March 2024 arXiv papers — page 165

Mandana Kariminejad, David Tormey, Marion McAfee

Injection moulding is a well-established automated process for manufacturing a wide variety of plastic components in large volumes and with high precision. There are, however, process control challenges associated with each stage of injection moulding, which should be monitored and controlled precisely to prevent defects in the injection moulded component. O

Farhad Nazari, Darius Nahavandi, Navid Mohajer, Abbas Khosravi

Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused o

C. Fransson, M. J. Barlow, P. J. Kavanagh, J. Larsson

The nearby Supernova 1987A was accompanied by a burst of neutrino emission, which indicates that a compact object (a neutron star or black hole) was formed in the explosion. There has been no direct observation of this compact object. In this work, we observe the supernova remnant with JWST spectroscopy finding narrow infrared emission lines of argon and sul

Martin Willbo, Aleksis Pirinen, John Martinsson, Edvin Listo Zec

Land cover classification and change detection are two important applications of remote sensing and Earth observation (EO) that have benefited greatly from the advances of deep learning. Convolutional and transformer-based U-net models are the state-of-the-art architectures for these tasks, and their performances have been boosted by an increased availabilit

Tomasz Winiarski, Daniel Giełdowski, Jan Kaniuka, Jakub Ostrysz

Tests and prototyping are vital in the research and development of robotic systems. Work with target hardware is problematic. Hence, in the article, a low-cost, miniaturised physical platform is presented to deal with experiments on heterogeneous robotic systems. The platform comprises a physical board with tiles of the standardised base, diverse mobile robo

Victor Rueskov Christiansen, Mads Middelhede Lund, Fan Yang, Klaus Mølmer

The Jaynes-Cummings model provides a simple and accurate description of the interaction between a two-level quantum emitter and a single mode of quantum radiation. Due to the multimode continuum of eigenmodes in free space and in waveguides, the Jaynes-Cummings model should not be expected to properly describe the interaction between an emitter and a traveli

Harshit Nigam, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff

Several tools have recently been proposed for assisting researchers during various stages of the research life-cycle. However, these primarily concentrate on tasks such as retrieving and recommending relevant literature, reviewing and critiquing the draft, and writing of research manuscripts. Our investigation reveals a significant gap in availability of too

Ruicong Liu, Takehiko Ohkawa, Mingfang Zhang, Yoichi Sato

The pursuit of accurate 3D hand pose estimation stands as a keystone for understanding human activity in the realm of egocentric vision. The majority of existing estimation methods still rely on single-view images as input, leading to potential limitations, e.g., limited field-of-view and ambiguity in depth. To address these problems, adding another camera t

Wolfgang Paier, Paul Hinzer, Anna Hilsmann, Peter Eisert

We present a new approach for video-driven animation of high-quality neural 3D head models, addressing the challenge of person-independent animation from video input. Typically, high-quality generative models are learned for specific individuals from multi-view video footage, resulting in person-specific latent representations that drive the generation proce

Souvik Deb, Megh Rathod, Rishi Balamurugan, Shankar K. Ghosh

To enhance the handover performance in fifth generation (5G) cellular systems, conditional handover (CHO) has been evolved as a promising solution. Unlike A3 based handover where handover execution is certain after receiving handover command from the serving access network, in CHO, handover execution is conditional on the RSRP measurements from both current

Kevin Klein, Pascal Hirmer, Steffen Becker

The amount of software in modern cars is increasing continuously with traditional electric/electronic (E/E) architectures reaching their limit when deploying complex applications, e.g., regarding bandwidth or computational power. To mitigate this situation, more powerful computing platforms are being employed and applications are developed as distributed app

Marta Benítez, Alfredo Bermúdez, Pedro Fontán, Iván Martínez

The aim of this work is to introduce a thermo-electromagnetic model for calculating the temperature and the power dissipated in cylindrical pieces whose geometry var\'ies with time and undergoes large deformations; the motion will be a known data. The work will be a first step towards building a complete thermoelectromagnetic-mechanical model suitable for si

Yuqi Liu, Guanyi Chen, Kees van Deemter

Theoretical linguists have suggested that some languages (e.g., Chinese and Japanese) are "cooler" than other languages based on the observation that the intended meaning of phrases in these languages depends more on their contexts. As a result, many expressions in these languages are shortened, and their meaning is inferred from the context. In this paper,

