March 2024 arXiv papers — page 81

Sai Ashish Somayajula, Youwei Liang, Abhishek Singh, Li Zhang

Pretrained Language Models (PLMs) have advanced Natural Language Processing (NLP) tasks significantly, but finetuning PLMs on low-resource datasets poses significant challenges such as instability and overfitting. Previous methods tackle these issues by finetuning a strategically chosen subnetwork on a downstream task, while keeping the remaining weights fix

Luca Anderlini, Larry Samuelson, Daniele Terlizzese

We examine an economy in which interactions are more productive if agents can trust others to refrain from cheating. Some agents are scoundrels, who cheat at every opportunity, while others cheat only if the cost of cheating, a decreasing function of the proportion of cheaters, is sufficiently low. The economy exhibits multiple equilibria. As the proportion

Gionnieve Lim, Simon T. Perrault

With fact-checking by professionals being difficult to scale on social media, algorithmic techniques have been considered. However, it is uncertain how the public may react to labels by automated fact-checkers. In this study, we investigate the use of automated warning labels derived from misinformation detection literature and investigate their effects on t

Jiajie Yang

We introduce the Pyramid Diffusion Model (PDM), a novel architecture designed for ultra-high-resolution image synthesis. PDM utilizes a pyramid latent representation, providing a broader design space that enables more flexible, structured, and efficient perceptual compression which enable AutoEncoder and Network of Diffusion to equip branches and deeper laye

André Milagre, Luís Lavoura

We impose partial-wave unitarity on $2 \to 2$ tree-level scattering processes to derive constraints on the dimensions of large scalar and fermionic multiplets of arbitrary gauge groups. We apply our results to scalar and fermionic extensions of the Standard Model, and also to the Grand Unified Theories (GUTs) based on the groups $SU(5)$, $SO(10)$, and $E_6$.

Gionnieve Lim, Simon T. Perrault

In Singapore, there has been a rise in misinformation on mobile instant messaging services (MIMS). MIMS support both small peer-to-peer networks and large groups. Misinformation in the former may spread due to recipients' trust in the sender while in the latter, misinformation can directly reach a wide audience. The encryption of MIMS makes it difficult to a

Kristen Van Gelder, Steffen N. Lindner, Andrew D. Hanson, Juannan Zhou

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular envir

Peter Bella, Julian Fischer, Marc Josien, Claudia Raithel

We prove quantitative decay estimates for the boundary layer corrector in stochastic homogenization in the case of a half-space boundary. Our estimates are of optimal order and show that the gradient of the boundary layer corrector features nearly fluctuation-order decay; its expected value decays even one order faster. As a corollary, we deduce estimates on

Lucy Xiaoyang Shi, Zheyuan Hu, Tony Z. Zhao, Archit Sharma

Hierarchical policies that combine language and low-level control have been shown to perform impressively long-horizon robotic tasks, by leveraging either zero-shot high-level planners like pretrained language and vision-language models (LLMs/VLMs) or models trained on annotated robotic demonstrations. However, for complex and dexterous skills, attaining hig

Anuj Abhishek, Alexander Katsevich, James W. Webber

In this paper, we investigate the reconstruction error, $N_\e^{\text{rec}}(x)$, when a linear, filtered back-projection (FBP) algorithm is applied to noisy, discrete Radon transform data with sampling step size $\epsilon$ in two-dimensions. Specifically, we analyze $N_\e^{\text{rec}}(x)$ for $x$ in small, $O(\e)$-sized neighborhoods around a generic fixed po

Carla Pinkney, Carolina Euan, Alex Gibberd, Ali Shojaie

Advances in modern technology have enabled the simultaneous recording of neural spiking activity, which statistically can be represented by a multivariate point process. We characterise the second order structure of this process via the spectral density matrix, a frequency domain equivalent of the covariance matrix. In the context of neuronal analysis, stati

R. Kumar, A. Moharana, S. Piridi, A. C. Pradhan

We report a new hot post-asymptotic giant branch (PAGB) star in the Galactic globular cluster (GC) E3, which is one of the first of the identified PAGB stars in a GC to show a binary signature. The star stands out as the brightest source in E3 in the \mbox{{\em Astrosat}}/UVIT images. We confirmed its membership with the cluster E3 using Gaia DR3 kinematics

