May 2024 arXiv papers — page 78

Kasra Rajabzadeh Dizaji, Ariq Haqq, Alicia B. Magann, Christian Arenz

We investigate the quantum Zeno effect as a framework for designing and analyzing quantum algorithms for Hamiltonian simulation. We show that frequent projective measurements of an ancilla qubit register can be used to simulate quantum dynamics on a target qubit register with a circuit complexity similar to randomized approaches. The classical sampling overh

K. Lodha, A. Shafieloo, R. Calderon, E. Linder

Baryon acoustic oscillation data from the first year of the Dark Energy Spectroscopic Instrument (DESI) provide near percent-level precision of cosmic distances in seven bins over the redshift range $z=0.1$-$4.2$. We use this data, together with other distance probes, to constrain the cosmic expansion history using some well-motivated physical classes of dar

Christian Holberg, Cristopher Salvi

We introduce a mathematically rigorous framework based on rough path theory to model stochastic spiking neural networks (SSNNs) as stochastic differential equations with event discontinuities (Event SDEs) and driven by c\`adl\`ag rough paths. Our formalism is general enough to allow for potential jumps to be present both in the solution trajectories as well

Alexis-Raja Brachet, Pierre-Yves Richard, Céline Hudelot

In the trend of hybrid Artificial Intelligence techniques, Physical-Informed Machine Learning has seen a growing interest. It operates mainly by imposing data, learning, or architecture bias with simulation data, Partial Differential Equations, or equivariance and invariance properties. While it has shown great success on tasks involving one physical domain,

Arvind Singh, Hynek Němec, Jan Kunc, Petr Kužel

Dynamics of plasmons in nanoribbons of (hydrogen intercalated) quasi-free-standing single layer graphene is studied by terahertz spectroscopy both in the steady state and upon photoexcitation by an ultrashort near infrared laser pulse. The use of two-dimensional frequency domain analysis of the optical pump - THz probe signals allows us to determine the evol

Qingming Li, Juzheng Miao, Puning Zhao, Li Zhou

In federated learning, client selection is a critical problem that significantly impacts both model performance and fairness. Prior studies typically treat these two objectives separately, or balance them using simple weighting schemes. However, we observe that commonly used metrics for model performance and fairness often conflict with each other, and a str

Roman Andriushchenko, Alexander Bork, Carlos E. Budde, Milan Češka

The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool support is available for computing basic properties such as expected rewards on basic models such as Markov chains. Previous editions of QComp, the comparison of tools for the analysis

Zheng An, Jiahui Wu, Zidong Lin, Xiaobo Yang

Recent advancements in quantum hardware and classical computing simulations have significantly enhanced the accessibility of quantum system data, leading to an increased demand for precise descriptions and predictions of these systems. Accurate prediction of quantum Hamiltonian dynamics and identification of Hamiltonian parameters are crucial for advancement

Zhendong Liu, Yuanbi Nie, Yingshui Tan, Xiangyu Yue

Benefiting from the powerful capabilities of Large Language Models (LLMs), pre-trained visual encoder models connected to an LLMs can realize Vision Language Models (VLMs). However, existing research shows that the visual modality of VLMs is vulnerable, with attackers easily bypassing LLMs' safety alignment through visual modality features to launch attacks.

Alexander Belinsky, Igor Dzhadan

A violation of Bell local realism inequalities in Clauser-Horn-Shimony-Holt (CHSH) form has been discovered in a relativistic GedanknExperiment. This means that there are no definite joint probabilities and this finds a classical explanation in the structure of Special relativity (SRT). The discovered nonlocality is weaker than Albert Einstein's quantum 'spo

Omar Moured, Jiaming Zhang, M. Saquib Sarfraz, Rainer Stiefelhagen

Chart summarization is a crucial task for blind and visually impaired individuals as it is their primary means of accessing and interpreting graphical data. Crafting high-quality descriptions is challenging because it requires precise communication of essential details within the chart without vision perception. Many chart analysis methods, however, produce

A. Morozov, A. Oreshina

Character expansions are among the most important approaches to modern quantum field theory, which substitute integrals by combinations of peculiar special functions from the Schur-Macdonald family. These formulas allow various deformations, which are not transparent in integral formulation. We analyze from this point of view the Itzykson-Zuber integral over

