April 2024 arXiv papers — page 42

Dimitrios Michael Manias, Ali Chouman, Abdallah Shami

Large language models (LLMs) are rapidly emerging in Artificial Intelligence (AI) applications, especially in the fields of natural language processing and generative AI. Not limited to text generation applications, these models inherently possess the opportunity to leverage prompt engineering, where the inputs of such models can be appropriately structured

A. S. Gordienko

Using the braided version of Lawvere's algebraic theories and Mac Lane's PROPs, we introduce polynomial identities for arbitrary algebraic structures in a braided monoidal category C as well as their codimensions in the case when C is linear over some field. The new cases include coalgebras, bialgebras, Hopf algebras, braided vector spaces, Yetter-Drinfel'd

Kostas N. Oikonomou

We introduce a new generalization of relative entropy to non-negative vectors with sums $\gt 1$. We show in a purely combinatorial setting, with no probabilistic considerations, that in the presence of linear constraints defining a convex polytope, a concentration phenomenon arises for this generalized relative entropy, and we quantify the concentration prec

Zhe Zhang, Yifei Guan, Junda Wang, Benjamin Apffel

Exploring and understanding topological phases in systems with strong distributed disorder requires developing fundamentally new approaches to replace traditional tools such as topological band theory. Here, we present a general real-space renormalization group (RG) approach for scattering models, which is capable of dealing with strong distributed disorder

Kenji Nakahira

A vector space is commonly defined as a set that satisfies several conditions related to addition and scalar multiplication. However, for beginners, it may be hard to immediately grasp the essence of these conditions. There are probably a fair number of people who have wondered if these conditions could be substituted with ones that seem more straightforward

Matteo Luca Ruggiero

We focus on the interaction of a plane gravitational wave with electromagnetic fields and we describe this interaction in the proper detector frame where, thanks to the introduction of Fermi coordinates, it is possible to refer to directly measurable quantities. The presence of a gravitational field can be addressed in terms of an effective electromagnetic m

Fabio Deelan Cunden, Marilena Ligabò, Maria Caterina Susca

For quantum observables $H$ truncated on the range of orthogonal projections $\Pi_N$ of rank $N$, we study the corresponding Weyl symbol in the phase space in the semiclassical limit of vanishing Planck constant $\hbar\to0$ and large quantum number $N\to\infty$, with $\hbar N$ fixed. Under certain assumptions, we prove the $L^2$- convergence of the Weyl symb

J. W. Zhou, Timothy A. Davis

We investigated the kinematics and dynamics of gas structures on galaxy-cloud scales in two spiral galaxies NGC5236 (M83) and NGC4321 (M100) using CO (2$-$1) line. We utilized the FILFINDER algorithm on integrated intensity maps for the identification of filaments in two galaxies. Clear fluctuations in velocity and density were observed along these filaments

Xavier Coiteux-Roy, Owidiusz Makuta, Fionnuala Curran, Remigiusz Augusiak

Bell's theorem proves that some quantum state correlations can only be explained by bipartite non-classical resources. The notion of genuinely multipartite nonlocality (GMNL) was later introduced to conceptualize the fact that nonclassical resources involving more than two parties in a nontrivial way may be needed to account for some quantum correlations. In

Brent Sordo, Ellen Rathje, Krishna Kumar

Tailings dams impound large amounts of saturated soil which can be highly susceptible to liquefaction. Liquefaction results in a severe loss of strength in the retained soil and potentially failure of the dam. If the dam is breached, a massive debris flow of liquefied soil is then released with potentially disastrous consequences downstream. Numerical models

Hong-Jun Yoon, Chris Keum, Alexander Witkowski, Joanna Ludzik

Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique used in biomedical research and clinical dermatology. It provides virtual high-resolution images of the skin and superficial tissues, reducing the need for physical biopsies. RCM employs a laser light source to illuminate the tissue, capturing the reflected light to generate detailed i

Malte Hansen, Andre Büttner

In light of the GDPR, data controllers (DC) need to allow data subjects (DS) to exercise certain data subject rights. A key requirement here is that DCs can reliably authenticate a DS. Due to a lack of clear technical specifications, this has been realized in different ways, such as by requesting copies of ID documents or by email address verification. Howev

Simone Soderi, Rocco De Nicola

Covert channel networks are a well-known method for circumventing the security measures organizations put in place to protect their networks from adversarial attacks. This paper introduces a novel method based on bit-rate modulation for implementing covert channels between devices connected over a wide area network. This attack can be exploited to exfiltrate

