October 2024 arXiv papers — page 45

Yugo Oshima, Yasuyuki Ishii, Francis L. Pratt, Isao Watanabe

The molecular triangular lattice system, beta'-EtMe3Sb[Pd(dmit)2]2, is considered as a candidate material for the quantum spin liquid (QSL) state, although ongoing debates arise from recent controversial results. Here, the results of electron spin resonance (ESR) and muon spin relaxation (muSR) measurements on beta'-EtMe3Sb[Pd(dmit)2]2 are presented. Both re

Yitong Chen, Danqi Jin, Jie Chen, Cedric Richard

This material introduces the D-Subspace algorithm derived on the basis of the centralized algorithm [1], which originally addresses parameter estimation problems under a subspace constraint.

Sarper Aydin, Ceyhun Eksin

We propose networked policy gradient play for solving Markov potential games with continuous and/or discrete state-action pairs. During the game, agents use parametrized and differentiable policies that depend on the current state and the policy parameters of other agents. During training, agents update their policy parameters following stochastic gradients.

Ben Goldys, Szymon Peszat

Let $P_s\phi(x)=\mathbb{E}\, \phi(X^x(s))$, be the transition semigroup on the space $B_b(E)$ of bounded measurable functions on a Banach space $E$, of the Markov family defined by the linear equation with additive noise $$ d X(s)= \left(AX(s) + a\right)ds + BdW(s), \qquad X(0)=x\in E. $$ We give a simple probabilistic proof of the fact that null-controlla\-

Yijie Zhang, Luzhe Huang, Nir Pillar, Yuzhu Li

Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study presents a diffusion model-based super-resolution virtual staining approach utilizing a Brownian bridge process to enhance both the spatial resolution and fidelity of label-free vi

Chuanqi Chen, Nan Chen, Yinling Zhang, Jin-Long Wu

Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents significant challenges for carrying out simultaneous data assimilation (DA), which is a crucial technique for state estimation, model identification, and reconstructing missing data.

Amelia Pompilio

Divisible convex sets have long been important in the study of Hilbert geometries. When a divisible convex set is an ellipsoid, the Hilbert geometry it induces is the hyperbolic space. In general, strictly convex divisible domains exhibit negative curvature properties, but only the ellipsoid is a CAT(0) space. The notion of p-uniform convexity from the theor

Naresh Kshetri, Will Carter, Seth Kern, Richard Mensah

The use of social media applications, hate speech engagement, and public debates among teenagers, primarily by university and college students, is growing day by day. The feelings of tremendous stress, anxiety, and depression via social media among our youths have a direct impact on their daily lives and personal workspace apart from delayed sleep, social me

Wen-Xiang Chen, Yao-Guang Zheng

This paper demonstrates the validity of the first law of horizon thermodynamics for Schwarzschild, RN, Kerr, and Kerr-Newman (KN) black holes within the framework of four-dimensional f(R) gravity coupled with dual scalar fields. Starting from a five-dimensional membrane world scenario, we derive the four-dimensional effective f(R) gravity action and obtain t

Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat, Olga Klopp

Graph Convolutional Networks (GCNs) have become a pivotal method in machine learning for modeling functions over graphs. Despite their widespread success across various applications, their statistical properties (e.g., consistency, convergence rates) remain ill-characterized. To begin addressing this knowledge gap, we consider networks for which the graph st

Hannah Lu, Lluis Salo-Salgado, Youssef M. Marzouk, Ruben Juanes

Geologic CO$_2$ storage is an important strategy for reducing greenhouse gas emissions to the atmosphere and mitigating climate change. In this process, coupling between mechanical deformation and fluid flow in fault zones is a key determinant of fault instability, induced seismicity, and CO$_2$ leakage. Using a recently developed methodology, PREDICT, we ob

Farnoush Baghestani, Mahdi Pirayesh Shirazi Nejad, Youngsun Kong, Ki H. Chon

Continuous monitoring of non-invasive skin sympathetic nerve activity (SKNA) holds promise for understanding the sympathetic nervous system (SNS) dynamics in various physiological and pathological conditions. However, muscle noise artifacts present a challenge in accurate SKNA analysis, particularly in real-life scenarios. This study proposes a deep convolut