Weilong Dong, Xinwei Wu, Renren Jin, Shaoyang Xu

Ensuring large language models (LLM) behave consistently with human goals, values, and intentions is crucial for their safety but yet computationally expensive. To reduce the computational cost of alignment training of LLMs, especially for those with a huge number of parameters, and to reutilize learned value alignment, we propose ConTrans, a novel framework

Adrien Poteaux, Martin Weimann

We obtain new complexity bounds for computing a triangular integral basis of a number field or a function field. We reach for function fields a softly linear cost with respect to the size of the output when the residual characteristic is zero or big enough. Analogous results are obtained for integral basis of fractional ideals, key ingredients towards fast c

Jiajie Jin, Yutao Zhu, Guanting Dong, Yuyao Zhang

With the advent of large language models (LLMs) and multimodal large language models (MLLMs), the potential of retrieval-augmented generation (RAG) has attracted considerable research attention. Various novel algorithms and models have been introduced to enhance different aspects of RAG systems. However, the absence of a standardized framework for implementa

Peiwang Tang, Weitai Zhang

Recent studies have attempted to refine the Transformer architecture to demonstrate its effectiveness in Long-Term Time Series Forecasting (LTSF) tasks. Despite surpassing many linear forecasting models with ever-improving performance, we remain skeptical of Transformers as a solution for LTSF. We attribute the effectiveness of these models largely to the ad

Congye Wang, Wilson Chen, Heishiro Kanagawa, Chris. J. Oates

An informal observation, made by several authors, is that the adaptive design of a Markov transition kernel has the flavour of a reinforcement learning task. Yet, to-date it has remained unclear how to actually exploit modern reinforcement learning technologies for adaptive MCMC. The aim of this paper is to set out a general framework, called Reinforcement L

Kaifeng Zhang, Zhao-Heng Yin, Weirui Ye, Yang Gao

Defining reward functions for skill learning has been a long-standing challenge in robotics. Recently, vision-language models (VLMs) have shown promise in defining reward signals for teaching robots manipulation skills. However, existing work often provides reward guidance that is too coarse, leading to insufficient learning processes. In this paper, we addr

Duc-Cuong Dang, Andre Opris, Dirk Sudholt

Runtime analysis has recently been applied to popular evolutionary multi-objective (EMO) algorithms like NSGA-II in order to establish a rigorous theoretical foundation. However, most analyses showed that these algorithms have the same performance guarantee as the simple (G)SEMO algorithm. To our knowledge, there are no runtime analyses showing an advantage

Wenbo Sui, Daniel Lichau, Josselin Lefèvre, Harold Phelippeau

Recent studies of multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images have highlighted the importance of exploiting the redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multimodal IAD in practical production lines remains a work in progress. It is essential to c

Zhiping Yu, Chenyang Liu, Liqin Liu, Zhenwei Shi

The recent advancement of generative foundational models has ushered in a new era of image generation in the realm of natural images, revolutionizing art design, entertainment, environment simulation, and beyond. Despite producing high-quality samples, existing methods are constrained to generating images of scenes at a limited scale. In this paper, we prese

Manaoj Aravind, Vaibhav Pachaulee, Mrinal Sarkar, Ishant Tiwari

A wide variety of engineered and natural systems are modelled as networks of coupled nonlinear oscillators. In nature, the intrinsic frequencies of these oscillators are not constant in time. Here, we probe the effect of such a temporal heterogeneity on coupled oscillator networks, through the lens of the Kuramoto model. To do this, we shuffle repeatedly the

Wanyu Hu, Vrizlynn L. L. Thing

With the drastic increase in the number of new vulnerabilities in the National Vulnerability Database (NVD) every year, the workload for NVD analysts to associate the Common Platform Enumeration (CPE) with the Common Vulnerabilities and Exposures (CVE) summaries becomes increasingly laborious and slow. The delay causes organisations, which depend on NVD for

Maximilian C. Hübl, Carl P. Goodrich

Modern experimental methods enable the creation of self-assembly building blocks with tunable interactions, but optimally exploiting this tunability for the self-assembly of desired structures remains an important challenge. Many studies of this inverse problem start with the so-called fully-addressable limit, where every particle in a target structure is di

Claudio Muñoz, Nicolás Valenzuela

We present a comprehensive framework for deriving rigorous and efficient bounds on the approximation error of deep neural networks in PDE models characterized by branching mechanisms, such as waves, Schr\"odinger equations, and other dispersive models. This framework utilizes the probabilistic setting established by Henry-Labord\`ere and Touzi. We illustrate

