May 2024 arXiv papers — page 7

Zhuonan Zheng, Sheng Zhou, Hongjia Xu, Ming Gu

Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar behaviors, while it may be violated in many real-world graphs. Recently, heterophilous graph neural networks (He

Ruixue Sun, Junli Hu

The traditional degumming method using sodium carbonate solution severely damages the structure of silk fibroin and results in low molecular weight, which limits the properties and applications of silk materials. In this study, we report a modified degumming method and compared it with the traditional one. The results indicate that compared with the traditio

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

Based on $(2712.4 \pm 14.3) \times 10^{6}$ $ e^{+}e^{-}\to\psi(3686)$ events collected with the BESIII detector operating at the BEPCII collider, we report the first evidence of $\chi_{c0}\to \Lambda\bar \Lambda \phi$ decays and the first observation of $\chi_{c1,2}\to \Lambda\bar \Lambda \phi$ decays, with significances of $4.5\sigma$, $11.3\sigma$ and $13.

Zhiguang Zhang, Yuxxiang Li

In this work, we study the no-flux initial-boundary value problem for the doubly degenerate nutrient taxis system \begin{align} \begin{cases}\tag{$\star$}\label{eq 0.1} u_t=\nabla \cdot(u v \nabla u)-\chi \nabla \cdot\left(u^{2} v \nabla v\right)+\ell u v, & x \in \Omega, t>0, \\ v_t=\Delta v-u v, & x \in \Omega, t>0 \end{cases} \end{align} in a smoothly bou

Hafiz Muhammad Shakir, Abdulsalam Aji Suleiman, Kübra Nur Kalkan, Amir Parsi

Excitons in monolayer transition metal dichalcogenides (TMDCs) offer intriguing new possibilities for optoelectronics with no analogues in bulk semiconductors. Yet, intrinsic defects in TMDCs limit the radiative exciton recombination pathways. As a result, the photoluminescence (PL) quantum yield (QY) is limited. Methods like superacid treatment, electrical

Caifeng Zou, Robert W. Clayton

We show reflectivity cross-sections for the San Gabriel, Chino, and San Bernardino basins north of Los Angeles, California determined from autocorrelations of ambient noise and teleseismic earthquake waves. These basins are thought to channel the seismic energy from earthquakes on the San Andreas Fault to Los Angeles and a more accurate model of their depth

Dipanjan Mitra, Rahul Basu, George I Melikizde

Radio observations from normal pulsars indicate that the coherent radio emission is excited by curvature radiation from charge bunches. In this review we provide a systematic description of the various observational constraints on the radio emission mechanism. We have discussed the presence of highly polarized time samples where the polarization position ang

Jing Xu, Anqi Zhu, Jingyu Lin, Qiuhong Ke

Human action recognition is crucial in computer vision systems. However, in real-world scenarios, human actions often fall outside the distribution of training data, requiring a model to both recognize in-distribution (ID) actions and reject out-of-distribution (OOD) ones. Despite its importance, there has been limited research on OOD detection in human acti

Lebo Molefe, Giuseppe A. Zampogna, John M. Kolinski, François Gallaire

Surface roughness significantly modifies the liquid film thickness entrained when dip coating a solid surface, particularly at low coating velocity. Using a homogenization approach, we present a predictive model for determining the liquid film thickness coated on a rough plate. A homogenized boundary condition at an equivalent flat surface is used to model t

Shiliang Zuo

We study a principal-agent team production model. The principal hires a team of agents to participate in a common production task. The exact effort of each agent is unobservable and unverifiable, but the total production outcome (e.g. the total revenue) can be observed. The principal incentivizes the agents to exert effort through contracts. Specifically, th

Byoungwoo Park, Jungwon Choi, Sungbin Lim, Juho Lee

Recent advancements in diffusion models and diffusion bridges primarily focus on finite-dimensional spaces, yet many real-world problems necessitate operations in infinite-dimensional function spaces for more natural and interpretable formulations. In this paper, we present a theory of stochastic optimal control (SOC) tailored to infinite-dimensional spaces,

Yige Liu, Bidan Zhao, Ji Xu, Junwu Wang

In order to resolve the pressure checkerboard field problem with collocated grid, it is essential to employ the momentum interpolation method when formulating the pressure equation, and the flux reconstruction method when updating the cell-centered velocity fields. In this study, we first derive a momentum interpolation method for Euler-Euler simulation of g

