December 2024 arXiv papers — page 169

Hongyin Tang, Di Xiu, Lanrui Wang, Xiurui Geng

The quadratic computational complexity of the attention mechanism in current Large Language Models (LLMs) renders inference with long contexts prohibitively expensive. To address this challenge, various approaches aim to retain critical portions of the context to optimally approximate Full Attention (FA) through Key-Value (KV) compression or Sparse Attention

Shivansh Chopra, Hussain Ahmad, Diksha Goel, Claudia Szabo

The increasing frequency and sophistication of cybersecurity vulnerabilities in software systems underscores the need for more robust and effective vulnerability assessment methods. However, existing approaches often rely on highly technical and abstract frameworks, which hinder understanding and increase the likelihood of exploitation, resulting in severe c

Anika Shrivastava, Renu Rameshan, Samar Agnihotri

Understanding the latent spaces learned by deep learning models is crucial in exploring how they represent and generate complex data. Autoencoders (AEs) have played a key role in the area of representation learning, with numerous regularization techniques and training principles developed not only to enhance their ability to learn compact and robust represen

Antonio De Felice, Shinji Tsujikawa

In general relativity with vector and scalar fields given by the Lagrangian ${\cal L}(F,\phi,X)$, where $F$ is a Maxwell term and $X$ is a kinetic term of the scalar field $\phi$, we study the linear stability of static and spherically symmetric objects without curvature singularities at their centers. We show that the background solutions are generally desc

Noah Vinod, Thanh Tran

Ferromagnetic magnetohydrodynamics concerns the study of conducting fluids with intrinsic magnetisation under the influence of a magnetic field. It is a generalisation of the magnetohydrodynamical equations and takes into account the dynamics of the magnetisation of a fluid. First proposed by Lingam (Lingam, `Dissipative effects in magnetohydrodynamical mode

Rajesh Mangannavar, Stefan Lee, Alan Fern, Prasad Tadepalli

We propose a novel approach to learn relational policies for classical planning based on learning to rank actions. We introduce a new graph representation that explicitly captures action information and propose a Graph Neural Network (GNN) architecture augmented with Gated Recurrent Units (GRUs) to learn action rankings. Unlike value-function based approache

Weihao Wang, Jing Guo, Siqiang Wang, Xinyi Wang

Despite the strong Doppler resilience capability, orthogonal time-frequency space (OTFS) modulation suffers from high channel estimation and equalization complexity at the receiver, hindering its applicability in air-ground transmission. In this paper, we propose a pre-equalization-based integrated sensing and communications-OTFS downlink transmission framew

Wenyong Huang, Xiang Zhang

Prelle and Singer showed in 1983 that if a system of ordinary differential equations defined on a differential field $K$ has a first integral in an elementrary field extension $L$ of $K$, then it must have a first integral consisting of algebraic elements over $K$ via their constant powers and logarithms. Based on this result they further proved that an elem

Karan Daga, Siddharth Agarwal, Zaeem Moti, Matthew BK Lee

Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate th

Sahar Rahimi Malakshan, Mohammad Saeed Ebrahimi Saadabadi, Ali Dabouei, Nasser M. Nasrabadi

Dataset Condensation (DC) aims to reduce deep neural networks training efforts by synthesizing a small dataset such that it will be as effective as the original large dataset. Conventionally, DC relies on a costly bi-level optimization which prohibits its practicality. Recent research formulates DC as a distribution matching problem which circumvents the cos

Kun Wu

As deep learning models scale, their training cost has surged significantly. Due to both hardware advancements and limitations in current software stacks, the need for data efficiency has risen. Data efficiency refers to the effective hiding of data access latency and the avoidance of unnecessary data movements. Major challenges arise from the growing dispar

Xuchan Bao, Judith Yue Li, Zhong Yi Wan, Kun Su

Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users' diverse and uncertain retrieval needs. To address this limitation, we introduce Diff4Steer, a novel generative retrieval framework that employs lightweight diffusion models to synthesize diverse seed embeddings from user queries th

Marco Volpe

In this paper, we study compactness and finiteness of an $\infty$-category $\mathcal{C}$ equipped with a conservative functor to a finite poset $P$. We provide sufficient conditions for $\mathcal{C}$ to be compact in terms of strata and homotopy links of $\mathcal{C}\rightarrow P$. Analogous conditions for $\mathcal{C}$ to be finite are also given. From thes

