October 2023 arXiv papers — page 91

Yimeng Zhang, Jinghan Jia, Xin Chen, Aochuan Chen

The recent advances in diffusion models (DMs) have revolutionized the generation of realistic and complex images. However, these models also introduce potential safety hazards, such as producing harmful content and infringing data copyrights. Despite the development of safety-driven unlearning techniques to counteract these challenges, doubts about their eff

Junaid Ali, Matthaeus Kleindessner, Florian Wenzel, Kailash Budhathoki

We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applicati

Liu Liu, Xuanqing Liu, Cho-Jui Hsieh, Dacheng Tao

Trust-region (TR) and adaptive regularization using cubics (ARC) have proven to have some very appealing theoretical properties for non-convex optimization by concurrently computing function value, gradient, and Hessian matrix to obtain the next search direction and the adjusted parameters. Although stochastic approximations help largely reduce the computati

Qinbin Li, Chulin Xie, Xiaojun Xu, Xiaoyuan Liu

Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, most existing federated learning studies focus on either horizontal or vertical data settings, where the data of different parties are assumed to be from the same feature or sample

Hongliang Zhong, Jingbo Zhang, Jing Liao

We propose VQ-NeRF, a two-branch neural network model that incorporates Vector Quantization (VQ) to decompose and edit reflectance fields in 3D scenes. Conventional neural reflectance fields use only continuous representations to model 3D scenes, despite the fact that objects are typically composed of discrete materials in reality. This lack of discretizatio

Yanyan Liang, Grisell Díaz Leines, Ralf Drautz, Jutta Rogal

Despite the fundamental importance of solid-solid transformations in many technologies, the microscopic mechanisms remain poorly understood. Here, we explore the atomistic mechanisms at the migrating interface during solid-solid phase transformations between the topologically closed-packed A15 and body-centred cubic phase in tungsten. The high energy barrier

Shiye Wang, Kaituo Feng, Changsheng Li, Ye Yuan

Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e.g., SGD or Adam) to learn network parameters, which makes training very time- and resource-intensive. In this paper, we propose a new training paradigm and formulate the parameter learning of ConvNets into a pred

Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza, Alina Geiger

Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java G

Gustavo Bergantiños, Juan D. Moreno-Ternero

We study the problem of sharing the revenues raised from subscriptions to music streaming platforms among content providers. We provide direct, axiomatic and game-theoretical foundations for two focal (and somewhat polar) methods widely used in practice: pro-rata and user-centric. The former rewards artists proportionally to their number of total streams. Wi

F. Javier Marañón-González, José Navarro-Salas

We analyze the adiabatic regularization scheme to renormalize Proca fields in a four-dimensional Friedmann-Lema\^{i}tre-Robertson-Walker spacetime. The adiabatic method is well established for scalar and spin-$1/2$ fields, but is not yet fully understood for spin-$1$ fields. We give the details of the construction and show that, in the massless limit, the re

Amir Mohammad Mirzaei

This paper introduces a simple framework for accurately predicting the fatigue lifetime of notched components by employing various machine learning algorithms applied to a wide range of materials, loading conditions, notch geometries, and fatigue lives. Traditional approaches for this task have relied on empirical relationships involving one of the mechanica

Limin Song, Shenyi Gao, Jina Ma, Liqin Tang

We demonstrate multiple flat bands and compact localized states (CLSs) in a photonic super-Kagome lattice (SKL) that exhibits coexistence of singular and nonsingular flat bands within its unique band structure. Specifically, we find that the upper two flat bands of an SKL are singular - characterized by singularities due to band touching with their neighbori

Yuqing Kong

This paper presents a distributed communication model to investigate multistable perception, where a stimulus gives rise to multiple competing perceptual interpretations. We formalize stable perception as consensus achieved through components exchanging information. Our key finding is that relationships between components influence monostable versus multista

Saleem Yousuf, Md Maruf Mridha, Rita Magri

A new superstructure of layered pristine LiNiO2 (LNO) was obtained optimizing a large supercell of the 166 space group, the one observed experimentally by XRD, and relaxing both cell parameters and internal positions. The crystal structure shows size and charge disproportionation of the NiO6 octahedra instead of the Jahn-Teller distortion. The decrease of th

