December 2024 arXiv papers — page 48

Yang Cao, Xiaoyu Li, Zhao Song

We introduce $\mathbf{G}$radient Descent with $\mathbf{A}$daptive $\mathbf{M}$omentum $\mathbf{S}$caling ($\mathbf{Grams}$), a novel optimization algorithm that decouples the direction and magnitude of parameter updates in deep learning. Unlike traditional optimizers that directly integrate momentum into updates, Grams separates the update direction, derived

Bithika Karmakar, Magdalena Djordjevic

We present a theoretical formalism for calculating first-order-in-opacity radiative energy loss that incorporates the spatial and temporal temperature evolution of the quark-gluon plasma (QGP) in a finite-size QCD medium with dynamical (i.e., moving) constituents. The derived expressions allow for arbitrary temperature profiles, enabling detailed evaluations

Lin Wu

We propose a method for detecting the electrode positions in lithium-ion batteries. The process begins by identifying the region of interest (ROI) in the battery's X-ray image through corner point detection. A convolutional neural network is then used to regress the pole positions within this ROI. Finally, the regressed positions are optimized and corrected

Jonas Wessén, Eliel Camargo-Molina

We present a Python package together with a practical guide for the implementation of a lightweight diversity-enhanced genetic algorithm (GA) approach for the exploration of multi-dimensional parameter spaces. Searching a parameter space for regions with desirable properties, e.g. compatibility with experimental data, poses a type of optimization problem whe

Megha Bhat, Rongdao Chen, Adityo Mamun, Ariana Verbanac

We study the homeomorphism groups of ordinals equipped with their order topology, focusing on successor ordinals whose limit capacity is also a successor. This is a rich family of groups that has connections to both permutation groups and homeomorphism groups of manifolds. For ordinals of Cantor--Bendixson degree one, we prove that the homeomorphism group is

Nathaniel Eldredge, Maria Gordina, Laurent Saloff-Coste

We prove that the uniform doubling property holds for every Lie group which can be written as a quotient group of $\operatorname{SU}(2) \times \mathbb{R}^n$ for some $n$. In particular, this class includes the four-dimensional unitary group $\operatorname{U}(2)$. As this class contain non-compact as well as compact Lie groups, we discuss a number of analytic

Yuntao Liu, Jayden John, Qian Wang

In recent years, quantum computing has started to demonstrate superior efficiency to classical computing. In quantum computing, quantum circuits that implement specific quantum algorithms are usually not directly executable on quantum computer hardware. Quantum circuit compilers decompose high-level quantum gates into the hardware's native gates and optimize

Sebastian Debus, Tobias Metzlaff

The action of a Weyl group on the associated weight lattice induces an additive action on the symmetric algebra and a multiplicative action on the group algebra of the lattice. We show that the coinvariant space of the multiplicative action affords the regular representation and is isomorphic to a space of multiplicative harmonics, which corresponds to exist

Bin Xia, Yuechen Zhang, Jingyao Li, Chengyao Wang

Currently, the success of large language models (LLMs) illustrates that a unified multitasking approach can significantly enhance model usability, streamline deployment, and foster synergistic benefits across different tasks. However, in computer vision, while text-to-image (T2I) models have significantly improved generation quality through scaling up, their

Eduard Emelyanov

Extensions of one-parameter operator semigroups on Archimedean vector lattices to their order/ru-completions are studied. Existence and uniqueness of the extension to the ru-completion is established in the class of positive semigroups. An extension theorem for positive ru-continuos semigroups on vector lattices with property (R) is proved. This theorem allo

Duarte Rocha, Detlef Lohse, Christian Diddens

The non-uniform evaporation rate at the liquid-gas interface of binary droplets induces solutal Marangoni flows. In glycerol-water mixtures (positive Marangoni number, where the more volatile fluid has higher surface tension), these flows stabilise into steady patterns. Conversely, in water-ethanol mixtures (negative Marangoni number, where the less volatile

Bo-Jun Yuan, Ni Yang, Hong-Yan Ge, Shi-Cai Gong

Extremal problems related to the enumeration of graph substructures, such as independent sets, matchings, and induced matchings, have become a prominent area of research with the advancement of graph theory. A subset of vertices is called a dissociation set if it induces a subgraph with vertex degree at most $1$, making it a natural generalization of these p

Md. Ahnaf Akib, Md. Muktadir Mazumder, Salman Ahsan

Code summarization aims to generate concise natural language descriptions for source code. Deep learning has been used more and more recently in software engineering, particularly for tasks like code creation and summarization. Specifically, it appears that the most current Large Language Models with coding perform well on these tasks. Large Language Models

