December 2024 arXiv papers — page 35

G. Ivanov

John's inclusion states that a convex body in $\mathbb{R}^d$ can be covered by the $d$-dilation of its maximal volume ellipsoid. We obtain a certain John-type inclusion for log-concave functions. As a byproduct of our approach, we establish the following asymptotically tight inequality: \\ \noindent For any log-concave function $f$ with finite, positive inte

Yuan Yuan, Yajing Xu, Wen Zhang

The core of the Knowledge Graph Completion (KGC) task is to predict and complete the missing relations or nodes in a KG. Common KGC tasks are mostly about inferring unknown elements with one or two elements being known in a triple. In comparison, the Triple Set Prediction (TSP) task is a more realistic knowledge graph completion task. It aims to predict all

Saskia Laura Schröer, Giovanni Apruzzese, Soheil Human, Pavel Laskov

Our society increasingly benefits from Artificial Intelligence (AI). Unfortunately, more and more evidence shows that AI is also used for offensive purposes. Prior works have revealed various examples of use cases in which the deployment of AI can lead to violation of security and privacy objectives. No extant work, however, has been able to draw a holistic

Nikhil Singh, Prabhat Kumar, Anupam Saxena

This paper provides a normalized field product approach for topology optimization to achieve close-to-binary optimal designs. The method employs a parameter-free density measure that implicitly enforces a minimum length scale on the solid phase, allowing for smooth and transition-free topologies. The density evaluation does not rely on weight functions; howe

Abdullah Khondoker, Enam Ahmed Taufik, Md Iftekhar Islam Tashik, S M Ishtiak mahmud

Evaluating text comprehension in educational settings is critical for understanding student performance and improving curricular effectiveness. This study investigates the capability of state-of-the-art language models-RoBERTa Base, Bangla-BERT, and BERT Base-in automatically assessing Bangla passage-based question-answering from the National Curriculum and

S. Patchkovskii, J. Mikosch

Cumulant mapping has been recently suggested [Frasinski, Phys. Chem. Chem. Phys. 24, 207767 (2022)] as an efficient approach to observing multi-particle fragmentation pathways, while bypassing the restrictions of the usual coincidence-measurement approach. We present a formal analysis of the cumulant-mapping technique in the presence of moderate external noi

Baptiste Chomet, Salim Basceken, Djamal Gacemi, Barbara Schneider

Frequency-modulated (FM) combs are produced by mode-locked lasers in which the electric field has a linearly chirped frequency and nearly constant amplitude. This regime of operation occurs naturally in certain laser systems and constitutes a valuable alternative to generate spectra with equidistant modes. Here, we use a low-noise fs-pulse comb as the local

Abdelmadjid Chergui, Grigor Bezirganyan, Sana Sellami, Laure Berti-Équille

Choosing a suitable deep learning architecture for multimodal data fusion is a challenging task, as it requires the effective integration and processing of diverse data types, each with distinct structures and characteristics. In this paper, we introduce MixMAS, a novel framework for sampling-based mixer architecture search tailored to multimodal learning. O

Pascal Auscher, Khalid Baadi

In this paper, we develop a universal, conceptually simple and systematic method to prove well-posedness to Cauchy problems for weak solutions of parabolic equations with non-smooth, time-dependent, elliptic part having a variational definition. Our classes of weak solutions are taken with minimal assumptions. We prove the existence and uniqueness of a funda

Tetsutaro Higaki, Junichiro Kawamura, Tatsuo Kobayashi, Kaito Nasu

We study the moduli stabilization by the radiative corrections due to the moduli dependent vector-like masses invariant under the finite modular symmetry. The radiative stabilization mechanism can stabilize the modulus $\tau$ of the finite modular symmetry $\Gamma_N$ ($N \in \mathbb{N}$) at $\mathrm{Im}\,\tau \gg 1$, where the shift symmetry $\tau \to \tau+1

Wontae Kim

This paper discusses the local Calder\'on-Zygmund type estimate for the singular parabolic double-phase system. The proof covers the counterpart $p<2$ of the result in [23]. Phase analysis is employed to determine an appropriate intrinsic geometry for each phase. Comparison estimates and scaling invariant properties for each intrinsic geometry are the main t

Pedro Cunha de Holanda, Macello Jales de Carvalho Fonseca Filho, Eduardo Walter da Silva

