November 2024 arXiv papers — page 140

Jiabei Cheng, Jiachen Li, Kaiyuan Yang, Hongbin Shen

The remarkable success of foundation models has sparked growing interest in their application to single-cell biology. Models like Geneformer and scGPT promise to serve as versatile tools in this specialized field. However, representing a cell as a sequence of genes remains an open question since the order of genes is interchangeable. Injecting the gene netwo

Easton J. Honaker, John E. Gizis

Brown dwarfs form the key, yet poorly understood, link between stellar and planetary astrophysics. These objects offer unique tests of Galactic structure, but observational limitations have inhibited their large-scale analysis to date. Major upcoming sky surveys will reveal unprecedented numbers of brown dwarfs, among even greater numbers of stellar objects,

Congyuan Duan, Wanteng Ma, Jiashuo Jiang, Dong Xia

This paper investigates regret minimization, statistical inference, and their interplay in high-dimensional online decision-making based on the sparse linear context bandit model. We integrate the $\varepsilon$-greedy bandit algorithm for decision-making with a hard thresholding algorithm for estimating sparse bandit parameters and introduce an inference fra

Hongxing Wang, Qiuli Ling, Tianhe Jiang, Shuangzhe Liu

The dual Drazin inverse is an important dual generalized inverse. In this paper, to extend it we introduce the weak dual Drazin inverse which is unique and exists for any square dual matrix. When the dual Drazin inverse exists, it coincides with the weak dual Drazin inverse. In addition, we introduce the weak dual group inverse and apply it to studying one t

Osvaldo M Velarde, Lucas C Parra

Conventional computer vision models rely on very deep, feedforward networks processing whole images and trained offline with extensive labeled data. In contrast, biological vision relies on comparatively shallow, recurrent networks that analyze sequences of fixated image patches, learning continuously in real-time without explicit supervision. This work intr

Yeguang Chi, Qionghua, Chu, Wenyan Hao

We empirically examine the intraday return- and volatility-forecasting power of on-chain flow data for Bitcoin(BTC), Ethereum(ETH), and Tether(USDT). We find ETH net inflows to strongly predict ETH returns and volatility in the 2017-2023 period. Our intraday frequencies are 1-6 hours. We find that differing significantly from forecasting patterns for BTC, ET

Shiyu Duan, Ziyi Wang, Shixiao Wang, Mengmeng Chen

In an era where user interaction with technology is ubiquitous, the importance of user interface (UI) design cannot be overstated. A well-designed UI not only enhances usability but also fosters more natural, intuitive, and emotionally engaging experiences, making technology more accessible and impactful in everyday life. This research addresses this growing

Rati Ludhani

The Nullstellensatz, proved by Hilbert in 1893, is a classical result that holds when the base field is algebraically closed. When the base field is finite, a version of Hilbert's Nullstellensatz is given by Terjanian in 1966. Laksov in 1987 generalized Hilbert's Nullstellensatz to a $K$-Nullstellensatz when the base field $K$ is not necessarily algebraicall

Brandon R. Feng, Reetam Majumder, Brian J. Reich, Mohamed A. Abba

Gaussian processes (GPs) are a ubiquitous tool for geostatistical modeling with high levels of flexibility and interpretability, and the ability to make predictions at unseen spatial locations through a process called Kriging. Estimation of Kriging weights relies on the inversion of the process' covariance matrix, creating a computational bottleneck for larg

Kento Kawaharazuka, Yuya Koga, Manabu Nishiura, Yusuke Omura

While musculoskeletal humanoids have the advantages of various biomimetic structures, it is difficult to accurately control the body, which is challenging to model. Although various learning-based control methods have been developed so far, they cannot completely absorb model errors, and recognition errors are also bound to occur. In this paper, we describe

Kento Kawaharazuka, Akihiro Miki, Yasunori Toshimitsu, Kei Okada

One of the important advantages of musculoskeletal humanoids is that the muscle arrangement can be easily changed and the number of muscles can be increased according to the situation. In this study, we describe an overall system of muscle addition for musculoskeletal humanoids and the adaptive body schema learning while taking into account the additional mu

