November 2024 arXiv papers — page 141

Arshia Hemmat, Kianoosh Vadaei, Mohammad Hassan Heydari, Afsaneh Fatemi

This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) to enhance information retrieval and query response systems for university-related question answering. By systematically extracting data from the university official webpage and employing advanced prompt engineering techniques,

Ning Wu, Han Huang, Yueting Xu, Zhifeng Hao

Architecture performance evaluation is the most time-consuming part of neural architecture search (NAS). Zero-Shot NAS accelerates the evaluation by utilizing zero-cost proxies instead of training. Though effective, existing zero-cost proxies require invoking backpropagations or running networks on input data, making it difficult to further accelerate the co

Gabriele Nebe

Together with David Schlang we computed the discriminants of the invariant Hermitian forms for all indicator $o$ even degree absolutely irreducible characters of the ATLAS groups supplementing the tables of orthogonal determinants computed in collaboration with Richard Parker, Tobias Braun and Thomas Breuer. The methods that are used in the unitary case are

Valentino Tosatti

In the setting of Donaldson's conjecture on the Calabi-Yau equation on symplectic 4-manifolds, we prove an a priori estimate which in the K\"ahler case resembles a classical estimate of Cheng-Yau.

M. R. Rajeshwari, S. K. Narasimhamurthy, H. M. Manjunatha

The main objective of this paper is to study semi-concurrent vector fields on a Finsler manifold. We show that the quasi-$C$-reducible Finsler space, $C3$-like Finsler space, $C^{h}$-recurrent Finsler space, and $P2$-like Finsler space are equivalent to Riemannian if they admit a semi-concurrent vector field. Further, we prove the necessary and sufficient co

Chaoran Sun, Milena Radenkovic

Enterprise networks are becoming increasingly complex, posing challenges for traditional WANs in terms of scalability, management, and operational costs. Software Defined Networking (SDN) and its application in Wide Area Networks (SD-WAN) offer solutions by decoupling the control plane from the data plane, providing centralized management, enhanced flexibili

Jing Huang, Hao Wen, Tianyi Zhou, Haozhe Lin

This paper focuses on spatially consistent hundreds of human pose and shape reconstruction from a single large-scene image with various human scales under arbitrary camera FoVs (Fields of View). Due to the small and highly varying 2D human scales, depth ambiguity, and perspective distortion, no existing methods can achieve globally consistent reconstruction

Nicodemo Di Pasquale, Jesus Algaba, Pablo Montero de Hijes, Ignacio Sanchez-Burgos

The theory of interfacial properties in liquid-liquid or liquid-vapour systems is nearly 200 years old. The advent of computational tools has greatly advanced the field, mainly through the use of Molecular Dynamics simulations. Despite the successes and advances in the theory of interfacial phenomena for liquid-liquid systems, the study of solid-liquid inter

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This article presents an innovative extension of the Smagorinsky model incorporating dynamic boundary conditions and advanced regularity methods. We formulate the modified Navier-Stokes equations with the Smagorinsky term to model dissipation in turbulence and prove theorems concerning the existence, uniqueness, and asymptotic behavior of solutions. The firs

Xinglei Wang, Tao Cheng, Stephen Law, Zichao Zeng

Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spatial information modelling, underutilisation of POI semantic attributes, and computational inefficiencies. To address these issues, we propose CaLLiPer (Contrastive Language-Locati

Clemente Pasti, Talu Karagöz, Anej Svete, Franz Nowak

Extracting finite state automata (FSAs) from black-box models offers a powerful approach to gaining interpretable insights into complex model behaviors. To support this pursuit, we present a weighted variant of Angluin's (1987) $\mathbf{L^*}$ algorithm for learning FSAs. We stay faithful to the original algorithm, devising a way to exactly learn deterministi

David Dickson

While binary merger events have been an active area of study in both simulations and observational work, the formation channels by which a high-mass star extends from Roche lobe overflow (RLO) in a decaying orbit of a black-hole (BH) companion to a binary black-hole (BBH) system merits further investigation. Variable length-scales must be employed to accurat

Reehab Jahangir, Filip Podjaski, Paransa Alimard, Sam A. J. Hillman

Organic based semiconductor materials offer emerging and sustainable solutions for solar energy conversion technologies and electronics. However, knowledge of their intrinsic (photo)physical properties and light-matter interactions is often limited, especially with respect to the frequency dependent dielectric properties on the relevant timescales of exciton