Frederick Truman-Williams

In many applied settings, the chemical Langevin equation and linear noise approximation are used in the simulation and data analysis of stochastic reaction networks. With the goal of exploring the sensitivities of reaction network paths to their initial conditions, we subject these modelling techniques to the analysis of random dynamical systems and stochast

Jinyu Zhang, Zhiyuan You, Jize Wang, Xinyi Le

Document Information Extraction (DIE) aims to extract structured information from Visually Rich Documents (VRDs). Previous full-training approaches have demonstrated strong performance but may struggle with generalization to unseen data. In contrast, training-free methods leverage powerful pre-trained models like Large Language Models (LLMs) to address vario

Alexei Onatski, Vladislav Kargin

Let $x_i$, $i\in\mathbb{Z}$ be a sequence of i.i.d. standard normal random variables. Consider rectangular Toeplitz $\mathbf{X}=\left(x_{j-i}\right)_{1\leq i\leq p,1\leq j\leq n}$ and circulant $\mathbf{X}=\left(x_{(j-i)\mod n}\right)_{1\leq i\leq p,1\leq j\leq n}$ matrices. Let $p,n\rightarrow\infty$ so that $p/n\rightarrow c\in(0,1]$. We prove that the sma

Arash Yavari, Alain Goriely

Most theories and applications of elasticity rely on an energy function that depends on the strains from which the stresses can be derived. This is the traditional setting of Green elasticity, also known as hyper-elasticity. However, in its original form the theory of elasticity does not assume the existence of a strain-energy function. In this case, called

Guilherme Cardoso, Abilio Rodrigues

The aim of this text is to present, in a technically accessible way, Tarski's definition of truth, the indefinability theorem, and to discuss two aspects of Tarski's work on truth, namely, whether or not the definition captures the notion of truth as correspondence, and Kripke's objection to the hierarchy of languages.

Yichi Zhang, Yuchen Zhang, Lipeng Zhu, Sa Xiao

This paper studies a sub-connected six-dimensional movable antenna (6DMA)-aided multi-user communication system. In this system, each sub-array is connected to a dedicated radio frequency chain and collectively moves and rotates as a unit within specific local regions. The movement and rotation capabilities of 6DMAs enhance design flexibility, facilitating t

Vasileios Evangelakos, Emmanuel Paspalakis, Dionisis Stefanatos

We consider the problem of maximizing the stored energy for a given charging duration in a quantum battery composed of a pair of spins-$1/2$ with Ising coupling starting from the spin-down state, using bounded transverse field control. We map this problem to an optimal control problem on a single qubit and using optimal control theory we show that, although

O. Kayali, E. Haliki, K. Bas, R. J. Nemiroff

Finding Dyson rings around distant pulsars may involve identifying light curve features that have not been previously identified. Previous studies covered the detection of a ring structure uniformly brightened by the central pulsar, mostly in infrared light. Here, more complex light curves are explored, which arise inherently from the pulsar beam spot's comm

Johannes Tölle

In this letter, we present the first fully analytic derivation and implementation of nuclear gradients for the G$_0$W$_0$ method. For this, we leverage the recently established connection between the G$_0$W$_0$ approach and equation-of-motion unitary coupled-cluster theory for charged excitations [J. Chem. Phys. 158, 124123 (2023)]. Analytic gradients are ob

Trey Cole, David Vanderbilt

Bands with non-trivial topological indices have a topological obstruction preventing them from being represented by exponentially localized Wannier states. Here, we propose a procedure to construct exponentially localized Wannier functions that span a subspace of topologically obstructed bands through the use of the projection method. These Wannier functions

S. Mossman, S. I. Mistakidis, G. C. Katsimiga, A. Romero-Ros

Modulational instability (MI) is a fundamental phenomenon in the study of nonlinear dynamics, spanning diverse areas such as shallow water waves, optics, and ultracold atomic gases. In particular, the nonlinear stage of MI has recently been a topic of intense exploration, and has been shown to manifest, in many cases, in the generation of dispersive shock wa

Igor Sokolov, Peter Richtárik

Non-smooth communication-efficient federated optimization is crucial for many machine learning applications, yet remains largely unexplored theoretically. Recent advancements have primarily focused on smooth convex and non-convex regimes, leaving a significant gap in understanding the non-smooth convex setting. Additionally, existing literature often overloo

