March 2024 arXiv papers — page 183

Biqing Qi, Xingquan Chen, Junqi Gao, Dong Li

Advanced life forms, sustained by the synergistic interaction of neural cognitive mechanisms, continually acquire and transfer knowledge throughout their lifespan. In contrast, contemporary machine learning paradigms exhibit limitations in emulating the facets of continual learning (CL). Nonetheless, the emergence of large language models (LLMs) presents pro

Boris Aronov, Abdul Basit, Indu Ramesh, Gianluca Tasinato

An {\em eight-partition} of a finite set of points (respectively, of a continuous mass distribution) in $\mathbb{R}^3$ consists of three planes that divide the space into $8$ octants, such that each open octant contains at most $1/8$ of the points (respectively, of the mass). In 1966, Hadwiger showed that any mass distribution in $\mathbb{R}^3$ admits an eig

Imad Eddine Toubal, Aditya Avinash, Neil Gordon Alldrin, Jan Dlabal

From content moderation to wildlife conservation, the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally, developing classifiers for such concepts requires substantial manual effort measured in hours, days, or even months to identify and annotate data needed for training. Even with recently

Hui Jiang, Lei Huang, Shengfan Wu

The semiparametric factor model serves as a vital tool to describe the dependence patterns in the data. It recognizes that the common features observed in the data are actually explained by functions of specific exogenous variables.Unlike traditional factor models, where the focus is on selecting the number of factors, our objective here is to identify the a

Yingrong Wang, Anpeng Wu, Haoxuan Li, Weiming Liu

This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other. For example, a higher dosage of medication might increase the speed of a patient's recovery (short-term) but could also result

Tuyen M. Pham, Duy H. Nguyen, Tuan Q. Do, W. F. Kao

In this paper, we would like to examine whether stable de Sitter inflationary solutions appear within power-law extensions of the Starobinsky model. In particular, we will address general constraints for the existence along with the stability of de Sitter inflationary solutions in a general case involving not only the Starobinsky $R^2$ term but also an addit

Yanchen Guan, Haicheng Liao, Zhenning Li, Jia Hu

In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process. World models have emerged as a transformative approach, enabling autonomous driving systems to synthesize and interpret vast amoun

Jin-Feng Wu, Li-Ying Zhu, Azizbek Matekov, Lin-jia Li

We presented the multi-filter light curves of CSS_J154915.7+375506 inaugurally, which were observed by the 1.5 m AZT-22 telescope at Maidanak Astronomical Observatory. A low-resolution spectrum obtained by LAMOST reveals it is an A-type close binary. By analyzing the BVRI total-eclipse light curves, we are able to derive a reliable photometric solution for t

Lin Deng, Ziqiang Cai, Yongmin Liu

Metasurface-based nano-printing has enabled ultrahigh-resolution grayscale or color image display. However, the maximum number of independent nano-printing images allowed by one single-layer metasurface is still limited despite many multiplexing methods that have been proposed to increase the design degree of freedom. In this work, we substantially push the

Aymen Rayane Khouas, Mohamed Reda Bouadjenek, Hakim Hacid, Sunil Aryal

Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has primarily been on the deployment and inference of Machine Learning (ML) models at the edge, the training aspect remains less explored. This survey, explores the concept of edge lear

Cui Chao, Zhao Jiankang

This paper presents a learning-based method for calibrating and denoising microelectromechanical system (MEMS) gyroscopes, which is designed based on a convolutional network, and only contains hundreds of parameters, so the network can be trained on a graphics processing unit (GPU) before being deployed on a microcontroller unit (MCU) with limited computatio

Xunjie Chen, Jingang Yi, Jerry Shan

Legged robots are well-suited for broad exploration tasks in complex environments with yielding terrain. Understanding robotic foot-terrain interactions is critical for safe locomotion and walking efficiency for legged robots. This paper presents a reduced-order resistive-force model for robotic-foot/mud interactions. We focus on vertical robot locomotion on

Haili Sun, Yan Huang, Lansheng Han, Cai Fu

Accurate detection and diagnosis of abnormal behaviors such as network attacks from multivariate time series (MTS) are crucial for ensuring the stable and effective operation of industrial cyber-physical systems (CPS). However, existing researches pay little attention to the logical dependencies among system working states, and have difficulties in explainin