Manishika Rawat, Matteo Pagin, Marco Giordani, Louis-Adrien Dufrene

Beam management in 5G NR involves the transmission and reception of control signals such as Synchronization Signal Blocks (SSBs), crucial for tasks like initial access and/or channel estimation. However, this procedure consumes energy, which is particularly challenging to handle for battery-constrained nodes such as RedCap devices. Specifically, in this work

Andrei Kataev, Konstantin Stepanyantz

We consider the group formed by finite renormalizations as an infinite-dimensional Lie group. It is demonstrated that for the finite renormalization of the gauge coupling constant its generators $\hat L_n$ with $n\ge 1$ satisfy the commutation relations of the Witt algebra and, therefore, form its subalgebra. The commutation relations are also written for th

Stanislav Alexeyev, Artem Baiderin, Alexandra Nemtinova, Oleg Zenin

With the help of Newman-Janis method new spinning black hole (BH) solution for a non-local gravity model was obtained. We show how to account the quantum gravitational correction part in BH shadows modelling using spinning BH metrics with a model independent approach. It is confirmed that in the future to follow the increasing of the experimental accuracy an

Tim S. Lyon, Ian Shillito, Alwen Tiu

It is well-known that extending the Hilbert axiomatic system for first-order intuitionistic logic with an exclusion operator, that is dual to implication, collapses the domains of models into a constant domain. This makes it an interesting problem to find a sound and complete proof system for first-order bi-intuitionistic logic with non-constant domains that

Haolin Wu, Jing Chen, Ruiying Du, Cong Wu

The increasing prevalence of audio deepfakes poses significant security threats, necessitating robust detection methods. While existing detection systems exhibit promise, their robustness against malicious audio manipulations remains underexplored. To bridge the gap, we undertake the first comprehensive study of the susceptibility of the most widely adopted

Julien Brémont, Olivier Bénichou, Raphaël Voituriez

Self-interacting random walks (SIRWs) show long-range memory effects that result from the interaction of the random walker at time $t$ with the territory already visited at earlier times $t'<t$. This class of non-Markovian random walks has applications in contexts as diverse as foraging theory, the behaviour of living cells, and even machine learning. Despit

Eneko Osaba, Esther Villar-Rodriguez

In this paper, we propose a quantum computing oriented benchmark for combinatorial optimization. This benchmark, coined as QOPTLib, is composed of 40 instances equally distributed over four well-known problems: Traveling Salesman Problem, Vehicle Routing Problem, one-dimensional Bin Packing Problem and the Maximum Cut Problem. The sizes of the instances in Q

Hannes Fassold

Deploying Large Language Models (LLMs) on mobile devices makes all the capabilities of natural language processing available on the device. An important use case of LLMs is question answering, which can provide accurate and contextually relevant answers to a wide array of user queries. We describe how we managed to port state of the art LLMs to mobile device

Edward Y. X. Ong, Anna R. Barth, Navneet Singh, Meera Ramaswamy

Systems driven far from equilibrium often retain structural memories of their processing history. This memory has, in some cases, been shown to dramatically alter the material response. For example, work hardening in crystalline metals can alter the hardness, yield strength, and tensile strength to prevent catastrophic failure. Whether memory of processing h

Elena Clabassi, Gianluca Balestra, Giulia Siciliano, Laura Polimeno

Optical Bound States in the Continuum are peculiar localized states within the continuous spectrum that are unaffected by any far-field radiation and intrinsic absorption, therefore possessing infinite mode lifetime and Q-factor. To date they have been widely studied in dielectric structures whereas their exploitation in lossy media, i.e. plasmonic nanostruc

Michaela Regneri, Alhassan Abdelhalim, Sören Laue

We present a novel approach to detecting noun abstraction within a large language model (LLM). Starting from a psychologically motivated set of noun pairs in taxonomic relationships, we instantiate surface patterns indicating hypernymy and analyze the attention matrices produced by BERT. We compare the results to two sets of counterfactuals and show that we

Russell Buchanan, S. Jack Tu, Marco Camurri, Stephen J. Mellon

Patellofemoral joint (PFJ) issues affect one in four people, with 20% experiencing chronic knee pain despite treatment. Poor outcomes and pain after knee replacement surgery are often linked to patellar mal-tracking. Traditional imaging methods like CT and MRI face challenges, including cost and metal artefacts, and there's currently no ideal way to observe