Samuel Zamour

We describe the classification of ranked definably quasi-Frobenius groups of odd type : dihedral configurations are isomorphic to PGL(2, K) for K an algebraically closed field of characteristic other than two; Frobenius groups are spilt and solvable if the characteristic of the underlying field is positive. To achieve this classification, we prove some resul

Xiaodi Chen, Meng Zhang, Zhengguang Wu, Ligang Wu

Load frequency control (LFC) is widely employed in power systems to stabilize frequency fluctuation and guarantee power quality. However, most existing LFC methods rely on accurate power system modeling and usually ignore the nonlinear characteristics of the system, limiting controllers' performance. To solve these problems, this paper proposes a model-free

Farhad Nazari, Navid Mohajer, Darius Nahavandi, Abbas Khosravi

Human walking is a complex activity with a high level of cooperation and interaction between different systems in the body. Accurate detection of the phases of the gait in real-time is crucial to control lower-limb assistive devices like exoskeletons and prostheses. There are several ways to detect the walking gait phase, ranging from cameras and depth senso

Leo Thomas, Miriam Schwarze, Hans Rabus

This work explores the enhancement of ionization clusters around a gold nanoparticle (NP), indicative of the induction of DNA lesions, a potential trigger for cell-death. Monte Carlo track structure simulations were performed to determine (a) the fluence of incident photons and electrons in water around a gold NP under charged particle equilibrium conditions

Grégoire Saerens, Günter Ellrott, Olesia Pashina, Ilya Deriy

Second harmonic generation (SHG) is prohibited in centrosymmetric crystals such as silicon or germanium due to the presence of inversion symmetry. However, the structuring of such materials makes it possible to break the inversion symmetry, thus achieving generation of second-harmonic. Moreover, various symmetry properties of the resulting structure, such as

Werner Bernreuther, Long Chen, Zong-Guo Si

We consider top-antitop quark $(t{\bar t})$ production at the Large Hadron Collider (LHC) with subsequent decays into dileptonic final states. We use and investigate a set of leptonic angular correlations and distributions with which all the independent coefficient functions of the top-spin dependent parts of the $t{\bar t}$ production spin density matrices

Grazia Sveva Ascione, Valerio Sterzi

The problem of disambiguation of company names poses a significant challenge in extracting useful information from patents. This issue biases research outcomes as it mostly underestimates the number of patents attributed to companies, particularly multinational corporations which file patents under a plethora of names, including alternate spellings of the sa

Karishma, Shrisha Rao

We propose a multi-agent system that enables groups of agents to collaborate and work autonomously to execute tasks. Groups can work in a decentralized manner and can adapt to dynamic changes in the environment. Groups of agents solve assigned tasks by exploring the solution space cooperatively based on the highest reward first. The tasks have a dependency s

Ang Li, Qiangchao Chen, Yiquan Wu, Ming Cai

Confusing charge prediction is a challenging task in legal AI, which involves predicting confusing charges based on fact descriptions. While existing charge prediction methods have shown impressive performance, they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. In the legal domain, constituent elements play a pi

Jiaqi Tang, Ruizheng Wu, Xiaogang Xu, Sixing Hu

In this paper, we study a new problem, Film Removal (FR), which attempts to remove the interference of wrinkled transparent films and reconstruct the original information under films for industrial recognition systems. We first physically model the imaging of industrial materials covered by the film. Considering the specular highlight from the film can be ef

Sascha Mücke

This article describes how to solve Sudoku puzzles using Quadratic Unconstrained Binary Optimization (QUBO). To this end, a QUBO instance with 729 variables is constructed, encoding a Sudoku grid with all constraints in place, which is then partially assigned to account for clues. The resulting instance can be solved with a Quantum Annealer, or any other str

Marzena Rams-Baron, Alfred Blazytko, Riccardo Casalini, Marian Paluch

Sizable glass formers feature numerous unique properties and potential applications, but many questions regarding their glass transition dynamics have not been resolved yet. Here we analyzed structural relaxation times measured as a function of temperature and pressure in combination with the equation of state obtained from pressure-volume-temperature (PVT)

Ang Li, Yiquan Wu, Yifei Liu, Fei Wu

Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions. While Pretrained Language Models (PLMs) have showcased their prowess in natural language generation, their application to the complex, knowledge-intensive domain

