April 2024 arXiv papers — page 132

Grigorios Fournodavlos, Elliot Marshall, Todd A. Oliynyk

We study the future stability of cosmological fluids, in spacetimes with an accelerated expansion, which exhibit extreme tilt behavior, ie. their fluid velocity becoming asymptotically null at timelike infinity. It has been predicted in the article \cite{LEUW} that the latter behavior is dominant for sound speeds beyond radiation $c_s=1/\sqrt{3}$, hence, bif

Hiromu Tanaka, Jakub Witaszek, Fuetaro Yobuko

We develop the theory of quasi-$F^e$-splittings, quasi-$F$-regularity, and quasi-$+$-regularity.

Wenqian Li, Yan Pang

Wasserstein distance is a key metric for quantifying data divergence from a distributional perspective. However, its application in privacy-sensitive environments, where direct sharing of raw data is prohibited, presents significant challenges. Existing approaches, such as Differential Privacy and Federated Optimization, have been employed to estimate the Wa

Subhasish Bag, Vikrant Saxena, Amita Das

Studies on finite-size plasma have attracted a lot of attention lately. They can form by ionizing liquid droplets by lasers. The dynamical behavior of such plasma droplets is, therefore, a topic of significant interest. In particular, questions related to the linear and nonlinear characteristics (associated with the inhomogeneous density typically at the edg

Urjit A. Yajnik

We set up a formalism for calculating the energy density generated in a quantized massive scalar field in the course of the drastic change in spacetime geometry at the end of the inflationary era. The calculation relies on the notion of adiabatic vacuum. The Bogolubov coefficients are computed by employing the sudden approximation. After obtaining a general

Pengcheng Ma, Kaveh Delfanazari, Reuben K. Puddy, Jiahui Li

The mass production and the practical number of cryogenic quantum devices producible in a single chip are limited to the number of electrical contact pads and wiring of the cryostat or dilution refrigerator. It is, therefore, beneficial to contrast the measurements of hundreds of devices fabricated in a single chip in one cooldown process to promote the scal

Hajime Ishikawa, Yuto Ishii, Takeshi Yajima, Yasuhiro H. Matsuda

We study the magnetism of a layered, spin-$\frac12$ organic-inorganic copper sulfate, which is a close realization of the star lattice antiferromagnet, one of the playgrounds of geometric frustration and resonating valence bond physics in two spatial dimensions. Our thermodynamic measurements show no ordering down to 0.1 K and a characteristic field-induced

Eduard Feireisl, Arnab Roy, Arghir Zarnescu

We show that the collective effect of $N$ rigid bodies $(\mathcal{S}_{n,N})_{n=1}^N$ of diameters $(r_{n,N})_{n=1}^N$ immersed in an incompressible non--Newtonian fluid is negligible in the asymptotic limit $N \to \infty$ as long as their total packing volume $\sum_{n=1}^N r_{n,N}^d$, $d=2,3$ tends to zero exponentially -- $\sum_{n=1}^N r_{n,N}^d \approx A^{

Victor Volfson

The paper proves a generalization of Wintner's theorem on the asymptotics of summation functions to the case of summation functions with nonlinear asymptotics. The class of arithmetic functions that have a logarithmic asymptotic mean is studied. The Kronecker lemma is generalized to the case when the corresponding Dirichlet series diverges. Several assertion

Ben Liu, Peng Zhang, Yi Feng, Xiaowei Lou

In this article, we propose a new method for calculating the mixed correlation coefficient (Pearson, polyserial and polychoric) matrix and its covariance matrix based on the GMM framework. We build moment equations for each coefficient and align them together, then solve the system with Two-Step IGMM algorithm. Theory and simulation show that this estimation

Fan Lu, Kwan-Yee Lin, Yan Xu, Hongsheng Li

Text-to-3D generation has achieved remarkable success via large-scale text-to-image diffusion models. Nevertheless, there is no paradigm for scaling up the methodology to urban scale. Urban scenes, characterized by numerous elements, intricate arrangement relationships, and vast scale, present a formidable barrier to the interpretability of ambiguous textual

Jinyu Song, Weitao You, Shuhui Shi, Shuxuan Guo

Chinese vector font generation is challenging due to the complex structure and huge amount of Chinese characters. Recent advances remain limited to generating a small set of characters with simple structure. In this work, we first observe that most Chinese characters can be disassembled into frequently-reused components. Therefore, we introduce the first eff