Changwoo J. Lee, David B. Dunson

In including random effects to account for dependent observations, the odds ratio interpretation of logistic regression coefficients is changed from population-averaged to subject-specific. This is unappealing in many applications, motivating a rich literature on methods that maintain the marginal logistic regression structure without random effects, such as

Giandomenico Palumbo

A novel oscillatory behaviour of the DC conductivity in Weyl semimetals with vacancies has recently been identified, occurring in the absence of external magnetic fields. Here, we argue that this effect has a geometric interpretation in terms of a magnetic-like field induced by an emergent Weyl connection. This geometric gauge field is related to the non-met

Runhua Chen, Haoxiang Luo, Gang Sun, Hongfang Yu

The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. Ad

Hiroto Kawakami, Kohei Saito, Akira Masuda, Shuto Yamamot

In this paper, we experimentally and theoretically show the improvement in noise characteristics in a distributed Raman amplifier (DRA) system for wavelength division multiplexing (WDM) transmission, utilizing our proposed pumping technique. We show that forward (Fwd) pumping is clearly superior to backward (Bwd) pumping in terms of noise figure (NF) defined

Yihui Li, Xiaoyue Yan, Hao Zhou, Borong Lin

In recent years, the critical role of green buildings in addressing energy consumption and environmental issues has become widely acknowledged. Research indicates that over 40% of potential energy savings can be achieved during the early design stage. Therefore, decision-making in green building design (DGBD), which is based on modeling and performance simul

Ryuji Kajikiya, Shingo Takeuchi

In the present paper, we study the first eigenvalue $\lambda(p)$ of the one-dimensional $p$-Laplacian in the interval $(-1,1)$. We give an upper and lower estimate of $\lambda(p)$ and study its asymptotic behavior as $p \to 1+0$ or $p \to \infty$.

Ningyi Liao, Zihao Yu, Siqiang Luo

Graph Transformer (GT) has recently emerged as a promising neural network architecture for learning graph-structured data. However, its global attention mechanism with quadratic complexity concerning the graph scale prevents wider application to large graphs. While current methods attempt to enhance GT scalability by altering model architecture or encoding h

Cade Gordon, Aniruddh Raghu, Peyton Greenside, Hunter Elliott

Antibody therapies have been employed to address some of today's most challenging diseases, but must meet many criteria during drug development before reaching a patient. Humanization is a sequence optimization strategy that addresses one critical risk called immunogenicity - a patient's immune response to the drug - by making an antibody more "human-like" i

Zhaoxing Gao, Ruey S. Tsay

This article considers a novel and widely applicable approach to modeling high-dimensional dependent data when a large number of explanatory variables are available and the signal-to-noise ratio is low. We postulate that a $p$-dimensional response series is the sum of a linear regression with many observable explanatory variables and an error term driven by

Shaojing Sun, Zhiyuan Liu, David Waxman

This work focuses on the nature of visibility in societies where the behaviours of humans and algorithms influence each other - termed algorithmically infused societies. We propose a quantitative measure of visibility, with implications and applications to an array of disciplines including communication studies, political science, marketing, technology desig

Gouranga Charan, Ahmed Alkhateeb

In the advent of next-generation wireless communication, millimeter-wave (mmWave) and terahertz (THz) technologies are pivotal for their high data rate capabilities. However, their reliance on large antenna arrays and narrow directive beams for ensuring adequate receive signal power introduces significant beam training overheads. This becomes particularly ch

Shengnan Guo, Tonglong Wei, Yiheng Huang, Yan Lin

Traffic data imputation is a critical preprocessing step in intelligent transportation systems, underpinning the reliability of downstream transportation services. Despite substantial progress in imputation models, model selection and development for practical applications remains challenging due to three key gaps: 1) the absence of a model taxonomy for traf

Tong Zhao, Shulin Zhong, Yuxin Sun, Defan Wu

Pressure-induced phase transformation occurs during silicon (Si) wafering processes. \b{eta}-tin (Si-II) phase is formed at high pressures, followed by the transformation to Si-XII, Si-III or/and amorphous Si ({\alpha}-Si) phases during the subsequent decompression. While the imposed pressure and its release rate are known to dictate the phase transformation

Yuqian Yang, Shusen Yang, Cong Zhao, Zongben Xu

Telecommunication Networks (TNs) have become the most important infrastructure for data communications over the last century. Operations and maintenance (O&M) is extremely important to ensure the availability, effectiveness, and efficiency of TN communications. Different from the popular O&M technique for IT systems (e.g., the cloud), artificial intelligence

