February 2024 arXiv papers — page 89

Victor Guedes, Shu Yan Lau, Cecilia Chirenti, Kent Yagi

Certain relations among neutron-star observables that are insensitive to the equation of state are known to exist. Such universal relations have been shown to be valid for cold and stationary neutron stars. Here, we study these relations in more dynamic scenarios: protoneutron stars and hypermassive neutron stars. First, we study protoneutron stars. We use a

Alexander Ororbia, Ankur Mali, Adam Kohan, Beren Millidge

One major criticism of deep learning centers around the biological implausibility of the credit assignment schema used for learning -- backpropagation of errors. This implausibility translates into practical limitations, spanning scientific fields, including incompatibility with hardware and non-differentiable implementations, thus leading to expensive energ

Kai Hugtenburg

In this paper we prove Gamma Conjecture $1$ for twistor bundles of hyperbolic $6$ manifolds, which are monotone symplectic manifolds which admit no K\"ahler structure. The proof involves a direct computation of the $J$-function, and a version of Laplace's method for estimating power series (as opposed to integrals). This method allows us to rephrase Gamma Co

Muhammad Shihab Rashid, Jannat Ara Meem, Yue Dong, Vagelis Hristidis

Large Language Models (LLMs) have achieved state-of-the-art performance in text re-ranking. This process includes queries and candidate passages in the prompts, utilizing pointwise, listwise, and pairwise prompting strategies. A limitation of these ranking strategies with LLMs is their cost: the process can become expensive due to API charges, which are base

David Jin, Sushrut Karmalkar, Harry Zhang, Luca Carlone

We investigate a variation of the 3D registration problem, named multi-model 3D registration. In the multi-model registration problem, we are given two point clouds picturing a set of objects at different poses (and possibly including points belonging to the background) and we want to simultaneously reconstruct how all objects moved between the two point clo

Christine Patterson, Marion Scheepers

For $D$ a natural number that is not a perfect square and for $k$ a non-zero integer, consider the subset $\mathbb{Z}_k(\sqrt{D})$ of the quadratic integer ring $\mathbb{Z}(\sqrt{D})$ consisting of elements $x+y\sqrt{D}$ for which $x^2 - Dy^2 = k$ . For each $k$ such that the set $\mathbb{Z}_k(\sqrt{D})$ is nonempty, $\mathbb{Z}_k(\sqrt{D})$ has a natural ar

D. S. M. Alencar, T. F. A. Alves, F. W. S. Lima, R. S. Ferreira

We use the pair heterogeneous mean-field (PHMF) approximation for an asynchronous version of the susceptible-infected-removed (SIR) model to estimate the epidemic thresholds on complex quenched networks. Our results indicate an improvement compared to the heuristic heterogeneous mean-field theory developed for one vertex (HMF) when the dynamic evolves on top

Ziyu Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani

Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of privacy-preserving techniques in handling sensitive health data. Despite strides in federated learning for me

Kristóf Bérczi, Karthekeyan Chandrasekaran, Tamás Király, Shubhang Kulkarni

We consider hypergraph network design problems where the goal is to construct a hypergraph that satisfies certain connectivity requirements. For graph network design problems where the goal is to construct a graph that satisfies certain connectivity requirements, the number of edges in every feasible solution is at most quadratic in the number of vertices. I

Diego B. S. Audiffred, André V. G. Cavalieri, Igor A. Maia, Eduardo Martini

We present an experimental study of reactive control of turbulent jets. We target axisymmetric disturbances associated with coherent structures, which are known to underpin the peak sound radiation of turbulent jets. We first consider a forced jet flow case, such that the coherent structures can be amplified above background levels, which makes it easier to

Nicoletta D'Angelo, Alessandro Albano, Andrea Gilardi, Giada Adelfio

In 2023, Sicily faced an escalating issue of uncontrolled fires, necessitating a thorough investigation into their spatio-temporal dynamics. Our study addresses this concern through point process theory. Each wildfire is treated as a unique point in both space and time, allowing us to assess the influence of environmental and anthropogenic factors by fitting

A. E. Atamuratov, B. O. Jabbarova, E. Sh. Xaitbayev, D. R. Rajapov

In this work, the self-heating effect in inversion mode FinFET and Junctionless (accumulation mode) FinFET is compared and the influence of the different electrical and geometric parameters on the self-heating effect are considered. It is shown, that the lattice temperature in the channel center is higher in junctionless (JL), accumulation mode FinFET than i

