November 2024 arXiv papers — page 182

Taro Kimura, Go Noshita

We further develop the BPS/CFT correspondence between quiver W-algebras/$qq$-characters and partition functions of gauge origami. We introduce $qq$-characters associated with multi-dimensional partitions with nontrivial boundary conditions which we call Donaldson--Thomas (DT) $qq$-characters. They are operator versions of the equivariant DT vertices of toric

Erna Begovic, Anita Carevic, Ivana Sain Glibic

Coupled decompositions are a widely used tool for data fusion. As the volume of data increases, so does the dimensionality of matrices and tensors, highlighting the need for more efficient coupled decomposition algorithms. This paper studies the problem of coupled matrix factorization (CMF), where two matrices represented in low-rank form share a common fact

Daniel Bonilla Licea, Giuseppe Silano, Hajar El Hammouti, Mounir Ghogho

A new class of Multi-Rotor Aerial Vehicles (MRAVs), known as omnidirectional MRAVs (o-MRAVs), has attracted significant interest in the robotics community. These MRAVs have the unique capability of independently controlling their 3D position and 3D orientation. In the context of aerial communication networks, this translates into the ability to control the p

Ting Wang, Xiaolong Wang, Guangrui Liao, Kai Zhu

An accurate description of the photon spectrum line shape is essential for extracting resonance parameters of the $\eta_c$ meson through the radiative transition $J/\psi \to \gamma \eta_{c}$. However, a persistent challenge remains in the form of a divergent tail at high photon energies, arising from the $E_{\gamma}^3$ factor in theoretical calculations. Var

Claudio Fontana, Eckhard Platen, Stefan Tappe

We develop a unified framework for modeling multiple term structures arising in financial, insurance, and energy markets, adopting an extended Heath-Jarrow-Morton (HJM) approach under the real-world probability. We study market viability and characterize the set of local martingale deflators. We conduct an analysis of the associated stochastic partial differ

Yijun Liu, Jiequan Cui, Zhuotao Tian, Senqiao Yang

Deep neural networks (DNNs) often suffer from the overconfidence issue, where incorrect predictions are made with high confidence scores, hindering the applications in critical systems. In this paper, we propose a novel approach called Typicalness-Aware Learning (TAL) to address this issue and improve failure detection performance. We observe that, with the

Stephan Mescher, Maximilian Stegemeyer

The Lusternik-Schnirelmann category of a space was introduced to obtain a lower bound on the number of critical points of a $C^1$-function on a given manifold. Related to Lusternik-Schnirelmann category and motivated by topological robotics, the topological complexity (TC) of a space is a numerical homotopy invariant whose topological properties are an activ

Lai Zhao, Meirong Tang, Zhaoyi Xu

For a short hair black hole(BH) which circumvents the "no-short-hair" theorem, it manifests intense hair behavior in the vicinity of the event horizon, accompanied by remarkable quantum effects. These effects may carry important information about the internal structure and dynamical evolution of BHs, thereby providing a new perspective on the problem of blac

Charles Camboulin, Diego Doimo, Aldo Glielmo

This work presents an analysis of the hidden representations of Variational Autoencoders (VAEs) using the Intrinsic Dimension (ID) and the Information Imbalance (II). We show that VAEs undergo a transition in behaviour once the bottleneck size is larger than the ID of the data, manifesting in a double hunchback ID profile and a qualitative shift in informati

Amr Abdelbari, Bülent Bilgehan, Fadi Al-Turjman

In the realm of digital communication, understanding and mitigating the probability of error is crucial, particularly in Rayleigh fading channels where signal impairments are common. This paper presents a unified approach to derive the probability of error formulations for two-users NOMA systems operating in Rayleigh fading channels. The methodologies and fi

Xiang Gao, Jingwen Li, Zijian Zhong, Xinghui Li

Large-area gratings play a crucial role in various engineering fields. However, traditional interference lithography is limited by the size of optical component apertures, making large-area fabrication a challenging task. Here, a method for fabricating laser interference lithography pattern arrays with a global alignment reference strategy is proposed. This

Ehsan Faghihi, Mohammedreza Zarenejad, Ali-Asghar Beheshti Shirazi

Capturing a video's meaning and critical concepts by analyzing the subtle details is a fundamental yet challenging task in video captioning. Identifying the dominant emotional tone in a video significantly enhances the perception of its context. Despite a strong emphasis on video captioning, existing models often need to adequately address emotional themes,

