October 2024 arXiv papers — page 85

Mehdi Monemi, Mehdi Rasti, Matti Latva-aho

This paper presents the characterization of near-field propagation regions for phased array antennas, with a particular focus on the propagation boundaries defined by Fraunhofer and Fresnel distances. These distances, which serve as critical boundaries for understanding signal propagation behavior, have been extensively studied and characterized in the liter

Ludovica Pannitto, Lorenzo Albanesi, Laura Marion, Federica Maria Martines

The paper presents a pilot exploration of the construction, management and analysis of a multimodal corpus. Through a three-layer annotation that provides orthographic, prosodic, and gestural transcriptions, the Gest-IT resource allows to investigate the variation of gesture-making patterns in conversations between sighted people and people with visual impai

Abhishek Pal Majumder

Examples of stochastic processes whose state space representations involve functions of an integral type structure $$I_{t}^{(a,b)}:=\int_{0}^{t}b(Y_{s})e^{-\int_{s}^{t}a(Y_{r})dr}ds, \quad t\ge 0$$ are studied under an ergodic semi-Markovian environment described by an $S$ valued jump type process $Y:=(Y_{s}:s\in\mathbb{R}^{+})$ that is ergodic with a limiti

Albino Carbognani, Marco Fenucci, Raffaele Salerno, Marco Micheli

In recent years, nine small near-Earth asteroids were discovered a few hours before the collision with the Earth: these are about one meter in diameter objects that have all disintegrated in the atmosphere, generating bright fireballs without causing damage. In some cases, several meteorites have been recovered. In cases like these, it is not always possible

Jose Blanchet, Aleksandar Mijatović, Wenhao Yang

Stochastic gradient descent is a classic algorithm that has gained great popularity especially in the last decades as the most common approach for training models in machine learning. While the algorithm has been well-studied when stochastic gradients are assumed to have a finite variance, there is significantly less research addressing its theoretical prope

Will Hawkins, Brent Mittelstadt, Chris Russell

Fine-tuning language models has become increasingly popular following the proliferation of open models and improvements in cost-effective parameter efficient fine-tuning. However, fine-tuning can influence model properties such as safety. We assess how fine-tuning can impact different open models' propensity to output toxic content. We assess the impacts of

Jinzhe Pan, Jingqing Wang, Zelin Yun, Zhiyong Xiao

The vast adoption of Wi-Fi and/or Bluetooth capabilities in Internet of Things (IoT) devices, along with the rapid growth of deployed smart devices, has caused significant interference and congestion in the industrial, scientific, and medical (ISM) bands. Traditional Wi-Fi Medium Access Control (MAC) design faces significant challenges in managing increasing

Camiel Oerlemans, Bram Grooten, Michiel Braat, Alaa Alassi

Predicting the behavior of road users accurately is crucial to enable the safe operation of autonomous vehicles in urban or densely populated areas. Therefore, there has been a growing interest in time series motion prediction research, leading to significant advancements in state-of-the-art techniques in recent years. However, the potential of using LiDAR d

Mao Fabrice Djete

In this paper, we address three Principal--Agent problems in a moral hazard context and show that they are connected. We start by studying the problem of Principal with multiple Agents in cooperation. The term cooperation is manifested here by the fact that the agents optimize their criteria through Pareto equilibria. We show that as the number of agents ten

Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang

Auctions are a vital economic mechanism used to determine the market value of goods or services through competitive bidding within a specific framework. However, much of the current research primarily focuses on the bidding algorithms used within auction mechanisms. This often neglects the potential benefits of incorporating individual users' unique preferen

Quentin Perez, Romain Lefeuvre, Thomas Degueule, Olivier Barais

The acceleration of software development and delivery requires rigorous continuous testing and deployment of software systems, which are being deployed in increasingly diverse, complex, and dynamic environments. In recent years, the popularization of DevOps and integrated software forges like GitLab and GitHub has largely democratized Continuous Integration

Bálint Máté, François Fleuret, Tristan Bereau

We present a method for computing free-energy differences using thermodynamic integration with a neural network potential that interpolates between two target Hamiltonians. The interpolation is defined at the sample distribution level, and the neural network potential is optimized to match the corresponding equilibrium potential at every intermediate time-st

Pei Liu, Nanfang Zheng, Yiqun Li, Junlan Chen

With the development of AI-assisted driving, numerous methods have emerged for ego-vehicle 3D perception tasks, but there has been limited research on roadside perception. With its ability to provide a global view and a broader sensing range, the roadside perspective is worth developing. LiDAR provides precise three-dimensional spatial information, while cam