Yuxing Shi

Let $(X, r)$ be any set-theoretical non-degenerate solution of the Yang-Baxter equation and $(X, \tilde r)$ be the derived solution of $(X, r)$. As for any braided vector space $(W_{X, r}, c)$ associated to $(X, r)$, is it possible to find some braided vector space $(W_{X, \tilde r}, \tilde c)$ which is t-equivalent to $(W_{X, r}, c)$? In case that $(X, r)$

Palash Kumar Pal, Md Sayeed Anwar, Dibakar Ghosh

In a predator-prey metapopulation, the two traits are adversely related: synchronization and persistence. A decrease in synchrony apparently leads to an increase in persistence and, therefore, necessitates the study of desynchrony in a metapopulation. In this article, we study predator-prey patches that communicate with one another while being interconnected

Luis Böttcher, Christian Fröhlich, Steffen Kortmann, Simon Braun

The shift towards clean energy brings about notable transformations to the energy system. In order to optimally plan a future energy system, it is necessary to consider the influence of several sectors as well as the interaction of the transmission grid and distribution grid. The concept of Feasible Operation Region (FOR) is a detailed approach to representi

Lulu Yu, Keping Bi, Jiafeng Guo, Xueqi Cheng

The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on the ULTRE-2 task. We recognize the issue of false negatives in the Baidu search data in this competition is very severe, much more severe than position bias. Hence, we adopt the Dual Le

Á. Süli, E. Forgács-Dajka

This work presents novel findings that broadens our understanding of the amount of water that can be transported to Earth. The key innovation lies in the combined usage of Smoothed Particle Hydrodynamics (SPH) and $N$-body codes to assess the role of collision fragments in water delivery. We also present a method for generating initial conditions that enable

Zhengyu Zhao, Hanwei Zhang, Renjue Li, Ronan Sicre

Transferable adversarial images raise critical security concerns for computer vision systems in real-world, black-box attack scenarios. Although many transfer attacks have been proposed, existing research lacks a systematic and comprehensive evaluation. In this paper, we systemize transfer attacks into five categories around the general machine learning pipe

Subir K. Das

Analytical descriptions of patterns concerning spread and fatality during an epidemic, covering natural as well as restriction periods, are important for reducing damage. We employ a scaling model to investigate this aspect in the real data of COVID-19. It transpires from our study that the statistics of fatality in many geographical regions follow unique be

J. Smalc-Koziorowska, J. Moneta, G. Muziol, W. Chrominski

(a+c) dislocations in hexagonal materials are typically observed to be dissociated into partial dislocations. Edge (a+c) dislocations are introduced into (0001) nitride semiconductor layers by the process of plastic relaxation. As there is an increasing interest in obtaining relaxed InGaN buffer layers for the deposition of high In content structures, the st

Manna Dai, Yang Jiang, Feng Yang, Joyjit Chattoraj

Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alterna

Malak Sadek, Rafael A. Calvo, Celine Mougenot

Conversational agents (CAs) are gaining traction in both industry and academia, especially with the advent of generative AI and large language models. As these agents are used more broadly by members of the general public and take on a number of critical use cases and social roles, it becomes important to consider the values embedded in these systems. This c

J. L. Chkareuli

The local $SL(2N,C)$ symmetry is shown to provide, when appropriately constrained, a viable framework for a consistent unification of the known elementary forces, including gravity. Such a covariant constraint implies that an actual gauge field multiplet in the $SL(2N,C)$ theory is ultimately determined by the associated tetrad fields which not only specify

Jie Liu, Yinmin Zhang, Chuming Li, Chao Yang

Building a single generalist agent with strong zero-shot capability has recently sparked significant advancements. However, extending this capability to multi-agent decision making scenarios presents challenges. Most current works struggle with zero-shot transfer, due to two challenges particular to the multi-agent settings: (a) a mismatch between centralize

Yufei Kuang, Xijun Li, Jie Wang, Fangzhou Zhu

Large-scale LP problems from industry usually contain much redundancy that severely hurts the efficiency and reliability of solving LPs, making presolve (i.e., the problem simplification module) one of the most critical components in modern LP solvers. However, how to design high-quality presolve routines -- that is, the program determining (P1) which presol

