November 2024 arXiv papers — page 77

Francesco Diana, Othmane Marfoq, Chuan Xu, Giovanni Neglia

Federated Learning (FL) enables multiple clients, such as mobile phones and IoT devices, to collaboratively train a global machine learning model while keeping their data localized. However, recent studies have revealed that the training phase of FL is vulnerable to reconstruction attacks, such as attribute inference attacks (AIA), where adversaries exploit

Ziheng Sun, Chris Ding, Jicong Fan

Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering. The goal of unsupervised feature selection is finding a subset of features such that the data points from different clusters are well separated. This paper presents a novel method called K-mea

Yuanbin Man, Ying Huang, Chengming Zhang, Bingzhe Li

The advancements in large language models (LLMs) have propelled the improvement of video understanding tasks by incorporating LLMs with visual models. However, most existing LLM-based models (e.g., VideoLLaMA, VideoChat) are constrained to processing short-duration videos. Recent attempts to understand long-term videos by extracting and compressing visual fe

Noga Klein Elmalem, Rica Gonen, Erel Segal-Halevi

We explore solutions for fairly allocating indivisible items among agents assigned weights representing their entitlements. Our fairness goal is weighted-envy-freeness (WEF), where each agent deems their allocated portion relative to their entitlement at least as favorable as any other's relative to their own. In many cases, achieving WEF necessitates moneta

Callum E. Flowerday, Nitish Bhardwaj, Ryan Thalman, Matthew C. Asplund

Absorption cross-sections for the 5th (6 $\leftarrow$ 0) and 6th (7 $\leftarrow$ 0) OH overtones for gas-phase methanol, ethanol, and isopropanol were measured using a slow flow cell and Incoherent Broadband Cavity-Enhanced Absorption Spectroscopy (IBBCEAS). Measurements were performed in two wavelength regions, 447-457 nm, and 508-518 nm, using two differen

Aleksander Bjørn Christiansen, Ivor van der Hoog, Eva Rotenberg

The densest subgraph problem is a classic problem in combinatorial optimisation. Danisch, Chan, and Sozio propose a definition for \emph{local density} that assigns to each vertex $v$ a value $\rho^*(v)$. This local density is a generalisation of the maximum subgraph density of a graph. I.e., if $\rho(G)$ is the subgraph density of a finite graph $G$, then $

Parisa Nozari, Sarah Sadavoy, Edwige Chapillon, Brian Mason

It is widely assumed that dust opacities in molecular clouds follow a power-law profile with an index, $\beta$. Recent studies of the Orion Molecular Cloud (OMC) 2/3 complex, however, show a flattening in the spectral energy distribution (SED) at $ \lambda > 2$ mm implying non-constant indices on scales $\gtrsim$ 0.08 pc. The origin of this flattening is not

Jiho Shin, Hoeseok Yang, Youngmin Yi

Leveraging sparsity is crucial for optimizing large language model inference. however, modern LLMs employing SiLU as their activation function exhibit minimal activation sparsity. Recent research has proposed replacing SiLU with ReLU to induce significant activation sparsity and showed no downstream task accuracy degradation through fine tuning. However, tak

Marco Flores

We investigate the modularity of formal Fourier--Jacobi series by establishing cohomological vanishing results for line bundles defined on compactifications of $\mathcal{A}_g$. Working over $\mathbb{C}$, we show that the minimal compactification of $\mathcal{A}_2$ has only rational singularities, which allows us to characterize, for sufficiently large weight

Tianxiang Zhu, Qipan Wang, Yibo Lin, Runsheng Wang

Thermomechanical stress induced by through-silicon vias (TSVs) plays an important role in the performance and reliability analysis of 2.5D/3D ICs. While the finite element method (FEM) adopted by commercial software can provide accurate simulation results, it is very time- and memory-consuming for large-scale analysis. Over the past decade, the linear superp

John Clapham, Kenneth Koltermann, Yanfu Zhang, Yuming Sun

Falls among seniors due to difficulties with tasks such as picking up objects pose significant health and safety risks, impacting quality of life and independence. Reliable, accessible assessment tools are critical for early intervention but often require costly clinic-based equipment and trained personnel, limiting their use in daily life. Existing wearable

Jad Sarkis, Yanchao Jiang, Pierluigi Poggiolini

As data transmission demands grow, long-haul optical transmission links face increasing pressure to increase their throughput. Expanding usable bandwidth through Ultra-Wide Band (UWB) systems has become the primary strategy for increasing transmission capacity. However, UWB systems present challenges, such as the reliance on backward Raman amplification and

