May 2024 arXiv papers — page 39

Mucong Ding, Yinhan He, Jundong Li, Furong Huang

The ubiquity of large-scale graphs in node-classification tasks significantly hinders the real-world applications of Graph Neural Networks (GNNs). Node sampling, graph coarsening, and dataset condensation are effective strategies for enhancing data efficiency. However, owing to the interdependence of graph nodes, coreset selection, which selects subsets of t

Sergio Hoyas, Ricardo Vinuesa, Peter Schmid, Hassan Nagib

Direct numerical simulations (DNS) stand out as formidable tools in studying turbulent flows. Despite the fact that the achievable Reynolds number remains lower than those available through experimental methods, DNS offers a distinct advantage: the complete knowledge of the velocity field, facilitating the evaluation of any desired quantity. This capability

Kai Wang, Mingjia Shi, Yukun Zhou, Zekai Li

Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called, which is based on a closer look at time steps. Our key findings are: i) Time steps can be empirically divided into acceleration, deceleration, and convergence areas based on the process increment. ii) The

Philip Schroeder, Nathaniel Morgan, Hongyin Luo, James Glass

Large language models (LLMs) have shown impressive capabilities across diverse settings, but still struggle as the length and complexity of the context increases. To address this challenge, we propose Thinking Recursively and Dynamically (ThReaD). THREAD frames model generation as a thread of execution that, based on the context, can run to completion or dyn

Litu Rout, Yujia Chen, Nataniel Ruiz, Abhishek Kumar

We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models. Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style image

Apurva Sinha, Ekta Gujral

Product attribute extraction is an growing field in e-commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Understanding the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment plan

Jiannan Huang, Jun Hao Liew, Hanshu Yan, Yuyang Yin

Recent text-to-image customization works have proven successful in generating images of given concepts by fine-tuning diffusion models on a few examples. However, tuning-based methods inherently tend to overfit the concepts, resulting in failure to create the concept under multiple conditions (*e.g.*, headphone is missing when generating "a `dog wearing a he

Digvijay Wadekar, Tejaswi Venumadhav, Javier Roulet, Ajit Kumar Mehta

Nearly all template-based gravitational wave (GW) searches only include the quasi-circular quadrupolar modes of the signals in their templates. Including additional degrees of freedom in the GW templates corresponding to higher-order harmonics, orbital precession, or eccentricity is challenging because: ($i$) the size of template banks and the matched-filter

Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain

The poor performance of transformers on arithmetic tasks seems to stem in large part from their inability to keep track of the exact position of each digit inside of a large span of digits. We mend this problem by adding an embedding to each digit that encodes its position relative to the start of the number. In addition to the boost these embeddings provide

Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta

World models can foresee the outcomes of different actions, which is of paramount importance for autonomous driving. Nevertheless, existing driving world models still have limitations in generalization to unseen environments, prediction fidelity of critical details, and action controllability for flexible application. In this paper, we present Vista, a gener

Niloofar Azizi, Mohsen Fayyaz, Horst Bischof

Understanding human behavior fundamentally relies on accurate 3D human pose estimation. Graph Convolutional Networks (GCNs) have recently shown promising advancements, delivering state-of-the-art performance with rather lightweight architectures. In the context of graph-structured data, leveraging the eigenvectors of the graph Laplacian matrix for positional

Sukannya Bhattacharya, Giulia Borghetto, Ameek Malhotra, Susha Parameswaran

Dynamical dark energy has gained renewed interest due to recent theoretical and observational developments. In the present paper, we focus on a string-motivated dark energy set-up, and perform a detailed cosmological analysis of exponential quintessence with potential $V=V_0 e^{-\lambda\phi}$, allowing for non-zero spatial curvature. We first gain some physi

Noga Mudrik, Ryan Ly, Oliver Ruebel, Adam S. Charles

Modern recordings of neural activity provide diverse observations of neurons across brain areas, conditions, and subjects; presenting an exciting opportunity to reveal the fundamentals of brain-wide dynamics. Current analysis methods often fail to harness the richness of such data, as they provide either uninterpretable representations or oversimplify models

Parsa Veysi, Mohsen Adeli, Nayerosadat Peirov Naziri, Ehsan Adeli

This research paper delves into the Linear Kalman Filter (LKF), highlighting its importance in merging data from multiple sensors. The Kalman Filter is known for its recursive solution to the linear filtering problem in discrete data, making it ideal for estimating states in dynamic systems by reducing noise in measurements and processes. Our focus is on lin