Moqbel Hamood, Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha

Clustered Federated Multi-task Learning (CFL) has emerged as a promising technique to address statistical challenges, particularly with non-independent and identically distributed (non-IID) data across users. However, existing CFL studies entirely rely on the impractical assumption that devices possess access to accurate ground-truth labels. This assumption

Willem Schooltink, Fabio Massimo Zennaro

Causal abstractions allow us to relate causal models on different levels of granularity. To ensure that the models agree on cause and effect, frameworks for causal abstractions define notions of consistency. Two distinct methods for causal abstraction are common in the literature: (i) graphical abstractions, such as Cluster DAGs, which relate models on a str

Damião J. Araújo, Andreas Minne, Edgard A. Pimentel

We study a general class of elliptic free boundary problems equipped with a Dirichlet boundary condition. Our primary result establishes an optimal $C^{1,1}$-regularity estimate for $L^p$-strong solutions at points where the free and fixed boundaries intersect. A key novelty is that no convexity or concavity assumptions are imposed on the fully nonlinear ope

D. S. Petrov

By using the Bogoliubov perturbation theory we describe the self-bound ground state and excited breathing states of $N$ two-dimensional bosons with zero-range attractive interactions. Our results for the ground state energy $B_N$ and size $R_N$ improve previously known large-$N$ asymptotes and we better understand the crossover to the few-body regime. The br

Jinzhi Wang, Qinfeng Song, Lidong Qian, Haozhou Li

The reliability of substation equipment is crucial to the stability of power systems, but traditional fault analysis methods heavily rely on manual expertise, limiting their effectiveness in handling complex and large-scale data. This paper proposes a substation equipment fault analysis method based on a multimodal large language model (MLLM). We developed a

Benjamin Aymard

In this article we introduce an original model in order to study the emergence of chaos in a reaction diffusion system in the presence of self- and cross-diffusion terms. A Fourier Spectral Method is derived to approximate equilibria and orbits of the latter. Special attention is paid to accuracy, a necessary condition when one wants to catch periodic orbits

Elham Peimani, Gurpreet Singh, Nisarg Mahyavanshi, Aman Arora

Retrieving semantically relevant documents in niche domains poses significant challenges for traditional TF-IDF-based systems, often resulting in low similarity scores and suboptimal retrieval performance. This paper addresses these challenges by introducing an iterative and semi-automated query refinement methodology tailored to Humber College's career serv

Ali Ghalavand, Sandi Klavžar, Xueliang Li

The local metric dimension ${\rm dim}_l$ in relation to the clique number $\omega$ is investigated. It is proved that if $\omega(G)\leq n(G)-3$, then ${\rm dim}_l(G) \leq n(G)-3$ and the graphs attaining the bound classified. Moreover, the graphs $G$ with ${\rm dim}_l(G) = n(G)-3$ are listed (with no condition on the clique number). It is proved that if $\om

Jose Luis Blázquez-Salcedo, Fech Scen Khoo, Burkhard Kleihaus, Jutta Kunz

Quasinormal modes are excited during the ringdown phase of black holes after merger. Determination of quasinormal modes of rapidly rotating black holes in alternative theories of gravity has remained a challenge for a long time. Here we discuss in detail our recently developed method to extract the quasinormal modes for rapidly rotating black holes in Einste

Marylou Fournier-Tondreau, Yanbo Pan, Kim Morel, David Lafrenière

In the era of exoplanet studies with JWST, the transiting, hot gas giant WASP-52 b provides an excellent target for atmospheric characterization through transit spectroscopy. WASP-52 b orbits an active K-type dwarf recognized for its surface heterogeneities, such as star-spots and faculae, which offers challenges to atmospheric characterization via transmiss

Lirui Feng

We consider a semiflow strongly focusing monotone with respect to a cone of rank k on a Banach space. We prove that the omega-limit set of a pseudo-ordered semiorbit is ordered, which is called as pseudo-ordered principle. Based on this principle, we obtain the solid Poincar\{'}e-Bendixson theorem with the rank k=2, that is, the omega-limit set of a pseudo-o

Mohammadreza Sharifi

In modern deep learning models, long training times and large datasets present significant challenges to both efficiency and scalability. Effective data curation and sample selection are crucial for optimizing the training process of deep neural networks. This paper introduces SALN, a method designed to prioritize and select samples within each batch rather

