March 2024 arXiv papers — page 181

Chenqiang Gao, Chuandong Liu, Jun Shu, Fangcen Liu

Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training. However, collecting such large-scale densely-supervised datasets is notoriously costly. To reduce the cumbersome data annotation process, we propose a novel sparsely-annotated framework, in which we just annotate one 3D object

Stav Cohen, Ron Bitton, Ben Nassi

In this paper, we show that when the communication between GenAI-powered applications relies on RAG-based inference, an attacker can initiate a computer worm-like chain reaction that we call Morris-II. This is done by crafting an adversarial self-replicating prompt that triggers a cascade of indirect prompt injections within the ecosystem and forces each aff

Marco Caliari, Fabio Cassini

In this paper, we consider the task of efficiently computing the numerical solution of evolutionary complex Ginzburg--Landau equations on Cartesian product domains with homogeneous Dirichlet/Neumann or periodic boundary conditions. To this aim, we employ for the time integration high-order exponential methods of splitting and Lawson type with constant time s

Quanyuan Chen, Yaqi Li

Let $X$ be a Banach algebra and $B(X)$ be the set of all bounded linear operators on $X$. Suppose that $\alpha: B(X) \rightarrow B(X)$ is an automorphism. We say that a mapping $\delta$ from $B(X)$ into itself is derivable at $G \in B(X)$ if $\delta(G) = \alpha(A)\delta(B) + \delta(A)\alpha(B)$ for all $A, B \in B(X)$ with $AB = G$. We say that an element $G

Ce Chi, Xing Wang, Kexin Yang, Zhiyan Song

Transformer has become one of the most popular architectures for multivariate time series (MTS) forecasting. Recent Transformer-based MTS models generally prefer channel-independent structures with the observation that channel independence can alleviate noise and distribution drift issues, leading to more robustness. Nevertheless, it is essential to note tha

Kevin Zwart

As a direct continuation of K. Zwart, arXiv:2304.02648, which is built on the work of M. M\"uger and L. Tuset, we reduce the Mathieu conjecture, formulated by O. Mathieu in 1997, for $Sp(N)$ and $G_2$ to a conjecture involving functions over $\mathbb{R}^n\times (S^1)^m$ with $n,m\in\mathbb{N}_0$. The proofs rely on Euler-style parametrizations of these group

A. B. Larionov, Yu. N. Uzikov

Relativistic beams of heavy ions interacting with various nuclear targets allow to study a broad range of problems starting from nuclear equation of state to the traditional nuclear structure. Some questions which were impossible to answer heretofore -- can be addressed nowadays by using inverse kinematics. These includes the structure of short-lived nuclei

Edoardo Caldarelli, Antoine Chatalic, Adrià Colomé, Cesare Molinari

In this paper, we study how the Koopman operator framework can be combined with kernel methods to effectively control nonlinear dynamical systems. While kernel methods have typically large computational requirements, we show how random subspaces (Nystr\"om approximation) can be used to achieve huge computational savings while preserving accuracy. Our main te

Chu Wang, Jinhong Wu, Yanzhi Wang, Zhijian Zha

Deep learning methods have access to be employed for solving physical systems governed by parametric partial differential equations (PDEs) due to massive scientific data. It has been refined to operator learning that focuses on learning non-linear mapping between infinite-dimensional function spaces, offering interface from observations to solutions. However

ATLAS Collaboration

The observation of the electroweak production of a $W$ boson and a photon in association with two jets, using $pp$ collision data at the Large Hadron Collider at a centre of mass energy of $\sqrt{s}=13$ TeV, is reported. The data were recorded by the ATLAS experiment from 2015 to 2018 and correspond to an integrated luminosity of 140 fb$^{-1}$. This process