Artem Trofimov, Mikhail Kostyukov, Sergei Ugdyzhekov, Natalia Ponomareva

Integrated development environments (IDEs) are prevalent code-writing and debugging tools. However, they have yet to be widely adopted for launching machine learning (ML) experiments. This work aims to fill this gap by introducing JetTrain, an IDE-integrated tool that delegates specific tasks from an IDE to remote computational resources. A user can write an

Federico Ghimenti, Ludovic Berthier, Grzegorz Szamel, Frédéric van Wijland

We explore the dynamics of a simple liquid whose particles, in addition to standard potential-based interactions, are also subjected to transverse forces preserving the Boltzmann distribution. We derive the effective dynamics of one and two tracer particles in the infinite-dimensional limit. We determine the amount of acceleration of the dynamics caused by t

Zhexin Liang, Zhaochen Li, Shangchen Zhou, Chongyi Li

Despite the existence of numerous colorization methods, several limitations still exist, such as lack of user interaction, inflexibility in local colorization, unnatural color rendering, insufficient color variation, and color overflow. To solve these issues, we introduce Control Color (CtrlColor), a multi-modal colorization method that leverages the pre-tra

S. F. Pate, V. Papavassiliou, J. P. Schaub, D. P. Trujillo

We present a global fit of neutral-current elastic (NCE) neutrino-scattering data and parity-violating electron-scattering (PVES) data with the goal of determining the strange quark contribution to the vector and axial form factors of the proton. Previous fits of this form included data from a variety of PVES experiments (PVA4, HAPPEx, G0, SAMPLE) and the NC

Rien Maertens, Maarten Van Neyghem, Maxiem Geldhof, Charlotte Van Petegem

Source code plagiarism is a significant issue in educational practice, and educators need user-friendly tools to cope with such academic dishonesty. This article introduces the latest version of Dolos, a state-of-the-art ecosystem of tools for detecting and preventing plagiarism in educational source code. In this new version, the primary focus has been on e

E. Redaelli, S. Bovino, A. Lupi, T. Grassi

Cosmic rays (CRs) heavily impact the chemistry and physics of cold and dense star-forming regions. However, characterising their ionisation rate is still challenging from an observational point of view. In the past, a few analytical formulas have been proposed to infer the cosmic-ray ionization rate $\zeta_2$ from molecular line observations. These have been

Mobina Mansoori, Sajjad Shahabodini, Jamshid Abouei, Arash Mohammadi

Digital pathology involves converting physical tissue slides into high-resolution Whole Slide Images (WSIs), which pathologists analyze for disease-affected tissues. However, large histology slides with numerous microscopic fields pose challenges for visual search. To aid pathologists, Computer Aided Diagnosis (CAD) systems offer visual assistance in efficie

Christodoulos Peltekis, Dionysios Filippas, Giorgos Dimitrakopoulos

Structured sparsity is an efficient way to prune the complexity of modern Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. In such cases, the acceleration of structured-sparse ML models is handled by sparse systolic tensor arrays. The increasing prevalence of ML in safety-critical systems requires enhancing the spar

Even Thingstad, Pierre Fromholz, Flavio Ronetti, Daniel Loss

Topological magnetic insulators host chiral gapless edge modes. In the presence of strong interaction effects, the spin of these modes may fractionalize. Studying a 2D array of coupled insulating spin-1/2 chains, we show how spatially modulated magnetic fields and Dzyaloshinskii-Moriya interactions can be exploited to realize chiral spin liquids or integer a

Francesca Astengo, Bianca Di Blasio, Fulvio Ricci

For a Gelfand pair $(G,K)$ with $G$ a Lie group of polynomial growth and $K$ a compact subgroup, the "Schwartz correspondence" states that the spherical transform maps the bi-$K$-invariant Schwartz space ${\mathcal S}(K\backslash G/K)$ isomorphically onto the space ${\mathcal S}(\Sigma_{\mathcal D})$, where $\Sigma_{\mathcal D}$ is an embedded copy of the Ge

Andrea Ferigo, Elia Cunegatti, Giovanni Iacca

One of the most striking capabilities behind the learning mechanisms of the brain is the adaptation, through structural and functional plasticity, of its synapses. While synapses have the fundamental role of transmitting information across the brain, several studies show that it is the neuron activations that produce changes on synapses. Yet, most plasticity

