October 2023 arXiv papers — page 42

Yuqing Wang, Prashanth Vijayaraghavan, Ehsan Degan

Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors affecting email marketing performance, limited research has focused on understanding email response behavior by considering email content and metadata. This study proposes a Prototy

Moses Charikar, Monika Henzinger, Lunjia Hu, Maxmilian Vötsch

Clustering is a fundamental problem in unsupervised machine learning with many applications in data analysis. Popular clustering algorithms such as Lloyd's algorithm and $k$-means++ can take $\Omega(ndk)$ time when clustering $n$ points in a $d$-dimensional space (represented by an $n\times d$ matrix $X$) into $k$ clusters. In applications with moderate to l

Jilali Seffadi, Hocine Bahlouli, Ahmed Jellal

We study the effect of the double potentials (barrier, well) on the Goos-H\"anchen (GH) shifts in phosphorene. We determine the solutions of the energy spectrum associated with the five regions that make up our system. By studying the phase shifts, we find that the GH shifts are highly sensitive to the incident energy, the $y$ directional wave vector, the po

Luca Ebner, Gideon Billings, Stefan Williams

In this work, we address the problem of real-time dense depth estimation from monocular images for mobile underwater vehicles. We formulate a deep learning model that fuses sparse depth measurements from triangulated features to improve the depth predictions and solve the problem of scale ambiguity. To allow prior inputs of arbitrary sparsity, we apply a den

Vineet Bhat, Preethi Jyothi, Pushpak Bhattacharyya

Disfluency correction (DC) is the process of removing disfluent elements like fillers, repetitions and corrections from spoken utterances to create readable and interpretable text. DC is a vital post-processing step applied to Automatic Speech Recognition (ASR) outputs, before subsequent processing by downstream language understanding tasks. Existing DC rese

Tianchen Yuan, Petros A. Ioannou

Numerous studies have shown the effectiveness of intelligent transportation system techniques such as variable speed limit (VSL), lane change (LC) control, and ramp metering (RM) in freeway traffic flow control. The integration of these techniques has the potential to further enhance the traffic operation efficiency of both freeway and adjacent arterial netw

Carsten Denker, Meetu Verma, Alexander G. M. Pietrow, Ioannis Kontogiannis

High-resolution solar spectroscopy provides a wealth of information from photospheric and chromospheric spectral lines. However, the volume of data easily exceeds hundreds of millions of spectra on a single observation day. Therefore, methods are needed to identify spectral signatures of interest in multidimensional datasets. Background-subtracted activity m

Nafis Irtiza Tripto, Adaku Uchendu, Thai Le, Mattia Setzu

Authorship Analysis, also known as stylometry, has been an essential aspect of Natural Language Processing (NLP) for a long time. Likewise, the recent advancement of Large Language Models (LLMs) has made authorship analysis increasingly crucial for distinguishing between human-written and AI-generated texts. However, these authorship analysis tasks have prim

Ilkin Aliyev. Kama Svoboda, Tosiron Adegbija

Spiking Neural Networks (SNNs) offer a promising alternative to Artificial Neural Networks (ANNs) for deep learning applications, particularly in resource-constrained systems. This is largely due to their inherent sparsity, influenced by factors such as the input dataset, the length of the spike train, and the network topology. While a few prior works have d

Joren Brunekreef, Andrzej Görlich, Renate Loll

We provide a hands-on introduction to Monte Carlo simulations in nonperturbative lattice quantum gravity, formulated in terms of Causal Dynamical Triangulations (CDT). We describe explicitly the implementation of Monte Carlo moves and the associated detailed-balance equations in two and three spacetime dimensions. We discuss how to optimize data storage and

Marco Davidovic, Heinz Pitsch

Mixture fraction-based models are widely employed for predicting turbulent non-premixed combustion processes due to their cost-effectiveness and well-established subfilter closure. In these models, the transport of reactive scalars in physical space is decomposed into two components: scalar transport relative to mixture fraction and transport of mixture frac

Arun Sehrawat

On the one hand, artificial neural networks have many successful applications in the field of machine learning and optimization. On the other hand, interferometers are integral parts of any field that deals with waves such as optics, astronomy, and quantum physics. Here, we introduce neural networks composed of interferometers and then build generative adver