Jean Barbier, Francesco Camilli, Justin Ko, Koki Okajima

Matrix denoising is central to signal processing and machine learning. Its statistical analysis when the matrix to infer has a factorised structure with a rank growing proportionally to its dimension remains a challenge, except when it is rotationally invariant. In this case the information theoretic limits and an efficient Bayes-optimal denoising algorithm,

Jesus S. Aguilar-Ruiz

Evaluating the performance of classifiers is critical in machine learning, particularly in high-stakes applications where the reliability of predictions can significantly impact decision-making. Traditional performance measures, such as accuracy and F-score, often fail to account for the uncertainty inherent in classifier predictions, leading to potentially

Yuping An, Blaise Goutéraux, Li Li

We explore instabilities in binary superfluids with a nonvanishing relative superflow, particularly focusing on counterflow and coflow instabilities. We extend recent results on the thermodynamic origin of finite superflow instabilities in single-component superfluids to binary systems and derive a criterion for the onset of instability through a hydrodynami

Jörn Bodenhausen, Laurenz Grote, Michael Rademacher, Martin Henze

With critical infrastructure increasingly relying on wireless communication, using end-to-end security such as TLS becomes imperative. However, TLS introduces significant overhead for resource-constrained devices and networks prevalent in critical infrastructure. In this paper, we propose to leverage the degrees of freedom in configuring TLS to dynamically a

Peter Horoschenkoff, Jasper Rödiger, Martin Wilske

This paper aims to address the challenge of designing secure and high performance Quantum Key Distribution Networks (QKDN), which are essential for encrypted communication in the era of quantum computing. Focusing on the control and management (CM) layer essential for monitoring and routing, the study emphasizes centrally managed software defined networks (S

Zhengyang Yu, Arthur Aubret, Marcel C. Raabe, Jane Yang

Toddlers learn to recognize objects from different viewpoints with almost no supervision. During this learning, they execute frequent eye and head movements that shape their visual experience. It is presently unclear if and how these behaviors contribute to toddlers' emerging object recognition abilities. To answer this question, we here combine head-mounted

Mario De Lucia, Paolo Dal Bo, Eugenia Di Giorgi, Tommaso Lari

Transition Edge Sensors (TESs) are amongst the most sensitive cryogenic detectors and can be easily optimized for the detection of massive particles or photons ranging from X-rays all the way down to millimetre radiation. Furthermore, TESs exhibit unmatched energy resolution while being easily frequency domain multiplexed in arrays of several hundred pixels.

Laurent Cervoni, Tonie Fares, Mehdi Roudesli

The coronavirus crisis has increased medical teleconsultations (in France) by 10 times between 2019 and the first 3 months of 2020. However, the consultation and teleconsultation process have remained much the same. To improve the patient pathway and the efficiency of a session, we have developed a conversational assistant to optimize the time spent with the

S Ballet, M Koutchoukali

In the theory of algebraic function fields and their applications to the information theory, the Riemann-Roch theorem plays a fundamental role. But its use, delicate in general, is efficient and practical for applications especially in the case of non-special divisors. So, in this paper, we give a survey of the known results concerning the non-special diviso

A. Mudit Adityaja, Saurabh J. Shigwan, Nitin Kumar

The data-intensive nature of supervised classification drives the interest of the researchers towards unsupervised approaches, especially for problems such as medical image segmentation, where labeled data is scarce. Building on the recent advancements of Vision transformers (ViT) in computer vision, we propose an unsupervised segmentation framework using a

Kenta Ohira, Toru Ohira, Hideki Ohira

Complex systems, such as biological networks, often exhibit intricate rhythmic behaviors that emerge from simple, small-amplitude dynamics in individual components. This study explores how significant oscillatory signals can arise from a minimal system consisting of just two interacting units, each governed by a simple non-autonomous delay differential equat

Christian Hercher

Erdos conjectured that every odd number greater than one can be expressed as the sum of a squarefree number and a power of two. Subsequently, Odlyzko and McCranie provided numerical verification of this conjecture up to $10^7$ and $1.4\cdot 10^9$. In this paper, we extend the verification to all odd integers up to $2^{50}$, thereby improving the previous bou