Yash Sarrof, Yana Veitsman, Michael Hahn

Recently, recurrent models based on linear state space models (SSMs) have shown promising performance in language modeling (LM), competititve with transformers. However, there is little understanding of the in-principle abilities of such models, which could provide useful guidance to the search for better LM architectures. We present a comprehensive theoreti

Sai Raj Kishore Perla, Yizhi Wang, Ali Mahdavi-Amiri, Hao Zhang

We present a novel approach for single-image mesh texturing, which employs a diffusion model with judicious conditioning to seamlessly transfer an object's texture from a single RGB image to a given 3D mesh object. We do not assume that the two objects belong to the same category, and even if they do, there can be significant discrepancies in their geometry

Cordula Reisch, Bao-Ngoc Tran, Juan Yang

We study a chemotaxis system that includes two competitive prey and one predator species in a two-dimensional domain, where the movement of prey (resp. predators) is driven by chemicals secreted by predators (resp. prey), called mutually repulsive (resp. mutually attractive) chemotactic effect. The kinetics for all species are chosen according to the competi

Ekaterina Kukleva, Vitaly Vanchurin

In artificial neural networks, the activation dynamics of non-trainable variables is strongly coupled to the learning dynamics of trainable variables. During the activation pass, the boundary neurons (e.g., input neurons) are mapped to the bulk neurons (e.g., hidden neurons), and during the learning pass, both bulk and boundary neurons are mapped to changes

Nimol Thuon, Wangrui Zhang, Sada Thuon

This paper introduces KSW, a Khmer-specific approach to keyword extraction that leverages a specialized stop word dictionary. Due to the limited availability of natural language processing resources for the Khmer language, effective keyword extraction has been a significant challenge. KSW addresses this by developing a tailored stop word dictionary and imple

Suma Murthy, Raffaella Morganti, Tom Oosterloo, Robert Schulz

We present deep kpc- and pc-scale neutral atomic hydrogen (HI) absorption observations of a very young radio source (< 5000 yrs), 4C 31.04, using the WSRT and the Global VLBI array. Using $z=0.0598$, we detect a broad absorption feature centred at the systemic velocity, and narrow absorption redshifted by 220 km/s both previously observed. Additionally, we d

Jamie Heredge, Maxwell West, Lloyd Hollenberg, Martin Sevior

We introduce several probabilistic quantum algorithms that overcome the normal unitary restrictions in quantum machine learning by leveraging the Linear Combination of Unitaries (LCU) method. Among our investigations are quantum native implementations of Residual Networks (ResNet), where we show that residual connections between layers of a variational ansat

Jimmy Fernandez Landivar, Khojiakbar Botirov, Hazem Sallouha, Marcos Katz

The sustainable design of Internet of Things (IoT) networks encompasses considerations related to energy efficiency and autonomy as well as considerations related to reliable communications, ensuring no energy is wasted on undelivered data. Under these considerations, this work proposes the design and implementation of energy-efficient Bluetooth Low Energy (

Zixian Huang, Wenhao Zhu, Gong Cheng, Lei Li

Reasoning capabilities are crucial for Large Language Models (LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-tune LLMs to relearn reasoning capabilities in non-English languages, while others replace non-English inputs with an external model's outputs such as English translation text to ci

Trond I. Andersen, Nikita Astrakhantsev, Amir H. Karamlou, Julia Berndtsson

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting

Fangneng Zhan, Hanxue Liang, Yifan Wang, Michael Niemeyer

The landscape of computer graphics has undergone significant transformations with the recent advances of differentiable rendering models. These rendering models often rely on heuristic designs that may not fully align with the final rendering objectives. We address this gap by pioneering \textit{evolutive rendering models}, a methodology where rendering mode

Andrea Giuliani, Martina Cardillo

In the 1960s, the remnants of supernova explosions (SNRs) were indicated as a possible source of galactic cosmic rays through the Diffusive Shock Acceleration (DSA) mechanism. Since then, the observation of gamma-ray emission from relativistic ions in these objects has been one of the main goals of high-energy astrophysics. A few dozen SNRs have been detecte