Étienne André

Railway scheduling consists in ensuring that a set of trains evolve in a shared rail network without collisions, while meeting schedule constraints. This problem is notoriously difficult, even more in the case of uncertain or even unknown train speeds. We propose here a modeling and verification approach for railway scheduling in the presence of uncertain sp

Keunwoo Peter Yu, Achal Dave, Rares Ambrus, Jean Mercat

Recent advances in vision-language models (VLMs) have shown great promise in connecting images and text, but extending these models to long videos remains challenging due to the rapid growth in token counts. Models that compress videos by local aggregation in time or space have become popular for handling long-form inputs; however, these pooling-based projec

Xinyan Yu, Yiyuan Wang, Tram Thi Minh Tran, Yi Zhao

The increasing transition of human-robot interaction (HRI) context from controlled settings to dynamic, real-world public environments calls for enhanced adaptability in robotic systems. This can go beyond algorithmic navigation or traditional HRI strategies in structured settings, requiring the ability to navigate complex public urban systems containing mul

Inju Ha, Donghun Ryou, Seonguk Seo, Bohyung Han

Deep learning-based image denoising techniques often struggle with poor generalization performance to out-of-distribution real-world noise. To tackle this challenge, we propose a novel noise translation framework that performs denoising on an image with translated noise rather than directly denoising an original noisy image. Specifically, our approach transl

Dipankar Srirag, Aditya Joshi, Jordan Painter, Diptesh Kanojia

Despite large language models (LLMs) being known to exhibit bias against non-standard language varieties, there are no known labelled datasets for sentiment analysis of English. To address this gap, we introduce BESSTIE, a benchmark for sentiment and sarcasm classification for three varieties of English: Australian (en-AU), Indian (en-IN), and British (en-UK

John R. Elton, Predrag Cvitanović, Jonathan Halcrow, John F. Gibson

Lagrangian tracer particle trajectories for invariant solutions of the Navier-Stokes equations confined to the three-dimensional geometry of plane Couette flow are studied. Treating the Eulerian velocity field of an invariant solution as a dynamical system, the transport of these passive scalars along Lagrangian flow trajectories reveals a rich repertoire of

Jixun Yao, Yuguang Yang, Yu Pan, Ziqian Ning

Zero-shot voice conversion (VC) aims to transfer the timbre from the source speaker to an arbitrary unseen speaker while preserving the original linguistic content. Despite recent advancements in zero-shot VC using language model-based or diffusion-based approaches, several challenges remain: 1) current approaches primarily focus on adapting timbre from unse

Zhanglin Hou, Ziluo Zhang, Jun Li, Kento Yasuda

Using an underdamped active Ornstein-Uhlenbeck particle, we propose two information swimmer models having either external or internal feedback control and perform their numerical simulations. Depending on the velocity that is measured after every fixed time interval (measurement time), the friction coefficient is modified in the externally controlled model,

Hitendra Sarkar, Madhurjya P. Bora

In this work, we have carried out a two-dimensional (2D) simulation of thermal instability (TI) in interstellar matter (ISM), considering it to be a weakly ionised inviscid plasma with radiation loss. We carry out the simulation using our multi-fluid flux-corrected transport (mFCT) code, which incorporates a background magnetic field and anisotropic pressure

Jarred M. Roberts, Steven Boggs, Thomas Siegert, John A. Tomsick

The Compton Spectrometer and Imager balloon payload (COSI-Balloon) is a wide-field-of-view Compton ${\gamma}$-ray telescope that operates in the 0.2 - 5 MeV bandpass. COSI-Balloon had a successful 46-day flight in 2016 during which the instrument observed the Crab Nebula, Cygnus X-1, and Centaurus A. Using the data collected by the COSI-Balloon instrument du

Haozhe Wang, Xiaodan Shao, Beixiong Zheng, Xiaoming Shi

Six-dimensional movable antenna (6DMA) is a promising solution for enhancing wireless network capacity through the adjustment of both three-dimensional (3D) positions and 3D rotations of distributed antenna surfaces. Previous works mainly consider 6DMA surfaces composed of active antenna elements, thus termed as active 6DMA. In this letter, we propose a new