Haojie Ren, Weixiao Shen

For a real analytic periodic function $\phi:\mathbb{R}\to\mathbb{R}^d$, an integer $b \ge 2$ and $\lambda\in(1/b,1)$, we prove that the box dimension and the Hausdorff dimension of the graph of the Weierstrass function $W(x)=\sum_{n=0}^{\infty}{{\lambda}^n\phi(b^nx)}$ are both equal to $$\min\left\{\log_{\lambda^{-1}}b,\,1+\left(\,d-q\,\right)\left(1+\log_b\

Xiaowen Huang, Tao Huang, Shushi Gu, Shuguang Zhao

Integrating artificial intelligence (AI) and federated learning (FL) in smart transportation has raised critical issues regarding their responsible use. Ensuring responsible AI is paramount for the stability and sustainability of intelligent transportation systems. Despite its importance, research on the responsible application of AI and FL in this domain re

Yu Qiao, Chaoning Zhang, Apurba Adhikary, Choong Seon Hong

Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks. However, challenges arise due to vulnerability to adversarial examples (AEs) and the non-independent and identically distributed (non-IID) nature of data distribution among devices, hindering the deployment of adversarially robu

Benchi Zhao, Kosuke Ito, Keisuke Fujii

Channel simulation using coherence, which refers to realizing a target channel with coherent states and free operations, is a fundamental problem in the quantum resource theory of coherence. The limitations of the accuracy of deterministic channel simulation motivate us to consider the more general probabilistic framework. In this paper, we develop the frame

Yuri Ravanal, Gabriel Gómez, Norman Cruz

In this paper, we investigate the behavior of a massive scalar field dark matter scenarios in the large mass limit around a central Reissner-Nordstr\"{o}m black hole. This study is motivated by observations from the Event Horizon Telescope collaboration, which does not exclude the possibility of the existence of such black holes. Through these inquiries, we

Sukhdeep Singh, Sudhir Rohilla, Anuj Sharma

Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. These deep learning algorithms have demonstrated great results in different fields. Deep learning has particularly witnessed for a great achievement of human level performance across a number of domains in comput

Jiahao Wang, Wenqi Shao, Mengzhao Chen, Chengyue Wu

This work examines whether decoder-only Transformers such as LLaMA, which were originally designed for large language models (LLMs), can be adapted to the computer vision field. We first "LLaMAfy" a standard ViT step-by-step to align with LLaMA's architecture, and find that directly applying a causal mask to the self-attention brings an attention collapse is

Pengwei Wang, Yilong Chen, Wan Su, Jie Wang

Deep learning models have become a powerful tool in knee angle estimation for lower limb prostheses, owing to their adaptability across various gait phases and locomotion modes. Current methods utilize Multi-Layer Perceptrons (MLP), Long-Short Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN), predominantly analyzing motion information fro

Xiejin Li, Yinghe Zhao, Jinming Bai

In this paper we investigate the stellar populations and star formation histories of 235 active galactic nuclei (AGN)-host dwarf galaxies, consisting of four samples identified separately with different methods (i.e., radio, X-ray, mid-IR and variability), utilizing the synthesis code STARLIGHT and spectra from the Sloan Digital Sky Survey (SDSS) Data Releas

Marco Majland, Patrick Ettenhuber, Nikolaj Thomas Zinner, Ove Christiansen

Quantum chemistry is one of the most promising applications for which quantum computing is expected to have significant impact. Despite considerable research in the field of electronic structure, calculating the vibrational properties of molecules on quantum computers remain a relatively unexplored field. In this work, we develop a vibrational ADAPT-VQE (vAD

Qi Deng, Zheng Fan, Zhi Li, Xinna Pan

The application of evolutionary algorithms (EAs) to multi-objective optimization problems has been widespread. However, the EA research community has not paid much attention to large-scale multi-objective optimization problems arising from real-world applications. Especially, Food-Energy-Water systems are intricately linked among food, energy and water that

Wajid M. Shaikh, Rupali S. Jain, B. Surendranath Reddy, Bhagyashri S. Patil

In this article, we present two new approaches to construct minimal linear codes of dimension $n+1$ over $\mathbb{F}_{3}$ using characteristic and ternary functions. We also obtain the weight distributions of these constructed minimal linear codes. We further show that a specific class of these codes violates Ashikhmin-Barg condition.

