecture, LLMs have revolutionized the NLP field with ever more powerful models. Nevertheless, their development came up with several challenges. The exponential growth in computational power and reasoning capabilities of language models has heightened concerns about their security. As models become more powerful

Benito A. Juárez-Aubry, Milton C. Mamani-Leqque

We obtain semiclassical gravity solutions in the Poincar\'e fundamental domain of $(3+1)$-dimensional Anti-de Sitter spacetime, PAdS$_4$, with a (massive or massless) Klein-Gordon field (with possibly non-trivial curvature coupling) with Dirichlet or Neumann boundary. Some results are explicitly and graphically presented for special values of the mass and cu

Jiao Hu, Jiaxu Cui, Bo Yang

Discovering governing equations of complex network dynamics is a fundamental challenge in contemporary science with rich data, which can uncover the mysterious patterns and mechanisms of the formation and evolution of complex phenomena in various fields and assist in decision-making. In this work, we develop a universal computational tool that can automatica

S. O. Adebusola, P. A. Owolawi, J. S. Ojo, P. S. Maswikaneng

Fibre optic communication system is expected to increase exponentially in terms of application due to the numerous advantages over copper wires. The optical network evolution presents several advantages such as over long-distance, low-power requirement, higher carrying capacity and high bandwidth among others Such network bandwidth surpasses methods of trans

David Jones

Close-binary central stars of planetary nebulae offer a unique tool with which to study the critical and yet poorly understood common-envelope phase of binary stellar evolution. Furthermore, as the nebula itself is thought to comprise the ionised remnant of the ejected common envelope, such planetary nebulae can be used to directly probe the mass, morphology

Hannah M. Bellenbaum, Benjamin Bachmann, Dominik Kraus, Thomas Gawne

Warm dense matter (WDM) plays an important role in astrophysical objects and technological applications, but the rigorous diagnostics of corresponding experiments is notoriously difficult. In this work, we present a model-free analysis of x-ray Thomson scattering (XRTS) measurements at multiple scattering angles. Specifically, we analyze scattering data that

Damien Jian, Yu-Chao Huang, Hsi-Sheng Goan

In this paper, we propose test-time training with the quantum auto-encoder (QTTT). QTTT adapts to (1) data distribution shifts between training and testing data and (2) quantum circuit error by minimizing the self-supervised loss of the quantum auto-encoder. Empirically, we show that QTTT is robust against data distribution shifts and effective in mitigating

Kurusch Ebrahimi-Fard, Frederic Patras, Anke Wiese

In this paper, we derive a Chen-Strichartz formula for stochastic differential equations driven by Levy processes, that is, we derive a series expansion of the logarithm of the flowmap of the stochastic differential equation in terms of commutators of vector fields with stochastic coefficients, and we provide an explicit formula for the components in this se

Kuiyao Dong, Xingyu Lou, Feng Liu, Ruian Wang

Mixture-of-Experts (MOE) has recently become the de facto standard in Multi-domain recommendation (MDR) due to its powerful expressive ability. However, such MOE-based method typically employs all experts for each instance, leading to scalability issue and low-discriminability between domains and experts. Furthermore, the design of commonly used domain-speci

M. Cortés-Contreras, J. A. Caballero, D. Montes, C. Cardona-Guillén

Aims. Our goals are to characterise the kinematic properties and to identify young and old stars among the M dwarfs of the CARMENES input catalogue. Methods. We compiled the spectral types, proper motions, distances, and radial velocities for 2187 M dwarfs. We used the public code SteParKin to derive their galactic space velocities and identify members in th

Megh Thakkar, Quentin Fournier, Matthew Riemer, Pin-Yu Chen

There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effe

Zeyd Boukhers, AmeerAli Khan, Qusai Ramadan, Cong Yang

Addressing the complexity of accurately classifying International Classification of Diseases (ICD) codes from medical discharge summaries is challenging due to the intricate nature of medical documentation. This paper explores the use of Large Language Models (LLM), specifically the LLAMA architecture, to enhance ICD code classification through two methodolo

Tingting Dai, Huiqiang Ding, Luan Cheng, Weining Zhang

We investigate the boundary effect of QGP droplet and self-similarity effect of hadrons on QGP-hadron phase transition. In intermediate or low energy collisions, when the transverse momentum is below QCD scale, QGP cannot be produced. However, if the transverse momentum fluctuates to a relatively large value, small scale QGP droplet is produced. The modified