Muhammad Waqas Ashraf, Ali Hassan, Imad Ali Shah

This paper introduces a real-time Vehicle Collision Avoidance System (V-CAS) designed to enhance vehicle safety through adaptive braking based on environmental perception. V-CAS leverages the advanced vision-based transformer model RT-DETR, DeepSORT tracking, speed estimation, brake light detection, and an adaptive braking mechanism. It computes a composite

David Colomer Matachana

Accurate identification of individual leopards across camera trap images is critical for population monitoring and ecological studies. This paper introduces a deep learning framework to distinguish between individual leopards based on their unique spot patterns. This approach employs a novel adaptive angular margin method in the form of a modified CosFace ar

Hichem Rami Ait El Hara, François Bobot, Guillaume Bury

Choices in the semantics and the signature of a theory are integral in determining how the theory is used and how challenging it is to reason over it. Our interest in this paper lies in the SMT theory of sequences. Various versions of it exist in the literature and in state-of-the-art SMT solvers, but it has not yet been standardized in the SMT-LIB. We refle

Youngwoo Choi, Woojin Kwon, Kate Pattle, Doris Arzoumanian

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicu

Tony J. Puthenpurakal

In this paper we consider projective and injective resolutions of Koszul complexes and give several applications to the study of Koszul homology modules.

Ilya Zisman, Alexander Nikulin, Viacheslav Sinii, Denis Tarasov

In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). However, existing in-context RL methods, such as Algorithm Distillation (AD), demand large, carefully curated datasets and can be unstable and costly to train due to the transient nat

Grigory Belousov, DongSeon Hwang

We classify del Pezzo surfaces with Picard number is equal to one and with four log terminal singular points.

Jiaxin Zhuang, Leon Yan, Zhenwei Zhang, Ruiqi Wang

Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data across various sectors. Anomalies in web service data, for example, can signal critical incidents such as system failures or server malfunctions, necessitating timely detection and response. However, most existing TSAD methodologies rely heavily on

Sichao Li, Tommy Liu, Quanling Deng, Amanda S. Barnard

Conflicting explanations, arising from different attribution methods or model internals, limit the adoption of machine learning models in safety-critical domains. We turn this disagreement into an advantage and introduce EXplanation AGREEment (EXAGREE), a two-stage framework that selects a Stakeholder-Aligned Explanation Model (SAEM) from a set of similar-pe

Pierre-Antoine Comby, Benjamin Lapostolle, Matthieu Terris, Philippe Ciuciu

Achieving high-quality Magnetic Resonance Imaging (MRI) reconstruction at accelerated acquisition rates remains challenging due to the inherent ill-posed nature of the inverse problem. Traditional Compressed Sensing (CS) methods, while robust across varying acquisition settings, struggle to maintain good reconstruction quality at high acceleration factors ($

Sarah Leyder, Jakob Raymaekers, Peter J. Rousseeuw, Thomas Servotte

Robust estimation provides essential tools for analyzing data that contain outliers, ensuring that statistical models remain reliable even in the presence of some anomalous data. While robust methods have long been available in R, users of Python have lacked a comprehensive package that offers these methods in a cohesive framework. RobPy addresses this gap b

Yajnaseni Dutta, Dominique Mattei, Evgeny Shinder

We study the sections, Tate--Shafarevich twists, and the period for an OG10 hyperk\"ahler Lagrangian associated to a cubic fourfold. To do so, we introduce the analytic relative Jacobian sheaf for a Lagrangian fibration of a hyperk\"ahler variety. The Tate--Shafarevich group parameterizing twists is isomorphic to the first cohomology group of this sheaf and

Mike Thelwall, Xiaorui Jiang, Peter A. Bath

Estimating the quality of published research is important for evaluations of departments, researchers, and job candidates. Citation-based indicators sometimes support these tasks, but do not work for new articles and have low or moderate accuracy. Previous research has shown that ChatGPT can estimate the quality of research articles, with its scores correlat

Tara Abrishami, Louis Esperet, Ugo Giocanti, Matthias Hamann

We study the existence of periodic colorings and orientations in locally finite graphs. A coloring or orientation of a graph $G$ is periodic if the resulting colored or oriented graph is quasi-transitive, meaning that $V(G)$ has finitely many orbits under the action of the group of automorphisms of $G$ preserving the coloring or the orientation. When such a

Evgenii Onishchuk, Maksim Dubovitskii, Eduard Horch

This empirical study presents the Decentralized Exchanges Comparison Service (DECS), a novel tool developed by 1inch Analytics to assess exchange efficiency in decentralized finance. The DECS utilizes swap transaction monitoring and simulation techniques to provide unbiased comparisons of swap rates across various DEXes and aggregators. Analysis of almost 1.