Hironori Yamaguchi, Tsuyoshi Okubo, Akira Matsuo, Takashi Kawakami

We introduce a model compound featuring a spin-1/2 frustrated square lattice partially coupled by spin-5/2. A significant magnetization plateau exceeding 60 T could be observed, indicating a quantum state formed by $S$ = 1/2 spins in the square lattice. The remaining $S$ = 5/2 spins exhibited paramagnetic behavior in the low-field regions. The numerical anal

Anand S Bharadwaj, Elisa Thiel, Pratik Suchde

In this paper, a new method to model solidification of thin liquid films is proposed. \blue{This method is targeted at applications like aircraft icing and tablet coating where the formation of liquid films from impinging droplets on a surface form a critical part of the physics of the process.} The proposed model takes into account the (i) unsteadiness in t

Yiru Wang, Wanting Yang, Zehui Xiong, Yuping Zhao

Semantic Communication (SemCom) is envisaged as the next-generation paradigm to address challenges stemming from the conflicts between the increasing volume of transmission data and the scarcity of spectrum resources. However, existing SemCom systems face drawbacks, such as low explainability, modality rigidity, and inadequate reconstruction functionality. R

Kiyoung Ko, Daewon Suk, Dohyeong Kim, Soobong Park

Ultra-high-Q optical resonators have facilitated recent advancements in on-chip photonics by effectively harnessing nonlinear phenomena providing useful functionalities. While these breakthroughs, primarily focused on the near-infrared region, have extended interest to longer wavelengths holding importance for monitoring and manipulating molecules, the absen

Zhilei Zhang

In [6], the authors construct a cochain complex $C\!H^*(\mathcal{Z}_{\mathcal{K}})$ on the cohomology of a moment-angle complex $\mathcal{Z}_{\mathcal{K}}$ and call the resulting cohomology the double cohomology, $H\!H^*(\mathcal{Z}_{\mathcal{K}})$. In this paper, we study the change of rank in double cohomology after gluing an $n$-simplex to a simplicial co

Zhengyuan Liu, Stella Xin Yin, Geyu Lin, Nancy F. Chen

Intelligent Tutoring Systems (ITSs) can provide personalized and self-paced learning experience. The emergence of large language models (LLMs) further enables better human-machine interaction, and facilitates the development of conversational ITSs in various disciplines such as math and language learning. In dialogic teaching, recognizing and adapting to ind

Jinghao Huang, Olga Sadovskaya, Fedor Sukochev, Dmitriy Zanin

We prove that the sequence space $\ell_{p,q}$ does not embed into $L_{p,q}(\mathcal{M},\tau)$ for any noncommutative probability space $(\mathcal{M},\tau)$, $1< p<\infty $, $1\le q<\infty$, $p\ne q$. Several applications to the isomorphic classification of noncommutative $L_{p,q}$-spaces are given, which extend and complement several earlier results.

Jianxiang Xiang, Zhenhua Liu, Haodong Liu, Yin Bai

In real-life conversations, the content is diverse, and there exists the one-to-many problem that requires diverse generation. Previous studies attempted to introduce discrete or Gaussian-based continuous latent variables to address the one-to-many problem, but the diversity is limited. Recently, diffusion models have made breakthroughs in computer vision, a

Kohki Iba, Kouji Yano

Long-time limit of one-dimensional Lévy processes weighted and normalized with respect to the exponential functional of two-point local times are studied. The limit processes may vary according to the choice of random clocks.

Kai Gao, Zihe Ye, Duo Zhang, Baichuan Huang

Long-horizon task and motion planning (TAMP) is notoriously difficult to solve, let alone optimally, due to the tight coupling between the interleaved (discrete) task and (continuous) motion planning phases, where each phase on its own is frequently an NP-hard or even PSPACE-hard computational challenge. In this study, we tackle the even more challenging goa

Jon Kleinberg, Sendhil Mullainathan

Although current large language models are complex, the most basic specifications of the underlying language generation problem itself are simple to state: given a finite set of training samples from an unknown language, produce valid new strings from the language that don't already appear in the training data. Here we ask what we can conclude about language

Kaixi Hu, Lin Li, Qing Xie, Xiaohui Tao

Granularity and accuracy are two crucial factors for crime event prediction. Within fine-grained event classification, multiple criminal intents may alternately exhibit in preceding sequential events, and progress differently in next. Such intensive intent dynamics makes training models hard to capture unobserved intents, and thus leads to sub-optimal genera

Zhihao Lu

We obtain almost optimal differential Harnack inequalities for a class of nonlinear parabolic equations on Riemannian manifolds with Bakry-\'{E}mery Ricci curvature bounded below, which includes the classical Fisher-KPP equation and Newell-Whitehead equation. Compared to existing research, we do not impose any additional conditions on the positive solutions.