Ekta Gavas, Kaustubh Olpadkar, Anoop Namboodiri

Fingerprint recognition stands as a pivotal component of biometric technology, with diverse applications from identity verification to advanced search tools. In this paper, we propose a unique method for deriving robust fingerprint representations by leveraging enhancement-based pre-training. Building on the achievements of U-Net-based fingerprint enhancemen

Kawa Atapour, S. Jamal Seyedmohammadi, Jamshid Abouei, Arash Mohammadi

This paper addresses the challenge of mitigating data heterogeneity among clients within a Federated Learning (FL) framework. The model-drift issue, arising from the noniid nature of client data, often results in suboptimal personalization of a global model compared to locally trained models for each client. To tackle this challenge, we propose a novel appro

Daniel Beltita, Alina Dobrogowska, Grzegorz Jakimowicz

We study Lie-Rinehart algebra structures in the framework provided by a duality pairing of modules over a unital commutative associative algebra. Thus, we construct examples of Lie brackets corresponding to a fixed anchor map whose image is a cyclic submodule of the derivation module, and therefore we call them cyclic Lie-Rinehart algebras. In a very special

Enea Mauri, Steef Smit, Mark Golden, H. T. C. Stoof

We show that the momentum-dependent scaling exponents of the holographic fermion self-energy of the conformal-to-AdS$_2$ Gubser-Rocha model can describe new findings from angle-resolved photoemission spectroscopy experiments on a single layer (Pb,Bi)$_{2}$Sr$_{2-x}$La$_x$CuO$_{6+\delta}$ copper-oxide. In particular, it was recently observed, in high-precisio

Achraf Atila, Sergey V. Sukhomlinov, Martin H. Müser

The low kinetic friction between ice and numerous counterbodies is commonly attributed to an interfacial water layer, which is believed to originate from pre-existing surface water or from melt water induced by high contact pressures or frictional heat. However, even the currently leading theory of frictional melting appears to defy direct experimental verif

Mohammad Reza Samadzadeh, Doost Ali Mojdeh, Reza Nadimi

\noindent A paired coalition in a graph $G=(V,E)$ consists of two disjoint sets of vertices $V_1$ and $V_2$, neither of which is a paired dominating set but whose union $V_1 \cup V_2$ is a paired dominating set. A paired coalition partition (abbreviated $pc$-partition) in a graph $G$ is a vertex partition $\pi= \lbrace V_1,V_2,\dots ,V_k \rbrace$ such that e

Martin Corless, Anthony Quinn, Sarah Boufelja, Robert Shorten

We derive iterative scaling algorithms of the Sinkhorn-Knopp (SK) type for constrained optimal transport. The constraints are in the form of prior-imposed zeroes in the transport plan. Based on classical Bregman arguments, we prove asymptotic convergence of our algorithms to a unique optimal solution. New insights obtained from the convergence proof are high

Rashid Yaaqib, Michael S. Petersen, Jorge Peñarrubia

We measure the kinematic signature arising from the Milky Way (MW) disc moving with respect to the outer stellar halo, which is observed as a dipole signal in the kinematics of stellar halo tracers. We quantify how the reflex motion varies as a function of Galactocentric distance, finding that (i) the amplitude of the dipole signal increases as a function of

Jesus Iñarrea, Gloria Platero-

We investigate irradiated high-mobility two-dimensional electron systems (2DES) under low or moderated magnetic fields. These systems present microwave-induced magnetoresistance oscillations (MIRO) which, as we demonstrate, reveal the presence of coherent states of the quantum harmonic oscillator. We also show that the principle of minimum uncertainty of coh

Michal Stransky, Thomas J. Lane, Alexander Gorel, Sébastien Boutet

Using X-ray free electron laser (XFEL) radiation to conduct an X-ray pump X-ray probe experiment, we studied strongly ionized water as part of our ongoing work on radiation damage. After irradiance with a pump pulse with a nominal fluence of ~$5 \times 10^5$ J/cm$^2$, we observed for pump-probe delays of 75 fs and longer an unexpected structural rearrangemen

Micael Said Garcia, Felipe Yukihide Yasumura

We compute the graded polynomial identities of the infinite dimensional upper triangular matrix algebra over an arbitrary field. If the grading group is finite, we prove that the set of graded polynomial identities admits a finite basis. We find conditions under which a grading on such an algebra satisfies a nontrivial graded polynomial identity. Finally, we

Sean Lawton, Sara Maloni, Frédéric Palesi

We study the relative SU(2,1)-character varieties of the one-holed torus, and the action of the mapping class group on them. We use an explicit description of the character variety of the free group of rank two in SU(2,1) in terms of traces, which allow us to describe the topology of the character variety. We then combine this description with a generalizati