Maxime Siber, Olivier J. J. Ronsin, Jens Harting

Understanding the morphology formation process of solution-cast photoactive layers (PALs) is crucial to derive design rules for optimized and reliable third generation solar cell fabrication. For this purpose, a Phase-Field (PF) computational framework dedicated to the simulation of PAL processing has recently been developed. In this study focused on non-eva

Yohann Benchetrit, Hubert Banville, Jean-Rémi King

In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution ($\approx$0.5 Hz) and t

Jonas Kuckling, Robin Luckey, Viktor Avrutin, Andrew Vardy

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the ro

Bosang Kim, Jonghyun Kim, Hyotae Lee, Lanying Jin

In general, hand pose estimation aims to improve the robustness of model performance in the real-world scenes. However, it is difficult to enhance the robustness since existing datasets are obtained in restricted environments to annotate 3D information. Although neural networks quantitatively achieve a high estimation accuracy, unsatisfied results can be obs

B. S. Ratanpal, Rinkal Patel

We studied a new class of interior solutions that are singular-free and useful for describing anisotropic compact star objects with spherically symmetric matter distribution. We have considered metric potential selecting B_0^2 (r)=1/(1-r^2/R^2 )^n ,where n>2 . The various physical characteristics of the model are specifically examined for the pulsar PSRJ1903

Olivier Cailloux, Matthieu Hervouin, Ali I. Ozkes, M. Remzi Sanver

A classification is a surjective mapping from a set of objects to a set of categories. A classification aggregation function aggregates every vector of classifications into a single one. We show that every citizen sovereign and independent classification aggregation function is essentially a dictatorship. This impossibility implies an earlier result of Maniq

Rohan Subramani, Marcus Williams, Max Heitmann, Halfdan Holm

Most algorithms in reinforcement learning (RL) require that the objective is formalised with a Markovian reward function. However, it is well-known that certain tasks cannot be expressed by means of an objective in the Markov rewards formalism, motivating the study of alternative objective-specification formalisms in RL such as Linear Temporal Logic and Mult

Chao-Yao Yang, Sheng-Huai Chen, Chih-Hsiang Tseng, Chang-Yang Kuo

Antiferromagnets (AFMs) exhibit spin arrangements with no net magnetization, positioning them as promising candidates for spintronics applications. While electrical manipulation of the single-crystal AFMs, composed of periodic spin configurations, is achieved recently, it remains a daunting challenge to characterize and to manipulate polycrystalline AFMs. Ut

Julian Tachella, Marcelo Pereyra

Scientific imaging problems are often severely ill-posed, and hence have significant intrinsic uncertainty. Accurately quantifying the uncertainty in the solutions to such problems is therefore critical for the rigorous interpretation of experimental results as well as for reliably using the reconstructed images as scientific evidence. Unfortunately, existin

Jonathan So, Richard E. Turner

Natural gradient methods have been used to optimise the parameters of probability distributions in a variety of settings, often resulting in fast-converging procedures. Unfortunately, for many distributions of interest, computing the natural gradient has a number of challenges. In this work we propose a novel technique for tackling such issues, which involve

Sebastian Baader, Levi Ryffel

We determine the cobordism distance between all positive 3-braid links and connected sums of trefoil knots, up to a constant error. This may be seen as a reinterpretation of Coxeter's finiteness result on the braid group on three strands modulo third powers of the generators. We go on to explore the limits of Coxeter's result, and find the cobordism distance

Aram Akram Mohammed

Cold storage is a strategy mainly used for ornamental cuttings for the purpose of prolonging production duration that provide the cuttings with the best condition at the time of planting. The results have been obtained from cold storage of carnations and chrysanthemums confirmed the fact that the herbaceous cuttings could be stored for about two months at no

Shubham Tiwari, Saksham Bhushan, Aryan Taneja, Mohamed Kassem

The Internet is going through a massive infrastructural revolution with the advent of low-flying satellite networks, 5/6G, WiFi7, and hollow-core fiber deployments. While these networks could unleash enhanced connectivity and new capabilities, it is critical to understand the performance characteristics to efficiently drive applications over them. Low-Earth