Ira J. S. Shokar, Rich R. Kerswell, Peter H. Haynes

We present a novel probabilistic deep learning approach, the 'Stochastic Latent Transformer' (SLT), designed for the efficient reduced-order modelling of stochastic partial differential equations. Stochastically driven flow models are pertinent to a diverse range of natural phenomena, including jets on giant planets, ocean circulation, and the variability of

Andrei Draghici, Christoph Haase, Andrew Ryzhikov

The recent years have seen remarkable progress in establishing the complexity of the reachability problem for vector addition systems with states (VASS), equivalently known as Petri nets. Existing work primarily considers the case in which both the VASS as well as the initial and target configurations are part of the input. In this paper, we investigate the

Julie Peeling, Jasmeet Judge, Vasubandhu Misra, C. B. Jayasankar

The rather unique sub-tropical, flat, peninsular region of Florida is subject to a unique climate with extreme weather events across the year that impacts agriculture, public health, and management of natural resources. Meteorological data at high temporal resolutions especially in the tropical latitudes are essential to understand diurnal and semi-diurnal v

Xi Wang, Hossein A. Rahmani, Jiqun Liu, Emine Yilmaz

Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However, the current state of conversational recommendation faces numerous challenges due to its relative novelty and limited existing contributions. In this study, we delve into benchmar

Giang Hoang Nguyen, Daniel Bessler, Simon Stelter, Mihai Pomarlan

Robots performing human-scale manipulation tasks require an extensive amount of knowledge about their surroundings in order to perform their actions competently and human-like. In this work, we investigate the use of virtual reality technology as an implementation for robot environment modeling, and present a technique for translating scene graphs into knowl

Habib Esmaili, Hosein Mohammadzadeh, Mehdi Biderang, Morteza Nattagh Najafi

We examine the thermodynamic characteristics of unified quantum statistics as a novel framework that undergoes a crossover between Bose-Einstein and Fermi-Dirac statistics by varying a generalization parameter $\delta$. We find an attractive intrinsic statistical interaction when $\delta\le0.5$ where the thermodynamic curvature remains positive throughout th

Wenlong Li, Zihao Li, Wenkai Li, Yueming Zhang

In the realm of data protection, a striking disconnect prevails between traditional domains of doctrinal, legal, theoretical, and policy-based inquiries and a burgeoning body of empirical evidence. Much of the scholarly and regulatory discourse remains entrenched in abstract legal principles or normative frameworks, leaving the empirical landscape uncharted

Selina Burkhard, Benjamin Dörich, Marlis Hochbruck, Caroline Lasser

In the present paper we consider the semiclassical magnetic Schr\"odinger equation, which describes the dynamics of particles under the influence of a magnetic field. The solution of the time-dependent Schr\"odinger equation is approximated by a single Gaussian wave packet via the time-dependent Dirac--Frenkel variational principle. For the approximation we

Kathryn Edmondson, Jordan Norris, Eamonn Kerins

We use a carefully selected subsample of 1053 confirmed exoplanets from the NASA Exoplanet Archive to construct empirical power-law exoplanet mass-radius-temperature ($M$-$R$-$T$) relations. Using orthogonal distance regression to account for errors in both mass and radius, we allow the data to decide: 1) the number of distinct planetary regimes; 2) whether

Yingjie Zhou, Zicheng Zhang, Wei Sun, Xiongkuo Min

In recent years, digital humans have been widely applied in augmented/virtual reality (A/VR), where viewers are allowed to freely observe and interact with the volumetric content. However, the digital humans may be degraded with various distortions during the procedure of generation and transmission. Moreover, little effort has been put into the perceptual q

Roshanak Mirzaee, Parisa Kordjamshidi

Spatial reasoning over text is challenging as the models not only need to extract the direct spatial information from the text but also reason over those and infer implicit spatial relations. Recent studies highlight the struggles even large language models encounter when it comes to performing spatial reasoning over text. In this paper, we explore the poten

Dong-Ki Kim, Sungryull Sohn, Lajanugen Logeswaran, Dongsub Shim

Recently, there has been an increasing interest in automated prompt optimization based on reinforcement learning (RL). This approach offers important advantages, such as generating interpretable prompts and being compatible with black-box foundation models. However, the substantial prompt space size poses challenges for RL-based methods, often leading to sub

