February 2024 arXiv papers — page 67

Fan Tian, Mirjeta Pasha, Misha E. Kilmer, Eric Miller

This paper is concerned with the problem of recovering third-order tensor data from limited samples. A recently proposed tensor decomposition (BMD) method has been shown to efficiently compress third-order spatiotemporal data. Using the BMD, we formulate a slicewise nuclear norm penalized algorithm to recover a third-order tensor from limited observed sample

Taygun R. Gungor, Ayse G. Gungor. Yvan Maciel

We investigate a turbulent boundary layer (TBL) with uniform pressure force variations, focusing on understanding its response to local pressure force, local pressure force variation (local disequilibrating effect), and upstream history. The studied flow starts as a zero-pressure-gradient (ZPG) TBL, followed by a uniform increase in the ratio of pressure for

Dripto Biswas, Igor Pesando

We define the framed DDF operators by introducing the concept of local frames in the usual formulation of DDF operators. In doing so it is possible to completely decouple the DDF operators from the associated tachyon and show that they are good zero-dimensional conformal operators. This allows for an explicit formulation of the general solution of the Viraso

Luca Mondada, Pablo Andrés-Martínez

Graph rewriting is a popular tool for the optimisation and modification of graph expressions in domains such as compilers, machine learning and quantum computing. The underlying data structures are often port graphs - graphs with labels at edge endpoints. A pre-requisite for graph rewriting is the ability to find graph patterns. We propose a new solution to

Andres Molares-Ulloa, Elisabet Rocruz, Daniel Rivero, Xosé A. Padin

Diarrhetic Shellfish Poisoning (DSP) is a global health threat arising from shellfish contaminated with toxins produced by dinoflagellates. The condition, with its widespread incidence, high morbidity rate, and persistent shellfish toxicity, poses risks to public health and the shellfish industry. High biomass of toxin-producing algae such as DSP are known a

Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang

We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on seed examples or existing datasets to construct instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of human knowledge and capabilities as input and generates l

Neelakash Biswas, Oliver R. H. Buxton

In this work we investigate the spatio-temporal nature of various coherent modes present in a rotor wake using a combination of new PIV experiments and data from Biswas and Buxton (2024). A multi-scale triple decomposition of the acquired velocity field is sought to extract the coherent modes and thereafter, the energy exchanges to and from them are studied

Sen Yuan, Francesco Fioranelli, Alexander Yarovoy

The problem of 3D high-resolution imaging in automotive multiple-input multiple-output (MIMO) side-looking radar using a 1D array is considered. The concept of motion-enhanced snapshots is introduced for generating larger apertures in the azimuth dimension. For the first time, 3D imaging capabilities can be achieved with high angular resolution using a 1D MI

Hao-Wei Chung, Ching-Hao Chiu, Yu-Jen Chen, Yiyu Shi

Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition. However, we see the deficiency in the previous logits space constraint methods. Therefore, we propose a novel framework, Logits-MMD, that achieves the fairness condition by imposing constraints on output logit

Morteza Rasouligandomani, Alex del Arco, Tomaso Villa, Luigi La Barbera

Background: Proximal Junctional Failure (PJF) is a post-operative complication in adult spine surgery, often requiring reoperation. Osteotomy is often used in revision surgeries, leading to 34.8% complications. Hence, suboptimal decisions might be extending hardware without osteotomy, which yields to severe Global Alignment and Proportion (GAP) scores. High

Flaviano Della Pia, Andrea Zen, Dario Alfè, Angelos Michaelides

Molecular crystals play a central role in a wide range of scientific fields, including pharmaceuticals and organic semiconductor devices. However, they are challenging systems to model accurately with computational approaches because of a delicate interplay of intermolecular interactions such as hydrogen bonding and van der Waals dispersion forces. Here, by

Tianxiang Zhan, Zhen Li, Yong Deng

Evidence theory is widely used in decision-making and reasoning systems. In previous research, Transferable Belief Model (TBM) is a commonly used evidential decision making model, but TBM is a non-preference model. In order to better fit the decision making goals, the Evidence Pattern Reasoning Model (EPRM) is proposed. By defining pattern operators and deci

Davide Giordano Ario Altamura, Matteo Carlesso, Sandro Donadi, Angelo Bassi

The Continuous Spontaneous Localisation (CSL) model is the most studied among collapse models, which describes the breakdown of the superposition principle for macroscopic systems. Here, we derive an upper bound on the parameters of the model by applying it to the rotational noise measured in a recent short-distance gravity experiment [Lee et al., Phys. Rev.