Guglielmo Gattiglio, Lyudmila Grigoryeva, Massimiliano Tamborrino

Parallel-in-time (PinT) techniques have been proposed to solve systems of time-dependent differential equations by parallelizing the temporal domain. Among them, Parareal computes the solution sequentially using an inaccurate (fast) solver, and then "corrects" it using an accurate (slow) integrator that runs in parallel across temporal subintervals. This wor

Chong Zhang, Mingyu Jin, Dong Shu, Taowen Wang

Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions through the user interface, thus causing LLM model security challenges like the language model not giving the correct answer. Although there is currently a large amount of research

Kemeng Huang, Xinyu Lu, Huancheng Lin, Taku Komura

Incremental Potential Contact (IPC) is a widely used, robust, and accurate method for simulating complex frictional contact behaviors. However, achieving high efficiency remains a major challenge, particularly as material stiffness increases, which leads to slower Preconditioned Conjugate Gradient (PCG) convergence, even with the state-of-the-art preconditio

Roman Chiva Gil, Daniel Jarne Ornia, Khaled A. Mustafa, Javier Alonso Mora

To safely and efficiently solve motion planning problems in multi-agent settings, most approaches attempt to solve a joint optimization that explicitly accounts for the responses triggered in other agents. This often results in solutions with an exponential computational complexity, making these methods intractable for complex scenarios with many agents. Whi

Igor Burban

In this paper, we elaborate the theory of exceptional hereditary curves over arbitrary fields. In particular, we study the category of equivariant coherent sheaves on a regular projective curve whose quotient curve has genus zero and prove existence of a tilting object in this case. We also give a link between wallpaper groups and real hereditary curves, pro

Lei Yu, Shiqi Chen, Hang Yuan, Peng Wang

With the rapid development of blockchain technology, smart contract security has become a critical challenge. Existing smart contract vulnerability detection methods face three main issues: (1) Insufficient quality of datasets, lacking detailed explanations and precise vulnerability locations. (2) Limited adaptability of large language models (LLMs) to the s

Detian Yang, Yaohua Liu, Liang Dai, Zhihang Xu

Oxide heterostructures provide versatile platforms for manipulating electronic and ionic conductive states. In this study, we demonstrate a remarkable billion-fold enhancement in room-temperature ionic conductivity within h-RMnO3/YSZ heterostructures, achieved through electric-field-assisted oxygen deficiency engineering. This enhancement is closely linked t

Saksham Gautam, Ratnangshu Das, Pushpak Jagtap

In this paper, we present a novel RRT*-based strategy for generating kinodynamically feasible paths that satisfy temporal logic specifications. Our approach integrates a robustness metric for Linear Temporal Logics (LTL) with the system's motion constraints, ensuring that the resulting trajectories are both optimal and executable. We introduce a cost functio

Didier Lesesvre, Ade Irma Suriajaya

Radziwill and Soundararajan unveiled a connection between low-lying zeros and central values of $L$-functions, which they instantiated in the case of quadratic twists of an elliptic curve. This paper addresses the case of the family of modular forms in the level aspect, and proves that the logarithms of central values of associated L-functions approximately

Moran Chen, Qiquan Zhang, Mingjiang Wang, Xiangyu Zhang

Voice user interfaces (VUIs) have facilitated the efficient interactions between humans and machines through spoken commands. Since real-word acoustic scenes are complex, speech enhancement plays a critical role for robust VUI. Transformer and its variants, such as Conformer, have demonstrated cutting-edge results in speech enhancement. However, both of them

E. Richter-Was, T. Yerniyazov, Z. Was

The consecutive steps of H to tau tau cascade can be useful for the measurement of Higgs couplings and parity. The analysis methos of ATLAS and CMS Collaborations was to fit a one-dimensional distribution of the phi* angle, phi*, which is sensitive to transverse spin correlations and, hence, to the CP mixing angle, phi^CP. Machine Learning techniques (ML) of

Peter Grindrod, Ka Man Yim

We propose a high dimensional generalisation of the standard Klein bottle, going beyond those considered previously. We address the problem of generating continuous scalar fields (distributions) and dynamical systems (flows) on such state spaces, which can provide a rich source of examples for future investigations. We consider a class of high dimensional dy