Zhuo Xu, Zhengping Yuan, Xue Zhang, Zhengde Xu

Replacing the ferromagnet with ferrimagnet (FiM) in the magnetic tunnel junction (MTJ) allows faster magnetization switching in picoseconds. The operation of a memory cell that consists of the MTJ and a transistor requires reversable magnetization switching. When a constant voltage is applied, we find that the spin-transfer torque can only switch the FiM-MTJ

Yuya Koga, Kento Kawaharazuka, Yasunori Toshimitsu, Manabu Nishiura

We proposed a method for learning the actual body image of a musculoskeletal humanoid for posture generation and object manipulation using inverse kinematics with redundancy in the shoulder complex. The effectiveness of this method was confirmed by realizing automobile steering wheel operation. The shoulder complex has a scapula that glides over the rib cage

Jari van Steen, Daan Stokbroekx, Nathan van de Wouw, Alessandro Saccon

Impact-aware robotic manipulation benefits from an accurate map from ante-impact to post-impact velocity signals to support, e.g., motion planning and control. This work proposes an approach to generate and experimentally validate such impact maps from simulations with a physics engine, allowing to model impact scenarios of arbitrarily large complexity. This

Junichi Akita

In this paper, we describe the algorithm to measure the stroke and the temperature of solenoid using PWM driving current at two points based on the electric characteristics of the solenoid with CNN, without mechanical attachments. We describe the evaluation experimental results of the stroke and the temperature prediction. We also describe the preliminary ex

Chenqing Hua, Jiarui Lu, Yong Liu, Odin Zhang

The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interaction prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and in

Shuang Chen, Haozheng Zhang, Amir Atapour-Abarghouei, Hubert P. H. Shum

Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. \revise{Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhibit similar patterns to the semanticly consistent regions}. This requires a model with a strong capaci

Sinan Pehlivanoglu, Malte Schwarzkopf

In this paper, we introduce Harpocrates, a compiler plugin and a framework pair for Scala that binds the privacy policies to the data during data creation in form of oblivious membranes. Harpocrates eliminates raw data for a policy protected type from the application, ensuring it can only exist in protected form and centralizes the policy checking to the pol

John Chen, Alexandros Lotsos, Lexie Zhao, Grace Wang

Inductive qualitative methods have been a mainstay of education research for decades, yet it takes much time and effort to conduct rigorously. Recent advances in artificial intelligence, particularly with generative AI (GAI), have led to initial success in generating inductive coding results. Like human coders, GAI tools rely on instructions to work, and how

M. C. Araújo, A. C. A. Ramos, J. Furtado

In this paper, we investigate the influence of the geometry in the electronic states of a quantum ripple surface. We have considered an electron governed by the spinless stationary Schrödinger equation constrained to move on the ripple surface due to a confining potential from which the Da Costa potential emerges. We investigate the role played by the geomet

Faramarz Rahmani

We present a novel perspective on gravity-induced wave function reduction using Bohmian trajectories. This study examines the quantum motion of both point particles and objects, identifying critical parameters for the transition from quantum to classical regimes. By analyzing the system's dynamics, we define the reduction time of the wave function throug

Taha Razzaq, Asim Iqbal

Medical brain imaging relies heavily on image registration to accurately curate structural boundaries of brain features for various healthcare applications. Deep learning models have shown remarkable performance in image registration in recent years. Still, they often struggle to handle the diversity of 3D brain volumes, challenged by their structural and co

Joshua Richland, Alexander Strang

We analyze the correlation between randomly chosen edge weights on neighboring edges in a directed graph. This shared-endpoint correlation controls the expected organization of randomly drawn edge flows when the flow on each edge is conditionally independent of the flows on other edges given its endpoints. To model different relationships between endpoints a

Jianqi Liu

We investigate a one-point restriction of conformal blocks on $(\mathbb{P}^1,\infty,1,0)$ associated with modules over a vertex operator algebra. By restricting the module attached to the point $\infty$ to its bottom degree, we obtain a new formula for computing fusion rules in terms of a left $A(V)$-module $M^1\odot M^2$ over the Zhu algebra $A(V)$. As a co

Mira Frick, Ryota Iijima, Yuhta Ishii

A multi-product monopolist faces a buyer who is privately informed about his valuations for the goods. As is well-known, optimal mechanisms are in general complicated, while simple mechanisms -- such as pure bundling or separate sales -- can be far from optimal and do not admit clear-cut comparisons. We show that this changes if the monopolist has sufficient

