October 2024 arXiv papers — page 47

Per Berglund, Giorgi Butbaia, Tristan Hübsch, Vishnu Jejjala

We introduce \texttt{cymyc}, a high-performance Python library for numerical investigation of the geometry of a large class of string compactification manifolds and their associated moduli spaces. We develop a well-defined geometric ansatz to numerically model tensor fields of arbitrary degree on a large class of Calabi-Yau manifolds. \texttt{cymyc} includes

Nicole Cho, Nishan Srishankar, Lucas Cecchi, William Watson

Financial intelligence generation from vast data sources has typically relied on traditional methods of knowledge-graph construction or database engineering. Recently, fine-tuned financial domain-specific Large Language Models (LLMs), have emerged. While these advancements are promising, limitations such as high inference costs, hallucinations, and the compl

Anna Jové

We study the boundaries of non-univalent simply connected Baker domains of transcendental maps (both entire and meromorphic), of hyperbolic and simply parabolic type. We prove non-ergodicity and non-recurrence for the boundary map, and additional properties concerning the Julia set and the set of singularities of the associated inner function, and the topolo

Vamshi Krishna Kancharla, Pavan Kumar Kaveti, Dasari Naga Raju

Video Capsule Endoscopy (VCE) has become an indispensable diagnostic tool for gastrointestinal (GI) disorders due to its non-invasive nature and ability to capture high-resolution images of the small intestine. However, the enormous volume of data generated during a single procedure makes manual inspection labor-intensive, time-consuming, and prone to inter-

Unique Subedi, Ambuj Tewari

We study active data collection strategies for operator learning when the target operator is linear and the input functions are drawn from a mean-zero stochastic process with continuous covariance kernels. With an active data collection strategy, we establish an error convergence rate in terms of the decay rate of the eigenvalues of the covariance kernel. We

Manishankar Ailiga, Shubhashis Mallik, Gaurav Narain

The path integral of 4D Einstein-Hilbert gravity for the de Sitter-like Universe with fluctuations is investigated, and the transition amplitude from one boundary configuration to another is computed. The gravitational system is described by lapse, scale factor and metric-fluctuation field. Variational consistency demands augmenting the bulk theory with suit

Yaochen Hu, Mai Zeng, Ge Zhang, Pavel Rumiantsev

Graph Neural Networks (GNN) exhibit superior performance in graph representation learning, but their inference cost can be high, due to an aggregation operation that can require a memory fetch for a very large number of nodes. This inference cost is the major obstacle to deploying GNN models with \emph{online prediction} to reflect the potentially dynamic no

Sahan Dissanayaka, Manjusri Wickramasinghe, Pasindu Marasinghe

The early detection of potential failures in industrial machinery components is paramount for ensuring the reliability and safety of operations, thereby preserving Machine Condition Monitoring (MCM). This research addresses this imperative by introducing an innovative approach to Real-Time Acoustic Anomaly Detection. Our method combines semi-supervised tempo

Andrei Constantinescu, Marc Dufay, Diana Ghinea, Roger Wattenhofer

Byzantine Agreement (BA) considers a setting of $n$ parties, out of which up to $t$ can exhibit byzantine (malicious) behavior. Honest parties must decide on a common value (agreement), which must belong to a set determined by the honest inputs (validity). Depending on the use case, this set can grow or shrink, leading to various possible desiderata collecti

Shilong Li, Yancheng He, Hui Huang, Xingyuan Bu

Recent advancements in Direct Preference Optimization (DPO) have significantly enhanced the alignment of Large Language Models (LLMs) with human preferences, owing to its simplicity and effectiveness. However, existing methods typically optimize a scalar score or ranking reward, thereby overlooking the multi-dimensional nature of human preferences. In this w

Mohamed Elshaarawy, Ashrakat Saeed, Mariam Sheta, Abdelrahman Said

This paper proposes a machine learning approach for classifying classical and new Egyptian music by composer and generating new similar music. The proposed system utilizes a convolutional neural network (CNN) for classification and a CNN autoencoder for generation. The dataset used in this project consists of new and classical Egyptian music pieces composed

Muath Alsuhaibani, Ali Pourramezan Fard, Jian Sun, Farida Far Poor

This review paper explores recent advances in deep learning approaches for non-invasive cognitive impairment detection. We examine various non-invasive indicators of cognitive decline, including speech and language, facial, and motoric mobility. The paper provides an overview of relevant datasets, feature-extracting techniques, and deep-learning architecture