Xudong Gao, Xiao Guang Gao, Jia Rong, Xiaowei Chen

Within the realm of image recognition, a specific category of multi-label classification (MLC) challenges arises when objects within the visual field may occlude one another, demanding simultaneous identification of both occluded and occluding objects. Traditional convolutional neural networks (CNNs) can tackle these challenges; however, those models tend to

P. -J. Wang, A. Huynh, T. -C. Hung, J. -K. Sheu

Many theories predict a quartic acoustic attenuation increase at sub-THz frequencies in glassy media for the excess vibrational modes known as the boson peak anomaly. Here by introducing phase-sensitive acoustic spectroscopy techniques with a THz bandwidth, we investigate the acoustic properties of vitreous silica at 15 and 300K in the crucial but unexplored

Vittoria Berta, Andrea Mignone, Matteo Bugli, Giancarlo Mattia

We present a novel implementation of a genuinely $4^{\rm th}$-order accurate finite volume scheme for multidimensional classical and special relativistic magnetohydrodynamics (MHD) based on the constrained transport (CT) formalism. The scheme introduces several novel aspects when compared to its predecessors yielding a more efficient computational tool. Amon

Kari A Noriy, Xiaosong Yang, Marcin Budka, Jian Jun Zhang

Multilingual speech processing requires understanding emotions, a task made difficult by limited labelled data. CLARA, minimizes reliance on labelled data, enhancing generalization across languages. It excels at fostering shared representations, aiding cross-lingual transfer of speech and emotions, even with little data. Our approach adeptly captures emotion

Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen

Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains. Meanwhile, the field of graph machine learning is witnessing a paradigm transition from shallow methods to more sophisticated deep learning approaches. The capab

Sen Yang, Mingze He, Chuchuan Hong, Josh Nordlander

Wavelength-selective thermal emitters (WS-EMs) hold considerable appeal due to the scarcity of cost-effective, narrow-band sources in the mid-to-long-wave infrared spectrum. WS-EMs achieved via dielectric materials typically exhibit thermal emission peaks with high quality factors (Q factors), but their optical responses are prone to temperature fluctuations

Rinkal Patel, B. S. Ratanpal

We obtain models of compact stars having pressure anisotropy on Finch-Skea spacetime by considering generalized equation of state (EoS), whose particular cases are linear, quadratic, polytropic, chaplygin and colour - flavor locked(CFL) equation of states. The physical viability of models are tested for strange star candidate 4U 1820 - 30 having mass M = 1.5

D. Basilico, G. Bellini, J. Benziger, R. Biondi

Borexino could efficiently distinguish between alpha and beta radiation in its liquid scintillator by the characteristic time profile of their scintillation pulse. This alpha/beta discrimination, first demonstrated at the tonne scale in the Counting Test Facility prototype, was used throughout the lifetime of the experiment between 2007 and 2021. With this m

Xiaoyan Li, Christoforos N. Hadjicostis, Zhiwu Li

Opacity is a property that captures security concerns in cyber-physical systems and its verification plays a significant role. This paper investigates the verifications of K-step and infinite-step weak and strong opacity for partially observed nondeterministic finite state automata. K-step weak opacity is checked by constructing, for some states in the obser

Alexander Berglund, Robin Stoll

These notes are based on a series of three lectures given (online) by the first named author at the workshop "Higher Structures and Operadic Calculus" at CRM Barcelona in June 2021. The aim is to give a concise introduction to rational homotopy theory through the lens of higher structures. The rational homotopy type of a simply connected space of finite type

Konrad Sakowski, Pawel Strak, Pawel Kempisty, Jacek Piechota

Defect-mediated nonradiative recombination, known as Shockley-Read-Hall (SRH) recombination is reformulated. The introduced model considers Coulomb attraction between charged deep defect and the approaching free carrier, showing that this effect may cause considerable increase of the carrier velocity approaching the recombination center. The effect considera