Peixian Ma, Boyan Li, Runzhi Jiang, Ju Fan

Existing Natural Language to SQL (NL2SQL) solutions have made significant advancements, yet challenges persist in interpreting and translating NL queries, primarily due to users' limited understanding of database schemas or memory biases toward specific table or column values. These challenges often result in incorrect NL2SQL translations. To address these i

Sheng Jin, Zhe Zhai, Qifu Tyler Sun, Zongpeng Li

Circular-shift linear network coding (LNC) is a class of vector LNC with local encoding kernels selected from cyclic permutation matrices, so that it has low coding complexities. However, it is insufficient to exactly achieve the capacity of a multicast network, so the data units transmitted along the network need to contain redundant symbols, which affects

David H. Brown, Davide Chicco

Machine learning continues to grow in popularity in academia, in industry, and is increasingly used in other fields. However, most of the common metrics used to evaluate even simple binary classification models have shortcomings that are neither immediately obvious nor consistently taught to practitioners. Here we present Interactive Classification Metrics (

Stepan Orevkov

Let $f(m,n)$ be the number of primitive lattice triangulations of an $m \times n$ rectangle. We express the limits $\lim_n f(m,n)^{1/n}$ for $m = 4$ and $m=5$ in terms of certain systems of Fredholm integral equations on generating functions (the case $m\le3$ was treated in a previous paper). Solving these equations numerically, we compute approximate values

Debashis Panda, Lyes Kahouadji, Laurette Tuckerman, Seungwon Shin

Parametric oscillations of an interface separating two fluid phases create nonlinear surface waves, called Faraday waves, which organise into simple patterns, like squares and hexagons, as well as complex structures, such as double hexagonal and superlattice patterns. In this work, we study the influence of surfactant-induced Marangoni stresses on the format

Esther Shizgal, Eitan Wagner, Renana Keydar, Omri Abend

This work presents a computational approach to analyze character development along the narrative timeline. The analysis characterizes the inner and outer changes the protagonist undergoes within a narrative, and the interplay between them. We consider transcripts of Holocaust survivor testimonies as a test case, each telling the story of an individual in fir

Jiasi Zhou, Chintha Tellambura, Geoffrey Ye Li

Integrated sensing and communication (ISAC) networks leverage extremely large antenna arrays and high frequencies. This inevitably extends the Rayleigh distance, making near-field (NF) spherical wave propagation dominant. This unlocks numerous spatial degrees of freedom, raising the challenge of optimizing them for communication and sensing tradeoffs. To thi

Hai Ye, Mingbao Lin, Hwee Tou Ng, Shuicheng Yan

Scaling laws for inference compute in multi-agent systems remain under-explored compared to single-agent scenarios. This work aims to bridge this gap by investigating the problem of data synthesis through multi-agent sampling, where synthetic responses are generated by sampling from multiple distinct language models. Effective model coordination is crucial f

Brice Flamencourt, Andrei Moroianu

We show that conformal vector fields on compact locally conformally product manifolds are orthogonal to the flat distribution and Killing with respect to the Gauduchon metric.

Zsolt Gyongyosi, Timothy J. Hollowood, S. Prem Kumar

Motivated by recent proposals for information recovery from black holes via non-isometric maps and post-selection in an effective description, we set up and investigate a teleportation scenario in a 2d CFT involving a local operator quench and projection on a portion of space onto a Cardy state with the theory in the vacuum state. Using conformal invariance

Marco Linton

In this article we study the following problem: given a chain complex $A_*$ of free $\mathbb{Z}G$-modules, when is $A_*$ isomorphic to the cellular chain complex of some simply connected $G$-CW-complex? Such a chain complex is called realisable. Wall studied this problem in the 60's and reduced it to a problem involving only the second differential $d_2$, no

Fabian Ridder, Malte Schilling

Detecting hallucinations in large language models (LLMs) is critical for enhancing their reliability and trustworthiness. Most research focuses on hallucinations as deviations from information seen during training. However, the opaque nature of an LLM's parametric knowledge complicates the understanding of why generated texts appear ungrounded: The LLM might

Mirko Mucciarini, Giulia Caselli, Daniele De Santis, Manuel Iori

The increase in non-renewable energy consumption and CO2 emissions, especially in the manufacturing sector, is moving radical shifts in energy supply policies and production models. Renewable energy integration and regulated pricing policies require new and effective scheduling strategies, as highlighted by the emerging field of energy-efficient scheduling.