OpenAI, :, Aaron Hurst, Adam Lerer

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232

Guilherme S. Y. Giardini, John F. Hardy, Carlo R. da Cunha

Understanding the mechanisms behind emergent behaviors in multi-agent systems is critical for advancing fields such as swarm robotics and artificial intelligence. In this study, we investigate how neural networks evolve to control agents' behavior in a dynamic environment, focusing on the relationship between the network's complexity and collective behavior

Vegard Berge, Chunlei Li

Traditional intrusion detection systems (IDSs) often rely on either network traffic or process data, but this single-source approach may miss complex attack patterns that span multiple layers within industrial control systems (ICSs) or persistent threats that target different layers of operational technology systems. This study investigates whether combining

Banadipa Chakraborty, Tamal Mukhopadhyay, Anamika Kotal, Ujjal Debnath

We present a comprehensive investigation exploring the theoretical framework of Einstein-Aether gravity theory when combined with two modified cosmological paradigms: the Barrow Agegraphic Dark Energy (BADE) and its newer variant, the New Barrow Agegraphic Dark Energy (NBADE). Our study focuses on reconstructing the functional form of the Einstein-Aether Lag

Lucas H. P. Cunha

Dipolar interactions govern the structure and dynamics of many soft-matter systems, from molecular to colloids assemblies. When dipole moments are induced by an external field, mutual interactions lead to a many-body magnetization response that cannot be described by fixed-dipole models. Here, we derive the interaction potential for a system of mutually inte

Hojun Chung, Junseo Lee, Minsoo Kim, Dohyeong Kim

Training agents that are robust to environmental changes remains a significant challenge in deep reinforcement learning (RL). Unsupervised environment design (UED) has recently emerged to address this issue by generating a set of training environments tailored to the agent's capabilities. While prior works demonstrate that UED has the potential to learn a ro

Joel Brewster Lewis, Alejandro H. Morales

We show by an explicit example that the Garsia--Remmel $q$-rook numbers of Ferrers boards do not all have unimodal sequences of coefficients. This resolves in the negative a question from 1986 by the aforementioned authors.

Giovanni Verza, Giulia Degni, Alice Pisani, Nico Hamaus

We provide an accurate forecast of the expected constraining power from the main void statistics -- the void size function and the void-galaxy cross-correlation function -- to be measured by the Roman reference High Latitude Spectroscopic Survey from the Nancy Grace Roman Space Telescope. Relying on a realistic galaxy mock lightcone, covering 2000 square deg

Md Faizal Karim, Shreya Bollimuntha, Mohammed Saad Hashmi, Autrio Das

Dual-arm manipulation is an area of growing interest in the robotics community. Enabling robots to perform tasks that require the coordinated use of two arms, is essential for complex manipulation tasks such as handling large objects, assembling components, and performing human-like interactions. However, achieving effective dual-arm manipulation is challeng

Xin Jiang, Peng Wang, Houwen Wu, Haitang Yang

The {\it finiteness} of the entanglement entropies between disjoint subsystems enables us to show that, the dynamical equation of the entanglement entropy in CFT$_2$ is precisely three dimensional Einstein's equation. We establish a profound relation between the cosmological constant and CFT$_2$ entanglement entropy. Thus entanglement entropies induce intern

Victor Michel-Dansac, Andrea Thomann

The present work concerns the derivation of a fully well-balanced Godunov-type finite volume scheme for the Euler equations with a gravitational potential based on an approximate Riemann solver in a one-dimensional framework. It is an extension to general equations of states of the entropy-stable and fully well-balanced scheme for ideal gases recently forwar

Eduardo Luiz Alba, Matheus Henrique Dal Molin Ribeiro, Gilson Adamczuk, Flavio Trojan

Educational institutions are essential for economic and social development. Budget cuts in Brazil in recent years have made it difficult to carry out their activities and projects. In the case of expenses with water and electricity, unexpected situations can occur, such as leaks and equipment failures, which make their management challenging. This study prop

Ramakant Kumar

The integration of Long Range Wide Area Network (LoRaWAN) with Mobile Ad-hoc Networks (MANETs) presents a promising solution for enhancing communication networks within campus environments. This paper explores the unique advantages of combining these two technologies, including scalability, energy efficiency, flexibility, and support for diverse applications