Christian Nikolajew, Holger Eichelberger

For Industry 4.0 applications, communication protocols and data formats even for legacy devices are fundamental. In this paper, we focus on the Modbus/TCP protocol, which is, e.g., used in energy metering. Allowing Industry 4.0 applications to include data from such protocols without need for programming would increase flexibility and, in turn, improve devel

Sadjad Rezvani, Mansoor Fateh, Yeganeh Jalali, Amirreza Fateh

Early detection of lung cancer is crucial as it increases the chances of successful treatment. Automatic lung image segmentation assists doctors in identifying diseases such as lung cancer, COVID-19, and respiratory disorders. However, lung segmentation is challenging due to overlapping features like vascular and bronchial structures, along with pixel-level

Yongguang Li, Yueqi Cao, Jindong Li, Qi Wang

Source-free Unsupervised Domain Adaptation (SF-UDA) aims to transfer a model's performance from a labeled source domain to an unlabeled target domain without direct access to source samples, addressing critical data privacy concerns. However, most existing SF-UDA approaches assume the availability of abundant source domain samples, which is often impractical

G. Canocchi, G. Morello, K. Lind, I. Carleo

Neutral sodium was the first atom detected in an exoplanetary atmosphere via transmission spectroscopy and remains the most frequently detected species due to its strong doublet in the optical. However, the center-to-limb variation (CLV) of these lines in the host star can bias the Na detection.When combined with the Rossiter-McLaughlin (RM) effect, the CLV

Jarod Hattab, Eran Palti

Integrating out supersymmetric M2 branes wrapped on two-cycles in Calabi-Yau manifolds is an important calculation: it allows the determination of, and in some ways defines, the free energy of topological strings. In these notes, based on a short course aimed at graduate students, we go through various aspects of this calculation in detail. The end result is

Oleg Savchenko, Florian List, Guillermo Franco Abellán, Noemi Anau Montel

Reconstructing cosmological initial conditions (ICs) from late-time observations is a difficult task, which relies on the use of computationally expensive simulators alongside sophisticated statistical methods to navigate multi-million dimensional parameter spaces. We present a simple method for Bayesian field reconstruction based on modeling the posterior d

B. van den Borne, M. Stryjczyk, R. P. de Groote, A. Kankainen

We report on the masses and hyperfine structure of ground and isomeric states in $^{114,116,118,120}$Ag isotopes, measured with the phase-imaging ion-cyclotron-resonance technique (PI-ICR) with the JYFLTRAP mass spectrometer and the collinear laser spectroscopy beamline at the Ion Guide Isotope Separator On-Line (IGISOL) facility, Jyv\"askyl\"a, Finland. We

Thomas Jerkovits, Hannes Bartz, Antonia Wachter-Zeh

The sum-rank metric generalizes the Hamming and rank metric by partitioning vectors into blocks and defining the total weight as the sum of the rank weights of these blocks, based on their matrix representation. In this work, we explore support-guessing algorithms for decoding sum-rank-metric codes. Support-guessing involves randomly selecting candidate supp

Tianyang Zhang, Zhuoxuan Jiang, Shengguang Bai, Tianrui Zhang

With the ever-increasing demands on Question Answering (QA) systems for IT operations and maintenance, an efficient and supervised fine-tunable framework is necessary to ensure the data security, private deployment and continuous upgrading. Although Large Language Models (LLMs) have notably improved the open-domain QA's performance, how to efficiently handle

Edi Sutoyo, Paris Avgeriou, Andrea Capiluppi

Self-Admitted Technical Debt (SATD) refers to circumstances where developers use textual artifacts to explain why the existing implementation is not optimal. Past research in detecting SATD has focused on either identifying SATD (classifying SATD items as SATD or not) or categorizing SATD (labeling instances as SATD that pertain to requirement, design, code,

Taorui Yang, Haifan Yin, Rongguang Song, Lianjie Zhang

This paper proposes a quantum algorithm for Dynamic Metasurface Antennas (DMA) beamforming to suppress interference for an amplify-and-forward relay system in multi-base station environments. This algorithm introduces an efficient dynamic block initialization and overarching block update strategy, which can enhance the Signal-to-Interference-plus-Noise Ratio

Andrea Berra, Viswa Narayanan Sankaranarayanan, Achilleas Santi Seisa, Julien Mellet