Jinman Zhao, Xueyan Zhang

We present a comprehensive evaluation of large language models(LLMs)' ability to reason about composition relations through a benchmark encompassing 1,500 test cases in English, designed to cover six distinct types of composition relations: Positional, Comparative, Personal, Mathematical, Identity, and Other. Acknowledging the significance of multilingual ca

Chaoying Meng, Miao Cai, Yufang Yang, Haodong Wu

Random numbers are at the heart of diverse fields, ranging from simulations of stochastic processes to classical and quantum cryptography. The requirement for true randomness in these applications has motivated various proposals for generating random numbers based on the inherent randomness of quantum systems. The generation of true random numbers with arbit

Penny Sweetser

Large language models (LLMs) hold interesting potential for the design, development, and research of video games. Building on the decades of prior research on generative AI in games, many researchers have sped to investigate the power and potential of LLMs for games. Given the recent spike in LLM-related research in games, there is already a wealth of releva

Guang-Xing Li

The Jeans criterion sets the foundation of our understanding of gravitational collapse. Jog studied the fragmentation of gas under external tides and derived a dispersion relation $$ l' = l_{\rm Jeans} \frac{1} {(1 + \lambda_0' / 4 \pi G \rho_0)^{1/2}} \;. $$ She further concludes that the Jeans mass is $m_{\rm incorrect}'=m_{\rm Jeans} ( 1/(1 + \lambda_0' /

Yuelin Zhang, Pengyu Zheng, Wanquan Yan, Chengyu Fang

Defocus blur is a persistent problem in microscope imaging that poses harm to pathology interpretation and medical intervention in cell microscopy and microscope surgery. To address this problem, a unified framework including the multi-pyramid transformer (MPT) and extended frequency contrastive regularization (EFCR) is proposed to tackle two outstanding cha

Pittawat Taveekitworachai, Febri Abdullah, Mury F. Dewantoro, Yi Xia

This paper presents the second ChatGPT4PCG competition at the 2024 IEEE Conference on Games. In this edition of the competition, we follow the first edition, but make several improvements and changes. We introduce a new evaluation metric along with allowing a more flexible format for participants' submissions and making several improvements to the evaluation

Wei Bao, Mi Zhang, Tao Zhang, Chengfu Huo

Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions.Today's QAC systems in real-world scenarios face two major challenges:1)intention equivocality(IE): during the user's typing process,the prefix often contains a combination of characters

Meixia Lin, Yangjing Zhang

We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices. Due to the complicated structure of Kronecker-product precision matrices in the commonly used matrix-variate Gaussian graphical models, a sparser Kronecker-sum structure was proposed recent

Zhen Gong, Lvyin Niu, Yang Zhao, Miao Xu

Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowadays, multi-slot display advertising is gradually becoming popular where many ads could be displayed in a list and shown as a whole to users. However, multi-slot display advertising

Z. Y. Zhao, K. Chen, F. Y. Wang, Z. G. Dai

Fast radio bursts (FRBs) are luminous millisecond-duration radio pulses with extragalactic origin, which were discovered more than a decade ago. Despite the numerous samples, the physical origin of FRBs remains poorly understood. FRBs have been thought to originate from young magnetars or accreting compact objects (COs). Massive stars or COs are predicted to

Marie Boër, Debaditya Biswas

This paper presents our project and perspectives to measure for the first time beam spin asymmetries from Double Deeply Virtual Compton Scattering in the $eP\to e'P' \mu^+\mu^-$ reaction at Jefferson Lab. Our goal is to constrain the so-called Generalized Parton Distribution (GPDs) in a kinematic region that isn't accessible from other reactions, such as Dee

Yu Li, Xiaojie Zhang, Ruihai Wu, Zilong Zhang

Learning a universal manipulation policy encompassing doors with diverse categories, geometries and mechanisms, is crucial for future embodied agents to effectively work in complex and broad real-world scenarios. Due to the limited datasets and unrealistic simulation environments, previous works fail to achieve good performance across various doors. In this