Zhen Qin, Xuyang Shen, Dong Li, Weigao Sun

We present the Linear Complexity Sequence Model (LCSM), a comprehensive solution that unites various sequence modeling techniques with linear complexity, including linear attention, state space model, long convolution, and linear RNN, within a single framework. The goal is to enhance comprehension of these models by analyzing the impact of each component fro

Zhixing Cao, Yiling Wang, Ramon Grima

We report the existence of deterministic patterns in plots showing the relationship between the mean and the Fano factor (ratio of variance and mean) of stochastic count data. These patterns are found in a wide variety of datasets, including those from genomics, paper citations, commerce, ecology, disease outbreaks, and employment statistics. We develop a th

Hyunseok Lee, Jihoon Tack, Jinwoo Shin

The remarkable capabilities and easy accessibility of large language models (LLMs) have significantly increased societal risks (e.g., fake news generation), necessitating the development of LLM-generated text (LGT) detection methods for safe usage. However, detecting LGTs is challenging due to the vast number of LLMs, making it impractical to account for eac

Zhen Qin, Weigao Sun, Dong Li, Xuyang Shen

We present Lightning Attention, the first linear attention implementation that maintains a constant training speed for various sequence lengths under fixed memory consumption. Due to the issue with cumulative summation operations (cumsum), previous linear attention implementations cannot achieve their theoretical advantage in a casual setting. However, this

Sureshkumar M, Prahlad Vaidyanathan

Given a separable, AF-algebra A and an inductive limit action on A of a finitely generated abelian group with finite Rokhlin dimension with commuting towers, we give a local description of the associated crossed product C*-algebra. In particular, when A is unital and $\alpha \in Aut(A)$ is approximately inner and has the Rokhlin property, we conclude that $A

Isaac H. Kim, Daniel Ranard

We prove the conjectured classification of topological phases in two spatial dimensions with gappable boundary, in a simplified setting. Two gapped ground states of lattice Hamiltonians are in the same quantum phase of matter, or topological phase, if they can be connected by a constant-depth quantum circuit. It is conjectured that the Levin-Wen string-net m

Ahmed Allam, Mohamed Shalan

Large Language Models (LLMs) have demonstrated potential in assisting with Register Transfer Level (RTL) design tasks. Nevertheless, there remains to be a significant gap in benchmarks that accurately reflect the complexity of real-world RTL projects. To address this, this paper presents RTL-Repo, a benchmark specifically designed to evaluate LLMs on large-s

Yuval Sharon, Yehuda Dar

In this paper, we elucidate how representations in deep neural networks (DNNs) evolve during training. Our focus is on overparameterized learning settings where the training continues much after the trained DNN starts to perfectly fit its training data. We examine the evolution of learned representations along the entire training process. We explore the repr

Mohamed Nabih Ali, Alessio Brutti, Daniele Falavigna

Automatic speech recognition models require large amounts of speech recordings for training. However, the collection of such data often is cumbersome and leads to privacy concerns. Federated learning has been widely used as an effective decentralized technique that collaboratively learns a shared prediction model while keeping the data local on different cli

Petar Bakić, Aleksander Horawa, Siyan Daniel Li-Huerta, Naomi Sweeting

Cuspidal automorphic representations $\tau$ of $\mathrm{PGL}_2$ correspond to global long root $A$-parameters for $\mathsf{G}_2$. Using an exceptional theta lift between $\mathrm{PU}_3$ and $\mathsf{G}_2$, we construct the associated global $A$-packet and prove the Arthur multiplicity formula for these representations when $\tau$ is dihedral and satisfies so

ShengYun Peng, Pin-Yu Chen, Matthew Hull, Duen Horng Chau

Safety alignment is crucial to ensure that large language models (LLMs) behave in ways that align with human preferences and prevent harmful actions during inference. However, recent studies show that the alignment can be easily compromised through finetuning with only a few adversarially designed training examples. We aim to measure the risks in finetuning

Sudipta Majumder, Vaibhav Walve, Rahul Chand, Gokul M. A.