Wei Liu, Xin Xu, Hua Chang, Xin Yuan

Current visible-infrared cross-modality person re-identification research has only focused on exploring the bi-modality mutual retrieval paradigm, and we propose a new and more practical mix-modality retrieval paradigm. Existing Visible-Infrared person re-identification (VI-ReID) methods have achieved some results in the bi-modality mutual retrieval paradigm

Jiajing Chen, Bingying Liu, Xiaoxuan Liao, Jia Gao

With the rapid development of natural language processing technology, large-scale language models (LLM) have achieved remarkable results in a variety of tasks. However, how to effectively train these huge models and improve their performance and computational efficiency remains an important challenge. This paper proposes an improved method based on adaptive

Matthew Nazari

The documentation of the Neo-Aramaic dialects before their extinction has been described as the most urgent task in all of Semitology today. The death of this language will be an unfathomable loss to the descendents of the indigenous speakers of Aramaic, now predominantly diasporic after forced displacement due to violence. This paper develops an ASR model t

Olivier Bourget, Alain Joye, Dominique Spehner

We analyse the discrete-time dynamics of a model of non-interacting fermions coupled to an infinite reservoir formed by a bosonic quantum walk on ${\mathbb Z}$. This dynamics consists of consecutive applications of free evolutions of the fermions and bosons followed by a local coupling between them. The unitary operator implementing this coupling accounts fo

Wei Hong, Zhen-Zhao Tao, Peng He, Tong-Jie Zhang

Gravitational waves (GWs) can convert into electromagnetic waves in the presence of a magnetic field via the Gertsenshtein-Zeldovich (GZ) effect. The characteristics of the magnetic field substantially affect this conversion probability. This paper confirms that strong magnetic fields in neutron stars significantly enhance the conversion probability, facilit

Sunung Mun, Jinhwan Nam, Sunghyun Cho, Jungseul Ok

Text-based image editing, powered by generative diffusion models, lets users modify images through natural-language prompts and has dramatically simplified traditional workflows. Despite these advances, current methods still suffer from a critical problem: attribute leakage, where edits meant for specific objects unintentionally affect unrelated regions or o

Hongjin Lin, Matthew Nazari, Derek Zheng

Reliable large-scale data on the state of forests is crucial for monitoring ecosystem health, carbon stock, and the impact of climate change. Current knowledge of tree species distribution relies heavily on manual data collection in the field, which often takes years to complete, resulting in limited datasets that cover only a small subset of the world's for

Yuichiro Kitajima

The CHSH inequality is an inequality used to test locality in quantum theory and is recognized as one of Bell's inequalities. In contrast, the KCBS inequality is employed to test noncontextuality in quantum theory. While certain quantum states are known to violate these inequalities individually, it was previously assumed that no state could violate both ine

Takumi Omori, Takashi Iida, Nobuo Hinohara, Kotaro Takahashi

Neutrinoless double beta decay (0v2b) has been investigated as a physical process that can provide evidence for the Majorana nature of neutrinos. The theoretical predictions of the 0v2b rate are subject to significant uncertainty, primarily due to nuclear matrix elements (NME). To reduce this uncertainty, experimental measurements of the half-lives of two-ne

Diluka Galappaththige, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou

Cell-free (CF) architecture and full-duplex (FD) communication are leading candidates for next-generation wireless networks. The CF framework removes cell boundaries in traditional cell-based systems, thereby mitigating inter-cell interference and improving coverage probability. In contrast, FD communication allows simultaneous transmission and reception on

Norbert Bodendorfer, Konstantin Eder, Xiangdong Zhang

Loop quantum gravity in its Hamiltonian form relies on a connection formulation of the gravitational phase space with three key properties: 1.) a compact gauge group, 2.) real variables, and 3.) canonical Poisson brackets. In conjunction, these properties allow to construct a well defined kinematical quantization of the holonomy flux-algebra on top of which

Tomoyuki Maruyama, Takehito Hayakawa, Ryoichi Hajima, Toshitaka Kajino

In the present study, we show calculation of nonlinear Compton scattering with circularly polarized photons in a cylindrical coordinate using Feynman diagram to calculate photon vortex generation in intermediate states considering conservation of angular momentum. We take two different vortex wave functions based on Bessel function for the emitted photon and

Evgeny Korotyaev

We consider a first order operator with a periodic 3x3 matrix potential on the real line. This operator appears in the problem of the periodic vector NLS equation. The spectrum of the operator covers the real line, it is union of the spectral bands of multiplicity 3, separated by spectral intervals of multiplicity 1. The main results of this work are the fol