Yufei Liu, Junwei Zhu, Junshu Tang, Shijie Zhang

Texturing 3D humans with semantic UV maps remains a challenge due to the difficulty of acquiring reasonably unfolded UV. Despite recent text-to-3D advancements in supervising multi-view renderings using large text-to-image (T2I) models, issues persist with generation speed, text consistency, and texture quality, resulting in data scarcity among existing data

Andrew M. Graff, Todd E. Humphreys

This paper presents methods of optimizing the placement and power allocations of pilots in an orthogonal frequency-division multiplexing (OFDM) signal to minimize time-of-arrival (TOA) estimation errors under power and resource allocation constraints. TOA errors in this optimization are quantified through the Ziv-Zakai bound (ZZB), which captures error thres

Linda Li, Orsolya Vasarhelyi, Balazs Vedres

Social media platforms have witnessed a substantial increase in social bot activity, significantly affecting online discourse. Our study explores the dynamic nature of bot engagement related to Extinction Rebellion climate change protests from 18 November 2019 to 10 December 2019. We find that bots exert a greater influence on human behavior than vice versa

Tilman Becker

In this paper, we provide new and simpler proofs of two theorems of Gluck and Harrison on contact structures induced by great circle or line fibrations. Furthermore, we prove that a geodesic vector field whose Jacobi tensor is parallel along flow lines (e.g. if the underlying manifold is locally symmetric) induces a contact structure if the 'mixed' sectional

ATLAS Collaboration

Fiducial and total $W^\pm$ and $Z$ boson cross sections, their ratios and the ratio of top-antitop-quark pair and $W$-boson fiducial cross sections are measured in proton-proton collisions at a centre-of-mass energy of $\sqrt{s}=13.6$ TeV, corresponding to an integrated luminosity of 29 fb$^{-1}$ of data collected in 2022 by the ATLAS experiment at the Large

Elijah D. S. Courtney, Mihir Pendharkar, Nathan J. Bittner, Aaron L. Sharpe

Over the past two decades, graphene has been intensively studied because of its remarkable mechanical, optical, and electronic properties. Initial studies were enabled by manual ``Scotch Tape'' exfoliation; nearly two decades later, this method is still widely used to obtain chemically-pristine flakes of graphene and other 2D van der Waals materials. Unfortu

Baolin Li, Yankai Jiang, Vijay Gadepally, Devesh Tiwari

The rapid advancement of Generative Artificial Intelligence (GenAI) across diverse sectors raises significant environmental concerns, notably the carbon emissions from their cloud and high performance computing (HPC) infrastructure. This paper presents Sprout, an innovative framework designed to address these concerns by reducing the carbon footprint of gene

John H. Schwarz

Motivated by string-theoretic swampland conjectures, the existence of a dark fifth dimension, whose size is roughly 1 -- 10 microns, has been proposed. A great deal of supporting evidence has been presented, and definitive experimental tests are likely to be carried out. The basic idea is that the four-dimensional spacetime that we observe lives on a brane t

Helena Bergold, Stefan Felsner, Meghana M. Reddy, Joachim Orthaber

A conjecture by Rafla from 1988 asserts that every simple drawing of the complete graph $K_n$ admits a plane Hamiltonian cycle. It turned out that already the existence of much simpler non-crossing substructures in such drawings is hard to prove. Recent progress was made by Aichholzer et al. and by Suk and Zeng who proved the existence of a plane path of len

G. Alencar, Kirill A. Bronnikov, Manuel E. Rodrigues, Diego Sáez-Chillón Gómez

One of the main issues in gravitation is the presence of singularities in the most common space-time solutions of General Relativity, as the case of black holes. A way of constructing regular solutions that remove spacelike singularities consists in implement a bounce on such space-time, leading to what is usually known as black bounce space-times. Such spac

Mankei Tsang

Using quantum information geometry, I derive quantum generalizations of the Onsager rate equations, which model the dynamics of an open system near a steady state. The generalized equations hold for a flexible definition of the forces as well as a large class of statistical divergence measures and quantum-Fisher-information metrics beyond the conventional de

Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan

Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for Visual Document Understanding are equipped with text recognition ability but lack general structure understanding abilities for text-rich document images. In this work, we emphasiz

Yuan Gao, Sangwook Kim, David E Austin, Chris McIntosh

Medical vision-language pretraining models (VLPM) have achieved remarkable progress in fusing chest X-rays (CXR) with clinical texts, introducing image-text data binding approaches that enable zero-shot learning and downstream clinical tasks. However, the current landscape lacks the holistic integration of additional medical modalities, such as electrocardio