Shamil Asgarli, Chi Hoi Yip

Given two irreducible conics $C$ and $D$ over a finite field $\mathbb{F}_q$ with $q$ odd, we show that there are $q^2/4+O(q^{3/2})$ points $P$ in $\mathbb{P}^2(\mathbb{F}_q)$ such that $P$ is external to $C$ and internal to $D$. This answers a question of Korchm\'{a}ros. We also prove the analogous result for higher-dimensional smooth quadric hypersurfaces i

Runfa Li, Upal Mahbub, Vasudev Bhaskaran, Truong Nguyen

Current monocular 3D scene reconstruction (3DR) works are either fully-supervised, or not generalizable, or implicit in 3D representation. We propose a novel framework - MonoSelfRecon that for the first time achieves explicit 3D mesh reconstruction for generalizable indoor scenes with monocular RGB views by purely self-supervision on voxel-SDF (signed distan

Sanjeev Kumar Pandey, Shaunak Sen, Indra Narayan Kar

Determining conditions on the coupling strength for the synchronization in networks of interconnected oscillators is a challenging problem in nonlinear dynamics. While sophisticated mathematical methods have been used to derive conditions, these conditions are usually only sufficient and/ or based on numerical methods. We addressed the gap between the suffic

Vikhyath Gupta, Srinivasa Rao P

In recent years, the use of open-source models has gained immense popularity in various fields, including legal language modelling and analysis. These models have proven to be highly effective in tasks such as summarizing legal documents, extracting key information, and even predicting case outcomes. This has revolutionized the legal industry, enabling lawye

Seth Lazar

Some have criticised Generative AI Systems for replicating the familiar pathologies of already widely-deployed AI systems. Other critics highlight how they foreshadow vastly more powerful future systems, which might threaten humanity's survival. The first group says there is nothing new here; the other looks through the present to a perhaps distant horizon.

Chuanqi Chen, Nan Chen, Jin-Long Wu

A new knowledge-based and machine learning hybrid modeling approach, called conditional Gaussian neural stochastic differential equation (CGNSDE), is developed to facilitate modeling complex dynamical systems and implementing analytic formulae of the associated data assimilation (DA). In contrast to the standard neural network predictive models, the CGNSDE i

Bo Qiang, Wenxian Shi, Yuxuan Song, Menghua Wu

Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally ``undruggable'' proteins by binding simultaneously to their targets and degradation-associated proteins. A key challenge in their rational design is understanding their structural basis of activity. Due to the lack of crystal structures (18 in the PDB), e

Vincent Henkel, Lukas Peter Wagner, Felix Gehlhoff, Alexander Fay

The rapid expansion of renewable energy sources has introduced significant volatility and unpredictability in the energy supply chain, necessitating advanced control strategies to ensure grid stability and reliability. Green hydrogen production via electrolysis offers a viable solution for converting and storing this volatile renewable energy. However, the i

Long Ji, Mingyu Ge, Yupeng Chen, Zhaosheng Li

We report accretion-powered pulsations for the first time during thermonuclear bursts in hard X-rays, which were observed with Insight-HXMT in 2022 during the outburst of the accreting X-ray millisecond pulsar MAXI J1816-195. By stacking 73 bursts, we detected pulse profiles in 8-30 keV and 30-100 keV during bursts, which are identical to those obtained from

Xiaojie Li, Song Bo, Xuewen Zhang, Yan Qin

In this work, we consider a state estimation problem for large-scale nonlinear processes in the absence of first-principles process models. By exploiting process operation data, both process modeling and state estimation design are addressed within a distributed framework. By leveraging the Koopman operator concept, a parallel subsystem modeling approach is

Hongyu Lu, Han-Qing Wu, Bin-Bin Chen, Zi Yang Meng

Collective excitations play a vital role in understanding the exotic phases of matter and phase transitions in quantum many-body systems. For the first time, we numerically (via exact diagonalization and density matrix renormalization group) report the microscopic realization of a transition from a translationally invariant fractional quantum anomalous Hall

Seraj Al Mahmud Mostafa, Jinbo Wang, Benjamin Holt, Jianwu Wang

Ocean eddies play a significant role both on the sea surface and beneath it, contributing to the sustainability of marine life dependent on oceanic behaviors. Therefore, it is crucial to investigate ocean eddies to monitor changes in the Earth, particularly in the oceans, and their impact on climate. This study aims to pinpoint ocean eddies using AWS cloud s