Jeongjin Park, Nicole Yang, Nisha Chandramoorthy

Conventional notions of generalization often fail to describe the ability of learned models to capture meaningful information from dynamical data. A neural network that learns complex dynamics with a small test error may still fail to reproduce its \emph{physical} behavior, including associated statistical moments and Lyapunov exponents. To address this gap,

D. Ray, N. Vassh, B. Liu, A. A. Valverde

Studies aiming to determine the astrophysical origins of nuclei produced by the rapid neutron capture process ($r$ process) rely on nuclear properties as inputs for simulations. The solar abundances can be used as a benchmark for such calculations, with the $r$-process rare-earth peak (REP) around mass number ($A$) 164 being of special interest due to its pr

Matteo Nerini, Gabriele Gradoni, Bruno Clerckx

Reconfigurable intelligent surface (RIS) enables the control of wireless channels to improve coverage. To further extend coverage, multi-RIS aided systems have been explored, where multiple RISs steer the signal via a multi-hop path. However, deriving a physics-compliant channel model for multi-RIS aided systems is still an open problem. In this study, we fi

Ezgi Demircan-Tureyen, Felix Lucka, Tristan van Leeuwen

Recent works demonstrate the effectiveness of diffusion models as unsupervised solvers for inverse imaging problems. Sparse-view computed tomography (CT) has greatly benefited from these advancements, achieving improved generalization without reliance on measurement parameters. However, this comes at the cost of potential hallucinations, especially when hand

Zitong Lan, Chenhao Zheng, Zhiwei Zheng, Mingmin Zhao

Realistic audio synthesis that captures accurate acoustic phenomena is essential for creating immersive experiences in virtual and augmented reality. Synthesizing the sound received at any position relies on the estimation of impulse response (IR), which characterizes how sound propagates in one scene along different paths before arriving at the listener's p

Chenran Li, Aolin Xu, Enna Sachdeva, Teruhisa Misu

The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning technologies, mainly the forward collision warning and unsafe lane change warning, can reduce the risk of collision caused by human errors. However, the current design methods have sever

Yusuf H. Erdogan, Masudul Haque

We consider non-Hermitian tight-binding one-dimensional Hamiltonians and show that imposing a certain symmetry causes all eigenvalues to pair up and the corresponding eigenstates to coalesce in pairs. This Pairwise Coalescence (PC) is an enhanced version of an exceptional point -- the complete spectrum pairs up, not just one pair of eigenstates. The symmetry

James Scully, Carter Hinsley, David Bloom, Hil G. E. Meijer

This paper investigates the origin and onset of chaos in a mathematical model of an individual neuron, arising from the intricate interaction between 3D fast and 2D slow dynamics governing its intrinsic currents. Central to the chaotic dynamics are multiple homoclinic connections and bifurcations of saddle equilibria and periodic orbits. This neural model re

Gabriel Gonzalo Guzman Ramos, Pedro Jesus Guzman Ramos

TiniScript is an intermediate programming language designed for educational robotics, aligned with STEM principles to foster integrative learning experiences. With its minimalist single-line syntax, such as F(2, 80) , TiniScript simplifies robotic programming, allowing users to bypass complex code uploading processes and enabling realtime direct instruction

Angel D. Cruz

We consider a translation invariant linear equation in four variables with integer coefficients of the form: $ax_1 +bx_2= cy_1+dy_2$. The main result of the paper states that any set on the real line with Fourier dimension greater than 1/2 must contain a nontrivial solution of such an equation.

Nicholas G. Ferraro, Jean L. Turner, Sara C. Beck, Edwin Alexani

We present 22 GHz imaging of regions of massive star formation within the Local Wolf-Rayet Galaxy Sample (LWRGS), a NSF's Karl G. Jansky Very Large Array (VLA) survey of 30 local galaxies showing spectral features of Wolf-Rayet (WR) stars. These spectral features are present in galaxies with young super star clusters (SSCs), and are an indicator of large con

Tito Spadini, Kenji Nose-Filho, Ricardo Suyama

This study focuses on Intelligent Fault Diagnosis (IFD) in rotating machinery utilizing a single microphone and a data-driven methodology, effectively diagnosing 42 classes of fault types and severities. The research leverages sound data from the imbalanced MaFaulDa dataset, aiming to strike a balance between high performance and low resource consumption. Th