Emma Casolin, Flora D. Salim, Ben Newell

Explainable AI (XAI) aims to support appropriate human-AI reliance by increasing the interpretability of complex model decisions. Despite the proliferation of proposed methods, there is mixed evidence surrounding the effects of different styles of XAI explanations on human-AI reliance. Interpreting these conflicting findings requires an understanding of the

Jun-Wei Li, Jia-Xing Zhang, Wei Chen

Twisted bilayer graphene (TBG) has attracted great interest in the last decade due to the novel properties it exhibited. It was revealed that e-phonon interaction plays an important role in a variety of phenomena in this system, such as superconductivity and exotic phases. However, due to its complexity, the e-phonon interaction in TBG is not well studied ye

Sabi Biswas, Nipen Saikia

The 2-adic valuation of an integer n which is the exponent of the highest power of 2 that divides n. In this paper, we give representations of certain restricted partition functions in terms of 2-adic valuation.

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

By analyzing $e^{+}e^{-}$ collision data with an integrated luminosity of 7.9~fb$^{-1}$ collected with the BESIII detector at the center-of-mass energy of 3.773~GeV, the branching fraction of $D^+\to\tau^+\nu_{\tau}$ is determined as $\mathcal{B}=(9.9\pm 1.1_\mathrm{stat}\pm 0.5_\mathrm{syst})\times10^{-4}$. Taking the most precise result $\mathcal{B}(D^+\to

Tasnim Sakib Apon, Md. Fahim-Ul-Islam, Nafiz Imtiaz Rafin, Joya Akter

Knee osteoarthritis(KO) is a degenerative joint disease that can cause severe pain and impairment. With increased prevalence, precise diagnosis by medical imaging analytics is crucial for appropriate illness management. This research investigates a comparative analysis between traditional machine learning techniques and new deep learning models for diagnosin

Caleb Eardley, Dalton Gomez, Ryan Dupuis, Michael Papadopoulos

The Multi-Capacity Fixed-Charge Network Flow (MC-FCNF) problem, a generalization of the Fixed-Charge Network Flow problem, aims to assign capacities to edges in a flow network such that a target amount of flow can be hosted at minimum cost. The cost model for both problems dictates that the fixed cost of an edge is incurred for any non-zero amount of flow ho

Ryo Tsumoto, Kentaro Yaji, Yutaka Nomaguchi, Kikuo Fujita

A generative design based on topology optimization provides diverse alternatives as entities in a computational model with a high design degree. However, as the diversity of the generated alternatives increases, the cognitive burden on designers to select the most appropriate alternatives also increases. Whereas the concept identification approach, which fin

Yong-Ning An, Rui Guo

In this paper, the relation between the integer partition theory and a kind of rational solution of the dispersion long wave equations is studied. For the integer partition {\lambda}= ({\lambda}1,{\lambda}2,... ,{\lambda}n) of positive integer N, with the degree vector m = (m1,m2,... ,mn), the corresponding M lump solution can be obtained where M = N + n mn.

Li Zhen, Weihua Gu

Demand-responsive connector (DRC) services are increasingly recognized for their convenience, comfort, and efficiency, offering seamless integrations between travelers' origins/destinations and major transportation hubs such as rail stations. Past analytical models for DRC optimization often failed to distinguish between two commonly used DRC operating strat

Jianqiao Mao, Max A. Little

A major limitation of machine learning (ML) prediction models is that they recover associational, rather than causal, predictive relationships between variables. In high-stakes automation applications of ML this is problematic, as the model often learns spurious, non-causal associations. This paper proposes mechanism learning, a simple method which uses caus

Sixu An, Xiangguo Sun, Yicong Li, Yu Yang

Personality analysis from online short videos has gained prominence due to its applications in personalized recommendation systems, sentiment analysis, and human-computer interaction. Traditional assessment methods, such as questionnaires based on the Big Five Personality Framework, are limited by self-report biases and are impractical for large-scale or rea

Bin Kang, Bin Chen, Junjie Wang, Yong Xu

Text-based person retrieval aims to identify the specific persons using textual descriptions as queries. Existing ad vanced methods typically depend on vision-language pre trained (VLP) models to facilitate effective cross-modal alignment. However, the inherent constraints of VLP mod-els, which include the global alignment biases and insuffi-cient self-feedb