Penghong Wang, Xingtao Wang, Wenrui Li, Xiaopeng Fan

Location awareness is a critical issue in wireless sensor network applications. For more accurate location estimation, the two issues should be considered extensively: 1) how to sufficiently utilize the connection information between multiple nodes and 2) how to select a suitable solution from multiple solutions obtained by the Euclidean distance loss. In th

William J. Wolf, Patrick M. Duerr

This paper argues that we ought to conceive of the Dark Energy problem -- the question of how to account for observational data, naturally interpreted as accelerated expansion of the universe -- as a crisis of underdetermined pursuit-worthiness. Not only are the various approaches to the Dark Energy problem evidentially underdetermined; at present, no compel

Xiaoying Yuan, Tingfa Xu, Xincong Liu, Ying Wang

In the realm of unmanned aerial vehicle (UAV) tracking, Siamese-based approaches have gained traction due to their optimal balance between efficiency and precision. However, UAV scenarios often present challenges such as insufficient sampling resolution, fast motion and small objects with limited feature information. As a result, temporal context in UAV trac

Yu-Xuan Zhang, Song Zhang, Yu-Gang Ma

It is important to understand whether $\alpha$-clustering structures can leave traces in ultra-relativistic heavy ion collisions. Using the modified AMPT model, we simulate three $\alpha$ + core configurations of $^{44}$Ti in $^{44}$Ti+$^{44}$Ti collisions at $\sqrt{s_{NN}}=5.02$ TeV as well as other systems with Woods-Saxon structures. One of these configur

Jiangshan Ju, Mingqiu Wang, Shengli Zhao

Subsampling algorithms for various parametric regression models with massive data have been extensively investigated in recent years. However, all existing studies on subsampling heavily rely on clean massive data. In practical applications, the observed covariates may suffer from inaccuracies due to measurement errors. To address the challenge of large data

Bastián Espinoza, Jennifer N. Jones-Baro

We study the stabilized automorphism group of minimal and, more generally, certain transitive dynamical systems. Our approach involves developing new algebraic tools to extract information about the rational eigenvalues of these systems from their stabilized automorphism groups. In particular, we prove that if two minimal system have isomorphic stabilized au

Mayank Mittal, Nikita Rudin, Victor Klemm, Arthur Allshire

Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively. Often, the learned behaviors fail to achieve the desired transformation invariances and suffer from motion artifacts. For instance, a quadruped may exhibit different gaits when c

T. Alboussière, Y. Ricard, S. Labrosse

Bounds on heat transfer have been the subject of previous studies concerning convection in the Boussinesq approximation: in the Rayleigh-B\'enard configuration, the first result obtained by \cite{howard63} states that $Nu < (3/64 \ Ra)^{1/2}$ for large values of the Rayleigh number $Ra$, independently of the Prandtl number $Pr$. This is still the best known

Greg K. Stretton, George Alex Koulieris

Tracking kinematic chains has many uses from healthcare to virtual reality. Inertial measurement units, IMUs, are well-recognised for their body tracking capabilities, however, existing solutions rely on gravity and often magnetic fields for drift correction. As humanity's presence in space increases, systems that don't rely on gravity or magnetism are requi

Karl Bringmann, Frank Staals, Karol Węgrzycki, Geert van Wordragen

The Earth Mover's Distance is a popular similarity measure in several branches of computer science. It measures the minimum total edge length of a perfect matching between two point sets. The Earth Mover's Distance under Translation ($\mathrm{EMDuT}$) is a translation-invariant version thereof. It minimizes the Earth Mover's Distance over all translations of

Sergio A. Dzib, Laurent Loinard, Ralf Launhardt, Jazmín Ordóñez-Toro

To increase the number of sources with Very Long Baseline Interferometry (VLBI) astrometry available for comparison with the Gaia results, we have observed 31 young stars with recently reported radio emission. These stars are all in the Gaia DR3 catalog and were suggested, on the basis of conventional interferometry observations, to be non-thermal radio emit

Mariana Carmelia Balanica-Dragomir

Carbon emissions have become a specific alarming indicators and intricate challenges that lead an extended argue about climate change. The growing trend in the utilization of fossil fuels for the economic progress and simultaneously reducing the carbon quantity has turn into a substantial and global challenge. The aim of this paper is to examine the driving