Wei-Jian Guo, Zhiwei Pan, Małgorzata Siudek, Jessica Nicole Aguilar

We present two cases of Ly$\alpha$ changing-look (CL) quasars (J1306 and J1512) along with two additional candidates (J1511 and J1602), all discovered serendipitously at $z >2$ through the Dark Energy Spectroscopic Instrument (DESI) and the Sloan Digital Sky Survey (SDSS). It is the first time to capture CL events in Ly$\alpha$ at high redshift, which is cru

Nathalie Blanpain

In 2023, 639,300 people died in France, 35,900 fewer than in 2022, a year of high mortality. Over the last twenty years, from 2004 to 2023, January 3rd was the deadliest day, while August 15th was the least deadly one. Elderly people die significantly less often in the summer. Deaths are also less frequent on public holidays and Sundays. Finally, the risk of

Giancarlo Cobino, Simone Farci

This research proposes a novel drift detection methodology for machine learning (ML) models based on the concept of ''deformation'' in the vector space representation of data. Recognizing that new data can act as forces stretching, compressing, or twisting the geometric relationships learned by a model, we explore various mathematical frameworks to quantify

Yunqiao Yang, Long-Kai Huang, Shengzhuang Chen, Kede Ma

Model editing aims to data-efficiently correct predictive errors of large pre-trained models while ensuring generalization to neighboring failures and locality to minimize unintended effects on unrelated examples. While significant progress has been made in editing Transformer-based large language models, effective strategies for editing vision Transformers

Anran Zhang, Xingfen Wang, Yuhan Zhao

Community detection plays a pivotal role in uncovering closely connected subgraphs, aiding various real-world applications such as recommendation systems and anomaly detection. With the surge of rich information available for entities in real-world networks, the community detection problem in attributed networks has attracted widespread attention. While prev

Joris Labarbe

This study examines the stability of a flexible material interface between two fluids of the same viscosity in interaction with a free surface. When the layers are motionless, we provide evidence for the onset of a novel instability by means of analytical and numerical solution of the associated boundary value problem in the region stable against Rayleigh--T

Shuang Peng, Songze Chen, Hong Liang, Chuang Zhang

Fast and accurate predictions of the spatiotemporal distributions of temperature are crucial to the multi-scale thermal management and safe operation of microelectronic devices. To realize it, an efficient semi-implicit Lax-Wendroff kinetic scheme is developed for numerically solving the transient phonon Boltzmann transport equation (BTE) from the ballistic

Lorenzo Manfredini, Özgür D. Gürcan

A new class of shell models is proposed, where the shell variables are defined on a recurrent sequence of integer wave-numbers such as the Fibonacci or the Padovan series, or their variations including a sequence made of square roots of Fibonacci numbers rounded to the nearest integer. Considering the simplest model, which involves only local interactions, t

Tam W. Nguyen, Kyoungseok Han, Kenji Hirata

This paper considers a class of thrust vectoring systems, which are nonlinear, overactuated, and time-invariant. We assume that the system is composed of two subsystems and there exist singular points around which the linearized system is uncontrollable. Furthermore, we assume that the system is stabilizable through a two-level control allocation. In this pa

M. Noirhomme, I. Mammadli, N. Vanesse, J. Pande

We present an innovative robotic device designed to provide controlled motion for studying active matter. Motion is driven by an internal vibrator powered by a small rechargeable battery. The system integrates acoustic and magnetic sensors along with a programmable microcontroller. Unlike conventional vibrobots, the motor induces horizontal vibrations, resul

Nick Huggett, James Ladyman, Karim P. Y. Thébault

Philosophers have claimed that: (a) Born-Oppenheimer approximation methods for solving molecular Schr\"odinger equations violate the Heisenberg uncertainty relations; therefore, (b) `quantum chemistry' is not fully quantum; and (c) therefore chemistry does not reduce to physics. This paper analyses the reasoning behind Born-Oppenheimer methods and shows that