Philip Arm, Mayank Mittal, Hendrik Kolvenbach, Marco Hutter

Legged robots have the potential to become vital in maintenance, home support, and exploration scenarios. In order to interact with and manipulate their environments, most legged robots are equipped with a dedicated robot arm, which means additional mass and mechanical complexity compared to standard legged robots. In this work, we explore pedipulation - usi

Sarigai Sarigai, Liping Yang, Katie Slack, K. Maria D. Lane

We are surrounded by overwhelming big data, which brings substantial advances but meanwhile poses many challenges. Geospatial big data comprises a big portion of big data, and is essential and powerful for decision-making if being utilized strategically. Volumes in size and high dimensions are two of the major challenges that prevent strategic decision-makin

Dhananjay Saikumar

Quantum Field Theory (QFT) forms the bedrock of the Standard Model (SM) of particle physics, a powerful framework that delineates the fundamental constituents and interactions of the universe. However, the SM's narrative is incomplete, as it conspicuously fails to account for several empirical phenomena that challenge our current understanding of particle ph

Federico Crippa

The causal inference model proposed by Lee (2008) for the regression discontinuity design (RDD) relies on assumptions that imply the continuity of the density of the assignment (running) variable. The test for this implication is commonly referred to as the manipulation test and is regularly reported in applied research to strengthen the design's validity. T

Hua Tang, Chong Zhang, Mingyu Jin, Qinkai Yu

Large language models (LLMs) have been applied in many fields and have developed rapidly in recent years. As a classic machine learning task, time series forecasting has recently been boosted by LLMs. Recent works treat large language models as \emph{zero-shot} time series reasoners without further fine-tuning, which achieves remarkable performance. However,

Qingnan Liang, Ruili Yao, Ruixuan Zhang, Zhibin Chen

Congestion tollings have been widely developed and adopted as an effective tool to mitigate urban traffic congestion and enhance transportation system sustainability. Nevertheless, these tolling schemes are often tailored on a city-by-city or even area-by-area basis, and the cost of conducting field experiments often makes the design and evaluation process c

Isak Björkman, Marko Kuzmanović, Gheorghe Sorin Paraoanu

Second-order processes introduce nonlinearities in quantum dynamics, unlocking a totally unexpected area of control operations. Here we show that the well-known Landau-Zener-St\"uckelberg-Majorana (LZSM) transition can be driven by a virtual process in a three-level system whereby two photons from a drive with linearly-modulated phase create excitations onto

Isaac Mooney

Jets, as collections of multi-scale objects, allow for insight into perturbative (high-momentum) processes, but gaining an understanding of the non-perturbative structure within jets such as hadronization effects and the underlying event has been more difficult. In observables sensitive to these non-perturbative effects, a large discrepancy is observed betwe

Ehtasham Naseer, Ali Imran Sandhu, Muhammad Adnan Siddique, Waqas W. Ahmed

Inverse scattering problems are inherently challenging, given the fact they are ill-posed and nonlinear. This paper presents a powerful deep learning-based approach that relies on generative adversarial networks to accurately and efficiently reconstruct randomly-shaped two-dimensional dielectric objects from amplitudes of multi-frequency scattered electric f

Yangyifei Luo, Zhuo Chen, Lingbing Guo, Qian Li

Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-of-the-art performance in EA yet faced interpretability challenges as they purely rely on the embedding distance and neglect the logic rules behind a pair of aligned entities. In t

António Galvão, Fernando Brito e Abreu, João Joanaz de Melo

Smart tourism (ST) stems from the concepts of e-tourism - focused on the digitalization of processes within the tourism industry, and digital tourism - also considering the digitalization within the tourist experience. The earlier ST references found regard ST Destinations and emerge from the development of Smart Cities. Our initial literature review on the

Benjamin Lemkin

Large language models (LLMs) are initially trained on vast amounts of data, then fine-tuned using reinforcement learning from human feedback (RLHF); this also serves to teach the LLM to provide appropriate and safe responses. In this paper, we present a novel method to manipulate the fine-tuned version into reverting to its pre-RLHF behavior, effectively era

Adam Chapman, S. Srimathy

We conjecture that a $p$-algebra over a complete discrete valued field $K$ contains a totally ramified purely inseparable subfield if and only if it contains a totally ramified cyclic maximal subfield. We prove the conjecture in several cases.