R. H. Drebotiy, H. A. Shynkarenko

We propose a novel finite element method scheme for singularly perturbed advection-diffusion-reaction problems, which combines certain quantum-assisted stabilization scheme with a classical h-adaptive approach to provide automatic error control and corresponding approximation refinement. Appropriate finite element a posteriori error estimates are proved. Des

K. V. Nikolaev, L. R. Muftakhova, G. M. Kuzmicheva, Yu. N. Malakhova

Ce-induced effects on the self-assembly of arachidic acid Langmuir monolayers was studied in this work. The monolayers were formed on the liquid subphase in the presence of Ce(III) ions. A new type of structural configuration is found for such monolayers, in which the monolayer maintains its structural ordering despite being compressed beyond the collapse po

Malay Kumar, S. Sarvajit Visagan, Tanish Sarang Mahajan, Anisha Natarajan

We have come up with a research that hopes to provide a bridge between the users of American Sign Language and the users of spoken language and Indian Sign Language (ISL). The research enabled us to create a novel framework that we have developed for Learner Systems. Leveraging art of Large models to create key features including: - Real-time translation bet

Ali Asgar Chandanwala, Srutakirti Bhowmik, Parna Chaudhury, Sheena Christabel Pravin

Applications in behavioural research, human-computer interaction, and mental health depend on the ability to recognize emotions. In order to improve the accuracy of emotion recognition using electroencephalography (EEG) data, this work presents a hybrid quantum deep learning technique. Conventional EEG-based emotion recognition techniques are limited by nois

Vanshika Jain, Noah Kravitz

For a subtorus $T \subseteq (\mathbb{R}/\mathbb{Z})^n$, let $D(T)$ denote the $L^\infty$-distance from $T$ to the point $(1/2, \ldots, 1/2)$. For a subtorus $U \subseteq (\mathbb{R}/\mathbb{Z})^n$, define $\mathcal{S}_1(U)$, the Lonely Runner spectrum relative to $U$, to be the set of all values of $D(T)$ as $T$ ranges over the $1$-dimensional subtori of $U$

Ewan T. Phillips, Benjamin Lindner, Holger Kantz

We provide a simple framework for the study of parametric (multiplicative) noise, making use of scale parameters. We show that for a large class of stochastic differential equations increasing the multiplicative noise intensity surprisingly causes the mass of the stationary probability distribution to become increasingly concentrated around the minima of the

Chaohui Fan, Xiangjian Qian, Hua-Chen Zhang, Rui-Zhen Huang

Clifford circuits can be utilized to disentangle quantum states with polynomial cost, thanks to the Gottesman-Knill theorem. Based on this idea, the Clifford circuits augmented matrix product states (CAMPS) method, which is a seamless integration of Clifford circuits within the density-matrix renormalization group algorithm, was proposed recently and was sho

Xiaoyang Wang, Xin Chen

The large-scale integration of inverter-interfaced renewable energy sources presents significant challenges to maintaining power balance and nominal frequency in modern power systems. This paper studies grid-level coordinated control of grid-forming (GFM) and grid-following (GFL) inverter-based resources (IBRs) for scalable and optimal frequency control. We

Dharanidharan S, Suhitha Renuka S, Ajishi Singh, Sheena Christabel Pravin

In order to support the creation of reliable machine learning models for anomaly detection, this project focuses on preprocessing, enhancing, and organizing a medical imaging dataset. There are two classifications in the dataset: normal and abnormal, along with extra noise fluctuations. In order to improve the photographs' quality, undesirable artifacts, inc

Jeremy T. Young, Alexey V. Gorshkov, Mohammad Maghrebi

In this work, we investigate an important class of nonequilibrium dynamics in the form of nonreciprocal interactions. In particular, we study how nonreciprocal coupling between two $O(n_i)$ order parameters (with $i=1,2$) affects the universality at a multicritical point, extending the analysis of [J.T. Young et al., Phys. Rev. X 10, 011039 (2020)], which co

Alina Chertock, Arsen S. Iskhakov, Anna Iskhakova, Alexander Kurganov

Uncertainty quantification (UQ) in mathematical models is essential for accurately predicting system behavior under variability. This study provides guidance on method selection for reliable UQ across varied functional behaviors in engineering applications. Specifically, we compare several interpolation and approximation methods within a stochastic collocati

Devakumar GR, JB Kaarthikeyan, Dominic Immanuel T, Sheena Christabel Pravin

Understanding the appropriate skin layer thickness in wounded sites is an important tool to move forward on wound healing practices and treatment protocols. Methods to measure depth often are invasive and less specific. This paper introduces a novel method that is non-invasive with deep learning techniques using classifying of skin layers that helps in measu