Martin Eckstein

We discuss the evaluation of the integrals for intermediate-order diagrams in the self-consistent strong-coupling expansion on the Keldysh contour using Tensor Cross Interpolation (TCI). TCI is used to factorize the nested parts of the integrand, allowing the integral to be computed as a recursion of convolution integrals, which are efficiently evaluated usi

Seifeddine Achour

Global minimization is a fundamental challenge in optimization, especially in machine learning, where finding the global minimum of a function directly impacts model performance and convergence. This article introduces a novel optimization method that we called Super Gradient Descent, designed specifically for one-dimensional functions, guaranteeing converge

Yinglun Xu, Zhiwei Wang, Gagandeep Singh

Thompson sampling is one of the most popular learning algorithms for online sequential decision-making problems and has rich real-world applications. However, current Thompson sampling algorithms are limited by the assumption that the rewards received are uncorrupted, which may not be true in real-world applications where adversarial reward poisoning exists.

Parthasarathy Suryanarayanan, Yunguang Qiu, Shreyans Sethi, Diwakar Mahajan

Quality molecular representations are key to foundation model development in bio-medical research. Previous efforts have typically focused on a single representation or molecular view, which may have strengths or weaknesses on a given task. We develop Multi-view Molecular Embedding with Late Fusion (MMELON), an approach that integrates graph, image and text

Anna Jové

In this paper, we develop Pesin theory for the boundary map of some Fatou components of transcendental functions, under certain hyptothesis on the singular values and the Lyapunov exponent. That is, we prove that generic inverse branches for such maps are well-defined and conformal. In particular, we study in depth the Lyapunov exponents with respect to harm

Naga VS Raviteja Chappa, Page Daniel Dobbs, Bhiksha Raj, Khoa Luu

The proliferation of tobacco-related content on social media platforms poses significant challenges for public health monitoring and intervention. This paper introduces a novel multi-modal deep learning framework named Flow-Attention Adaptive Semantic Hierarchical Fusion (FLAASH) designed to analyze tobacco-related video content comprehensively. FLAASH addre

Xiangyu Zeng, Kunchang Li, Chenting Wang, Xinhao Li

Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in short video understanding. However, understanding long-form videos still remains challenging for MLLMs. This paper proposes TimeSuite, a collection of new designs to adapt the existing short-form video MLLMs for long video understanding, including a simple yet efficient fram

Biman Barua, M. Shamim Kaiser

This paper investigates the inclusion of microservices architecture in the development of scalable and reliable airline reservation systems. Most of the traditional reservation systems are very rigid and centralized which makes them prone to bottlenecks and a single point of failure. As such, systems do not meet the requirements of modern airlines which are

M. A. Arroyo-Ureña, J. Lorenzo Díaz-Cruz, E. A. Herrera-Chacón, T. A. Valencia-Pérez

We present a study on the possibility of observing a hypothetical particle known as the Flavon $H_F$, which is predicted in an extension of the standard model that includes the so-called Froggatt-Nielsen mechanism. The proposed decay channel is through a $b\bar{b}h$ final state, where the Higgs boson $(h)$ decays to a pair of photons or a pair of $b$ quarks

Shijun Chu, Ashley P. Willis, Elena Marensi

It has been observed that flattening the mean velocity profile of pipe flow by body force can laminarise turbulence, a promising means to reduce frictional drag substantially. To explore whether there is a more efficient body force to eliminate turbulence, we consider time-independent active body forces with varying spatial dependencies. Results confirm that

Lorenzo Pizzuti, Federico Fantoccoli, Valeria Broccolato, Andrea Biviano

Refracted Gravity (RG) is a a classical theory of gravity where a gravitational permittivity $ a monotonically-increasing function of the local density rho , is introduced in the Poisson equation to mimic the effect of dark matter at astrophysical scales. We use high precision spectroscopic data of two massive galaxy clusters, MACS J1206.2-0847 at redshift z

Kaixian Qu, Jie Tan, Tingnan Zhang, Fei Xia

Navigating efficiently to an object in an unexplored environment is a critical skill for general-purpose intelligent robots. Recent approaches to this object goal navigation problem have embraced a modular strategy, integrating classical exploration algorithms-notably frontier exploration-with a learned semantic mapping/exploration module. This paper introdu