Krishanu Maity, A. S. Poornash, Sriparna Saha, Pushpak Bhattacharyya

In an era of rapidly evolving internet technology, the surge in multimodal content, including videos, has expanded the horizons of online communication. However, the detection of toxic content in this diverse landscape, particularly in low-resource code-mixed languages, remains a critical challenge. While substantial research has addressed toxic content dete

Sree Oh, Matthew Colless, Stefania Barsanti, Henry R. M. Zovaro

We investigate the influence of star formation and instantaneous AGN feedback processes on the ionized gas velocity dispersion in a sample of 1285 emission-line galaxies with stellar masses $\log\,(M_*/M_{\odot}) \geq 9$ from the integral-field spectroscopy SAMI Galaxy Survey. We fit both narrow and broad emission line components using aperture spectra integ

Shengyu Zhang, Ziqi Jiang, Jiangchao Yao, Fuli Feng

Recommendation performance usually exhibits a long-tail distribution over users -- a small portion of head users enjoy much more accurate recommendation services than the others. We reveal two sources of this performance heterogeneity problem: the uneven distribution of historical interactions (a natural source); and the biased training of recommender models

Atharva Gundawar, Mudit Verma, Lin Guan, Karthik Valmeekam

As the applicability of Large Language Models (LLMs) extends beyond traditional text processing tasks, there is a burgeoning interest in their potential to excel in planning and reasoning assignments, realms traditionally reserved for System 2 cognitive competencies. Despite their perceived versatility, the research community is still unraveling effective st

Qianyu Huang, Tongfang Zhao

Entity matching (EM) is a critical task in data integration, aiming to identify records across different datasets that refer to the same real-world entities. Traditional methods often rely on manually engineered features and rule-based systems, which struggle with diverse and unstructured data. The emergence of Large Language Models (LLMs) such as GPT-4 offe

Georg Meinhardt, Kai Yi, Laurent Condat, Peter Richtárik

In Federated Learning (FL), both client resource constraints and communication costs pose major problems for training large models. In the centralized setting, sparse training addresses resource constraints, while in the distributed setting, local training addresses communication costs. Recent work has shown that local training provably improves communicatio

Huaduo Wang, Gopal Gupta

We present a novel and systematic method, called Superfast Selection, for selecting the "optimal split" for decision tree and feature selection algorithms over tabular data. The method speeds up split selection on a single feature by lowering the time complexity, from O(MN) (using the standard selection methods) to O(M), where M represents the number of inpu

Yige Liu, Mingming He, Jianhua Chen, Wen Li

Numerical solution of Euler-Euler model using different in-house, open source and commercial software can generate significantly different results, even when the governing equations and the initial and boundary conditions are exactly same. Unfortunately, the underlying reasons have not been identified yet. In this article, three methods for calculating the g

Adithya Vasudev

The Lottery Ticket hypothesis proposes that ideal, sparse subnetworks, called lottery tickets, exist in untrained dense neural networks. The Early Bird hypothesis proposes an efficient algorithm to find these winning lottery tickets in convolutional neural networks, using the novel concept of distance between subnetworks to detect convergence in the subnetwo

Alyssa Shuang Sha, Bernardo Pereira Nunes, Armin Haller

This survey investigates the multifaceted nature of forgetting in machine learning, drawing insights from neuroscientific research that posits forgetting as an adaptive function rather than a defect, enhancing the learning process and preventing overfitting. This survey focuses on the benefits of forgetting and its applications across various machine learnin

Md Nurul Anwar, James M. McCaw, Alexander E. Zarebski, Roslyn I. Hickson

Plasmodium vivax is the most geographically widespread malaria parasite due to its ability to remain dormant (as a hypnozoite) in the human liver and subsequently reactivate. Given the majority of P. vivax infections are due to hypnozoite reactivation, targeting the hypnozoite reservoir with a radical cure is crucial for achieving P. vivax elimination. Stoch

Chengdi Ma, Jizu Huang, Hao Luo, Chao Yang

Compared with the remarkable progress made in parallel numerical solvers of partial differential equations,the development of algorithms for generating unstructured triangular/tetrahedral meshes has been relatively sluggish. In this paper, we propose a novel, consistent parallel advancing front technique (CPAFT) by combining the advancing front technique, th