Millicent Li, Tongfei Chen, Benjamin Van Durme, Patrick Xia

Document retrieval for tasks such as search and retrieval-augmented generation typically involves datasets that are unstructured: free-form text without explicit internal structure in each document. However, documents can have a structured form, consisting of fields such as an article title, message body, or HTML header. To address this gap, we introduce Mul

Xiaoyang Qin, Hao Huang, Shuaichen Lin, Xinhao Zeng

Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent apposition holds pivotal importance in averting and identifying PCI complications that lead to in-stent restenosis. Here we proposed a novel three-dimensional (3D) distance-color-coded asse

Dhanush Tella, Chandra Teja Tiriveedhi, Naphtali Rishe, Dan E. Tamir

We consider the task of classifying trajectories of boat activities as a proxy for assessing maritime threats. Previous approaches have considered entropy-based metrics for clustering boat activity into three broad categories: random walk, following, and chasing. Here, we comprehensively assess the accuracy of neural network-based approaches as alternatives

Xiaohui Gao, Yue Cheng, Peiyang Li, Yijie Niu

Neural encoding of artificial neural networks (ANNs) links their computational representations to brain responses, offering insights into how the brain processes information. Current studies mostly use linear encoding models for clarity, even though brain responses are often nonlinear. This has sparked interest in developing nonlinear encoding models that ar

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This article presents a comprehensive analysis of the formation and dissipation of vortices within chaotic fluid flows, leveraging the framework of Sobolev and Besov spaces on Riemannian manifolds. Building upon the Navier-Stokes equations, we introduce a hypercomplex bifurcation approach to characterize the regularity and critical thresholds at which vortic

Abeer Badawi, Somayya Elmoghazy, Samira Choudhury, Sara Elgazzar

Dementia is a neurodegenerative condition that combines several diseases and impacts millions around the world and those around them. Although cognitive impairment is profoundly disabling, it is the noncognitive features of dementia, referred to as Neuropsychiatric Symptoms (NPS), that are most closely associated with a diminished quality of life. Agitation

Daniel Erman, Eric Riedl

We apply ideas related to the strength of polynomials to provide new cases of unirational hypersurfaces. It is famously known that hypersurfaces that are smooth in very high codimension are unirational, and a simple corollary then implies that any polynomial of sufficiently high strength will give rise to a unirational hypersurface. Our main result shows tha

Lei Li, Xiangxu Zhang, Xiao Zhou, Zheng Liu

Medical information retrieval (MIR) is essential for retrieving relevant medical knowledge from diverse sources, including electronic health records, scientific literature, and medical databases. However, achieving effective zero-shot dense retrieval in the medical domain poses substantial challenges due to the lack of relevance-labeled data. In this paper,

Xun Shi

Most research on astrophysical lensing has been conducted using the geometric optics framework, where there exists a clear concept of lensing images. However, wave optics effects can be important for coherent sources, e.g. pulsars, fast raio bursts, and gravitational waves observed at long wavelengths. There, the concept of lensing images needs an extension.

Chen Chen, Weimin Yi, Zhicheng He, Fred Hamann

Investigating line-locked phenomena within quasars is crucial for understanding the dynamics of quasar outflows, the role of radiation pressure in astrophysical flows, and the star formation history and metallicity of the early universe. We have initiated the Tracking Outflow by Line-Locking (TOLL) project to study quasar outflow by studying line-locking sig

Xincheng Yao, Zixin Chen, Chao Gao, Guangtao Zhai

This paper explores the problem of class-generalizable anomaly detection, where the objective is to train one unified AD model that can generalize to detect anomalies in diverse classes from different domains without any retraining or fine-tuning on the target data. Because normal feature representations vary significantly across classes, this will cause the

Yang Zhou, Zhen Dong, Ellick Chan, Dhiraj Kalamkar

Large-scale recommendation models are currently the dominant workload for many large Internet companies. These recommenders are characterized by massive embedding tables that are sparsely accessed by the index for user and item features. The size of these 1TB+ tables imposes a severe memory bottleneck for the training and inference of recommendation models.