Dong Chen, Gengzhuo Liu, Hongyan Du, Benjamin Jones

The continuous evolution of the SARS-CoV-2 virus poses a significant challenge to global public health. Of particular concern is the potential resistance to the widely prescribed drug PAXLOVID, of which the main ingredient nirmatrelvir inhibits the viral main protease (Mpro). Here, we developed CAPTURE (direCted flAg laPlacian Transformer for drUg Resistance

Gordon Baym, Jen-Chieh Peng

We re-introduce, in light of our modern understanding of neutrinos, the concept of ``macroscopic neutrinoless double beta decay" (MDBD) for Majorana neutrinos. In this process an antineutrino produced by a nucleus undergoing beta decay, $X\to Y + e^- + \bar \nu_e$, is absorbed as a neutrino by another identical $X$ nucleus via the inverse beta decay reaction

Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren

For image super-resolution (SR), bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a novel "Low-Res Leads the Way" (LWay) training framework, merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. O

Hyunwook Lee, Sungahn Ko

Accurate traffic forecasting is challenging due to the complex dependency on road networks, various types of roads, and the abrupt speed change due to the events. Recent works mainly focus on dynamic spatial modeling with adaptive graph embedding or graph attention having less consideration for temporal characteristics and in-situ modeling. In this paper, we

Zhijie Tang, Alex Lu Dou, Arit Kumar Bishwas

Portfolio optimization (PO) is extensively employed in financial services to assist in achieving investment objectives. By providing an optimal asset allocation, PO effectively balances the risk and returns associated with investments. However, it is important to note that as the number of involved assets and constraints increases, the portfolio optimization

Sotirios Panagiotis Chytas, Vishnu Suresh Lokhande, Peiran Li, Vikas Singh

Small sample sizes are common in many disciplines, which necessitates pooling roughly similar datasets across multiple institutions to study weak but relevant associations between images and disease outcomes. Such data often manifest shift/imbalance in covariates (i.e., secondary non-imaging data). Controlling for such nuisance variables is common within sta

Shokoufe Faraji, João Luís Rosa

This work investigates observational properties, namely the shadow and photon ring structure, of emission profiles originating near compact objects. In particular, we consider a distorted and deformed compact object characterised by two quadrupole parameters and surrounded by an optically thin and geometrically thin accretion disk with different emission pro

Kenta Sato

In this paper, we give a partial affirmative answer to the BAB conjecture for $3$-folds in characteristic $p>5$. Specifically, we prove that a set $\mathcal{D}$ of weak Fano $3$-folds over an uncountable algebraically closed field is bounded, if each element $X \in \mathcal{D}$ satisfies certain conditions regarding the Gorenstein index, a complement and Kod

Ziheng Guo, Igor Cialenco, Ming Zhong

We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term togeth

Tarik M. Salman, Teresa Nieves-Chinchilla, Lan K. Jian, Noé Lugaz

We present a statistical investigation of the radial evolution of 28 interplanetary coronal mass ejections (ICMEs), measured in situ by the Parker Solar Probe (PSP) spacecraft from 2018 October to 2022 August. First, by analyzing the radial distribution of ICME classification based on magnetic hodograms, we find that coherent configurations are more likely t

Zhi Yee Chng, Thomas Britz, Ta Sheng Tan, Kok Bin Wong

The Ramsey numbers $R(T_n,W_8)$ are determined for each tree graph $T_n$ of order $n\geq 7$ and maximum degree $\Delta(T_n)$ equal to either $n-4$ or $n-5$. These numbers indicate strong support for the conjecture, due to Chen, Zhang and Zhang and to Hafidh and Baskoro, that $R(T_n,W_m) = 2n-1$ for each tree graph $T_n$ of order $n\geq m-1$ with $\Delta(T_n)

Pavel Guerzhoy

The weight two Eisenstein series may be considered as the first example of a Katz $p$-adic modular form. Classically, its values are defined for the primes of ordinary reduction. We offer a modified definition which applies uniformly to all primes of good reduction, both ordinary and supersingular. We show that, in the case of complex multiplication, these $