Yuning Zhang, Thomas Choi, Zihang Cheng, Issei Kanno

The design of cell-free massive MIMO (CF-mMIMO) systems requires accurate, measurement-based channel models. This paper provides the first results from the by far most extensive outdoor measurement campaign for CF-mMIMO channels in an urban environment. We measured impulse responses between over 20,000 potential access point (AP) locations and 80 user equipm

Zhen-Kai Gao, Chen-Fatt Lim, Wei-Hao Wang, Chian-Chou Chen

We present confusion-limited SCUBA-2 450-$\mu$m observations in the COSMOS-CANDELS region as part of the JCMT Large Program, SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). Our maps at 450 and 850 $\mu$m cover an area of 450 arcmin$^2$. We achieved instrumental noise levels of $\sigma_{\mathrm{450}}=$ 0.59 mJy beam$^{-1}$ and $\sigma_{\mathrm{850}}=$ 0.09 m

James Ripple, Anish Agashe

The Tolman VII space-time is one of the few physically acceptable exact solutions in general relativity. In this paper, we derive a generalised Tolman VII solution which includes a charge and a cosmological constant. We analyse the spatial geometry of the solution and present conditions for zero and non-zero spatial curvature. We show that for a particular v

Junming Ren, Zhoujian Xiao, Yujia Zhang, Yujie Yang

In the preclinical translational studies, drug candidates with remarkable anti-epileptic efficacy demonstrate long-term suppression of spontaneous recurrent seizures (SRSs), particularly convulsive seizures (CSs), in mouse models of chronic epilepsy. However, the current methods for monitoring CSs have limitations in terms of invasiveness, specific laborator

Alyssa Huang, Oishi Banerjee, Kay Wu, Eduardo Pontes Reis

The current gold standard for evaluating generated chest x-ray (CXR) reports is through radiologist annotations. However, this process can be extremely time-consuming and costly, especially when evaluating large numbers of reports. In this work, we present FineRadScore, a Large Language Model (LLM)-based automated evaluation metric for generated CXR reports.

Hanzhang Zhou, Zijian Feng, Zixiao Zhu, Junlang Qian

Large language models (LLMs) have demonstrated impressive capabilities in various tasks using the in-context learning (ICL) paradigm. However, their effectiveness is often compromised by inherent bias, leading to prompt brittleness, i.e., sensitivity to design settings such as example selection, order, and prompt formatting. Previous studies have addressed L

Gary A. McCully, John D. Hastings, Shengjie Xu, Adam Fortier

Detecting vulnerabilities within compiled binaries is challenging due to lost high-level code structures and other factors such as architectural dependencies, compilers, and optimization options. To address these obstacles, this research explores vulnerability detection using natural language processing (NLP) embedding techniques with word2vec, BERT, and RoB

Wooseok Shin, Hyun Joon Park, Jin Sob Kim, Juan Yun

In semi-supervised semantic segmentation, the Mean Teacher- and co-training-based approaches are employed to mitigate confirmation bias and coupling problems. However, despite their high performance, these approaches frequently involve complex training pipelines and a substantial computational burden, limiting the scalability and compatibility of these metho

Ravikiran Kalluri

The purpose of this research study was to study the influence of key psychological factors on emergence of Agile team autonomy that leads to Agile project success in software organizations.

Cheng Liu, Wei Xiang, Bang Wang

Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of identifying after learning paradigm, where events' representations are first learned and then used for the identification. Furthermore, they mainly focus on the causality existence, but ignoring causal

Yuanjiang Luo, Hongxiang Li, Xuan Wu, Meng Cao

Existing mainstream approaches follow the encoder-decoder paradigm for generating radiology reports. They focus on improving the network structure of encoders and decoders, which leads to two shortcomings: overlooking the modality gap and ignoring report content constraints. In this paper, we proposed Textual Inversion and Self-supervised Refinement (TISR) t

Yang Chen, Tian He, Junfeng Fu, Ling Wang

Skeleton-based action representation learning aims to interpret and understand human behaviors by encoding the skeleton sequences, which can be categorized into two primary training paradigms: supervised learning and self-supervised learning. However, the former one-hot classification requires labor-intensive predefined action categories annotations, while t

Jung H. Lee, Sujith Vijayan

Deep learning (DL) enables deep neural networks (DNNs) to automatically learn complex tasks or rules from given examples without instructions or guiding principles. As we do not engineer DNNs' functions, it is extremely difficult to diagnose their decisions, and multiple lines of studies proposed to explain the principles of their operations. Notably, one li