Hongyu Li, Matteo Nerini, Shanpu Shen, Bruno Clerckx

This work studies the wideband modeling and beamforming design of beyond diagonal reconfigurable intelligent surface (BD-RIS), which generalizes and goes beyond conventional RIS with diagonal phase shift matrices to achieve enhanced channel gain. Specifically, we investigate the response of BD-RIS in wideband systems by going back to its hardware circuit rea

Do Hai Son, Nguyen Huu Hung, Pham Duy Hung, Tran Thi Thuy Quynh

This study focuses on developing an experimental system for estimating communication channels in a multi-robot mobile system using software-defined radio (SDR) devices. The system consists of two mobile robots programmed for two scenarios: one where the robot remains stationary and another where it follows a predefined trajectory. Communication within the sy

Rui Liu, Amisha Bhaskar, Pratap Tokekar

In this study, we introduce a novel visual imitation network with a spatial attention module for robotic assisted feeding (RAF). The goal is to acquire (i.e., scoop) food items from a bowl. However, achieving robust and adaptive food manipulation is particularly challenging. To deal with this, we propose a framework that integrates visual perception with imi

Zhongwen Tang, Jin Li, Gangsong Leng

We present a complete classification of $\operatorname{SL}(n)$ contravariant, $C(\mathbb{R}^n\setminus\{o\})$-valued valuations on polytopes, without any additional assumptions.It extends the previous results of the second author [Int. Math. Res. Not. 2020] which have a good connection with the $L_p$ and Orlicz Brunn-Minkowski theory. Additionally, our resul

Rubén Seoane Souto, Dushko Kuzmanovski, Ignacio Sardinero, Pablo Burset

Superconductivity and magnetism are competing effects that can coexist in certain regimes. Their co-existence leads to unexpected new behaviors that include the onset of exotic electron pair mechanisms and topological phases. In this work, we study the properties of a Josephson junction between two spin-split superconductors. The spin-splitting in the superc

Felix-Ekkehard von Horstig, Lorenzo Peri, Virginia N. Ciriano-Tejel, Sylvain Barraud

In semiconductor nanostructures, spin blockade (SB) is the most scalable mechanism for electrical spin readout, requiring only two bound spins for its implementation. In conjunction with charge sensing techniques, SB has led to high-fidelity readout of spins in semiconductor-based quantum processors. However, various mechanisms may lift SB, such as strong sp

Raphaël Barboni, Gabriel Peyré, François-Xavier Vialard

We study the convergence of gradient flow for the training of deep neural networks. If Residual Neural Networks are a popular example of very deep architectures, their training constitutes a challenging optimization problem due notably to the non-convexity and the non-coercivity of the objective. Yet, in applications, those tasks are successfully solved by s

Federico Nocentini, Claudio Ferrari, Stefano Berretti

The domain of 3D talking head generation has witnessed significant progress in recent years. A notable challenge in this field consists in blending speech-related motions with expression dynamics, which is primarily caused by the lack of comprehensive 3D datasets that combine diversity in spoken sentences with a variety of facial expressions. Whereas literat

Cilon F. Perusato, Franco D. Vega

We examine the long-time behavior of solutions (and their derivatives) to the micropolar equations with nonlinear velocity damping. Additionally, we get a speed-up gain of $ t^{1/2} $ for the angular velocity, consistent with established findings for classic micropolar flows lacking nonlinear damping. Consequently, we also obtain a sharper result regarding t

Fucai Ke, Zhixi Cai, Simindokht Jahangard, Weiqing Wang

Recent advances in visual reasoning (VR), particularly with the aid of Large Vision-Language Models (VLMs), show promise but require access to large-scale datasets and face challenges such as high computational costs and limited generalization capabilities. Compositional visual reasoning approaches have emerged as effective strategies; however, they heavily

Churan Zhi, Junbao Zhuo, Shuhui Wang

In this paper, we address unsupervised domain adaptation under noisy environments, which is more challenging and practical than traditional domain adaptation. In this scenario, the model is prone to overfitting noisy labels, resulting in a more pronounced domain shift and a notable decline in the overall model performance. Previous methods employed prototype

Jennifer Brown, Nathan Geer

We show that the quantum invariants arising from typical representations of the quantum group $U_h\mathfrak{sl}(2|1)$ are q-holonomic. In particular, this implies the existence of an underlying field theory for which this family of invariants are partition functions.