As is widely known, the flavour and mass eigenbasis in neutrino sector can be related by a reduced unitary matrix, described by 3 real parameters and one phase, for Dirac neutrinos, and 2 extra phases if neutrinos are Majorana particles. Neutrino flavour oscillation experiments are insensitive to Majorana phases, which leads to using the PMNS matrix with onl

O. Deniz Akyildiz, Pierre Del Moral, Joaquín Miguez

Entropic optimal transport problems are regularized versions of optimal transport problems. These models play an increasingly important role in machine learning and generative modelling. For finite spaces, these problems are commonly solved using Sinkhorn algorithm (a.k.a. iterative proportional fitting procedure). However, in more general settings the Sinkh

Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita

Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce GeAR, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional bas

Zhongyan Liu, Jingke Xu, Zhifang Zhang

The I/O cost, defined as the amount of data accessed at helper nodes during the repair process, is a crucial metric for repair efficiency of Reed-Solomon (RS) codes. Recently, a formula that relates the I/O cost to the Hamming weight of some linear spaces was proposed in [Liu\&Zhang-TCOM2024]. In this work, we introduce an effective method for calculating th

Chenxi Zhou, Zhe Guo, Qifeng Li, Gaojie Zhang

Spin orbit torque (SOT) has become a promising approach to efficiently manipulate the magnetization switching in spintronic devices. As a main factor to impact the device performance, the high quality interface is essentially desired, which can be readily acquired by using the two-dimensional (2D) van der Waals (vdW) materials. Recently, a 2D ferromagnetic m

Farhad Nooralahzadeh, Yi Zhang, Jonathan Furst, Kurt Stockinger

International enterprises, organizations, and hospitals collect large amounts of multi-modal data stored in databases, text documents, images, and videos. While there has been recent progress in the separate fields of multi-modal data exploration as well as in database systems that automatically translate natural language questions to database query language

Manting Lin, Hongjing Pan

Global solution curve and exact multiplicity of positive solutions for a class of fourth-order beam equations with clamped boundary conditions are derived. The results extend atheorem of P. Korman (2004) by allowing the presence of a singularity in the nonlinearity. The paper also establishes an a priori estimate for C^3-norm of positive solutions, which is

Kangjia Zhao, Jiahui Song, Leigang Sha, Haozhan Shen

Nowadays, research on GUI agents is a hot topic in the AI community. However, current research focuses on GUI task automation, limiting the scope of applications in various GUI scenarios. In this paper, we propose a formalized and comprehensive environment to evaluate the entire process of automated GUI Testing (GTArena), offering a fair, standardized enviro

M. Golafshan, M. Rigo, M. Whiteland

Two finite words are k-binomially equivalent if each subword (i.e., subsequence) of length at most k occurs the same number of times in both words. The k-binomial complexity of an infinite word is a function that maps the integer $n\geq 0$ to the number of k-binomial equivalence classes represented by its factors of length n. The Thue--Morse (TM) word and it

Chao Deng, Jiale Yuan, Pi Bu, Peijie Wang

Large vision language models (LVLMs) have improved the document understanding capabilities remarkably, enabling the handling of complex document elements, longer contexts, and a wider range of tasks. However, existing document understanding benchmarks have been limited to handling only a small number of pages and fail to provide a comprehensive analysis of l

Arunima Ray

There are two main approaches to building locally flat embedded surfaces in 4-manifolds: direct methods which geometrically manipulate a given map of a surface, and more indirect methods using surgery theory. Both rely on Freedman-Quinn's disc embedding theorem. In this expository article, we give an introduction to these methods by sketching proofs of the f

Mahima Upadhyay, Mahesh Choudhary, Namrata Singh, Punit Dubey

The current study presents the cross-section measurement of $^{181}$Ta(n,$\gamma$)$^{182}$Ta reaction at 1.37 $\pm$ 0.13, 2.06 $\pm$ 0.14, 2.56 $\pm$ 0.15, and 3.05 $\pm$ 0.17 MeV neutron energies utilizing offline $\gamma$-ray spectroscopy. The neutrons were generated through the $^{7}$Li(p,n)$^{7}$Be reaction. The $^{115}$In(n,n'$\gamma$)$^{115m}$In reacti

Qice Qin, Yuki Hirakawa, Ryotaro Shimizu, Takuya Furusawa

Image generation in the fashion domain has predominantly focused on preserving body characteristics or following input prompts, but little attention has been paid to improving the inherent fashionability of the output images. This paper presents a novel diffusion model-based approach that generates fashion images with improved fashionability while maintainin