Hao Peng, Xinguo Ren

In wavefunction-based $\textit{ab-initio}$ quantum mechanical calculations, achieving absolute convergence with respect to the one-electron basis set is a long-standing challenge. In this work, using the random phase approximation (RPA) electron correlation energy as an example, we show how to compute the basis-error-free RPA correlation energy for diatomic

Oleksandr Kosenkov, Parisa Elahidoost, Tony Gorschek, Jannik Fischbach

Context: As the diversity and complexity of regulations affecting Software-Intensive Products and Services (SIPS) is increasing, software engineers need to address the growing regulatory scrutiny. As with any other non-negotiable requirements, SIPS compliance should be addressed early in SIPS engineering - i.e., during requirements engineering (RE). Objectiv

Ivan I. Shevchenko

The problem of analytical estimation of the Lyapunov exponents and Lyapunov timescales of the motion in multiplets of interacting nonlinear resonances is considered. To this end, we elaborate a unified framework, based on the separatrix map theory, which incorporates both an earlier approach for the first fundamental model of perturbed resonance (given by th

Wolfgang Kuhle

The present paper shows that it can be advantageous for traders to publish their information on the true value of an asset even if they (i) cannot build a position in the asset prior to the publication of their information and (ii) cannot charge for the provision of information. The model also shows that the informational content of prices is U-shaped in the

Konstantin E. Osetrin, Vladimir Y. Epp, Altair E. Filippov

An exact non-perturbative model of a gravitational wave with pure radiation is constructed. It is shown that the presence of dust matter in this model contradicts Einstein's field equations. Exact solution of Einstein equations for gravitational wave and pure radiation is obtained. The trajectories of propagation and characteristics of radiation are found. F

Joonas Hilska, Janne Puustinen, Eero Koivusalo, Mircea Guina

We report on the electronic, structural, and optical properties of epitaxial GaSbBi films with varying Bi-concentration (up to 7%Bi) grown on semi-insulating GaAs(100) substrates. The 1 $\mu$m thick GaSbBi epilayers exhibit fully relaxed narrow X-ray diffraction peaks and smooth surface morphology comparable to that of high-quality GaSb epilayers on GaAs. Lo

Pawel Nurowski, Katja Sagerschnig, Dennis The

For any 4D split-signature conformal structure, there is an induced twistor distribution on the 5D space of all self-dual totally null 2-planes, which is $(2,3,5)$ when the conformal structure is not anti-self-dual. Several examples where the twistor distribution achieves maximal symmetry (the split-real form of the exceptional simple Lie algebra of type $\m

Steffen Bötzel, Frank Lechermann, Takasada Shibauchi, Ilya M. Eremin

Recently discovered high-T$_c$ superconductivity in pressurized bilayer nickelate La$_3$Ni$_2$O$_7$ (La-327) is believed to be driven by the non-phononic repulsive interaction. Depending on the strength of the interlayer repulsion, the symmetry of the superconducting order parameter is expected to be either $d$-wave or sign-changing bonding-antibonding $s_{\

Bing-Sui Lu

We consider the Casimir-Polder interaction between a two-level atomic system and a Chern insulator for both the resonant and nonresonant channels. For a right circularly polarized excited atomic state near a Chern insulator with a negative Chern number $C$, the resonant Casimir-Polder force can be monotonically repulsive over a large range of separations. In

Filippo Bracci, Eva A. Gallardo-Gutiérrez

Let $\mathbb H$ be the finite direct sums of $H^2(\mathbb D)$. In this paper, we give a characterization of the closed subspaces of $\mathbb H$ which are invariant under the shift, thus obtaining a concrete Beurling-type theorem for the finite index shift. This characterization presents any such a subspace as the finite intersection, up to an inner function,

Maxim Virta, Jasper Parkkila, Dong Jo Kim

Improved constraints on current model parameters in a heavy-ion collision model are established using the latest measurements from three distinct collision systems. Various observables are utilized from Au--Au collisions at $\sqrt{s_\mathrm{NN}}=200$~GeV and Pb--Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02$~TeV and $\sqrt{s_\mathrm{NN}}=2.76$~TeV. Additionall

Julien Nicolas, César Sabater, Mohamed Maouche, Sonia Ben Mokhtar

The randomized power method has gained significant interest due to its simplicity and efficient handling of large-scale spectral analysis and recommendation tasks. However, its application to large datasets containing personal information (e.g., web interactions, search history, personal tastes) raises critical privacy problems. This paper addresses these is