Zehui Chen, Kuikun Liu, Qiuchen Wang, Wenwei Zhang

Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. How to integrate agent ability into general LLMs becomes a crucial and urgent problem. This paper first delivers three key observations: (1) the current agent training corpus is entangled

Luiza S. C. Piancastelli, Nial Friel

Rankings are a type of preference elicitation that arise in experiments where assessors arrange items, for example, in decreasing order of utility. Orderings of n items labelled {1,...,n} denoted are permutations that reflect strict preferences. For a number of reasons, strict preferences can be unrealistic assumptions for real data. For example, when items

O. B. Zaslavskii

We consider the Penrose process with the charged particles in the Reissner-Nordstr\"{o}m (RN) background. Let parent particle 0 decay to particles 1 and 2. With the assumption that all three particles move in the equatorial plane, the exact formulas for characteristics of particles 1 and 2 in terms of those of particle 0 are derived. We concentrate on scenar

Emily Fox, Amir Nayyeri, Jonathan James Perry, Benjamin Raichel

We define and investigate the Fr\'{e}chet edit distance problem. Given two polygonal curves $\pi$ and $\sigma$ and a threshhold value $\delta>0$, we seek the minimum number of edits to $\sigma$ such that the Fr\'{e}chet distance between the edited $\sigma$ and $\pi$ is at most $\delta$. For the edit operations we consider three cases, namely, deletion of ver

Bach Nguyen-Xuan, Thien Nguyen-Hoang, Thanh-Huy Nguyen, Nhu Tai-Do

Facial Expression Recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-F

Roshan J. Samuel, Mathis Bode, Janet D. Scheel, Katepalli R. Sreenivasan

We study the dynamics of thermal and momentum boundary regions in three-dimensional direct numerical simulations of Rayleigh-B\'enard convection for the Rayleigh number range $10^5 \le Ra \le 10^{11}$ and $Pr=0.7$. Using a Cartesian slab with horizontal periodic boundary conditions and an aspect ratio of 4, we obtain statistical homogeneity in the horizontal

Amisha Bhaskar, Rui Liu, Vishnu D. Sharma, Guangyao Shi

Robotic Assisted Feeding (RAF) addresses the fundamental need for individuals with mobility impairments to regain autonomy in feeding themselves. The goal of RAF is to use a robot arm to acquire and transfer food to individuals from the table. Existing RAF methods primarily focus on solid foods, leaving a gap in manipulation strategies for semi-solid and def

Stefano Bonaccorsi, Fulvia Confortola

In this paper, we focus on solving the optimal control problem for integral stochastic Volterra equations in a finite dimensional setting. In our setting, the noise term is driven by a pure jump L\'evy noise and the control acts on the intensity of the jumps. We use recent techniques proposed by Hamaguchi, where a crucial requirement is that the convolution

Yuchen Ding

We answer negatively a 2014 problem of Yang and Chen on Romanoff type representations. Sharp results involving their problem were also obtained in this article.

Joshua Edward Hammond, Ricardo A. Lara Orozco, Michael Baldea, Brian A. Korgel

We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. The output irradiance values are transformed to focus on unknown short-term dynamics. Inspired by control theory, a noise input is used to re

I. Busa, S. Cabibbo, C. Trigilio, F. Leone

Context. Investigating the diagnostic power of H$\alpha$/H$\beta$ Charge-Exchange (CE) emission as Low-Energy Galactic Cosmic Rays(LECRs) tracer in diffuse regions. Aims. In this work, we define and test a spectroscopic indicator of CE reactions between LECRs protons and neutral hydrogen atoms of the diffuse medium. This indicator can be used for mapping LEC

Spiros Maggioros, Nikos Tsalkitzis

Convolutional Neural Networks (CNNs) have proven instrumental across various computer science domains, enabling advancements in object detection, classification, and anomaly detection. This paper explores the application of CNNs to analyze geospatial data specifically for identifying wildfire-affected areas. Leveraging transfer learning techniques, we fine-t

Nissim Maruani, Maks Ovsjanikov, Pierre Alliez, Mathieu Desbrun

Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applications. In this work, we introduce a novel learnable mesh representation through a set of local 3D sample Points and their associated Normals and Quadric error metrics (QEM) w.r.t. the