Qianyu He, Jie Zeng, Qianxi He, Jiaqing Liang

It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following). Yet, it remains under-explored how to enhance the ability of LLMs to follow complex instructions with multiple constraints. To bridge the gap, we initially study what training data is effective in enhancing complex const

Maja Stahl, Leon Biermann, Andreas Nehring, Henning Wachsmuth

Individual feedback can help students improve their essay writing skills. However, the manual effort required to provide such feedback limits individualization in practice. Automatically-generated essay feedback may serve as an alternative to guide students at their own pace, convenience, and desired frequency. Large language models (LLMs) have demonstrated

Kun Chen

Nonlocal effective interactions are inherent to non-relativistic quantum many-body systems, but their systematic resummation poses a significant challenge known as the ``vertex problem" in many-body perturbation theory. We introduce a renormalization scheme based on a projection-based renormalization condition that selectively resums the most essential nonlo

Vaisakh Shaj

Machines that can replicate human intelligence with type 2 reasoning capabilities should be able to reason at multiple levels of spatio-temporal abstractions and scales using internal world models. Devising formalisms to develop such internal world models, which accurately reflect the causal hierarchies inherent in the dynamics of the real world, is a critic

Ryan Martin, Jonathan P. Williams

The inferential model (IM) framework offers alternatives to the familiar probabilistic (e.g., Bayesian and fiducial) uncertainty quantification in statistical inference. Allowing this uncertainty quantification to be imprecise makes it possible to achieve exact validity and reliability. But is imprecision and exact validity compatible with attainment of the

Aamer Mohamed Huroon, Baris Donmez, Gunes Karabulut Kurt, Li-Chun Wang

This study introduces a novel approach to enhance communication networks in the Cislunar space by leveraging Reconfigurable Intelligent Surfaces (RIS). Using the ability of RIS to dynamically control electromagnetic waves, this paper tackles the challenges of signal attenuation, directivity, and divergence in Cislunar missions, primarily caused by immense di

Géza Csima, Jenő Szirmai

After having investigated the geodesic triangles and their angle sums in Nil and $Sl\times\mathbb{R}$ geometries we consider the analogous problem in Sol space that is one of the eight 3-dimensional Thurston geometries. We analyse the interior angle sums of geodesic triangles and we prove that it can be larger than, less than or equal to $\pi$. Moreover, we

Simone Dovetta, Louis Jeanjean, Enrico Serra

We consider the existence of normalized solutions to nonlinear Schr\"odinger equations on noncompact metric graphs in the $L^2$ supercritical regime. For sufficiently small prescribed mass ($L^2$ norm), we prove existence of positive solutions on two classes of graphs: periodic graphs, and noncompact graphs with finitely many edges and suitable topological a

Tim S. Lyon, Jonas Karge

We introduce a constructive method applicable to a large number of description logics (DLs) for establishing the concept-based Beth definability property (CBP) based on sequent systems. Using the highly expressive DL RIQ as a case study, we introduce novel sequent calculi for RIQ-ontologies and show how certain interpolants can be computed from sequent calcu

Carsten Gundlach, Thomas W. Baumgarte, David Hilditch

We present the first numerical simulations in null coordinates of the collapse of nonspherical regular initial data to a black hole. We restrict to twist-free axisymmetry, and re-investigate the critical collapse of a non-spherical massless scalar field. We find that the Choptuik solution governing scalar field critical collapse in spherical symmetry persist

Ping He, Bo-Qiang Ma

The confirmation of the existence of GZK cut-off was tortuous, leading to activities to explore new physics, such as the cosmic-ray new components, unidentified cosmic-ray origins, unknown propagation mechanism, and the modification of fundamental physics concepts like the tiny Lorentz invariance violation (LV). The confirmation of the GZK cut-off provides a

Diederick Vermetten, Johannes Lengler, Dimitri Rusin, Thomas Bäck

Optimization problems in dynamic environments have recently been the source of several theoretical studies. One of these problems is the monotonic Dynamic Binary Value problem, which theoretically has high discriminatory power between different Genetic Algorithms. Given this theoretical foundation, we integrate several versions of this problem into the IOHpr