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The tar

Lu Dai, Hao Liu, Hui Xiong

Retrieval module can be plugged into many downstream NLP tasks to improve their performance, such as open-domain question answering and retrieval-augmented generation. The key to a retrieval system is to calculate relevance scores to query and passage pairs. However, the definition of relevance is often ambiguous. We observed that a major class of relevance

Anna Sepliarskaia, Sophie Langer, Johannes Schmidt-Hieber

We study the generalization capabilities of Group Convolutional Neural Networks (GCNNs) with ReLU activation function by deriving upper and lower bounds for their Vapnik-Chervonenkis (VC) dimension. Specifically, we analyze how factors such as the number of layers, weights, and input dimension affect the VC dimension. We further compare the derived bounds to

Ralf Römer, Tim Emmert, Angela P. Schoellig

Autonomous drone racing requires powerful perception, planning, and control and has become a benchmark and test field for autonomous, agile flight. Existing work usually assumes static race tracks with known maps, which enables offline planning of time-optimal trajectories, performing localization to the gates to reduce the drift in visual-inertial odometry

Qi Zhou, Zhigui Lin, Michael Pedersen

This paper develops an impulsive faecal-oral model with free boundary to in order to understand how the exposure to a periodic disinfection and expansion of the infected region together influences the spread of faecal-oral diseases. We first check that this impulsive model has a unique globally nonnegative classical solution. The principal eigenvalues of the

Julien Mellet, Andrea Berra, Achilleas Santi Seisa, Viswa Sankaranarayanan

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400~g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop

Peng Qiu, Xiang Lin, Yurong Liang, Hao Yan

Tilt-to-length (TTL) noise, caused by angular jitter and misalignment, is a major noise source in the inter-satellite interferometer for gravitational wave detection. However, the required level of axis alignment of the optical components is beyond the current state of the art. A set of optical parallel plates, called beam alignment mechanism (BAM), is propo

Takayuki Kihara

In this article, we investigate the arithmetical hierarchy from the perspective of realizability theory. An experimental observation in classical computability theory is that the notion of degrees of unsolvability for natural arithmetical decision problems only plays a role in counting the number of quantifiers, jumps, or mind-changes. In contrast, we reveal

Hanseon Joo, Eunji Lee, Minjong Cheon

Water segmentation is critical to disaster response and water resource management. Authorities may employ high-resolution photography to monitor rivers, lakes, and reservoirs, allowing for more proactive management in agriculture, industry, and conservation. Deep learning has improved flood monitoring by allowing models like CNNs, U-Nets, and transformers to

Wen Ge, Guanyi Mou, Emmanuel O. Agu, Kyumin Lee

Human Activity Recognition (HAR) is essential in ubiquitous computing, with far-reaching real-world applications. While recent SOTA HAR research has demonstrated impressive performance, some key aspects remain under-explored. Firstly, HAR can be both highly contextualized and personalized. However, prior work has predominantly focused on being Context-Aware

Enrique Cervero-Martín, Laura Mančinska, Elias Theil

We formalize the task of unitary Schur sampling -- an extension of weak Schur sampling -- which is the process of measuring the Young label and the unitary group register of an input $m$ qudit state. Intuitively, this task is equivalent to applying the Schur transform, projecting onto the isotypic subspaces of the unitary and symmetric groups indexed by the

Heng Zhai, Jilin Mei, Chen Min, Liang Chen

3D semantic occupancy prediction is an essential part of autonomous driving, focusing on capturing the geometric details of scenes. Off-road environments are rich in geometric information, therefore it is suitable for 3D semantic occupancy prediction tasks to reconstruct such scenes. However, most of researches concentrate on on-road environments, and few me

Georges Kassis, Andrea Macrina

Randomised arcade processes are a class of continuous stochastic processes that interpolate in a strong sense, i.e., omega by omega, between any given ordered set of random variables, at fixed pre-specified times. Utilising these processes as generators of partial information, a class of continuous-time martingale -- the filtered arcade martingales (FAMs) --

Songyi Liu, Yongjun Wang, Baoshan Wang, Yunyi Jia

Fully revealing the mathmatical structure of quantum contextuality is a significant task, while some known contextuality theories are only applicable for rank-1 projectors. That is because they adopt the observable-based definitions. This paper overcomes the challenges faced by some known contextuality theories by establishing an event-based contextuality th