Filip Bártek, Karel Chvalovský, Martin Suda

To achieve the best performance, automatic theorem provers often rely on schedules of diverse proving strategies to be tried out (either sequentially or in parallel) on a given problem. In this paper, we report on a large-scale experiment with discovering strategies for the Vampire prover, targeting the FOF fragment of the TPTP library and constructing a sch

E. Braaten, R. Bruschini

Hidden-heavy hadrons can decay into pairs of heavy hadrons through transitions from confining Born-Oppenheimer potentials to hadron-pair potentials with the same Born-Oppenheimer quantum numbers. The transitions are also constrained by conservation of angular momentum and parity. From these constraints, we derive model-independent selection rules for decays

Carrie Clark, Richard S. Laugesen

The $q$-th moment ($q>0$) of electrostatic equilibrium measure is shown to be minimal for a centered ball among $3$-dimensional sets of given capacity, while among $2$-dimensional sets a centered disk is the minimizer for $0<q \leq 2$. Analogous results are developed for Newtonian capacity in higher dimensions and logarithmic capacity in $2$ dimensions. Open

Carlos F. D. Faurby, Lorenzo Carosini, Huan Cao, Patrik I. Sund

Indistinguishability between photons is a key requirement for scalable photonic quantum technologies. We experimentally demonstrate that partly distinguishable single photons can be purified to reach near-unity indistinguishability by the process of quantum interference with ancillary photons followed by heralded detection of a subset of them. We report on t

Ben Van Dusen, Jayson Nissen, Odis Johnson

The success of collaborative instruction in helping students achieve higher grades in introductory science, technology, engineering, and mathematics (STEM) courses has led many educators and researchers to assume these methods also address inequities. However, little evidence tests this assumption. Structural inequities in our society have led to the chronic

Jiaxin Qiu, Qingchen Liu, Jiahu Qin, Dewang Cheng

The role of a motion planner is pivotal in quadrotor applications, yet existing methods often struggle to adapt to complex environments, limiting their ability to achieve fast, safe, and robust flight. In this letter, we introduce a performance-enhanced quadrotor motion planner designed for autonomous flight in complex environments including dense obstacles,

Daniel Lakey, Tim Schlippe

Spacecraft operations are highly critical, demanding impeccable reliability and safety. Ensuring the optimal performance of a spacecraft requires the early detection and mitigation of anomalies, which could otherwise result in unit or mission failures. With the advent of deep learning, a surge of interest has been seen in leveraging these sophisticated algor

Alessio Caminata, Samuel Shideler, Kevin Tucker, Francesco Zerman

Let $f$ be a diagonal hypersurface in $A_p=\mathbb{F}_p[[x_1,\dots,x_n]]$. We study the behavior of the function $\phi_{f,p}({a}/{p^e})=p^{-ne}\dim_{\mathbb{F}_p}\big(A_p/(x_1^{p^e},\dots,x_n^{p^e},f^a)\big)$ which encodes information about the F-threshold, the Hilbert-Kunz, and the F-signature functions. We prove that when $p$ goes to infinity $\phi_{f,p}$

Mark Stefik

Today's robots do not learn the general skills needed for such services as providing home care, being nursing assistants, or doing household chores. Addressing such aspirational goals requires improving how AIs and robots are created. Today's mainstream AIs are not created by agents learning from experiences doing real world tasks and interacting with people

Minsu Kim, James Thorne

This paper investigates the inherent knowledge in language models from the perspective of epistemological holism. The purpose of this paper is to explore whether LLMs exhibit characteristics consistent with epistemological holism. These characteristics suggest that core knowledge, such as general scientific knowledge, each plays a specific role, serving as t

Jun Yamada, Shaohong Zhong, Jack Collins, Ingmar Posner

Mastering dexterous robotic manipulation of deformable objects is vital for overcoming the limitations of parallel grippers in real-world applications. Current trajectory optimisation approaches often struggle to solve such tasks due to the large search space and the limited task information available from a cost function. In this work, we propose D-Cubed, a

Mark Saaltink

A recent paper showed how to find sets of finite affine or projective planes constructed on a common set of points, so that lines of one plane meet lines of a different plane in at most two points. In this paper, those results are generalized in two different ways to spaces of higher dimension. The simpler of the two generalizations admits many solutions, bo

Liang Zhang, Niao He, Michael Muehlebach

Variational inequality problems are recognized for their broad applications across various fields including machine learning and operations research. First-order methods have emerged as the standard approach for solving these problems due to their simplicity and scalability. However, they typically rely on projection or linear minimization oracles to navigat