Manushree Vijayvergiya, Małgorzata Salawa, Ivan Budiselić, Dan Zheng

Modern code review is a process in which an incremental code contribution made by a code author is reviewed by one or more peers before it is committed to the version control system. An important element of modern code review is verifying that code contributions adhere to best practices. While some of these best practices can be automatically verified, verif

Ziming Wang

This article focuses on the problem of adaptive tracking control for a specific type of nonlinear system that is subject to full-state constraints via a hybrid event-triggered control (HETC) strategy. With the auxiliary system, we proposed a 'log' function to deal with the full-state constraint. Additionally, a disturbance observer (DO) is constructed to han

Julius H. P. Breuer, Peter F. Pelz

The European Union's climate targets challenge the building sector to reduce energy use while ensuring comfort. Ventilation systems play an important role in achieving these goals. During system planning, the primary focus tends to lie on reducing life-cycle costs, including energy and investment expenses. Acoustic considerations which contribute significant

Pallabi Das

After the Higgs boson discovery in 2012, the experiments at the LHC are continuing to study this particle and look for physics beyond the standard model. Some of the Higgs boson properties, such as the mass, has been measured with sub-percent level accuracy. Yet the present integrated luminosity is still a limiting factor for measuring the Higgs boson self-c

Wanli Ni, Hui Tian

The proliferation of Internet of Things (IoT) systems demands scalable artificial intelligence (AI) solutions that can operate in computing-heterogeneous environments with diverse hardware capabilities and non-independent and identically distributed data. This paper proposes a semi-federated learning (SemiFL) framework that integrates centralized learning (C

Marco Longinetti, Paolo Manselli, Adriana Venturi

Let a $R$-body be a closed set, complement of union of open balls of radius $R$ in the Euclidean space. Properties generalizing similar ones for convex sets are proved for the family of $R$-bodies; properties for the family of sets supported by spheres of radius $R$ (extension of the supporting hyperplane to convex bodies) are investigated. Comparison of tha

Shalosh B. Ekhad, Doron Zeilberger

On March 16, 2024, Daniel Litt, in an X-post, proposed the following brainteaser: "Flip a fair coin 100 times. It gives a sequence of heads (H) and tails (T). For each HH in the sequence of flips, Alice gets a point; for each HT, Bob does, so e.g. for the sequence THHHT Alice gets 2 points and Bob gets 1 point. Who is most likely to win?" We show the power o

Yizhe Zhang, Yucheng Jin, Li Chen, Ting Yang

Conversational recommender systems (CRS) enable users to articulate their preferences and provide feedback through natural language. With the advent of large language models (LLMs), the potential to enhance user engagement with CRS and augment the recommendation process with LLM-generated content has received increasing attention. However, the efficacy of LL

Anjan Mukherjee, Biswanth Banerjee

Most of the tailored materials are heterogeneous at the ingredient level. Analysis of those heterogeneous structures requires the knowledge of microstructure. With the knowledge of microstructure, multiscale analysis is carried out with homogenization at the micro level. Second-order homogenization is carried out whenever the ingredient size is comparable to

Silvia Leanza, Cristina Pallanca, Francesco R. Ferraro, Barbara Lanzoni

We present the results of a study aimed at characterizing the kinematics of the inner regions of the halo globular cluster M75 (NGC 6864) based on data acquired as part of the ESO-VLT Multi-Instrument Kinematic Survey (MIKiS) of Galactic globular clusters. Our analysis includes the first determination of the line-of-sight velocity dispersion profile in the c

Luca Savant Aira, Antonio Montanaro, Emanuele Aiello, Diego Valsesia

Generating videos with realistic and physically plausible motion is one of the main recent challenges in computer vision. While diffusion models are achieving compelling results in image generation, video diffusion models are limited by heavy training and huge models, resulting in videos that are still biased to the training dataset. In this work we propose

Brendan K. Beare, Alexis Akira Toda

Recent work on random growth models with light-tailed Markov-modulated additive shocks has shown that irreducible modulation yields tail behavior resembling an exponential distribution. We show that with reducible modulation the tail behavior more generally resembles an Erlang distribution. Our main technical contribution is a theorem on the order of a real