Sung Hoon Choi, Donggyu Kim

In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Ito semimartingale models using high-frequency financial data. Several studies have highlighted stylized volatility time series features, such as interday auto-regressive dynamics and the intraday U-shaped pattern. To accommodate these volatility features, we

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed

We report a measurement of decay-time dependent charge-parity ($CP$) asymmetries in $B^0 \rightarrow K^0_S K^0_S K^0_S$ decays. We use $387 \times 10^6 B\bar{B}$ pairs collected at the $\Upsilon(4S)$ resonance with the Belle II detector at the SuperKEKB asymmetric-energy electron-positron collider. We reconstruct 220 signal events and extract the $CP$-violat

Mou Wu, Haibin Liao, Zhengtao Ding, Yonggang Xiao

Gradient-type distributed optimization methods have blossomed into one of the most important tools for solving a minimization learning task over a networked agent system. However, only one gradient update per iteration is difficult to achieve a substantive acceleration of convergence. In this paper, we propose an accelerated framework named as MUSIC allowing

Jacob Granley, Galen Pogoncheff, Alfonso Rodil, Leili Soo

Neural activity in the visual cortex of blind humans persists in the absence of visual stimuli. However, little is known about the preservation of visual representation capacity in these cortical regions, which could have significant implications for neural interfaces such as visual prostheses. In this work, we present a series of analyses on the shared repr

Yongshun Luo, Min Yang, Sirui Li, Yana Di

Amphiphilic polymers in aqueous solutions can self-assemble to form bilayer membranes, and their elastic properties can be captured by the well-known Helfrich model involving several elastic constants. In this paper, we employ the self-consistent field model to simulate sinusoidal bilayers self-assembled from diblock copolymers where a proper constraint term

Debaditya Biswas, Marie Boër

Generalized parton Distributions (GPDs) are important functions to understand the three dimensional structure of the nucleon. Deeply Virtual Compton Scattering is one of the reaction accessing GPDs, and has been measured for the past $\sim$20 years. However, to move forward, we need to look for other reactions, such as Timelike Compton Scattering (TCS), its

Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Derek Ma

Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches yield sporadic successes, yet generally fall short of expectations. In this work, we aim to improve zero-shot event detection by training models to better follow event definitions.

Yan Pan, Yiming Bian, Yang Li, Xuesong Xu

Quantum key distribution can provide information-theoretical secure communication, which is now heading towards building the quantum secure network for real-world applications. In most built quantum secure networks, point-to-multipoint (PTMP) topology is one of the most popular schemes, especially for quantum access networks. However, due to the lack of cust

Jianfeng Ning, Fuqun Han, Jun Zou

We consider in this work an inverse acoustic scattering problem when only phaseless data is available. The inverse problem is highly nonlinear and ill-posed due to the lack of the phase information. Solving inverse scattering problems with phaseless data is important in applications as the collection of physically acceptable phased data is usually difficult

Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao

Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics. However, modeling genomic sequences introduces challenges such as the need to model long-range token interactions, the effects of upstream and downstream regions of the genome, and the reverse complementarity (RC) of DNA. Here, we propose an architecture motiv

Yuan Huang, Yinan Chen, Xiangping Chen, Junqi Chen

The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs). Pre-trained models based on architectures such as BERT and Transformer, as well as LLMs like ChatGPT, have demonstrated remarkable language cap

Yang P. Liu

We study connections between the problem of fully dynamic $(1-\epsilon)$-approximate maximum bipartite matching, and the dual $(1+\epsilon)$-approximate vertex cover problem, with the online matrix-vector ($\mathsf{OMv}$) conjecture which has recently been used in several fine-grained hardness reductions. We prove that there is an online algorithm that maint

Zhiyuan Chang, Mingyang Li, Junjie Wang, Cheng Li

Visual entailment (VE) is a multimodal reasoning task consisting of image-sentence pairs whereby a promise is defined by an image, and a hypothesis is described by a sentence. The goal is to predict whether the image semantically entails the sentence. VE systems have been widely adopted in many downstream tasks. Metamorphic testing is the commonest technique