Alessandro Carlotto, Yangyang Li, Zhihan Wang

We obtain a generic regularity result for stationary integral $n$-varifolds with only strongly isolated singularities inside $N$-dimensional Riemannian manifolds, in absence of any restriction on the dimension ($n\geq 2$) and codimension. As a special case, we prove that for any $n\geq 2$ and any compact $(n+1)$-dimensional manifold $M$ the following holds:

Fei Ren, Chao Ren, Tianyi Lyu

This study proposes the IoT-Enhanced Pose Optimization Network (IE-PONet) for high-precision 3D pose estimation and motion optimization of track and field athletes. IE-PONet integrates C3D for spatiotemporal feature extraction, OpenPose for real-time keypoint detection, and Bayesian optimization for hyperparameter tuning. Experimental results on NTURGB+D and

Yan B. Bandeira, Victor P. Goncalves, Wolfgang Schäfer

The cross-section for the associated production of a jet with an electroweak gauge boson ($G = W^{\pm}, Z^0, \gamma$) at forward rapidities in $pp$ and $pA$ collisions is derived within the color - dipole $S$ - matrix framework. We present the full expressions for the differential cross-section of the $q p \rightarrow G q X$ process in the transverse momentu

Yiwen Lu, Jiayi Tong, Yuqing Lei, Alex J. Sutton

We introduce OrigamiPlot, an open-source R package and Shiny web application designed to enhance the visualization of multivariate data. This package implements the origami plot, a novel visualization technique proposed by Duan et al. in 2023, which improves upon traditional radar charts by ensuring that the area of the connected region is invariant to the o

Stéphane Lhaut, Johan Segers

The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins. Its statistical recovery is an important step in learning problems involving observations far away from the center. In this paper, we test the goodness-of-fit of a g

Iman Valiulahi, Christos Masouros, Athina P. Petropulu

This paper presents an off-the-grid estimator for ISAC systems using lifted atomic norm minimization (LANM). The main challenge in the ISAC systems is the unknown nature of both transmitted signals and radar-communication channels. We use a known dictionary to encode transmit signals and show that LANM can localize radar targets and decode communication symb

Stefano De Giorgis, Aldo Gangemi, Alessandro Russo

The development of artificial intelligence systems capable of understanding and reasoning about complex real-world scenarios is a significant challenge. In this work we present a novel approach to enhance and exploit LLM reactive capability to address complex problems and interpret deeply contextual real-world meaning. We introduce a method and a tool for cr

Akram Aldroubi, Victor Bailey, Ilya Krishtal, Brendan Miller

We investigate the dynamical sampling space-time trade-off problem within a graph setting. Specifically, we derive necessary and sufficient conditions for space-time sampling that enable the reconstruction of an initial band-limited signal on a graph. Additionally, we develop and test numerical algorithms for approximating the optimal placement of sensors on

Xingwei Zhong, Kui Cai, Guanghui Song, Weijie Wang

Resistive random access memory (ReRAM) is a promising emerging non-volatile memory (NVM) technology that shows high potential for both data storage and computing. However, its crossbar array architecture leads to the sneak path problem, which may severely degrade the reliability of data stored in the ReRAM cell. Due to the complication of memory physics and

Giuseppe Clemente, Massimo D'Elia, Dániel Németh, Gianmarco Simonetti

We consider 4D $SU(N)$ gauge theories coupled to gravity in the Causal Dynamical Triangulations (CDT) approach, focusing on the topological classification of the gauge path integral over fixed triangulations. We discretize the topological charge and, after checking the emergence of topology and the continuum scaling on flat triangulations, we show that topol

Kazi Hasibul Kabir, Md. Zahiruddin Aqib, Sharmin Sultana, Shamim Akhter

Monitoring agricultural activities is important to ensure food security. Remote sensing plays a significant role for large-scale continuous monitoring of cultivation activities. Time series remote sensing data were used for the generation of the cropping pattern. Classification algorithms are used to classify crop patterns and mapped agriculture land used. S

Matteo Luigi De Pascali, Francesco Casella

The forthcoming energy transition calls for a new generation of thermal power generation systems with low- or zero-emission and highly flexible operation. Dynamic modelling and simulation is a key enabling factor in this field, as controlling such plants is a difficult task for which there is no previous experience and very short design times are expected. T

Benjamin Eyre, David Madras

The availability of machine learning systems that can effectively perform arbitrary tasks has led to synthetic labels from these systems being used in applications of statistical inference, such as data analysis or model evaluation. The Prediction Powered Inference (PPI) framework provides a way of leveraging both a large pool of pseudo-labelled data and a s

Y. Azimi

Let $R$ and $S$ be commutative rings with unity, $f:R\to S$ a ring homomorphism and $J$ an ideal of $S$. Then the subring $R\bowtie^fJ:=\{(a,f(a)+j)\mid a\in R$ and $j\in J\}$ of $R\times S$ is called the amalgamation of $R$ with $S$ along $J$ with respect to $f$. In this paper, we determine when $R\bowtie^fJ$ is a (generalized) filter ring.