Erkan Bayram, Melih Bastopcu, Mohamed-Ali Belabbas, Tamer Başar

We consider an information update system on a gossip network, where a source node encodes information into $n$ total keys such that any subset of at least $k+1$ keys can fully reconstruct the original information. This encoding process follows the principles of a $k$-out-of-$n$ threshold system. The encoded updates are then disseminated across the network th

Zepei Xing, Santiago Torres, Ylva Götberg, Alessandro A. Trani

Although planets have been found orbiting binary systems, whether they can survive binary interactions is debated. While the tightest-orbit binaries should host the most dynamically stable and long-lived circumbinary planetary systems, they are also the systems that are expected to experience mass transfer, common envelope evolution, or stellar mergers. In t

Yifei Zhang, Hao Zhu, Aiwei Liu, Han Yu

Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. However, the enormous size of LLMs poses significant challenges in terms of computational complexity and resource requirements. Low-Rank Adaptation (LoRA) has emerged as a promising solution. However, there exists a gap between the pra

Georgios Papagiannis, Edward Johns

Data collection in imitation learning often requires significant, laborious human supervision, such as numerous demonstrations, and/or frequent environment resets for methods that incorporate reinforcement learning. In this work, we propose an alternative approach, MILES: a fully autonomous, self-supervised data collection paradigm, and we show that this ena

Clemencia Siro, Yifei Yuan, Mohammad Aliannejadi, Maarten de Rijke

Generating diverse and effective clarifying questions is crucial for improving query understanding and retrieval performance in open-domain conversational search (CS) systems. We propose AGENT-CQ (Automatic GENeration, and evaluaTion of Clarifying Questions), an end-to-end LLM-based framework addressing the challenges of scalability and adaptability faced by

Bowen Huang, Haibo Yuan, Shuai Xu, Kai Xiao

We apply the stellar locus method to synthetic $(BP-RP)_{XPSP}$ and $(BP-G)_{XPSP}$ colors derived from corrected Gaia BP/RP (XP) spectra to obtain precise estimates of metallicity for about 100 million stars in the Milky Way (34 million giants in the color range $0.6 < (BP-RP)_0 < 1.75$ and 65 million dwarfs in the color range $0.2 < (BP-RP)_0 < 1.5$). The

Anna Abbatiello, Donatella Donatelli

We study free boundary compressible viscous models that may include nonlinear viscosities. These are compressible/incompressible Navier-Stokes type systems for a non-Newtonian stress tensor. They describe the motion of a possibly non-Newtonian fluid in free flow and in congested regions. In the congested regions it appears the pressure that is the Lagrange m

Amit Das, Tanmay Shukla, Naofumi Tomita, Ryland Richards

Grading inflammatory bowel disease (IBD) activity using standardized histopathological scoring systems remains challenging due to resource constraints and inter-observer variability. In this study, we developed a deep learning model to classify activity grades in hematoxylin and eosin-stained whole slide images (WSIs) from patients with IBD, offering a robus

Benjamin Anderson-Sackaney, Tim de Laat, Ebrahim Samei, Matthew Wiersma

Let $G$ be a countable group and $\mu$ a probability measure on $G$. We build a new framework to compute asymptotic quantities associated with the $\mu$-random walk on $G$, using methods from harmonic analysis on groups and Banach space theory, most notably complex interpolation. It is shown that under mild conditions, the Lyapunov exponent of the $\mu$-rand

Jianhao He, Chengchang Liu, Xutong Liu, Lvzhou Li

We explore whether quantum advantages can be found for the zeroth-order feedback online exp-concave optimization problem, which is also known as bandit exp-concave optimization with multi-point feedback. We present quantum online quasi-Newton methods to tackle the problem and show that there exists quantum advantages for such problems. Our method approximate

Abhay Kumar, Vigneshwaran Shankaran, Rajesh Sharma

In the recent years online social media platforms has been flooded with hateful remarks such as racism, sexism, homophobia etc. As a result, there have been many measures taken by various social media platforms to mitigate the spread of hate-speech over the internet. One particular concept within the domain of hate speech is instigating hate, which involves