Javier González-Delgado, Mathis Deronzier, Juan Cortés, Pierre Neuvial

Recent work by Gao et al. (JASA 2022) has laid the foundations for post-clustering inference, establishing a theoretical framework allowing to test for differences between means of estimated clusters. Additionally, they studied the estimation of unknown parameters while controlling the selective type I error. However, their theory was developed for independe

Kai Wang, Qing-Wen Tang, Yan-Qiu Zhang, Chao Zheng

Acceleration of hadrons in relativistic shocks has been long expected and invoked to model GRB high-energy photon and neutrino emissions. However, so far there has been no direct observational evidence of hadronic emission from GRBs. The B.O.A.T. ("brightest of all time") gamma-ray burst (GRB) 221009A had extreme energies (with an isotropic energy exceeding

Vera Serganova

We call an affine algebraic supergroup quasireductive if its underlying algebraic group is reductive. We obtain some results about the structure and representations of reductive supergroups.

Robert Kraft, Sabri Koraltan, Markus Gattringer, Florian Bruckner

Speeding up computationally expensive problems, such as numerical simulations of large micromagnetic systems, requires efficient use of parallel computing infrastructures. While parallelism across space is commonly exploited in micromagnetics, this strategy performs poorly once a minimum number of degrees of freedom per core is reached. We use magnum.pi, a f

Zengguang Hao, Jie Zhang, Binxia Xu, Yafang Wang

Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services. The conventional approaches typically cast the intent mining process as a classification task. Although neural classifiers have proven adept at such classification tasks

Aitor De Andres, Shikha Bhadoria, Javier Tello Marmolejo, Alexander Muschet

Acceleration of electrons in vacuum directly by intense laser fields, often termed vacuum laser acceleration (VLA), holds great promise for the creation of compact sources of high-charge, ultrashort, relativistic electron bunches. However, while the energy gain is expected to be higher with tighter focusing (i.e. stronger electric field), this does not accou

A. Saleh, M. I. Jaghoub

We performed a least-square fit analysis to reproduce the elastic angular distributions for $\alpha$ scattering on various nuclei form $^{12}$C to $^{208}$Pb for incident energies in the range 18 - 70 MeV using a velocity-dependent optical model. The model reproduced the experimental data well including the enhanced angular distributions in the large-angle s

Omkar Nabar, Gautam Shroff

Price movements in financial markets are well known to be very noisy. As a result, even if there are, on occasion, exploitable patterns that could be picked up by machine-learning algorithms, these are obscured by feature and label noise rendering the predictions less useful, and risky in practice. Traditional rule-learning techniques developed for noisy dat

Ali Nauman, Haya Mesfer Alshahrani, Nadhem Nemri, Kamal M. Othman

This study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy grad

Bo-Heng Song, Tian-Qi Huang, Kai Wang

Recently, the Large High Altitude Air Shower Observatory (LHAASO) identified 12 $\gamma$-ray sources emitting gamma rays with energies above 100 TeV, making them potential PeV cosmic-ray accelerators (PeVatrons). Neutrino observations are crucial in determining whether the gamma-ray radiation process is of hadronic or leptonic origin. In this paper, we study

Deepak Ajwani, Rob H. Bisseling, Katrin Casel, Ümit V. Çatalyürek

Large networks are useful in a wide range of applications. Sometimes problem instances are composed of billions of entities. Decomposing and analyzing these structures helps us gain new insights about our surroundings. Even if the final application concerns a different problem (such as traversal, finding paths, trees, and flows), decomposing large graphs is

Ahmed Tawfik Aboukhadra, Jameel Malik, Nadia Robertini, Ahmed Elhayek

3D reconstruction of hand-object manipulations is important for emulating human actions. Most methods dealing with challenging object manipulation scenarios, focus on hands reconstruction in isolation, ignoring physical and kinematic constraints due to object contact. Some approaches produce more realistic results by jointly reconstructing 3D hand-object int

Xukun Feng, Zeying Zhang, Weikang Wu, Xian-Lei Sheng

Quadratic nodal point (QNP) in two dimensions has so far been reported only in nonmagnetic materials and in the absence of spin-orbit coupling. Here, by first-principles calculations and symmetry analysis, we predict stable QNP near Fermi level in a two-dimensional kagome metal-organic framework material, Cr$_3$(HAB)$_2$, which features noncollinear antiferr