Jacob Carducci, Jeremy D. Brown

Traditional approaches to measurement in upper-limb therapy have gaps that electronic sensing and recording can help fill. We highlight shortcomings in current kinematic recording devices, and we introduce a wrist sensing device that performs multimodal sensing during single-axis rotation. Our goal is to characterize normative kinesthetic perception and real

David Picard, Nicolas Dufour

Diffusion models based on Multi-Head Attention (MHA) have become ubiquitous to generate high quality images and videos. However, encoding an image or a video as a sequence of patches results in costly attention patterns, as the requirements both in terms of memory and compute grow quadratically. To alleviate this problem, we propose a drop-in replacement for

Craig T. Russell, Jean-Marie Burel, Awais Athar, Simon Li

We introduce bia-binder (BioImage Archive Binder), an open-source, cloud-architectured, and web-based coding environment tailored to bioimage analysis that is freely accessible to all researchers. The service generates easy-to-use Jupyter Notebook coding environments hosted on EMBL-EBI's Embassy Cloud, which provides significant computational resources. The

Yu Min

We use the Tannakian formalism to define the Emerton--Gee stack for general groups. For a flat algebraic group G over Z_p, we are able to prove the associated Emerton--Gee stack is a formal algebraic stack locally of finite presentation over Spf(Z_p). We also define a derived stack of Laurent F-crystals with G-structure on the absolute prismatic site, whose

Jonas D. Fortmann, Alexander Kaßen, Christian Brand, Thomas Duden

The implementation of a monolithic fiber-optically coupled CMOS-based TemCam-XF416 camera into our ultra-high vacuum (UHV) ultrafast reflection high-energy electron diffraction setup is reported. A combination of a pumpable gate valve and a self-built cooling collar allows UHV conditions to be reached without the need to remove the heat-sensitive device. The

Laihao Ding, Jun Gao, Hong Liu, Bingyu Luan

A graph $G$ is $(c,t)$-sparse if for every pair of vertex subsets $A,B\subset V(G)$ with $|A|,|B|\geq t$, $e(A,B)\leq (1-c)|A||B|$. In this paper we prove that for every $c>0$ and integer $\ell$, there exists $C>1$ such that if an $n$-vertex graph $G$ is $(c,t)$-sparse for some $t$, and has at least $C t^{1-1/\ell}n^{1+1/\ell}$ edges, then $G$ contains an in

Huazhi Dong, Ronald B. Liu, Leo Micklem, Peisan Sharel E

Electrical Impedance Tomography (EIT)-inspired tactile sensors are gaining attention in robotic tactile sensing due to their cost-effectiveness, safety, and scalability with sparse electrode configurations. This paper presents a data augmentation strategy for learning-based tactile reconstruction that amplifies the original single-frame signal measurement in

Marie-Noëlle Célérier, Nilton O. Santos

We present a study of shearfree gravitational collapse using cylindrically symmetric spacetimes whose interior is a non-rotating dissipative fluid bounded by a cylindrical hypersurface beyond which is an Einstein-Rosen vacuum exterior. We consider three different pressure configurations: axially, azimuthally, and radially directed, for which we find new exac

Vahid Nasirimarekani, Zuzana Ditte, Eberhard Bodenschatz

Recent advances in optical technology have significantly enhanced the resolution of imaging of living cells, achieving nanometer-scale precision. However, the crowded three-dimensional environment within cells presents a challenge for measuring the spatio-temporal dynamics of cellular components. One solution to this issue is expansion microscopy, which cann

Florian Huber, Massimiliano Marcellino, Tommaso Tornese

We study the distributional implications of uncertainty shocks by developing a model that links macroeconomic aggregates to the US distribution of earnings and consumption. We find that: initially, the fraction of low-earning workers decreases, while the share of households reporting low consumption increases; at longer horizons, the fraction of low-income w

Tianshu Li, Yihong Chen, Qian Huang

This paper introduces the discrete-velocity-direction model (DVDM) in conjunction with the hyperbolic quadrature method of moments (HyQMOM) to develop a multidimensional spatial-temporal approximation of the BGK equation, termed DVD-HyQMOM. Serving as a multidimensional extension of HyQMOM, DVD-HyQMOM model achieves higher accuracy than other DVDM submodels,