Rui Wang, Ying D. Liu, L. P. Chitta, Huidong Hu

An extreme ultraviolet (EUV) close-up view of the Sun offers unprecedented detail of heating events in the solar corona. Enhanced temporal and spatial images obtained by the Solar Orbiter during its first science perihelion enabled us to identify clustered EUV bright tadpoles (CEBTs) occurring near the footpoints of coronal loops. Combining SDO/AIA observati

Longfei Song, Sebastjan Glinsek, Nagamalleswara Rao Alluri, Veronika Kovacova

Acoustic haptic technology adds touch sensations to human-machine interfaces by integrating piezoelectric actuators onto touchscreens. Traditional piezoelectric haptic technologies use opaque lead-containing ceramics that are both toxic and visible. We have developed a highly transparent lead-free piezoelectric haptic device using potassium sodium niobate (K

Hanseul Cho, Jaeyoung Cha, Srinadh Bhojanapalli, Chulhee Yun

Transformers often struggle with length generalization, meaning they fail to generalize to sequences longer than those encountered during training. While arithmetic tasks are commonly used to study length generalization, certain tasks are considered notoriously difficult, e.g., multi-operand addition (requiring generalization over both the number of operands

Jianke Tian, Jia Li, Hengbo Liu, Yan Li

Valley-related multiple Hall effect and piezoelectric response are novel transport characteristics in low-dimensional system, however few studies have reported their coexistence in a single system as well as their coupling relationships. By first-principles calculations, we propose a multifunctional Janus semiconductor, i.e. FeO$_2$SiGeN$_2$ monolayer with l

M V Vismaya, M V Sangaranarayanan

We demonstrate the applicability of the $\epsilon$-convergence algorithm in extracting the critical temperatures and critical exponents of three-dimensional Ising models. We analyze the low temperature magnetization as well as high temperature susceptibility series of simple cubic, body-centered cubic, face-centered cubic and diamond lattices, using two diff

Zhen-Yan Xian, Yan Ya, Rong An

Charge radii can be generally used to encode information about various fine structures of finite nuclei. In this work, a constructed Bayesian neural network based on the Monte Carlo dropout approach is proposed to accurately describe the charge radii of nuclei with proton number $Z\geq20$ and mass number $A\geq40$. More motivated underlying mechanisms are in

Luoyao Chu, Yan Li, Shunyu Yao, Yuru Li

The prevalent high intrinsic absorption in the crystalline state of phase-change materials (PCMs), typically leads to a decline in modulation efficiency for phase-change metasurfaces, underutilizing their potential for quasi-continuous phase-state tuning. This research introduces a concise design approach that maximizes the exploitation of the quasi-continuo

Clara Torres-Latorre

We obtain boundary nondegeneracy and regularity estimates for solutions to non-divergence equations in $C^1$ domains, providing an explicit modulus of continuity. Our results extend the classical Hopf-Oleinik lemma and boundary Lipschitz regularity for domains with $C^{1,\mathrm{Dini}}$ boundaries, while also recovering the known $C^{1-\varepsilon}$ regulari

L. Casamiquela, D. R. Reese, Y. Lebreton, M. Haywood

Reliable stellar age estimates are fundamental for testing several problems in modern astrophysics, in particular since they set the time scales of Galactic dynamical and chemical evolution. In this study, we determine ages using only Gaia DR3 photometry and parallaxes, in combination with interstellar extinction maps, spectroscopic metallicities and $\alpha

Ziyi Liu, Claudio Affolter, Sidi Wu, Yizi Chen

Historical maps provide valuable information and knowledge about the past. However, as they often feature non-standard projections, hand-drawn styles, and artistic elements, it is challenging for non-experts to identify and interpret them. While existing image captioning methods have achieved remarkable success on natural images, their performance on maps is

Jianke Tian, Jia Li, Hengbo Liu, Yan Li

The electronic transport characteristics of two-dimensional (2D) systems have widespread application prospects in the fabrication of multifunctional nanodevices. However, the current research for basic transport phenomena, such as anomalous valley Hall effect (AVHE) and piezoelectric response, is limited to discrete discussion. Here, we theoretically propose

Sheng Liu, Haotian Ye, Lei Xing, James Zou

Hallucination poses a challenge to the deployment of large vision-language models (LVLMs) in applications. Unlike in large language models (LLMs), hallucination in LVLMs often arises from misalignments between visual inputs and textual outputs. This paper investigates the underlying mechanisms of hallucination, focusing on the unique structure of LVLMs that