Yuming Zhang, Stéphane Guerrier, Runze Li

Inference for high-dimensional logistic regression models using penalized methods has been a challenging research problem. As an illustration, a major difficulty is the significant bias of the Lasso estimator, which limits its direct application in inference. Although various bias corrected Lasso estimators have been proposed, they often still exhibit substa

Xiaotian Zhang, Zhanhai Yu, Hongrui Zhang, Di Xiang

We realize a ring cavity strongly interacting with an atom array with configurable spatial structures. By preparing the atom array with a maximized structure factor, we observe the emergence of a cavity dark mode, where the standing-wave nodes are dynamically locked to the positions of the atoms. The dark mode is decoupled from the atoms, protecting the syst

Eliuvish Cuicizion, Haowen Xu, Weng Kee Wong

Optimization plays an important role in tackling public health problems. Animal instincts can be used effectively to solve complex public health management issues by providing optimal or approximately optimal solutions to complicated optimization problems common in public health. BAT algorithm is an exemplary member of a class of nature-inspired metaheuristi

Joshua Jordaan, Alexander Minovich, Dragomir Neshev, Isabelle Staude

Metalenses, advanced nanostructured alternatives to conventional lenses, significantly enhance the compactness and functionality of optical systems. Despite progress in monochromatic applications, scaling metalenses to centimeter-sized apertures for broadband or multiwavelength use remains challenging due to limitations in achieving the necessary group delay

Min Fu, Lipeng Zhu, Rui Zhang

In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To achieve this, we assume that there is a given set of candidate sites in the target area for deploying IRSs and divide the area into multiple grid

Adit Jain, Soumyabrata Pal, Sunav Choudhary, Ramasuri Narayanam

We investigate the high-dimensional sparse linear bandits problem in a data-poor regime where the time horizon is much smaller than the ambient dimension and number of arms. We study the setting under the additional blocking constraint where each unique arm can be pulled only once. The blocking constraint is motivated by practical applications in personalize

Shira Faigenbaum-Golovin, Ingrid Daubechies

We present results of a long-term team collaboration of mathematicians and biologists. We focus on building a mathematical framework for the shape space constituted by a collection of homologous bones or teeth from many species. The biological application is to quantitative morphological understanding of the evolutionary history of primates in particular, an

Fan Fei, Jinhyun Choo

The phase-field method has become popular for the numerical modeling of fluid-filled fractures, thanks to its ability to represent complex fracture geometry without algorithms. However, the algorithm-free representation of fracture geometry poses a significant challenge in calculating the crack opening (aperture) of phase-field fracture, which governs the fr

Louise Schmidt, Cristian Lillo, Javier Bustos

In this article we revise the football's performance score called PlayeRank, designed and evaluated by Pappalardo et al.\ in 2019. First, we analyze the weights extracted from the Linear Support Vector Machine (SVM) that solves the classification problem of "which set of events has a higher impact on the chances of winning a match". Here, we notice that the

Ron Sun

To better understand existing LLMs, we may examine the human mental (cognitive/psychological) architecture, and its components and structures. Based on psychological, philosophical, and cognitive science literatures, it is argued that, within the human mental architecture, existing LLMs correspond well with implicit mental processes (intuition, instinct, and

Saket Kumar, Abul Ehtesham, Aditi Singh, Tala Talaei Khoei

The application of artificial intelligence (AI) in civil engineering presents a transformative approach to enhancing design quality and safety. This paper investigates the potential of the advanced LLM GPT4 Turbo vision model in detecting architectural flaws during the design phase, with a specific focus on identifying missing doors and windows. The study ev

Vighnesh Subramaniam, David Mayo, Colin Conwell, Tomaso Poggio

We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. We call a network untrainable when it overfits, underfits, or converges to poor results even when tuning their hyperparameters. For example, fully connected networks overfit on object recognition wh

Wenqiang Chen, Jiaxuan Cheng, Leyao Wang, Wei Zhao

Visual Question-Answering, a technology that generates textual responses from an image and natural language question, has progressed significantly. Notably, it can aid in tracking and inquiring about daily activities, crucial in healthcare monitoring, especially for elderly patients or those with memory disabilities. However, video poses privacy concerns and

Doosung Choi

We represent a matrix representation of the Neumann-Poincar\'e operator defined on the boundaries of a torus. A torus is a doubly connected domain in three dimensions. There is a well-known parametrization for the shape of the torus, the toroidal coordinate system. Based on the coordinate system, we use toroidal harmonics to get an expansion of the NP operat