Xuguang Li, Zhonglin Zuo, Zheng Dong, Yang Yang

Natural gas pipeline leaks pose severe risks, leading to substantial economic losses and potential hazards to human safety. In this study, we develop an accurate model for the early prediction of pipeline leaks. To the best of our knowledge, unlike previous anomaly detection, this is the first application to use internal pipeline data for early prediction of

Jennifer L. Chen, Faisal Ladhak, Daniel Li, Noémie Elhadad

Given the black-box nature and complexity of large transformer language models (LM), concerns about generalizability and robustness present ethical implications for domains such as hate speech (HS) detection. Using the content rich Social Bias Frames dataset, containing human-annotated stereotypes, intent, and targeted groups, we develop a three stage analys

Masoud Kargar, Nasim Jelodari, Alireza Assadzadeh

Graphs, comprising nodes and edges, visually depict relationships and structures, posing challenges in extracting high-level features due to their intricate connections. Multiple connections introduce complexities in discovering patterns, where node weights may affect some features more than others. In domains with diverse topics, graph representations illus

Seth Bullock, Nirav Ajmeri, Mike Batty, Michaela Black

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scal

Nadja Egner, Marino Gran

We prove that the category 2-$ \mathrm{Grpd}(\mathscr{C}) $ of internal $2$-groupoids is a Birkhoff subcategory of the category $ \mathrm{Grpd}^2(\mathscr{C}) $ of double groupoids in a regular Mal'tsev category $\mathscr{C}$ with finite colimits. In particular, when $\mathscr{C}$ is a Mal'tsev variety of universal algebras, the category 2-$ \mathrm{Grpd}(\m

Adam Czornik, Konrad Kitzing, Stefan Siegmund

In this note we introduce a notion of dichotomy which generalizes the classical concept of exponential dichotomy and the recent notion of Bohl dichotomy. A key attribute is the discussion of the sets of subspaces of the state space on which the dichotomy estimates are uniform. Two main results are a dichotomy spectral theorem based on our notion of dichotomy

Xinghua Zhang, Haiyang Yu, Cheng Fu, Fei Huang

In the realm of large language models (LLMs), the ability of models to accurately follow instructions is paramount as more agents and applications leverage LLMs for construction, where the complexity of instructions are rapidly increasing. However, on the one hand, there is only a certain amount of complex instruction evaluation data; on the other hand, ther

Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao

Large Language Models (LLMs) often struggle with dynamically changing knowledge and handling unknown static information. Retrieval-Augmented Generation (RAG) is employed to tackle these challenges and has a significant impact on improving LLM performance. In fact, we find that not all questions need to trigger RAG. By retrieving parts of knowledge unknown to

Mohsen Yavartanoo, Sangmin Hong, Reyhaneh Neshatavar, Kyoung Mu Lee

The generation of industrial Computer-Aided Design (CAD) models from user requests and specifications is crucial to enhancing efficiency in modern manufacturing. Traditional methods of CAD generation rely heavily on manual inputs and struggle with complex or non-standard designs, making them less suited for dynamic industrial needs. To overcome these challen

Jiayao Zhang, Yuran Bi, Mengye Cheng, Jinfei Liu

Data is the new oil of the 21st century. The growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place as a result of data buyers and data sellers being in contact with one another, either directly or thro

Xiaoyan Zhao, Zuo-tang Liang, Tianbo Liu, Ya-jin Zhou

We investigate $\Lambda$ production in semi-inclusive deep-inelastic scattering using a polarized lepton beam and find that the spin transfer is significantly suppressed by target fragmentation. As further demonstrated by a model estimation, experimental data can be well described once the target fragmentation is taken into account, which alleviates the tens

Gregor P. Henze, Kevin J. Kircher, James E. Braun

Over the last two decades, research and development efforts have shown that advanced control of heating, ventilation, and air conditioning (HVAC) equipment in commercial buildings can improve energy efficiency, reduce emissions, and turn buildings into active participants in the power grid. Despite these efforts, advanced commercial HVAC control has not yet

Patrick Copinger, James P. Edwards, Anton Ilderton, Karthik Rajeev

We revisit particle creation in strong fields, and backreaction on those fields, from an amplitudes perspective. We describe the strong field by an initial coherent state of photons which we explicitly evolve in time, thus going beyond the background field approximation, and then consider observables which quantify the effects of backreaction. We present exp