Elyes Boughattas

Yanchevski\u{i} had asked whether conic bundle surfaces over $\mathbf{P}^1_k$ are unirational when $k$ is a finite field. We give a partial answer to his question by showing that for quasi-finite fields $k$ (e.g. finite fields) a regular conic bundle $X$ over $\mathbf{P}^1_k$ is unirational if all non-split fibres lie over rational points. For large finite f

Jesús Aranda, Juan Francisco Díaz, David Gaona, Frank Valencia

We generalize the classic multi-agent DeGroot model for opinion dynamics to incorporate the Spiral of Silence theory from political science. This theory states that individuals may withhold their opinions when they perceive them to be in the minority. As in the DeGroot model, a community of agents is represented as a weighted directed graph whose edges indic

Hun Jang, Joonbum Bae, Kevin Haninger

Soft robotic fingers can improve adaptability in grasping and manipulation, compensating for geometric variation in object or environmental contact, but today lack force capacity and fine dexterity. Integrated tactile sensors can provide grasp and task information which can improve dexterity,but should ideally not require object-specific training. The total

Tom Banks

We introduce a hierarchical system of approximations for summing both conventional perturbation theory and large N vector expansions of models in quantum field theory and condensed matter physics. Each stage of the hierarchy consists of a closed set of nonlinear equations for one particle irreducible correlation functions with no more than K points and captu

Awa Diop, Caroline Sirois, Jason R. Guertin, Mireille E. Schnitzer

The R package trajmsm provides functions designed to simplify the estimation of the parameters of a model combining latent class growth analysis (LCGA), a trajectory analysis technique, and marginal structural models (MSMs) called LCGA-MSM. LCGA summarizes similar patterns of change over time into a few distinct categories called trajectory groups, which are

Pablo García-Sánchez, Alberto Tonda, Antonio J. Fernández-Leiva, Carlos Cotta

Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to develop age

Chao Chen, Yu-Shen Liu, Zhizhong Han

It is important to estimate an accurate signed distance function (SDF) from a point cloud in many computer vision applications. The latest methods learn neural SDFs using either a data-driven based or an overfitting-based strategy. However, these two kinds of methods are with either poor generalization or slow convergence, which limits their capability under

Mehdi Naimi, Mohammed Benharrat, Faouzi Hireche

This paper presents a study of the generalized Davis-Wielandt radius of Hilbert space operators. New lower bounds for the generalized Davis-Wielandt radius and numerical radius are provided. An alternative of the triangular inequality for operators is also derived.

Yang Bai, Ting-Kuo Chen

The determination of whether the ground state of baryon matter in Quantum Chromodynamics (QCD) is the ordinary nucleus or a quark matter state remains a long-standing question in physics. A critical parameter in this investigation is the bag parameter $B$, which quantifies the QCD vacuum energy and can be computed using nonperturbative methods such as Lattic

Cameron R D Bunney, Robert B Mann

We present a comprehensive study of the C-metric in $2+1$ dimensions, placing it within a shell of stress energy and matching it to an exterior vacuum AdS metric. The $2+1$ C-metric is not circularly symmetric and hence neither are the constructed shells, which instead take on a cuspoidal or teardrop shape. We interpret the stress energy of the shells as a p

Briony Eldridge

We give a homotopy equivalence for the loop space of the moment-angle complex associated with a simplicial complex formed by the polyhedral join operation, and give necessary conditions for this loop space to be a finite type product of spheres and loops on spheres.

Ethan Harvey, Mikhail Petrov, Michael C. Hughes

A number of popular transfer learning methods rely on grid search to select regularization hyperparameters that control over-fitting. This grid search requirement has several key disadvantages: the search is computationally expensive, requires carving out a validation set that reduces the size of available data for model training, and requires practitioners

Maurice Genevieva Almeida, Tarkeshwar Singh

Let G=(V,E) be a graph of order n without isolated vertices. A bijection f:V -- {1,2,...n} is called a local distance antimagic labeling if the weights of any two adjacent vertices are not equal, where the weight of a vertex is defined to be the sum of labels of adjacent vertices. The local distance antimagic chromatic number is defined to be the minimum num

Michael Detzel, Gabriel Nobis, Jackie Ma, Wojciech Samek

We incorporate prior graph topology information into a Neural Controlled Differential Equation (NCDE) to predict the future states of a dynamical system defined on a graph. The informed NCDE infers the future dynamics at the vertices of simulated advection data on graph edges with a known causal graph, observed only at vertices during training. We investigat