Alberto Bressan, Khai T. Nguyen

The paper is concerned with a scalar balance law, where the source term depends on a control function $\alpha(t)$. Given a control $\alpha\in \mathbf{L}^\infty\bigl([0,T]\bigr)$, it is proved that, for generic initial data $\bar u \in \mathcal{C}^3(\mathbb{R})$, the solution has finitely many shocks, interacting at most two at a time. Moreover, at the termin

Yun-Ning Fan, Yong Shao, Wen-Cong Chen

The magnetic braking (MB) plays an important role in driving the evolution of low-mass X-ray binaries (LMXBs). The modified MB prescription, convection and rotation boosted (CARB) model, is very successful in reproducing the detected mass-transfer rates of persistent neutron star (NS) LMXBs. In this work, we investigate whether the CARB MB prescription could

Xuanchi Ren, Yifan Lu, Hanxue Liang, Zhangjie Wu

We present SCube, a novel method for reconstructing large-scale 3D scenes (geometry, appearance, and semantics) from a sparse set of posed images. Our method encodes reconstructed scenes using a novel representation VoxSplat, which is a set of 3D Gaussians supported on a high-resolution sparse-voxel scaffold. To reconstruct a VoxSplat from images, we employ

Takeshi Fukasawa

This study proposes a simple procedure to compute Efficient Pseudo Likelihood (EPL) estimator proposed by Dearing and Blevins (2024) for estimating dynamic discrete games, without computing Jacobians of equilibrium constraints. EPL estimator is efficient, convergent, and computationally fast. However, the original algorithm requires deriving and coding the J

Mohamed El Hichami, Youssef El Hadfi

In this paper, we investigate the existence and uniqueness of solutions for the following model problem, involving singularities and inhomogeneous Robin boundary conditions \begin{equation*} \left\{ \begin{array}{ll} -\Delta_{p}u_{p}=\frac{f}{u_{p}^{\gamma}}& \hbox{in $\Omega,$} \frac{\partial u_{p}}{\partial \sigma}+\lambda\vert u_{p}\vert^{p-2} u_{p}+\vert

Shihao Cai, Jizhi Zhang, Keqin Bao, Chongming Gao

Large language model-based agents are increasingly applied in the recommendation field due to their extensive knowledge and strong planning capabilities. While prior research has primarily focused on enhancing either the recommendation agent or the user agent individually, the collaborative interaction between the two has often been overlooked. Towards this

Hao Ding, Yuqian Zhang, Wenzheng Cheng, Xinyu Wang

Surgical phase recognition (SPR) is an integral component of surgical data science, enabling high-level surgical analysis. End-to-end trained neural networks that predict surgical phase directly from videos have shown excellent performance on benchmarks. However, these models struggle with robustness due to non-causal associations in the training set. Our go

Zhijian Luo, Shaohua Zhang, Jianzhen Chen, Zhu Chen

This study focuses on transforming galaxy images between astronomical surveys, specifically enhancing images from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Camera Legacy Survey (DECaLS) to achieve quality comparable to the Hyper Suprime-Cam survey (HSC). We proposed a hybrid model called Pix2WGAN, which integrates the pix2pix framework with the

Mikhail Rumiantsau, Aliaksei Vertsel, Ilya Hrytsuk, Isaiah Ballah

Large Language Models (LLMs) have become increasingly important in natural language processing, enabling advanced data analytics through natural language queries. However, these models often generate "hallucinations"-inaccurate or fabricated information-that can undermine their reliability in critical data-driven decision-making. Addressing the challenge of

Qianggang Ding, Santiago Miret, Bang Liu

Material discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a novel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery and design of new solid-state materials. Inspired by the workflow of human materials design experts, our appr

Xue-Na Zhu, Zhi-Xiang Jin, Gui Bao, Shao-Ming Fei

Coherence is a fundamental resource in quantum information processing, which can be certified by a coherence witness. In order to detect all the coherent states, we introduce a useful concept of coherence witness and structure the set of coherence witnesses $C^{d}_{[m,M]}$. We present necessary and sufficient conditions of detecting quantum coherence of $d-$

Theodore Glavas, Joud Chataoui, Florence Regol, Wassim Jabbour

The vast size of Large Language Models (LLMs) has prompted a search to optimize inference. One effective approach is dynamic inference, which adapts the architecture to the sample-at-hand to reduce the overall computational cost. We empirically examine two common dynamic inference methods for natural language generation (NLG): layer skipping and early exitin