Paweł Zyblewski

Rapid technological advances are inherently linked to the increased amount of data, a substantial portion of which can be interpreted as data stream, capable of exhibiting the phenomenon of concept drift and having a high imbalance ratio. Consequently, developing new approaches to classifying difficult data streams is a rapidly growing research area. At the

J. -W. Zhang, B. Wang, W. -F. Yuan, J. -C. Li

Although entanglement is considered as an essential resource for quantum information processing, whether entanglement helps for energy conversion or output in the quantum regime is still lack of experimental witness. Here we report on an energy-conversion device operating as a quantum engine with the working medium acted by two entangled ions confined in a h

Christian Heidorn, Frank Hannig, Dominik Riedelbauch, Christoph Strohmeyer

The AURIX 2xx and 3xx families of TriCore microcontrollers are widely used in the automotive industry and, recently, also in applications that involve machine learning tasks. Yet, these applications are mainly engineered manually, and only little tool support exists for bringing neural networks to TriCore microcontrollers. Thus, we propose OpTC, an end-to-en

C. A. Bertulani, Shubhchintak

The conventional Big Bang model successfully anticipates the initial abundances of 2H(D), 3He, and 4He, aligning remarkably well with observational data. However, a persistent challenge arises in the case of 7Li, where the predicted abundance exceeds observations by a factor of approximately three. Despite numerous efforts employing traditional nuclear physi

Albert Beardo, Prajit Rawte, Chia-Nien Tsai, Mahmoud I. Hussein

Thermal transport in crystals is influenced by chemistry, boundaries, and nanostructure. The anharmonic phonon band structure extracted from molecular-dynamics simulations provides an illuminating view of both the type and extent of prevalence of wavelike mechanisms underlying the transport, yet falls short of elucidating the nature of thermal evolution for

Hanfu Zhang, Yidan Mei, Erwu Liu, Rui Wang

This letter introduces a novel unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) structure into near-field localization systems to enhance the design flexibility of IRS, thereby obtaining additional performance gains. Specifically, a UAV-IRS is utilized to improve the harsh wireless environment and provide localization possibilities. To impr

Monibor Rahman, Adam Carpenter, Khan Iftekharuddin, Chris Tennant

Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizi

Johannes Aspman, Vyacheslav Kungurtsev, Jakub Marecek

Optimal control of closed quantum systems is a well studied geometrically elegant set of computational theory and techniques that have proven pivotal in the implementation and understanding of quantum computers. The design of a circuit itself corresponds to an optimal control problem of choosing the appropriate set of gates (which appear as control operands)

Jeff Maki, Fei Zhou

Conformal symmetry heavily constrains the dynamics of non-relativistic quantum gases tuned to a nearby quantum critical point. One important consequence of this symmetry is that entropy production can be absent in far away from equilibrium dynamics of strongly interacting three-dimensional (3D) and one-dimensional (1D) quantum gases placed inside a soft harm

Michael P. Adams, Evelyn P. Sinaga, Stefan Liscak, Andreas Michels

Within the framework of the recently introduced multi-nanoparticle power-series expansion method for the polarized small-angle neutron scattering (SANS) cross section, we present analytical expressions for the polarized SANS observables arising from dilute nanoparticle assemblies with antisymmetric vortex-type spin structures. We establish connections betwee

Tao Hu, Xianzhou Shao, Mingkai Bai, Xinpei Jia

We study the impact of top SiO2 interlayer thickness on memory window of Si channel FeFET with TiN/SiO2/Hf0.5Zr0.5O2/SiOx/Si (MIFIS) gate structure. The memory window increases with thicker top SiO2. We realize the memory window of 6.3 V for 3.4 nm top SiO2. Moreover, we find that the endurance characteristic degrades with increasing the initial memory windo

Shutaro Ueda, Yuto Ichinohe

We present a detailed study of three-dimensional (3D) thermodynamic structures of the intracluster medium (ICM) across edges in the X-ray surface brightness of four massive, bright, dynamically-active galaxy clusters (A3667, A2319, A520, and A2146), with the Chandra X-ray Observatory. Based on a forward modeling approach developed in previous work, we extend

Oliver Bause, Paul Palomero Bernardo, Oliver Bringmann

As machine learning applications continue to evolve, the demand for efficient hardware accelerators, specifically tailored for deep neural networks (DNNs), becomes increasingly vital. In this paper, we propose a configurable memory hierarchy framework tailored for per layer adaptive memory access patterns of DNNs. The hierarchy requests data on-demand from t