Balqees Talal Hasan, Ali Kadhum Idrees

Over the past few years, The idea of edge computing has seen substantial expansion in both academic and industrial circles. This computing approach has garnered attention due to its integrating role in advancing various state-of-the-art technologies such as Internet of Things (IoT) , 5G, artificial intelligence, and augmented reality. In this chapter, we int

Jie Ren, Qipeng Guo, Hang Yan, Dongrui Liu

Although large language models (LLMs) have demonstrated remarkable performance, the lack of transparency in their inference logic raises concerns about their trustworthiness. To gain a better understanding of LLMs, we conduct a detailed analysis of the operations of attention heads and aim to better understand the in-context learning of LLMs. Specifically, w

Alexander Raßloff, Paul Seibert, Karl A. Kalina, Markus Kästner

Tailoring materials to achieve a desired behavior in specific applications is of significant scientific and industrial interest as design of materials is a key driver to innovation. Overcoming the rather slow and expertise-bound traditional forward approaches of trial and error, inverse design is attracting substantial attention. Targeting a property, the de

Theophanes K. Karydas, Bradley J. Kavanagh, Gianfranco Bertone

Dark matter overdensities around black holes can alter the dynamical evolution of a companion object orbiting around it, and cause a dephasing of the gravitational waveform. Here, we present a refined calculation of the co-evolution of the binary and the dark matter distribution, taking into account the accretion of dark matter particles on the companion bla

ATLAS Collaboration

This Letter presents a differential cross-section measurement of Lund subjet multiplicities, suitable for testing current and future parton shower Monte Carlo algorithms. This measurement is made in dijet events in 140 fb$^{-1}$ of $\sqrt{s}=13$ TeV proton-proton collision data collected with the ATLAS detector at CERN's Large Hadron Collider. The data are u

C. Douglas Haessig, Steven Sperber

The symmetric power L-function of the hyper-Kloosterman family is a rational function over the integers. Its degree and complex absolute values of its zeros and poles are now known through the work of Fu and Wan. The purpose of this paper is to study the p-adic absolute value of these zeros and poles. In particular, we give a uniform lower bound, independent

Marco Caroccia, Riccardo Scala

Given any $\Gamma=\gamma(\mathbb{S}^1)\subset\mathbb{R}^2$, image of a Lipschitz curve $\gamma:\mathbb{S}^1\rightarrow \mathbb{R}^2$, not necessarily injective, we provide an explicit formula for computing the value of \[ \mathcal A(\gamma):=\inf\left\{\left. \int_{B_1(0)}|\mathrm{det}(\nabla u)| \mathrm{d} x \ \right| \ u=\gamma \text{ on }\mathbb{S}^1\righ

Samuel Epstein

An overwhelming majority of quantum (pure and mixed) states, when undertaking a POVM measurement, will result in a classical probability with no algorithmic information. Thus most quantum states produce white noise when measured. Furthermore most non-pointer states, when undergoing the decoherence process, will produce white noise. These results can be seen

Zihao Wei, Liang Pang, Hanxing Ding, Jingcheng Deng

Efficient knowledge editing of large language models is crucial for replacing obsolete information or incorporating specialized knowledge on a large scale. However, previous methods implicitly assume that knowledge is localized and isolated within the model, an assumption that oversimplifies the interconnected nature of model knowledge. The premise of locali

Caio F. B. Macedo, João Luís Rosa, Diego Rubiera-Garcia

Black holes in General Relativity are described by space-time metrics that are simpler in comparison to non-vacuum compact objects. However, given the universality of the gravitational pull, it is expected that dark matter accumulates around astrophysical black holes, which can have an impact in the overall gravitational field, especially at galactic centers

Sandra Saade, Hugh G. A. Burton

State-specific complete active space self-consistent field (SS-CASSCF) theory has emerged as a promising route to accurately predict electronically excited energy surfaces away from molecular equilibria. However, its accuracy and practicality for chemical systems of photochemical interest has yet to be fully determined. We investigate the performance of SS-C