P. Francis, Abraham M. Illickan, Lijo M. Jose, Deepak Rajendraprasad

A dominating set of a graph $G$ is a subset $S$ of its vertices such that each vertex of $G$ not in $S$ has a neighbor in $S$. A face-hitting set of a plane graph $G$ is a set $T$ of vertices in $G$ such that every face of $G$ contains at least one vertex of $T$. We show that the vertex-set of every plane (multi-)graph without isolated vertices, self-loops o

Xiaodong Duan, Fulai Guo

Multi-wavelength observations indicate that the intracluster medium in some galaxy clusters contains cold filaments, while their formation mechanism remains debated. Using hydrodynamic simulations, we show that cold filaments could naturally condense out of hot gaseous wake flows uplifted by the jet-inflated active galactic nucleus (AGN) bubbles. Consistent

Eelco van Kampen, Tom Bakx, Carlos De Breuck, Chian-Chou Chen

During the most active period of star formation in galaxies, which occurs in the redshift range 1<z<3, strong bursts of star formation result in significant quantities of dust, which obscures new stars being formed as their UV/optical light is absorbed and then re-emitted in the infrared, which redshifts into the mm/sub-mm bands for these early times. To get

Peter Scheirich, Petr Pravec, Alex J. Meyer, Harrison F. Agrusa

The NASA Double Asteroid Redirection Test (DART) spacecraft successfully impacted the Didymos-Dimorphos binary asteroid system on 2022 September 26 UTC. We provide an update to its pre-impact mutual orbit and estimate the post-impact physical and orbital parameters, derived using ground-based photometric observations taken from July 2022 to February 2023. We

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

Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. However, recent studies have shown that FL is vulnerable to adversarial examples (AEs), leading to a significant drop in its performance. Meanwhile, the non-independent and identically distributed (no

Konstantin Avrachenkov, B. R. Vinay Kumar, Lasse Leskelä

We consider the community recovery problem on a one-dimensional random geometric graph where every node has two independent labels: an observed location label and a hidden community label. A geometric kernel maps the locations of pairs of nodes to probabilities. Edges are drawn between pairs of nodes based on their communities and the value of the kernel cor

Richard I. Anderson

Classical Cepheids were the first stellar standard candles and have played a crucial role for astronomical distance measurements ever since the discovery of the Leavitt law (period-luminosity relation). Enormous improvements in distance accuracy have been achieved since Hertzsprung's first application of Leavitt's law to measure the distance to the Small Mag

Xuzhen Cao, Chunyu Jia, Ying Hu, Zhaoxin Liang

The nonlinear Thouless pumping is an exciting frontier of topological physics. While recent works have revealed the quantized motion of solitons in Thouless pumps, the interplay between the topology, nonlinearity and disorder remains largely unexplored. Here, we investigate the nonlinear Thouless pumping of solitons in the presence of an impurity in the cont

Yaochen Zhu, Rui Xia, Jiajun Zhang

Model merging is to combine fine-tuned models derived from multiple domains, with the intent of enhancing the model's proficiency across various domains. The principal concern is the resolution of parameter conflicts. A substantial amount of existing research remedy this issue during the merging stage, with the latest study focusing on resolving this issue t

Oleg Ivrii, Artur Nicolau

In this paper, we study analytic self-maps of the unit disk which distort hyperbolic area of large hyperbolic disks by a bounded amount. We give a number of characterizations involving angular derivatives, Lipschitz extensions, M\"obius distortion, the distribution of critical points and Aleksandrov-Clark measures. We also study Lyapunov exponents of their A

Matthias Roeper, Samuel Dominic Seddon, Zeeshan H. Amber, Michael Rüsing

Piezoresponse Force Microscopy (PFM) is one of the most widespread methods for investigating and visualizing ferroelectric domain structures down to the nanometer length scale. PFM makes use of the direct coupling of the piezoelectric response to the crystal lattice, and hence is most often applied to spatially map the 3-dimensional (3D) near-surface domain