Takuto Fukushima, Ryusuke Miyamoto

Motor imagery classification based on electroencephalography (EEG) signals is one of the most important brain-computer interface applications, although it needs further improvement. Several methods have attempted to obtain useful information from EEG signals by using recent deep learning techniques such as transformers. To improve the classification accuracy

Mika Juvela, Devika Tharakkal

The fitting of spectral lines is a common step in the analysis of line observations and simulations. However, the observational noise, the presence of multiple velocity components, and potentially large data sets make it a non-trivial task. We present a new computer program Spectrum Iterative Fitter (SPIF) for the fitting of spectra with Gaussians or with hy

Ronaldo Rodrigues Pela, Claudia Draxl

Currently, many ab initio codes are being prepared for exascale computing. A first and important step is to significantly improve the efficiency of existing implementations by devising better algorithms that can accomplish the same tasks with enhanced scalability. This manuscript addresses this challenge for real-time time-dependent density functional theory

Yu-Chiung Lin, Albert K. H. Kong

We propose a self-supervised learning model to denoise gravitational wave (GW) signals in the time series strain data without relying on waveform information. Denoising GW data is a crucial intermediate process for machine-learning-based data analysis techniques, as it can simplify the model for downstream tasks such as detections and parameter estimations.

Leonhard Hahn, Tobias Bader, Christian Carlowitz, Martin Vossiek

This paper reports on a novel compact, low-loss bending technique for additively manufactured dielectric image lines between 140 GHz and 220 GHz. Conventional bending approaches require either large curvature radii or technologically challenging permittivity variations to prevent radiation loss at line discontinuities, e.g. caused by narrow bends. In contras

Tyler J. Hughes, Karl Glazebrook, Colin Jacobs

We provide an analysis of a convolutional neural network's ability to identify the lensing signal of single dark matter subhalos in strong galaxy-galaxy lenses in the presence of increasingly complex source light morphology. We simulate a balanced dataset of 800,000 strong lens images both perturbed and unperturbed by a single subhalo ranging in virial mass

Laurent Condat, Artavazd Maranjyan, Peter Richtárik

In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a communication-efficient algorithm that leverages the two popular and effective techniques of Local training, which reduces the communication frequency, and Compression, in which sh

Thiago Carvalho Corso, Tobias Ried

We explicitly solve a variational problem related to upper bounds on the optimal constants in the Cwikel--Lieb--Rozenblum (CLR) and Lieb--Thirring (LT) inequalities, which has recently been derived in [Invent. Math. 231 (2023), no.1, 111-167. https://doi.org/10.1007/s00222-022-01144-7 ] and [J. Eur. Math. Soc. (JEMS) 23 (2021), no.8, 2583-2600. https://doi.o

Cunqing Huangfu, Kang Sun, Yi Zeng, Yuwei Wang

The exponential growth of neuroscience literature presents a significant challenge for researchers seeking to efficiently access and utilize relevant information. To address this issue, we introduce the Brain Knowledge Engine (BrainKnow), an automated system designed to extract, link, and synthesize neuroscience knowledge from scientific publications. BrainK

Enrico Bernardi, Alberto Lanconelli, Christopher S. A. Lauria

Simple Exponential Smoothing is a classical technique used for smoothing time series data by assigning exponentially decreasing weights to past observations through a recursive equation; it is sometimes presented as a rule of thumb procedure. We introduce a novel theoretical perspective where the recursive equation that defines simple exponential smoothing o

Boning Li, Zhixuan Fang, Longbo Huang

Effective action abstraction is crucial in tackling challenges associated with large action spaces in Imperfect Information Extensive-Form Games (IIEFGs). However, due to the vast state space and computational complexity in IIEFGs, existing methods often rely on fixed abstractions, resulting in sub-optimal performance. In response, we introduce RL-CFR, a nov

Yanqi Dai, Yong Wang, Zebin You, Dong Jing

Visual instruction tuning is a key training stage of large multimodal models. However, when learning multiple visual tasks simultaneously, this approach often results in suboptimal and imbalanced overall performance due to latent knowledge conflicts across tasks. To mitigate this issue, we propose a novel Adaptive Task Balancing approach tailored for visual

Jeffrey C. Lagarias, David Harry Richman

An approximate divisor order is a partial order on the positive integers $\mathbb{N}^+$ that refines the divisor order and is refined by the additive total order. A previous paper studied such a partial order on $\mathbb{N}^+$, produced using the floor function. A positive integer $d$ is a floor quotient of $n$, denoted $d \,\preccurlyeq_{1}\, n$, if there i