Marcin Sendera, Amin Sorkhei, Tomasz Kuśmierczyk

Gaussian Processes (GPs) provide a convenient framework for specifying function-space priors, making them a natural choice for modeling uncertainty. In contrast, Bayesian Neural Networks (BNNs) offer greater scalability and extendability but lack the advantageous properties of GPs. This motivates the development of BNNs capable of replicating GP-like behavio

Chang-Gi Lee, Varatharaja Nallathambi, TaeHyeok Kang, Leonardo Shoji Aota

The development of magnetic refrigerators that operate at room temperature without the use of environmentally harmful substances represents a significant advancement in eco-friendly technology. These refrigerators employ the magnetocaloric effect (MCE), which has traditionally been achieved using expensive rare-earth elements such as gadolinium. To facilitat

Ashley Sheil, Jacob Camilleri, Moya Cronin, Melanie Gruben

The term `Digital Divide' emerged in the mid-1990s, highlighting the gap between those with access to emerging information technologies and those without. This gap persists for older adults even in the 21st century. To address this, our study focused on how older adults in Ireland can feel safer online. We conducted a two-phase study. In Phase I, 58 particip

Qiao Sun, Huimin Wang, Jiahao Zhan, Fan Nie

Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, model architecture, reward design, training strategies, and planner pipelines. These planners promise better generalizations on complicated and few-shot cases than previous methods. H

Thanadon Kongkoom, Frank W. Nijhoff, Sikarin Yoo-Kong

We consider the variational principle for the Lagrangian 1-form structure for long-range models of Calogero-Moser (CM) type. The multiform variational principle involves variations with respect to both the field variables as well as the independent variables corresponding to deformations of the time-curves in a multi-time space. The ensuing generalised Euler

Thomas George, Pierre Nodet, Alexis Bondu, Vincent Lemaire

Mislabeled examples are ubiquitous in real-world machine learning datasets, advocating the development of techniques for automatic detection. We show that most mislabeled detection methods can be viewed as probing trained machine learning models using a few core principles. We formalize a modular framework that encompasses these methods, parameterized by onl

Julien Verges

We study two continuous and isotropic analogues of the model of greedy lattice animals introduced by Cox, Gandolfi, Griffin and Kesten in 1993. In our framework, animals collect masses scattered on a Poisson point process on $\mathbb R^d$, and are allowed to have vertices outside the process or not, depending on the model. The author recently proved in a mor

Sidi Wu, Katharina Henggeler, Yizi Chen, Lorenz Hurni

Maps are broadly relevant across various fields, serving as valuable tools for presenting spatial phenomena and communicating spatial knowledge. However, map-making is still largely confined to those with expertise in GIS and cartography due to the specialized software and complex workflow involved, from data processing to visualization. While generative AI

Jianke Tian, Jia Li, Hengbo Liu, Yan Li

The discovery of collinear symmetric-compensated altermagnets (AM) with intrinsic spin splitting provides a route towards energy-efficient and ultrafast device applications. Here, using first-principles calculations and symmetry analysis, we propose a series of AM Cr2SX (X=O, S, Se) monolayer and explore the spin splitting in Cr2SX multilayer. A general desi

Qi Bing, Chaoyi Zhang, Weidong Cai

Creating and understanding art has long been a hallmark of human ability. When presented with finished digital artwork, professional graphic artists can intuitively deconstruct and replicate it using various drawing tools, such as the line tool, paint bucket, and layer features, including opacity and blending modes. While most recent research in this field h

Yi Zhang, Yidong Zhao, Qian Tao

Deformable image registration is inherently a multi-objective optimization (MOO) problem, requiring a delicate balance between image similarity and deformation regularity. These conflicting objectives often lead to poor optimization outcomes, such as being trapped in unsatisfactory local minima or experiencing slow convergence. Deep learning methods have rec

Maximilian Ulmer, Leonard Klüpfel, Maximilian Durner, Rudolph Triebel

We investigate the efficacy of data augmentations to close the domain gap in spaceborne computer vision, crucial for autonomous operations like on-orbit servicing. As the use of computer vision in space increases, challenges such as hostile illumination and low signal-to-noise ratios significantly hinder performance. While learning-based algorithms show prom

Tianlin Liu, Jannes Münchmeyer, Laura Laurenti, Chris Marone

We introduce the Seismic Language Model (SeisLM), a foundational model designed to analyze seismic waveforms -- signals generated by Earth's vibrations such as the ones originating from earthquakes. SeisLM is pretrained on a large collection of open-source seismic datasets using a self-supervised contrastive loss, akin to BERT in language modeling. This appr