Ramesh Prasad Panda

Let $\Gamma$ be an undirected and simple graph. A set $ S $ of vertices in $\Gamma$ is called a {cyclic vertex cutset} of $\Gamma$ if $\Gamma - S$ is disconnected and has at least two components containing cycles. If $\Gamma$ has a cyclic vertex cutset, then it is said to be {cyclically separable}. The {cyclic vertex connectivity} of $\Gamma$ is the minimum

Luca Francone

Let $\widehat G \subseteq G$ be complex reductive algebraic groups. The branching problem that aims to study $G$-modules as $\widehat G$-modules is encoded by a collection of branching multiplicities parameterised by pairs of dominant weights. The branching algebra $Br(G,\widehat G)$ is a graded algebra whose dimension of homogeneous components are precisely

Ya-Dong Wu, Yan Zhu, Yuexuan Wang, Giulio Chiribella

Characterizing multipartite quantum systems is crucial for quantum computing and many-body physics. The problem, however, becomes challenging when the system size is large and the properties of interest involve correlations among a large number of particles. Here we introduce a neural network model that can predict various quantum properties of many-body qua

Xiao-Jian Chen, Quanhua Dong, Changjiang Xiao, Zhou Huang

The spatial arrangement of taxi hotspots indicates their inherent distribution relationships, reflecting their spatial organization structure, and has received attention in urban studies. Previous studies have primarily explored large-scale hotspots through visual analysis or simple indices, which typically spans hundreds or even thousands of meters. However

Khattiya Pongsirijinda, Zhiqiang Cao, Muhammad Shalihan, Benny Kai Kiat Ng

Nowadays, several real-world tasks require adequate environment coverage for maintaining communication between multiple robots, for example, target search tasks, environmental monitoring, and post-disaster rescues. In this study, we look into a situation where there are a human operator and multiple robots, and we assume that each human or robot covers a cer

Indre Zliobaite, Jesse Read

Machine learning from data streams is an active and growing research area. Research on learning from streaming data typically makes strict assumptions linked to computational resource constraints, including requirements for stream mining algorithms to inspect each instance not more than once and be ready to give a prediction at any time. Here we review the h

Kazuya Yokota, Takahiko Kurahashi, Masajiro Abe

This study devised a physics-informed neural network (PINN) framework to solve the wave equation for acoustic resonance analysis. The proposed analytical model, ResoNet, minimizes the loss function for periodic solutions and conventional PINN loss functions, thereby effectively using the function approximation capability of neural networks while performing r

Raphael Bertrand-Delgado, Ravindra Desai, Fernando Zorto-Aguilera, Zeqi Zhang

The advent of the modular CubeSat satellite architecture has heralded a revolution in satellite missions, drastically lowering the technical and financial barriers to space. Surface charging resulting from energetic electron poses a direct risk to satellites in space, causing electric arcing and breakdowns. This risk is exacerbated for small technology demon

Weian Mao, Muzhi Zhu, Zheng Sun, Shuaike Shen

Innovations like protein diffusion have enabled significant progress in de novo protein design, which is a vital topic in life science. These methods typically depend on protein structure encoders to model residue backbone frames, where atoms do not exist. Most prior encoders rely on atom-wise features, such as angles and distances between atoms, which are n

Rikteem Bhowmick, Navaneeth Krishnan Mohan, Devesh Kumar, Rohit Chaurasiya

After the emergence of quantum mechanics and realising its need for an accurate understanding of physical systems, numerical methods were being used to undergo quantum mechanical treatment. With increasing system correlations and size, numerical methods fell rather inefficient, and there was a need to simulate quantum mechanical phenomena on actual quantum c

Roman Gerasimov, Adam J. Burgasser, Ilaria Caiazzo, Derek Homeier

Despite their shared origin, members of globular clusters display star-to-star variations in composition. The observed pattern of element abundances is unique to these stellar environments, and cannot be fully explained by any proposed mechanism. It remains unclear whether stars form with chemical heterogeneity, or inherit it from interactions with other mem