Atomic vacancies, such as chalcogen vacancies in 2D TMDs, are important in changing the host material's electronic structure and transport properties. We present a straightforward one-step method for growing monolayer MoS2 utilizing oxidized Molybdenum (Mo) foil using CVD and delve into the transport properties of as-grown samples. Devices fabricated from th

Zikang Zhou, Haibo Hu, Xinhong Chen, Jianping Wang

Simulating realistic behaviors of traffic agents is pivotal for efficiently validating the safety of autonomous driving systems. Existing data-driven simulators primarily use an encoder-decoder architecture to encode the historical trajectories before decoding the future. However, the heterogeneity between encoders and decoders complicates the models, and th

Takashi U. Ito, Wataru Higemoto, Akihiro Koda, Jumpei G. Nakamura

We report on muon spin relaxation ($\mu^+$SR) measurements in a mixed perovskite compound, (LaAlO$_3$)$_{x}$(SrAl$_{0.5}$Ta$_{0.5}$O$_3$)$_{1-x}$ with $x\simeq 0.3$ (LSAT), which is widely used as a single-crystalline substrate for thin film deposition. In zero applied field (ZF), muon depolarization due to the distribution of nuclear dipole fields was obser

Yunian Pan, Quanyan Zhu

Meta-learning has been proposed as a promising machine learning topic in recent years, with important applications to image classification, robotics, computer games, and control systems. In this paper, we study the problem of using meta-learning to deal with uncertainty and heterogeneity in ergodic linear quadratic regulators. We integrate the zeroth-order o

Amin Ahmadi Kasani, Hedieh Sajedi

The way organs are positioned and moved in the workplace can cause pain and physical harm. Therefore, ergonomists use ergonomic risk assessments based on visual observation of the workplace, or review pictures and videos taken in the workplace. Sometimes the workers in the photos are not in perfect condition. Some parts of the workers' bodies may not be in t

J. D. Peiffer, Kunal Shah, Shawana Anarwala, Kayan Abdou

Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors provide high-resolution measurements of specific body segments. A robust method to fuse these modalities and obtain biomechanically accurate kinematics would have substantial uti

Rafael de Oliveira Moura, Alexandre Nolasco de Carvalho, José A. Langa

The aim of this paper is to find an upper bound for the box-counting dimension of uniform attractors for non-autonomous dynamical systems. Contrary to the results in literature, we do not ask the symbol space to have finite box-counting dimension. Instead, we ask a condition on the semi-continuity of pullback attractors of the system as time goes to infinity

Elad Hadar, Yoav Etsion

Coarse-grain reconfigurable architectures (CGRAs) are gaining traction thanks to their performance and power efficiency. Utilizing CGRAs to accelerate the execution of tight loops holds great potential for achieving significant overall performance gains, as a substantial portion of program execution time is dedicated to tight loops. But loop parallelization

Matteo Torcoli, Mhd Modar Halimeh, Thomas Leitz, Yannik Grewe

The introduction and regulation of loudness in broadcasting and streaming brought clear benefits to the audience, e.g., a level of uniformity across programs and channels. Yet, speech loudness is frequently reported as being too low in certain passages, which can hinder the full understanding and enjoyment of movies and TV programs. This paper proposes expan

Christian Guzman Ruiz, Mario Acosta, Oriol Jorba, Eduardo Cesar Galobardes

Earth system models (ESM) demand significant hardware resources and energy consumption to solve atmospheric chemistry processes. Recent studies have shown improved performance from running these models on GPU accelerators. Nonetheless, there is room for improvement in exploiting even more GPU resources. This study proposes an optimized distribution of the ch

Arash Yavari, Alain Goriely

For a given material, \emph{controllable deformations} are those deformations that can be maintained in the absence of body forces and by applying only boundary tractions. For a given class of materials, \emph{universal deformations} are those deformations that are controllable for any material within the class. In this paper, we characterize the universal d

Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni

This paper reveals a key insight that a one-layer decoder-only Transformer is equivalent to a two-layer Recurrent Neural Network (RNN). Building on this insight, we propose ARC-Tran, a novel approach for verifying the robustness of decoder-only Transformers against arbitrary perturbation spaces. Compared to ARC-Tran, current robustness verification technique

Lander Guerrero Sánchez, Henrique Souza

We study the asymptotic behaviour of the cohomology of subgroups $\Gamma$ of an algebraic group $G$ with coefficients in the various irreducible rational representations of $G$ and raise a conjecture about it. Namely, we expect that the dimensions of these cohomology groups approximate the $\ell^2$-Betti numbers of $\Gamma$ with a controlled error term. We p