Ayoub Jadouli, Chaker El Amrani

Wildfires pose significant threats to ecosystems, economies, and communities worldwide, necessitating advanced predictive methods for effective mitigation. This study introduces a novel and comprehensive dataset specifically designed for wildfire prediction in Morocco, addressing its unique geographical and climatic challenges. By integrating satellite obser

Philipp Gohlke, Andrew Mitchell

We study a class of dynamical systems generated by random substitutions, which contains both intrinsically ergodic systems and instances with several measures of maximal entropy. In this class, we show that the measures of maximal entropy are classified by invariance under an appropriate symmetry relation. All measures of maximal entropy are fully supported

Yukti Makhija, Rishi Saket

In learning from aggregate labels, the training data consists of sets or "bags" of feature-vectors (instances) along with an aggregate label for each bag derived from the (usually {0,1}-valued) labels of its instances. In learning from label proportions (LLP), the aggregate label is the average of the bag's instance labels, whereas in multiple instance learn

Gabriel Albertin Amici, José Andrés Guzmán Morán, Emanuel Fernandes de Lima

We introduce the one-dimensional Morse-soft-Coulomb (MsC) potential consisting of a Morse repulsive barrier smoothly connected with a soft-core Coulomb potential at the origin. This new potential has a single parameter that controls the softness of the repulsive barrier and the well depth. When this softening-depth parameter tends to zero, the MsC potential

Leo Li, Ye Luo, Tingyou Pan

The Orion-1 model by OpenAI is claimed to have more robust logical reasoning capabilities than previous large language models. However, some suggest the excellence might be partially due to the model "memorizing" solutions, resulting in less satisfactory performance when prompted with problems not in the training data. We conduct a comparison experiment usin

Azam Sadat Emadi, Iman Masoumi, Seyed Reza Musawi

Let $G=(V,E)$ be a simple graph of order $n$. A Majority Roman Dominating Function (MRDF) on a graph G is a function $f: V\rightarrow\{-1, +1, 2\}$ if the sum of its function values over at least half the closed neighborhoods is at least one , this is , for at least half of the vertices $v\in V$, $f(N[v])\geq 1$. Moreover, every vertex u with $f(u)=-1$ is ad

Shukun Jia, Shiyu Hu, Yichao Cao, Feng Yang

Multi-object tracking (MOT) is primarily dominated by two paradigms: tracking-by-detection (TBD) and tracking-by-query (TBQ). While TBD offers modular efficiency, its fragmented association pipeline often limits robustness in complex scenarios. Conversely, TBQ enhances semantic modeling end-to-end but suffers from high training costs and slow inference due t

Shaowei Lan, Zuowen Liu, Like Liu, Shusu Shi

The collective flow provides valuable insights into the anisotropic expansion of particles produced in heavy-ion collisions and is sensitive to the equation of the state of nuclear matter in high-baryon-density regions. In this paper, we use the hadronic transport model SMASH to investigate the elliptic flow ($v_2$), quadrangular flow ($v_4$), and their rati

Margherita Disertori, Franz Merkl, Silke W. W. Rolles

We prove that the restriction of the vertex-reinforced jump process to a subset of the vertex set is a mixture of vertex-reinforced jump processes. A similar statement holds for the non-linear hyperbolic supersymmetric sigma model. This is then applied to vertex-reinforced jump processes on subdivided versions of graphs of bounded degree, where every edge is

Hillary Dawkins, Isar Nejadgholi, Chi-kiu Lo

We assess the difficulty of gender resolution in literary-style dialogue settings and the influence of gender stereotypes. Instances of the test suite contain spoken dialogue interleaved with external meta-context about the characters and the manner of speaking. We find that character and manner stereotypes outside of the dialogue significantly impact the ge

Jie Guo, Meiting Wang, Hang Yin, Bin Song

Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted great interests from both academia and industry. Although some noteworthy advancements have been made in this area, a comprehensive exploration

Nicolas Nguyen, James Ridgway, Claire Vernade

Portfolio construction is the science of balancing reward and risk; it is at the core of modern finance. In this paper, we tackle the question of optimal decision-making within a Bayesian paradigm, starting from a decision-theoretic formulation. Despite the inherent intractability of the optimal decision in any interesting scenarios, we manage to rewrite it

Yu Liu, Shu Yang, Jingtao Ding, Quanming Yao

By representing knowledge in a primary triple associated with additional attribute-value qualifiers, hyper-relational knowledge graph (HKG) that generalizes triple-based knowledge graph (KG) has been attracting research attention recently. Compared with KG, HKG is enriched with the semantic qualifiers as well as the hyper-relational graph structure. However,