Paavo Sattler, Dennis Dobler

Covariance matrices of random vectors contain information that is crucial for modelling. Specific structures and patterns of the covariances (or correlations) may be used to justify parametric models, e.g., autoregressive models. Until now, there have been only a few approaches for testing such covariance structures and most of them can only be used for one

Guy Avni, Kaushik Mallik, Suman Sadhukhan

Many sequential decision-making tasks require satisfaction of multiple, partially contradictory objectives. Existing approaches are monolithic, namely all objectives are fulfilled using a single policy, which is a function that selects a sequence of actions. We present auction-based scheduling, a modular framework for multi-objective decision-making problems

Rohit Mohan, Kiran Kumaraswamy, Juana Valeria Hurtado, Kürsat Petek

Deep learning has led to remarkable strides in scene understanding with panoptic segmentation emerging as a key holistic scene interpretation task. However, the performance of panoptic segmentation is severely impacted in the presence of out-of-distribution (OOD) objects i.e. categories of objects that deviate from the training distribution. To overcome this

F. Sun, S. Mishra, U. Stockert, R. Daou

In many physical situations in which many-body assemblies exist at temperature $T$, a characteristic quantum-mechanical time scale of approximately $\hbar/k_{B}T$ can be identified in both theory and experiment, leading to speculation that it may be the shortest meaningful time in such circumstances. When this behaviour is investigated by probing the scatter

Jasleen Kaur, Rajinder Kaur

This research work introduces the structure of invariant and screen semi-invariant lightlike submanifolds of a metallic semi-Riemannian manifold with a quarter symmetric non-metric connection, elaborated with examples. It delves into the characterization of integrability and parallelism of distributions inherent in the structure of these submanifolds. Additi

B. Griffiths, Y. S. Lo, J. F. Dynes, R. I. Woodward

The ability to create phase-controlled pulses of light with wavelength tunability has applications spanning quantum and classical communications networks. Traditionally, optical transmitters are able to either produce phase-controlled pulses at a fixed wavelength or require a chain of bulky and expensive external modulators to convert wavelength tunable cont

Xinyi Ding, Meiqi Chang

Sonodynamic therapy (SDT) has received a lot of interest due to its deep tissue penetration and lack of invasiveness. However, SDT still prioritizes the creation of highly effective, multifunctional, and biocompatible sonosensitizers to improve the therapeutic efficiency. In this study, sodium niobate (NaNbO3) nanosonosensitizers are rationed synthesized for

Noriaki Takasugi, Masaya Kinoshita, Yasuhisa Kamikawa, Ryoichi Tsuzaki

To achieve safe legged locomotion, it is important to generate motion in real-time considering various constraints in robots and environments. In this study, we propose a lightweight real-time perspective motion control system for the newly developed six-wheeled-telescopic-legged robot, Tachyon 3. In the proposed method, analytically smoothed constraints inc

Shanjun Mao, Xiao Huang, Runjiu Chen, Chenyang Zhang

Motivation: Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Due to the lack of comprehensive integration of spatial and temporal dimensions of brain gene expression data, previous studies have mainly focused on individual brain regions or a certain developmental stage. Ou

Zekun Ji, Shuling Wang, Xiong Xu

Message passing is a fundamental element in software development, ranging from concurrent and mobile computing to distributed services, but it suffers from communication errors such as deadlocks. Session types are a typing discipline for enforcing safe structured interactions between multiple participants. However, each typed interaction is restricted to hav

Shuai Sun, Jiayun Li, Yilin Mo

This paper is concerned with the finite sample identification performance of an n dimensional discrete-time Multiple-Input Multiple-Output (MIMO) Linear Time-Invariant system, with p inputs and m outputs. We prove that the widely-used Ho-Kalman algorithm and Multivariable Output Error State Space (MOESP) algorithm are ill-conditioned for MIMO systems when n/

Shengzhu Shi, Yao Li, Zhichang Guo, Boying Wu

Physics-informed neural networks (PINNs) have shown great promise in solving partial differential equations (PDEs). However, vanilla PINNs often face challenges when solving complex PDEs, especially those involving multi-scale behaviors or solutions with sharp or oscillatory characteristics. To precisely and adaptively locate the critical regions that fail i