Robert Grimm

Alarmist and sensationalist statements about the "explosion" of online child sexual exploitation or CSE dominate much of the public discourse about the topic. Based on a new dataset collecting the transparency disclosures for 16 US-based internet platforms and the national clearinghouse collecting legally mandated reports about CSE, this study seeks answers

Jianhao Yuan, Shuyang Sun, Daniel Omeiza, Bo Zhao

We need to trust robots that use often opaque AI methods. They need to explain themselves to us, and we need to trust their explanation. In this regard, explainability plays a critical role in trustworthy autonomous decision-making to foster transparency and acceptance among end users, especially in complex autonomous driving. Recent advancements in Multi-Mo

Jinlu Li

In this paper, we investigate the properties and the precise solutions of the Mordukhovich derivatives of the set-valued metric projection operator onto some closed balls in some general Banach spaces. In the Banach space c, we find the properties of Mordukhovich derivatives of the set-valued metric projection operator onto the closed subspace c0. We show th

Nicolas Daans

We study linkage of $(d+1)$-fold quadratic Pfister forms over function fields in one variable over a henselian valued field of 2-cohomological dimension $d$. Specifically, we characterise this property in terms of linkage of quadratic Pfister forms over function fields over the residue field of the henselian valued field; in full generality in characteristic

Yuma Fujimoto, Kaito Ariu, Kenshi Abe

Learning in games discusses the processes where multiple players learn their optimal strategies through the repetition of game plays. The dynamics of learning between two players in zero-sum games, such as Matching Pennies, where their benefits are competitive, have already been well analyzed. However, it is still unexplored and challenging to analyze the dy

Gianluca Stefanucci

The extraordinary quantum properties of nonequilibrium systems governed by dissipative dynamics have become a focal point in contemporary scientific inquiry. The Nonequilibrium Green's Functions (NEGF) theory provides a versatile method for addressing driven {\em non-dissipative} systems, utilizing the powerful diagrammatic technique to incorporate correlati

Jarod Alper, Felix Janda

Motivated by localization theorems on moduli spaces, we prove a structural classification of Deligne-Mumford stacks with an action of a torus where the induced action on the coarse moduli space is trivial. We also establish a general local structure theorem for morphisms of algebraic stacks.

David Soler, Iván Cillero, Carlos Dafonte, Manuel Fernández-Veiga

The first Quantum Key Distribution (QKD) networks are currently being deployed, but the implementation cost is still prohibitive for most researchers. As such, there is a need for realistic QKD network simulators. The \textit{QKDNetSim} module for the network simulator NS-3 focuses on the representation of packets and the management of key material in a QKD

Ruchika Desure, Gutha Jaya Krishna

Designing proper treatment plans to manage diabetes requires health practitioners to pay heed to the individuals remaining life along with the comorbidities affecting them. Older adults with Type 2 Diabetes Mellitus (T2DM) are prone to experience premature death or even hypoglycaemia. The structured dataset utilized has 68 potential mortality predictors for

Divin Yan, Lu Qi, Vincent Tao Hu, Ming-Hsuan Yang

Diffusion models have made significant advances recently in high-quality image synthesis and related tasks. However, diffusion models trained on real-world datasets, which often follow long-tailed distributions, yield inferior fidelity for tail classes. Deep generative models, including diffusion models, are biased towards classes with abundant training imag

Alfredo Reichlin, Miguel Vasco, Hang Yin, Danica Kragic

We study the problem of learning optimal behavior from sub-optimal datasets for goal-conditioned offline reinforcement learning under sparse rewards, invertible actions and deterministic transitions. To mitigate the effects of \emph{distribution shift}, we propose MetricRL, a method that combines metric learning for value function approximation with weighted

Gutha Jaya Krishna

Serial-parallel redundancy is a reliable way to ensure service and systems will be available in cloud computing. That method involves making copies of the same system or program, with only one remaining active. When an error occurs, the inactive copy can step in as a backup right away, this provides continuous performance and uninterrupted operation. This ap

Rasmus N. Larsen, Gaurang Parkar, Alexander Rothkopf, Johannes Heinrich Weber

We re-investigate the interactions between static color sources in a finite temperature gluonic medium using both high resolution isotropic and anisotropic quenched lattice QCD ensembles. The underlying ill-posed inverse problem, related to the extraction of spectral functions, is attacked with a range of different methods, including Bayesian inference, Pad\