David Cimasoni, Livio Ferretti

In 2018, Kashaev introduced a square matrix indexed by the regions of a link diagram, and conjectured that it provides a novel way of computing the Levine-Tristram signature and Alexander polynomial of the corresponding oriented link. In this article, we show that for the classical signature (i.e. the Levine-Tristram signature at -1), this conjecture follows

Yannick Kuhl, Florian Mueller, Stephan Naunheim, Matthias Bovelett

Preclinical research and organ-dedicated applications require high-resolution positron emission tomography (PET) detectors to visualize small structures and understand biological processes at a finer level of detail. Current commercial systems often employ finely pixelated or monolithic scintillators, each with its limitations. We present a semi-monolithic d

Ronald Schnitzer, Andreas Hapfelmeier, Sven Gaube, Sonja Zillner

Recent advancements in the field of Artificial Intelligence (AI) establish the basis to address challenging tasks. However, with the integration of AI, new risks arise. Therefore, to benefit from its advantages, it is essential to adequately handle the risks associated with AI. Existing risk management processes in related fields, such as software systems, n

Anna Fino, Asia Mainenti

A $p$-K\"ahler structure on a complex manifold of complex dimension $n$ is given by a $d$-closed transverse real $(p,p)$-form. In the paper we study the existence of $p$-K\"ahler structures on compact quotients of simply connected Lie groups by discrete subgroups endowed with an invariant complex structure. In particular, we discuss the existence of $p$-K\"a

Parvathy S Nair, Hadi Zadeh-Haghighi, Christoph Simon

The N-methyl-D-aspartate receptor is a prominent player in brain development and functioning. Perturbations to its functioning through external stimuli like magnetic fields can potentially affect the brain in numerous ways. Various studies have shown that magnetic fields of varying strengths affect these receptors. We propose that the radical pair mechanism,

Ahmet M. Elbir, Kumar Vijay Mishra, Symeon Chatzinotas

Automotive radar at terahertz (THz) band has the potential to provide compact design. The availability of wide bandwidth at THz-band leads to high range resolution. Further, very narrow beamwidth arising from large arrays yields high angular resolution up to milli-degree level direction-of-arrival (DoA) estimation. At THz frequencies and extremely large arra

Pablo Krupa, Daniel Limon, Alberto Bemporad, Teodoro Alamo

Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased performance and domain of attraction with respect to other MPC formulations. This article presents an extensio

Pyei Phyo Lin, Matthias Wächter, Joachim Peinke, M. Reza Rahimi Tabar

The measured time series from complex systems are renowned for their intricate stochastic behavior, characterized by random fluctuations stemming from external influences and nonlinear interactions. These fluctuations take diverse forms, ranging from continuous trajectories reminiscent of Brownian motion to noncontinuous trajectories featuring jump events. T

Noah Goodall

Transportation agencies routinely collect weather data to support maintenance activities. With the proliferation of smartphones, many agencies have begun using crowdsourced data in operations. This study evaluates a novel unplowed roads dataset from the largest crowdsourced transportation data provider Waze. User-reported unplowed roads in Virginia were comp

Kasia Budzik

Twisted holography relates the two-dimensional chiral algebra subsector of $\mathcal{N}=4$ SYM to the B-model topological string theory on the deformed conifold $SL(2,\mathbb{C})$. We review the relevant aspects of the duality and its two generalizations: the correspondence between determinant operators and "Giant Graviton" branes and the extension to non-co

Aleksandra Nelson, Evelyn Tang

Topological theory predicts the necessary conditions for robust dimensional reduction in a host of quantum and classical systems. Models have recently been proposed for stochastic systems which describe many biological and chemical phenomena. However, general theoretical principles are lacking for this class of systems, exemplified by the breakdown of the ce

Siegfried Carl, Hossein Tehrani

In this paper our main goal is to present a new global $L^\infty$-estimate for a general class of quasilinear elliptic equations of the form $$ -div \mathcal{A}(x,u,\nabla u)=\mathcal{B}(x,u,\nabla u) $$ under minimal structure conditions on the functions $\mathcal{A}$ and $\mathcal{B}$, and in arbitrary domains of $\mathbb{R}^N$. The main focus and the nove

Alfredo Miranda, Julio D. Rossi

In this paper we find viscosity solutions to the two membranes problem (that is a system with two obstacle-type equations) with two different $p-$Laplacian operators taking limits of value functions of a sequence of games. We analyze two-player zero-sum games that are played in two boards with different rules in each board. At each turn both players (one ins