Paul Goldsmith-Pinkham

This paper updates Currie, Kleven, and Zwiers (2020) by examining the credibility revolution across fields, including finance and macroeconomics, using NBER working papers up to May 2024. While the growth in terms related to identification and research designs have continued, finance and macroeconomics have lagged behind applied micro. Difference-in-differen

Ke Xu, Yu Cheng, Shiqing Long, Junjie Guo

This paper focuses on the application and optimization of LSTM model in financial risk prediction. The study starts with an overview of the architecture and algorithm foundation of LSTM, and then details the model training process and hyperparameter tuning strategy, and adjusts network parameters through experiments to improve performance. Comparative experi

Seunghwan An, Gyeongdong Woo, Jaesung Lim, ChangHyun Kim

In this paper, our goal is to generate synthetic data for heterogeneous (mixed-type) tabular datasets with high machine learning utility (MLu). Since the MLu performance depends on accurately approximating the conditional distributions, we focus on devising a synthetic data generation method based on conditional distribution estimation. We introduce MaCoDE b

Antonio R. Linero

A recent trend in Bayesian research has been revisiting generalizations of the likelihood that enable Bayesian inference without requiring the specification of a model for the data generating mechanism. This paper focuses on a Bayesian nonparametric extension of Wedderburn's quasi-likelihood, using Bayesian additive regression trees to model the mean functio

Kaicheng Fu, Changde Du, Xiaoyu Chen, Jie Peng

Emotion decoding plays an important role in affective human-computer interaction. However, previous studies ignored the dynamic real-world scenario, where human experience a blend of multiple emotions which are incrementally integrated into the model, leading to the multi-label class incremental learning (MLCIL) problem. Existing methods have difficulty in s

Fan Wu, Xuefeng Zhang, Yi Chen, Ding Pei

In two-dimensional van der Waals magnetic materials, the interplay between magnetism and electron correlation can give rise to new ground states and lead to novel transport and optical properties. A fundamental question in these materials is how the electron correlation manifests and interacts with the magnetic orders. In this study, we demonstrate that the

Joon Young Park, Young Jae Shin, Jeacheol Shin, Jehyun Kim

Atomically thin van der Waals (vdW) films provide a novel material platform for epitaxial growth of quantum heterostructures. However, unlike the remote epitaxial growth of three-dimensional bulk crystals, the growth of two-dimensional (2D) material heterostructures across atomic layers has been limited due to the weak vdW interaction. Here, we report the do

Jiachen Liang, Ruibing Hou, Hong Chang, Bingpeng Ma

Traditional semi-supervised learning (SSL) assumes that the feature distributions of labeled and unlabeled data are consistent which rarely holds in realistic scenarios. In this paper, we propose a novel SSL setting, where unlabeled samples are drawn from a mixed distribution that deviates from the feature distribution of labeled samples. Under this setting,

Yinxiao Zhuo, Tianqi Mao, Haojin Li, Chen Sun

Integrated sensing and communication (ISAC) has been envisioned as a critical enabling technology for the next-generation wireless communication, which can realize location/motion detection of surroundings with communication devices. This additional sensing capability leads to a substantial network quality gain and expansion of the service scenarios. As the

Li Ji-An, Marcus K. Benna

Backpropagation, a foundational algorithm for training artificial neural networks, predominates in contemporary deep learning. Although highly successful, it is widely considered biologically implausible, because it relies on precise symmetry between feedforward and feedback weights to accurately propagate gradient signals that assign credit. The so-called w

Juncal Arbelaiz, Alessio Franci, Naomi Ehrich Leonard, Rodolphe Sepulchre

We propose and analyze the suitability of a spiking controller to engineer the locomotion of a soft robotic crawler. Inspired by the FitzHugh-Nagumo model of neural excitability, we design a bistable controller with an electrical flipflop circuit representation capable of generating spikes on-demand when coupled to the passive crawler mechanics. A propriocep

Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu

In this paper, we introduce LInK, a novel framework that integrates contrastive learning of performance and design space with optimization techniques for solving complex inverse problems in engineering design with discrete and continuous variables. We focus on the path synthesis problem for planar linkage mechanisms. By leveraging a multimodal and transforma