Jixian Liu, Tao Xu, Jianping He, Chongrong Fang

The predict-then-optimize (PTO) framework is indispensable for addressing practical stochastic decision-making tasks. It consists of two crucial steps: initially predicting unknown parameters of an optimization model and subsequently solving the problem based on these predictions. Elmachtoub and Grigas [1] introduced the Smart Predict-then-Optimize (SPO) los

Samir Canning, Hannah Larson, Sam Payne, Thomas Willwacher

We study the appearances of $\mathsf{LS}_{12}$ and $\mathsf{S}_{16}$ in the weight-graded compactly supported cohomology of moduli spaces of curves. As applications, we prove new nonvanishing results for the middle cohomology groups of $\mathcal{M}_9$ and $\mathcal{M}_{11}$ and give evidence to support the conjecture that the dimension fo $H^{2g + k}_c(\math

Yu. A. Budkov, P. E. Brandyshev

In this paper, we use the formalism of finite-temperature quantum field theory to investigate the Casimir force between flat, ideally conductive surfaces containing confined, but mobile ions. We demonstrate that, in the Gaussian approximation, the ionic fluctuations contribute separately from the electromagnetic fluctuations that are responsible for the stan

Biman Barua, M. Shamim Kaiser

The growing complexity of the operations of airline reservations requires a smart solution for the adoption of novel approaches to the development of quick, efficient, and adaptive reservation systems. This paper outlines in detail a conceptual framework for the implementation of edge computing microservices in order to address the shortcomings of traditiona

Levent Toksoz, Mukund Srinath, Gang Tan, C. Lee Giles

A novel pseudocode search engine is designed to facilitate efficient retrieval and search of academic papers containing pseudocode. By leveraging Elasticsearch, the system enables users to search across various facets of a paper, such as the title, abstract, author information, and LaTeX code snippets, while supporting advanced features like combined facet s

Y. Azimi, M. R. Doustimehr

Let $R$ and $S$ be commutative rings with identity, $f:R\to S$ a ring homomorphism and $J$ an ideal of $S$. Then the subring $R\bowtie^fJ:=\{(r,f(r)+j)\mid r\in R$ and $j\in J\}$ of $R\times S$ is called the amalgamation of $R$ with $S$ along $J$ with respect to $f$. In this paper, we generalize and improve recent results on the computation of the diameter o

Edson D. Leonel, Celia M. Kuwana, Diego F. M. Oliveira

The unbounded diffusion observed for the standard mapping in a regime of high nonlinearity is suppressed by dissipation due to the violation of Liouville's theorem. The diffusion coefficient becomes important for the description of scaling invariance particularly for the suppression of the unbounded action diffusion. When the dynamics start in the regime of

Vedant Dhruv, Ben Prather, George Wong, Charles F. Gammie

General Relativistic Magnetohydrodynamics (GRMHD) simulations are an indispensable tool in studying accretion onto compact objects. The Event Horizon Telescope (EHT) frequently uses libraries of ideal GRMHD simulations to interpret polarimetric, event-horizon-scale observations of supermassive black holes at the centers of galaxies. In this work, we present

Yihao Xu, Tao Chen, Zongzheng Zhou, Jesús Salas

The number $n_s$ of clusters (per site) of size $s$, a central quantity in percolation theory, displays at criticality an algebraic scaling behavior of the form $n_s\simeq s^{-\tau}\, A\, (1+B s^{-\Omega})$. For the Fortuin--Kasteleyn representation of the $Q$-state Potts model in two dimensions, the Fisher exponent $\tau$ is known as a function of the real

Francesco Marzari

A large sample of planet-planet scattering events for three planet systems with different orbital separations and masses is analyzed with a multiple regression model. The dependence of the time for the onset of instability on the masses of the planets and on their initial orbital separations is modeled with a quadratic function. The same analysis is applied

Ye Liu, Rui Meng, Shafiq Joty, Silvio Savarese

Despite the success of text retrieval in many NLP tasks, code retrieval remains a largely underexplored area. Most text retrieval systems are tailored for natural language queries, often neglecting the specific challenges of retrieving code. This gap leaves existing models unable to effectively capture the diversity of programming languages and tasks across

Vinay Kumar Sankarapu, Chintan Chitroda, Yashwardhan Rathore, Neeraj Kumar Singh

The rapid growth of AI has led to more complex deep learning models, often operating as opaque "black boxes" with limited transparency in their decision-making. This lack of interpretability poses challenges, especially in high-stakes applications where understanding model output is crucial. This work highlights the importance of interpretability in fosterin