Rui Zhou, Yanxia Zhang, Chenyang Yuan, Frank Permenter

This paper introduces a generative model designed for multimodal control over text-to-image foundation generative AI models such as Stable Diffusion, specifically tailored for engineering design synthesis. Our model proposes parametric, image, and text control modalities to enhance design precision and diversity. Firstly, it handles both partial and complete

Myungheon Chin, Sarah J Zou, Garry Chinn, Craig S. Levin

Positron emission tomography (PET) enables quantification of dynamic physiological processes through time-resolved imaging. In Rb-82 myocardial perfusion PET, kinetic compartment modeling is used to estimate physiological parameters and derive myocardial blood flow. However, conventional nonlinear least squares (NLLS) estimation is sensitive to model misspec

Neill Lambert, Eric Giguère, Paul Menczel, Boxi Li

QuTiP, the Quantum Toolbox in Python, has been at the forefront of open-source quantum software for the past 13 years. It is used as a research, teaching, and industrial tool, and has been downloaded millions of times by users around the world. Here we introduce the latest developments in QuTiP v5, which are set to have a large impact on the future of QuTiP

Abulhair Saparov, Srushti Pawar, Shreyas Pimpalgaonkar, Nitish Joshi

Search is an ability foundational in many important tasks, and recent studies have shown that large language models (LLMs) struggle to perform search robustly. It is unknown whether this inability is due to a lack of data, insufficient model parameters, or fundamental limitations of the transformer architecture. In this work, we use the foundational graph co

Zhijie Sun, Ruofan Chen, Zhenyu Li, Chu Guo

An emergent numerical approach to solve quantum impurity problems is to encode the impurity path integral as a matrix product state. For time-dependent problems, the cost of this approach generally scales with the evolution time. Here we consider a common non-equilibrium scenario where an impurity, initially in equilibrium with a thermal bath, is driven out

Francesco Di Colandrea, Tareq Jaouni, John Grace, Dilip Paneru

The evolution of a quantum system interacting with an environment can be described as a unitary process acting on both the system and the environment. In this framework, the system's evolution can be predicted by tracing out the environmental degrees of freedom. Here, we establish a precise mapping between the global unitary dynamics and the quantum operatio

Hao Yu, Zebin Huang, Yutong Li, Xinliang Guo

Robotic devices hold great potential for efficient and reliable assessment of neuromotor abnormalities in post-stroke patients. However, spasticity caused by stroke is still assessed manually in clinical settings. The limited and variable nature of data collected from patients has long posed a major barrier to quantitatively modelling spasticity with robotic

Jan-Hendrik Pöhls, Chun-Wan Timothy Lo, Marissa MacIver, Yu-Chih Tseng

Systemic optimization of thermoelectric materials is arduous due to their conflicting electrical and thermal properties. A strategy based on Design of Experiments and machine learning is developed to optimize the thermoelectric efficiency of AgSb1+xTe2+y, an established thermoelectric. From eight experiments, high thermoelectric performance in AgSb1.021Te2.0

Hieu Nguyen, Jun Li, Shahram Ghandeharizadeh

This paper introduces a novel one-hop sub-query result cache for processing graph read transactions, gR-Txs, in a graph database system. The one-hop navigation is from a vertex using either its in-coming or out-going edges with selection predicates that filter edges and vertices. Its cache entry identifies a unique one-hop sub-query (key) and its result set

Tatsuki Koga, Ruihan Wu, Zhiyuan Zhang, Kamalika Chaudhuri

With the recent remarkable advancement of large language models (LLMs), there has been a growing interest in utilizing them in the domains with highly sensitive data that lies outside their training data. For this purpose, retrieval-augmented generation (RAG) is particularly effective -- it assists LLMs by directly providing relevant information from the ext

V. V. Kashikhin, S. Cohan, J. DiMarco, O. Kiemschies

Rare-Earth Barium Copper Oxide (REBCO) coated conductors are an attractive option for application in high field accelerator magnets due to their high critical field and the convenience of fabrication without heat treatment compared to some other superconductors. A small REBCO accelerator magnet was previously fabricated and tested in liquid nitrogen, demonst

Lehel Csillag, Julio Marny Hoff da Silva, Tudor Patuleanu

Wigner's classification has led to the insight that projective unitary representations play a prominent role in quantum mechanics. The physics literature often states that the theory of projective unitary representations can be reduced to the theory of ordinary unitary representations by enlarging the group of physical symmetries. Nevertheless, the enlargeme