Krutika Tawri

We prove the existence of martingale solutions to a stochastic fluid-structure interaction problem involving a viscous, incompressible fluid flow, modeled by the Navier-Stokes equations, through a deformable elastic tube modeled by shell/membrane equations. The fluid and the structure are nonlinearly coupled via the kinematic and dynamic coupling conditions

Wansong Liu, Sibo Tian, Boyi Hu, Xiao Liang

This paper presents a deep learning enhanced adaptive unscented Kalman filter (UKF) for predicting human arm motion in the context of manufacturing. Unlike previous network-based methods that solely rely on captured human motion data, which is represented as bone vectors in this paper, we incorporate a human arm dynamic model into the motion prediction algor

Shubham Yadav, M. Mishra, Tapomoy Guha Sarkar

We attempt to find the impact of a modified Tolman Oppenheimer Volkoff (TOV) system of equations on the luminosities of direct photons, neutrinos and axions for a particular axion mass in the presence of a magnetic field. We employ two different equation of states (EoSs) namely APR and FPS to generate the profiles of mass and pressure for spherically symmetr

Seanie Lee, Jianpeng Cheng, Joris Driesen, Alexandru Coca

Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt learning. Previous works use raw dialogue context as search keys and queries, and a retriever is fine-tuned with annotated dialogues to achieve superior performance. However, the ap

Jiahao Ai, Zhimei Ren

We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts. This framework distinguishes the shift in covariate distributions from that in the conditional relationship between the outcome ($Y$) and the covariates ($X$). We propose to reweight the training samples to adjust for an identifiable covaria

Morteza Rasouligandomani, Alex del Arco, Francis Kiptengwer Chemorion, Marc-Antonio Bisotti

Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalized FE models is often complex and t

Haisong Gong, Qiang Liu, Shu Wu, Liang Wang

Text-guided molecule generation is a task where molecules are generated to match specific textual descriptions. Recently, most existing SMILES-based molecule generation methods rely on an autoregressive architecture. In this work, we propose the Text-Guided Molecule Generation with Diffusion Language Model (TGM-DLM), a novel approach that leverages diffusion

Paul C. Duffell, Alexander J. Dittmann, Daniel J. D'Orazio, Alessia Franchini

We have performed numerical calculations of a binary interacting with a gas disk, using eleven different numerical methods and a standard binary-disk setup. The goal of this study is to determine whether all codes agree on a numerically converged solution, and to determine the necessary resolution for convergence and the number of binary orbits that must be

Martin Jacquet, Kostas Alexis

This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework exploiting a Deep Neural Network for processing onboard-captured depth images for collision avoidance in trajectory-tracking tasks with UAVs. The network is trained on simulated depth images to output a collision score for queried 3D points within the sensor field of view. Then, thi

Maksim Bobrin, Nazar Buzun, Dmitrii Krylov, Dmitry V. Dylov

Offline Reinforcement Learning (RL) addresses the problem of sequential decision-making by learning optimal policy through pre-collected data, without interacting with the environment. As yet, it has remained somewhat impractical, because one rarely knows the reward explicitly and it is hard to distill it retrospectively. Here, we show that an imitating agen

Shoutao Guo, Shaolei Zhang, Zhengrui Ma, Min Zhang

Simultaneous Machine Translation (SiMT) generates translations while reading the source sentence, necessitating a policy to determine the optimal timing for reading and generating words. Despite the remarkable performance achieved by Large Language Models (LLM) across various NLP tasks, existing SiMT methods predominantly focus on conventional transformers,

Che Zhang, Zhenyang Xiao, Chengcheng Han, Yixin Lian

Self-correction has achieved impressive results in enhancing the style and security of the generated output from large language models (LLMs). However, recent studies suggest that self-correction might be limited or even counterproductive in reasoning tasks due to LLMs' difficulties in identifying logical mistakes. In this paper, we aim to enhance the self-c

Sara Sandh, Hamed Radpour, Benjamin Rainer, Markus Hofer

Ray tracing accelerated with graphics processing units (GPUs) is an accurate and efficient simulation technique of wireless communication channels. In this paper, we extend a GPU-accelerated ray tracer (RT) to support the effects of reconfigurable intelligent surfaces (RISs). To evaluate the electric field, we derived a RIS path loss model that can be integr