Ben Wilks, Fabien Montiel, Luke G. Bennetts, Sarah Wakes

Eigenmodes are studied for a fluid-filled rectangular tank containing one or more vertical barriers, and on which either Dirichlet or Neumann boundary conditions are prescribed on the lateral walls. In the case where the tank contains a single barrier, the geometry of the tank is equivalent to the unit cell of the cognate periodic array, and its eigenmodes a

Xiaokang Zhang, Zijun Yao, Jing Zhang, Kaifeng Yun

Detecting non-factual content is a longstanding goal to increase the trustworthiness of large language models (LLMs) generations. Current factuality probes, trained using humanannotated labels, exhibit limited transferability to out-of-distribution content, while online selfconsistency checking imposes extensive computation burden due to the necessity of gen

Xingyu Song, Zhan Li, Shi Chen, Xin-Qiang Cai

Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications. Despite significant improvements brought by Convolutional Neural Networks (CNNs), these models suffer performance declines when trained with discontinuous video frames, which is a frequent scenario in real-world settings. This decline primarily resul

Akihiro Miki, Yuta Sahara, Kazuhiro Miyama, Shunnosuke Yoshimura

The human joint is an open-type joint composed of bones, cartilage, ligaments, synovial fluid, and joint capsule, having advantages of flexibility and impact resistance. However, replicating this structure in robots introduces friction challenges due to the absence of bearings. To address this, our study focuses on mimicking the fluid-exuding function of hum

V. K. Sharma, J. Gupta, J. Bhatt Mitra, H. Srinivasan

The bactericidal potency of ionic liquids (ILs) is well-established, yet their precise mechanism of action remains elusive. Here, we show evidence that the bactericidal action of ILs primarily involves permeabilizing the bacterial cell membrane. Our findings reveal that ILs exert their effects by directly interacting with the lipid bilayer and enhancing the

Xiaojie Li, Adrian Wing-Keung Law, Xunyuan Yin

In this paper, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach. First, we formulate a linear full-information estimation design within a distributed framework as the basis for developing our approach. Second, the analytical solution to the local optimization pr

Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath

Copyright infringement may occur when a generative model produces samples substantially similar to some copyrighted data that it had access to during the training phase. The notion of access usually refers to including copyrighted samples directly in the training dataset, which one may inspect to identify an infringement. We argue that such visual auditing l

Liuquan Yao, Zhichao Liu, Yuan Li, Huazi Zhang

In this paper, we define partial orders (POs) of polar codes based on the Bhattacharyya parameter and the bit-error probability, respectively. These POs are applicable to arbitrary binary memoryless symmetric channel (BMSC). Leveraging the extremal inequalities of polarization transformation, we derive new POs for BMSC based on the corresponding POs observed

Qi Zhang, Bing Li, Lingzhou Xue

Motivated by modern data forms such as images and multi-view data, the multi-attribute graphical model aims to explore the conditional independence structure among vectors. Under the Gaussian assumption, the conditional independence between vectors is characterized by blockwise zeros in the precision matrix. To relax the restrictive Gaussian assumption, in t

Adrian Xiao Bin Yong, Tianyu Su, Elif Ertekin

Generative models have received significant attention in recent years for materials science applications, particularly in the area of inverse design for materials discovery. However, these models are usually assessed based on newly generated, unverified materials, which provide a narrow evaluation of a model's performance. Also, current efforts for inorganic

Jessica Y. Bo, Pan Hao, Brian Y. Lim

Many explainable AI (XAI) techniques strive for interpretability by providing concise salient information, such as sparse linear factors. However, users either only see inaccurate global explanations, or highly-varying local explanations. We propose to provide more detailed explanations by leveraging the human cognitive capacity to accumulate knowledge by in

Jie Wang, Yash Vardhan Pant, Lei Zhao, Michał Antkiewicz

With the increasing presence of autonomous vehicles (AVs) on public roads, developing robust control strategies to navigate the uncertainty of human-driven vehicles (HVs) is crucial. This paper introduces an advanced method for modeling HV behavior, combining a first-principles model with Gaussian process (GP) learning to enhance velocity prediction accuracy