Avinash Maurya, Jie Ye, M. Mustafa Rafique, Franck Cappello

Transformers and large language models~(LLMs) have seen rapid adoption in all domains. Their sizes have exploded to hundreds of billions of parameters and keep increasing. Under these circumstances, the training of transformers is very expensive and often hits a ``memory wall'', i.e., even when using 3D parallelism (pipeline, tensor, data) and aggreg

Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai

Cross-lingual summarization (CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at various English tasks. However, unlike languages such as English, Chinese or Spanish, for those relatively low-resource languages with limited usage or data, recent studies have sh

Francisco Pernice, Oscar Sprumont, Mary Wootters

It is known that the Shannon capacity of the q-ary symmetric channel (qSC) is the same as the list-decoding capacity of an adversarial channel, raising the question of whether there is a formal (and black-box) connection between the two. We show that there is: Any linear code $C\subseteq \mathbb{F}_q^n$ that has minimum distance $d_{\min}=\omega(q^3)$ and ac

Poojitha Thota, Shirin Nilizadeh

Large Language Models have introduced novel opportunities for text comprehension and generation. Yet, they are vulnerable to adversarial perturbations and data poisoning attacks, particularly in tasks like text classification and translation. However, the adversarial robustness of abstractive text summarization models remains less explored. In this work, we

Kyle B. Hatch, Ashwin Balakrishna, Oier Mees, Suraj Nair

Image and video generative models that are pre-trained on Internet-scale data can greatly increase the generalization capacity of robot learning systems. These models can function as high-level planners, generating intermediate subgoals for low-level goal-conditioned policies to reach. However, the performance of these systems can be greatly bottlenecked by

Ge Gao, Xi Yang, Qitong Gao, Song Ju

In human-centric tasks such as healthcare and education, the heterogeneity among patients and students necessitates personalized treatments and instructional interventions. While reinforcement learning (RL) has been utilized in those tasks, off-policy selection (OPS) is pivotal to close the loop by offline evaluating and selecting policies without online int

Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai

Vertical text input is commonly encountered in various real-world applications, such as mathematical computations and word-based Sudoku puzzles. While current large language models (LLMs) have excelled in natural language tasks, they remain vulnerable to variations in text formatting. Recent research demonstrates that modifying input formats, such as vertica

Nicolas Pan, Tabaré Gallardo

The main objective of this paper is to fully study 1:1 mean-motion resonance in the Solar System. We calculated stability points applying a resonant semi-analytic theory valid for any value of eccentricity or inclination. The location of each equilibrium point changes as the orbital elements of an object change, which led us to map the location of them. For

Patrick Shaw, Rupert A. C. Croft, R. Benton Metcalf

The apparent angular positions of quasars are deflected on the sky by the gravitational field sourced by foreground matter. This weak lensing effect is measurable through the distortions it introduces in the lensed quasar spectra. Discrepancies in the statistics of the Lyman-$\alpha$ forest spectral absorption features can be used to reconstruct the foregrou

Qingfeng Lyu

For any non-simple (1,1)-knot in $S^3$ or a lens space, we construct a co-oriented taut foliation in its complement that intersects the boundary torus transversely in a suspension foliation of the knot meridian, or the infinity slope. This provides new evidence for a conjecture made by Boyer, Gordon and Hu using slope detections, related to the L-space conje

Cristina G. Fernandes, Carla N. Lintzmayer, Juan P. Peña, Giovanne Santos

For a digraph $D$ of order $n$ and an integer $1 \leq k \leq n-1$, the $k$-token digraph of $D$ is the graph whose vertices are all $k$-subsets of vertices of $D$ and, given two such $k$-subsets $A$ and $B$, $(A,B)$ is an arc in the $k$-token digraph whenever $\{a\} = A \setminus B$, $\{b\} = B \setminus A$, and there is an arc $(a,b)$ in $D$. Token digraphs

Roberto Lo Conte, Jens Wiebe, Stephan Rachel, Dirk K. Morr

Magnet-superconductor hybrid (MSH) systems have recently emerged as one of the most significant developments in condensed matter physics. This has generated, in the last decade, a steadily rising interest in the understanding of their unique properties. They have been proposed as one of the most promising platforms for the establishment of topological superc

Carlo Rovelli

I discuss recent claims according to which relational understandings of quantum physics would undermine the credibility of science.