Kai Li, Yupeng Deng, Yunlong Kong, Diyou Liu

More accurate extraction of invisible building footprints from very-high-resolution (VHR) aerial images relies on roof segmentation and roof-to-footprint offset extraction. Existing methods based on instance segmentation suffer from poor generalization when extended to large-scale data production and fail to achieve low-cost human interaction. This prompt pa

Marco Molinaro, Valerio Formato, Carmelo Magnafico, Federico Benvenuto

The project CAESAR (Comprehensive spAce wEather Studies for the ASPIS prototype Realization) is aimed to tackle all the relevant aspects of Space Weather (SWE) and realize the prototype of the scientific data centre for Space Weather of the Italian Space Agency (ASI) called ASPIS (ASI SPace Weather InfraStructure). This contribution is meant to bring attenti

Federica Quaglia, Anastasia Chasapi, Maria Victoria Nugnes, Maria Cristina Aspromonte

The DisProt database is a significant resource containing manually curated data on experimentally validated intrinsically disordered proteins (IDPs) and regions (IDRs) from the literature. Developed in 2005, its primary goal was to collect structural and functional information into proteins that lack a fixed three-dimensional (3D) structure. Today, DisProt h

Caelan Atamanchuk, Luc Devroye, Massimo Vicenzo

Given some binary matrix $M$, suppose we are presented with the collection of its rows and columns in independent arbitrary orderings. From this information, are we able to recover the unique original orderings and matrix? We present an algorithm that identifies whether there is a unique ordering associated with a set of rows and columns, and outputs either

Kilian Holzapfel, Christian Spannfellner, Omid Aghaei, Andrew Baron

Since 2018, the potential for a high-energy neutrino telescope, named the Pacific Ocean Neutrino Experiment (P-ONE), has been thoroughly examined by two pathfinder missions, STRAW and STRAW-b, short for short for Strings for Absorption Length in Water. The P-ONE project seeks to install a neutrino detector with a one cubic kilometer volume in the Cascadia Ba

Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin

Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning. In this work, we present SkyMath, a large language model for mathematics with 13 billion parameters. By applying self-compare fine-tuning, we have enhanced mathematical reasoning abilities of Skywork-13B-Bas

Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Dujian Ding

In this work, we utilize Large Language Models (LLMs) for a novel use case: constructing Performance Predictors (PP) that estimate the performance of specific deep neural network architectures on downstream tasks. We create PP prompts for LLMs, comprising (i) role descriptions, (ii) instructions for the LLM, (iii) hyperparameter definitions, and (iv) demonst

Jan Jäderberg, Katarina Bendtz, Kristoffer Lindvall, Jan Scheffel

A new magnetic mirror-cusp concept is described - the Novatron - with the potential to confine compact and stable fusion plasmas. Traditionally, the major challenges for open field line designs include MHD interchange modes, drift cyclotron loss-cone (DCLC) modes, neoclassical transport, and axial losses of particles and energy. The novel magnetic field conf

Hiroshi Ohki, Shohei Uemura

We study physical implications of general CP symmetry including CP-like symmetry. Various scattering amplitudes of CP asymmetry are calculated in CP-like symmetric models. We explicitly show that the CP-like transformation leads to a specific relation between different CP asymmetries. The resultant relation is similar to the one obtained in GUT baryogenesis

Bin-Bin Mao, Yi-Ming Ding, Zhe Wang, Shijie Hu

The reduced density matrix (RDM) plays a key role in quantum entanglement and measurement, as it allows the extraction of almost all physical quantities related to the reduced degrees of freedom. However, restricted by the degrees of freedom in the environment, the total system size is often limited, let alone the subsystem. To address this challenge, we pro

Giuseppe Arnone, Florinda Capone

In the present paper, we study the existence, uniqueness and behaviour in time of the solutions to the Darcy-B\'enard problem for an extended-quasi-thermal-incompressible fluid-saturated porous medium uniformly heated from below. Unlike the classical problem, where the compressibility factor of the fluid vanishes, in this paper we allow the fluid to be sligh

Alberto Bressan

Aim of these notes is provide a brief review of the current well-posedness theory for hyperbolic systems of conservation laws in one space dimension, also pointing out open problems and possible research directions. They supplement the slides of the short course given by the author in Erice, May 2023, available at: sites.google.com/view/erice23/speakers-and-