Vasilis Chasiotis, Lin Wang, Dimitris Karlis

In the field of big data analytics, the search for efficient subdata selection methods that enable robust statistical inferences with minimal computational resources is of high importance. A procedure prior to subdata selection could perform variable selection, as only a subset of a large number of variables is active. We propose an approach when both the si

Mei Qiu, Lauren Ann Christopher, Stanley Chien, Lingxi Li

Vision Transformers (ViTs) have shown exceptional performance in vehicle re-identification (ReID) tasks. However, non-square aspect ratios of image or video inputs can negatively impact re-identification accuracy. To address this challenge, we propose a novel, human perception driven, and general ViT-based ReID framework that fuses models trained on various

Alice Petrov

The study of differential forms that are closed but not exact reveals important information about the global topology of a manifold, encoded in the de Rham cohomology groups $H^k(M)$, named after Georges de Rham (1903-1990). This expository paper provides an explanation and exploration of de Rham cohomology and its equivalence to singular cohomology. We pres

Xingzhi Guo

With the recent advance of representation learning algorithms on graphs (e.g., DeepWalk/GraphSage) and natural languages (e.g., Word2Vec/BERT) , the state-of-the art models can even achieve human-level performance over many downstream tasks, particularly for the task of node and sentence classification. However, most algorithms focus on large-scale models fo

Hamed Rahimi Nohooji, Holger Voos

This paper presents a hierarchical, performance-based framework for the design optimization of multi-fingered soft grippers. To address the need for systematically defined performance indices, the framework structures the optimization process into three integrated layers: Task Space, Motion Space, and Design Space. In the Task Space, performance indices are

Francisco R. Ortega, Victoria Interrante, Sara Lotemplio, Rachel Masters

The growing demand for accessible therapeutic options has led to the exploration of Virtual Reality (VR) as a platform for forest bathing, which aims to reduce stress and improve cognitive functions. This paper brings together findings from three studies by the authors. The first study compared environments with and without plant life to examine how biomass

Yuriy Biktairov, Leszek Gąsieniec, Wanchote Po Jiamjitrak, Namrata

In Polyamorous Scheduling, we are given an edge-weighted graph and must find a periodic schedule of matchings in this graph which minimizes the maximal weighted waiting time between consecutive occurrences of the same edge. This NP-hard problem generalises Bamboo Garden Trimming and is motivated by the need to find schedules of pairwise meetings in a complex

Ahmed Y. Radwan, Mohammad Shehab, Mohamed-Slim Alouini

Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, often raise privacy concerns and demand significant on-device computational resources. Centralized learning (CL) on the edge provides an energy-efficient alternative but requires collecting raw data, compromising user privacy. While federated learning (FL) e

Peter Markowich, Simone Portaro

We propose a partial differential-integral equation (PDE) framework for deep neural networks (DNNs) and their associated learning problem by taking the continuum limits of both network width and depth. The proposed model captures the complex interactions among hidden nodes, overcoming limitations of traditional discrete and ordinary differential equation (OD

Jamal Shabani, Kaushik Bhattacharya, Blaise Bourdin

We investigate the systematic design of compliant morphing structures composed of materials reacting to an external stimulus. We add a perimeter penalty term to ensure existence of solutions. We propose a phase-field approximation of this sharp interface problem, prove its convergence as the regularization length approaches 0 and present an efficient numeric

Hamed Rahimi Nohooji, Holger Voos

This paper introduces the Progressive Barrier Lyapunov Function (p-BLF) for output- and full-state-constrained nonlinear control systems. Unlike traditional BLF methods, where control effort continuously increases as the state approaches the constraint boundaries, the p-BLF maintains minimal control effort in unconstrained regions and increases it progressiv

Arshia Hemmat, Adam Davies, Tom A. Lamb, Jianhao Yuan

Despite the importance of shape perception in human vision, early neural image classifiers relied less on shape information for object recognition than other (often spurious) features. While recent research suggests that current large Vision-Language Models (VLMs) exhibit more reliance on shape, we find them to still be seriously limited in this regard. To q

Bruno Jacob, Amanda A. Howard, Panos Stinis

Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use multilayer perceptrons (MLPs) as their underlying architecture, recent advancements have explored alternative neural network structures. One such innovation is the Kolmogorov-Arnold N