Artavazd Maranjyan, Abdurakhmon Sadiev, Peter Richtárik

Coordinate Descent (CD) methods have gained significant attention in machine learning due to their effectiveness in solving high-dimensional problems and their ability to decompose complex optimization tasks. However, classical CD methods were neither designed nor analyzed with data privacy in mind, a critical concern when handling sensitive information. Thi

Huiwen Wu, Deyi Zhang, Xiaohan Li, Xiaogang Xu

The emergence of the Large Language Model (LLM) has shown their superiority in a wide range of disciplines, including language understanding and translation, relational logic reasoning, and even partial differential equations solving. The transformer is the pervasive backbone architecture for the foundation model construction. It is vital to research how to

Shaina Raza, Caesar Saleh, Azib Farooq, Emrul Hasan

Detecting bias in multimodal news requires models that reason over text--image pairs, not just classify text. In response, we present ViLBias, a VQA-style benchmark and framework for detecting and reasoning about bias in multimodal news. The dataset comprises 40,945 text--image pairs from diverse outlets, each annotated with a bias label and concise rational

Aleksandr Lobanov, Alexander Gasnikov, Eduard Gorbunov, Martin Takáč

The gradient descent (GD) method -- is a fundamental and likely the most popular optimization algorithm in machine learning (ML), with a history traced back to a paper in 1847 (Cauchy, 1847). It was studied under various assumptions, including so-called $(L_0,L_1)$-smoothness, which received noticeable attention in the ML community recently. In this paper, w

Jiangbo Yu, Jinhua Zhao, Luis Miranda-Moreno, Matthew Korp

Surveys and interviews are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. Recently, various domains have begun to explore the use of conversational agents (chatbots) powered by generative artificial intelligence (AI) technologies. Ho

Hankun Wang, Haoran Wang, Yiwei Guo, Zhihan Li

Although text-based large language models exhibit human-level writing ability and remarkable intelligence, speech language models (SLMs) still struggle to generate semantically coherent outputs. There are several potential reasons for this performance degradation: (A) speech tokens mainly provide phonetic information rather than semantic information, (B) the

Jihong Huang, Shun Zhou

In this paper, we perform a complete calculation of the differential cross section for elastic neutrino-electron scattering at the one-loop level in the Standard Model (SM), by using up-to-date values of relevant input parameters in the on-shell renormalization scheme. A careful comparison with the calculation done by Sarantakos, Sirlin and Marciano more tha

Ying Gu, Xue-Guang Zhang, Xing-Qian Chen, Xing Yang

We report a high-redshift ($z=1.404$) tidal disruption event (TDE) candidate in SDSS J000118.70+003314.0 (SDSS J0001), which is a quasar with apparent broad Mg~{\sc ii} emission line. The long-term variability in its nine-year photometric $ugriz$-band light curves, obtained from the SDSS Stripe82 and the PHOTOOBJALL databases, can be described by the convent

Robson Christie, James Trayford

We explore an unconventional bridge between quantum mechanical density matrices and sound by mapping elements of the density matrix and their phases to auditory signals, thus introducing a framework for Open Quantum Sonification. Employing the eigenstates of the Hamiltonian operator as a basis, each quantum state contributes a frequency proportional to its e

Konstantinos Sourounis, Aurélien Manchon

Despite the tremendous interest raised by the recent realization of magnetic Weyl semimetals and the observation of giant anomalous Hall signals, most of the theories used to interpret experimental data overlook the influence of magnetic fluctuations, which are ubiquitous in such materials and can massively impact topological and transport properties. In thi

Yang Qi, Jiexiang Wang, Weiyang Ding, Gustavo Deco

Cortical neurons exhibit a hierarchy of timescales across brain regions in response to input stimuli, which is thought to be crucial for information processing of different temporal scales. Modeling studies suggest that both intra-regional circuit dynamics as well as cross-regional connectome may contribute to this timescale diversity. Equally important to d

Luoxu Jin, Hiroshi Watanabe

With the development of video generation models has advanced significantly in recent years, we adopt large-scale image-to-video diffusion models for video frame interpolation. We present a conditional encoder designed to adapt an image-to-video model for large-motion frame interpolation. To enhance performance, we integrate a dual-branch feature extractor an

Yu-Bo Liu, Hongyi Sun, Ming Zhang, Qihang Liu

The discovery of high-temperature superconductivity (SC) with $T_c\approx 80$ K in the pressurized La$_3$Ni$_2$O$_{7}$ has aroused great interests. Currently, due to technical difficulties, most experiments on La$_3$Ni$_2$O$_{7}$ can only be performed at ambient pressure (AP). Particularly, various experiments have revealed the presence of spin-density wave