Andres Felipe Bocanegra Vargas, Tao E. Li

Recent experiments have demonstrated that polariton formation provides a novel strategy for modifying local molecular processes when a large ensemble of molecules is confined within an optical cavity. Herein, a numerical strategy based on coupled Maxwell--Schr\"odinger equations is examined for simulating local molecular processes in a realistic cavity struc

Aristomenis Tsopelakos, Georgios Fellouris

The problem of sequential anomaly detection and identification is considered, where multiple data sources are simultaneously monitored and the goal is to identify in real time those, if any, that exhibit ``anomalous" statistical behavior. An upper bound is postulated on the number of data sources that can be sampled at each sampling instant, but the decision

Neel Guha, Mayee F. Chen, Trevor Chow, Ishan S. Khare

Large language models (LLMs) are increasingly used in applications where LLM inputs may span many different tasks. Recent work has found that the choice of LLM is consequential, and different LLMs may be good for different input samples. Prior approaches have thus explored how engineers might select an LLM to use for each sample (i.e. routing). While existin

Hidehisa Arai, Keishi Ishihara, Tsubasa Takahashi, Yu Yamaguchi

World models have emerged as promising neural simulators for autonomous driving, with the potential to supplement scarce real-world data and enable closed-loop evaluations. However, current research primarily evaluates these models based on visual realism or downstream task performance, with limited focus on fidelity to specific action instructions - a cruci

Robert Meadows, Y. Xue, Nicholas Allbritton, G. P. Zhang

Laser-driven electron transport across a sample has garnered enormous attentions over several decades, as it provides a much faster way to control electron dynamics. Light is an electromagnetic wave, so how and why an electron can acquire a longitudinal velocity remains unanswered. Here we show that it is the magnetic field that steers the electron to the li

Xuan Chen, Tong Lu, Zhichun Wang

Entity Alignment (EA) seeks to identify and match corresponding entities across different Knowledge Graphs (KGs), playing a crucial role in knowledge fusion and integration. Embedding-based entity alignment (EA) has recently gained considerable attention, resulting in the emergence of many innovative approaches. Initially, these approaches concentrated on le

Brett Parker

How should we model an observer within quantum mechanics or quantum field theory? How can classical physics emerge from a quantum model, and why should classical probability be useful? How can we model a selective measurement entirely within a closed quantum system? This paper sketches a new physical theory of probability based on an attempt to model classic

Seyedparsa Dajkhosh

Procedural terrain generation plays a vital role in creating virtual landscapes for games, simulations, and various applications. The WaveFunctionCollapse (WFC) algorithm has proven effective in generating content by learning patterns from example data. In this research, we adapt WFC to generate terrain height maps using Shuttle Radar Topography Mission (SRT

Nguyen Duy Cuong, Alexander Y. Kruger

We show that the existing generalized separation statements including the conventional extremal principle and its extensions differ {in the ways norms on product spaces are defined}. We prove a general separation statement with arbitrary product norms covering the existing results of this kind. The proof is divided into a series of claims and exposes the key

Jiyuan Wang, Jason Teoh, Muhammand Ali Gulza, Qian Zhang

Many symptoms of poor performance in big data analytics such as computational skews, data skews, and memory skews are input dependent. However, due to the lack of inputs that can trigger such performance symptoms, it is hard to debug and test big data analytics. We design PerfGen to automatically generate inputs for the purpose of performance testing. PerfGe

Simon Florian Koch, Brian Moser, Antonín Lindner, Valerio Dao

The ITk is a new silicon tracker for the ATLAS experiment designed to increase detector resolution, readout capacity, and radiation hardness, in preparation for the larger number of simultaneous proton-proton interactions at the High Luminosity LHC. This paper presents the first direct measurement of the material budget of an ATLAS ITk pixel module, performe

Qun Chen, Hongbing Qiu

When the domain is a complete noncompact Riemannian manifold with nonnegative Bakry--Emery Ricci curvature and the target is a complete Riemannian manifold with sectional curvature bounded above by a positive constant, by carrying out refined gradient estimates, we obtain a better Liouville theorem for ancient solutions to the V-harmonic map heat flows. Furt