Feng-Hua Qiao, Xian-Gai Deng, Yu-Gang Ma

Within the Ultra-relativistic Quantum Molecular Dynamics model (UrQMD) coupled with nucleon coalescence model and Mini-Spanning-Tree model, the yields of light nuclei have been stimulated in Au + Au collisions over an energy range of \(\sqrt{s_{NN}}=2.0\sim7.7\ \rm{GeV}\) and the momentum correlation functions of light nuclei pairs have been calculated by bo

Belle Collaboration, X. Dong, S. M. Zou, H. Y. Zhang

Using the data samples of 102 million $\Upsilon(1S)$ and 158 million $\Upsilon(2S)$ events collected by the Belle detector, we search for a pentaquark state in the $pJ/\psi$ final state from $\Upsilon(1,2S)$ inclusive decays. Here, the charge-conjugate $\bar{p}J/\psi$ is included. We observe clear $pJ/\psi$ production in $\Upsilon(1,2S)$ decays and measure t

Vladimiro Benedetti, Fabio Tanturri

In this paper we address a conjecture by Kleppe and Mir\'o-Roig stating that suitable twists by line bundles (on the smooth locus) of the exterior powers of the normal sheaf of a standard determinantal locus are arithmetically Cohen--Macaulay, and even Ulrich when the locus is linear determinantal. We do so by providing a very simple locally free resolution

Tan Peng, Yong-Chen Xiong, Chun-Bo Hua, Zheng-Rong Liu

Recently, the structural disorder-induced topological phase transitions in periodic systems have attracted much attention. However, in aperiodic systems such as quasicrystalline systems, the interplay between structural disorder and band topology is still unclear. In this work, we investigate the effects of structural disorder on a quantum spin Hall insulato

Abdoulaye Gamatié, Yuyang Wang

This paper investigates the application of eXplainable Artificial Intelligence (XAI) in the design of embedded systems using machine learning (ML). As a case study, it addresses the challenging problem of static silent store prediction. This involves identifying redundant memory writes based only on static program features. Eliminating such stores enhances p

Xinxiang Guo, Yifen Mu, Xiaoguang Yang

In this paper, we consider the $n \times n$ two-payer zero-sum repeated game in which one player (player X) employs the popular Hedge (also called multiplicative weights update) learning algorithm while the other player (player Y) adopts the myopic best response. We investigate the dynamics of such Hedge-myopic system by defining a metric $Q(\textbf{x}_t)$,

Emmanuel Fricain, Romain Lebreton

In this paper, we study the cyclic vectors of the shift operator $S_b$ acting on de Branges-Rovnyak space $\mathcal H(b)$ associated to a non-extreme point of the closed unit ball of $H^\infty$. We highlight an interesting link with a completeness problem that we study using the Cauchy transform. This enables us to obtain some nice consequences on cyclicity.

Jin Hu, Constantine Sideris

This paper introduces an efficient approach for solving the Electric Field Integral Equation (EFIE) with high-order accuracy by explicitly enforcing the continuity of the impressed current densities across boundaries of the surface patch discretization. The integral operator involved is discretized via a Nystr\"om-collocation approach based on Chebyshev poly

Amala Sonny, Abhinav Kumar, Linga Reddy Cenkeramaddi

Indoor human positioning has become increasingly important for applications such as health monitoring, breath monitoring, human identification, safety and rescue operations, and security surveillance. However, achieving robust indoor human positioning remains challenging due to various constraints. Numerous attempts have been made in the literature to develo

Romain Peretti, Mélanie Lavancier, Nabil Vindas-Yassine, Juliette Vlieghe

We present a robust method, as well as a new metric, for the comparison of permittivity models in terahertz timedomain spectroscopy (THz-TDS). In this work, we perform an extensive noise analysis of a THz-TDS system, we remove and model the unwanted deterministic noises and implement them into our fitting process. This is done using our open-source software,

Abdelghani Maddi, Luis Miotti

Amidst the ever-expanding realm of scientific production and the proliferation of predatory journals, the focus on peer review remains paramount for scientometricians and sociologists of science. Despite this attention, there is a notable scarcity of empirical investigations into the tangible impact of peer review on publication quality. This study aims to a