Tassilo Wald, Constantin Ulrich, Jonathan Suprijadi, Sebastian Ziegler

The field of self-supervised learning (SSL) for 3D medical images lacks consistency and standardization. While many methods have been developed, it is impossible to identify the current state-of-the-art, due to i) varying and small pretraining datasets, ii) varying architectures, and iii) being evaluated on differing downstream datasets. In this paper, we br

Eric Hedlin, Munawar Hayat, Fatih Porikli, Kwang Moo Yi

To efficiently adapt large models or to train generative models of neural representations, Hypernetworks have drawn interest. While hypernetworks work well, training them is cumbersome, and often requires ground truth optimized weights for each sample. However, obtaining each of these weights is a training problem of its own-one needs to train, e.g., adaptat

Kevin Shen, Andrii Kurkin, Adrián Pérez-Salinas, Elvira Shishenina

Expectation Value Samplers (EVSs) are quantum generative models that can learn high-dimensional continuous distributions by measuring the expectation values of parameterized quantum circuits. However, these models can demand impractical quantum resources for good performance. We investigate how observable choices affect EVS performance and propose an Observa

Sipeng Shen, Yunming Zhang, Dengpan Ye, Xiuwen Shi

While face recognition (FR) models have brought remarkable convenience in face verification and identification, they also pose substantial privacy risks to the public. Existing facial privacy protection schemes usually adopt adversarial examples to disrupt face verification of FR models. However, these schemes often suffer from weak transferability against b

K. Ho, S. Perna, S. Wittrock, S. Tsunegi

Exceptional points (EPs), branch singularities parameter space of non-Hermitian eigenvalue manifolds, display unique topological phenomena linked to eigenvalue and eigenvector switching: the parameter space states are highly sensitive to the system's parameter changes. Therefore, we suggest investigating the parameter space in the presence of an EP by experi

Antonio Laface, Alex Massarenti, William D. Montoya

We take a first step towards the classification of singular Mori dream $K3$ surfaces. We prove that if the Picard lattice of a singular $K3$ surface is Mori dream, then the surface is Mori dream. Moreover, we show that for singular $K3$ surfaces, of Picard rank two, being Mori dream is equivalent to contain two negative curves intersecting each other, and ap

Bang Huang, Sajid Ahmed, Mohamed-Slim Alouini

This paper, addressing the integration requirements of radar imaging and communication for High-Altitude Platform Stations (HAPs) platforms, designs a waveform based on linear frequency modulated (LFM) frequency-hopping signals that combines synthetic aperture radar (SAR) and communication functionalities. Specifically, each pulse of an LFM signal is segment

Lang Gao, Jiahui Geng, Xiangliang Zhang, Preslav Nakov

Jailbreaking in Large Language Models (LLMs) is a major security concern as it can deceive LLMs to generate harmful text. Yet, there is still insufficient understanding of how jailbreaking works, which makes it hard to develop effective defense strategies. We aim to shed more light into this issue: we conduct a detailed large-scale analysis of seven differen

Fabrizio Catanese, Wenfei Liu, Matthias Schütt

In this second part we study first the group $Aut_{\mathbb Q}(S)$ of numerically trivial automorphisms of an algebraic properly elliptic surface $S$, that is, of a minimal algebraic surface with Kodaira dimension $\kappa(S)=1$, in the case $\chi(S) \geq 1$. Our first surprising result is that, against what has been believed for over 40 years, there exist non

Jie He, Nan Hu, Wanqiu Long, Jiaoyan Chen

Large language models (LLMs) have demonstrated impressive capabilities in various reasoning tasks but face significant challenges with complex, knowledge-intensive multi-hop queries, particularly those involving new or long-tail knowledge. Existing benchmarks often fail to fully address these challenges. To bridge this gap, we introduce MINTQA (Multi-hop Que

Lovisa Hagström, Sara Vera Marjanović, Haeun Yu, Arnav Arora

Retrieval-augmented generation (RAG) helps address the limitations of parametric knowledge embedded within a language model (LM). In real world settings, retrieved information can vary in complexity, yet most investigations of LM utilisation of context has been limited to synthetic text. We introduce DRUID (Dataset of Retrieved Unreliable, Insufficient and D

Haru-Tada Sato

We introduce supersymmetric extensions of the Hom-Lie deformation of the Virasoro algebra (super Curtright-Zachos algebra), as realized in the GL(1,1) quantum superspace, for Bloch electron systems under Zeeman effects. By examining the duality inherent in quantum superspace scaling operators, we establish a correspondence between quantum superspace and its