Ju Chen, Chang Liu, Yun-Long Zhang, Gang Wang

The detection of parity violation in the isotropic stochastic gravitational wave background (SGWB) will serve a crucial probe for new physics, particularly in parity-violating theories of gravity. The joint observations by the planned space-borne gravitational wave detectors, LISA and TAIJI, will offer a unique opportunity to observe such effects in the mill

Zihan Zhou, Ziyi Zeng, Wenhao Jiang, Yihui Zhu

As social changes accelerate, the incidence of psychosomatic disorders has significantly increased, becoming a major challenge in global health issues. This necessitates an innovative knowledge system and analytical methods to aid in diagnosis and treatment. Here, we establish the ontology model and entity types, using the BERT model and LoRA-tuned LLM for n

Cuimei Cao, Wei Duan, Xiaoyu Feng, Yan Xu

Neuromorphic computing, which seeks to replicate the brain's ability to process information, has garnered significant attention due to its potential to achieve brain-like computing efficiency and human cognitive intelligence. Spin-orbit torque (SOT) devices can be used to simulate artificial synapses with non-volatile, high-speed processing and endurance cha

Zhibin Wang, Yanxin Cai, Jiayi Zhou, Yangming Zhang

The growing field of remote sensing faces a challenge: the ever-increasing size and volume of imagery data are exceeding the storage and transmission capabilities of satellite platforms. Efficient compression of remote sensing imagery is a critical solution to alleviate these burdens on satellites. However, existing compression methods are often too computat

Zihan Wang, Xiaocui Yang, Yongkang Liu, Shi Feng

Current conversational recommendation systems focus predominantly on text. However, real-world recommendation settings are generally multimodal, causing a significant gap between existing research and practical applications. To address this issue, we propose Muse, the first multimodal conversational recommendation dataset. Muse comprises 83,148 utterances fr

Avinash Anand, Kritarth Prasad, Chhavi Kirtani, Ashwin R Nair

Large Language Models (LLMs) excel in linguistic tasks but struggle with mathematical reasoning, particularly in non English languages like Hindi. This research aims to enhance the mathematical reasoning skills of smaller, resource efficient open-source LLMs in both Hindi and English. We evaluate models like OpenHathi 7B, LLaMA-2 7B, WizardMath 7B, Mistral 7

Shihang Xu, Shibing Chu, Rami Mrad, Zhejun Zhang

Generative model for 2D materials has shown significant promise in accelerating the material discovery process. The stability and performance of these materials are strongly influenced by their underlying symmetry. However, existing generative models for 2D materials often neglect symmetry constraints, which limits both the diversity and quality of the gener

Sushant Kumar Behera, Ruggero Sala, Abhirup Roy Karmakar, Matteo Moioli

The Chirality-Induced Spin Selectivity (CISS) effect describes the ability of chiral molecules and crystals to transmit spin-polarized currents, a phenomenon first identified in 1999. Although this effect holds great promise for a broad spectrum of different applications in device physics and synthetic chemistry (including, e.g., spintronics, quantum computi

Boran Mert, Usta Ahmet, Kayhan Cenk

In this study, we present the results of the relationship between spectral type (ST) and the projected stellar rotational velocity ($vsini$), utilising a sample of approximately 50,000 single stars across a range of evolutionary stages. The STs of the stars included in this study span a broad range, from O0 to M9. We examine the stars in our data set, which

Gaston Giribet, Mauricio Leston, Pedro Schmied, Bruno Sivilotti

We consider a generalization of the double Liouville theory, which can be thought of as a two-parameter family of marginal deformations of the so-called Virasoro Minimal String (VMS). The latter consists of a timelike ($c_{-}<1$) and a spacelike ($c_{+}>25$) Liouville field theory formulated on a fluctuating Riemann surface. For the deformed theory, we compu

Dongran Zhang, Jiangnan Yan, Kemal Polat, Adi Alhudhaif

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling me

Denis Bernardes, Orlando Verducci Junior, Francisco Rodrigues, Claudia Vilega Rodrigues

SPARC4 is a new astronomical instrument developed entirely by Brazilian institutions, currently installed on the 1.6-m Perkin-Elmer telescope of the Pico dos Dias Observatory. It allows the user to perform photometric or polarimetric observations simultaneously in the four SDSS bands (g, r, i, and z). In this paper, we describe the control system developed f