Nan Sun, Yuantian Miao, Hao Jiang, Ming Ding

In the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between innovation and regulation has become an emerging focus of our society. Despite tremendous advancements in AI's capabilities to excel in specific tasks and contribute to diverse sectors, establishing a high degree of trust in AI-generated outputs and decisions nece

Hisashi Oshima, Tsuyoshi Ishizone, Tomoyuki Higuchi

Recent developments in the unsupervised domain adaptation (UDA) enable the unsupervised machine learning (ML) prediction for target data, thus this will accelerate real world applications with ML models such as image recognition tasks in self-driving. Researchers have reported the UDA techniques are not working well under large co-variate shift problems wher

Whittney Easterwood, Anusha Kalyaan, Andrea Banzatti

Volatiles like $H_2O$ are present as ice in solids in the outer cold regions of protoplanetary disks and as vapor in the warm inner regions within the water snow line. Icy pebbles drifting inwards from the outer disk sublimate after crossing the snow line, enriching the inner disk with solid mass and water vapor. Meanwhile, proto-planets forming within the d

Jaihyun Lew, Soohyuk Jang, Jaehoon Lee, Seungryong Yoo

Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success lies in the attention mechanism, which models relationships among tokens. While the tokenization process in NLP inherently ensures that a single token does not contain multiple se

John C. Howell, Brian Flores, Juan Javier Naranjo, Angel Mendez

We report on an educational pilot program for low-cost physics experimentation run in Ecuador, South Africa, and the United States. The program was developed after having needs-based discussions with African educators, researchers, and leaders. It was determined that the need and desire for low-cost, skills-building, and active-learning tools is very high. F

Daniela Ivanova, Marco Aversa, Paul Henderson, John Williamson

We propose an unsupervised image segmentation method using features from pre-trained text-to-image diffusion models. Inspired by classic spectral clustering approaches, we construct adjacency matrices from self-attention layers between image patches and recursively partition using Normalised Cuts. A key insight is that self-attention probability distribution

Ian Lu, Hao Jia, Sebastian Gonzalez, Deniz Sogutlu

With the approach of the High Luminosity Large Hadron Collider (HL-LHC) era set to begin particle collisions by the end of this decade, it is evident that the computational demands of traditional collision simulation methods are becoming increasingly unsustainable. Existing approaches, which rely heavily on first-principles Monte Carlo simulations for modeli

Andor Menczer, Örs Legeza

We introduce efficient solutions to optimize the cost of tree-like tensor network state method calculations when an expensive GPU-accelerated hardware is utilized. By supporting a main powerful compute node with additional auxiliary, but much cheaper nodes to store intermediate, precontracted tensor network scratch data, the IO time can be hidden behind the

Iván Alfonso, Aaron Conrardy, Jordi Cabot

With the promise of accelerating software development, low-code platforms (LCPs) are becoming popular across various industries. Nevertheless, there are still barriers hindering their adoption. Among them, vendor lock-in is a major concern, especially considering the lack of interoperability between these platforms. Typically, after modeling an application i

David Garcia-Garcia, Jose A. R. Cembranos

This work explores the intersection of quantum mechanics and curved spacetime by employing the Wigner formalism to investigate quantum systems in the vicinity of black holes. Specifically, we study the quantum dynamics of a probe particle bound to a Schwarzschild black hole using a phase-space representation of quantum mechanics. The analysis begins with a r

Peng He, Xue-Ping Wang

This article intends to characterize triangular norms on a finite lattice. We first give a method for generating a triangular norm on an atomistic lattice by the values of atoms. Then we prove that every triangular norm on a non-Boolean atomistic lattice is not left-continuous and $T_M$ is the uniquely left-continuous triangular norm on an atomistic Boolean

Dmitry V. Malyshev

About one third of Fermi Large Area Telescope (LAT) sources are unassociated. We perform multi-class classification of Fermi-LAT sources using machine learning with the goal of probabilistic classification of the unassociated sources. A particular attention is paid to the fact that the distributions of associated and unassociated sources are different as fun

Bin Wu, Guo-Liang Yu, Zhi-Gang Wang, Ze Zhou

This article is devoted to calculating the form factors of $B_c \to D^{*}$, $B_c \to D$, $B_c \to D_s^{*}$ and $B_c \to D_s$ transitions in the framework of three-point QCD sum rules. At the QCD side, the contributions of $\langle\overline{q}q\rangle$, $\langle\overline{q}g_{s}σGq\rangle$, $\langle g_{s}^{2}G^{2}\rangle$, $\langle f^{3}G^{3}\rangle$ and $\la