Timo Knispel, Vasily Tseplyaev, Gustav Bihlmayer, Stefan Blügel

Ultrathin magnetic films on heavy metal substrates with strong spin-orbit coupling provide versatile platforms for exploring novel spin textures. So far, structurally open fcc(110) substrates remain largely terra incognita. Here, we stabilize a metastable, unreconstructed Ir(110)-$(1 \times 1)$ surface supporting two layers of Fe. Combining spin-polarized sc

Yixiao Zhang

The field of AI-assisted music creation has made significant strides, yet existing systems often struggle to meet the demands of iterative and nuanced music production. These challenges include providing sufficient control over the generated content and allowing for flexible, precise edits. This thesis tackles these issues by introducing a series of advancem

Weiwen Ji, Jin Feng, Yueqi Liu, Yulu Qiu

Recently, deep-learning weather forecasting models have surpassed traditional numerical models in terms of the accuracy of meteorological variables. However, there is considerable potential for improvements in precipitation forecasts, especially for heavy precipitation events. To address this deficiency, we propose Leadsee-Precip, a global deep learning mode

Meibao Qin, Feng Wu, Tingting Liu, Dandan Zhang

Bound states in the continuum (BICs) exhibit significant electric field confinement capabilities and have recently been employed to enhance nonlinear optics response at the nanoscale. In this study, we achieve substantial enhancement of third-harmonic generation (THG) and degenerate four-wave mixing (dFWM) by implementing Brillouin zone folding-induced BICs

Kaili Xie, Marie Corpart, Antoine Deblais, Daniel Bonn

The coalescence of liquid drops is a fundamental process that remains incompletely understood, particularly in the intermediate regimes where capillary, viscous, and inertial forces are comparable. Here, we experimentally investigate the dynamics of drop-to-drop coalescence during the transition between viscous and inertial regimes using high-speed imaging.

Alexander Wietfeld, Marina Wendrich, Sebastian Schmidt, Wolfgang Kellerer

Proposals for molecular communication networks as part of a future internet of bio-nano-things have become more intricate and the question of practical implementation is gaining more importance. One option is to apply detailed chemical modeling to capture more realistic effects of computing processes in biological systems. In this paper, we present ChemSICal

Pascal Tribel, Gianluca Bontempi

Seismic data is often sparse and unevenly distributed due to the high costs and logistical challenges associated with deploying physical seismometers, limiting the application of Machine Learning (ML) in earthquake analysis. While simulation methods exist, no tool allows the generation of large datasets containing simulated measurements of the ground motion.

Luoxi Zhang, Pragyan Shrestha, Yu Zhou, Chun Xie

The precise reconstruction of 3D objects from a single RGB image in complex scenes presents a critical challenge in virtual reality, autonomous driving, and robotics. Existing neural implicit 3D representation methods face significant difficulties in balancing the extraction of global and local features, particularly in diverse and complex environments, lead

F. Sgrignuoli, I. Viti, Z. G. Yu, E. Allridge

Silicon Carbide is renowned for its exceptional thermal stability, making it a crucial material for high-temperature power devices in extreme environments. While optically detected magnetic resonance in SiC has been widely studied for magnetometry, it requires complex setups involving optical and microwave sources. Similarly, electrically detected magnetic r

Ari Stern, Milo Viviani

Runge-Kutta methods are affine equivariant: applying a method before or after an affine change of variables yields the same numerical trajectory. However, for some applications, one would like to perform numerical integration after a quadratic change of variables. For example, in Lie-Poisson reduction, a quadratic transformation reduces the number of variabl

Vitalis Vosylius, Edward Johns

Following the impressive capabilities of in-context learning with large transformers, In-Context Imitation Learning (ICIL) is a promising opportunity for robotics. We introduce Instant Policy, which learns new tasks instantly (without further training) from just one or two demonstrations, achieving ICIL through two key components. First, we introduce inducti

Olfa Ben Yahia, William Ferguson, Sumit Chakravarty, Nesrine Benchoubane

The rapid evolution of communication technologies, compounded by recent geopolitical events such as the Viasat cyberattack in February 2022, has highlighted the urgent need for fast and reliable satellite missions for military and civil security operations. Consequently, this paper examines two Earth observation (EO) missions: one utilizing a single low Eart

Ziqian Tang, Hanyu Xue, Zizhao Han, Zikuan Kan

The interface between quantum mechanics and gravity remains an unresolved issue. Recent advances in precision measurement suggest that detecting gravity-induced entanglement in oscillator systems could provide key evidence for the quantum nature of gravity. However, thermal decoherence imposes strict constraints on system parameters. For entanglement to occu