Qiong Li, Christopher J. Conselice, Florian Sarron, Tom Harvey

In this paper we describe our search for galaxy protocluster candidates at $4.5< z < 10$ and explore the environmental and physical properties of their member galaxies identified through JWST wide-field surveys within the CEERS, JADES, and PEARLS NEP-TDF fields. Combining with HST data, we identify 2948 robust $z>4.5$ candidates within an area of 185.4 arcmi

Chenhao Lu, Ruizhe Shi, Yuyao Liu, Kaizhe Hu

Sequential decision-making algorithms such as reinforcement learning (RL) in real-world scenarios inevitably face environments with partial observability. This paper scrutinizes the effectiveness of a popular architecture, namely Transformers, in Partially Observable Markov Decision Processes (POMDPs) and reveals its theoretical limitations. We establish tha

Yulong Mao, Kaiyu Huang, Changhao Guan, Ganglin Bao

Fine-tuning large-scale pre-trained models is inherently a resource-intensive task. While it can enhance the capabilities of the model, it also incurs substantial computational costs, posing challenges to the practical application of downstream tasks. Existing parameter-efficient fine-tuning (PEFT) methods such as Low-Rank Adaptation (LoRA) rely on a bypass

Yu-Ao Chen, Gilad Gour, Xin Wang, Lei Zhang

Magic states are essential for achieving universal quantum computation. This study introduces a reversible framework for the manipulation of magic states in odd dimensions, delineating a necessary and sufficient condition for the exact transformations between magic states under maps that preserve the trace of states and positivity of discrete Wigner represen

Robert M. Gray, Ryoto Sekine, Luis Ledezma, Gordon H. Y. Li

Arrays of nonlinear resonators offer a fertile ground for a wide range of complex phenomena and opportunities for advanced photonic sensing and computing. Recently, significant attention has focused on studying coupled resonators in special-purpose configurations either on chips or in table-top experiments. However, a path to realizing a large-scale programm

Simone Cavazzoni, Paolo Bordone, Matteo G. A. Paris

Discrete-time quantum walk (DTQW) represents a convenient mathematical framework for describing the motion of a particle on a discrete set of positions when this motion is conditioned by the values of certain internal degrees of freedom, which are usually referred to as the {\em coin} of the particle. As such, and owing to the inherent dependence of the posi

Yucheng Liu, Romain Panis, Gordon Slade

We consider the Ising model on a $d$-dimensional discrete torus of volume $r^d$, in dimensions $d>4$ and for large $r$, in the vicinity of the infinite-volume critical point $\beta_c$. We prove that for $\beta=\beta_c- {\rm const}\, r^{-d/2}$ (with a suitable constant) the susceptibility is bounded above and below by multiples of $r^{d/2}$. Additionally, aga

Batuhan K. Karaman, Mert R. Sabuncu

In this study, we employ a transformer encoder model to characterize the significance of longitudinal patient data for forecasting the progression of Alzheimer's Disease (AD). Our model, Longitudinal Forecasting Model for Alzheimer's Disease (LongForMAD), harnesses the comprehensive temporal information embedded in sequences of patient visits that incorporat

Yujie Wang, Praneeth Chakravarthula, Baoquan Chen

3D Gaussian Splatting (3DGS) techniques have recently enabled high-quality 3D scene reconstruction and real-time novel view synthesis. These approaches, however, are limited by the pinhole camera model and lack effective modeling of defocus effects. Departing from this, we introduce DOF-GS--a new 3DGS-based framework with a finite-aperture camera model and e

J. Socorro, J. Juan Rosales, Leonel Toledo-Sesma

In this work, we will explore the effects of non-commutativity in fractional classical and quantum schemes using the anisotropicc Bianchi Type I cosmological model coupled to a scalar field in the K-essence formalism. We introduce non-commutative variables considering that all minisuperspace variables $q^i_{nc}$ do not commute, so the symplectic structure wa

Anna Bobyleva, Alexander Gorsky, Sergei Nechaev, Olga Valba

In this study, we explore the fundamental principles behind the architecture of the human brain's structural connectome, from the perspective of spectral analysis of Laplacian and adjacency matrices. Building on the idea that the brain strikes a balance between efficient information processing and minimizing wiring costs, we aim to understand the impact of t

Matthieu Vanhoecke, Marco Schirò

Continuously monitoring a quantum system can strongly affect its properties and even suppress its coherent evolution via the Quantum Zeno effect. Well understood for few body quantum systems, the role of quantum measurements on entangled many-body states is still largely unexplored. Here we focus on one of the simplest entangled many-body state, arising via