Jaume de Haro, Supriya Pan

The reheating temperature plays a crucial role in the early universe's evolution, marking the transition from inflation to the radiation-dominated era. It directly impacts the number of $e$-folds and, consequently, the observable parameters of inflation, such as the spectral index of scalar perturbations. By establishing a relationship between the gravitatio

Dinkar Mishra, Saumya Singh, Bhupesh Kumar, Pallavi Jha

We present analytical and simulation study of twisted terahertz (THz) radiation generation via propagation of a circularly polarized Laguerre Gaussian (LG) laser pulse in homogeneous plasma embedded in an axial magnetic field. Analytical formulation is based on perturbation technique and quasistatic approximation. Longitudinal and transverse wakefields gener

Syed Masood

Lorentz invariance violation (LIV) is a topic of significant interest in quantum gravity and in extensions of the Standard Model of particle physics. Recently, new classes of black hole solutions have been proposed, involving vector fields and rank-two antisymmetric tensor fields that acquire nontrivial vacuum expectation values, resulting in the Bumblebee a

Junjie Hu, Na Ruan

Mining attacks enable an adversary to procure a disproportionately large portion of mining rewards by deviating from honest mining practices within the PoW-based blockchain system. In this paper, we demonstrate that the security vulnerabilities of PoW-based blockchain extend beyond what these mining attacks initially reveal. We introduce a novel mining strat

Poonam Sharma, Vaishali Roondhe, Alok Shukla

Recently, the successful synthesis of the pentagonal form of PdTe$_{2}$ monolayer (\emph{p}-PdTe$_{2}$) was reported [Liu~\emph{et al.}, Nature Materials \textbf{23}, 1339 (2024)]. In this work, we present an extensive first-principles density-functional theory (DFT) based computational study of vacancies in this material. Our study covers the evolution of t

Stéphane Crépey, Mekonnen Tadese, Gauthier Vermandel

Emissions markets play a vital role in emissions reduction by incentivizing firms to minimize costs. However, their effectiveness heavily depends on the decisions of policymakers, future economic activity, and the availability of abatement technologies. This study investigates how variations in regulatory standards, firms' abatement costs, and emission level

Wenhao Chi, Haiping Liu, Hongqiao Dong, Wenhua Liang

In the field of non-invasive medical imaging, radiomic features are utilized to measure tumor characteristics. However, these features can be affected by the techniques used to discretize the images, ultimately impacting the accuracy of diagnosis. To investigate the influence of various image discretization methods on diagnosis, it is common practice to eval

Adrian Hess, Alexander M. Kübler, Benedek Forrai, Mehmet Dogar

Biomimetic and compliant robotic hands offer the potential for human-like dexterity, but controlling them is challenging due to high dimensionality, complex contact interactions, and uncertainties in state estimation. Sampling-based model predictive control (MPC), using a physics simulator as the dynamics model, is a promising approach for generating contact

Hongming Shen, Zhenyu Wu, Wei Wang, Qiyang Lyu

In recent years, infrastructure-based localization methods have achieved significant progress thanks to their reliable and drift-free localization capability. However, the pre-installed infrastructures suffer from inflexibilities and high maintenance costs. This poses an interesting problem of how to develop a drift-free localization system without using the

Daniel Gilo, Tzofi Klinghoffer, Or Litany

Neural field methods, initially successful in the inverse rendering domain, have recently been extended to CT reconstruction, marking a paradigm shift from traditional techniques. While these approaches deliver state-of-the-art results in sparse-view CT reconstruction, they struggle in limited-angle settings, where input projections are captured over a restr

Yuhan Zhao, Juntao Chen, Yingdong Lu, Quanyan Zhu

Mean field control provides a robust framework for coordinating large-scale populations with complex interactions and has wide applications across diverse fields. However, the inherent nonlinearity and the presence of unknown system dynamics pose significant challenges for developing effective analytic or numerical solutions. There is a pressing need for dat

Mayank Verma, Ashoke De

This study numerically investigates the influence of the taper on the flow-induced vibrations of an elastically mounted circular cylinder under Vortex-induced vibrations. The dynamic response of three different taper ratios defined as 12 (highly tapered cylinder), 20 (medium tapered cylinder), and 40 (low tapered cylinder))is studied at a fixed Reynolds numb