Kamil Olejník, Zdeněk Kašpar, Jan Zubáč, Sjoerd Telkamp

We demonstrate that epitaxial thin film antiferromagnet Mn2As exhibits the quench-switching effect, which was previously reported only in crystallographically similar antiferromagnetic CuMnAs thin films. Quench switching in Mn2As shows stronger increase in resistivity, reaching hundreds of percent at 5K, and significantly longer retention time of the metasta

Mohammad Zbeeb, Mohammad Ghorayeb, Mariam Salman

Artificial Intelligence (AI) research often aims to develop models that can generalize reliably across complex datasets, yet this remains challenging in fields where data is scarce, intricate, or inaccessible. This paper introduces a novel approach that leverages three generative models of varying complexity to synthesize one of the most demanding structured

Xinlin Wang, Zsófia Kräussl, Mats Brorsson

Bankruptcy prediction is an important research area that heavily relies on data science. It aims to help investors, managers, and regulators better understand the operational status of corporations and predict potential financial risks in advance. To improve prediction, researchers and practitioners have begun to utilize a variety of different types of data,

R. Saglia, K. Mehrgan, S. de Nicola, J. Thomas

Core ellipticals, massive early-type galaxies have an almost constant inner surface brightness profile. The size of the core region correlates with the mass of the finally merged black hole. Here we report the first Euclid-based dynamical mass determination of a supermassive black hole. We study the centre of NGC 1272, the second most luminous elliptical gal

Kohki Kawabata

We investigate two-dimensional conformal field theories (CFTs) with affine $\widehat{su}(2)$ and $\widehat{su}(3)$ algebra symmetry. Their bosonic modular-invariant partition functions have been fully classified based on the ADE classification. In this work, we extend the classification to include fermionic and parafermionic CFTs with the same affine symmetr

Sharat Agarwal

Objects, in the real world, rarely occur in isolation and exhibit typical arrangements governed by their independent utility, and their expected interaction with humans and other objects in the context. For example, a chair is expected near a table, and a computer is expected on top. Humans use this spatial context and relative placement as an important cue

Masoud Shokrnezhad, Hamidreza Mazandarani, Tarik Taleb

The effective distribution of user transmit powers is essential for the significant advancements that the emergence of 6G wireless networks brings. In recent studies, Deep Neural Networks (DNNs) have been employed to address this challenge. However, these methods frequently encounter issues regarding fairness and computational inefficiency when making decisi

Zhichao Shao, Xiaojun Yuan, Rodrigo C. de Lamare, Yong Zhang

This work considers an uplink asynchronous massive random access scenario in which a large number of users asynchronously access a base station equipped with multiple receive antennas. The objective is to alleviate the problem of massive collision due to the limited number of orthogonal preambles of an access scheme in which user activity detection is perfor

Jhon Edgar Amaya, Carlos Cotta, Antonio J. Fernández-Leiva, Pablo García-Sánchez

Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic optimization algorithms that interact among them in a cooperative way in order to deal with a specific optimiza

Bharti Garg, Santanu Sarkar

The aim of this paper is to study the vector valued de Branges spaces, which are based on $J$-contractive operator valued analytic functions, and to explore their role in the functional models for simple, closed, densely defined, symmetric operators with infinite deficiency indices.

Camille Moisset, Stéphane Mathis, Paul Billant, Junho Park

With the ongoing characterisation of the atmospheres of exoplanets by the JWST, we are unveiling a large diversity of planetary atmospheres, both in terms of composition and dynamics. As such, it is necessary to build coherent atmospheric models for exoplanetary atmospheres to study their dynamics in any regime of thickness, stratification and rotation. Howe

Teng Bin, Jianming Yao, Tin Lun Lam, Tianwei Zhang

We present a novel algorithm for real-time planar semantic mapping tailored for humanoid robots navigating complex terrains such as staircases. Our method is adaptable to any odometry input and leverages GPU-accelerated processes for planar extraction, enabling the rapid generation of globally consistent semantic maps. We utilize an anisotropic diffusion fil

Yuan Li, Tao Li, Xiaoxue Xu, Xiang Dong

Numerous real-world systems, including manufacturing processes, supply chains, and robotic systems, involve multiple independent entities with diverse objectives. The potential for conflicts arises from the inability of these entities to accurately predict and anticipate each other's actions. To address this challenge, we propose the Preemptive Holistic Coll