Kiri Daust, Adam Monahan

Adapting to the changing climate requires accurate local climate information, a computationally challenging problem. Recent studies have used Generative Adversarial Networks (GANs), a type of deep learning, to learn complex distributions and downscale climate variables efficiently. Capturing variability while downscaling is crucial for estimating uncertainty

Daniel Messenger, Greg Dwyer, Vanja Dukic

Species subject to predation and environmental threats commonly exhibit variable periods of population boom and bust over long timescales. Understanding and predicting such behavior, especially given the inherent heterogeneity and stochasticity of exogenous driving factors over short timescales, is an ongoing challenge. A modeling paradigm gaining popularity

Junzhi Wen, Rafal A. Angryk

Time series data plays a crucial role across various domains, making it valuable for decision-making and predictive modeling. Machine learning (ML) and deep learning (DL) have shown promise in this regard, yet their performance hinges on data quality and quantity, often constrained by data scarcity and class imbalance, particularly for rare events like solar

Alexandre Galvao Patriota

This paper introduces a novel training methodology that enables a Transformer model to generalize the addition of two-digit numbers to numbers with unseen lengths of digits. The proposed approach employs an autoregressive generation technique, processing from right to left, which mimics a common manual method for adding large numbers. To the best of my knowl

Zheng Wang, Zheng Wang, Zhaopeng Peng, Zihui Wang

Federated Learning (FL) stands to gain significant advantages from collaboratively training capacity-heterogeneous models, enabling the utilization of private data and computing power from low-capacity devices. However, the focus on personalizing capacity-heterogeneous models based on client-specific data has been limited, resulting in suboptimal local model

Taolin Zhang, Qizhou Chen, Dongyang Li, Chengyu Wang

Recently, while large language models (LLMs) have demonstrated impressive results, they still suffer from hallucination, i.e., the generation of false information. Model editing is the task of fixing factual mistakes in LLMs; yet, most previous works treat it as a one-time task, paying little attention to ever-emerging mistakes generated by LLMs. We address

Yunpeng Jiang, Yutong Ban, Paul Weng

Data augmentation is widely applied and has shown its benefits in different machine learning tasks. However, as recently observed, it may have an unfair effect in multi-class classification. While data augmentation generally improves the overall performance (and therefore is beneficial for many classes), it can actually be detrimental for other classes, whic

Amr M. Zaki, Sara A. Elsayed, Khalid Elgazzar, Hossam S. Hassanein

Task offloading in Vehicular Edge Computing (VEC) can advance cooperative perception (CP) to improve traffic awareness in Autonomous Vehicles. In this paper, we propose the Quality-aware Cooperative Perception Task Offloading (QCPTO) scheme. Q-CPTO is the first task offloading scheme that enhances traffic awareness by prioritizing the quality rather than the

Donghoon Ha, Jeong San Kim

We consider multipartite quantum state discrimination and provide a specific relation between the properties of entanglement witness and quantum nonlocality inherent in the confidence of measurements. We first provide the definition of the confidence of measurements as well as its useful properties for various types of multipartite measurements. We show that

Mohammed-Khalil Ghali, Abdelrahman Farrag, Hajar Sakai, Hicham El Baz

In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical gap in current information extraction techniques. This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs) to efficiently ext

Yisu Liu, Jinyang An, Wanqian Zhang, Dayan Wu

With the development of diffusion-based customization methods like DreamBooth, individuals now have access to train the models that can generate their personalized images. Despite the convenience, malicious users have misused these techniques to create fake images, thereby triggering a privacy security crisis. In light of this, proactive adversarial attacks

Yu Chen, Hongwei Lin, Jiacong Yan

Widely employed in cognitive psychology, Gestalt theory elucidates basic principles in visual perception. However, the Gestalt principles are validated mainly by psychological experiments, lacking quantitative research supports and theoretical coherence. In this paper, we utilize persistent homology, a mathematical tool in computational topology, to develop

Alissa A. Valentine, Lauren A. Lepow, Lili Chan, Alexander W. Charney

Negative patient descriptions and stigmatizing language can contribute to generating healthcare disparities in two ways: (1) read by patients, they can harm their trust and engagement with the medical center; (2) read by physicians, they may negatively influence their perspective of a future patient. In psychiatry, the patient-clinician therapeutic alliance