Esla Timothy Anzaku, Seyed Amir Mousavi, Arnout Van Messem, Wesley De Neve

Deep neural networks (DNNs) are typically evaluated under the assumption that each image has a single correct label. However, many images in benchmarks like ImageNet contain multiple valid labels, creating a mismatch between evaluation protocols and the actual complexity of visual data. This mismatch can penalize DNNs for predicting correct but unannotated l

Daniel Peraltai, Xin Qin

Cyber-physical systems (CPS) combine cyber and physical components engineered to make decisions and interact within dynamic environments. Ensuring the safety of CPS is of great importance, requiring extensive testing across diverse and complex scenarios. To generate as many testing scenarios as possible, previous efforts have focused on describing scenarios

Siavash Ameli, Siyuan Zhuang, Ion Stoica, Michael W. Mahoney

Large language models (LLMs) have transformed natural language processing, with frameworks like Chatbot Arena providing pioneering platforms for evaluating these models. By facilitating millions of pairwise comparisons based on human judgments, Chatbot Arena has become a cornerstone in LLM evaluation, offering rich datasets for ranking models in open-ended c

Aleix Boquet-Pujadas

Mechanobiology is gaining more and more traction as the fundamental role of physical forces in biological function becomes clearer. Forces at the microscale are often measured indirectly using inverse problems such as Traction Force Microscopy because biological experiments are hard to access with physical probes. In contrast with the experimental nature of

JeongYeon Nam, Jinbae Im, Wonjae Kim, Taeho Kil

Recent vision-language foundation models still frequently produce outputs misaligned with their inputs, evidenced by object hallucination in captioning and prompt misalignment in the text-to-image generation model. Recent studies have explored methods for identifying misaligned elements, aiming not only to enhance interpretability but also to improve model p

Jorge Silva, Martin Plöschner, Karl Bertling, Mukund Ghantala

Terahertz imaging holds great potential for non-destructive material inspection, but practical implementation has been limited by resolution constraints. In this study, we present a single-pixel THz imaging system based on a confocal microscope architecture, utilising a quantum cascade laser as both transmitter and phase-sensitive receiver. Our approach inte

Joan Hernández

In this paper we study removable singularities for regular $(1,\frac{1}{2s})$-Lipschitz solutions of the $s$-fractional heat equation for $1/2<s<1$. To do so, we define a Lipschitz fractional caloric capacity and study its critical dimension and the $L^2$-boundedness of a pair of singular integral operators, whose kernels will be the gradient of the fundamen

Ce Wang

In this paper, we introduce and investigate a model of magnetic quantum walk on a general hypercube. We first construct a set of unitary involutions associated with a magnetic potential $\nu$ by using quantum Bernoulli noises. And then, with these unitary involutions as the magnetic shift operators, we define the evolution operator $\mathsf{W}^{(\nu)}$ for t

Albert Bruno Piek, Evgeniy Petrov

We introduce a metric on the set of permutations of given order, which is a weighted generalization of Kendall's $\tau$ rank distance and study its properties. Using the edge graph of a permutohedron, we give a criterion which guarantees that a permutation lies metrically between another two fixed permutations. In addition, the conditions under which four po

Bahruz Suleymanli, B. Tanatar

The theory of layered superconductors is extended in the presence of Rashba and Dresselhaus spin-orbit interactions (SOIs). Using the intralayer BCS-like pairing interaction and employing the Gor'kov formalism, we obtain analytical expressions for the temperature Green's functions and determine the gap function $\Delta$ which becomes complex in the presence

Jiajian Zhang, Lingna Wang, Yong-Ju Hai, Jiawei Zhang

Quantum metrology has emerged as a powerful tool for timekeeping, field sensing, and precision measurements in fundamental physics. With the advent of distributed quantum metrology, its capabilities have extended to probing spatially distributed parameters across networked quantum systems. However, scalable implementations of distributed quantum metrology wi

Ning Sun, Pengfei Zhang, Lei Feng

Symmetry breaking plays a central role in classifying the phases of quantum many-body systems. Recent developments have highlighted a novel symmetry-breaking pattern, in which the strong symmetry of a density matrix spontaneously breaks to the week symmetry. This strong-to-weak symmetry breaking is typically detected using multi-replica correlation functions