Hamid Kazemi, Atoosa Chegini, Jonas Geiping, Soheil Feizi

We employ an inversion-based approach to examine CLIP models. Our examination reveals that inverting CLIP models results in the generation of images that exhibit semantic alignment with the specified target prompts. We leverage these inverted images to gain insights into various aspects of CLIP models, such as their ability to blend concepts and inclusion of

Aditya Cowsik, Tamra Nebabu, Xiao-Liang Qi, Surya Ganguli

We investigate forward signal propagation and gradient back propagation in deep, randomly initialized transformers, yielding simple necessary and sufficient conditions on initialization hyperparameters that ensure trainability of deep transformers. Our approach treats the evolution of the representations of $n$ tokens as they propagate through the transforme

Priidik Gallagher

A recently proposed pregeometric auxiliary vector mediated gauge theory is studied in its canonical domain, by performing the Legendre transform on a curved background and by considering its covariant phase space, with further application to duality. The constraints become differential equations, but the Dirac-Bergmann algorithm appears consistent with elect

Zixian Dong, Lei Shi, Bing Wang, Mengwu Huo

Kagome system usually attracts great interest in condensed matter physics due to its unique structure hosting various exotic states such as superconductivity (SC), charge density wave (CDW), and nontrivial topological states. Topological semimetal RbTi3Bi5 consisting of the kagome layer of Ti shares a similar crystal structure to topological correlated mater

Xinbing Wang, Luoyi Fu, Xiaoying Gan, Ying Wen

The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis

Anh Ho Hoai, Jian Huang, L. N. Pfeiffer, K. W. West

Topology provides an essential concept for achieving unchanged (or protected) quantum properties in the presence of perturbations. A challenge facing realistic applications is that the level of protection displayed in real systems is subject to substantial variations. Some key differences stem from mechanisms influencing the reconstruction behaviors of exten

Yutong Li, Lu Chen, Aiwei Liu, Kai Yu

The literature review is an indispensable step in the research process. It provides the benefit of comprehending the research problem and understanding the current research situation while conducting a comparative analysis of prior works. However, literature summary is challenging and time consuming. The previous LLM-based studies on literature review mainly

Zhongdong Liu, Keyuan Zhang, Bin Li, Yin Sun

We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the Age-of-Information. The source must ba

Felix Lokin, Fenghui Yu

This paper studies the fill probabilities of limit orders placed at different price levels in a limit order book. These probabilities play a central role in execution optimization, as limit orders are not guaranteed to be executed and inherently involve a trade-off between execution cost and execution risk. We model the limit order book within a general stat

Sana Hafeez, Runze Cheng, Lina Mohjazi, Yao Sun

Unmanned Aerial Vehicles (UAVs) have significant potential for agile communication and relief coordination in post-disaster scenarios, particularly when ground infrastructure is compromised. However, efficiently coordinating and securing flocks of heterogeneous UAVs from different service providers poses significant challenges related to privacy, scalability

Zihao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang

Recent developments have underscored the critical role of \textit{differential privacy} (DP) in safeguarding individual data for training machine learning models. However, integrating DP oftentimes incurs significant model performance degradation due to the perturbation introduced into the training process, presenting a formidable challenge in the {different

Atsuto Seko

Machine learning potentials (MLPs) have become indispensable for conducting accurate large-scale atomistic simulations and for the efficient prediction of crystal structures. Polynomial MLPs, defined by polynomial rotational invariants, have been systematically developed for a wide range of elemental, alloy, and ionic systems. This study introduces a highly

Pamela Muñoz Obreque, Oscar Garrido, Diego Romero, Hartmut Löwen

Artificial active particles, exemplified by Hexbugs (HB), serve as valuable tools for investigating the intricate dynamics of active matter systems. Leveraging their stochastic motion, Hexbugs provides an excellent experimental model. In this study, we utilize Hexbugs equipped with disk-like armor and embedded magnetic dipoles, transforming them into Magneti

JooYoung Seo, Sile O'Modhrain, Yilin Xia, Sanchita Kamath

While recent years have seen a growing interest in accessible visualization tools and techniques for blind people, little attention is paid to the learning opportunities and teaching strategies of data science and visualization tailored for blind individuals. Whereas the former focuses on the accessibility issues of data visualization tools, the latter is co