Soojong Kim, Kwanho Kim, Claire Wonjeong Jo

Objective. Vaccination has engendered a spectrum of public opinions, with social media acting as a crucial platform for health-related discussions. The emergence of artificial intelligence technologies, such as large language models (LLMs), offers a novel opportunity to efficiently investigate public discourses. This research assesses the accuracy of ChatGPT

Sonae Hadama, Younghun Hong

The nonlinear Hartree equation (NLH) in the Heisenberg picture admits steady states of the form $\gamma_f=f(-\Delta)$ representing quantum states of infinitely many particles. In this article, we consider the time evolution of perturbations from a large class of such steady states via the three-dimensional NLH. We prove that if the interaction potential $w$

Evan Krul, Hye-young Paik, Sushmita Ruj, Salil S. Kanhere

Digital identity is evolving from centralized systems to a decentralized approach known as Self-Sovereign Identity (SSI). SSI empowers individuals to control their digital identities, eliminating reliance on third-party data custodians and reducing the risk of data breaches. However, the concept of trust in SSI remains complex and fragmented. This paper syst

Rishi Veerapaneni, Jonathan Park, Muhammad Suhail Saleem, Maxim Likhachev

With the advent of machine learning, there have been several recent attempts to learn effective and generalizable heuristics. Local Heuristic A* (LoHA*) is one recent method that instead of learning the entire heuristic estimate, learns a "local" residual heuristic that estimates the cost to escape a region (Veerapaneni et al 2023). LoHA*, like other supervi

Sibeak Lee, Kyeongsu Kang, Seongbo Ha, Hyeonwoo Yu

We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for additional networks, making it particularly suited for challenging observations and uncontrolled image environments. NeRF diverges from traditional geometric methods by providing an e

MohammadMahdi Ghadaksaz, Mobeen Mahmood, Tho Le-Ngoc

This study focuses on a multi-user massive multiple-input multiple-output (MU-mMIMO) system by incorporating an unmanned aerial vehicle (UAV) as a decode-and-forward (DF) relay between the base station (BS) and multiple Internet-of-Things (IoT) devices. Our primary objective is to maximize the overall achievable rate (AR) by introducing a novel framework tha

Kevin Linka, Ellen Kuhl

The ability to automatically discover interpretable mathematical models from data could forever change how we model soft matter systems. For convex discovery problems with a unique global minimum, model discovery is well-established. It uses a classical top-down approach that first calculates a dense parameter vector, and then sparsifies the vector by gradua

Hoan-Phung Bui, Joost Vercruysse, Gabor Wiese

This paper extends Hopf-Galois theory to infinite field extensions and provides a natural definition of subextensions. For separable (possibly infinite) Hopf-Galois extensions, it provides a Galois correspondence. This correspondence also is a refinement of what was known in the case of finite separable Hopf-Galois extensions.

Yingbo Ma, Suraj Kolla, Zhenhong Hu, Dhruv Kaliraman

Modern electronic health records (EHRs) hold immense promise in tracking personalized patient health trajectories through sequential deep learning, owing to their extensive breadth, scale, and temporal granularity. Nonetheless, how to effectively leverage multiple modalities from EHRs poses significant challenges, given its complex characteristics such as hi

Kun Wang, Zheng Chen, Jun Li

This paper presents a Neural Networks (NNs) based approach for designing the Fuel-Optimal Powered Descent Guidance (FOPDG) for lunar pinpoint landing. According to Pontryagin's Minimum Principle, the optimality conditions are first derived. To generate the dataset of optimal trajectories for training NNs, we formulate a parameterized system, which allows for

Norrathep Rattanavipanon, Ivan De Oliveira Nunes

The rise in IoT-driven distributed data analytics, coupled with increasing privacy concerns, has led to a demand for effective privacy-preserving and federated data collection/model training mechanisms. In response, approaches such as Federated Learning (FL) and Local Differential Privacy (LDP) have been proposed and attracted much attention over the past fe

Moise Blanchard

In this paper we provide oracle complexity lower bounds for finding a point in a given set using a memory-constrained algorithm that has access to a separation oracle. We assume that the set is contained within the unit $d$-dimensional ball and contains a ball of known radius $\epsilon>0$. This setup is commonly referred to as the feasibility problem. We sho

Yu Kitabeppu

We prove the Shannon's inequality on non-collapsing $\mathsf{RCD}(0,N)$ spaces. In the proof, we use the characterization of the $\mathsf{EVI}_{0,N}$-gradient flow of the relative entropy and the infinitesimal behavior of the heat kernel. Also we have a cone rigidity result. As an application, we have the so-called uncertainty principle inequality on such sp