Swetarka Das, Dharmendra Yadav, Abhisek Kumar Gupta, Debopam Das

This paper explores experimental and numerical investigation of the spatio-temporal dynamics of a finite-length vortex column in three dimensions using Particle Image Velocimetry (PIV) and Large Eddy Simulation (LES). The research examines the combined impact of bending, buckling, and core-splitting on a finite-length vortex column. More precisely, the work

Danila Cherkashin, Pavel Prozorov

Counting the number of spanning trees in specific classes of graphs has attracted increasing attention in recent years. In this note, we present unified proofs and generalizations of several results obtained in the 2020s. The main method is to study the behavior of the vertex (degree) enumerator of a distance-hereditary graph under the operations of copying

Yew Ken Chia, Liying Cheng, Hou Pong Chan, Chaoqun Liu

The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multimodal contents such as texts, figures, and tables, which are very time-consuming for humans to read thoroughly. Hence, there is an urgent need to develop effective and automated met

Shan Zhong, Jiahao Zeng, Yongxin Yu, Bohong Lin

This paper proposes a Clustering, Labeling, then Augmenting framework that significantly enhances performance in Semi-Supervised Text Classification (SSTC) tasks, effectively addressing the challenge of vast datasets with limited labeled examples. Unlike traditional SSTC approaches that rely on a predefined small set of labeled data to generate pseudo-labels

Feiyang Huang

This paper presents ViTOC (Vision Transformer and Object-aware Captioner), a novel vision-language model for image captioning that addresses the challenges of accuracy and diversity in generated descriptions. Unlike conventional approaches, ViTOC employs a dual-path architecture based on Vision Transformer and object detector, effectively fusing global visua

Jianda Chen, Wen Zheng Terence Ng, Zichen Chen, Sinno Jialin Pan

In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. Recent advancements in metric learning, such as deep bisimulation metric approaches, have shown promising results in learning structured low-dimensional representation space from pixel observations, where the distance between states

Weijie Ma, Jingwei Jiang, Yang Yang, Zehui Chen

With the attention gained by camera-only 3D object detection in autonomous driving, methods based on Bird-Eye-View (BEV) representation especially derived from the forward view transformation paradigm, i.e., lift-splat-shoot (LSS), have recently seen significant progress. The BEV representation formulated by the frustum based on depth distribution prediction

Zilin Huang, Xiulai Li, Xinyi Cao, Ke Chen

In the digital age, users store personal data in corporate databases, making data security central to enterprise management. Given the extensive attack surface, assets face challenges like weak authentication, vulnerabilities, and malware. Attackers may exploit vulnerabilities to gain unauthorized access, masquerading as legitimate users. Such attacks can le

Jinghan He, Haiyun Guo, Kuan Zhu, Zihan Zhao

Continual learning (CL) is crucial for language models to dynamically adapt to the evolving real-world demands. To mitigate the catastrophic forgetting problem in CL, data replay has been proven a simple and effective strategy, and the subsequent data-replay-based distillation can further enhance the performance. However, existing methods fail to fully explo

Takol Tangphati, İzzet Sakallı, Ayan Banerjee, Anirudh Pradhan

The structure and stability of quark stars (QSs) made of interacting quark matter are discussed in this study, taking color superconductivity and perturbative QCD corrections into account. By combining this EoS with the Tolman-Oppenheimer-Volkoff (TOV) equations, we explore the mass-radius ($M-R$) relations of QSs. The analysis is conducted within the framew

Edcarlos D. Silva, Elaine A. F. Leite, Maxwell L. da Silva

In this work, we shall investigate existence and multiplicity of solutions for a nonlocal elliptic systems driven by the fractional Laplacian. Specifically, we establish the existence of two positive solutions for following class of nonlocal elliptic systems: \begin{equation*} \left\{\begin{array}{lll} (-\Delta)^su +V_1(x)u = \lambda|u|^{p - 2}u+ \frac{\alph

Edcarlos D. Silva, Marcos. L. M. Carvalho, Márcia S. B. A. Cardoso

In the present work, we consider existence and multiplicity of positive solutions for nonlocal elliptic problems driven by the Stein-Weiss problem with concave-convex nonlinearities defined in the whole space $\mathbb{R}^N$. More precisely, we consider the following nonlocal elliptic problem: \begin{equation*} - \Delta u + V(x)u = \lambda a(x) |u|^{q-2} u +