Enrique Nueve, Bo Waggoner, Dhamma Kimpara, Jessie Finocchiaro

In multiclass classification over $n$ outcomes, the outcomes must be embedded into the reals with dimension at least $n-1$ in order to design a consistent surrogate loss that leads to the "correct" classification, regardless of the data distribution. For large $n$, such as in information retrieval and structured prediction tasks, optimizing a surrogate in $n

Matyas Barczy, Zsolt Páles

We prove that the values of a generalized $\psi$-estimator (introduced by Barczy and P\'ales in 2025) on samples of arbitrary length but having only two different observations uniquely determine the values of the estimator on any sample of arbitrary length without any restriction on the number of different observations. In other words, samples of arbitrary l

Nadieh Khalili, Joey Spronck, Francesco Ciompi, Jeroen van der Laak

Deep learning algorithms, often critiqued for their 'black box' nature, traditionally fall short in providing the necessary transparency for trusted clinical use. This challenge is particularly evident when such models are deployed in local hospitals, encountering out-of-domain distributions due to varying imaging techniques and patient-specific pathologies.

Richeng Jin, Yujie Gu, Kai Yue, Xiaofan He

Distributed training of deep neural networks faces three critical challenges: privacy preservation, communication efficiency, and robustness to fault and adversarial behaviors. Although significant research efforts have been devoted to addressing these challenges independently, their synthesis remains less explored. In this paper, we propose TernaryVote, whi

Tesshu Hanaka, Noleen Köhler, Michael Lampis

Additively Separable Hedonic Game (ASHG) are coalition-formation games where we are given a graph whose vertices represent $n$ selfish agents and the weight of each edge $uv$ denotes how much agent $u$ gains (or loses) when she is placed in the same coalition as agent $v$. We revisit the computational complexity of the well-known notion of core stability of

Tommaso Salvatori, Beren Millidge, Yuhang Song, Rafal Bogacz

An autoassociative memory model is a function that, given a set of data points, takes as input an arbitrary vector and outputs the most similar data point from the memorized set. However, popular memory models fail to retrieve images even when the corruption is mild and easy to detect for a human evaluator. This is because similarities are evaluated in the r

Bianka Kovács, Sadamori Kojaku, Gergely Palla, Santo Fortunato

Graph embeddings learn the structure of networks and represent it in low-dimensional vector spaces. Community structure is one of the features that are recognized and reproduced by embeddings. We show that an iterative procedure, in which a graph is repeatedly embedded and its links are reweighted based on the geometric proximity between the nodes, reinforce

Qi Shi, Han Cui, Haofeng Wang, Qingfu Zhu

Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data. Various approaches have been proposed to address these challenges. One approach involves training specialized retrievers to select relevant information, thereby redu

Tiancheng Hu, Nigel Collier

Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. This study investigates how integrating persona variables-demographic, social, and behavioral factors-impacts LLMs' ability to simulate diverse perspectives. We find that persona variables account for <10% variance in annotations in existing subjective NLP d

Zihao Li, Boyi Liu, Zhuoran Yang, Zhaoran Wang

We study the Constrained Convex Markov Decision Process (MDP), where the goal is to minimize a convex functional of the visitation measure, subject to a convex constraint. Designing algorithms for a constrained convex MDP faces several challenges, including (1) handling the large state space, (2) managing the exploration/exploitation tradeoff, and (3) solvin

Dmitrii Zakharov

For $g \ge 2$, we show that the number of positive integers at most $X$ which can be written as sum of two base $g$ palindromes is at most $\frac{X}{\log^c X}$. This answers a question of Baxter, Cilleruelo and Luca.

M. F. Islam, Kushantha P. K. Withanage, C. M. Canali, Mark R. Pederson

Frustrated triangular molecular magnets (MMs) with anti-ferromagnetic ground states (GS) are an important class of magnetic systems with potential applications in quantum information processing. The two-fold degenerate GS of these molecules, characterized by spin chirality, can be utilized to encode qubits for quantum computing. Furthermore, because of the l

Ce Jin, Michael Kapralov, Sepideh Mahabadi, Ali Vakilian

We study the $k$-connectivity augmentation problem ($k$-CAP) in the single-pass streaming model. Given a $(k-1)$-edge connected graph $G=(V,E)$ that is stored in memory, and a stream of weighted edges $L$ with weights in $\{0,1,\dots,W\}$, the goal is to choose a minimum weight subset $L'\subseteq L$ such that $G'=(V,E\cup L')$ is $k$-edge connected. We give