Z. G. Xu, C. M. S Cohen, R. A. Leske, G. D. Muro

In this study, we employ the combined charged particle measurements from Integrated Science Investigation of the Sun (\ISOIS) onboard the Parker Solar Probe (PSP) and Energetic Particle Detector (EPD) onboard the Solar Orbiter (SolO) to study the composition variation of the solar energetic particle (SEP) event occurring on May 16, 2023. During the event, So

Vitaly Fedoseev, Hanzhen Lin, Yu-Kun Lu, Yoo Kyung Lee

We study light scattering of single atoms in free space and discuss the results in terms of atom-photon entanglement and which-way information. Using ultracold atoms released from an optical lattice, we realize a Gedanken experiment which interferes single photons scattering off of Heisenberg uncertainty-limited wavepackets. We unify the free-space and trapp

Corentin Lanore, Federico Grasselli, Xavier Valcarce, Jean-Daniel Bancal

Nonlocal quantum realizations, certified by the violation of a Bell inequality, are core resources for device-independent quantum information processing. Although proof-of-principle experiments demonstrating device-independent quantum information processing have already been reported, identifying physical platforms that are realistically closer to practical,

Thomas Y. He, C. S. Huang, H. X. Li, X. Zhang

Recently, Andrews and Newman studied the minimal excludant of a partition, which is defined as the smallest positive integer that is not a part of a partition. In this article, we consider the minimal excludant size of an overpartition, which is an overpartition analogue of the minimal excludant of a partition. We define three types of overpartition related

Jia Liu, Long Cheng, Mingyue Zhang, Junkun Zha

In two-dimensional (2D) limit, the quantum fluctuation is significantly enhanced which could induce a quantum phase transition. Investigating the quantum criticality is an effective approach to elucidate the underlying physics of 2D superconductivity. Here we report the observation of different universality classes of quantum criticality at the superconducti

Sajjad Karimi, Shirin Karimi, Amit J. Shah, Gari D. Clifford

Cardiovascular diseases are best diagnosed using multiple modalities that assess both the heart's electrical and mechanical functions. While effective, imaging techniques like echocardiography and nuclear imaging are costly and not widely accessible. More affordable technologies, such as simultaneous electrocardiography (ECG) and phonocardiography (PCG), may

Piero Deidda, Martin Burger, Mario Putti, Francesco Tudisco

We analyze various formulations of the $\infty$-Laplacian eigenvalue problem on graphs, comparing their properties and highlighting their respective advantages and limitations. First, we investigate the graph $\infty$-eigenpairs arising as limits of $p$-Laplacian eigenpairs, extending key results from the continuous setting to the discrete domain. We prove t

Hongjia Wu, Hui Zeng, Zehui Xiong, Jiawen Kang

Timely updating of Internet of Things data is crucial for achieving immersion in vehicular metaverse services. However, challenges such as latency caused by massive data transmissions, privacy risks associated with user data, and computational burdens on metaverse service providers (MSPs) hinder the continuous collection of high-quality data. To address thes

Isaac M. Craig, B. Junsuh Kim, David T. Limmer, D. Kwabena Bediako

Van der Waals hosts intercalated with transition metal (TM) ions exhibit a range of magnetic properties strongly influenced by the structural order of the intercalants. However, predictive computational models for the intercalant ordering phase diagram are lacking, complicating experimental pursuits to target key structural phases. Here we use Density Functi

Yvonne Geyer, Jiachen Guo, Ricardo Monteiro, Lecheng Ren

We find a direct map that determines moduli-space integrands for one-loop superstring amplitudes in terms of field-theory loop integrands in the BCJ form. The latter can be computed using efficient unitarity methods, so our map provides an alternative to worldsheet CFT techniques. This construction is a one-loop higher-point analogue of a recent conjecture f

Hamad El Kahza, Jing-Mei Qiu, Luis Chacon, William Taitano

We consider the adaptive-rank integration of {2D and 3D} time-dependent advection-diffusion partial differential equations (PDEs) with variable coefficients. We employ a standard finite-difference method for spatial discretization coupled with diagonally implicit Runge-Kutta temporal schemes. The discrete equation is a generalized Sylvester equation (GSE), w