Mirza Alim Mutasodirin, Rafi Dwi Rizqullah, Andie Setiyoko, Aniati Murni Arymurthy

In disease risk spatial analysis, many researchers especially in Indonesia are still modelling population density as the ratio of total population to administrative area extent. This model oversimplifies the problem, because it covers large uninhabited areas, while the model should focus on inhabited areas. This study uses settlement mapping against satellit

Emilio Pelaez, Victory Omole, Pranav Gokhale, Rich Rines

Average circuit eigenvalue sampling (ACES) was introduced by Flammia in arXiv:2108.05803 as a protocol to characterize the Pauli error channels of individual gates across the device simultaneously. The original paper posed using ACES to characterize near-term devices as an open problem. This work advances in this direction by presenting a full implementation

Mirco Theile, Hongpeng Cao, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli

In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance. However, ensuring that the deep RL policy and value networks are respectively equivariant and invariant to exploit these symmetries is a substantial challenge. Related works try to design networks that are equivariant and inv

Mihai Pavel, Julius Ross, Matei Toma

We prove uniform boundedness statements for semistable pure sheaves on projective manifolds. For example, we prove that the set of isomorphism classes of pure sheaves of dimension 2 that are slope semistable with respect to ample classes that vary in a compact set $K$ are bounded. We also prove uniform boundedness for pure sheaves of higher dimension, but wi

Matteo Muratori, Troy Petitt, Fernando Quirós

We investigate the asymptotic behavior as $t\to+\infty$ of solutions to a weighted porous medium equation in $ \mathbb{R}^N $, whose weight $\rho(x)$ behaves at spatial infinity like $ |x|^{-\gamma} $ with subcritical power, namely $ \gamma \in [0,2) $. Inspired by some results by Alikakos-Rostamian and Kamin-Ughi from the 1980s on the unweighted problem, we

Minghui Zhao, Junxi Xia, Kaiyuan Hou, Yanchen Liu

Foundation models (FM) have shown immense human-like capabilities for generating digital media. However, foundation models that can freely sense, interact, and actuate the physical domain is far from being realized. This is due to 1) requiring dense deployments of sensors to fully cover and analyze large spaces, while 2) events often being localized to small

Lingting Zhu, Noel Codella, Dongdong Chen, Zhenchao Jin

The limited availability of 3D medical image datasets, due to privacy concerns and high collection or annotation costs, poses significant challenges in the field of medical imaging. While a promising alternative is the use of synthesized medical data, there are few solutions for realistic 3D medical image synthesis due to difficulties in backbone design and

Yan-Qiu Zhang, Shao-Lin Xiong, Ji-Rong Mao, Shuang-Nan Zhang

As the brightest gamma-ray burst ever observed, GRB 221009A provided a precious opportunity to explore spectral line features. In this paper, we performed a comprehensive spectroscopy analysis of GRB 221009A jointly with GECAM-C and Fermi/GBM data to search for emission and absorption lines. For the first time we investigated the line feature throughout this

Mohammadsadeq Garshasbi Herabad, Javid Taheri, Bestoun S. Ahmed, Calin Curescu

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or rem

Yao Wei, Martin Renqiang Min, George Vosselman, Li Erran Li

Compositional 3D scene synthesis has diverse applications across a spectrum of industries such as robotics, films, and video games, as it closely mirrors the complexity of real-world multi-object environments. Conventional works typically employ shape retrieval based frameworks which naturally suffer from limited shape diversity. Recent progresses have been

Wenjun Zou, Yao Lyu, Jie Li, Yujie Yang

Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems. Existing studies in safe RL implicitly assume continuity in policy functions, where policies map states to actions in a smooth, uninterrupted manner; however, our research finds that in some scenarios, the feasible policy should be discontinuous or multi-value

Garry Goldstein

In this work we introduce a Linearized version of the Koringa Khon and Rostoker method (LKKR) and show it to be equivalent to the Linearized Muffin Tin Orbitals method (LMTO). We then present higher derivative versions of both methods, e.g. HDKKR and HDMTO and show them to be partially distinct (not equivalent). In particular HDKKR basis set does not have an

Nikola Bugarin, Jovana Bugaric, Manuel Barusco, Davide Dalle Pezze

Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distribution, which may cause a significant decrease in performance. In this study, we investigate the problem of Pixel-Level Anomaly Detection in the Con