Siqi Zhou, Sotiris Papatheodorou, Stefan Leutenegger, Angela P. Schoellig

In this paper, we consider a Micro Aerial Vehicle (MAV) system teleoperated by a non-expert and introduce a perceptive safety filter that leverages Control Barrier Functions (CBFs) in conjunction with Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) and dense 3D occupancy mapping to guarantee safe navigation in complex and unstructured environ

Ganzhinov Mikhail, Östergård Patric R. J

Let $\textrm{S}(n,t,k)$ be the maximum size of a code containing only vectors of the $k$th shell of the integer lattice $\mathbb{Z}^n$ such that the inner product between distinct vectors does not exceed $t$. In this paper we compute lower bounds for $\textrm{S}(n,t,k)$ for small values of $n$, $t$ and $k$ by carrying out computer searches for codes with pre

Jingfeng Wang, Guanghui Hu

In this paper, a novel mechanism-driven reinforcement learning framework is proposed for airfoil shape optimization. To validate the framework, a reward function is designed and analyzed, from which the equivalence between the maximizing the cumulative reward and achieving the optimization objectives is guaranteed theoretically. To establish a quality explor

Nobuo Koida, Koji Shirai

This paper develops a novel characterization for random utility models (RUM), which turns out to be a dual representation of the characterization by Kitamura and Stoye (2018, ECMA). For a given family of budgets and its "patch" representation \'a la Kitamura and Stoye, we construct a matrix $\Xi$ of which each row vector indicates the structure of possible r

Humam Kourani, Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst

ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techniques. Beyond automating the generation of complex process models, ProMoAI also supports process model optimization. Users can interact with

Zhongjun Ni, Chi Zhang, Magnus Karlsson, Shaofang Gong

Digital transformation in the built environment generates vast data for developing data-driven models to optimize building operations. This study presents an integrated solution utilizing edge computing, digital twins, and deep learning to enhance the understanding of climate in buildings. Parametric digital twins, created using an ontology, ensure consisten

Changjiang Gao, Jixing Li, Jiajun Chen, Shujian Huang

The process of meaning composition, wherein smaller units like morphemes or words combine to form the meaning of phrases and sentences, is essential for human sentence comprehension. Despite extensive neurolinguistic research into the brain regions involved in meaning composition, a computational metric to quantify the extent of composition is still lacking.

Shuzhen Yang, Wenqing Zhang

In this study, we propose the sublinear expectation structure under countable state space. To describe an interesting "nonlinear randomized" trial, based on a convex compact domain, we introduce a family of probability measures under countable state space. Corresponding the sublinear expectation operator introduced by S. Peng, we consider the related notatio

Chungang Shi

In the paper, a class of second-order McKean-Vlasov stochastic evolution equation driven by Poisson jumps with non-Lipschitz conditions is considered. The existence and uniqueness of the mild solution is established by means of the Carath${\rm \acute{e}}$odory approximation technique. Furthermore, an averaging principle is obtained between the solution of th

Pierluigi Mansueto, Fabio Schoen

In this paper, we propose an extension for semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problems of MDEClust, a memetic framework based on the Differential Evolution paradigm for unsupervised clustering. In semi-supervised MSSC, background knowledge is available in the form of (instance-level) "must-link" and "cannot-link" constraints, each of wh

Leigang Qu, Wenjie Wang, Yongqi Li, Hanwang Zhang

Despite advancements in text-to-image generation (T2I), prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions involve cross-attention manipulation for better compositional understanding or integrating large language models for improved layout planning. However, the inherent alignment capa

P. Bilski, B. Marczewska, M. Sankowska, A. Kilian

Fluorescent nuclear track detectors based on LiF crystals were successfully applied for detection of proton induced tracks. Irradiations were performed with protons with energy ranging from 1 MeV up to about 56 MeV and for all proton energies the fluorescent tracks were observed. The tracks are not continuous, but consist of a series of bright spots. The gap

Vladyslav Unukovych, Andrii Sotnikov

We study SU(4)-symmetric ultracold fermionic mixture in the cubic optical lattice with the variable tunneling amplitude along one particular crystallographic axis in the crossover region from the two- to three-dimensional spatial geometry. To theoretically analyze emerging magnetic phases and physical observables, we describe the system in the framework of t