Xiangyu Zhao, Zehui Li, Mingzhu Shen, Guy-Bart Stan

Graph augmentation methods play a crucial role in improving the performance and enhancing generalisation capabilities in Graph Neural Networks (GNNs). Existing graph augmentation methods mainly perturb the graph structures, and are usually limited to pairwise node relations. These methods cannot fully address the complexities of real-world large-scale networ

Sean J. Gunderson, Kenneth G. Gayley, David P. Huenemoerder, Pragati Pradhan

We fit the high resolution \textit{Chandra} X-ray spectra of the O supergiant $\zeta$ Puppis using the variable boundary condition (VBC) line model to test the stability of its mass-loss rate between two epochs of observation: 2000 March and 2018 July -- 2019 August. At issue is whether the observed variations are induced by global changes in the cool (unsho

Damien A. Easson, Joseph E. Lesnefsky

The inflationary paradigm has transformed our understanding of the early universe; yet most inflationary models are considered geodesically past-incomplete, suggesting a beginning of time or a primordial Big Bang singularity. The Borde-Guth-Vilenkin (BGV) theorem is often cited as demonstrating that all eternally inflating spacetimes must be past-incomplete.

Emin Moghadas, Nikolaus Dräger, Alessandro Toschi, Jiawei Zang

Precise algorithms capable of providing controlled solutions in the presence of strong interactions are transforming the landscape of quantum many-body physics. Particularly exciting breakthroughs are enabling the computation of non-zero temperature correlation functions. However, computational challenges arise due to constraints in resources and memory limi

Balqees Talal Hasan, Ali Kadhum Idrees

With the help of a new architecture called Edge/Fog (E/F) computing, cloud computing services can now be extended nearer to data generator devices. E/F computing in combination with Deep Learning (DL) is a promisedtechnique that is vastly applied in numerous fields. To train their models, data producers in conventional DL architectures with E/F computing ena

Haisong Gong, Weizhi Xu, Shu wu, Qiang Liu

Fact checking aims to predict claim veracity by reasoning over multiple evidence pieces. It usually involves evidence retrieval and veracity reasoning. In this paper, we focus on the latter, reasoning over unstructured text and structured table information. Previous works have primarily relied on fine-tuning pretrained language models or training homogeneous

Kourosh Parand, Saeed Setayeshi, Mir Mohsen Pedram, Ali Yoonesi

Cognitive decision-making processes are crucial aspects of human behavior, influencing various personal and professional domains. This research delves into the application of differential equations in analyzing decision-making accuracy by leveraging eye-tracking data within a virtual industrial town setting. The study unveils a systematic approach to transfo

Helmut Prodinger

Dispersed Dyck paths are Dyck paths, with possible flat steps on level 0. We revisit and augment questions about them from the Encyclopedia of Integer Sequences, in a systematic way that uses generating functions and the kernel method.

Ying-Jia Lin, Chun-Yi Lin, Chia-Jen Yeh, Yi-Ting Li

We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as "Supports", "Refutes", or "Not Enough Info" to depict its degree of factualness. Similar to the FEVER dataset, claims in the "Supports" and "Refutes" cate

Mersedeh Sadeghi, Lars Herbold, Max Unterbusch, Andreas Vogelsang

Explainability is crucial for complex systems like pervasive smart environments, as they collect and analyze data from various sensors, follow multiple rules, and control different devices resulting in behavior that is not trivial and, thus, should be explained to the users. The current approaches, however, offer flat, static, and algorithm-focused explanati

Lucas Girard, Yannick Guyonvarch

In the 1990s, Joshua Angrist and Guido Imbens studied the causal interpretation of Instrumental Variable estimates (a widespread methodology in economics) through the lens of potential outcomes (a classical framework to formalize causality in statistics). Bridging a gap between those two strands of literature, they stress the importance of treatment effect h

Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu

The growth of social media, characterized by its multimodal nature, has led to the emergence of diverse phenomena and challenges, which calls for an effective approach to uniformly solve automated tasks. The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods,

Robert Righi, Zhongwei Shen

In this paper we establish $W^{1,p}$ estimates for solutions $u_\varepsilon$ to Laplace's equation with the Dirichlet condition in a bounded and perforated, not necessarily periodically, $C^1$ domain $\Omega_{\varepsilon, \eta}$ in $\mathbb{R}^d$. The bounding constants depend explicitly on two small parameters $\varepsilon$ and $\eta$, where $\varepsilon$ r