Harold Erazo, Luke Jeffreys, Carlos Gustavo Moreira

Let $L$ and $M$ denote the classical Lagrange and Markov spectra, respectively. It is known that $L\subset M$ and that $M\setminus L\neq\varnothing$. Inspired by three questions asked by the third author in previous work investigating the fractal geometric properties of the Lagrange and Markov spectra, we investigate the function $d_{loc}(t)$ that gives the

Depeng Gao, Yang Gao, Yuanzhi Zhang, Hongwei Lin

Porous structures are materials consisting of minuscule pores, where the microstructure morphology significantly impacts their macroscopic properties. Integrating different porous structures through a blending method is indispensable to cater to diverse functional regions in heterogeneous models. Previous studies on blending methods for porous structures hav

Mingyang Jiang, Yueyuan Li, Songan Zhang, Siyuan Chen

Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due to their incapability to handle the diverse and complex parking scenarios in reality. While non-learning methods provide reliable planning results, they are vulnerable to intricate

Nhan Phan-Thien, Dingyi Pan, Mona A. Kanso, Alan Jeffrey Giacomin

The modelling of symmetric rigid dumbbell particles suspended in a Newtonian fluid, as a model of a rigid-rod polymeric solution, has been accomplished exclusively through the diffusion equation, which has been detailed elegantly by Bird et al. [Chapter 14 of $Dynamics \ of \ Polymeric \ Liquids$, Vol 2, Ed 2, (1987)]. In this tutorial, a straightforward app

Xiao-Bin Lai, Yu-Qi Dong, Yu-Qiang Liu, Yu-Xiao Liu

We study the polarization modes of gravitational waves in general Einstein-vector theory with an arbitrary constant background vector field under a Minkowski background. We compare these polarization modes with those of other vector-tensor theories and constrain the parameter spaces based on the gravitational-wave event GW170817 with its electromagnetic coun

Shang Liu, Yang Cao, Takao Murakami, Weiran Liu

Collaborative graph analysis across multiple institutions is becoming increasingly popular. Realistic examples include social network analysis across various social platforms, financial transaction analysis across multiple banks, and analyzing the transmission of infectious diseases across multiple hospitals. We define the federated graph analytics, a new pr

Jacqueline Hidalgo-Jimenez, Taner Akbay, Yuji Ikeda, Tatsumi Ishihara

High-pressure torsion (HPT) can facilitate phase transformations in titanium dioxide (TiO2) and stabilize its high-pressure columbite phase, as an active photocatalyst, by shear straining under high pressure. This study aims to understand the mechanism underlying the acceleration of the anatase-to-columbite phase transformation by shear strain. A mechanism b

Chanjun Park, Hyeonwoo Kim, Dahyun Kim, Seonghwan Cho

This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as vital tools for evaluating Large Language Models (LLMs) in Korean. Incorporating private test sets while mirroring the English Open LLM Leaderboard, we establish a robust evaluation framework that has been well integrated in the Korean LLM community. We perform data leakage analysis

A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban, Byung-Jun Yoon

In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks aimed at optimizing specific molecular properties. However

Yangyang Guo, Lukáš F Pašteka, Yuichiro Nagame, Tetsuya K. Sato

The calculations of the first and the second ionization potentials of lawrencium and lutetium and the electron affinity of lawrencium are performed within the relativistic coupled cluster framework. These results are corrected by including the contributions of extrapolation to the complete basis set limit and higher order contributions due to relativity and

Daesung Kim, Hyunchul Park

We investigate the explicit expression for the principal eigenvalue $\lambda_{1}^{X}(D)$ for a large class of compound Poisson processes $X$ on a bounded open set $D$ by examining its spectral heat content. When the jump density of the compound Poisson process is radially symmetric and strictly decreasing, we demonstrate that balls are the unique minimizers

Nosratollah Jafari

In doubly special relativity (DSR) theories the speed of the light usually depends on the frequency. Thus, we expect that light from very distant stars and gamma ray bursts have additional time delays with respect to special relativity. In this paper, we will find the general DSR transformations in the first order of the Planck length which have zero time de

Severin Engelmann, Madiha Zahrah Choksi, Angelina Wang, Casey Fiesler

Education plays an indispensable role in fostering societal well-being and is widely regarded as one of the most influential factors in shaping the future of generations to come. As artificial intelligence (AI) becomes more deeply integrated into our daily lives and the workforce, educational institutions at all levels are directing their focus on resources