Jie Ma, Tianchi Yang

In this paper, we investigate the hypergraph Tur\'an number $ex(n,K^{(r)}_{s,t})$. Here, $K^{(r)}_{s,t}$ denotes the $r$-uniform hypergraph with vertex set $\left(\cup_{i\in [t]}X_i\right)\cup Y$ and edge set $\{X_i\cup \{y\}: i\in [t], y\in Y\}$, where $X_1,X_2,\cdots,X_t$ are $t$ pairwise disjoint sets of size $r-1$ and $Y$ is a set of size $s$ disjoint fr

Jihoon Tack, Jaehyung Kim, Eric Mitchell, Jinwoo Shin

Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. To address the crucial need to keep models updated, online learning has emerged as a critical tool when utilizing LLMs for real-world applications. However, given the ever-expanding corpus of unseen documents

Chiara Cecchini, Mariaveronica De Angelis, William Giarè, Massimiliano Rinaldi

There is solid theoretical and observational motivation behind the idea of scale-invariance as a fundamental symmetry of Nature. We consider a recently proposed classically scale-invariant inflationary model, quadratic in curvature and featuring a scalar field non-minimally coupled to gravity. We go beyond earlier analytical studies, which showed that the mo

Ha Eum Kim, Kabgyun Jeong

Various modified quantum teleportation schemes are proposed to overcome experimental constraints or to meet specific application requirements for quantum communication. Hence, most schemes are developed and studied with unique methodologies, each with its inherent challenges. Our research focuses on interconnecting these schemes appearing to be unrelated to

Yuwei Zhang, Siffi Singh, Sailik Sengupta, Igor Shalyminov

Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional embeddings allowing one to adjust semantics over the embedding space using prompts, are being viewed as a panacea for these downstream conversational tasks. However, traditional ev

Philipp Krah, Arthur Marmin, Beata Zorawski, Julius Reiss

We present a new methodology for decomposing flows with multiple transports that further extends the shifted proper orthogonal decomposition (sPOD). The sPOD tries to approximate transport-dominated flows by a sum of co-moving data fields. The proposed methods stem from sPOD but optimize the co-moving fields directly and penalize their nuclear norm to promot

Greg Martin, Chi Hoi Yip

We derive an asymptotic formula for the number of solutions in a given subfield to certain system of equations over finite fields. As an application, we construct new families of maximal cliques in generalized Paley graphs. Given integers $d\ge2$ and $q \equiv 1 \pmod d$, we show that for each positive integer $m$ such that $\operatorname{rad}(m) \mid \opera

Deepti Raghavan, Keshav Santhanam, Muhammad Shahir Rahman, Nayani Modugula

Compound AI applications chain together subcomponents such as generative language models, document retrievers, and embedding models. Applying traditional systems optimizations such as parallelism and pipelining in compound AI systems is difficult because each component has different constraints in terms of the granularity and type of data that it ingests. Ne

Qinghua Lei, Didier Sornette

Landslides exhibit intermittent gravity-driven downslope movements developing over days to years before a possible major collapse, commonly boosted by external events like precipitations and earthquakes. The reasons behind these episodic movements and how they relate to the final instability remain poorly understood. Here, we develop a novel "endo-exo" theor

Mingyuan Li, Tong Jia, Hao Wang, Bowen Ma

Prohibited item detection in X-ray images is one of the most essential and highly effective methods widely employed in various security inspection scenarios. Considering the significant overlapping phenomenon in X-ray prohibited item images, we propose an Anti-Overlapping DETR (AO-DETR) based on one of the state-of-the-art general object detectors, DINO. Spe

Chungang Shi, Yan Lv, Wei Wang

The small mass limit is derived for a McKean-Vlasov equation subject to environmental noise with state-dependent friction. By applying the averaging approach to a non-autonomous stochastic slow-fast system with the microscopic and macroscopic scales, the convergence in distribution is obtained.