Chia Xin Liang, Pu Tian, Caitlyn Heqi Yin, Yao Yua

This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, images, video and audio, to enable complex

Georgios Georgalis, Alejandro Becerra, Kenneth Budzinski, Matthew McGurn

The goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification analysis of a complicated physics-based simulation like a 2D slab burner direct numerical simulation (DNS). The UQ framework includes the development of data-driven surrogate models, propagation of parametric uncertaintie

Gopal Hazra, Aline A. Vidotto, Stephen Carolan, Carolina Villarreal D'Angelo

Coronal Mass Ejections (CMEs) erupting from the host star are expected to have effects on the atmospheric erosion processes of the orbiting planets. For planets with a magnetosphere, the embedded magnetic field in the CMEs is thought to be the most important parameter to affect planetary mass loss. In this work, we investigate the effect of different magneti

Leon Kopitar, Nejc Plohl, Mojca Tancer Verboten, Gregor Štiglic

The current system of scholarly publishing is often criticized for being slow, expensive, and not transparent. The rise of open access publishing as part of open science tenets, promoting transparency and collaboration, together with calls for research assesment reforms are the results of these criticisms. The emergence of new open access publishers presents

Fabrice Nonez

The goal of this paper is to introduce a process that generates, given Hilbert space $H$ and symmetric operator $A$, an embedding of $H$ into an $L_2$-space through which $A$ is extended by a multiplication operator. This process will depend on two parameters, the nonstandard sampling and the standard-biased scale. We will use that process to prove diverse v

Sergey Bezuglyi, Artem Dudko, Olena Karpel

We introduce and study dynamical systems and measures on stationary generalized Bratteli diagrams $B$ that are represented as the union of countably many classical Pascal-Bratteli diagrams. We describe all ergodic tail invariant measures on $B$. For every probability tail invariant measure $\nu_p$ on the classical Pascal-Bratteli diagram, we approximate the

Youngjae Kim, Vedasri Godavarthi, Laura Victoria Rolandi, Joseph T. Klamo

We investigate the effect of three-dimensionality on the synchronization characteristics of the wake behind an oscillating circular cylinder at Re = 300. Cylinder oscillations in rotation, transverse translation, and streamwise translation are considered. We utilize phase-reduction analysis, which quantifies the phase-sensitivity function of periodic flows,

V. Pelgrims, M. Unger, I. C. Maris

Bubbles and super-bubbles are ubiquitous in the interstellar medium and influence their local magnetic field. Starting from the assumption that bubbles result from violent explosions that sweep matter away in a thick shell, we derive the analytical equations for the divergence-free, regular magnetic field in the shell. The explosion velocity field is assumed

Mehdi Hennequin, Abdelkrim Zitouni, Khalid Benabdeslem, Haytham Elghazel

The PAC-Bayesian framework has significantly advanced the understanding of statistical learning, particularly for majority voting methods. Despite its successes, its application to multi-view learning -- a setting with multiple complementary data representations -- remains underexplored. In this work, we extend PAC-Bayesian theory to multi-view learning, int

Brian Hsuan-Cheng Liao, Yingjie Xu, Chih-Hong Cheng, Hasan Esen

This paper presents a novel monitoring framework that infers the level of collision risk for autonomous vehicles (AVs) based on their object detection performance. The framework takes two sets of predictions from different algorithms and associates their inconsistencies with the collision risk via fuzzy inference. The first set of predictions is obtained by

G. A. Domínguez-Castro, L. Santos, L. A. Peña Ardila

Impurities immersed in hard-core Bose gases offer exciting opportunities to explore polaron and bipolaron physics. We investigate the ground state properties of a single and a pair of impurities throughout the superfluid and insulating (charge density wave) phases of the bosonic environment. In the superfluid phase, the impurity exhibits polaron-like behavio

Guangming Hu, Ziping Lei, Yanlin Li, Hao Yu

In 1991, Beardon and Stephenson [2] generalized the classical Schwarz-Pick lemma in hyperbolic geometry to the discrete Schwarz-Pick lemma for Andreev circle packings. This paper continues to investigate the discrete Schwarz-Pick lemma for generalized circle packings (including circle, horocycle or hypercycle) in hyperbolic background geometry. Since the dis