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A comprehensive study of the local and nonlocal amplitudes contributing to the decay $B^0\rightarrow K^{*0}(\to K^+\pi^-) \mu^+\mu^-$ is performed by analysing the phase-space distribution of the decay products. The analysis is based on $pp$ collision data corresponding to an integrated luminosity of 8.4fb$^{-1}$ collected by the LHCb experiment. This measur

Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng

Large language models (LLMs) have demonstrated remarkable performances in various tasks. However, the performance of LLMs heavily depends on the input prompt, which has given rise to a number of recent works on prompt optimization. However, previous works often require the availability of a numeric score to assess the quality of every prompt. Unfortunately,

Alejandro Tlaie

Large Language Models (LLMs) have become central to advancing automation and decision-making across various sectors, raising significant ethical questions. This study proposes a comprehensive comparative analysis of the most advanced LLMs to assess their moral profiles. We subjected several state-of-the-art models to a selection of ethical dilemmas and found

Jiwoon Park, Gordon Slade

We obtain precise plateau estimates for the two-point function of the finite-volume weakly-coupled hierarchical $|\varphi|^4$ model in dimensions $d \ge 4$, for both free and periodic boundary conditions, and for any number $n \ge 1$ of components of the field $\varphi$. We prove that, within a critical window around their respective effective critical point

Laura DeMarco, Niki Myrto Mavraki, Hexi Ye

For each integer $d\geq 2$, let $M_d$ denote the moduli space of maps $f: \mathbb{P}^1\to \mathbb{P}^1$ of degree $d$. We study the geometric configurations of subsets of postcritically finite (or PCF) maps in $M_d$. A complex-algebraic subvariety $Y \subset M_d$ is said to be PCF-special if it contains a Zariski-dense set of PCF maps. Here we prove that the

Michael Lau, Neha Patankar, Jesse D. Jenkins

As decarbonization agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is Modeling to Generate Alternatives (MGA), which systematically explores new objectives without explicit

C. S. Kim, Dibyakrupa Sahoo, K. N. Vishnudath

We conduct a model-independent analysis of the distinct signatures of various generic new physics possibilities in the decay $B \to K \, \nu \, \overline{\nu}$ by analyzing the branching ratio as well as the missing mass-square distribution. Considering the final neutrinos to be of the same flavor with non-zero mass, we discuss the new physics contributions

Jesse Jääsaari

We obtain $\Omega$-results for linear exponential sums with rational additive twists of small prime denominators weighted by Hecke eigenvalues of Maass cusp forms for the group $\mathrm{SL}_3(\mathbb Z)$. In particular, our $\Omega$-results match the expected conjectural upper bounds when the denominator of the twist is sufficiently small compared to the len

Carlo Cena, Umberto Albertin, Mauro Martini, Silvia Bucci

The unique challenges posed by the space environment, characterized by extreme conditions and limited accessibility, raise the need for robust and reliable techniques to identify and prevent satellite faults. Fault detection methods in the space sector are required to ensure mission success and to protect valuable assets. In this context, this paper proposes

Yanlong Shi, Kyle Kremer, Philip F. Hopkins

The origins and mergers of supermassive black holes (BHs) remain a mystery. We describe a scenario from a novel multi-physics simulation featuring rapid ($\lesssim 1\,$Myr) hyper-Eddington gas capture by a $\sim 1000\,{\rm M}_{\odot}$ ``seed'' BH up to supermassive ($\gtrsim 10^{6}\,M_{\odot}$) masses, in a massive, dense molecular cloud complex typical of h

Junnan Dong, Qinggang Zhang, Chuang Zhou, Hao Chen

Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledge during pre-training. We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for

Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li

In the domain of Document AI, parsing semi-structured image form is a crucial Key Information Extraction (KIE) task. The advent of pre-trained multimodal models significantly empowers Document AI frameworks to extract key information from form documents in different formats such as PDF, Word, and images. Nonetheless, form parsing is still encumbered by notab

Hideo Suganuma, Hiroki Ohata, Masakiyo Kitazawa

Using SU(3) lattice QCD, we study for the first time the effective potential of the Polyakov loop $\langle P \rangle$ at finite temperature, i.e., the thermodynamic potential, in the field-theoretical way. In the framework of the reweighting method in lattice QCD, we express the effective potential $V_{\rm eff}(\langle P \rangle)$ using the expectation value