Bryan Li, Tamer Alkhouli, Daniele Bonadiman, Nikolaos Pappas

The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we introduce a multilingual structured reasoning and explanation dataset, termed xSTREET, that covers four tasks across six languages. xSTREET exposes a gap in base LLM performance b

Mathias S. Fischer, Martin C. Fischer

In optical experiments, shutters are devices that open or close a path of light. They are often used to limit the duration of light exposure onto a target or onto a detector in order to reduce possible light-induced damage. Many commercial shutters are available for different applications - some provide very fast opening and closing times, some can handle la

Runmin Jiang, Zhaoxin Fan, Junhao Wu, Lenghan Zhu

3D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning. Recent advances in deep learning have significantly enhanced fully supervised medical image segmentation. However, this approach heavily relies on labor-intensive and time-consuming fully annotated ground-truth labels, particularly for

Zhiqing Wei, Haotian Liu, Zhiyong Feng, Huici Wu

With the emerging Integrated Sensing and Communication (ISAC) technique, exploiting the mobile communication system with multi-domain resources, multiple network elements, and large-scale infrastructures to realize cooperative sensing is a crucial approach satisfying the requirements of high-accuracy and large-scale sensing in IoE. In this article, the deep

Aashaka Desai, Maartje De Meulder, Julie A. Hochgesang, Annemarie Kocab

Growing research in sign language recognition, generation, and translation AI has been accompanied by calls for ethical development of such technologies. While these works are crucial to helping individual researchers do better, there is a notable lack of discussion of systemic biases or analysis of rhetoric that shape the research questions and methods in t

José Burillo, Sean Cleary, Brita Nucinkis

We describe standard forms for elements of the higher-dimensional Thompson groups $nV$ arising from gridding subdivision processes. These processes lead to standard normal form descriptions for elements in these groups, and sizes of these standard forms estimate the word length with respect to finite generating sets. These gridded forms lead to standard alge

Xiaoyu Zhan, Jianxin Yang, Yuanqi Li, Jie Guo

The field of 3D detailed human mesh reconstruction has made significant progress in recent years. However, current methods still face challenges when used in industrial applications due to unstable results, low-quality meshes, and a lack of UV unwrapping and skinning weights. In this paper, we present SHERT, a novel pipeline that can reconstruct semantic hum

Rizwanul Karim

This study uses the GARCH (1,1) model to examine the impact of COVID-19 cases (log value) on the volatility of the Exchange rate return of Bangladeshi taka (BDT) over the US dollar (USD), Japanese Yen (JPY), and Swedish Krona (SEK). The result shows that an increase in the number of COVID-19-affected cases in Bangladesh has a significant and positive impact

Filippo Pecci, Jesse D. Jenkins

We consider electricity capacity expansion models, which optimize investment and retirement decisions by minimizing both investment and operation costs. In order to provide credible support for planning and policy decisions, these models need to include detailed operations and time-coupling constraints, consider multiple possible realizations of weather-rela

Brenda Y. Miao, Irene Y. Chen, Christopher YK Williams, Jaysón Davidson

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks,

Sara Faridi, Iresha Madduwe Hewalage

In 2021, Hibi et. al. studied lattice points in $\mathbb{N}^2$ that appear as $(\depth R/I,\dim R/I)$ when $I$ is the edge ideal of a graph on $n$ vertices, and showed these points lie between two convex polytopes. When restricting to the class of Cameron--Walker graphs, they showed that these pairs do not form a convex lattice polytope. In this paper, for t

Ryan A. Nelson, Jesaiah King, Shuyu Cheng, Archibald J. Williams

EuCd_{2}As_{2} has attracted considerable interest as one of the few magnetic Weyl semimetal candidate materials, although recently there have been emerging reports that claim it to have a semiconducting electronic structure. To resolve this debate, we established the growth of n-type EuCd_{2}As_{2} crystals, to directly visualize the nature of the conductio