M. Fava, S. Gopalakrishnan, R. Vasseur, S. A. Parameswaran

We consider one dimensional many-particle systems that exhibit kinematically protected single-particle excitations over their ground states. We show that momentum and time-resolved 4-point functions of operators that create such excitations diverge linearly in particular time differences. This behaviour can be understood by means of a simple semiclassical an

Peter Gavriel, Adam Norton, Kenneth Kimble, Megan Zimmerman

Training data is an essential resource for creating capable and robust vision systems which are integral to the proper function of many robotic systems. Synthesized training data has been shown in recent years to be a viable alternative to manually collecting and labelling data. In order to meet the rising popularity of synthetic image training data we propo

Johannes Moerland

In physics literature about supersymmetry, many authors refer to "super Minkowski spaces". These spaces are affine supermanifolds with certain distinguished spin structures. In these notes, we make the notion of such spin structures precise and generalise the setup to curved supermanifolds. This leads to the more general notion of spinorial superspaces. By w

Cagdas Ulus Agca, Bayram Tekin

The membrane paradigm of black holes is an effective theory that replaces the event horizon with a fictitious yet tangible fluid. It has provided us with valuable insights, especially in understanding the environment of black holes. The paradigm establishes a fluid/gravity correspondence that allows the computation of the thermal properties of the black hole

Rhombik Roy, Sunayana Dutta, Ofir E. Alon

The ground state properties of strongly rotating bosons confined in an asymmetric anharmonic potential exhibit a split density distribution. However, the out-of-equilibrium dynamics of this split structure remain largely unexplored. Given that rotation is responsible for the breakup of the bosonic cloud, we investigate the out-of-equilibrium dynamics by abru

S. I. Kruglov

The heat engine of magnetic black holes in Einstein-AdS gravity coupled to rational nonlinear electrodynamics, as the working substance, is studied. The dynamical negative cosmological constant is considered as a thermodynamic pressure. We investigate the efficiency of black hole heat engines in extended space thermodynamics for rectangle closed path in the

Priyansh Khare

The article uses an arithmetic-geometric Fibonacci series to find the expected value of trials needed to observe k consecutive successes for the first time in a Bernoulli experiment using a recurrence relation. It is important to note that this is not a new result, but to the best of the authors' knowledge at the time of uploading, this is a novel derivation

Boqiang Li, Xun Chen, Yuqian Zhao, Zhaohua Ma

The spin-1/2 ferromagnetic XXZ chain is a prototypical many-body quantum model, exactly solvable via the integrable Bethe ansatz method, hosting a Tomonaga-Luttinger spin liquid. However, its clear experimental realizations remain absent. Here, we present a thorough investigation of the magnetism of the structurally disorder-free compound LuCu(OH)$_3$SO$_4$.

Gholamreza Davarpanah, Hossein Shirzad, Jawad Faiz

Despite their simple and robust structure, low cost, and simple cooling system, switched reluctance motors (SRMs) face the challenge of low mean torque. A possible solution is to change the structure of SRMs. This article introduces an innovative combination of the number of rotor teeth and stator teeth of a two-phase switch reluctance motor (TPSRM) with eig

Jingyi Zhou, Senlin Luo, Haofan Chen

Text emotion detection constitutes a crucial foundation for advancing artificial intelligence from basic comprehension to the exploration of emotional reasoning. Most existing emotion detection datasets rely on manual annotations, which are associated with high costs, substantial subjectivity, and severe label imbalances. This is particularly evident in the

Zhi Zhang, Yan Liu, Sheng-hua Zhong, Gong Chen

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to

Mingyu Yang, Liuchang Jing, Wentao Li, Wei Wang

Approximate $k$-nearest neighbor (AKNN) search is a fundamental problem with wide applications. To reduce memory and accelerate search, vector quantization is widely adopted. However, existing quantization methods either rely on codebooks -- whose query speed is limited by costly table lookups -- or adopt dimension-wise quantization, which maps each vector d

V. Squicciarini, J. Mazoyer, A. -M. Lagrange, A. Chomez

Direct imaging (DI) campaigns are uniquely suited to probing the outer regions around young stars and looking for giant exoplanet and brown dwarf companions, hence providing key complementary information to radial velocity (RV) and transit searches for the purpose of demographic studies. However, the critical 5-20 au region, where most giant planets are thou