Paolo Penna, Manvir Schneider

In the face of limited block size, miners (e.g., in Bitcoin) prioritize high-bid transactions, forming a large part of their revenue. If the block size were to expand significantly, meeting all transaction demand due to infrastructure or protocol improvements, bids could drop to zero or to a minimum fee, reducing mining incentives and potentially affecting s

Mohd Ashhad, Ricardo Henao

Synthetically generated data can improve privacy, fairness, and data accessibility; however, it can be challenging in specialized scenarios such as survival analysis. One key challenge in this setting is censoring, i.e., the timing of an event is unknown in some cases. Existing methods struggle to accurately reproduce the distributions of both observed and c

Arya Ramteke, Sankalp Tembhurne, Gunesh Sonawane, Ratnmala N. Bhimanpallewar

Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. This study investigates the detection of such dark patterns. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patter

Thomas Lam

These are lecture notes for five lectures given at MPI Leipzig in May 2024. We study the moduli space M_{0,n} of n distinct points on P^1 as a positive geometry and a binary geometry. We develop mathematical formalism to study Cachazo-He-Yuan's scattering equations and the associated scalar and Yang-Mills amplitudes. We discuss open superstring amplitudes an

David Santiago Quevedo, Robin C. Verstraten, Cristiane Morais Smith

We study the fractional Langevin equation with fractional $\alpha$-order and linear friction terms of a system coupled to white and colored thermal baths using both analytical and numerical methods. We find analytical expressions for the position and the mean squared displacement (MSD) of the system using the Prabhakar-Mittag-Leffler function. The MSD exhibi

Sérgio Luiz E. F. da Silva, Felipe T. Costa, Ammir Karsou, Adriano de Souza

We develop a workflow based on full-waveform inversion (FWI) to estimate P-wave velocities in a deepwater Brazilian pre-salt field using the recently introduced circular shot ocean bottom node (OBN) acquisition geometry. Such a geometry comprises a source vessel sailing in large radius concentric circular trajectories and seismic signals are recorded by OBN

Haichao Sha, Yang Cao, Yong Liu, Yuncheng Wu

Differentially Private Stochastic Gradient Descent (DPSGD) is widely utilized to preserve training data privacy in deep learning, which first clips the gradients to a predefined norm and then injects calibrated noise into the training procedure. Existing DPSGD works typically assume the gradients follow sub-Gaussian distributions and design various clipping

Yaqiong Zhao, Jindan Xu, Wei Xu, Kezhi Wang

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output communication system with multiple antennas at both the base station (BS) and the user. We plan to maximize the achievable rate through jointly optimizing the transmit precoding matrix, the receive combining matrix, and the RIS reflection matrix unde

Logan Sowadski, Sean Anderson, Cameron Lerch, Julia Medvedeva

We revisit the magnetic properties of the hexagonal ferrite PbFe$_{12-x}$Ga$_x$O$_{19}$. Recent experiments have reported puzzling dependencies of the ordering temperature and the saturation magnetization on the Ga concentration $x$. To explain these observations, we perform large-scale Monte Carlo simulations, focusing on the effects of an unequal distribut

Garima Aggarwal, Adeem Saeed Mirza, Stefania Riva, Corrado Comparotto

Exploring the conduction mechanism in the chalcogenide perovskite BaZrS$_3$ is of significant interest due to its potential suitability as a top absorber layer in silicon-based tandem solar cells and other optoelectronic applications. Theoretical and experimental studies anticipate native ambipolar doping in BaZrS$_3$, although experimental validation remain

Krish Didwania, Durga Toshniwal, Amit Agarwal

Legal documents are indispensable in every country for legal practices and serve as the primary source of information regarding previous cases and employed statutes. In today's world, with an increasing number of judicial cases, it is crucial to systematically categorize past cases into subgroups, which can then be utilized for upcoming cases and practices.