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

We measure the Born cross section for the reaction $e^{+}e^{-} \rightarrow \eta h_c$ from $\sqrt{s} = 4.129$ to $4.600$~GeV using data sets collected by the BESIII detector running at the BEPCII collider. A resonant structure in the cross section line shape near 4.200~GeV is observed with a statistical significance of 7$\sigma$. The parameters of this resona

Jennifer Mickel

Racial diversity has become increasingly discussed within the AI and algorithmic fairness literature, yet little attention is focused on justifying the choices of racial categories and understanding how people are racialized into these chosen racial categories. Even less attention is given to how racial categories shift and how the racialization process chan

Kaiyuan Yang, Weilong Wei, Xiafei Ma, Botao Chen

Optical interferometric imaging enables astronomical observation at extremely high angular resolution. The necessary optical information for imaging, such as the optical path differences and visibilities, is easy to extract from fringes generated by the combination of two beams. With more than two apertures, the image-plane interference pattern becomes an in

Aakash Kumar, Chen Chen, Ajmal Mian, Neils Lobo

3D detection is a critical task that enables machines to identify and locate objects in three-dimensional space. It has a broad range of applications in several fields, including autonomous driving, robotics and augmented reality. Monocular 3D detection is attractive as it requires only a single camera, however, it lacks the accuracy and robustness required

Xincan Feng, Akifumi Yoshimoto

Recent advancements in Natural Language Processing (NLP) have seen Large-scale Language Models (LLMs) excel at producing high-quality text for various purposes. Notably, in Text-To-Speech (TTS) systems, the integration of BERT for semantic token generation has underscored the importance of semantic content in producing coherent speech outputs. Despite this,

Yuan Tang

This article conducts a reexamination of the research conducted by Kwasniewski et al., focusing on their adaptation of the 2.5D LU factorization algorithm with tournament pivoting, known as \func{COnfLUX}. Our reexamination reveals potential concerns regarding the upper bound, empirical investigation methods, and lower bound, despite the original study provi

Yamin Wang

We study the regularity of the free boundary in one-phase Stefan problem with nonlinear operator. Using the Hodograph transform and a linearization technique, we prove that flat free boundaries are $C^{1, \alpha}$ in space and time. When the operator is concave (or convex) and smooth, the free boundary is smooth.

Murong Yue, Wenhan Lyu, Jennifer Suh, Yixuan Zhang

Collaborative problem solving (CPS) is essential in mathematics education, fostering deeper learning through the exchange of ideas. Yet, classrooms often lack the resources, time, and peer dynamics needed to sustain productive CPS. Recent advancements in Large Language Models (LLMs) offer a promising avenue to enhance CPS in mathematical education. We design

Shangyu Chen, Zibo Zhao, Yuanyuan Zhao, Xiang Li

The emergence of Large Language Models (LLMs) has innovated the development of dialog agents. Specially, a well-trained LLM, as a central process unit, is capable of providing fluent and reasonable response for user's request. Besides, auxiliary tools such as external knowledge retrieval, personalized character for vivid response, short/long-term memory for

Gaole Dai, Zhenyu Wang, Qinwen Xu, Ming Lu

One of the most critical factors in achieving sharp Novel View Synthesis (NVS) using neural field methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) is the quality of the training images. However, Conventional RGB cameras are susceptible to motion blur. In contrast, neuromorphic cameras like event and spike cameras inherently capture

Longwei Zou, Qingyang Wang, Han Zhao, Jiangang Kong

The fast-growing large scale language models are delivering unprecedented performance on almost all natural language processing tasks. However, the effectiveness of large language models are reliant on an exponentially increasing number of parameters. The overwhelming computation complexity incurs a high inference latency that negatively affects user experie

Aleksandr Bekshaev, Oleg Angelsky, Claudia Zenkova

The Wigner distribution function and its spatial-angular moments (intensity-moments) are known as efficient instruments for characterization of complex quasimonochromatic light beams and their transformations. In this paper, the generalization of the WF-based approach to spatio-temporal (ST) light fields (wave packets, short pulses) is considered. It is show

Keian Noori, Ben A. Olsen, Aleksandr Rodin

Ionic conductivity in solid electrolytes is commonly expected to exhibit Arrhenius dependence on temperature, determined by a well-defined activation energy. Consequently, a standard approach involves calculating this energy using quasi-static methods and using the Arrhenius form to extrapolate the numerical results from one temperature range to another. Des