Jan Diekhoff, Jörn Fischer

Reinforcement learning is a popular method of finding optimal solutions to complex problems. Algorithms like Q-learning excel at learning to solve stochastic problems without a model of their environment. However, they take longer to solve deterministic problems than is necessary. Q-learning can be improved to better solve deterministic problems by introduci

Kentaro Kojima, Yuri Okubo, Carolina Sayuri Takeda

We analyze the vacuum structure of an eight-dimensional non-abelian gauge theory with a compactified four-dimensional torus as the extra dimensions. As a non-trivial background configuration of the gauge field of an $SU(n)$ gauge group, we suppose a magnetic flux in two extra dimensions, and continuous Wilson line phases are also involved. We introduce matte

Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek, Peter Schneider-Kamp

With the growing demand for synthetic data to address contemporary issues in machine learning, such as data scarcity, data fairness, and data privacy, having robust tools for assessing the utility and potential privacy risks of such data becomes crucial. SynthEval, a novel open-source evaluation framework distinguishes itself from existing tools by treating

Felix Thimm

We compute the equivariant K-theoretic Donaldson--Thomas invariants of $[\mathbb{C}^2/\mu_r]\times \mathbb{C}$ using factorization and rigidity techniques. For this, we develop a generalization of Okounkov's factorization technique that applies to Hilbert schemes of points on orbifolds. We show that the (twisted) virtual structure sheaves of Hilbert schemes

Lin Ding, Song Bian, Penggao He, Yan Xu

Fully Homomorphic Encryption (FHE) is known to be extremely computationally-intensive, application-specific accelerators emerged as a powerful solution to narrow the performance gap. Nonetheless, due to the increasing complexities in FHE schemes per se and multi-scheme FHE algorithm designs in end-to-end privacy-preserving tasks, existing FHE accelerators of

Tumpa Mahato, Ayaka Shimizu

A set of regions of a link projection is said to be isolated if any pair of regions in the set share no crossings. The isolate-region number of a link projection is the maximum value of the cardinality for isolated sets of regions of the link projection. In this paper, all the link projections of isolate-region number one are determined. Also, estimations fo

Yahan Li, Yuan Wu

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. The most recent UDA methods always resort to adversarial training to yield state-of-the-art results and a dominant number of existing UDA methods employ convolutional neural networks (CNNs) as feature extractors to learn domain invarian

A. Abergel, K. Misselt, K. D. Gordon, A. Noriega-Crespo

The JWST has captured the sharpest IR images ever taken of the Horsehead nebula, a prototypical moderately irradiated PDR that is fully representative of most of the UV-illuminated molecular gas in the Milky Way and star-forming galaxies. We investigate the impact of FUV radiation of a molecular cloud and constrain the structure of the edge of the PDR and it

Yan-Kang Wang, Chengyi Xing, Yi-Lin Wei, Xiao-Ming Wu

In this work, we explore a novel task of generating human grasps based on single-view scene point clouds, which more accurately mirrors the typical real-world situation of observing objects from a single viewpoint. Due to the incompleteness of object point clouds and the presence of numerous scene points, the generated hand is prone to penetrating into the i

Hyunsu Kim, Jongmin Yoon, Juho Lee

Deep Ensemble (DE) approach is a straightforward technique used to enhance the performance of deep neural networks by training them from different initial points, converging towards various local optima. However, a limitation of this methodology lies in its high computational overhead for inference, arising from the necessity to store numerous learned parame

Mohammadreza Banjafar, Lisa Randolph, Lingen Huang, S. V. Rahul

Particle-in-Cell (PIC) method is a powerful plasma simulation tool for investigating high-intensity femtosecond laser-matter interaction. However, its simulation capability at high-density plasmas around the Fermi temperature is considered to be inadequate due, among others, to the necessity of implementing atomic-scale collisions. Here, we performed a one-d

Edward Sharkey, Philip Treleaven

The paper benchmarks several Transformer models [4], to show how these models can judge sentiment from a news event. This signal can then be used for downstream modelling and signal identification for commodity trading. We find that fine-tuned BERT models outperform fine-tuned or vanilla GPT models on this task. Transformer models have revolutionized the fie