Deven Manam

We identify Drinfeld's formal group on the prismatization of $\mathrm{Spf}\,\mathbb{Z}_p$ with a formal group arising from homotopy theory, given locally by the Quillen formal group of a decompleted variant of topological periodic cyclic homology. We also prove that this formal group extends to the syntomization of $\mathrm{Spf}\,\mathbb{Z}_p$, and that it a

Jiaoyue Yuan, Runqing Yang, Lokanath Patra, Bolin Liao

Magnetic cooling based on the magnetocaloric effect is a promising solid-state refrigeration technology for a wide range of applications in different temperature ranges. Previous studies have mostly focused on near room temperature (300 K) and cryogenic temperature (< 10 K) ranges, while important applications such as hydrogen liquefaction call for efficient

Carla Farsi, Hannah Mobley, Christopher Seaton

We give explicit computations of the $\Gamma$-Euler characteristic of several families of orbit space definable translation groupoids. These include the translation groupoids associated to finite-dimensional linear representations of the circle and real and unitary representations of the real $2\times 2$ orthogonal group. In the case of translation groupoids

W. Rzesa, G. Kornakov, A. R. Kisiel, Yu. M. Sinyukov

Femtoscopy studies of pion radiation in heavy-ion collisions have been conducted extensively at all available collider energies, both theoretically and experimentally. In all these studies a special interest is given to $m_{T}$ dependency of pion femtoscopy radii, usually approximated by a power-law function at transverse momenta above 200 MeV/$c$. However,

George Lusztig, Eric Sommers

We establish a connection between constructible representations (arising in the study of left cells in Weyl groups) and Catalan numbers.

Simon Sundelin, Mohammed Ali Aamir, Vyom Manish Kulkarni, Claudia Castillo-Moreno

While dephasing noise frequently presents obstacles for quantum devices, it can become an asset in the context of a Brownian-type quantum refrigerator. Here we demonstrate a novel quantum thermal machine that leverages noise-assisted quantum transport to fuel a cooling engine in steady state. The device exploits symmetry-selective couplings between a superco

Yashraj M. Wani, Penelope Grace Kovakas, Arash Nikoubashman, Michael P. Howard

We determine the long-time self-diffusion coefficient and sedimentation coefficient for suspensions of nanoparticles with anisotropic shapes (octahedra, cubes, tetrahedra, and spherocylinders) as a function of nanoparticle concentration using mesoscale simulations. We use a discrete particle model for the nanoparticles, and we account for solvent-mediated hy

Sameh Othman, Jiarul Midya, Thorsten Auth, Gerhard Gompper

Self-propelled particles that are subject to noise are a well-established generic model system for active matter. A homogeneous alignment field can be used to orient the direction of the self-propulsion velocity and to model systems like phoretic Janus particles with a magnetic dipole moment or magnetotactic bacteria in an external magnetic field. Computer s

Leonid Gorodetsky, Nikita Markarian

The main result of the paper is a description of conormal Lie algebras of Feigin-Odesskii Poisson structures. In order to obtain it we introduce a new variant of a definition of a Feigin-Odesskii Poisson structure: we define it using a differential on the second page of a certain spectral sequence. In the general case this spectral sequence computes morphism

C. Coelho, M. Fernanda P. Costa, L. L. Ferrás

Real-world systems are often formulated as constrained optimization problems. Techniques to incorporate constraints into Neural Networks (NN), such as Neural Ordinary Differential Equations (Neural ODEs), have been used. However, these introduce hyperparameters that require manual tuning through trial and error, raising doubts about the successful incorporat

Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter, Miguel A. Bessa

Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task. The human brain, however, developed specialized regions for different tasks and performs inference with a small fraction of its neuronal connections. We propose an iterative pruning strategy introducing a simple importance-scor

Daniela Ruggiano

The two-particle angular correlations in the $\Delta$y,$\Delta\varphi$ space provide valuable insights into the properties of hadronization mechanisms and quark-gluon plasma properties. The correlation functions are influenced by several physical sources, including mini-jet correlations, Bose-Einstein quantum statistics, resonance decays, conservation of ene