Jingyu Li, Zhiyong Feng, Dongxiao He, Hongqi Chen

Interactive Recommendation (IR) has gained significant attention recently for its capability to quickly capture dynamic interest and optimize both short and long term objectives. IR agents are typically implemented through Deep Reinforcement Learning (DRL), because DRL is inherently compatible with the dynamic nature of IR. However, DRL is currently not perf

Somesh Chamoli, Xubo Wang, Chaoqun Zhang, Malaya K. Nayak

We present an efficient and cost-effective implementation for the exact two-component atomic mean field (X2CAMF) based coupled cluster (CC) method, which integrates frozen natural spinors (FNS) and the Cholesky decomposition (CD) technique. The use of CD approximation greatly reduces the storage requirement of the calculation without any significant reductio

Hong Zhu, Xun Qian

This paper presents a stochastic block-coordinate proximal Newton method for minimizing the sum of a blockwise Lipschitz-continuously differentiable function and a separable nonsmooth convex function. At each iteration, the method randomly selects one block and approximately solves a strongly convex regularized quadratic subproblem built from a second-order

Xiuting Ge, Chunrong Fang, Xuanye Li, Ye Shang

Static Code Analyzers (SCAs) have played a critical role in software quality assurance. However, SCAs with various static analysis techniques suffer from different levels of false positives and false negatives, thereby yielding the varying performance in SCAs. To detect more defects in a given project, it is a possible way to use more available SCAs for scan

Kang Chen, Yan Lin, Shuhui Yang

In this article, we introduce a class of multilinear strongly singular integral operators with generalized kernels on the RD-space. The boundedness of these operators on weighted Lebesgue spaces is established. Moreover, two types of endpoint estimates and their boundedness on generalized weighted Morrey spaces are obtained. Our results further generalize th

Haosheng He, Jianpeng Qi, Chao Liu, Junyu Dong

In compute-first networking, maintaining fresh and accurate status information at the network edge is crucial for effective access to remote services. This process typically involves three phases: Status updating, user accessing, and user requesting. However, current studies on status effectiveness, such as Age of Information at Query (QAoI), do not comprehe

Xiaoping Wu, Jie Hu, Xiaoming Wei

Diffusion Probabilistic Models (DPMs) have emerged as the de facto approach for high-fidelity image synthesis, operating diffusion processes on continuous VAE latent, which significantly differ from the text generation methods employed by Large Language Models (LLMs). In this paper, we introduce a novel generative framework, the Recurrent Diffusion Probabili

Elisa Marchetto, Hannah Eichhorn, Daniel Gallichan, Julia A. Schnabel

Purpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics' robustness to typica

J. X. Lu

In this paper, we address how to implement T duality to the closed string tree cylinder amplitude between a Dp brane and a Dp$'$ brane with $p - p' = 2 \,n$. For this, we first compute the closed string tree cylinder amplitude between these two D branes with common longitudinal and transverse circle compactifications. We then show explicitly how to perform a

Tenghui Li, Guoxu Zhou, Xuyang Zhao, Qibin Zhao

Large language models have demonstrated predictable scaling behaviors with respect to model parameters and training data. This study investigates whether a similar scaling relationship exist for vision-language models with respect to the number of vision tokens. A mathematical framework is developed to characterize a relationship between vision token number

Sagnik Majumder, Tushar Nagarajan, Ziad Al-Halah, Kristen Grauman

We introduce SWITCH-A-VIEW, a model that learns to automatically select the viewpoint to display at each timepoint when creating a how-to video. The key insight of our approach is how to train such a model from unlabeled -- but human-edited -- video samples. We pose a pretext task that pseudo-labels segments in the training videos for their primary viewpoint

Huan Wang, Xiaojie Liu, Hui Wang, Yin Wang

The high contact resistance between MoS$_2$ and metals hinders its potential as an ideal solution for overcoming the short channel effect in silicon-based FETs at sub-3nm scales. We theoretically designed a MoS$_2$-based transistor, featuring bilayer MoS$_2$ connected to Cu-intercalated bilayer MoS$_2$ electrodes. At 0.6 V, contact resistance is 16.7 $\Omega

Dileep Kumar

The isotope selective grazing-incidence nuclear resonance scattering (GI-NRS) technique is demonstrated to be depth-resolved under x-ray standing wave (XSW) conditions to probe the magnetism of the two interfaces of the Fe layers (Fe-on-Tb and Tb-on-Fe interface) independently in Tb/Fe/Tb trilayer structures. Depth resolution was achieved by placing an ultra