Moises Diaz, Miguel A. Ferrer, Donato Impedovo, Muhammad Imran Malik

Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10

Amelia Hassoun, Ariel Abonizio, Katy Osborn, Cameron Wu

Advances in generative AI (GenAI) have raised concerns about detecting and discerning AI-generated content from human-generated content. Most existing literature assumes a paradigm where 'expert' organized disinformation creators and flawed AI models deceive 'ordinary' users. Based on longitudinal ethnographic research with misinformation creators and consum

Jan-Ole Koslik

The well-established methodology for the estimation of hidden semi-Markov models (HSMMs) as hidden Markov models (HMMs) with extended state spaces is further developed to incorporate covariate influences across all aspects of the state process model, in particular, regarding the distributions governing the state dwell time. The special case of periodically v

Alexey Dubinsky

Quantum gravitational corrections to the entropy of the Schwarzschild black hole, derived using the Wald entropy formula within an effective field theory framework, were presented in [X. Calmet, F. Kuipers Phys.Rev.D 104 (2021) 6, 066012]. These corrections result in a Schwarzschild spacetime that is deformed by the quantum correction. However, it is observe

Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi

Causal structure discovery from observations can be improved by integrating background knowledge provided by an expert to reduce the hypothesis space. Recently, Large Language Models (LLMs) have begun to be considered as sources of prior information given the low cost of querying them relative to a human expert. In this work, firstly, we propose a set of met

Paolo Bernuzzi, Henk A. Dijkstra, Christian Kuehn

In this paper, we construct and discuss early-warning signs of the approach of a parameter to a deterministic bifurcation on a stochastic partial differential equation (SPDE) model with Gaussian white-noise on the boundary. We specifically focus on the qualitative behaviour of the time-asymptotic autocovariance and autocorrelation of the solutions of the lin

Peng Wang, Dongsheng Han, Yashuai Cao, Wanli Ni

Integrated sensing and communication (ISAC) opens up new service possibilities for sixth-generation (6G) systems, where both communication and sensing (C&S) functionalities co-exist by sharing the same hardware platform and radio resource. In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under di

Wei Zhang, Xianfu Cheng, Yi Zhang, Jian Yang

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the she

Mustafa Yildirim, Barkin Dagda, Saber Fallah

Autonomous driving is a complex task which requires advanced decision making and control algorithms. Understanding the rationale behind the autonomous vehicles' decision is crucial to ensure their safe and effective operation on highway driving. This study presents a novel approach, HighwayLLM, which harnesses the reasoning capabilities of large language mod

Monika Jain, Raghava Mutharaju, Kuldeep Singh, Ramakanth Kavuluru

Relation extraction (RE) is a well-known NLP application often treated as a sentence- or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct from the single document case because different documents often focus on disparate themes, while text within a document t

Yelyzaveta Velizhanina, Bernard Knaepen

We consider the influence of a transverse magnetic field on the transient growth of perturbations in a liquid-metal circular pipe flow with an electrically insulating or conducting wall. In this configuration, the mean flow profile and the amplification of perturbations are strongly affected by the applied magnetic field, leading to a rich dynamical landscap

Gislaine Varão, Iarley P. Lobo, Valdir B. Bezerra

This letter extends previous findings on the modified Schr\"odinger evolution inspired by quantum gravity phenomenology. By establishing a connection between this approach and fractional quantum mechanics, we provide insights into a potential deep infrared regime of quantum gravity, characterized by the emergence of fractal dimensions, similar to behaviors o

Miguel Garcia-Bohigues, Carmengelys Cordova, Joaquin Taverner, Javier Palanca

This paper presents an extended version of the SPADE platform, which aims to empower intelligent agent systems with normative reasoning and value alignment capabilities. Normative reasoning involves evaluating social norms and their impact on decision-making, while value alignment ensures agents' actions are in line with desired principles and ethical guidel

Michal Pliska, Matouš Vrba, Tomáš Báča, Martin Saska

A unique approach for the mid-air autonomous aerial interception of non-cooperating UAV by a flying robot equipped with a net is presented in this paper. A novel interception guidance method dubbed EPN is proposed, designed to catch agile maneuvering targets while relying on onboard state estimation and tracking. The proposed method is compared with state-of