David Montero, Miguel D. Mahecha, César Aybar, Clemens Mosig

The Sentinel-2 (S2) mission from the European Space Agency's Copernicus program provides essential data for Earth surface analysis. Its Level-2A products deliver high-to-medium resolution (10-60 m) surface reflectance (SR) data through the MultiSpectral Instrument (MSI). To enhance the accuracy and comparability of SR data, adjustments simulating a nadir vie

Rupert Hölzl, Philip Janicki, Wolfgang Merkle, Frank Stephan

Speedable numbers are real numbers which are algorithmically approximable from below and whose approximations can be accelerated nonuniformly. We begin this article by answering a question of Barmpalias by separating a strict subclass that we will refer to as superspeedable from the speedable numbers; for elements of this subclass, acceleration is possible u

Jiwen Chen, Laichuan Shen, Hongyu An, Xichao Zhang

Domain wall bimerons (DWBMs) are nanoscale spin textures residing within the magnetic domain walls of in-plane magnets. In this study, we employ both numerical and analytical methods to explore the stabilization of N\'eel-type domain wall bimerons and their dynamics when excited by spin-orbit torque. Our findings reveal two unique and intriguing dynamic mech

Joao Magueijo

We examine local physics in the presence of global variables: variables associated with the whole of the spacelike surfaces of a foliation. These could be the (pseudo-)constants of nature and their conjugate times, but our statements are more general. Interactions between the local and the global (for example, dependence of the local action on global times d

Sohan Kumar Jha

This article is devoted to the comparative study of the effects of the Lorentz symmetry violation (LV) arising in Kalb-Ramond (KR) and Bumblebee (BM) field models. We study optical appearance with accretion, Quasinormal modes, ringdown waveforms, Hawking radiation, and weak gravitational lensing. The horizon radius, photon radius, and the critical impact par

Zhiwen Xie, Yi Zhang, Guangyou Zhou, Jin Liu

Knowledge Graph Completion (KGC) has garnered massive research interest recently, and most existing methods are designed following a transductive setting where all entities are observed during training. Despite the great progress on the transductive KGC, these methods struggle to conduct reasoning on emerging KGs involving unseen entities. Thus, inductive KG

Chuang Liu, Yuyao Wang, Yibing Zhan, Xueqi Ma

Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a straightforward random masking strategy for nodes or edges during training. However, this strategy fails to consider the varying significance of different nodes within the graph structu

Shujian Jiao, Bingxuan Li, Lei Wang, Xiaojin Zhang

Proteins are essential to life's processes, underpinning evolution and diversity. Advances in sequencing technology have revealed millions of proteins, underscoring the need for sophisticated pre-trained protein models for biological analysis and AI development. Facebook's ESM2, the most advanced protein language model to date, leverages a masked prediction

Michael Fore, Simranjit Singh, Dimitrios Stamoulis

In this preliminary study, we investigate a GPT-driven intent-based reasoning approach to streamline tool selection for large language models (LLMs) aimed at system efficiency. By identifying the intent behind user prompts at runtime, we narrow down the API toolset required for task execution, reducing token consumption by up to 24.6\%. Early results on a re

Samik Basu, Shilpa Gondhali, Fathima Safikaa

The flip Stiefel manifolds (FV_{m,2s}) are defined as the quotient of the real Stiefel manifolds (V_{m,2s}) induced by the simultaneous pairwise flipping of the co-ordinates by the cyclic group of order 2. We calculate the complex (K)-ring of the flip Stiefel manifolds, $K^\ast(FV_{m,2s})$, for $s$ even. Standard techniques involve the representation theory

Zhong Ji, Yimu Su, Yan Zhang, Jiacheng Hou

Video Wire Inpainting (VWI) is a prominent application in video inpainting, aimed at flawlessly removing wires in films or TV series, offering significant time and labor savings compared to manual frame-by-frame removal. However, wire removal poses greater challenges due to the wires being longer and slimmer than objects typically targeted in general video i

Yexin Liu, Lin Wang

In conventional online T-shirt customization, consumers, \ie, users, can achieve the intended design only after repeated adjustments of the design prototypes presented by sellers in online dialogues. However, this process is prone to limited visual feedback and cumbersome communication, thus detracting from users' customization experience and time. This pape

Xiao-Wu Chen, Zhi-Wei Li, Xiaojin Zhang, Zhibing Zhao

We give a direct proof of the following known result: the Grothendieck group of a triangulated category with a silting subcategory is isomorphic to the split Grothendieck group of the silting subcategory. Moreover, we obtain its cluster-tilting analogue.