Eli Newby, Wenlong Shi, Yang Jiao, Reka Albert

Disordered hyperuniform many-particle systems are recently discovered exotic states of matter, characterized by a complete suppression of normalized infinite-wavelength density fluctuations and lack of conventional long-range order. Here, we begin a program to quantify the structural properties of nonhyperuniform and hyperuniform networks. In particular, lar

Xiaojun Wu, Junxi Liu, Huanyi Su, Zhouchi Lin

As large language models (LLMs) increasingly permeate the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. Existing financial benchmarks often suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation. To address these limitations, we i

Yahya Ladghami, Aatifa Bargach, Amine Bouali, Touafik Ouali

In this paper, we investigate the emergent thermodynamic phenomena arising from spacetime foam and its impact on black hole behavior. Within this framework, we adopt the Barrow model, where the structure of spacetime at small scales is modeled by analogy with the Koch snowflake, implying that black hole surfaces acquire a quasi-fractal structure due to quant

Haim Diamant

We present a phenomenological, model-free theory for the large-distance hydrodynamic response of a viscous fluid hosting colloidal particles. The flow of the host fluid is affected by the presence of the particles, thus reflecting their liquid or solid state. On the liquid side of the glass transition we identify a dynamic length scale $\ell$ beyond which th

Uttamasha Monjoree, Wei Yan

Spatial intelligence is important in Architecture, Construction, Science, Technology, Engineering, and Mathematics (STEM), and Medicine. Understanding three-dimensional (3D) spatial rotations can involve verbal descriptions and visual or interactive examples, illustrating how objects change orientation in 3D space. Recent studies show Artificial Intelligence

Thuan Pham, Xingpeng Li

Power system networks are often modeled as homogeneous graphs, which limits the ability of graph neural network (GNN) to capture individual generator features at the same nodes. By introducing the proposed virtual node-splitting strategy, generator-level attributes like costs, limits, and ramp rates can be fully captured by GNN models, improving GNN's learni

Caio V. P. de Brito, David Wagner, Gabriel S. Denicol, Dirk H. Rischke

In this letter, we demonstrate for the first time that the moment expansion for an ultrarelativistic gas undergoing Bjorken flow diverges. We then show how this series can be resummed using the Borel-Pad\'e method and use this to determine the single-particle distribution function of the gas. Finally, we compare the exact resummed solution of the single-part

Lu Meng, Emmanuel Ortiz-Pacheco, Vadim Baru, Evgeny Epelbaum

Experimentally, the doubly charm tetraquark channel $cc\bar q\bar q$ with $q\!=\!u,d$ features an exotic hadron, $T_{cc}$, with isospin $I\!=\!0$ near the $DD^*$ threshold, while no peak was observed for $I\!=\!1$. We present a lattice QCD study of this channel with $I\!=\!1$, $J^P\!=\!1^+$ and $m_\pi\simeq 280~$MeV. Finite-volume energies calculated across

John W. Moffat

This paper explores the quantum and classical descriptions of gravitational wave detection in interferometers like LIGO. We demonstrate that a graviton scattering and quantum optics model succeeds in explaining the observed arm displacements, while the classical gravitational wave approach and a quantum graviton energy method also successfully predict the co

MD Ragib Shahriyear

Although rapid advancements in Large Language Models (LLMs) are facilitating the integration of artificial intelligence-based applications and services in healthcare, limited research has focused on the systematic evaluation of medical notes for guideline adherence. This paper introduces GuidelineGuard, an agentic framework powered by LLMs that autonomously

Josue Ndeko, Shaba Shaon, Aubrey Beal, Avimanyu Sahoo

This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split Learning (FSL) with Differential Privacy (DP) over edge networks. Our FSL-DP framework leverages both accelerometer and gyroscope data, achieving significant improvements in HAR accuracy. The evaluation includes a detailed comparison be

Mahzabin Tamanna, Mohd Anwar, Joseph D. W. Stephens

Software updates are essential to enhance security, fix bugs, and add better features to the existing software. While some users accept software updates, non-compliance remains a widespread issue. While some users accept software updates, non-compliance remains a widespread issue. End users' systems remain vulnerable to security threats when security updates