Alexander Kamenshchik

We discuss the problem of singularity crossing in isotropic and anisotropic universes. We study at which conditions singularities can disappear in quantum cosmology and how quantum particles behave in the vicinity of singularities. Some attempts to develop general approach to the connection between the field reparametrization and the elimination of singulari

Yuchen He, Xiangfeng Wang

Federated learning is a specific distributed learning paradigm in which a central server aggregates updates from multiple clients' local models, thereby enabling the server to learn without requiring clients to upload their private data, maintaining data privacy. While existing federated learning methods are primarily designed for static data, real-world app

Suvir Mirchandani, David D. Yuan, Kaylee Burns, Md Sazzad Islam

In recent years, imitation learning from large-scale human demonstrations has emerged as a promising paradigm for training robot policies. However, the burden of collecting large quantities of human demonstrations is significant in terms of collection time and the need for access to expert operators. We introduce a new data collection paradigm, RoboCrowd, wh

Jiaxing Yang, Liyu Zhang, Kai Wang, Chen Zhang

The strong coupling between photons and phonons in polar materials gives rise to phonon-polaritons that encapsulate a wealth of physical information, offering crucial tools for the ultrafast terahertz sources and the topological engineering of terahertz light. However, it is still quite challenging to form and manipulate the terahertz phonon-polaritons under

Severin Jakob, David Mayweg, Mattias Thuvander

The evaporation mechanisms during laser-assisted field evaporation of thermally grown chromia is investigated with the newest generation of commercial atom probe tomography instrument, equipped with a deep-UV laser (257.5 nm, 4.8 eV photon energy). By holding the voltage constant, the electrostatic field is kept constant, and the evolution of detection rate

Ye Yuan, Genghua Yan, Samah Akel, Uwe Rau

Lead-halide perovskites are semiconductor materials with attractive properties for photovoltaic and other optoelectronic applications. However, determining crucial electronic material parameters, such as charge-carrier mobility and lifetime, is plagued by a wide range of reported values and inconsistencies caused by interpreting and reporting data originatin

Xiaoshan Li, Guicong Su

We prove an isoperimetric inequalitie on the complex hyperbolic ball with Assumption \ref{assumption}}. As an application, we prove a contraction property for the holomorphic functions in Hardy and weighted Bergman spaces on the complex hyperbolic ball with this assumption. The results can be seen as partial generalization of Kulikov's result on the complex

Shi-Dong Liu, Xiao-Yun Wang, Xiao-Hai Liu, Gang Li

We used an effective Lagrangian approach to investigate the production of the anticharmed pentaquark state $P_{\bar{c}s}^{(*)-}$ with the minimal quark content $\bar{c}sudd$ in the $\Lambda_b^0$ decay and its decay into the $\Lambda D^{(\ast)-}$ and $\Sigma D^{(\ast)-}$. In our calculation, the $P_{\bar{c}s}^{(*)-}$ is considered as the molecule state of the

Michael Kurenkov, Sajad Marvi, Julian Schmidt, Christoph B. Rist

The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices and ensure seamless integration into human-dominated environments. This paper presents a comparative eva

Marcos Moreno-Gonzalez, Antonio Artuñedo, Jorge Villagra

Model-Free Control (MFC) has been applied to a wide variety of systems in which it has shown its performance. MFC offers "model-free operation", but the controller design requires some information from the nominal plant. This paper introduces a new design method for model-free controllers that uses minimal data about the system and retrieves a set of stable

A. Missiaen, H. Mayaffre, S. Krämer, D. Zhao

Although spin and charge stripes in high-Tc cuprates have been extensively studied, the exact range of carrier concentration over which they form a static order remains uncertain, complicating efforts to understand their significance. In La2-xSrxCuO4 (LSCO) and in zero external magnetic field, static spin stripes are confined to a doping range well below p*,

Hanyi Liu, Xianbin Cao, Peng Yang, Zehui Xiong

This paper is concerned with the theoretical modeling and analysis of uplink connection performance of a radiosonde network deployed in a typhoon. Similar to existing works, the stochastic geometry theory is leveraged to derive the expression of the uplink connection probability (CP) of a radiosonde. Nevertheless, existing works assume that network nodes are