Chien Van Nguyen, Xuan Shen, Ryan Aponte, Yu Xia

Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, making them ideal for various settings including on-device, mobile, edge devices, among many others. In this article, we present a comprehensive survey on SLMs, focusing on their archi

Margarita Bugueño, Hazem Abou Hamdan, Gerard de Melo

Heterogeneous graph neural networks have recently gained attention for long document summarization, modeling the extraction as a node classification task. Although effective, these models often require external tools or additional machine learning models to define graph components, producing highly complex and less intuitive structures. We present GraphLSS,

Mitsuaki Kimura, Takeshi Matsumoto, Takashi Sakajo, Hiroshi Takeuchi

The dynamics and statistical properties of two-dimensional (2D) turbulence are often investigated through numerical simulations of incompressible, viscous fluids in doubly periodic domains. A key challenge in 2D turbulence research is accurately identifying and describing statistical properties of its coherent vortex structures within complex flow patterns.

Beñat Martinez de Aguirre Jokisch, Rasmus Ellebæk Christiansen, Ole Sigmund

Precise spatial manipulation of particles via optical forces is essential in many research areas, ranging from biophysics to atomic physics. Central to this effort is the challenge of designing optical systems that are optimized for specific applications. Traditional design methods often rely on trial-and-error methods, or on models that approximate the part

Zheng Zhao, Yftah Ziser, Shay B. Cohen

Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models that can solve various natural language processing (NLP) tasks. However, where and to what extent these models retain task-specific knowledge remains largely unexplored. This study investigates the task-specific information encode

Danqing Wang, Zhuorui Ye, Fei Fang, Lei Li

Enhancing the reasoning capabilities of large language models (LLMs) is crucial for enabling them to tackle complex, multi-step problems. Multi-agent frameworks have shown great potential in enhancing LLMs' reasoning capabilities. However, the lack of effective cooperation between LLM agents hinders their performance, especially for multi-step reasoning task

Hong Zhao, Huyunting Huang, Tonglin Zhang, Baijian Yang

A 3D point cloud is an unstructured, sparse, and irregular dataset, typically collected by airborne LiDAR systems over a geological region. Laser pulses emitted from these systems reflect off objects both on and above the ground, resulting in a dataset containing the longitude, latitude, and elevation of each point, as well as information about the correspon

Manuel Sage, Joshua Campbell, Yaoyao Fiona Zhao

Energy arbitrage is one of the most profitable sources of income for battery operators, generating revenues by buying and selling electricity at different prices. Forecasting these revenues is challenging due to the inherent uncertainty of electricity prices. Deep reinforcement learning (DRL) emerged in recent years as a promising tool, able to cope with unc

Alexander Thomas, Shubham Mishra, Kaiyuan Chen, John Kubiatowicz

We present PSL, a lightweight, secure and stateful Function-as-a-Serivce (FaaS) framework for Trusted Execution Environments (TEEs). The framework provides rich programming language support on heterogeneous TEE hardware for statically compiled binaries and/or WebAssembly (WASM) bytecodes, with a familiar Key-Value Store (KVS) interface to secure, performant,

Nikolaos Pavlidis, Vasileios Perifanis, Eleni Briola, Christos-Chrysanthos Nikolaidis

Early identification of Autism Spectrum Disorder (ASD) is considered critical for effective intervention to mitigate emotional, financial and societal burdens. Although ASD belongs to a group of neurodevelopmental disabilities that are not curable, researchers agree that targeted interventions during childhood can drastically improve the overall well-being o

Reinaldo J. Gleiser

An experimental setup capable, in principle, to test the isotropy of the {\em one way} propagation of light to 1 part in $10^8$ (or better), is suggested.