Chukwudubem Umeano, Oleksandr Kyriienko

We introduce a quantum data embedding protocol based on the preparation of a ground state of a parameterized Hamiltonian. We analyze the corresponding quantum feature map, recasting it as an adiabatic state preparation procedure with Trotterized evolution. We compare the properties of underlying quantum models with ubiquitous Fourier-type quantum models, and

Romain Ait Abdelmalek-Lomenech, Julien Bect, Vincent Chabridon, Emmanuel Vazquez

We consider an unknown multivariate function representing a system-such as a complex numerical simulator-taking both deterministic and uncertain inputs. Our objective is to estimate the set of deterministic inputs leading to outputs whose probability (with respect to the distribution of the uncertain inputs) of belonging to a given set is less than a given t

Yoshinao Katsu, Jiawn Zhang, Ya Ao, Michael E. Baker

While the classical function of human mineralocorticoid receptor (MR) is to regulate sodium and potassium homeostasis through aldosterone activation of the kidney MR, the MR also is highly expressed in the brain, where the MR is activated by cortisol in response to stress. Here, we report the half-maximal response (EC50) and fold-activation by cortisol, aldo

Haleh Damirchi, Ali Etemad, Michael Greenspan

Pedestrian trajectory prediction remains a challenge for autonomous systems, particularly due to the intricate dynamics of social interactions. Accurate forecasting requires a comprehensive understanding not only of each pedestrian's previous trajectory but also of their interaction with the surrounding environment, an important part of which are other pedes

Hugo Marsan, Mathieu Sablik

We add small random perturbations to a cellular automaton and consider the one-parameter family $(F_\epsilon)_{\epsilon>0}$ parameterized by $\epsilon$ where $\epsilon>0$ is the level of noise. The objective of the article is to study the set of limiting invariant distributions as $\epsilon$ tends to zero denoted $\mathcal{M}_0^l$. Some topological obstructi

Bethia Sun, Maurice Pagnucco, Yang Song

Since the inception of the classicalist vs. connectionist debate, it has been argued that the ability to systematically combine symbol-like entities into compositional representations is crucial for human intelligence. In connectionist systems, the field of disentanglement has gained prominence for its ability to produce explicitly compositional representati

Mrigi Munjal, Jaewan Lee, Changyoung Park, Sehui Han

Most prediction models for crystal properties employ a unimodal perspective, with graph-based representations, overlooking important non-local information that affects crystal properties. Some recent studies explore the impact of integrating graph and textual information on crystal property predictions to provide the model with this "missing" information by

Murdock Grewar, Glenn Myers, Andrew Kingston

Global backprojection-convolution (GBC) is a recently developed theory for exact reconstruction in transmission cone-beam computed tomography (CBCT). It is the first exact inversion theory that applies when the X-ray source points comprise a multidimensional `source locus' $X \subset \mathbb R^3$. Theoretically, GBC is computationally highly expedient due to

Ali Abbasi, Shima Imani, Chenyang An, Gayathri Mahalingam

With the rapid scaling of neural networks, data storage and communication demands have intensified. Dataset distillation has emerged as a promising solution, condensing information from extensive datasets into a compact set of synthetic samples by solving a bilevel optimization problem. However, current methods face challenges in computational efficiency, pa

Sadra Sabouri, Sepand Haghighi

Long sessions of computer use introduce physical and mental health risks, particularly for programmers and intensive computer users. Breathing exercises can improve focus, reduce stress, and overall well-being. However, existing tools for such practices are often app-based, requiring users to leave their workspace. In this technical report, we introduce Nafa

Kebin Peng, Haotang Li, Zhenyu Qi, Huashan Chen

State-of-the-art monocular depth estimation (MDE) models often struggle in challenging environments, primarily because they overlook robust physical information. To demonstrate this, we first conduct an empirical study by computing the covariance between a model's prediction error and atmospheric attenuation. We find that the error of existing SOTAs increase

Karthik Shetty, Annette Birkhold, Bernhard Egger, Srikrishna Jaganathan

We present a novel approach for 3D human pose estimation by employing probabilistic modeling. This approach leverages the advantages of normalizing flows in non-Euclidean geometries to address uncertain poses. Specifically, our method employs normalizing flow tailored to the SO(3) rotational group, incorporating a coupling mechanism based on the M\"obius tra