Amir Hossein Barshooi, Elmira Bagheri

This paper aims to enhance the ability to predict nighttime driving behavior by identifying taillights of both human-driven and autonomous vehicles. The proposed model incorporates a customized detector designed to accurately detect front-vehicle taillights on the road. At the beginning of the detector, a learnable pre-processing block is implemented, which

Dai Hai Nguyen, Tetsuya Sakurai, Hiroshi Mamitsuka

Variational inference (VI) can be cast as an optimization problem in which the variational parameters are tuned to closely align a variational distribution with the true posterior. The optimization task can be approached through vanilla gradient descent in black-box VI or natural-gradient descent in natural-gradient VI. In this work, we reframe VI as the opt

Suzan Zuurmond, AnneMarie Borg, Matthijs van Kempen, Remi Wieten

We propose a human-centred explanation method for rule-based automated decision-making systems in the legal domain. Firstly, we establish a conceptual framework for developing explanation methods, representing its key internal components (content, communication and adaptation) and external dependencies (decision-making system, human recipient and domain). Se

Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca

Volatility smile and skewness are two key properties of option prices that are represented by the implied volatility (IV) surface. However, IV surface calibration through nonlinear interpolation is a complex problem due to several factors, including limited input data, low liquidity, and noise. Additionally, the calibrated surface must obey the fundamental f

Giulia Guarda, Domenico Ribezzo, Tommaso Occhipinti, Alessandro Zavatta

Photonic integrated circuits (PICs) are key in advancing quantum technologies for secure communications. They offer inherent stability, low losses and compactness compared to standard fiber-based and free-space systems. Our reasearch demonstrates PIC's effectivness in enhancing quantum communications, implementing a three-state BB84 protocol with decoy-state

Peter Frankl, János Pach, Dömötör Pálvölgyi

Extending the notion of sunflowers, we call a family of at least two sets an odd-sunflower if every element of the underlying set is contained in an odd number of sets or in none of them. It follows from the Erd\H os--Szemer\'edi conjecture, recently proved by Naslund and Sawin, that there is a constant $\mu<2$ such that every family of subsets of an $n$-ele

Luigi Asprino, Enrico Daga, Justin Dowdy, Paul Mulholland

What should a data integration framework for knowledge engineers look like? Recent research on Knowledge Graph construction proposes the design of a fa\c{c}ade, a notion borrowed from object-oriented software engineering. This idea is applied to SPARQL Anything, a system that allows querying heterogeneous resources as-if they were in RDF, in plain SPARQL 1.1

Haiyu Meng, Yee Sin Ang, Ching Hua Lee

Exceptional points are complex branching singularities of non-Hermitian bands that have lately attracted considerable interest, particularly in non-Hermitian photonics. In this article, we review some recent developments in non-Hermitian photonic platforms such as waveguides, photonic crystals, Fabry-Perot resonators and plasmonic systems, and suggest how op

Minjie Wang, Xiaotong Shen, Wei Pan

This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships. The proposed approach addresses this

Franziska Eberle

We study a fundamental online scheduling problem where jobs with processing times, weights, and deadlines arrive online over time at their release dates. The task is to preemptively schedule these jobs on a single or multiple (possibly unrelated) machines with the objective to maximize the weighted throughput, the total weight of jobs that complete before th

Etienne Le Naour, Ghislain Agoua, Nicolas Baskiotis, Vincent Guigue

Deep learning has made significant advances in creating efficient representations of time series data by automatically identifying complex patterns. However, these approaches lack interpretability, as the time series is transformed into a latent vector that is not easily interpretable. On the other hand, Symbolic Aggregate approximation (SAX) methods allow t

Christian Harder, Moritz Fuchs, Yuri Tolkach, Anirban Mukhopadhyay

Traditional initialisation methods, e.g. He and Xavier, have been effective in avoiding the problem of vanishing or exploding gradients in neural networks. However, they only use simple pointwise distributions, which model one-dimensional variables. Moreover, they ignore most information about the architecture and disregard past training experiences. These l

Yuejun Jiao, Song Qiu, Mingsong Chen, Dingding Han

In recent years, vehicle re-identification (Re-ID) has gained increasing importance in various applications such as assisted driving systems, traffic flow management, and vehicle tracking, due to the growth of intelligent transportation systems. However, the presence of extraneous background information and occlusions can interfere with the learning of discr