Bo Yuan, Jiazi Hu

Reflection is widely recognized as a cornerstone of student development, fostering critical thinking, self-regulation, and deep conceptual understanding. Traditionally, reflective skills have been cultivated through structured feedback, mentorship, and guided self-assessment. However, these approaches often face challenges such as limited scalability, diffic

J. G. F. Campos, A. Mohammadi, T. Romanczukiewicz

In this paper, we explored a class of potentials with three minima that support kink solutions exhibiting one long-range tail. We analyzed antikink-kink interactions using an effective Lagrangian based on collective coordinates and compared the results to those obtained from full dynamical simulations. To this end, we constructed the collective coordinates w

Nikhil Garuda, John F. Wu, Dylan Nelson, Annalisa Pillepich

Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses ($\rm{M}_{\rm{halo}}$) must be inferred indirectly. We present a graph neural network (GNN) model for predicting $\rm{M}_{\rm{halo}}$ from stellar mass ($\rm{M}_{*}$) in simulated galaxy clusters using data from the IllustrisTNG simulation suite. Unlike t

Jibril Ben Achour, Dario Benedetti, Martin Bojowald, Robert Brandenberger

This collection of perspective pieces captures recent advancements and reflections from a dynamic research community dedicated to bridging quantum gravity, hydrodynamics, and emergent cosmology. It explores four key research areas: (a) the interplay between hydrodynamics and cosmology, including analog gravity systems; (b) phase transitions, continuum limits

William J Rider, Dennis R Liles

This paper presents a generalized flux-corrected transport (FCT) algorithm, which is shown to be total variation diminishing under some conditions. The new algorithm has improved properties from the standpoint of use and analysis. Results show that the new FCT algorithm performs better than the older FCT algorithms and is comparable with other modern methods

Elliott Abel, Andrew J. Steindl, Selma Mazioud, Ellie Schueler

Drawing motivation from the manifold hypothesis, which posits that most high-dimensional data lies on or near low-dimensional manifolds, we apply manifold learning to the space of neural networks. We learn manifolds where datapoints are neural networks by introducing a distance between the hidden layer representations of the neural networks. These distances

Manuel H. Muñoz-Arias, Pablo M. Poggi

Quantum annealers play a major role in the ongoing development of quantum information processing and in the advent of quantum technologies. Their functioning is underpinned by the many-body adiabatic evolution connecting the ground state of a simple system to that of an interacting classical Hamiltonian which encodes the solution to an optimization problem.

Yinxia Zhang, Matthias Bartosik, Steffen Brinckmann, Ujjval Bansal

The effect of columnar grain boundaries on the fracture toughness was investigated using micro-cantilever fracture testing with a bridge notch, and a unique hard coating consisting of two distinct microstructures: one with columnar grains and another with an epitaxial layer. The bridge-failure sequence qualitatively demonstrated the lower fracture toughness

Riccardo Passeggeri

Point processes and, more generally, random measures are ubiquitous in modern statistics. However, they can only take positive values, which is a severe limitation in many situations. In this work, we introduce and study random signed measures, also known as real-valued random measures, and apply them to constrcut various Bayesian non-parametric models. In p

Matthew L. Peters, Guoqing Wang, David C. Spierings, Niv Drucker

Using the strong dispersive coupling to a high-cooperativity cavity, we demonstrate fast and non-destructive number-resolved detection of atoms in optical tweezers. We observe individual atom-atom collisions, quantum state jumps, and atom loss events with a time resolution of $100\ \mu$s through continuous measurement of cavity transmission. Using adaptive f

Paolo Fiore, Ilario Filippini, Danilo De Donno

Growing congestion in current mobile networks necessitates innovative solutions. This paper explores the potential of mmWave 5G networks in urban settings, focusing on Integrated Access and Backhaul (IAB) and the Smart Radio Environment (SRE). The mmWave traffic will be mainly made of short bursts to transfer large volumes of data and long idle periods where

Matteo Toso, Stefano Fiorini, Stuart James, Alessio Del Bue

World-wide detailed 2D maps require enormous collective efforts. OpenStreetMap is the result of 11 million registered users manually annotating the GPS location of over 1.75 billion entries, including distinctive landmarks and common urban objects. At the same time, manual annotations can include errors and are slow to update, limiting the map's accuracy. Ma

Adithya TG, Abhinavaram N, Gowri Srinivasa

This paper presents a new approach to multiple language learning, with Hindi the language to be learnt in our case, by using the integration of virtual reality environments and AI enabled tutoring systems using OpenAIs GPT api calls. We have developed a scenario which has a virtual campus environment using Unity which focuses on a detailed representation of