Yuu Jinnai, Ukyo Honda

Preference optimization is a standard approach to fine-tuning large language models to align with human preferences. The quantity, diversity, and representativeness of the preference dataset are critical to the effectiveness of preference optimization. However, obtaining a large amount of preference annotations is difficult in many applications. This raises

Dan Zhang, Jingjing Wang, Feng Luo

In this paper, we present the Directly Denoising Diffusion Model (DDDM): a simple and generic approach for generating realistic images with few-step sampling, while multistep sampling is still preserved for better performance. DDDMs require no delicately designed samplers nor distillation on pre-trained distillation models. DDDMs train the diffusion model co

Ryusuke Numata

How thermal equilibrium is determined in a weakly collisional plasma is a fundamental question in plasma physics. This letter shows that the turbulence driven by the magnetic curvature and density gradient tends to equilibrate the temperature between species without collisions in a magnetospheric plasma. The classical stability analysis in terms of energetic

Hai Ni, Rui Wang, Scarlett Liu

Railway detection is critical for the automation of railway systems. Existing models often prioritize either speed or accuracy, but achieving both remains a challenge. To address the limitations of presetting anchor groups that struggle with varying track proportions from different camera angles, an ultra-fast adaptive track detection network is proposed in

Sam A. Whitaker, Andrew Golightly, Colin S. Gillespie, Theodore Kypraios

Epidemics are inherently stochastic, and stochastic models provide an appropriate way to describe and analyse such phenomena. Given temporal incidence data consisting of, for example, the number of new infections or removals in a given time window, a continuous-time discrete-valued Markov process provides a natural description of the dynamics of each model c

Tobias Leemann, Alina Fastowski, Felix Pfeiffer, Gjergji Kasneci

We address the critical challenge of applying feature attribution methods to the transformer architecture, which dominates current applications in natural language processing and beyond. Traditional attribution methods to explainable AI (XAI) explicitly or implicitly rely on linear or additive surrogate models to quantify the impact of input features on a mo

Yinsong Chen, Samson S. Yu, Zhong Li, Chee Peng Lim

In recent years, inconsistency in Bayesian deep learning has attracted significant attention. Tempered or generalized posterior distributions are frequently employed as direct and effective solutions. Nonetheless, the underlying mechanisms and the effectiveness of generalized posteriors remain active research topics. In this work, we interpret posterior temp

Jack Kohav, Nir Lazarovich

We prove that a hyperbolic group cannot contain a strictly ascending chain of free quasiconvex subgroups of constant rank.

François Gay-Balmaz, Tudor S. Ratiu, Alice B. Tumpach

The restricted Siegel disc is a homogeneous space related to the connected component $T_0(1)$ of the Universal Teichm\"uller space via the period mapping. In this paper we show that it is a coadjoint orbit of the universal central extension of the restricted symplectic group or, equivalently, an affine coadjoint orbit of the restricted symplectic group with

Zhaojun Guo, Jinghui Lu, Xuejing Liu, Rui Zhao

Despite the notable advancements achieved by leveraging pre-trained vision-language (VL) models through few-shot tuning for downstream tasks, our detailed empirical study highlights a significant dependence of few-shot learning outcomes on the careful selection of training examples - a facet that has been previously overlooked in research. In this study, we

Alberto Fernández-de-Marcos, Eduardo García-Portugués

We introduce a test of uniformity for (hyper)spherical data motivated by the stereographic projection. The closed-form expression of the test statistic and its null asymptotic distribution are derived using Gegenbauer polynomials. The power against rotationally symmetric local alternatives is provided, and simulations illustrate the non-null asymptotic resul

Hanna Ek Fälth, Fredrik Hedenus, Lina Reichenberg, Niclas Mattsson

Previous research has raised concerns about energy droughts in renewables-based energy systems. This study explores the ability of reservoir hydropower to sustain a high output and, thereby, mitigate such energy droughts. Using detailed modelling, we estimate that Swedish hydropower can sustain 67-92% of its installed capacity for 3 weeks, with higher values

Huasheng Zhang

In native speakers' lexical choices, a concept can be more readily expressed by one expression over another grammatical one, a phenomenon known as nativelike selection (NLS). In previous research, arbitrary chunks such as collocations have been considered crucial for this phenomenon. However, this study examines the possibility of analyzing the semantic moti