Yuhao Yang, Jiabin Tang, Lianghao Xia, Xingchen Zou

Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrated through knowledge graphs. In this wor

Ruslan Nakibov, Varvara Karteyeva, Igor Petrashkevich, Maksim Ozhiganov

We present a new description of the 7.7~$\mu$m region towards the high-mass star-forming region IRAS 23385+6053 taken from open James Webb Space Telescope Mid-Infrared Instrument Medium Resolution Spectrometer (JWST MIRI/MRS) data. This area is commonly attributed to the $\nu_4$ deformation mode of methane ice. For the first time gaseous and solid methane we

Jing-Min Zhu

TThe organization and structure of bipartite mixed-state quantum entanglement (QE) are more complex and less well understood compared to bipartite pure-state QE. Bipartite mixed-state QEs and their measures play a crucial role in both theory and practical applications. Some existing measures involve quantifying the minimum QE and reflect the inherently compl

Jia-Hui Bi, Shaoshi Yang, Ping Zhang, Sheng Chen

The memristive crossbar array (MCA) has been successfully applied to accelerate matrix computations of signal detection in massive multiple-input multiple-output (MIMO) systems. However, the unique property of massive MIMO channel matrix makes the detection performance of existing MCA-based detectors sensitive to conductance deviations of memristive devices,

Jia-Hui Bi, Shaoshi Yang, Ping Zhang, Sheng Chen

The emerging analog matrix computing technology based on memristive crossbar array (MCA) constitutes a revolutionary new computational paradigm applicable to a wide range of domains. Despite the proven applicability of MCA for massive multiple-input multiple-output (MIMO) detection, existing schemes do not take into account the unique characteristics of mass

Yaoting Gui, Yuqiao Li, Jun Sun

This paper investigates the volume-preserving harmonic mean curvature flow in asymptotically Schwarzschild spaces. We demonstrate the long-time existence and exponential convergence of this flow with a coordinate sphere of large radius serving as the initial surface in the asymptotically flat end, which eventually converges to a constant harmonic mean curvat

Marcin Osial, Daniel Marczak, Bartosz Zieliński

Model merging combines knowledge from task-specific models into a unified multi-task model to avoid joint training on all task data. However, current methods face challenges due to representation bias, which can interfere with tasks performance. As a remedy, we propose IntervMerge, a novel approach to multi-task model merging that effectively mitigates repre

Bin Shen

This manuscript investigates the curvature and topological properties of certain $\infty$-Einstein Finsler metrics on Finsler metric measure spaces. By imposing symmetry conditions, we construct a series of special metrics and analyze their equivalence on special manifolds. Provided a Ricci curvature bound, we establish a linear growth lower bound estimate f

Zhengqian Wu, Ruizhe Li, Zijun Xu, Zhongyuan Wang

Video question answering (VideoQA) aims to answer natural language questions according to the given videos. Although existing models perform well in the factoid VideoQA task, they still face challenges in deep video understanding (DVU) task, which focuses on story videos. Compared to factoid videos, the most significant feature of story videos is storylines,

Cuong Le, Tien Mai, Ngan Ha Duong, Minh Hoang Ha

We study a competitive facility location problem, where customer behavior is modeled and predicted using a discrete choice random utility model. The goal is to strategically place new facilities to maximize the overall captured customer demand in a competitive marketplace. In this work, we introduce two novel considerations. First, the total customer demand

A. Zhang, X. -L. Wu, R. Yang, A. -F. Wang

In Kagome magnet FeGe, the coexistence of electron correlation, charge-density wave (CDW), and magnetism renders it ideal to study their interactions. Here, we combined the optical spectroscopy and the first-principles calculations to investigate the band structures of FeGe annealed at different temperatures. Our observations reveal that the sample annealed

Shahar Katz, Lior Wolf

The success of Transformer-based Language Models (LMs) stems from their attention mechanism. While this mechanism has been extensively studied in explainability research, particularly through the attention values obtained during the forward pass of LMs, the backward pass of attention has been largely overlooked. In this work, we study the mathematics of the

Yewen Li, Shuai Mao, Jingtong Gao, Nan Jiang

Auto-bidding is essential in facilitating online advertising by automatically placing bids on behalf of advertisers. Generative auto-bidding, which generates bids based on an adjustable condition using models like transformers and diffusers, has recently emerged as a new trend due to its potential to learn optimal strategies directly from data and adjust fle