Greg Martin, Chau Nguyen

Let $S(n)$ denote the least primary factor in the primary decomposition of the multiplicative group $M_n = (\Bbb Z/n\Bbb Z)^\times$. We give an asymptotic formula, with order of magnitude $x/(\log x)^{1/2}$, for the counting function of those integers $n$ for which $S(n) \ne 2$. We also give an asymptotic formula, for any prime power $q$, for the counting fu

Masaki Takeuchi, Hiroki Kusuyama, Daisuke Iwai, Kosuke Sato

Projection mapping (PM) is a technique that enhances the appearance of real-world surfaces using projected images, enabling multiple people to view augmentations simultaneously, thereby facilitating communication and collaboration. However, PM typically requires a dark environment to achieve high-quality projections, limiting its practicality. In this paper,

Maksim E. Eren, Ryan Barron, Manish Bhattarai, Selma Wanna

National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens, motivating the use of data-driven machine learning (ML) methods for analysis. However, shortcomings in existing ML approaches hinder their mass adoption. These challenges incl

Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie

Scaling laws play an instrumental role in the sustainable improvement in model quality. Unfortunately, recommendation models to date do not exhibit such laws similar to those observed in the domain of large language models, due to the inefficiencies of their upscaling mechanisms. This limitation poses significant challenges in adapting these models to increa

Mariusz Bujny, Katarzyna Jesionek, Jakub Nalepa, Karol Miszalski-Jamka

Precise localization of coronary arteries in Computed Tomography (CT) scans is critical from the perspective of medical assessment of coronary artery disease. Although various methods exist that offer high-quality segmentation of coronary arteries in cardiac contrast-enhanced CT scans, the potential of less invasive, non-contrast CT in this area is still not

Scott Aaronson, Sabee Grewal, Vishnu Iyer, Simon C. Marshall

We define and study a variant of QMA (Quantum Merlin Arthur) in which Arthur can make multiple non-collapsing measurements to Merlin's witness state, in addition to ordinary collapsing measurements. By analogy to the class PDQP defined by Aaronson, Bouland, Fitzsimons, and Lee (2014), we call this class PDQMA. Our main result is that PDQMA = NEXP; this resul

Zoran Sukurma, Martin Schlipf, Moritz Humer, Amir Taheridehkordi

We report modifications of the ph-AFQMC algorithm that allow the use of large time steps and reliable time step extrapolation. Our modified algorithm eliminates size-consistency errors present in the standard algorithm when large time steps are employed. We investigate various methods to approximate the exponential of the one-body operator within the AFQMC f

Hyeonseop Lee, Patrick McGlynn, Cedric Simenel

Quasi-fission (QF) reactions occur in fully damped heavy-ion collisions without the formation of an equilibrated compound nucleus, leading to the formation of fragments with similar properties as in fission reactions. Similar shell effects are expected to affect fragment formation in both fission and QF. Our purpose is to investigate QF dynamics in different

Everton M. C. Abreu

We have different definitions of the surface gravity (SG) of a horizon since we can say we have distinct classifications of horizons. The SG has an underlying role in the laws of black hole (BH) thermodynamics, being constant in the event horizon. The SG also acts in the emission of Hawking radiation being connected to its temperature. Concerning this last i

Linda Amoafo, Shiyao Xu, Elizabeth Platz, Daniel Scharfstein

In this paper, we develop a semiparametric sensitivity analysis approach designed to address unmeasured confounding in observational studies with time-to-event outcomes. We target estimation of the marginal distributions of potential outcomes under competing exposures using influence function-based techniques. We derive the non-parametric influence function

Ryuji Chiba, Toru Kojo, Daiki Suenaga

We study thermal effects on equations of state (EOS) in isospin QCD, utilizing a quark-meson model coupled to a Polyakov loop. The quark-meson model is analyzed at one-loop that is the minimal order to include quark substructure constraints on pions which condense at finite isospin density. In the previous study we showed that the quark-meson model at zero t

Enrico Ventura

Statistical mechanics has made significant contributions to the study of biological neural systems by modeling them as recurrent networks of interconnected units with adjustable interactions. Several algorithms have been proposed to optimize the neural connections to enable network tasks such as information storage (i.e. associative memory) and learning prob