Jiaheng Ma, Chengyuan Wang, Bingbing Li, Yun Chen

Orbital angular momentum (OAM) entangled photon pairs with narrow bandwidths play a crucial role in the interaction of light and quantum states of matter. In this article, we demonstrate an approach for generating OAM entangled photon pairs with a narrow bandwidth by using a single driving beam in a $^{85}$Rb atomic vapor cell. This single driving beam is ab

Holger F. Hofmann

Quantum contextuality describes situations where the statistics observed in different measurement contexts cannot be explained by a measurement independent reality of the system. The most simple case is observed in a three-dimensional Hilbert space, with five different measurement contexts related to each other by shared measurement outcomes. The quantum for

Srijoni Majumdar, Edith Elkind, Evangelos Pournaras

Recent breakthroughs in generative artificial intelligence (AI) and large language models (LLMs) unravel new capabilities for AI personal assistants to overcome cognitive bandwidth limitations of humans, providing decision support or even direct representation of abstained human voters at large scale. However, the quality of this representation and what unde

Geng Sun, Wenwen Xie, Dusit Niyato, Fang Mei

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (G

Jiarong Kang, Yi Wang, Xiaobin Xiong

In this paper, we present a fast and decentralized state estimation framework for the control of legged locomotion. The nonlinear estimation of the floating base states is decentralized to an orientation estimation via Extended Kalman Filter (EKF) and a linear velocity estimation via Moving Horizon Estimation (MHE). The EKF fuses the inertia sensor with visi

Kei Iida, Etsuko Itou, Kotaro Murakami, Daiki Suenaga

We investigate the phase structure and the equation of state (EoS) for dense two-color QCD (QC$_2$D) at low temperature ($T = 40$ MeV, $32^4$ lattice) for the purpose of extending our previous works~\cite{Iida:2019rah, Iida:2022hyy} at $T=80$ MeV ($16^4$ lattice). Indeed, a rich phase structure below the pseudo-critical temperature $T_c$ as a function of qua

Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu

Incorporating Knowledge Graphs into Recommendation has attracted growing attention in industry, due to the great potential of KG in providing abundant supplementary information and interpretability for the underlying models. However, simply integrating KG into recommendation usually brings in negative feedback in industry, due to the ignorance of the followi

Giampaolo Bonomi

Why do politicians sharpen divisions when elections reward broad appeal? I model how two identical, office-motivated parties turn voter disagreement into partisan conflict. Elections allocate political power. Each party empowers its most valuable members first, so power has diminishing returns. Platforms never converge: differentiation builds constituencies

Jung-Chao Ban, Nai-Zhu Huang, Guan-Yu Lai

In this paper, we introduce the concepts of $\omega$-limit sets and pseudo orbits for a tree-shift defined on a Markov-Cayley tree, extending the results of tree-shifts defined on $d$-trees [5,6]. Firstly, we establish the relationships between $\omega$-limit sets and we introduce a modified definition of $\omega$-limit set based on complete prefix sets (The

Wei Li

Flow instabilities play important roles in a wide range of engineering, geophysical, and astrophysical flows, ranging from supernova explosion in crab nebula, formation of clouds in sky, waves on ocean, to inertial confinement fusion capsules, making fusion energy a viable alternative energy source in the future. The potential for life is directly related to

Haroon Miah, Dimitrios Kollias, Giacinto Luca Pedone, Drew Provan

Primary Immune thrombocytopenia (ITP) is a rare autoimmune disease characterised by immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP is challenging because there is no established test to confirm the disease and no biomarker with which one can pred

Tianle Sun, Ningyu He, Jiang Xiao, Yinliang Yue

In Ethereum, the practice of verifying the validity of the passed addresses is a common practice, which is a crucial step to ensure the secure execution of smart contracts. Vulnerabilities in the process of address verification can lead to great security issues, and anecdotal evidence has been reported by our community. However, this type of vulnerability ha

Lujie Tang, Minxian Xu, Chengzhong Xu, Kejiang Ye

Due to the limited resource capacity of edge servers and the high purchase costs of edge resources, service providers are facing the new challenge of how to take full advantage of the constrained edge resources for Internet of Things (IoT) service hosting and task scheduling to maximize system performance. In this paper, we study the joint optimization probl