Ruben Monten, Richard M. Myers, Konstantinos Roumpedakis

We investigate the locality properties of $T \overline T$-deformed CFTs within perturbation theory. Up to third order in the deformation parameter, we find a Hamiltonian operator which solves the flow equation, reproduces the Zamolodchikov energy spectrum, and is consistent with quasi-locality of the theory. This Hamiltonian includes terms proportional to th

Eduardo Lourenço Fabio de Lima

This work delves into the study of flavor invariants and, in special, invariants capable of detecting CP (Charge-Parity) violation. Through the mathematical tool of the Hilbert series, we systematically enumerate and explore flavor invariants that are unchanged under weak basis transformations. After reviewing the Hilbert series and the flavor invariants of

O'Bryan Cárdenas-Andaur

Given a tied link $L$, the invariant $\langle\langle\cdot\rangle\rangle$ generalizes the Kauffman bracket of classical links. However, the analogues of Kauffman states and their relationship to this invariant are not immediately clear. We address this question by defining the Aicardi-Juyumaya states, and show that the contribution of each AJ-state to $\langl

Kenneth M. Lanzetta, Stefan Gromoll, Michael M. Shara, David Valls-Gabaud

We used the Condor Array Telescope to obtain deep imaging observations through luminance broad-band and He II, [O III], He I, H$\alpha$, [N II], and [S II] narrow-band filters of an extended region of the M81 Group spanning $\approx 8 \times 8$ deg$^2$ on the sky centered near M81 and M82. Here we report aspects of these observations that are specifically re

Roberto A Sussman, Sebastian Najera

We propose a new phenomenological second order gravity theory to be denoted as ''Schouten-Codazzi' Gravity'' (SCG), as it is based on Schouten and Codazzi tensors. The theory is related, but is clearly distinct from Cotton Gravity. By assuming as source the energy momentum of General Relativity, we form a second order system with its geometric sector given b

Chenqing Hua

Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowledge graphs. In this paper, we present an exhaustive review of the latest advancements in graph representation learning and Graph Neural Networks (GNNs). GNNs, tailored to handle g

Charles L. A. Clarke

This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the underlying indexing framework for databases that support retrieval augmented generation, knowledge graphs, entity retrieval, semi-structured

Kenneth M. Lanzetta, Stefan Gromoll, Michael M. Shara, David Valls-Gabaud

We used the Condor Array Telescope to obtain deep imaging observations through the luminance broad-band and He II 468.6 nm, [O III] 500.7 nm, He I 587.5 nm, H$\alpha$, [N II] 658.4 nm, and [S II] 671.6 nm narrow-band filters of an extended region comprising 13 "Condor fields" spanning $\approx 8 \times 8$ deg$^2$ on the sky centered near M81 and M82. Here we

Yuheng Wang

Knowledge representation and reasoning systems represent knowledge as collections of facts and rules. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain e

Tiberiu Burlacu, Mischa Hoogeman, Danny Lathouwers, Zoltán Perkó

Objective: To assess the performance of a probabilistic deep learning based algorithm for predicting inter-fraction anatomical changes in head and neck patients. Approach: A probabilistic daily anatomy model for head and neck patients $(\mathrm{DAM}_{\mathrm{HN}})$ is built on the variational autoencoder architecture. The model approximates the generative jo

Aditya Parashar, Aditya Vikram Singh, Avinash Amballa, Jinlin Lai

Large language models (LLMs) are often equipped with multi-sample decoding strategies. An LLM implicitly defines an arithmetic code book, facilitating efficient and embarrassingly parallelizable \textbf{arithmetic sampling} to produce multiple samples using quasi-random codes. Traditional text generation methods, such as beam search and sampling-based techni

Jaspreet Kaur, Meenu Goyal

In this paper, we describe two novel changes to the Baskakov-Durrmeyer operators that improve their approximation performance. These improvements are especially designed to produce higher rates of convergence, with orders of one or two. This is a major improvement above the linear rate of convergence commonly associated with conventional Baskakov-Durrmeyer o

Aydin Zaboli, Seong Lok Choi, Junho Hong

This study addresses critical challenges of cybersecurity in digital substations by proposing an innovative task-oriented dialogue (ToD) system for anomaly detection (AD) in multicast messages, specifically, generic object oriented substation event (GOOSE) and sampled value (SV) datasets. Leveraging generative artificial intelligence (GenAI) technology, the