Yongqi Li, Wenjie Wang, Leigang Qu, Liqiang Nie

The recent advancements in generative language models have demonstrated their ability to memorize knowledge from documents and recall knowledge to respond to user queries effectively. Building upon this capability, we propose to enable multimodal large language models (MLLMs) to memorize and recall images within their parameters. Given a user query for visua

Oleg K. Cheremnykh, Viktor Fedun, Volodymyr M. Lashkin

We present an analytical model of the self-consistent equilibrium of a magnetic flux rope which is obtained in cylindrical geometry. The equilibrium azimuthal magnetic field and plasma pressure are determined in a self-consistent way through the current density which is derived as a solution of a nonlinear equation. By minimizing the energy functional, it wa

Johann Lussange, Stefano Vrizzi, Stefano Palminteri, Boris Gutkin

Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent syste

Haotian Si, Jianhui Li, Changhua Pei, Hang Cui

Time series anomaly detection (TSAD) has gained significant attention due to its real-world applications to improve the stability of modern software systems. However, there is no effective way to verify whether they can meet the requirements for real-world deployment. Firstly, current algorithms typically train a specific model for each time series. Maintain

Andrea Brilli, Andrea Cristofari, Giampaolo Liuzzi, Stefano Lucidi

In this paper, we analyze a derivative-free line search method designed for bound-constrained problems. Our analysis demonstrates that this method exhibits a worst-case complexity comparable to other derivative-free methods for unconstrained and linearly constrained problems. In particular, when minimizing a function with $n$ variables, we prove that at most

Julian Frattini, Davide Fucci, Richard Torkar, Daniel Mendez

The quality of requirements specifications may impact subsequent, dependent software engineering (SE) activities. However, empirical evidence of this impact remains scarce and too often superficial as studies abstract from the phenomena under investigation too much. Two of these abstractions are caused by the lack of frameworks for causal inference and frequ

Patrick Ilg

As more and more promising applications of magnetic nanoparticles in complicated environments are explored, their flow properties in porous media are of increasing interest. We here propose a hybrid approach based on the Multiparticle Collision Dynamics Method extended to porous media via friction forces and coupled with Brownian Dynamics simulations of the

Bram Vanherle, Vittorio Pippi, Silvia Cascianelli, Nick Michiels

Styled Handwritten Text Generation (HTG) has received significant attention in recent years, propelled by the success of learning-based solutions employing GANs, Transformers, and, preliminarily, Diffusion Models. Despite this surge in interest, there remains a critical yet understudied aspect - the impact of the input, both visual and textual, on the HTG mo

Alberto Cabezas, Adrien Corenflos, Junpeng Lao, Rémi Louf

BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by taking a functional approach to the algorithms' implementation. BlackJAX is written in Python, using JAX to compile and run NumpPy-like samplers and variational methods on CPUs, GPUs,

Takahiro Uto, Daigo Oue

We study parametric instability in a magnomechanical system, specifically examining magnon tunneling between moving ferromagnetic insulators. Our analysis reveals that quantum fluctuations generate spin currents above a critical velocity threshold, while no spin currents occur below this threshold at low temperatures. The critical velocity depends on magnon

Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona

Crowdsourced machine learning on competition platforms such as Kaggle is a popular and often effective method for generating accurate models. Typically, teams vie for the most accurate model, as measured by overall error on a holdout set, and it is common towards the end of such competitions for teams at the top of the leaderboard to ensemble or average thei

Adolfo Arroyo-Rabasa, Paolo Bonicatto, Giacomo Del Nin

We introduce the concept of local Poincar\'e constant of a $BV$ function as a tool to understand the relation between its mean oscillation and its total variation at small scales. This enables us to study a variant of the BMO-type seminorms on $\varepsilon$-size cubes introduced by Ambrosio, Bourgain, Brezis, and Figalli. More precisely, we relax the size co

David Buterez, Jon Paul Janet, Dino Oglic, Pietro Lio

There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersede handcrafted operators characteristic of message passing schemes. However, concerns over their empirical effectiveness, scalability, and complexity of the pre-processing steps hav

Tobias frederico, Rafael Mendes Francisco, Dérick dos Santos Rosa, Gastão Inácio Krein

We explore the effect of a finite two-body energy in the discrete scale symmetry regime of two heavy bosonic impurities immersed in a light bosonic system. By means of the Born-Oppenheimer approximation in non-integer dimensions $(D)$, we discuss the effective potential of the heavy-particles Schrodinger equation. We study how including the two-body energy i

V. G. Ramesh, S. R. K. Rodriguez

The time-integrated intensity transmitted by a laser driven resonator obeys L\'evy's arcsine laws [Ramesh \textit{et al.}, Phys. Rev. Lett. \textit{in press} (2024)]. Here we demonstrate the implications of these laws for optical sensing. We consider the standard goal of resonant optical sensors, namely to report a perturbation to their resonance frequency.