Yiwei Guo, Zhihan Li, Chenpeng Du, Hankun Wang

Although discrete speech tokens have exhibited strong potential for language model-based speech generation, their high bitrates and redundant timbre information restrict the development of such models. In this work, we propose LSCodec, a discrete speech codec that has both low bitrate and speaker decoupling ability. LSCodec adopts a multi-stage unsupervised

Lingfang Li, Yating Wei, Gangzhou Wu, Yang Ruan

We report the exact closed-form solutions for higher-order topological states as well as explicit energy-spectrum relationships in two-dimensional (2D) non-Hermitian multi-orbital lattices with generalized boundary conditions. These analytical solutions unequivocally confirm that topological edge states in a 2D non-Hermitian system which feature point-gap to

Renyuan Li, Zhehui Chen, Guanyi Wang

Multi-Output Regression (MOR) has been widely used in scientific data analysis for decision-making. Unlike traditional regression models, MOR aims to simultaneously predict multiple real-valued outputs given an input. However, the increasing dimensionality of the outputs poses significant challenges regarding interpretability and computational scalability fo

Qi Bing, Chaoyi Zhang, Weidong Cai

In contrast to the well-established technique of rasterization, vectorization of images poses a significant challenge in the field of computer graphics. Recent learning-based methods for converting raster images to vector formats frequently suffer from incomplete shapes, redundant path prediction, and a lack of accuracy in preserving the semantics of the ori

María Jesús Carro, Sheldy Ombrosi

Given a bilinear (or sub-bilinear) operator $B$, we prove restricted weighted weak type inequalities of the form $$ ||B(f_1, f_2)||_{L^{p, \infty}(w_1^{p/p_1}w_2^{p/p_2})}\lesssim ||f_1||_{L^{p_1, 1}(w_1)}||f_2||_{L^{p_2, 1}(w_2)}, $$ whenever $B(f_1, f_2)= (T_1f_1) (T_2 f_2)$ is the product of two singular integral operators satisfying Dini conditions. Addi

Ismael Illán García, Francesc D. Muñoz-Escoí, Jordi Arjona Aroca, F. Javier Fernández-Bravo Peñuela

Digital product passports (DPP) have been proposed in the European Ecodesign for Sustainable Products Regulation (ESPR) as a means to keep and provide product information that facilitates product reusage, reparation, and recycling. Thus, DPPs should provide a positive effect on the environmental impact of future manufactured products, preventing waste and pr

Uma Namangalam, Salvi Mohandas, Hemanth Dinesan, Sunil Kumar S

In high-resolution mass spectrometry, an electrospray ionization source is often paired with an ion-funnel to enhance ion transmission. Although it is established that ions experience collision-induced dissociation as they pass through this device, the impact of gas-flow dynamics on ion fragmentation remains unexplored. The present work demonstrates that the

Tianyu Chen, Shuai Lu, Shan Lu, Yeyun Gong

Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obstacle lies in the severe lack of data-there is much fewer proofs than code snippets for Large Language Models (LLMs) t

Xingming Huang, Yuanhong Wang, Min Jiang, Xiang Kang

Recent advancements in space science and technologies offer exciting prospects for investigating novel research that is unattainable within terrestrial laboratories. Here we propose the implementation of space-based quantum sensing to explore ultralight new particles beyond the standard model. The central idea involves probing long-range interactions between

P. B. Blakie

The honeycomb supersolid state is predicted to form in a dipolar Bose-Einstein condensate with a planar confining potential. Our results for its excitation spectrum reveal the gapless bands and the emergence of Dirac points at the Brillouin zone edge, manifesting as points where the second sound and transverse sound bands touch. The honeycomb supersolid has

Joshua Amavi, Mirian Halfeld Ferrari, Nicolas Hiot

This paper focuses on a domain expert querying system over databases. It presents a solution designed for a French enterprise interested in offering a natural language interface for its clients. The approach, based on entity enrichment, aims at translating natural language queries into database queries. In this paper, the database is treated through a logica

Yanchen Fana, Xiang Chenb, Dominik Legut, Qianfan Zhang

Rechargeable lithium metal batteries (LMBs) with an ultrahigh theoretical energy density have attracted more and more attentions for their crucial applications of portable electronic devices, electric vehicles, and smart grids. However, the implementation of LMBs in practice is still facing numerous challenges, such as low Coulombic e ciency, poor cycling pe