Max Bergmann, Jürgen Belz, Oliver Maßmeyer, Badrosadat Ojaghi Dogahe

In this study, we investigate excitonic properties of epitaxially grown WS2, which is of particular interest for various applications due to its potential for upscaling to wafer sized structures. Understanding the effect of the dielectric environment due to changing layer numbers and multi-material heterostructures on the optical properties is crucial for ta

Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia

Neurosymbolic AI is a growing field of research aiming to combine neural networks learning capabilities with the reasoning abilities of symbolic systems. This hybridization can take many shapes. In this paper, we propose a new formalism for supervised multi-label classification with propositional background knowledge. We introduce a new neurosymbolic techniq

Haibin Wu, Huang-Cheng Chou, Kai-Wei Chang, Lucas Goncalves

Speech emotion recognition (SER) is a pivotal technology for human-computer interaction systems. However, 80.77% of SER papers yield results that cannot be reproduced. We develop EMO-SUPERB, short for EMOtion Speech Universal PERformance Benchmark, which aims to enhance open-source initiatives for SER. EMO-SUPERB includes a user-friendly codebase to leverage

Nicolas Moreno, David Vazquez-Cortes, Eliot Fried

Chiral objects have intrigued scientists across several disciplines, including mathematics, crystallography, chemistry, and biology. A M\"obius band, an emblematic chiral structure, can be made by connecting the ends of a strip after applying an odd number of twists. Traditionally, the direction of the twist governs its rotational behaviour during sedimentat

Vincent Jung, Lonneke van der Plas

We study the effect of one type of imbalance often present in real-life multilingual classification datasets: an uneven distribution of labels across languages. We show evidence that fine-tuning a transformer-based Large Language Model (LLM) on a dataset with this imbalance leads to worse performance, a more pronounced separation of languages in the latent s

Igor Yu. Potemine

We reexamine the SIMBAD database with incorporated Gaia DR3 parallaxes and proper motions. Appropriate query searches allow us to find several nearby stars with measured radial velocities having flybies within 1 ly from the Sun (in linear approximation). The closest past flyby $\approx 215.7$ kyr ago at $\approx 0.136$ pc is attributed to the star UCAC4 323-

A. A. Araújo Filho, J. R. Nascimento, A. Yu. Petrov, P. J. Porfírio

Within the framework of the spontaneous Lorentz symmetry breaking, we consider a metric--affine generalization of the gravitational sector of the Standard--Model Extension (SME), including the Lorentz--violating (LV) coefficients $u$ and $s^{\mu\nu}$. In this model, we derive the modified Einstein field equations in order to obtain a new axisymmetric vacuum

Demin Song, Honglin Guo, Yunhua Zhou, Shuhao Xing

The programming skill is one crucial ability for Large Language Models (LLMs), necessitating a deep understanding of programming languages (PLs) and their correlation with natural languages (NLs). We examine the impact of pre-training data on code-focused LLMs' performance by assessing the comment density as a measure of PL-NL alignment. Given the scarcity o

Mishio Kawashita, Wakako Kawashita

In this paper, the inverse problem of the wave equation by the enclosure method for a medium with multiple types of cavities is discussed. In the case considered here, the sign of the indicator function of the enclosure method is not determined and sign cancellation may occur, resulting in loss of information. By examining the top terms of the indicator func

Kehuan Feng, Songlin Han, Minyu Feng, Attila Szolnoki

Reputation plays a crucial role in social interactions by affecting the fitness of individuals during an evolutionary process. Previous works have extensively studied the result of imitation dynamics without focusing on potential irrational choices in strategy updates. We now fill this gap and explore the consequence of such kind of randomness, or one may in

Stefano Melacci, Achille Globo, Leonardo Rigutini

Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks. Despite the recent introduction of Word Embeddings and Recurrent Neural Networks to design powerful context-related features, the interest in improving WSD models using Semantic Lexical Resources (SLRs) is mostly restricted to kno