Michalis Chatzittofi, Ramin Golestanian, Jaime Agudo-Canalejo

The dynamics of many nanoscale biological and synthetic systems such as enzymes and molecular motors are activated by thermal noise, and driven out-of-equilibrium by local energy dissipation. Because the energies dissipated in these systems are comparable to the thermal energy, one would generally expect their dynamics to be highly stochastic. Here, by study

Rishi Shah, Krishnanshu Jain, Sahil Manchanda, Sourav Medya

Graph partitioning aims to divide a graph into disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhibit NP-hardness due to their combinatorial nature. Conventional methods, like approximation algorithms or heuristics, are designed for distinct partitioning objectives and fail to a

C. H. Zhang, Y. B. Shi, Z. Song

In general, the summation of a set of sub-Hamiltonians cannot share a common eigenstate of each one, only if it is an unentangled product state, such as a phantom helix state in quantum spin system. Here we present a method, referred to as the building block method (BBM), for constructing possible spin-1/2 XXZ Heisenberg lattice systems possessing phantom he

Masoud Sabzevari

We construct complete normal forms for $5$-dimensional real hypersurfaces in $\mathbb C^3$ which are $2$-nondegenerate and also of Levi non-uniform rank zero at the origin point ${\bf p} =0$. The latter condition means that the rank of the Levi form vanishes at ${\bf p}$ but not identically in a neighborhood of it. The mentioned hypersurfaces are the only fi

Xinhua Cheng, Tianyu Yang, Jianan Wang, Yu Li

Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current methods struggle to generate correct 3D content for a complex prompt in semantics, i.e., a prompt describing multiple interacted objects binding with different attributes. In this wo

Maciej Trzebinski

The status of the ATLAS Roman Pot detectors (AFP and ALFA) for LHC Run 3 after all refurbishments and improvements done during Long Shutdown 2 is discussed.

Yibin Zhang

Given a bounded smooth domain $\Omega$ in $\mathbb{R}^2$, we study the following anisotropic elliptic problem $$ \begin{cases} -\nabla\big(a(x)\nabla \upsilon\big)= a(x)\big[e^{\upsilon}-s\phi_1-4\pi\alpha\delta_q-h(x)\big]\,\,\,\, \,\textrm{in}\,\,\,\,\,\Omega,\\[2mm] \upsilon=0 \qquad\qquad\qquad\qquad\qquad \qquad\qquad\qquad\qquad\quad \textrm{on}\,\ \,\

Côme Peladeau, Geoffroy Peeters

Blind Estimation of Audio Effects (BE-AFX) aims at estimating the Audio Effects (AFXs) applied to an original, unprocessed audio sample solely based on the processed audio sample. To train such a system traditional approaches optimize a loss between ground truth and estimated AFX parameters. This involves knowing the exact implementation of the AFXs used for

Felix Stollenwerk, Joey Öhman, Danila Petrelli, Emma Wallerö

This handbook is a hands-on guide on how to approach text annotation tasks. It provides a gentle introduction to the topic, an overview of theoretical concepts as well as practical advice. The topics covered are mostly technical, but business, ethical and regulatory issues are also touched upon. The focus lies on readability and conciseness rather than compl

Sagnik Mondal, Marc G. Genton

We introduce a new family of multivariate distributions by taking the component-wise Tukey-h transformation of a random vector following a skew-normal distribution. The proposed distribution is named the skew-normal-Tukey-h distribution and is an extension of the skew-normal distribution for handling heavy-tailed data. We compare this proposed distribution t

Qichao Wang, Tian Bian, Yian Yin, Tingyang Xu

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products. While these exceptional AIGC products are gaining increasing recognition and sparking enthusiasm among consumers, the questions regarding whether, when, and how these models

Jie Zhou, Qian Yu

In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the

Geoffroy Berthelot, Liubov Tupikina, Min-Yeong Kang, Jérôme Dedecker

Transport in complex networks can describe a variety of natural and human-engineered processes including biological, societal and technological ones. However, how the properties of the source and drain nodes can affect transport subject to random failures, attacks or maintenance optimization in the network remain unknown. In this paper, the effects of both t