Jialin Liu, Yi Bu

AIGC tools such as ChatGPT have profoundly changed scientific research, leading to widespread attention on its use on academic writing. Leveraging preprints from large language models, this study examined the use of AIGC in manuscript writing and its correlation with author profiles. We found that: (1) since the release of ChatGPT, the likelihood of abstract

Tian Hong Loh, Tas Emrah, Frederic Pythoud, Wei Fan

Emerging wireless technologies with Gbps connectivity, such as the 5th generation (5G) and 6th generation (6G) of mobile networks, require improved and substantiating documentation for the wireless standards concerning the radio signals, systems, transmission environments used, and the radio frequency exposures created. Current challenges faced by the teleco

Andrea Petrocchi, Matthias K. Scharrer, Franz Pichler, Stefan Volkwein

Accurate parameter dependent electro-chemical numerical models for lithium-ion batteries are essential in industrial application. The exact parameters of each battery cell are unknown and a process of estimation is necessary to infer them. The parameter estimation generates an accurate model able to reproduce real cell data. The field of optimal input/experi

T. I. Strasser, E. C. W. de Jong, M. Sosnina, J. E. Rodriguez-Seco

Power system operation is of vital importance and must be developed far beyond today's practice to meet future needs. Almost all European countries are facing an abrupt and very important increase of renewables with intrinsically varying yields which are difficult to predict. In addition, an increase of new types of electric loads and a reduction of traditio

Pedro Alonso, Jun Zhang, Cong Liu

Reconstruction of the point spread function (PSF) plays an important role in many areas of astronomy, including photometry, astrometry, galaxy morphology, and shear measurement. The atmospheric and instrumental effects are the two main contributors to the PSF, both of which may exhibit complex spatial features. Current PSF reconstruction schemes typically re

Bryan Lim, Manon Flageat, Antoine Cully

Quality-Diversity (QD) approaches are a promising direction to develop open-ended processes as they can discover archives of high-quality solutions across diverse niches. While already successful in many applications, QD approaches usually rely on combining only one or two solutions to generate new candidate solutions. As observed in open-ended processes suc

Yinan Yu, Dominik Gutt, Warut Khern-am-nuai

Evaluating the helpfulness of online reviews supports consumers who must sift through large volumes of online reviews. Online review platforms have increasingly adopted review evaluating systems, which let users evaluate whether reviews are helpful or not; in turn, these evaluations assist review readers and encourage review contributors. Although review hel

Karl Svozil

Matrix pencils provide a robust method for finding simultaneous eigensystems of mutually commuting degenerate operators. In this paper, we utilize these techniques to investigate the quantum logical structures of the Peres-Mermin square and the Greenberger-Horne-Zeilinger-Mermin configuration. Our analysis uncovers analogous complete contradictions between c

Marcos V. de S. Silva, Manuel E. Rodrigues

In this work, the trajectories of particles around a black bounce spacetime are considered, with the Simpson-Visser model serving as an example. Trajectories for massless and massive particles are obtained through the study of null and time-like geodesics. As the Simpson-Visser solution is derived via the Einstein equations for a source involving nonlinear e

Xuanxuan Zhou, Tingchun Wang, Yong Wu, Yongyong Cai

In this paper, we introduce a conservative Crank-Nicolson-type finite difference schemes for the regularized logarithmic Schr\"{o}dinger equation (RLSE) with Dirac delta potential in 1D. The regularized logarithmic Schr\"{o}dinger equation with a small regularized parameter $0<\eps \ll 1$ is adopted to approximate the logarithmic Schr\"{o}dinger equation (LS

Oriol Barbany, Michael Huang, Xinliang Zhu, Arnab Dhua

Multimodal search has become increasingly important in providing users with a natural and effective way to ex-press their search intentions. Images offer fine-grained details of the desired products, while text allows for easily incorporating search modifications. However, some existing multimodal search systems are unreliable and fail to address simple quer

Teng Hu, Jiangning Zhang, Ran Yi, Yating Wang

The emergence of diffusion models has greatly propelled the progress in image and video generation. Recently, some efforts have been made in controllable video generation, including text-to-video generation and video motion control, among which camera motion control is an important topic. However, existing camera motion control methods rely on training a tem