Alison Litherland, Patrick Litherland, Sam Nelson, Steven Wallace

Richard A. Litherland was born in 1953 in England. He received his PhD at Trinity College in Cambridge in 1979 and moved to the USA in 1983. He had a lengthy and distinguished career as a professor of mathematics and researcher of low-dimensional topology, based primarily at Louisiana State University (LSU) in Baton Rouge until his untimely passing in Novemb

Stergios Athanasoglou, Somouaoga Bonkoungou

We consider a group of voters that needs to decide between two candidates. We propose a novel family of neutral and strategy-proof rules, which we call sequential unanimity rules. By demonstrating their formal equivalence to the M-winning coalition rules of Moulin (1983), we show that sequential unanimity rules are characterized by neutrality and strategy-pr

Qihao Cheng, Da Yan, Tianhao Wu, Lyuheng Yuan

Finding cohesive subgraphs in a large graph has many important applications, such as community detection and biological network analysis. Clique is often a too strict cohesive structure since communities or biological modules rarely form as cliques for various reasons such as data noise. Therefore, $k$-plex is introduced as a popular clique relaxation, which

Elodie Germani, Nikhil Baghwat, Mathieu Dugré, Rémi Gau

Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to coho

Binglin Zhou, Linhao Zhong, Wentao Chen

Dataset distillation, a pragmatic approach in machine learning, aims to create a smaller synthetic dataset from a larger existing dataset. However, existing distillation methods primarily adopt a model-based paradigm, where the synthetic dataset inherits model-specific biases, limiting its generalizability to alternative models. In response to this constrain

Manvi Agarwal, Changhong Wang, Gaël Richard

Music generated by deep learning methods often suffers from a lack of coherence and long-term organization. Yet, multi-scale hierarchical structure is a distinctive feature of music signals. To leverage this information, we propose a structure-informed positional encoding framework for music generation with Transformers. We design three variants in terms of

Sara Vera Marjanović, Isabelle Augenstein, Christina Lioma

Explainable AI methods facilitate the understanding of model behaviour, yet, small, imperceptible perturbations to inputs can vastly distort explanations. As these explanations are typically evaluated holistically, before model deployment, it is difficult to assess when a particular explanation is trustworthy. Some studies have tried to create confidence est

Jonathan Dan, Una Pale, Alireza Amirshahi, William Cappelletti

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This hetero

David Gimeno-Gómez, Carlos-D. Martínez-Hinarejos

Thanks to the rise of deep learning and the availability of large-scale audio-visual databases, recent advances have been achieved in Visual Speech Recognition (VSR). Similar to other speech processing tasks, these end-to-end VSR systems are usually based on encoder-decoder architectures. While encoders are somewhat general, multiple decoding approaches have

Sheng-Qiang Zhong, Shun-Cai Zhao, Sheng-Nan Zhu

Revealing the quantum regime of photovoltaics is crucial to enhancing the internal quantum efficiency of a double quantum dots (DQDs) photocell housed in a cavity. In this study, the performance of a quantum photovoltaic is evaluated based on the current-voltage and power-voltage characteristics in a cavity-coupled DQDs photocell. The results show that the c

Fabio E. Furcas, Shishir Mundra, Barbara Lothenbach, Camelia N. Borca

Accurate model predictions of corrosion-driven damage in reinforced concrete structures necessitate a comprehensive understanding of the rate of corrosion product formation. Here, we investigate the influence of dissolved Si characteristic of cementitious systems on the rate of corrosion product transformation at alkaline pH. Compared to systems aged in the

F. J. Beron-Vera

We have previously shown that the nonlinear growth of a finite-amplitude perturbation to a basic state given by a baroclinic zonal flow on the $\beta$-plane in a thermal quasigeostrophic reduced-gravity model can be a priori bounded. In this note we show that, unlike we stated earlier, Lyapunov stability can be proved even when buoyancy varies linearly with

Ammar Daskin

Graph states are used to represent mathematical graphs as quantum states on quantum computers. They can be formulated through stabilizer codes or directly quantum gates and quantum states. In this paper we show that a quantum graph neural network model can be understood and realized based on graph states. We show that they can be used either as a parameteriz

Lars Herbold, Mersedeh Sadeghi, Andreas Vogelsang

Human explanations are often contrastive, meaning that they do not answer the indeterminate "Why?" question, but instead "Why P, rather than Q?". Automatically generating contrastive explanations is challenging because the contrastive event (Q) represents the expectation of a user in contrast to what happened. We present an approach that predicts a potential