Sachin Thukral, Suyash Sangwan, Vipul Chauhan, Arnab Chatterjee

As an increasingly large number of people turn to platforms like Reddit, YouTube, Twitter, Instagram, etc. for financial advice, generating insights about the content generated and interactions taking place within these platforms have become a key research question. This study proposes content and interaction analysis techniques for a large repository create

Zhiying Zhu, Yiming Yang, Zhiqing Sun

Hallucinations pose a significant challenge to the reliability of large language models (LLMs) in critical domains. Recent benchmarks designed to assess LLM hallucinations within conventional NLP tasks, such as knowledge-intensive question answering (QA) and summarization, are insufficient for capturing the complexities of user-LLM interactions in dynamic, r

Yao Jiang, Xinyu Yan, Ge-Peng Ji, Keren Fu

The advent of large vision-language models (LVLMs) represents a remarkable advance in the quest for artificial general intelligence. However, the model's effectiveness in both specialized and general tasks warrants further investigation. This paper endeavors to evaluate the competency of popular LVLMs in specialized and general tasks, respectively, aiming to

M. S. Aswathy, Marco E Rosti

This study explores the dynamics of finite-size fibers suspended freely in a viscoelastic turbulent flow. For a fiber suspended in Newtonian flows, two different flapping regimes were identified previously by Rosti et al (2018). Here we explore, how the fiber dynamics is modified by the elasticity of the carrier fluid by performing Direct Numerical Simulatio

Fabian Kleischmann, Paolo Luzzatto-Fegiz, Eckart Meiburg, Bernhard Vowinckel

We present a systematic simulation campaign to investigate the pairwise interaction of two mobile, monodisperse particles submerged in a viscous fluid and subjected to monochromatic oscillating flows. To this end, we employ the immersed boundary method to geometrically resolve the flow around the two particles in a non-inertial reference frame. We neglect gr

Jialin Li, Qiang Nie, Weifu Fu, Yuhuan Lin

Deep learning models, particularly those based on transformers, often employ numerous stacked structures, which possess identical architectures and perform similar functions. While effective, this stacking paradigm leads to a substantial increase in the number of parameters, posing challenges for practical applications. In today's landscape of increasingly l

Martin Duchaň, Martin Šiler, Petr Jákl, Oto Brzobohatý

A contactless control of mean values and fluctuations of position and velocity of a nanoobject belongs among the key methods needed for ultra-precise nanotechnology and the upcoming quantum technology of macroscopic systems. An analysis of experimental implementations of such a control, including assessments of linearity and the effects of added noise, is re

Daihei Ise, Satoshi Kobayashi

This paper deals with the control generation of right linear grammars with unknown behaviors (RLUBs, for short) in which derivation behavior is not determined completely. In particular, we consider a physical property of control devices used in control systems and formulate it as a partial order over control alphabet of the control system. We give necessary

Abdul Q. Batin, Suranjana Ghosh, Utpal Roy, David Vitali

We provide a scheme by utilizing a two-cavity setup to generate useful quantum mechanically entangled states of two cavity fields, which themselves are prepared in Schrodinger cat states. The underlying atom-field interaction is considered off-resonant and three atoms are successively sent through the cavities, initially fed with coherent fields. Analytical

Haojie Xin, Xiaodong Zhang, Renzhi Tang, Songyang Yan

Simulation is pivotal in evaluating the performance of autonomous driving systems due to the advantages of high efficiency and low cost compared to on-road testing. Bridging the gap between simulation and the real world requires realistic agent behaviors. However, the existing works have the following shortcomings in achieving this goal: (1) log replay offer

Sachin Thukral, Suyash Sangwan, Arnab Chatterjee, Lipika Dey

This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed methods are generic and applicable to all platforms. Along with an extension of topic extraction method using Latent Di

Qiqing Xia, Huiqin Xie, Li Yang

Steane code is one of the most widely studied quantum error-correction codes, which is a natural choice for fault-tolerant quantum computation (FTQC). However, the original Steane code is not fault-tolerant because the CNOT gates in an encoded block may cause error propagation. In this paper, we first propose a fault-tolerant encoding and decoding scheme, wh

Shengbin Wang, Peng Wang, Guihui Li, Shubin Zhao

Great efforts have been dedicated in recent years to explore practical applications for noisy intermediate-scale quantum (NISQ) computers, which is a fundamental and challenging problem in quantum computing. As one of the most promising methods, the variational quantum eigensolver (VQE) has been extensively studied. In this paper, VQE is applied to solve por

Long Zhong, Shenghao Li

A formally second order correct Boussinesq-type equation that describes unidirectional shallow water waves is derived, $$u_{tt} - u_{xx} - u_{xxxx} - u_{xxxxxx} - (u^2)_{xx} - (u^2)_{xxxx} - (uu_{xx})_{xx} - (u^3)_{xx} = 0.$$ Such equation is analogous to original Boussinesq equation but with higher order approximation which may ensure a more accuracy descri