Trever Schirmer, Tobias Pfandzelter, David Bermbach

Running microbenchmark suites often and early in the development process enables developers to identify performance issues in their application. Microbenchmark suites of complex applications can comprise hundreds of individual benchmarks and take multiple hours to evaluate meaningfully, making running those benchmarks as part of CI/CD pipelines infeasible. I

Wei Zeng, Xian He, Ye Wang

Piano audio-to-score transcription (A2S) is an important yet underexplored task with extensive applications for music composition, practice, and analysis. However, existing end-to-end piano A2S systems faced difficulties in retrieving bar-level information such as key and time signatures, and have been trained and evaluated with only synthetic data. To addre

Joshua Southern, Francesco Di Giovanni, Michael Bronstein, Johannes F. Lutzeyer

While message passing neural networks (MPNNs) have convincing success in a range of applications, they exhibit limitations such as the oversquashing problem and their inability to capture long-range interactions. Augmenting MPNNs with a virtual node (VN) removes the locality constraint of the layer aggregation and has been found to improve performance on a r

Christopher J. N. Coveney, Jonah B. Haber, Antonios M. Alvertis, Jeffrey B. Neaton

Understanding the processes governing the dissociation of excitons to free charge carriers in semiconductors and insulators is of central importance for photovoltaic applications. Dyson's $\mathcal{S}$-matrix formalism provides a framework for computing scattering rates between quasiparticle states derived from the same underlying Hamiltonian, often reducing

Sergio Sánchez Cruz, Marina Kolosova, Clara Ramón Álvarez, Giovanni Petrucciani

We introduce the usage of equivariant neural networks in the search for violations of the charge-parity ($\textit{CP}$) symmetry in particle interactions at the CERN Large Hadron Collider. We design neural networks that take as inputs kinematic information of recorded events and that transform equivariantly under the a symmetry group related to the $\textit{

Horst Lenske, Jessica Bellone, Danilo Gambacurta, José-Antonio Lay

The role of initial state (ISI) and final state (FSI) ion-ion interactions in heavy-ion double-charge-exchange (DCE) reactions $A(Z,N)\to A(Z\pm 2,N\mp 2)$ are studied for double single-charge-exchange (DSCE) reactions given by sequential actions of the isovector nucleon-nucleon (NN) T-matrix. In momentum representation, the second-order DSCE reaction amplit

Zhijian Xu, Hao Wang, Qiang Xu

Traditional time series forecasting methods predominantly rely on historical data patterns, neglecting external interventions that significantly shape future dynamics. Through control-theoretic analysis, we show that the implicit "self-stimulation" assumption limits the accuracy of these forecasts. To overcome this limitation, we propose an Intervention-Awar

Kavita Jain, Sakuntala Chatterjee

We consider a run-and-tumble particle whose speed and tumbling rate are space-dependent on an infinite line. Unlike most of the previous work on such models, here we make the physical assumption that at large distances, these rates saturate to a constant. For our choice of rate functions, we show that a stationary state exists, and the exact steady state dis

Enrico Facca, Jan Martin Nordbotten, Erik Andreas Hanson

In this work, we present a novel tool for reconstructing networks from corrupted images. The reconstructed network is the result of a minimization problem that has a misfit term with respect to the observed data, and a physics-based regularizing term coming from the theory of optimal transport. Through a range of numerical tests, we demonstrate that our sugg

Yi Yuan, Li Chen, Qirui Yang, Lingran Gu

Topology optimization (TO) is a powerful method to design innovative structures with improved heat transfer performance. In the present study, a multi-fidelity TO method with a delicately defined objective function is developed for flow boiling heat transfer in microchannels. Low-fidelity TO is conducted for the reduced-order process of single-phase laminar

Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid, Muhammad Haris Khan

The emergence of foundational models has significantly advanced segmentation approaches. However, challenges still remain in dense scenarios, where occlusions, scale variations, and clutter impede precise instance delineation. To address this, we propose PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for Personalized instance S

Baizhou Huang, Xiaojun Wan

With the increasing use of large language models (LLMs) in daily life, concerns have emerged regarding their potential misuse and societal impact. Watermarking is proposed to trace the usage of specific models by injecting patterns into their generated texts. An ideal watermark should produce outputs that are nearly indistinguishable from those of the origin