Roman Ferrer, Rafael Benitez, Vicente J. Bolos

This paper examines the interdependence between green financial instruments, represented by green bonds and green stocks, and a set of major conventional assets, such as Treasury, investment-grade and high-yield corporate bonds, general stocks, crude oil, and gold. To that end, a novel wavelet-based network approach that allows for assessing the degree of in

Houwang Li, Tianhao Liu, Wenming Zou

This paper focuses on the existence and multiplicity of normalized solutions for the coupled Schrodinger system with Sobolev critical coupling term. We present several existence and multiplicity results under some explicit conditions. Furthermore, we present a non-existence result for the defocusing case. This paper, together with the paper [T. Bartsch, H. W

Peiji Yang, Fengping Wang, Yicheng Zhong, Huawei Wei

Neural speech codecs have demonstrated their ability to compress high-quality speech and audio by converting them into discrete token representations. Most existing methods utilize Residual Vector Quantization (RVQ) to encode speech into multiple layers of discrete codes with uniform time scales. However, this strategy overlooks the differences in informatio

Shaonan Wu, Shuai Lu, Yeyun Gong, Nan Duan

Formal proofs are challenging to write even for experienced experts. Recent progress in Neural Theorem Proving (NTP) shows promise in expediting this process. However, the formal corpora available on the Internet are limited compared to the general text, posing a significant data scarcity challenge for NTP. To address this issue, this work proposes Alchemy,

Jiang Hua, Michael Bewong, Selasi Kwashie, MD Geaur Rahman

Data imputation addresses the challenge of imputing missing values in database instances, ensuring consistency with the overall semantics of the dataset. Although several heuristics which rely on statistical methods, and ad-hoc rules have been proposed. These do not generalise well and often lack data context. Consequently, they also lack explainability. The

Elin Dypvik Sødahl, Jesús Carrete, Georg K. H. Madsen, Kristian Berland

Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to properties such as room temperature ferroelectricity, low-energy synthesis, malleability, and potential for multiaxial polarization. The ferroelectric molecular salt HdabcoClO4 is of p

Li-Ming Cao, Long-Yue Li, Xia-Yuan Liu

Quantum Improved Regular Kerr (QIRK) black hole is a rotating regular black hole model constructed based on the asymptotic safety method. The model eliminates the ring singularity and prevents the formation of closed timelike curves, while retaining well-defined thermodynamic properties. Given these properties, probing the observable features of the QIRK bla

Yankai Jiang, Wenhui Lei, Xiaofan Zhang, Shaoting Zhang

Recent advancements in medical vision-language pre-training models have driven significant progress in zero-shot disease recognition. However, transferring image-level knowledge to pixel-level tasks, such as lesion segmentation in 3D CT scans, remains a critical challenge. Due to the complexity and variability of pathological visual characteristics, existing

Maximilian Maurer, Tanise Ceron, Sebastian Padó, Gabriella Lapesa

Political discourse on Twitter is a moving target: politicians continuously make statements about their positions. It is therefore crucial to track their discourse on social media to understand their ideological positions and goals. However, Twitter data is also challenging to work with since it is ambiguous and often dependent on social context, and consequ

Mohammad Hasan Ahmadilivani, Jaan Raik, Masoud Daneshtalab, Maksim Jenihhin

The growing exploitation of Machine Learning (ML) in safety-critical applications necessitates rigorous safety analysis. Hardware reliability assessment is a major concern with respect to measuring the level of safety in ML-based systems. Quantifying the reliability of emerging ML models, including Convolutional Neural Networks (CNNs), is highly complex due

Nick Scharrer, Samuel T. Spencer, Vikas Joshi, Alison M. W. Mitchell

Supernova remnants (SNRs) are likely sources of hadronic particle acceleration within our galaxy, contributing to the galactic cosmic ray flux. Next-generation instruments, such as the Southern Wide-field Gamma-ray Observatory (SWGO), will be of crucial importance in identifying new candidate SNRs. SWGO will observe two-thirds of the gamma-ray sky, covering

Bernardo Carvalho

We prove the transitivity of real Anosov diffeomorphisms, which are Anosov diffeomorphisms where stable and unstable spaces decompose into a continuous sum of invariant one-dimensional sub-spaces with uniform contraction/expansion over the ambient manifold. We prove that if a stable/unstable curve has a well-defined length in a conformal hyperbolic distance,