Zhen-Xuan He, Ji-Yang Zhou, Wu-Xi Lin, Qiang Li

Color centers in silicon carbide (SiC) have demonstrated significant promise for quantum information processing. However, the undesirable ionization process that occurs during optical manipulation frequently causes fluctuations in the charge state and performance of these defects, thereby restricting the effectiveness of spin-photon interfaces. Recent predic

Ryan Soh-Eun Shim, Kalvin Chang, David R. Mortensen

Received wisdom in linguistic typology holds that if the structure of a language becomes more complex in one dimension, it will simplify in another, building on the assumption that all languages are equally complex (Joseph and Newmeyer, 2012). We study this claim on a micro-level, using a tightly-controlled sample of Dutch dialects (across 366 collection sit

Hippolyte Gisserot-Boukhlef, Manuel Faysse, Emmanuel Malherbe, Céline Hudelot

Neural Information Retrieval (NIR) has significantly improved upon heuristic-based Information Retrieval (IR) systems. Yet, failures remain frequent, the models used often being unable to retrieve documents relevant to the user's query. We address this challenge by proposing a lightweight abstention mechanism tailored for real-world constraints, with particu

Thibaut Arnoulx de Pirey, Yariv Kafri, Sriram Ramaswamy

We show that an inclusion placed inside a dilute Stokesian suspension of microswimmers induces power-law number-density modulations and flows. These take a different form depending on whether the inclusion is held fixed by an external force, for example an optical tweezer, or if it is free. When the inclusion is held in place, the far-field fluid flow is a S

Łukasz Rudnicki, Tomasz Linowski

We perform an analysis supplementing the metrology toolbox in the time-frequency domain. While the relevant time-frequency-based metrological protocols can be borrowed from the spatial domain, where they have recently been well developed, their ultimate practical usefulness is shown to be restricted by limits put on the bandwidth of both the signal and measu

Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou

With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (a.k.a. IID assumption), and exhibits significant declines under distribution shifts. Distri

Kexin Chen, Yuyang Du, Junyou Li, Hanqun Cao

The development of AI-assisted chemical synthesis tools requires comprehensive datasets covering diverse reaction types, yet current high-throughput experimental (HTE) approaches are expensive and limited in scope. Chemical literature represents a vast, underexplored data source containing thousands of reactions published annually. However, extracting reacti

Yves Colin de Verdière, Jérémie Vidal

In geophysical environments, wave motions that are shaped by the action of gravity and global rotation bear the name of gravito-inertial waves. We present a geometrical description of gravito-inertial surface waves, which are low-frequency waves existing in the presence of a solid boundary. We consider an idealized fluid model for an incompressible fluid enc

Martin Gubri, Dennis Ulmer, Hwaran Lee, Sangdoo Yun

Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them. Assessing the compliance of the released LLMs is crucial, as these rules protect the interests of the LLM contributor and prevent misuse. In this context, we describe the novel fingerprinting problem of Black-box Identity Verification (B

Nikolay D. Gagunashvili

Experimental data in Particle and Nuclear physics, Particle Astrophysics and Radiation Protection Dosimetry are obtained from experimental facilities comprising a complex array of sensors, electronics and software. Computer simulation is used to study the measurement process. Probability Density Functions (PDFs) of measured physical parameters deviate from t

Alessia Silvia Ivani, Manuel G. Catalano, Giorgio Grioli, Matteo Bianchi

Our research investigates vibrotactile perception in four prosthetic hands with distinct kinematics and mechanical characteristics. We found that rigid and simple socket-based prosthetic devices can transmit tactile information and surprisingly enable users to identify the stimulated finger with high reliability. This ability decreases with more advanced pro

Chunfeng Cui, Yong Lu, Liqun Qi, Ligong Wang

In this paper, we study dual quaternion and dual complex unit gain graphs and their spectral properties in a unified frame of dual unit gain graphs. Unit dual quaternions represent rigid movements in the 3D space, and have wide applications in robotics and computer graphics. Dual complex numbers found application in brain science recently. We establish the i

Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu

Incremental learning is a machine learning approach that involves training a model on a sequence of tasks, rather than all tasks at once. This ability to learn incrementally from a stream of tasks is crucial for many real-world applications. However, incremental learning is a challenging problem on graph-structured data, as many graph-related problems involv