Lukas Jelinek, Kurt Schab, Viktor Hruska, Miloslav Capek

The explicit connection between the transition matrix and boundary element method integral operators is formulated. This enables the calculation of characteristic modes via eigenvalue problems involving either set of operators, leading to convenient orthogonality properties facilitating scattering analysis, solution of inverse problems, and the design of exc

Robin van Haastrecht, Genkai Zhang

We prove Wehrl-type $L^2(G)-L^{p}(G)$ inequalities for matrix coefficients of vector-valued holomorphic discrete series of $G$, for even integers $p=2n$. The optimal constant is expressed in terms of Harish-Chandra formal degrees for the discrete series. We prove the maximizers are precisely the reproducing kernels.

Qiuyi Gu, Zhaocheng Ye, Jincheng Yu, Jiahao Tang

Collaborative perception in unknown environments is crucial for multi-robot systems. With the emergence of foundation models, robots can now not only perceive geometric information but also achieve open-vocabulary scene understanding. However, existing map representations that support open-vocabulary queries often involve large data volumes, which becomes a

Yiling Yao, Bing Zhang, Wenjuan Zhang, Lianru Gao

Novel View Synthesis (NVS) can reconstruct scenes from multi-view images and synthesize novel images from new viewpoints, which provides technical support for tasks such as target recognition and environmental perception. Aerial remote sensing can conveniently capture a wealth of multi-view images with just a few flights. However, the challenges brought by l

Xuan Luo, Zichao Jiang, Yi Zhang, Qinghe Yao

A novel particle tracking method based on a convolutional neural network (CNN) is proposed to improve the efficiency of Lagrangian-Eulerian (L-E) approaches. Relying on the successive neighbor search (SNS) method for particle tracking, the L-E approaches face increasing computational and parallel overhead as simulations grow in scale. This issue arises prima

Xinping Zhao, Baotian Hu, Yan Zhong, Shouzheng Huang

Although prevailing supervised and self-supervised learning augmented sequential recommendation (SeRec) models have achieved improved performance with powerful neural network architectures, we argue that they still suffer from two limitations: (1) Preference Drift, where models trained on past data can hardly accommodate evolving user preference; and (2) Imp

Shani Goren, Oren Kalinsky, Tomer Stav, Yuri Rapoport

The rise of LLMs has deflected a growing portion of human-computer interactions towards LLM-based chatbots. The remarkable abilities of these models allow users to interact using long, diverse natural language text covering a wide range of topics and styles. Phrasing these messages is a time and effort consuming task, calling for an autocomplete solution to

Raven Adam, Marie Lisa Kogler

This study introduces Bidirectional Topic Matching (BTM), a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM is a flexible framework that can incorporate various topic modeling approaches, including BERTopic, Top2Vec, and Latent Dirichlet Allocation (LDA). BTM employs a dual-model approach, tra

Andre Opris

This article addresses theory in evolutionary many-objective optimization and focuses on the role of crossover operators. The advantages of using crossover are hardly understood and rigorous runtime analyses with crossover are lagging far behind its use in practice, specifically in the case of more than two objectives. We present two many-objective problems

Aditya M. Natu, S. Hassan HosseinNia

Nanopositioning systems frequently encounter limitations in control bandwidth due to their lightly damped resonance behavior. This paper presents a novel Non-Minimum-Phase Resonant Controller (NRC) aimed at active damping control within dual closed-loop architectures, specifically applied to piezo-actuated nanopositioning systems. The control strategy is str

Fumiya Hori, Hiroyasu Matsudaira, Shunsaku Kitagawa, Kenji Ishida

We have investigated the changes in the magnetic properties on YbCuS2 by Lu or Se substitutions from a microscopic perspective. In general, it is expected that nonmagnetic Lu substitution dilutes the magnetic Yb3+ concentration, and that Se substitution induces the negative pressure in YbCuS2. The 63/65Cu-nuclear quadrupole resonance (NQR) measurements on po

A. Jiménez-Vargas, M. I. Ramírez

We study the injectivity of normed ideals of weighted holomorphic mappings. To be more precise, the concept of injective hull of normed weighted holomorphic ideals is introduced and characterized in terms of a domination property. The injective hulls of those ideals -- generated by the procedures of composition and dual -- are described and these description

Kaiwen Ning, Jiachi Chen, Jingwen Zhang, Wei Li

AI agents are systems capable of perceiving their environment, autonomously planning and executing tasks. Recent advancements in LLM have introduced a transformative paradigm for AI agents, enabling them to interact with external resources and tools through prompts. In such agents, the workflow integrates developer-written code, which manages framework const