Shenyang Huang, Boyang Yu, Yixuan Ma, Chenghao Pan

Bright dipolar excitons, which contain electrical dipoles and have high oscillator strength, are an ideal platform for studying correlated quantum phenomena. They usually rely on carrier tunneling between two quantum wells or two layers to hybridize with nondipolar excitons to gain oscillator strength. In this work, we uncovered a new type of bright infrared

Yuchen He, Chuyun Shen, Xiangfeng Wang, Bo Jin

Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usually employ typical rehearsal mechanisms, which could result in privacy violations or additional onerous storag

Aditya Jain, Amin A. Nizami

Using polarization spinor methods in conjunction with the superspace formalism, we construct 3-point superconformal invariants that are used to determine the form of 3-point correlators of spinning superfield operators in $\mathcal{N}=1$ superconformal field theories (SCFTs) in 4-dimensions. We enumerate the structural form of various spinning 3-point correl

Heiko Geppert, Frank Dürr, Kurt Rothermel

Many applications of cyber-physical systems require real-time communication: manufacturing, automotive, etc. Recent Ethernet standards for Time Sensitive Networking (TSN) offer time-triggered scheduling in order to guarantee low latency and jitter bounds. This requires precise frame transmission planning, which becomes especially hard when dealing with many

Aleksei Aleksandrov, Vladimir Peller

We study the behaviour of functions of self-adjoint operators under relatively bounded and relatively trace class perturbation We introduce and study the class of relatively operator Lipschitz functions. An essential role is played by double operator integrals. We also consider study the class of resolvent Lipschitz functions. Then we obtain a trace formula

Mohammed Azzouzi, Steven Bennett, Victor Posligua, Roberto Bondesan

Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in

Kaixiang Hu, Caixia Kou, Jianhua Yuan

The continuous nonlinear resource allocation problem (CONRAP) has broad applications in economics, engineering, production and inventory management, and often serves as a subproblem in complex programming. Without relying on monotonicity assumptions for the objective and constraint functions, we propose two Lagrangian dual algorithms for solving two types of

Riccardo Poiani, Marc Jourdan, Emilie Kaufmann, Rémy Degenne

We study the fixed-confidence best-arm identification problem in unimodal bandits, in which the means of the arms increase with the index of the arm up to their maximum, then decrease. We derive two lower bounds on the stopping time of any algorithm. The instance-dependent lower bound suggests that due to the unimodal structure, only three arms contribute to

Aoming Liang, Zhaoyang Mu, Qi liu, Ruipeng Li

Partial Differential Equations are foundational in modeling science and natural systems such as fluid dynamics and weather forecasting. The Latent Evolution of PDEs method is designed to address the computational intensity of classical and deep learning-based PDE solvers by proposing a scalable and efficient alternative. To enhance the efficiency and accurac

Longfeng Shen, Yanqi Hou, Jiacong Chen, Liangjin Diao

Accurate segmentation of brain tumors plays a key role in the diagnosis and treatment of brain tumor diseases. It serves as a critical technology for quantifying tumors and extracting their features. With the increasing application of deep learning methods, the computational burden has become progressively heavier. To achieve a lightweight model with good se

André Storhaug, Jingyue Li

Parameter-efficient fine-tuning (PEFT) methods, which fine-tune only a subset of model parameters, offer a promising solution by reducing the computational costs of tuning large language models (LLMs) while maintaining their performance. Existing studies have explored using PEFT and LLMs for various code-related tasks and found that the effectiveness of PEFT

Musaab H. Hamed-Ahmed, Paula Fraga-Lamas, Tiago M. Fernandez-Carames

This paper details the creation of a novel Virtual Reality-based application for the Industrial Metaverse aimed at shipboard fire emergency training for fire drill and evacuation, aligned with the Safety of Life at Sea (SOLAS) convention requirements. Specifically, the application includes gamified scenarios with different levels (e.g., varying fire intensit

Hongliang Lai, Qingzhu Luo

This paper investigates triangle functions induced by tensor products of triangular norms and conorms. For any left continuous t-norm $T$ on $[0,1]$ and any right continuous t-conorm $L$ on $[0,\infty]$, the tensor product $L\otimes T$ induces a triangle function on $\Delp$, giving rise to a partially ordered monoid structure on $(\Delta^+, L \otimes T)$. Th