Andrea Tognazzi, Paolo Franceschini, Jonas Biechteler, Enrico Baù

Layered van der Waals (vdW) materials have emerged as a promising platform for nanophotonics due to large refractive indexes and giant optical anisotropy. Unlike conventional dielectrics and semiconductors, the absence of covalent bonds between layers allows for novel degrees of freedom in designing optically resonant nanophotonic structures down to the atom

Zhewen Xu, Baoxiang Pan, Hongliang Li, Xiaohui Wei

The rapid development of large climate models has created the requirement of storing and transferring massive atmospheric data worldwide. Therefore, data compression is essential for meteorological research, but an efficient compression scheme capable of keeping high accuracy with high compressibility is still lacking. As an emerging technique, Implicit Neur

Zbigniew Drogosz, Wojciech Florkowski, Natalia Łygan, Radoslaw Ryblewski

A recently formulated extension of perfect spin hydrodynamics, which includes second-order corrections in the spin polarization tensor to the energy-momentum tensor and baryon current, is studied in the case of a one-dimensional boost-invariant expansion. The presence of second-order corrections introduces feedback from spin dynamics on the hydrodynamic back

Zarullo Rakhmonov, Firuz Rakhmonov

For $n \geq 3$, an asymptotic formula is derived for the number of representations of a sufficiently large natural number $N$ as a sum of $r = 2^n + 1$ summands, each of which is an $n$-th power of natural numbers $x_i$, $i = \overline{1, r}$, satisfying the conditions $$ |x_i^n-\mu_iN|\le H,\qquad H\ge N^{1-\theta(n,r)+\varepsilon},\qquad \theta(n,r)=\frac2

Xi Deng, Zhen-hua Jiang, Omar K. Matar, Chao Yan

This short note introduces a novel diagnostic tool for evaluating the convection boundedness properties of numerical schemes across discontinuities. The proposed method is based on the convection boundedness criterion and the normalised variable diagram. By utilising this tool, we can determine the CFL conditions for numerical schemes to satisfy the convecti

Vera Pavlova, Mohammed Makhlouf

The widespread use of large language models (LLMs) has dramatically improved many applications of Natural Language Processing (NLP), including Information Retrieval (IR). However, domains that are not driven by commercial interest often lag behind in benefiting from AI-powered solutions. One such area is religious and heritage corpora. Alongside similar doma

Sebastian Ankargren, Mattias Frånberg, Mårten Schultzberg

Online controlled experiments, or A/B tests, are large-scale randomized trials in digital environments. This paper investigates the estimands of the difference-in-means estimator in these experiments, focusing on scenarios with repeated measurements on users. We compare cumulative metrics that use all post-exposure data for each user to windowed metrics that

Luca Tanganelli Castrillón

In this note, we show a classical result on the local existence and uniqueness of a solution to an initial value problem subject to a Lipschitz condition. We use only elementary tools from mathematical analysis, without involving any integration. We proceed by showing that the Cauchy iterates converge on a dense subset of the interval and subsequently provin

Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial

The Digital Twin Oriented Complex Networked System (DT-CNS) aims to build and extend a Complex Networked System (CNS) model with progressively increasing dynamics complexity towards an accurate reflection of reality -- a Digital Twin of reality. Our previous work proposed evolutionary DT-CNSs to model the long-term adaptive network changes in an epidemic out

Qiang Kang, Chenguang Hu

The transfer matrix method (TMM) is widely used to analyze the transport properties of one-dimensional or quasi-one-dimensional systems, such as nanostructures and layered materials in spintronics. However, its application in quantifying the influence of different crystallographic orientations on tunneling magnetoresistance (TMR) remains underexplored [1, 2]

Zhiyu Zhu, Zhibo Jin, Hongsheng Hu, Minhui Xue

AI systems, in particular with deep learning techniques, have demonstrated superior performance for various real-world applications. Given the need for tailored optimization in specific scenarios, as well as the concerns related to the exploits of subsurface vulnerabilities, a more comprehensive and in-depth testing AI system becomes a pivotal topic. We have

Pengyu Xue, Linhao Wu, Zhen Yang, Chengyi Wang

In recent years, Large Language Models (LLMs) have dramatically advanced the performance of automated code translation, making their computational accuracy score reach up to over 80% on many previous benchmarks. However, most code samples in these benchmarks are short, standalone, statement/method-level, and algorithmic, which is not aligned with practical c