Henry Pinkard, Leyla Kabuli, Eric Markley, Tiffany Chien

Imaging systems have traditionally been designed to mimic the human eye and produce visually interpretable measurements. Modern imaging systems, however, process raw measurements computationally before or instead of human viewing. As a result, the information content of raw measurements matters more than their visual interpretability. Despite the importance

Brendan T. Reed, Rahul Somasundaram, Soumi De, Cassandra L. Armstrong

Gravitational-wave observations of binary neutron-star (BNS) mergers have the potential to revolutionize our understanding of the nuclear equation of state (EOS) and the fundamental interactions that determine its properties. However, Bayesian parameter estimation frameworks do not typically sample over microscopic nuclear-physics parameters that determine t

Jong Hyuk Yoon

I show that the recently proposed (2+2) Hamiltonian reduction of Einstein's equations of 4-dimensional spacetimes is consistent with general covariance. The consistency proof is {\it extrinsic}, as it follows from the fact that Hamilton's equations derived from the non-zero gravitational Hamiltonian are identical to the Ricci-flat condition of 4-dimensional

You Li, Guannan Zhao, Shuyu Kong, Yunqi He

A globally robust deep neural network resists perturbations on all meaningful inputs. Current robustness certification methods emphasize local robustness, struggling to scale and generalize. This paper presents a systematic and efficient method to evaluate and verify global robustness for deep neural networks, leveraging the PAC verification framework for so

Linjiajie Fang, Ruoxue Liu, Jing Zhang, Wenjia Wang

In offline reinforcement learning, it is necessary to manage out-of-distribution actions to prevent overestimation of value functions. One class of methods, the policy-regularized method, addresses this problem by constraining the target policy to stay close to the behavior policy. Although several approaches suggest representing the behavior policy as an ex

Sebastian Casalaina-Martin, Xianyu Hu, Xun Lin, Shizhuo Zhang

Let $Y$ be a cubic threefold with a non-Eckardt type involution $\tau$. Our first main result is that the $\tau$-equivariant category of the Kuznetsov component $\mathcal{K}u_{\mathbb{Z}_2}(Y)$ determines the isomorphism class of $Y$ for general $(Y,\tau)$. We shall prove this categorical Torelli theorem via three approaches: a noncommutative Hodge theoretic

Kazuaki A. Homma, Satoshi Okuzumi, Sota Arakawa, Ryota Fukai

High-precision isotopic measurements of meteorites revealed that they are classified into non-carbonaceous (NC) and carbonaceous (CC) meteorites. One plausible scenario for achieving this grouping is the early formation of Jupiter because massive planets can create gaps that suppress the mixing of dust across the gap in protoplanetary disks. However, the eff

Larry Guth, James Maynard

We prove new bounds for how often Dirichlet polynomials can take large values. This gives improved estimates for a Dirichlet polynomial of length $N$ taking values of size close to $N^{3/4}$, which is the critical situation for several estimates in analytic number theory connected to prime numbers and the Riemann zeta function. As a consequence, we deduce a

Dorin Pomian, Abhiram Bellur, Malinda Dilhara, Zarina Kurbatova

Excessively long methods, loaded with multiple responsibilities, are challenging to understand, debug, reuse, and maintain. The solution lies in the widely recognized Extract Method refactoring. While the application of this refactoring is supported in modern IDEs, recommending which code fragments to extract has been the topic of many research tools. Howeve

Coleman DuPlessie, Aidan Gao

Machine learning models have recently enjoyed a significant increase in size and popularity. However, this growth has created concerns about dataset privacy. To counteract data leakage, various privacy frameworks guarantee that the output of machine learning models does not compromise their training data. However, this privatization comes at a cost by adding

Peter Jan van Leeuwen, J. Christine Chiu, C. Kevin Yang

We present a critical survey on the consistency of uncertainty quantification used in deep learning and highlight partial uncertainty coverage and many inconsistencies. We then provide a comprehensive and statistically consistent framework for uncertainty quantification in deep learning that accounts for all major sources of uncertainty: input data, training

Mu'taz A. Momani, Mehdi Hosseinzadeh

Explicit reference governor (ERG) is an add-on unit that provides constraint handling capability to pre-stabilized systems. The main idea behind ERG is to manipulate the derivative of the applied reference in continuous time such that the satisfaction of state and input constraints is guaranteed at all times. However, ERG should be practically implemented in