Paulina Quintanilla, Daniel Navia, Stephen Neethling, Pablo Brito-Parada

We present the implementation of a novel economic model predictive control (E-MPC) strategy for froth flotation, the largest tonnage mineral separation process. A previously calibrated and validated dynamic model incorporating froth physics was used, which overcomes the limitations of previous simplified models reported in the literature. The E-MPC's optimal

Gabriel Arslan Waltersson, Yiannis Karayiannidis

Dexterous in-hand manipulation offers significant potential to enhance robotic manipulator capabilities. This paper presents a sensori-motor architecture for in-hand slip-aware control, being embodied in a sensorized gripper. The gripper in our architecture features rapid closed-loop, low-level force control, and is equipped with sensors capable of independe

Mary Crone Odekon, Trevor W. Viscardi, Jake Rabinowitz, Brandon Young

We present a strategy for estimating the mass per unit length along supercluster-scale filaments that are oriented across the sky, based on mock redshift surveys of 264 filaments from the Millennium simulation. In our fiducial scenario, we place each simulated filament at a distance of 300 Mpc, perpendicular to the line of sight, and calculate the redshift d

F. Naderi, A. Rezaei-Aghdam

We study the propagation of massless fermionic fields, implementing a family of special functions: Heun functions, in solving the wave equation in three three-dimensional backgrounds, including the BTZ black hole in string theory and Lifshitz black hole solutions in conformal gravity and Hu-Sawicki $F(R)$ theory. The main properties of the selected black hol

Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu

3D Gaussian Splatting (3DGS) has shown convincing performance in rendering speed and fidelity, yet the generation of Gaussian Splatting remains a challenge due to its discreteness and unstructured nature. In this work, we propose DiffGS, a general Gaussian generator based on latent diffusion models. DiffGS is a powerful and efficient 3D generative model whic

Huaxiaoyue Wang, Nathaniel Chin, Gonzalo Gonzalez-Pumariega, Xiangwan Sun

Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances. We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for placement collide with physical limitations. The robot must infer user preferences based on a small set of demonstrations,

Jens Knappe

The year 2022 marks a watershed in technology, and arguably in human history, with the release of powerful generative AIs capable of convincingly performing creative tasks. With the help of these systems, anyone can create something that would previously have been considered a remarkable work of art. In human-AI collaboration, the computer seems to have beco

Fengbin Zhu, Ziyang Liu, Xiang Yao Ng, Haohui Wu

Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated. Existing benchmarks either contain limited fine-grained evaluation samples that are mixed with other data, or are confined to object-level assessments in natural

Maria M. Popova, Sergei N. Yudin, Alexei N. Grum-Grzhimailo, Elena V. Gryzlova

The RABBITT setup is theoretically studied for various combinations of XUV and IR field components polarization: 'linear+linear', `linear+circular' with crossed propagation directions, and `circular+circular' with parallel propagation directions. The general properties of photoelectron angular distributions and their responses to the variation of the IR puls

Honey Boban, Mohammed Qahosh, Xiao Hou, Tomasz Sobol

Recent years have witnessed a steady progress towards blending 2D quantum materials into technology, with future applications often rooted in the electronic structure. Since crossings and inversions of electronic bands with different orbital characters determine intrinsic quantum transport properties, knowledge of the orbital character is essential. Here, we

Nicola Pedreschi, Renaud Lambiotte, Alexandre Bovet

Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can be estimated. In this work, we extend the recently developed

Gábor Czédli

Since Henrik Strietz's 1975 paper proving that the lattice Part($n$) of all partitions of an $n$-element finite set is four-generated, more than half a dozen papers have been devoted to four-element generating sets of this lattice. We prove that each element of Part($n$) with height one or two (in particular, each atom) belongs to a four-element generating s

Annika Lang, Björn Müller

As an extension of isotropic Gaussian random fields and Q-Wiener processes on d-dimensional spheres, isotropic Q-fractional Brownian motion is introduced and sample H\"older regularity in space-time is shown depending on the regularity of the spatial covariance operator Q and the Hurst parameter H. The processes are approximated by a spectral method in space

Amir Algom, Michael Hochman, Meng Wu

For self-similar sets $X,Y\subseteq \mathbb{R}$, we obtain new results towards the affine embeddings conjecture of Feng-Huang-Rao (2014), and the equivalent weak intersections conjecture. We show that the conjecture holds when the defining maps of $X,Y$ have algebraic contraction ratios, and also for arbitrary $Y$ when the maps defining $X$ have algebraic-co