Zhi Yan, Ruixia Yang, Cheng Fang, Wentian Lu

Van der Waals multiferroic tunnel junctions (vdW-MFTJs) with multiple nonvolatile resistive states are highly suitable for new physics and next-generation storage electronics. However, currently reported vdW-MFTJs are based on two types of materials, i.e., vdW ferromagnetic and ferroelectric materials, forming a multiferroic system. This undoubtedly introduc

Stefanos Laskaridis, Kleomenis Katevas, Lorenzo Minto, Hamed Haddadi

Transformers have revolutionized the machine learning landscape, gradually making their way into everyday tasks and equipping our computers with "sparks of intelligence". However, their runtime requirements have prevented them from being broadly deployed on mobile. As personal devices become increasingly powerful and prompt privacy becomes an ever more press

Hanwen Liu

The stretched Gau{\ss}ian function $f(\mathbf{x})=\exp \left(-\|\mathbf{x}\|^s\right)$, as a real function defined on $\mathbb{R}^d$, has found numerous applications in mathematics and physics. For instance, to describe results from spectroscopy or inelastic scattering, the Fourier transform of the stretched Gau{\ss}ian function is needed. For $s \in(0,2]$,

William Clark, Leonardo Colombo, Anthony Bloch

Hybrid systems are dynamical systems with continuous-time and discrete-time components in their dynamics. When hybrid systems are defined on a principal bundle we are able to define two classes of impacts for the discrete-time transition of the dynamics: interior impacts and exterior impacts. In this paper we define hybrid systems on principal bundles, study

E. Guzmán Ccolque, M. Fernández López, M. M. Vazzano, I. de Gregorio

The protostar IRAS 15398-3359 is associated with a bipolar molecular outflow ejected in an nearly northeast-southwest (NE-SW) direction which has been extensively studied. It has been suggested previous episodic accretion events by this source. Furthermore, the analysis of the morphology and kinematics of the molecular outflow revealed the presence of four $

Lei You, Rui-bo Wang, Shi-Jie Ma, Jian-Bo Deng

The optical properties of Euler-Heisenberg (EH) black hole (BH) surrounded by Cold Dark Matter (CDM) halo are investigated. By changing BH's parameters, we found that the radius of horizon r_{h} and radius of photon sphere r_{ph} will transparently increase as CDM halo parameters R and \rho increase. To show the influence of CDM halo on the BH's optical char

Mingqi Shao, Feng Xiong, Hang Zhang, Shuang Yang

Neural radiance fields~(NeRF) have recently been applied to render large-scale scenes. However, their limited model capacity typically results in blurred rendering results. Existing large-scale NeRFs primarily address this limitation by partitioning the scene into blocks, which are subsequently handled by separate sub-NeRFs. These sub-NeRFs, trained from scr

Renee DiResta, Josh A. Goldstein

Much of the research and discourse on risks from artificial intelligence (AI) image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers - seemingly motivated by profit or clout, not ideology - are already using AI-generated images to

Kurran Singh, Jungseok Hong, Nicholas R. Rypkema, John J. Leonard

Despite recent advances in semantic Simultaneous Localization and Mapping (SLAM) for terrestrial and aerial applications, underwater semantic SLAM remains an open and largely unaddressed research problem due to the unique sensing modalities and the object classes found underwater. This paper presents an object-based semantic SLAM method for underwater enviro

Thando Nkomozake

We use mathematical statistics theory to derive the Compound-Dirichlet-Multinomial (CDM) prediction model. We then use this model to predict winning numbers for the 6-number, 5-number, pick-4 and pick-3 lottery games. We also develop a strategy which we call the 3-strategy, for generating profit by predicting winning numbers for the pick-3 lottery game.

Jieming Cui, Tengyu Liu, Nian Liu, Yaodong Yang

Traditional approaches in physics-based motion generation, centered around imitation learning and reward shaping, often struggle to adapt to new scenarios. To tackle this limitation, we propose AnySkill, a novel hierarchical method that learns physically plausible interactions following open-vocabulary instructions. Our approach begins by developing a set of

Karol Gotkowski, Klaus H. Maier-Hein, Fabian Isensee

Scribble supervision has emerged as a promising approach for reducing annotation costs in medical 3D segmentation by leveraging sparse annotations instead of voxel-wise labels. While existing methods report strong performance, a closer analysis reveals that the majority of research is confined to the cardiac domain, predominantly using ACDC and MSCMR dataset

Kai Toyosawa

We show that the amalgamated free product of weakly exact von Neumann algebras is weakly exact. This is done by using a universal property of Toeplitz-Pimsner algebras and a locally convex topology on bimodules of von Neumann algebras, which is used to characterize weakly exact von Neumann algebras.