Atul Kedia, Richard O'Shaughnessy, Leslie Wade, Anjali Yelikar

The wide range of nuclear densities achieved in neutron stars makes them probes of dense nuclear behavior in the form of the nuclear equation of state (EoS). Studying neutron stars both in isolation, with X-ray measurements and pulse profiling, and in dynamic events, such as neutron star mergers, have provided insight into these high nuclear densities. Thoug

Uraz Odyurt, Nadezhda Dobreva, Zef Wolffs, Yue Zhao

Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorithmic design effort by utilising a simplified simulator (REDVID) to generate training data that is specifically

Chinmaya Kausik, Kevin Tan, Ambuj Tewari

Leveraging offline data is an attractive way to accelerate online sequential decision-making. However, it is crucial to account for latent states in users or environments in the offline data, and latent bandits form a compelling model for doing so. In this light, we design end-to-end latent bandit algorithms capable of handing uncountably many latent states.

Tingwei Liu, Yasutomo Kawanishi, Takahiro Komamizu, Ichiro Ide

This paper focuses on tracking birds that appear small in a panoramic video. When the size of the tracked object is small in the image (small object tracking) and move quickly, object detection and association suffers. To address these problems, we propose Adaptive Slicing Aided Hyper Inference (Adaptive SAHI), which reduces the candidate regions to apply de

L. A. Torres, Carlos J. Barrios H, Yves Denneulin

Matrix multiplication is fundamental in the backpropagation algorithm used to train deep neural network models. Libraries like Intel's MKL or NVIDIA's cuBLAS implemented new and optimized matrix multiplication techniques that increase performance and reduce computational costs. These techniques can also be implemented in CUDA and SYCL and functions with AVX2

Himangshu Sekhar Sarmah, Subhradip Ghosh

We have performed Density Functional Theory (DFT) based calculations to investigate the effects of stacking patterns on the electronic and magnetic properties of several Nitride MXenes. MXenes, a relatively new addition to the family of two-dimensional materials, have exhibited fascinating properties on several occasions, primarily due to their compositional

Alberto Cabada, Lucía López-Somoza

In this paper we obtain an explicit formula of the parameter dependence of the partial derivatives of the Green's functions related to two-point boundary conditions. Such expression follows as an integral of both kernels times the difference of the corresponding parameters of each Green's function. As a direct consequence, we deduce a simpler proof of the mo

Sabine Jansen

Motivated by metastability in the zero-range process, we consider i.i.d.\ random variables with values in $\N_0$ and Weibull-like (stretched exponential) law $\mathbb P(X_i =k) = c \exp( - k^\alpha)$, $\alpha \in (0,1)$. We condition on large values of the sum $S_n= \mu n + s n^\gamma$ and prove large deviation principles for the rescaled maximum $M_n /n^\ga

Mihyun Kim, Piotr Kokoszka

We propose a coefficient that measures dependence in paired samples of functions. It has properties similar to the Pearson correlation, but differs in significant ways: (i) it is designed to measure dependence between curves, (ii) it focuses only on extreme curves. The new coefficient is derived within the framework of regular variation in Banach spaces. A c

J-M Rax

The impact of earth's gravity on neutral kaons oscillations is analyzed. The main effect of a Newtonian potential is to couple the strangeness oscillation and the strange quarks zitterbewegung. This coupling is responsible for the observed CP violations. Gravity induced CP violation is in fact a CPT violation with T conservation rather than a T violation wit

Augusto Ghiotto, LingNan Wei, Larry Song, Jiawei Zang

Moir\'e transition metal dichalcogenide (TMD) systems provide a tunable platform for studying electron-correlation driven quantum phases. Such phases have so far been found at rational fillings of the moir\'e superlattice, and it is believed that lattice commensurability plays a key role in their stability. In this work, we show via magnetotransport measurem

Vadim Ezhov, Hyoungseob Park, Zhaoyang Zhang, Rishi Upadhyay

We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tackle the problem through a multi-sensor fusion approach, where we take as input an additional synchronized sparse point cloud (i.e., from a LiDAR) projected onto the image plane as

Christian Komusiewicz, Jannik Schestag

In the NP-hard Optimizing PD with Dependencies (PDD) problem, the input consists of a phylogenetic tree $T$ over a set of taxa $X$, a food-web that describes the prey-predator relationships in $X$, and integers $k$ and $D$. The task is to find a set $S$ of $k$ species that is viable in the food-web such that the subtree of $T$ obtained by retaining only the

Eric Larson, Ravi Vakil, Isabel Vogt

The interpolation problem is a natural and fundamental question whose roots trace back to ancient Greece. The story is long and rich, with many chapters, and a complete solution has been obtained only recently. Exploring it leads us on a tour through a number of general themes in geometry. This concrete problem motivates fundamental concepts such as moduli s