Savvas Andronicou, Emmanouil Milakis

In this article we prove that if $ X $ is a normed space and $ U $ is a polygonally-connected subset of $ X $ with $M:=\{S_i:\ i\in I\}\subset \mathcal{P}\left( U\right) $, a non-empty arbitrary family of discrete, non-empty subsets of $ U, $ then the property of polygonal-connectedness is also preserved in the resulting set $ U\setminus\left( \bigcup_{i\in

Tirawut Worrakitpoonpon

We investigate the effect of a finite particle number $N$ on the violent relaxation leading to the Quasi-Stationary State (QSS) in a one-dimensional self-gravitating system. From the theoretical point of view, we demonstrate that the local Poissonian fluctuations embedded in the initial state give rise to an additional term proportional to $1/N$ in the Vlaso

Sebastian Doerrich, Francesco Di Salvo, Julius Brockmann, Christian Ledig

The integration of deep learning based systems in clinical practice is often impeded by challenges rooted in limited and heterogeneous medical datasets. In addition, the field has increasingly prioritized marginal performance gains on a few, narrowly scoped benchmarks over clinical applicability, slowing down meaningful algorithmic progress. This trend often

Jiaming Lei, Lin Li, Chunping Wang, Jun Xiao

Benefiting from strong generalization ability, pre-trained vision language models (VLMs), e.g., CLIP, have been widely utilized in zero-shot scene understanding. Unlike simple recognition tasks, grounded situation recognition (GSR) requires the model not only to classify salient activity (verb) in the image, but also to detect all semantic roles that partici

Ivan Kayongo, Haonan Zhao, Leonardo Malcotti, Fausto Giunchiglia

Pervasive sensors have become essential in research for gathering real-world data. However, current studies often focus solely on objective data, neglecting subjective human contributions. We introduce an approach and system for collecting big-thick data, combining extensive sensor data (big data) with qualitative human feedback (thick data). This fusion ena

Daniel Saragih, Paridhi Goel, Tejas Balaji, Alyssa Li

Bit flipping attacks are one class of attacks on neural networks with numerous defense mechanisms invented to mitigate its potency. Due to the importance of ensuring the robustness of these defense mechanisms, we perform an empirical study on the Aegis framework. We evaluate the baseline mechanisms of Aegis on low-entropy data (MNIST), and we evaluate a pre-

Marta Borchiellini, Leandro Mano, Fernando Barão, Manuela Vecchi

Isotopic composition measurements of singly charged cosmic rays (CR) provide essential insights into CR transport in the Galaxy. The Alpha Magnetic Spectrometer (AMS-02) can identify singly charged isotopes up to about 10 GeV/n. However, their identification presents challenges due to the small abundance of CR deuterons compared to the proton background. In

Thomas Kosch, Sebastian Feger

Reproducibility is a major concern across scientific fields. Human-Computer Interaction (HCI), in particular, is subject to diverse reproducibility challenges due to the wide range of research methodologies employed. In this article, we explore how the increasing adoption of Large Language Models (LLMs) across all user experience (UX) design and research act

Chih-Chung Hsu, Chih-Yu Jian, Eng-Shen Tu, Chia-Ming Lee

This paper addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a Real-Time Compressed Sensing (RTCS) network designed to be lightweight and require only relatively few training samples for efficient and robus

Yuya Fukuhara, Satoshi Okuzumi

The spatial distribution of dust particles in protoplanetary disks affects dust evolution and planetesimal formation processes. The vertical shear instability (VSI) is one of the candidate hydrodynamic mechanisms that can generate turbulence in the outer disk region and affect dust diffusion. Turbulence driven by the VSI has a predominant vertical motion tha

Jin Won Kim, Amirhossein Taghvaei, Prashant G. Mehta

This paper is divided into two parts. The first part reviews the formulae for f-divergences in the study of continuous-time Markov processes and explores their applications in areas such as stochastic stability, the second law of thermodynamics, and its non-equilibrium extensions. This sets the foundation for the second part, which focuses on f-divergence in

Haifeng Qian, Sujan Kumar Gonugondla, Sungsoo Ha, Mingyue Shang

Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. However, most existing implementations focus on generating a single sequence. Real-world generative AI applications often require multiple responses and how to perform speculative decoding in a batched setting while preserving its latency

Yining Huang, Keke Tang, Meilian Chen, Boyuan Wang

Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs) such as GPT and BERT have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation. These models have shown potential to transform the medical field, highlighting the necessity for specialized evaluati