Mingye Zhu, Yi Liu, Lei Zhang, Junbo Guo

Recently, tremendous strides have been made to align the generation of Large Language Models (LLMs) with human values to mitigate toxic or unhelpful content. Leveraging Reinforcement Learning from Human Feedback (RLHF) proves effective and is widely adopted by researchers. However, implementing RLHF is complex, and its sensitivity to hyperparameters renders

Yixiong Chen, Weichuan Fang

In recent years, with the development of quantum machine learning, quantum neural networks (QNNs) have gained increasing attention in the field of natural language processing (NLP) and have achieved a series of promising results. However, most existing QNN models focus on the architectures of quantum recurrent neural network (QRNN) and self-attention mechani

Muhammad Shakeel, Yui Sudo, Yifan Peng, Shinji Watanabe

End-to-end (E2E) automatic speech recognition (ASR) can operate in two modes: streaming and non-streaming, each with its pros and cons. Streaming ASR processes the speech frames in real-time as it is being received, while non-streaming ASR waits for the entire speech utterance; thus, professionals may have to operate in either mode to satisfy their applicati

Shivam Patel, Vivek Borkar

Risk sensitive decision making finds important applications in current day use cases. Existing risk measures consider a single or finite collection of random variables, which do not account for the asymptotic behaviour of underlying systems. Conditional Value at Risk (CVaR) is the most commonly used risk measure, and has been extensively utilized for modelli

Simon Baeuerle, Andreas Steimer, Ralf Mikut

Thermal management of power electronics and Electronic Control Units is crucial in times of increasing power densities and limited assembly space. Electric and autonomous vehicles are a prominent application field. Thermal Interface Materials are used to transfer heat from a semiconductor to a heatsink. They are applied along a dispense path onto the semicon

Carl Feghali, Felicia Lucke

We verify a recent conjecture of Sehrawat, Kumar and Ahlawat on the minimum bisection width (a notion that was rediscovered in 2020 by Acharya and Kureethara under the name of rna number) of powers of cycles.

Tomás Hüttebräucker, Mohamed Sana, Emilio Calvanese Strinati

We relax the constraint of a shared language between agents in a semantic and goal-oriented communication system to explore the effect of language mismatch in distributed task solving. We propose a mathematical framework, which provides a modelling and a measure of the semantic distortion introduced in the communication when agents use distinct languages. We

Salihah Thabet Alwadani

The theory of monotone operators plays a major role in modern optimization and many areas of nonlinera analysis. The central classes of monotone operators are matrices with a positive semidefinite symmetric part and subsifferential operators. In this paper, we complete our study to the displacement mappings. We derive formulas for set-valued and Moore-Penros

Sen Xue, Chuanhou Gao

The generalized assignment problem with routing constraints, e.g. the vehicle routing problem, has essential practical relevance. This paper focuses on addressing the complexities of the problem by learning a surrogate model with reduced variables and reconstructed constraints. A surrogate model framework is presented with a class of surrogate models and a l

Iustin Floroiu

Given the high advances of large language models (LLM) it is of vital importance to study their behaviors and apply their utility in all kinds of scientific fields. Psychology has been, in recent years, poorly approached using novel computational tools. One of the reasons is the high complexity of the data required for a proper analysis. Moreover, psychology

Jakob Stubenrauch, Benjamin Lindner

We consider a dynamic system that is driven by an intensity-modulated Poisson process with intensity $\Lambda(t)=\lambda(t)+\varepsilon\nu(t)$. We derive an exact relation between the input-output cross-correlation in the spontaneous state ($\varepsilon=0$) and the linear response to the modulation ($\varepsilon>0$). If $\varepsilon$ is sufficiently small, l

Akihiro Minamide, Youichi Yanase

Theory of the superconducting parity transition is extended by incorporating the vortex degree of freedom. We employ the bilayer Rashba model representing locally noncentrosymmetric layered superconductors and derive the Ginzburg-Landau free energy functional. This formulation reveals the parity transition, where the even-parity superconducting state changes

Aitor R. Gomez, Manuela L. Bujorianu, Rafal Wisniewski

We often rely on probabilistic measures -- e.g. event probability or expected time -- to characterize systems' safety. However, determining these quantities for extremely low-probability events is generally challenging, as standard safety methods usually struggle due to conservativeness, high-dimension scalability, tractability or numerical limitations. We a