Zbigniew Drogosz, Wojciech Florkowski, Mykhailo Hontarenko, Radoslaw Ryblewski

Classical pseudo-gauge transformations are discussed in the context of hydrodynamic models of heavy-ion collisions. A decomposition of the pseudo-gauge transformation into Lorentz-invariant tensors is made, which allows for better interpretation of its physical consequences. For pseudo-gauge transformations connecting two symmetric energy-momentum tensors, w

Yongye Su, Yuqing Wu

The ability of large language models to generate complex texts allows them to be widely integrated into many aspects of life, and their output can quickly fill all network resources. As the impact of LLMs grows, it becomes increasingly important to develop powerful detectors for the generated text. This detector is essential to prevent the potential misuse o

Andrew Beveridge, Kristin Heysse, Rhys O'Higgins, Lola Vescovo

We characterize the extremal structure for the exact mixing time for random walks on trees $T_{n,d}$ of order $n$ with diameter $d$. Given a graph $G=(V,E)$, let $H(v,\pi)$ denote the expected length of an optimal stopping rule from vertex $v$ to the stationary distributon $\pi$. We show that the quantity $\max_{G \in T_{n,d} } T_{\mbox{mix}}(G) = \max_{G \i

Krishna Neupane, Igor Griva

According to The Exchange Act, 1934 unlawful insider trading is the abuse of access to privileged corporate information. While a blurred line between "routine" the "opportunistic" insider trading exists, detection of strategies that insiders mold to maneuver fair market prices to their advantage is an uphill battle for hand-engineered approaches. In the cont

Hjørdis Schlüter

Hybrid inverse problems such as Acousto-Electric Tomography, Current Density Imaging or Magnetic Resonance Electric Impedance Tomography are concerned with reconstructing the electrical conductivity from interior measurements. For a two-dimensional object the measurements correspond to two different functions imposed as the Neumann boundary condition to an e

M. A. Lkabous, Z. Palmowski

In this paper, we analyze some distributions involving the longest and shortest negative excursions of spectrally negative L\'evy processes using the binomial expansion approach. More specifically, we study the distributions of such excursions and related quantities such as the joint distribution of the shortest and longest negative excursion and their diffe

Mingzhao Zhou, Nadine Aburumman

In real life, grasping is one of the fundamental and effective forms of interaction when manipulating objects. This holds true in the physical and virtual world; however, unlike the physical world, virtual reality (VR) is grasped in a complex formulation that includes graphics, physics, and perception. In virtual reality, the user's immersion level depends o

Sepanta Zeighami, Cyrus Shahabi

Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (locating items in an array), cardinality estimation (estimating the number of matching records in a database), and range-sum estimation (estimating aggregate attribute values for q

Adrien Abgrall

We provide a new geometric characterization for the spine of untwisted outer space of a right-angled Artin group, constructed by Charney, Stambaugh, and Vogtmann. We realize the spine as the natural simplicial complex associated with the category of spatial cube complexes, a new class of locally CAT(0) cube complexes with a simple definition in terms of hype

Sepanta Zeighami, Cyrus Shahahbi

Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits. However, when datasets change and data distribution shifts, empirical results also show performance degradation for learned models, possibly to worse than non-learned alternatives. This, together

Jan Dhaene, Rodrigue Kazzi, Emiliano A. Valdez

In this paper, we present axiomatic characterizations of some simple risk-sharing (RS) rules, such as the uniform, the mean-proportional and the covariance-based linear RS rules. These characterizations make it easier to understand the underlying principles when applying these rules. Such principles typically include maintaining some degree of anonymity rega

Scott Freitas, Amir Gharib

Defending against today's increasingly sophisticated and large-scale cyberattacks demands accurate, real-time threat intelligence. Traditional approaches struggle to scale, integrate diverse telemetry, and adapt to a constantly evolving security landscape. We introduce Threat Intelligence Tracking via Adaptive Networks (TITAN), an industry-scale graph mining

Nai-Bo Zhang, Bao-An Li

The density dependence of nuclear symmetry energy $E_{\rm sym}(\rho)$ remains the most uncertain aspect of the equation of state (EOS) of supradense neutron-rich nucleonic matter. Implications of observational crustal fraction of neutron star (NS) moment of inertia $\Delta I/I$ on the $E_{\rm sym}(\rho)$ are examined in the present work, utilizing an isospin