The Tien Mai

This work addresses the problem of high-dimensional classification by exploring the generalized Bayesian logistic regression method under a sparsity-inducing prior distribution. The method involves utilizing a fractional power of the likelihood resulting the fractional posterior. Our study yields concentration results for the fractional posterior, not only o

Frank Lechermann, Steffen Bötzel, Ilya M. Eremin

Using advanced dynamical mean-field theory on a realistic level we study the normal-state correlated electronic structure of the high-pressure superconductor La$_3$Ni$_2$O$_7$ and compare the features of the conventional bilayer (2222) Ruddelsden-Popper crystal structure with those of a newly-identified monolayer-trilayer (1313) alternation. Both structural

Cheng-Long Wang, Qi Li, Zihang Xiang, Yinzhi Cao

By adopting a more flexible definition of unlearning and adjusting the model distribution to simulate training without the targeted data, approximate machine unlearning provides a less resource-demanding alternative to the more laborious exact unlearning methods. Yet, the unlearning completeness of target samples-even when the approximate algorithms are exec

David M. Ambrose, Megan Griffin-Pickering, Alpár R. Mészáros

We prove well-posedness of a class of kinetic-type Mean Field Games, which typically arise when agents control their acceleration. Such systems include independent variables representing the spatial position as well as velocity. We consider non-separable Hamiltonians without any structural conditions, which depend locally on the density variable. Our analysi

N. M. Tri, D. A. Tuan

In this paper we are mainly concerned with nontrivial positive solutions to the Dirichlet problem for the degenerate elliptic equation \begin{gather} -\frac{\partial^2 u}{\partial x^2} -\left|x\right|^{2k}\frac{\partial^2 u}{\partial y^2}=|x|^{2k}u^p+f(x,y,u) \quad\text{ in }\Omega, \ u=0 \quad\text{ on }\partial\Omega,\label{equ0} \end{gather} where $\Omega

Qiuyun Li, Manda Riehl

Profiling is a process that finds similarities between different RNA secondary structures by extracting signals from the Boltzmann sampling. The reproducibility of profiling can be identified by the standard deviation of number of features among Boltzmann samples. We found a strong relationship between the frequency of each helix class and its standard devia

Behzad Ghahremani, Hessam Babaee

We present a novel tensor interpolation algorithm for the time integration of nonlinear tensor differential equations (TDEs) on the tensor train and Tucker tensor low-rank manifolds, which are the building blocks of many tensor network decompositions. This paper builds upon our previous work (Donello et al., Proceedings of the Royal Society A, Vol. 479, 2023

Manuel Estevez, Erika Roldan, Henry Segerman

Which surfaces can be realized with two-dimensional faces of the five-dimensional cube (the penteract)? How can we visualize them? In recent work, Aveni, Govc, and Roldan, show that there exist 2690 connected closed cubical surfaces up to isomorphism in the 5-cube. They give a classification in terms of their genus $g$ for closed orientable cubical surfaces

Luca Gamberale, Daniele Garbelli

We present a simple argument confirming the spontaneous quantum condensation of an electromagnetic field in matter in the case of a multi-level atomic system coupled to a single-mode electromagnetic field in the dipole approximation. The obtained result disproves the validity of a ``no-go theorem'' and fully rehabilitates theories of spontaneous quantum cond

Yuanhua Zhong, Jianzhong Lu, Min Li, Jinlu Li

In this paper, we study the Cauchy problem of the Euler-Poincar\'{e} equations in $\R^d$ with initial data belonging to the Triebel-Lizorkin spaces. We prove the local-in-time unique existence of solutions to the Euler-Poincar\'{e} equations in $F^s_{p,r}(\R^d)$. Furthermore, we obtain that the data-to-solution of this equation is continuous but not uniforml

Benjamin Callewaert, Joost Vennekens

Payroll management is a critical business task that is subject to a large number of rules, which vary widely between companies, sectors, and countries. Moreover, the rules are often complex and change regularly. Therefore, payroll management systems must be flexible in design. In this paper, we suggest an approach based on a flexible Answer Set Programming (