Seth Gossage, Rocio Kiman, Kristina Monsch, Amber A. Medina

The relationship between magnetic activity and Rossby number is one way through which stellar dynamos can be understood. Using measured rotation rates and X-ray to bolometric luminosity ratios of an ensemble of stars, we derive empirical convective turnover times based on recent observations and re-evaluate the X-ray activity-Rossby number relationship. In d

G. Coslovich, R. P. Smith, S. -F. Shi, J. H. Buss

We employ ultrabroadband terahertz (THz) spectroscopy to expose the high-frequency transport properties of Dirac fermions in monolayer graphene. By controlling the carrier concentration via tunable electrical gating, both equilibrium and transient optical conductivities are obtained for a range of Fermi levels. The frequency-dependent equilibrium response is

Shakhrul Iman Siam, Hyunho Ahn, Li Liu, Samiul Alam

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form

Anton M. Zeitlin

These lecture notes are devoted to the recent progress in the geometric aspects of quantum integrable systems based on quantum groups solved using the Bethe ansatz technique. One part is devoted to their enumerative geometry realization through the quantum K-theory of Nakajima quiver varieties. The other part describes a recently studied $q$-deformation of t

Yuxin Chen, Zijian Wu, Adam Schmidt, Septimiu E. Salcudean

Purpose: Tissue tracking is critical for downstream tasks in robot-assisted surgery. The Sparse Efficient Neural Depth and Deformation (SENDD) model has previously demonstrated accurate and real-time sparse point tracking, but struggled with occlusion handling. This work extends SENDD to enhance occlusion detection and tracking consistency while maintaining

Kasra Naftchi-Ardebili, Karanpartap Singh, Gerald R. Popelka, Kim Butts Pauly

Transcranial ultrasound (TUS) has emerged as a promising tool in clinical and research settings due to its potential to modulate neuronal activity, open the blood-brain barrier, facilitate targeted drug delivery via nanoparticles, and perform thermal ablation, all non-invasively. By delivering focused ultrasound waves to precise regions anywhere in the brain

Hadi Amarloo, Mohammad Noaman, Su-Peng Yu, Donald Booth

Rydberg atom-based sensors use atoms dressed by lasers to detect and measure radio frequency electromagnetic fields. The absorptive properties of the atomic gas, configured as a Rydberg atom-based sensor, change in the presence of a radio frequency electromagnetic field. While these sensors are reasonably sensitive, the best conventional radio frequency sens

Anzhelika Kolinko, Md Hasibul Amin, Ramtin Zand, Jason Bakos

Given the growing focus on memristive crossbar-based in-memory computing (IMC) architectures as a potential alternative to current energy-hungry machine learning hardware, the availability of a fast and accurate circuit-level simulation framework could greatly enhance research and development efforts in this field. This paper introduces XbarSim, a domain-spe

Francesco Maria Iudica

The Kudla lift studied in this article is a classical version for Picard modular forms of the automorphic theta lift between $\text{GU}(2)$ and $\text{GU}(3)$. We construct an explicit $p$-adic analytic family of Picard modular forms varying with respect to the weight and level, which interpolates a so-called $p$-modification of the lift at arithmetic weight

Adolfo S. Carvalho, Greg Doppmann, Kyle B. Westfall, Debora Pelliccia

We present a data reduction pipeline (DRP) for Keck/NIRSPEC built as an addition to the PypeIt Python package. The DRP is capable of reducing multi-order echelle data taken both before and after the detector upgrade in 2018. As part of developing the pipeline, we implemented major improvements to the capabilities of the PypeIt package, including manual wavel

Ricardo Baptista, Michael Brennan, Youssef Marzouk

Gradient-based dimension reduction decreases the cost of Bayesian inference and probabilistic modeling by identifying maximally informative (and informed) low-dimensional projections of the data and parameters, allowing high-dimensional problems to be reformulated as cheaper low-dimensional problems. A broad family of such techniques identify these projectio

Ondrej Biza, Thomas Weng, Lingfeng Sun, Karl Schmeckpeper

Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-robot runtime, due to inefficient exploration when learning from a sparse reward signal. Designing dense reward functions is labour-intensive and requires domain expertise. In our w

Victor V. Golovko

A new and robust statistics was applied to previous measurements of the 97Ru half-life. This process incorporates the most frequent value (MFV) technique along with hybrid parametric bootstrap (HPB) method to deliver a more precise estimate of the 97Ru half-life. The derived value is T1/2,MFV(HPB) = 2.8385+0.0022-0.0075 days. This estimate corresponds to a 6

M. Andrecut

The single-layer feedforward neural network with random weights is a recurring motif in the neural networks literature. The advantage of these networks is their simplified training, which reduces to solving a ridge-regression problem. A general assumption is that these networks require a large number of hidden neurons relative to the dimensionality of the da