J. R. Weaver, S. Taamoli, C. J. R. McPartland, L. Zalesky

This paper presents a search for high redshift galaxies from the Euclid Early Release Observations program "Magnifying Lens." The 1.5 deg$^2$ area covered by the twin Abell lensing cluster fields is comparable in size to the few other deep near-infrared surveys such as COSMOS, and so provides an opportunity to significantly increase known samples of rare UV-

H. Atek, R. Gavazzi, J. R. Weaver, J. M. Diego

We present the first analysis of the Euclid Early Release Observations (ERO) program that targets fields around two lensing clusters, Abell 2390 and Abell 2764. We use VIS and NISP imaging to produce photometric catalogs for a total of $\sim 500\,000$ objects. The imaging data reach a $5\,\sigma$ typical depth in the range 25.1-25.4 AB in the NISP bands, and

M. Kluge, N. A. Hatch, M. Montes, J. B. Golden-Marx

We study the intracluster light (ICL) and intracluster globular clusters (ICGCs) in the nearby Perseus galaxy cluster using Euclid's EROs. By modelling the isophotal and iso-density contours, we mapped the distributions and properties of the ICL and ICGCs out to radii of 200-600 kpc (up to ~1/3 of the virial radius) from the brightest cluster galaxy (BCG). W

F. R. Marleau, J. -C. Cuillandre, M. Cantiello, D. Carollo

We make use of the unprecedented depth, spatial resolution, and field of view of the Euclid Early Release Observations of the Perseus galaxy cluster to detect and characterise the dwarf galaxy population in this massive system. The Euclid high resolution VIS and combined VIS+NIR colour images were visually inspected and dwarf galaxy candidates were identifie

J. -C. Cuillandre, M. Bolzonella, A. Boselli, F. R. Marleau

The Euclid ERO programme targeted the Perseus cluster of galaxies, gathering deep data in the central region of the cluster over 0.7 square degree, corresponding to approximately 0.25 r_200. The data set reaches a point-source depth of IE=28.0 (YE, JE, HE = 25.3) AB magnitudes at 5 sigma with a 0.16" and 0.48" FWHM, and a surface brightness limit of 30.1 (29

T. Saifollahi, K. Voggel, A. Lançon, Michele Cantiello

We present an analysis of Euclid observations of a 0.5 deg$^2$ field in the central region of the Fornax galaxy cluster that were acquired during the performance verification phase. With these data, we investigate the potential of Euclid for identifying GCs at 20 Mpc, and validate the search methods using artificial GCs and known GCs within the field from th

L. K. Hunt, F. Annibali, J. -C. Cuillandre, A. M. N. Ferguson

Euclid is poised to make significant advances in the study of nearby galaxies in the local Universe. Here we present a first look at 6 galaxies observed for the Nearby Galaxy Showcase as part of the Euclid Early Release Observations acquired between August and November, 2023. These targets, 3 dwarf galaxies (HolmbergII, IC10, NGC6822) and 3 spirals (IC342, N

D. Massari, E. Dalessandro, D. Erkal, E. Balbinot

As part of the Euclid Early Release Observations (ERO) programme, we analyse deep, wide-field imaging from the VIS and NISP instruments of two Milky Way globular clusters (GCs), namely NGC 6254 (M10) and NGC 6397, to look for observational evidence of their dynamical interaction with the Milky Way. We search for such an interaction in the form of structural

E. L. Martín, M. {Ž}erjal, H. Bouy, D. Martin-Gonzalez

We provide an early assessment of the imaging capabilities of the Euclid space mission to probe deeply into nearby star-forming regions and associated very young open clusters, and in particular to check to what extent it can shed light on the new-born free-floating planet population. This paper focuses on a low-reddening region observed in just one Euclid p

J. -C. Cuillandre, E. Bertin, M. Bolzonella, H. Bouy

The Euclid ERO showcase Euclid's capabilities in advance of its main mission, targeting 17 astronomical objects, from galaxy clusters, nearby galaxies, globular clusters, to star-forming regions. A total of 24 hours observing time was allocated in the early months of operation, engaging the scientific community through an early public data release. We descri

Euclid Collaboration, F. J. Castander, P. Fosalba, J. Stadel

We present the Flagship galaxy mock, a simulated catalogue of billions of galaxies designed to support the scientific exploitation of the Euclid mission. Euclid is a medium-class mission of the European Space Agency optimised to determine the properties of dark matter and dark energy on the largest scales of the Universe. It probes structure formation over m