José Vinaixa, Begoña Mula, Alfredo Deaño, Silvia N. Santalla

We characterize the long-term state of the 1D Dirac vacuum stirred by an impenetrable object, modeled as the ground state of a finite free-fermionic chain dynamically perturbed by a moving classical obstacle which suppresses the local hopping amplitudes. We find two different regimes, depending on the velocity of the obstacle. For a slow motion, the effectiv

Christian Müller, Martin H. Müser, Giuseppe Carbone, Nicola Menga

We study how the commonly neglected coupling of normal and in-plane elastic response affects tribological properties when Hertzian or randomly rough indenters slide past an elastic body. Compressibility-induced coupling is found to substantially increase maximum tensile stresses, which cause materials to fail, and to decrease friction such that Amontons law

Erik Kalz, Abhinav Sharma, Ralf Metzler

A first-principles approach for active chiral hard disks is presented, that explicitly accounts for steric interactions on the two-body level. We derive an effective one-body equation for the joint probability distribution of ositions and angles of the particles. By projecting onto the angular modes, we write a hierarchy for the lowest hydrodynamic modes, i.

Caterina Gregorio, Giovanni Baj, Giulia Barbati, Francesca Ieva

In recent years, research interest in personalised treatments has been growing. However, treatment effect heterogeneity and possibly time-varying treatment effects are still often overlooked in clinical studies. Statistical tools are needed for the identification of treatment response patterns, taking into account that treatment response is not constant over

Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov

Causal discovery is crucial for causal inference in observational studies, as it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation. However, global causal discovery is notoriously hard in the nonparametric setting, with exponential time and sample complexity in the worst case. To address this, we propose local discov

Renjith Mathew Roy, Sudip Pal, Run Yang, Seulki Roh

The heavy-fermion compound CeRhIn$_5$ can be tuned through a quantum critical point, when In is partially replaced by Sn. This way additional charge carriers are introduced and the antiferromagnetic order is gradually suppressed to zero temperature. Here we investigate the temperature-dependent optical properties of CeRh(In$_{1-x}$Sn$_x$)$_5$ single crystals

Philipp Scholl, Maged Iskandar, Sebastian Wolf, Jinoh Lee

In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the manufacturing process using robots, especially in collaboration with humans, is highly intricate. In particular, modeling the friction torque in robotic joints is a longstanding pro

Hare Krishna

In this paper, we analyze the loop corrections to celestial OPE for gluons and gravitons. Even at the loop level, the soft gluons and gravitons have conformal dimensions $\Delta=1-\mathbb Z_{\geq 0}$. The only novelty is the presence of higher poles. At one loop level, there are two types of conformal soft gluons with a single pole and a double pole in the $

Michael Beukman, Devon Jarvis, Richard Klein, Steven James

While reinforcement learning has achieved remarkable successes in several domains, its real-world application is limited due to many methods failing to generalise to unfamiliar conditions. In this work, we consider the problem of generalising to new transition dynamics, corresponding to cases in which the environment's response to the agent's actions differs

Amanda Ferrari Iaquinta, Gustavo Voltani von Atzingen

The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and

Yung Jer Wong, Teck Khim Ng

Diffusion models (DMs) are generative models that learn to synthesize images from Gaussian noise. DMs can be trained to do a variety of tasks such as image generation and image super-resolution. Researchers have made significant improvements in the capability of synthesizing photorealistic images in the past few years. These successes also hasten the need to

Alfredo Pérez, Stefan Prohazka, Ali Seraj

In theories with conserved dipole moment, isolated charged particles (fractons) are immobile, but dipoles can move. We couple these dipoles to the fracton gauge theory and analyze the universal infrared structure. This uncovers an observable double kick memory effect which we relate to a novel dipole soft theorem. Together with their asymptotic symmetries th

Daniel Lozano-Gómez, Vincent Noculak, Jaan Oitmaa, Rajiv R. P. Singh

Gauge theories are powerful tools in theoretical physics, allowing complex phenomena to be reduced to simple principles, and are used in both high-energy and condensed matter physics. In the latter context, gauge theories are becoming increasingly popular for capturing the intricate spin correlations in spin liquids, exotic states of matter in which the dyna