Takahiko Nobukawa, Tatsushi Shimazaki

We provide the special values of the skew version of the $K$-theoretic Schur $P$- and $Q$-functions. Using these special values, we show an oddness property of the number of shifted set-valued skew tableaux. Additionally, we generalize these special values to another skew case. Based on these special values, we give pairs among certain shifted set-valued ske

Bo Li, Ankang Sun, Shiji Xing

In this paper, we study the allocation of indivisible chores and consider the problem of finding a fair allocation that is approximately efficient. We shift our attention from the multiplicative approximation to the additive one. Our results are twofold, with (1) bounding how the optimal social cost escalates resulting from fairness requirements and (2) pres

Quang Hieu Pham, Hoang Ngo, Anh Tuan Luu, Dat Quoc Nguyen

Retrieval-augmented generation (RAG) methods are viable solutions for addressing the static memory limits of pre-trained language models. Nevertheless, encountering conflicting sources of information within the retrieval context is an inevitable practical challenge. In such situations, the language models are recommended to transparently inform users about t

CH. Pallavi, C. Padma, R. Kiran Kumar, T. Suguna

The main areas of research in VLSI system design include area, high speed, and power-efficient data route logic systems. The amount of time needed to send a carry through the adder limits the pace at which addition can occur in digital adders. One of the quickest adders, the Carry Select Adder (CSLA), is utilized by various data processing processors to carr

Abhishek Thakur

With the advancements in open-source models, training (or finetuning) models on custom datasets has become a crucial part of developing solutions which are tailored to specific industrial or open-source applications. Yet, there is no single tool which simplifies the process of training across different types of modalities or tasks. We introduce AutoTrain (ak

Guo Yan

We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its computational scalability in the number of covariates. For instance, even when assuming a normal error distribution as in pr

Hajime Fukuda, Qiang Li, Takeo Moroi, Atsuya Niki

We study the non-thermal production of the Higgsino dark matter (DM). Assuming that the lightest neutral Higgsino is the lightest supersymmetric particle (LSP) in minimal supersymmetric standard model, we calculate the relic abundance of the Higgsino LSP produced by the decay of late-decaying scalar field. In the calculation of the relic abundance, we have p

Xumeng Han, Longhui Wei, Zhiyang Dou, Zipeng Wang

Mixture-of-Experts (MoE) models embody the divide-and-conquer concept and are a promising approach for increasing model capacity, demonstrating excellent scalability across multiple domains. In this paper, we integrate the MoE structure into the classic Vision Transformer (ViT), naming it ViMoE, and explore the potential of applying MoE to vision through a c

Xi Gao, Jinxin Xiong, Akang Wang, Qihong Duan

Solving constrained nonlinear programs (NLPs) is of great importance in various domains such as power systems, robotics, and wireless communication networks. One widely used approach for addressing NLPs is the interior point method (IPM). The most computationally expensive procedure in IPMs is to solve systems of linear equations via matrix factorization. Re

Yu Sheng, Runfeng Lin, Lidian Wang, Quecheng Qiu

Combining accurate geometry with rich semantics has been proven to be highly effective for language-guided robotic manipulation. Existing methods for dynamic scenes either fail to update in real-time or rely on additional depth sensors for simple scene editing, limiting their applicability in real-world. In this paper, we introduce MSGField, a representation

Yannis Montreuil, Shu Heng Yeo, Axel Carlier, Lai Xing Ng

The Two-Stage Learning-to-Defer (L2D) framework has been extensively studied for classification and, more recently, regression tasks. However, many real-world applications require solving both tasks jointly in a multi-task setting. We introduce a novel Two-Stage L2D framework for multi-task learning that integrates classification and regression through a uni

Cristian Meo, Akihiro Nakano, Mircea Lică, Aniket Didolkar

Unsupervised object-centric learning from videos is a promising approach towards learning compositional representations that can be applied to various downstream tasks, such as prediction and reasoning. Recently, it was shown that pretrained Vision Transformers (ViTs) can be useful to learn object-centric representations on real-world video datasets. However

Vahagn Nersesyan, Meng Zhao

We study the mixing properties of the white-forced Navier-Stokes system in the whole space $\mathbb{R}^2$. Assuming that the noise is sufficiently non-degenerate, we prove the uniqueness of stationary measure and polynomial mixing in the dual-Lipschitz metric. The proof combines the coupling method with a Foia\c{s}-Prodi type estimate, weighted growth estima