Samuel Teuber, Stefan Mitsch, André Platzer

While neural networks (NNs) have potential as autonomous controllers for Cyber-Physical Systems, verifying the safety of NN based control systems (NNCSs) poses significant challenges for the practical use of NNs, especially when safety is needed for unbounded time horizons. One reason is the intractability of analyzing NNs, ODEs and hybrid systems. To this e

Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Dmitry Sorokin

This paper addresses the challenge of processing long documents using generative transformer models. To evaluate different approaches, we introduce BABILong, a new benchmark designed to assess model capabilities in extracting and processing distributed facts within extensive texts. Our evaluation, which includes benchmarks for GPT-4 and RAG, reveals that com

Gokhan Aydin, Gokhan Silahtaroglu

Background Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come. Objective This study aims to investigate the current landscape of smartphone apps that focus on improving and sustaining health and wellbeing. Understanding

Nataly Rafat sabbah, Mohamed Ghaleb Al-Masaeed, Ahmed Al-Jamel

The variational method is used to study the hard confinement of a two-particle quantum system in two potential models, the Cornell potential and the global potential, with Dirichlet-type boundary conditions at various cut-off radii. The trial wavefunction is constructed as the product of the $1S$ free hydrogen atom wavefunction or $1S$ free harmonic oscillat

Xuan Shen, Peiyan Dong, Zhenglun Kong, Yifan Gong

A growing trend has emerged in designing high-quality Small Language Models (SLMs) with a few million parameters. This trend is driven by the increasing concerns over cloud costs, privacy, and latency. Considering that full parameter training is feasible for SLMs on mobile devices, Quantization-Aware Training (QAT) is employed to improve efficiency by reduci

Subhrata Dey, Arti Goyal, Katarzyna Małek, Tanio Díaz-Santos

We present detailed spectral energy distribution (SED) modeling of 14 local ultraluminous infrared galaxies (ULIRGs) with outstanding photometric data from the literature covering the ultraviolet--infrared (FIR) and radio bands ($\sim$50 MHz to $\sim$30 GHz). We employ the CIGALE SED fitting code to model the ultraviolet--FIR--radio SED. For the radio-only S

Stephen P. Reynolds, Kazimierz J. Borkowski

We present a deep Chandra observation of the shell supernova remnant G32.4+0.1, whose featureless X-ray spectrum has led to its classification as an X-ray synchrotron-dominated supernova remnant (SNR). We find a partial shell morphology whose outline is quite circular, with a radius of about 11 pc at an assumed distance of 11 kpc. Thermal and power-law spect

Juan Ramón Muñoz de Nova, Fernando Sols

We study simultaneous symmetry breaking in spontaneous Floquet states, focusing on the specific case of an atomic condensate. We first describe the quantization of the Nambu-Goldstone (NG) modes for a stationary state simultaneously breaking several symmetries of the Hamiltonian by invoking the generalized Gibbs ensemble, which enables a thermodynamical desc

Jordan Kuschner, Yugarshi Shashwat, Sarthak Yadav, Marek Chrobak

Selective families of sets, or selectors, are combinatorial tools used to "isolate" individual members of sets from some set family. Given a set $X$ and an element $x\in X$, to isolate $x$ from $X$, at least one of the sets in the selector must intersect $X$ on exactly $x$. We study (k,N)-permutation selectors which have the property that they can isolate ea

Rohit Singh, Aryan Kaushik, Wonjae Shin, Marco Di Renzo

Over the past few decades, wireless communication has witnessed remarkable growth, experiencing several transformative changes. This article aims to provide a comprehensive overview of wireless communication technologies, from the foundations to the recent wireless advances. Specifically, we take a neutral look at the state-of-the-art technologies for 5G and

Natalia Saburova

We consider discrete Schr\"odinger operators with periodic potentials on periodic graphs. Their spectra consist of a finite number of bands. We perturb a periodic graph by adding edges in a periodic way (without changing the vertex set) and show that if the added edges are long enough, then the perturbed graph is asymptotically isospectral to some periodic g