Xingmeng Zhao, Tongnian Wang, Sheri Osborn, Anthony Rios

Language models have seen significant growth in the size of their corpus, leading to notable performance improvements. Yet, there has been limited progress in developing models that handle smaller, more human-like datasets. As part of the BabyLM shared task, this study explores the impact of reinforcement learning from human feedback (RLHF) on language model

Hitesh Chhabra, Jayaseelan Dhakshinamoorthy, Ajaya K. Nayak

The dynamic behavior of non-collinear atomic spin arrangements in a topologically protected magnetic skyrmion plays a pivotal role in potential future spintronic technologies, including racetrack memory based ultra-high-density storage devices. However, the topological nature of the skyrmion comes with an unwanted skyrmion Hall effect (SkHE) that poses a sig

Alexander A. Gaifullin

In 1987 Brehm and K\"uhnel showed that any triangulation of a $d$-manifold (without boundary) that is not homeomorphic to the sphere has at least $3d/2+3$ vertices. Moreover, triangulations with exactly $3d/2+3$ vertices may exist only for `manifolds like projective planes', which can have dimensions $2$, $4$, $8$, and $16$ only. There is a $6$-vertex triang

Olive Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas

Collaborative machine learning (ML) is widely used to enable institutions to learn better models from distributed data. While collaborative approaches to learning intuitively protect user data, they remain vulnerable to either the server, the clients, or both, deviating from the protocol. Indeed, because the protocol is asymmetric, a malicious server can abu

Niki Maria Foteinopoulou, Ioannis Patras

Schizophrenia is a severe yet treatable mental disorder, it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms, therefore there is a need for accurate, personalised assessments. However, the process can be both time-consuming and subjective; hence, there is a m

Tao Shi, Xiao Liang, Yaoyuan Liang, Xinyi Tong

Emotion recognition in conversations (ERC) is a rapidly evolving task within the natural language processing community, which aims to detect the emotions expressed by speakers during a conversation. Recently, a growing number of ERC methods have focused on leveraging supervised contrastive learning (SCL) to enhance the robustness and generalizability of lear

Jiechen Chen, Sangwoo Park, Osvaldo Simeone

Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gains can be obtained if decisions are taken as early as possible as a function of the complexity of the input time series. The decision on when to stop inference and produce a decisi

Christiane Quesne

The known asymptotic relations interconnecting Jacobi, Laguerre, and Hermite classical orthogonal polynomials are generalized to the corresponding exceptional orthogonal polynomials of codimension $m$. It is proved that $X_m$-Laguerre exceptional orthogonal polynomials of type I, II, or III can be obtained as limits of $X_m$-Jacobi exceptional orthogonal pol

Paheli Bhattacharya, Manojit Chakraborty, Kartheek N S N Palepu, Vikas Pandey

Automating code documentation through explanatory text can prove highly beneficial in code understanding. Large Language Models (LLMs) have made remarkable strides in Natural Language Processing, especially within software engineering tasks such as code generation and code summarization. This study specifically delves into the task of generating natural-lang

Martin Johannes Dechant, Olga Lukashova-Sanz, Siegfried Wahl

Trust is essential for our interactions with others but also with artificial intelligence (AI) based systems. To understand whether a user trusts an AI, researchers need reliable measurement tools. However, currently discussed markers mostly rely on expensive and invasive sensors, like electroencephalograms, which may cause discomfort. The analysis of gaze d

Gaurav Sawant

We extend the work of An, Guan and Kleinbock on bounded orbits of diagonalizable flows on $\mathrm{SL}_3(\mathbb{R})/\mathrm{SL}_3(\mathbb{Z})$ to $\mathrm{SL}_3(\mathbb{C})/\mathrm{SL}_3(\mathcal{O}_{\mathbb{K}})$, where $\mathbb{K}$ is an imaginary quadratic field. To achieve this, we first prove a complex analogue of Minkowski's Linear Forms Theorem. We t

Francesco Palma, Matteo Martucci, Coralie Neubüser, Alessandro Sotgiu

In this paper we report the detection of five strong Gamma-Ray Bursts (GRBs) by the High-Energy Particle Detector (HEPD-01) mounted on board the China Seismo-Electromagnetic Satellite (CSES-01), operational since 2018 on a Sun-synchronous polar orbit at a $\sim$ 507 km altitude and 97$^\circ$ inclination. HEPD-01 was designed to detect high-energy electrons