Jinhyeok Choi, Heehyeon Kim, Joyce Jiyoung Whang

Graph neural networks (GNNs) have emerged as an effective tool for fraud detection, identifying fraudulent users, and uncovering malicious behaviors. However, attacks against GNN-based fraud detectors and their risks have rarely been studied, thereby leaving potential threats unaddressed. Recent findings suggest that frauds are increasingly organized as gang

Bernhard Andraschko, Martin Kreuzer, Le Ngoc Long

In this paper we continue the development of a new technique for computing elimination ideals by substitution which has been called $Z$-separating re-embeddings. Given an ideal $I$ in the polynomial ring $K[x_1,\dots,x_n]$ over a field $K$, this method searches for tuples $Z=(z_1,\dots,z_s)$ of indeterminates with the property that $I$ contains polynomials o

John M. Campbell

Given an identity relating families of Schur and power sum symmetric functions, this may be thought of as encoding representation-theoretic properties according to how the $p$-to-$s$ transition matrices provide the irreducible character tables for symmetric groups. The case of the Murnaghan-Nakayama rule for cycles provides that $p_{n} = \sum_{i = 0}^{n-1} (

Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang

The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. We introduce GIST, a large-scale multilingual AI terminology dataset containing 5K terms extracted from top AI conference papers spanning 2000 to 2023. The terms are translated into Arabic, Chinese, Fre

Kostas Filippas

Recently, we introduced a symmetry on the structure of angular momentum which interchanges internal and external degrees of freedom. The spin-orbit duality is a holographic map that projects a massive theory in four-dimensional flat spacetime onto the three-dimensional $\mathbb{S}^2\times\mathbb{R}$ null infinity. This cylinder has radius $R\sim1/m$ and, qua

Meixia He, Peican Zhu, Keke Tang, Yangming Guo

Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group identity of hyperedges, thereby resulting in limited attack performance and detectable attacks. In this manuscript,

Piek Vossen, Selene Báez Santamaría, Lenka Bajčetić, Thomas Belluci

Obtaining an explicit understanding of communication within a Hybrid Intelligence collaboration is essential to create controllable and transparent agents. In this paper, we describe a number of Natural Language Understanding models that extract explicit symbolic triples from social conversation. Triple extraction has mostly been developed and tested for Kno

Kotaro Inami

Motivated by a recent work of Schippa (2022), we consider local smoothing estimates for Schr\"{o}dinger equations in modulation spaces. By using the C\'{o}rdoba-Fefferman type reverse square function inequality and the bilinear Strichartz estimate, we can refine the summability exponent of modulation spaces. Next, we will also discuss a new type of randomize

Jangseop Park, Namwoo Kang

Nonlinear structural analyses in engineering often require extensive finite element simulations, limiting their applicability in design optimization and real-time control. Conventional deep learning surrogates often struggle with complex, non-parametric three-dimensional (3D) geometries and directionally varying loads. This work presents Point-DeepONet, an o

Ken Wang, Zuyi Zhang, Tao Zheng, Peng Zhu

We show the existence and uniqueness of solutions to a generalized Monge-Amp\`{e}re equation on closed almost K\"ahler surfaces, where the equation depends only on the underlying almost K\"ahler structure. As an application, we prove Donaldson's conjecture for tamed almost complex 4-manifolds.

Mircea Lazar

In this work, we consider the problem of learning nonlinear operators that correspond to discrete-time nonlinear dynamical systems with inputs. Given an initial state and a finite input trajectory, such operators yield a finite output trajectory compatible with the system dynamics. Inspired by the universal approximation theorem of operators tailored to radi

Pujun Liu, Rui-Dong Zhu

We give the connection formulae for ordinary differential equations with 5 and 6 (and in principle can be generalized to more) regular singularities from the data of instanton partition functions of quiver gauge theories. We check the consistency of these connection formulae by numerically computing the quasinormal modes (QNMs) of Reissner-Nordstr\"om de Sit

Soumyaditya Das, Soumyajyoti Biswas, Bikas K. Chakrabarti

We propose and demonstrate numerically a fast classical annealing scheme for the Sherrington-Kirkpatrick (SK) spin glass model, employing the Suzuki-Kubo meanfield Ising dynamics (supplemented by a modified Thouless-Anderson-Palmer reaction field). The resultant dynamics, starting from any arbitrary paramagnetic phase (with local magnetizations $m_i=\pm 1$ f