Lianqiang Li, Chenqian Yan, Yefei Chen

To accelerate and compress deep neural networks (DNNs), many network quantization algorithms have been proposed. Although the quantization strategy of any algorithm from the state-of-the-arts may outperform others in some network architectures, it is hard to prove the strategy is always better than others, and even cannot judge that the strategy is always th

Xiaojie Li, Song Bo, Yan Qin, Xunyuan Yin

In this paper, partition-based distributed state estimation of general linear systems is considered. A distributed moving horizon state estimation scheme is developed via decomposing the entire system model into subsystem models and partitioning the global objective function of centralized moving horizon estimation (MHE) into local objective functions. The s

Enrico Valdinoci

We recall the classical theory of capillarity, describing the shape of a liquid droplet in a container, and present a recent approach which aims at accounting for long-range particle interactions. This nonlocal setting recovers the classical notion of surface tension in the limit. We provide some regularity results and the determination of the contact angle,

Guohang Shan, Shuangcheng Jia

In this paper, we introduce a novel Convolution-based Probability Gradient (CPG) loss for semantic segmentation. It employs convolution kernels similar to the Sobel operator, capable of computing the gradient of pixel intensity in an image. This enables the computation of gradients for both ground-truth and predicted category-wise probabilities. It enhances

Cyrus Cousins

The original position or veil of ignorance argument of John Rawls, perhaps the most famous argument for egalitarianism, states that our concept of fairness, justice, or welfare should be decided from behind a veil of ignorance, and thus must consider everyone impartially (invariant to our identity). This can be posed as a zero-sum game, where a Daemon constr

Hanyu Meng, Vidhyasaharan Sethu, Eliathamby Ambikairajah

There is increasing interest in the use of the LEArnable Front-end (LEAF) in a variety of speech processing systems. However, there is a dearth of analyses of what is actually learnt and the relative importance of training the different components of the front-end. In this paper, we investigate this question on keyword spotting, speech-based emotion recognit

Yuheng He, Changliang Zou, Yi Zhao

In the high-dimensional landscape, addressing the challenges of covariance regression with high-dimensional covariates has posed difficulties for conventional methodologies. This paper addresses these hurdles by presenting a novel approach for high-dimensional inference with covariance matrix outcomes. The proposed methodology is illustrated through its appl

Hao Lu, Jiaqi Tang, Xinli Xu, Xu Cao

The emergence of Multi-Camera 3D Object Detection (MC3D-Det), facilitated by bird's-eye view (BEV) representation, signifies a notable progression in 3D object detection. Scaling MC3D-Det training effectively accommodates varied camera parameters and urban landscapes, paving the way for the MC3D-Det foundation model. However, the multi-view fusion stage of t

Junghyo Jo, Alexandre Wagemakers, Vipul Periwal

The Newton-Raphson method is a fundamental root-finding technique with numerous applications in physics. In this study, we propose a parameterized variant of the Newton-Raphson method, inspired by principles from physics. Through analytical and empirical validation, we demonstrate that this novel approach offers increased robustness and faster convergence du

Navid Mohammad Mirzaei, Panayotis G. Kevrekidis, Leili Shahriyari

Breast cancer is one of the most challenging global health problems among women. This study investigates the intricate breast tumor microenvironment (TME) dynamics utilizing data from Mammary-specific Polyomavirus Middle T Antigen Overexpression mouse models (MMTV-PyMT). It incorporates Endothelial Cells (ECs), oxygen, and Vascular Endothelial Growth Factors

Yuan Tang

This paper presents a reexamination of the research paper titled "Communication-Avoiding Parallel Algorithms for \proc{TRSM}" by Wicky et al. We focus on the communication bandwidth cost analysis presented in the original work and identify potential issues that require clarification or revision. The problem at hand is the need to address inconsistencies and

Thomas A. Metzger, Christopher T. Franck

Linear modeling is ubiquitous, but performance can suffer when the model is misspecified. We have recently demonstrated that latent groupings in the levels of categorical predictors can complicate inference in a variety of fields including bioinformatics, agriculture, industry, engineering, and medicine. Here we present the R package slgf which enables the u

Koffka Khan

The demand for high-quality video streaming has propelled the evolution of adaptive streaming systems. Efficient resource allocation is paramount to ensuring optimal viewer experience, considering dynamic factors such as server load, network bandwidth, and viewer demand. This review paper investigates the application of fuzzy logic to enhance resource alloca