P B Jones

A recent phenomenological study of radio emission from normal and millisecond pulsars by Karastergiou et al has lead these authors to state that they are unable to exclude a common physics process as the source although the rotation periods and magnetic fields of these two classes are very different. This has bearing on the nature of that source and it is th

Vivek Singh, Shikha Chaganti, Matthias Siebert, Sowmya Rajesh

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards indi

Sathvik Sanagala, Kaushik Bhattacharya

Photomechanically active materials undergo reversible deformation on illumination, making them ideal for remote, tether-free actuation. Much of the work on these materials has focused on one-dimensional structures, such as strips. In this paper, we explore photomechanically active two-dimensional structures such as sheets and shells. When illuminated, such s

El Mahdi Chayti, Nikita Doikov, Martin Jaggi

We study stochastic second-order methods for solving general non-convex optimization problems. We propose using a special version of momentum to stabilize the stochastic gradient and Hessian estimates in Newton's method. We show that momentum provably improves the variance of stochastic estimates and allows the method to converge for any noise level. Using t

Nicolás Nieto, Simon B. Eickhoff, Christian Jung, Martin Reuter

Machine learning (ML) models benefit from large datasets. Collecting data in biomedical domains is costly and challenging, hence, combining datasets has become a common practice. However, datasets obtained under different conditions could present undesired site-specific variability. Data harmonization methods aim to remove site-specific variance while retain

Pranav Gupta, Pratham Gohil, Sridhar S

The accurate prediction of danger levels in video content is critical for enhancing safety and security systems, particularly in environments where quick and reliable assessments are essential. In this study, we perform a comparative analysis of various machine learning and deep learning models to predict danger ratings in a custom dataset of 100 videos, eac

Filippo Maria Balli, Alex Edison, Oliver Schlotterer

Loop amplitudes in string theories reduce to those of gauge theories and (super)gravity in their worldline description as the inverse string tension $\alpha'$ tends to zero. The appearance of reducible diagrams in these $\alpha' \rightarrow 0$ limits is determined through so-called pinching rules in the worldline literature. In this work, we extend these pin

Michael Hochman

We show that the Feng-Xiong lower bound of $1/2$ for the box dimension of $\alpha\beta$-sets is tight. We also study how much of an $\alpha\beta$-orbit ``carries the dimension'': deleting an arbitararily small positive density set of times can cause the box dimension to drop to zero, but the Assouad dimension cannot drop below $1/4$.

Liu Yunhao, Ding Hong, Zhang Ziming, Wang Huixin

Autonomous driving technology has seen significant advancements, but existing models often fail to fully capture the complexity of multi-agent environments, where interactions between dynamic agents are critical. To address this, we propose the Planning-Integrated Forecasting Model (PIFM), a novel framework inspired by neural mechanisms governing decision-ma

Sam Hiken, Nicole Wein

We study the token swapping problem, in which we are given a graph with an initial assignment of one distinct token to each vertex, and a final desired assignment (again with one token per vertex). The goal is to find the minimum length sequence of swaps of adjacent tokens required to get from the initial to final assignment. The token swapping problem is kn

Riccardo Rende, Federica Gerace, Alessandro Laio, Sebastian Goldt

The remarkable capability of over-parameterised neural networks to generalise effectively has been explained by invoking a ``simplicity bias'': neural networks prevent overfitting by initially learning simple classifiers before progressing to more complex, non-linear functions. While simplicity biases have been described theoretically and experimentally in f

Md Shafayat Hossain, Zahir Muhammad, Rajibul Islam, Zi-Jia Cheng

Nematic quantum fluids appear in strongly interacting systems and break the rotational symmetry of the crystallographic lattice. In metals, this is connected to a well-known instability of the Fermi liquid-the Pomeranchuk instability. Using scanning tunneling microscopy, we identified this instability in a highly unusual setting: on the surface of an element

Shenghao Fu, Junkai Yan, Qize Yang, Xihan Wei

Recent vision foundation models can extract universal representations and show impressive abilities in various tasks. However, their application on object detection is largely overlooked, especially without fine-tuning them. In this work, we show that frozen foundation models can be a versatile feature enhancer, even though they are not pre-trained for objec