Kaushal Kumar Kesharpu, Evgenii A. Kochetov, Alvaro Ferraz

The effect of the Rashba spin orbit coupling (RSOC) on the topological properties of the one-dimensional (1D) extended $s$-wave superconducting Hamiltonian, in the presence of strong electron-electron correlation, is investigated. It is found that a non-zero RSOC increases the periodicity of the effective Hamiltonian, which results in the folding of the Bril

Basit Iqbal, Kingshuk Sarkar

We employ Monte Carlo techniques, utilizing the Metropolis and Wolff algorithms, to investigate phase behavior and phase transitions in anisotropic Ising models. Our study encompasses the thermodynamic properties, evaluating energy, magnetization, specific heat, magnetic susceptibility, magnetic entropy, and the Binder cumulant. Additionally, we examine the

Ruizhe Yan, Jie Su, Jin Wang

Self-propelled particles with alignment, displaying ordered collective motions such as swarming, can be investigated by the well-known Vicsek model. However, challenges still remain regarding the nature of the associated phase transition. Here, we use the landscape-flux approach combined with the coarse-grained mapping method to reveal the underlying mechani

Stefano Marcantoni, Marco Merkli

We study the dynamics of an open quantum system linearly coupled to a bosonic reservoir. We show that, in the ultrastrong coupling limit, the system undergoes a nonselective measurement and then evolves unitarily according to an effective Zeno Hamiltonian. This dynamical process is largely independent of the reservoir state. We examine the entanglement break

Patricio Gallardo, Javier González-Anaya, José Luis González

We introduce and study different compactifications of the moduli space of $n$ distinct weighted labeled points in a flag of affine spaces. We construct these spaces via the weighted and generalized Fulton-MacPherson compactifications of Routis and Kim-Sato. For certain weights, our compactifications are toric and isomorphic to the polypermutohedral and polys

Aditya Soni, Mayukh Das, Anjaly Parayil, Supriyo Ghosh

The difficulty of exploring and training online on real production systems limits the scope of real-time online data/feedback-driven decision making. The most feasible approach is to adopt offline reinforcement learning from limited trajectory samples. However, after deployment, such policies fail due to exogenous factors that temporarily or permanently dist

Neli Laštovičková Streshkova, Petr Koutenský, Tomáš Novotný, Martin Kozák

Inelastic interaction between coherent light with constant frequency and free electrons enables periodic phase modulation of electron wave packets leading to periodic side-bands in the electron energy spectra. In this Letter we propose a generalization of the interaction by considering linearly chirped electron wave packets interacting with chirped optical f

K. K. L. Charlton, J. Delhaize, K. Thorat, I. Heywood

In this study we report spatially resolved, wideband spectral properties of three giant radio galaxies (GRGs) in the COSMOS field: MGTC J095959.63+024608.6 , MGTC J100016.84+015133.0 and MGTC J100022.85+031520.4. One such galaxy MGTC J100022.85+031520.4 is reported here for the first time with a projected linear size of 1.29 Mpc at a redshift of 0.1034. Unli

Xabier E. Barandiaran, Marta Pérez-Verdugo

This paper introduces the concept of ``generative midtended cognition'', exploring the integration of generative AI with human cognition. The term "generative" reflects AI's ability to iteratively produce structured outputs, while "midtended" captures the potential hybrid (human-AI) nature of the process. It stands between traditional conceptions of intended

Mobina Zibandehpoor, Fatemeh Alizadehziri, Arash Abbasi Larki, Sobhan Teymouri

In the quest for understanding human executive function, eye movements represent a unique insight into how we process and comprehend our environment. Eye movements reveal patterns in how we focus, navigate, and make decisions across various contexts. The proposed dataset includes electrooculography (EOG) signals from 27 healthy subjects, capturing both verti

Daria Tsereh, Mark Mirgaleev, Ivan Molodetskikh, Roman Kazantsev

Learning-based image compression methods have improved in recent years and started to outperform traditional codecs. However, neural-network approaches can unexpectedly introduce visual artifacts in some images. We therefore propose methods to separately detect three types of artifacts (texture and boundary degradation, color change, and text corruption), to