Peng Jiang, Fucai Li, Jinkai Ni

In this paper, the compressible Navier-Stokes-Fourier-$P_1$ (NSF-$P_1$) approximation model in radiation hydrodynamics is investigated in the whole space $\mathbb{R}^3$. This model consists of the compressible NSF equations of fluid coupled with the transport equations of the radiation field propagation. Assuming that the initial data are a small perturbatio

Yi-Chi Zhang, Hua Bai, Chong Chen, Lei Han

The emerging altermagnetic RuO2 with both compensated magnetic moments and broken time-reversal symmetry possesses nontrivial magneto-electronic responses and nonrelativistic spin currents, which are closely related to magnetic easy axis. To probe the N\'eel order in RuO2, we conducted Ru M3-edge X-ray magnetic linear dichroism (XMLD) measurement. For epitax

Luan Pham, Hongyu Zhang, Huong Ha, Flora Salim

Root cause analysis (RCA) for microservice systems has gained significant attention in recent years. However, there is still no standard benchmark that includes large-scale datasets and supports comprehensive evaluation environments. In this paper, we introduce RCAEval, an open-source benchmark that provides datasets and an evaluation environment for RCA in

Till Böhmer, Florian Pabst, Jan Gabriel, Rolf Zeißler

Structural relaxation in deeply supercooled liquids is non-exponential. In susceptibility representation, $\chi^{\prime\prime}(\nu)$, the spectral shape of the structural relaxation is observed as an asymmetrically broadened peak with a $\nu^{1}$ low- and $\nu^{-\beta}$ high-frequency behavior. In this perspective article we discuss common notions, recent re

Yichi Zhang, Hua Bai, Lei Han, Jiankun Dai

Due to nonrelativistic altermagnetic spin splitting effect (ASSE), altermagnets can generate time-reversal-odd spin current and spin splitting torque (SST) with spin polarization parallel to the N\'eel vector. Hence the effective manipulation of SST would provide plenty of opportunities for designable spintronic devices, which remains elusive. Here, the elec

S. Elgammal

In this analysis, we examine the angular distribution of low-mass dimuon pairs produced in simulated electron-positron collisions at the proposed International Linear Collider (ILC), which operates at a center-of-mass energy of 500 GeV and has an integrated luminosity of 4 ab\(^{-1}\). Our focus is on the cos\(\theta_{\text{CS}}\) variable, which is defined

Fethi Bencherki, Anders Rantzer

An adaptive controller is proposed and analyzed for the class of infinite-horizon optimal control problems in positive linear systems presented in (Ohlin et al., 2024b). This controller is derived from the solution of a "data-driven algebraic equation" constructed using the model-free Bellman equation from Q-learning. The equation is driven by data correlati

Yiyi Tao, Yixian Shen, Hang Zhang, Yanxin Shen

The increasing deployment of Large Language Models (LLMs) in various applications necessitates a rigorous evaluation of their robustness against adversarial attacks. In this paper, we present a comprehensive study on the robustness of GPT LLM family. We employ two distinct evaluation methods to assess their resilience. The first method introduce character-le

Dana Rasul Hamad, Tarik A. Rashid

Learner Performance-based Behavior using Simulated Annealing (LPBSA) is an improvement of the Learner Performance-based Behavior (LPB) algorithm. LPBSA, like LPB, has been proven to deal with single and complex problems. Simulated Annealing (SA) has been utilized as a powerful technique to optimize LPB. LPBSA has provided results that outperformed popular al

Dominik Boras

The influence of detector setup configuration and scintillator material choice on spectrum quality in Positron Annihilation Lifetime Spectroscopy (PALS) is fundamentally acknowledged primarily by empirical observation. However, this study quantifies the effects of ultra-fast plastic scintillators (BC422Q) within a conventional collinear (180-degree) detector

Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell

In many real-world settings, regulations and economic incentives permit the sharing of models but not data across institutional boundaries. In such scenarios, practitioners might hope to adapt models to new domains, without losing performance on previous domains (so-called catastrophic forgetting). While any single model may struggle to achieve this goal, le

Zijian Zhou, Xinyi Xu, Daniela Rus, Bryan Kian Hsiang Low

For gradient-based machine learning (ML) methods commonly adopted in practice such as stochastic gradient descent, the de facto differential privacy (DP) technique is perturbing the gradients with random Gaussian noise. Data valuation attributes the ML performance to the training data and is widely used in privacy-aware applications that require enforcing DP