Sergio Mazzola, Samuel Riedel, Luca Benini

Systolic arrays and shared-L1-memory manycore clusters are commonly used architectural paradigms that offer different trade-offs to accelerate parallel workloads. While the first excel with regular dataflow at the cost of rigid architectures and complex programming models, the second are versatile and easy to program but require explicit dataflow management

Rishabh Bubna, Hans-Werner Hammer, Fabian Müller, Jin-Yi Pang

We derive the modified L\"uscher equation in the presence of the long-range force caused by the exchange of a light particle. It is shown that the use of this equation enables one to circumvent the problems related to the strong partial-wave mixing and the t-channel sub-threshold singularities. It is also demonstrated that the present method is intrinsically

Xuanwen Huang, Kaiqiao Han, Yang Yang, Dezheng Bao

Recently, large language models (LLMs) have demonstrated superior capabilities in understanding and zero-shot learning on textual data, promising significant advances for many text-related domains. In the graph domain, various real-world scenarios also involve textual data, where tasks and node features can be described by text. These text-attributed graphs

Gennady I. Zebrev, Nikolay N. Samotaev, Rustem G. Useinov, Artur M. Galimov

A new physics-based compact model, which makes it possible to simulate in a unified way the neutron and proton of cosmic ray induced SEU cross sections, including effects from nuclear reaction products and from direct ionization by low-energy protons, has been proposed and vali-dated. The proposed approach is analytical and based on explicit analytical relat

Mirko D'Ovidio

We extend the results obtained in \cite{Dov22} by introducing a new class of boundary value problems involving non-local dynamic boundary conditions. We focus on the problem to find a solution to a local problem on a domain $\Omega$ with non-local dynamic conditions on the boundary $\partial \Omega$. Due to the pioneering nature of the present research, we p

Christoph Bock

Roughly speaking, functional analysis is the study of vector spaces of arbitrary dimension over the field of real or complex numbers, and the continuous linear mappings between such spaces. Naturally, the notion of continuity requires a topology - or more specifically, a norm - on these vector spaces, bringing both analytic and algebraic tools into play. The

Alexander Mangulad Christgau, Anton Rask Lundborg, Niels Richard Hansen

Covariate adjustment is a ubiquitous method used to estimate the average treatment effect (ATE) from observational data. Assuming a known graphical structure of the data generating model, recent results give graphical criteria for optimal adjustment, which enables efficient estimation of the ATE. However, graphical approaches are challenging for high-dimensi

Balázs Pozsgay, Rustem Sharipov, Anastasiia Tiutiakina, István Vona

We revisit the problem of integrability breaking in free fermionic quantum spin chains. We investigate the so-called adiabatic gauge potential (AGP), which was recently proposed as an accurate probe of quantum chaos. We also study the so-called weak integrability breaking, which occurs if the dynamical effects of the perturbation do not appear at leading ord

Phillip Baumann, Kevin Sturm

This paper is concerned with the minimisation of peak stresses occurring in linear elasticity. We propose to minimise the maximal von Mises stress of the elastic body. This leads to a nonsmooth shape functional. We derive the shape derivative and associate it with the Clarke sub-differential. Using a steepest descent algorithm we present numerical simulation

Luc Dessart, Claudia P. Gutierrez, Andrea Ercolino, Harim Jin

Over the last decade, evidence has accumulated that massive stars do not typically evolve in isolation but instead follow a tumultuous journey with a companion star on their way to core collapse. While Roche-lobe overflow appears instrumental for the production of a large fraction of supernovae (SNe) of Type Ib and Ic, variations in the initial orbital perio

Philipp Mondorf, Barbara Plank

Deductive reasoning plays a pivotal role in the formulation of sound and cohesive arguments. It allows individuals to draw conclusions that logically follow, given the truth value of the information provided. Recent progress in the domain of large language models (LLMs) has showcased their capability in executing deductive reasoning tasks. Nonetheless, a sig

Miaoran Zhang, Vagrant Gautam, Mingyang Wang, Jesujoba O. Alabi

In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without needing any parameter updates. Although there have been extensive studies on English in-context learning, multilingual in-context learning remains under-explored, and we lack an in-depth understanding of the role of dem