Itai Carmeli, Chanoch Carmeli

The interaction of light with photosynthetic proteins is an extremely efficient process and has been thoroughly investigated. However, exploring light-matter interactions in hybrid nano-solid-photosynthetic proteins is a relatively new and existing field of research. The properties of these hybrid materials significantly influence the energy levels, non-radi

Junliang Du, Guiran Liu, Jia Gao, Xiaoxuan Liao

This study proposed a knowledge graph entity extraction and relationship reasoning algorithm based on a graph neural network, using a graph convolutional network and graph attention network to model the complex structure in the knowledge graph. By building an end-to-end joint model, this paper achieves efficient recognition and reasoning of entities and rela

Kota Numajiri, Kazumasa Okabayashi, Shinji Mukohyama

We examine the vacuum state and its corresponding renormalized stress-energy tensor (RSET) in static horizonless regular spacetime in both two and four dimensions. Using the local field formulation of the anomaly-induced effective action, we show that the regularities of the spacetime and the RSET dictate the appropriate vacuum state. Furthermore, through a

Alexander J. Heilman, Weiyi Gong, Qimin Yan

Graph representations of solid state materials that encode only interatomic distance lack geometrical resolution, resulting in degenerate representations that may map distinct structures to equivalent graphs. Here we propose a hypergraph representation scheme for materials that allows for the association of higher-order geometrical information with hyperedge

Jiaqi Yang, Nitish Mehta, Xiaoling Hu, Chao Chen

Accurate segmentation of Optical Coherence Tomography (OCT) images is crucial for diagnosing and monitoring retinal diseases. However, the labor-intensive nature of pixel-level annotation limits the scalability of supervised learning for large datasets. Weakly Supervised Semantic Segmentation (WSSS) offers a promising alternative by using weaker forms of sup

Jeremy L. Smallwood, Rebecca Nealon, Hsi-Wei Yen, Christophe Pinte

Misaligned circumbinary disks will produce dust traffic jams during alignment or anti-alignment to the binary orbital plane. We conduct a hydrodynamical simulation of an initially misaligned circumbinary disk undergoing polar alignment with multiple dust species. Due to differential precession between the gas and dust components, multiple dust traffic jams a

V. S. Ryumshin, S. V. Nuzhin, Yu. D. Panov, A. S. Moskvin

Rare-earth orthonickelates RNiO3 are Jahn-Teller magnets, unstable with respect to the anti-Jahn-Teller disproportionation reaction with the formation of a system equivalent to a system of effective spin-triplet composite bosons moving in a non-magnetic lattice. Within the framework of the two-sublattice approximation, we have developed a mean field theory f

Kamyar Barakati, Yu Liu, Chris Nelson, Maxim A. Ziatdinov

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hype

Vishakha Gupta, Patrick Winkel, Neel Thakur, Peter van Vlaanderen

Lumped-element inductors are an integral component in the circuit QED toolbox. However, it is challenging to build inductors that are simultaneously compact, linear and low-loss with standard approaches that either rely on the geometric inductance of superconducting thin films or on the kinetic inductance of Josephson junctions arrays. In this work, we overc

Akram Youssry, Alberto Peruzzo

Implementing arbitrary unitary transformations is crucial for applications in quantum computing, signal processing, and machine learning. Unitaries govern quantum state evolution, enabling reversible transformations critical in quantum tasks like cryptography and simulation and playing key roles in classical domains such as dimensionality reduction and signa

Philippe Michel, Dinakar Ramakrishnan, Liyang Yang

Fix a Dirichlet character $\chi$ and a cuspidal GL$(2)$ eigenform $\phi$ with relatively prime conductors. Then we show that there are infinitely many cusp forms $\pi$ on GL$(3)$ such that $L(1/2, \pi \times \chi)$ and $L(1/2, \pi \times \phi)$ are simultaneously non-zero. We achieve this by use of Jacquet's Relative Trace Formula. We derive an expression of

Haoqian Pan, Changhong Lu, Yuqing Zheng, Chunxing Yan

Perfect Domination Problem (PDP), a canonical challenge in combinatorial optimization, finds critical applications in real-world systems such as error-correcting codes, wireless communication networks, and social networks. Decades of research have firmly established its NP-completeness across numerous graph classes. Motivated by rapid advances in quantum com

G. Su, S. T. Millmore, X. Zhang, N. Nikiforakis

We present a single-fluid approach for the simulation of partially-ionized plasmas (PIPs) which is designed to capture the non-ideal effects introduced by neutrals while remaining close in computational efficiency to single-fluid MHD. This is achieved using a model which treats the entire partially-ionized plasma as a single mixture, which renders internal i