David Kofroň, Petr Kotlařík, Oldřich Semerák

We present a new method for generating the gravitational field of thin disks within the Weyl class of static and axially symmetric spacetimes. Such a gravitational field is described by two metric functions: one satisfies the Laplace equation and represents the gravitational potential, while the other is determined by line integration. We show how to obtain

Anna Yesypenko, Chao Chen, Per-Gunnar Martinsson

This work introduces a kernel-independent, multilevel, adaptive algorithm for efficiently evaluating a discrete convolution kernel with a given source distribution. The method is based on linear algebraic tools such as low rank approximation and ``skeleton representations'' to approximate far-field interactions. While this work is related to previous linear

Chuofan Ma, Yi Jiang, Xin Wen, Zehuan Yuan

Deriving reliable region-word alignment from image-text pairs is critical to learn object-level vision-language representations for open-vocabulary object detection. Existing methods typically rely on pre-trained or self-trained vision-language models for alignment, which are prone to limitations in localization accuracy or generalization capabilities. In th

Markus Linckelmann

We show that dualising transfer maps in Hochschild cohomology of symmetric algebras over complete discrete valuations rings commutes with Tate duality. This is analogous to a similar result for Tate cohomology of symmetric algebras over fields. We interpret both results in the broader context of Calabi-Yau triangulated categories.

Lingrui Li, Yanfeng Zhou, Ge Yang

Unsupervised Domain Adaptation (UDA) is a learning technique that transfers knowledge learned in the source domain from labelled training data to the target domain with only unlabelled data. It is of significant importance to medical image segmentation because of the usual lack of labelled training data. Although extensive efforts have been made to optimize

Yunsheng Ma, Juanwu Lu, Can Cui, Sicheng Zhao

Vehicle-to-vehicle (V2V) communications have greatly enhanced the perception capabilities of connected and automated vehicles (CAVs) by enabling information sharing to "see through the occlusions", resulting in significant performance improvements. However, developing and training complex multi-agent perception models from scratch can be expensive and unnece

Siqi Zheng, Juan Li, Junzhi Wang, Yao Wang

Peptide-like molecule, which has a close connection with the origin of life, has been detected in universe. Mapping observations of HCONH$_2$ and CH$_3$CONH$_2$, two simplest peptide-like molecules, are performed towards Sagittarius B2 (Sgr B2) complex with the IRAM 30m telescope. Seven transitions of HCONH$_2$ and five transitions of CH$_3$CONH$_2$ are used

Qi Dai, Zhanpeng Lu, Zhihao Xu

In this paper, we explore the localization features of wave functions in a family of mosaic quasiperiodic chains obtained by continuously interpolating between two limits: the mosaic Aubry-Andr\'{e} (AA) model, known for its exact mobility edges with extended states in the band-center region, and localized ones in the band-edge regions for a large enough mod

Hana Ahmadzadeh Sarhangi, Dorsa Beigifard, Elahe Farmani, Hamidreza Bolhasani

Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep learning, a promising technology in computer vision, has e

Christian H. Johansen, Johannes Lang, Francesco Piazza

We consider continuous time-crystalline phases in dissipative many-body systems of atoms in cavities, focusing on the role of short-range interatomic interactions. First, we show that the latter can alter the nature of the time crystal by changing the type of the underlying critical bifurcation. Second, we characterize the heating mechanism and dynamics resu

Zachary M. Geballe, Francesca Miozzi, Chris F. Anto, Javier Rojas

Accurate and precise measurements of spectroradiometric temperature are crucial for many high pressure experiments that use diamond anvil cells or shock waves. In experiments with sub-millisecond timescales, specialized detectors such as streak cameras or photomultiplier tubes are required to measure temperature. High accuracy and precision are difficult to

Qizhen Wu, Kexin Liu, Lei Chen, Jinhu Lü

Traditional methods plan feasible paths for multiple agents in the stochastic environment. However, the methods' iterations with the changes in the environment result in computation complexities, especially for the decentralized agents without a centralized planner. Although reinforcement learning provides a plausible solution because of the generalization f

Mohit Singh, Mihir Dharmadhikari, Kostas Alexis

This work presents a camera model for refractive media such as water and its application in underwater visual-inertial odometry. The model is self-calibrating in real-time and is free of known correspondences or calibration targets. It is separable as a distortion model (dependent on refractive index $n$ and radial pixel coordinate) and a virtual pinhole mod