Liang Chen, Yang Li, Jun Cai, Songlin Gu

This paper introduces a novel approach for tracking the dynamic trajectories of integrated natural gas and power systems, leveraging a Kalman filter-based structure. To predict the states of the system, the Holt's exponential smoothing techniques and nonlinear dynamic equations of gas pipelines are applied to establish the power and gas system equations, res

Simon Kohaut, Benedict Flade, Julian Eggert, Devendra Singh Dhami

The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is essential to understand their inherent uncer

Hao Yu, Xin Yang, Le Zhang, Hanlin Gu

Federated continual learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenarios. However, FCL needs to address not only spatial data heterogeneity between clients but also temporal data heterogeneity between tasks. In this paper, empirical experiments demon

Viviane Clay, Niels Leadholm, Jeff Hawkins

Artificial intelligence has advanced rapidly in the last decade, driven primarily by progress in the scale of deep-learning systems. Despite these advances, the creation of intelligent systems that can operate effectively in diverse, real-world environments remains a significant challenge. In this white paper, we outline the Thousand Brains Project, an ongoi

Nikita Fedosov, Daria Medvedeva, Oleg Shevtsov, Alexei Ossadtchi

This study presents an analysis of sensorimotor rhythms using an advanced, optically-pumped magnetoencephalography (OPM-MEG) system - a novel and rapidly developing technology. We conducted real-movement and motor imagery experiments with nine participants across two distinct magnetically-shielded environments: one featuring an analog active suppression syst

Timofey V. Maximov, Norayr A. Asriyan, Igor L. Kurbakov, Yurii E. Lozovik

We present a theoretical model for a coherent terahertz radiation source based on Bose-Einstein condensate of incoherently pumped microcavity photons. Energy relaxation is provided by inelastic photon scattering on a two-dimensional electron gas in magnetic field. The proposed setup evades the standard lasing mechanisms: neither population inversion nor ligh

Zhongjian Hu, Peng Yang, Bing Li, Zhenqi Wang

Large Language Models (LLMs) have achieved impressive results in knowledge-based Visual Question Answering (VQA). However existing methods still have challenges: the inability to use external tools autonomously, and the inability to work in teams. Humans tend to know whether they need to use external tools when they encounter a new question, e.g., they tend

Sizhuo Jin, Shuo Chen, Jianjun Qian, Ying Tai

Density Functional Theory (DFT) stands as a widely used and efficient approach for addressing the many-electron Schr\"odinger equation across various domains such as physics, chemistry, and biology. However, a core challenge that persists over the long term pertains to refining the exchange-correlation (XC) approximation. This approximation significantly inf

Andreas Knoblauch

Neural associative memories are single layer perceptrons with fast synaptic learning typically storing discrete associations between pairs of neural activity patterns. Previous works have analyzed the optimal networks under naive Bayes assumptions of independent pattern components and heteroassociation, where the task is to learn associations from input to o

Stefano Damiano, Federico Miotello, Mirco Pezzoli, Alberto Bernardini

Sound field reconstruction aims to estimate pressure fields in areas lacking direct measurements. Existing techniques often rely on strong assumptions or face challenges related to data availability or the explicit modeling of physical properties. To bridge these gaps, this study introduces a zero-shot, physics-informed dictionary learning approach to perfor

Simon Kohaut, Felix Divo, Benedict Flade, Devendra Singh Dhami

Predicting agents impacted by legal policies, physical limitations, and operational preferences is inherently difficult. In recent years, neuro-symbolic methods have emerged, integrating machine learning and symbolic reasoning models into end-to-end learnable systems. Hereby, a promising avenue for expressing high-level constraints over multi-modal input dat

Priyanka Sharma, Edward J. Oughton, Aleksan Shanoyan

This paper provides the first incentive analysis of open radio access networks (ORAN) using game theory. We assess strategic interactions between telecom supply chain stakeholders: mobile network operators (MNOs), network infrastructure suppliers (NIS), and original equipment manufacturers (OEMs) across three procurement scenarios: (i) Traditional, (ii) Pred

Krzysztof Bogdan, Dominik Kutek, Katarzyna Pietruska-Pałuba

We define Bregman variation of semimartingales. We give its pathwise representation, It\^o-type isometry for martingales, and applications to harmonic analysis.