R. Wang, M. Elimelech, P. M. Biesheuvel

Membranes consist of pores and the walls of these pores are often charged. In contact with an aqueous solution, the pores fill with water and ions migrate from solution into the pores until chemical equilibrium is reached. The distribution of ions between outside and pore solution is governed by a balance of chemical potential, and the resulting model is cal

Joy He-Yueya, Noah D. Goodman, Emma Brunskill

Creating effective educational materials generally requires expensive and time-consuming studies of student learning outcomes. To overcome this barrier, one idea is to build computational models of student learning and use them to optimize instructional materials. However, it is difficult to model the cognitive processes of learning dynamics. We propose an a

YaoDan Zhang, Zidong Wang, Ru Jia, Ru Li

In recent years, personalized recommendation technology has flourished and become one of the hot research directions. The matrix factorization model and the metric learning model which proposed successively have been widely studied and applied. The latter uses the Euclidean distance instead of the dot product used by the former to measure the latent space ve

ATLAS Collaboration

Measurements of inclusive, differential cross-sections for the production of events with missing transverse momentum in association with jets in proton-proton collisions at $\sqrt{s}=13~$TeV are presented. The measurements are made with the ATLAS detector using an integrated luminosity of $140~$fb$^{-1}$ and include measurements of dijet distributions in a r

Isao Kiuchi, Sumaia Saad Eddin

Let $\gcd(m,n)$ denote the greatest common divisor of the positive integers $m$ and $n$, and let $\mu$ represent the M\" obius function. For any real number $x>5$, we define the summatory function of the M\" obius function involving the greatest common divisor as $ S_{\mu}(x) := \sum_{mn\leq x} \mu(\gcd(m,n)). $ In this paper, we present an asymptotic formul

Dongzi Xie, Xinming Wu, Zhixiang Guo, Heting Hong

Distributed Acoustic Sensing (DAS) is promising for traffic monitoring, but its extensive data and sensitivity to vibrations, causing noise, pose computational challenges. To address this, we propose a two-step deep-learning workflow with high efficiency and noise immunity for DAS-based traffic monitoring, focusing on instance vehicle trajectory segmentation

Maurizio Salaris, Simon Blouin, Santi Cassisi, Luigi R. Bedin

Recent Monte Carlo plasma simulations to study in crystallizing carbon-oxygen (CO) white dwarfs (WDs) the phase separation of Ne22 (the most abundant metal after carbon and oxygen) have shown that, under the right conditions, a distillation process that transports Ne22 toward the WD centre is efficient and releases a considerable amount of gravitational ener

Liwenying Yang, Ming Li

By introducing a simple competition mechanism for bond insertion in random graphs, explosive percolation exhibits a sharp phase transition with rich critical phenomena. We investigate high-order connectivity in explosive percolation using an event-based ensemble, focusing on biconnected clusters, where any two sites are connected by at least two independent

Steven C. Pan, Jia Yi Han, Fun Man Fung

Prequestioning is an instructional strategy that involves taking practice tests on to-be-learned information followed by studying the correct answers. Despite promising results in laboratory studies, it has rarely been examined in authentic educational settings. The current study investigated the pedagogical benefits of prequestioning as a learning intervent

Yu. B. Ivanov, M. Kozhevnikova

We present results of simulations of directed flow of various hadrons in Au+Au collisions at collision energies of $\sqrt{s_{NN}}=$ 3 and 4.5 GeV. Simulations are performed within the model three-fluid dynamics (3FD) and the event simulator based on it (THESEUS). The results are compared with recent STAR data. The directed flows of various particles provide

So Yeon Kim, Sehee Wang, Eun Kyung Choe

Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from

Yuri Ashrafyan, Diogo Gomes

Here, we examine a fully-discrete Semi-Lagrangian scheme for a mean-field game price formation model. We show the existence of the solution of the discretized problem and that it is monotone as a multivalued operator. Moreover, we show that the limit of the discretization converges to the weak solution of the continuous price formation mean-field game using

Lingyan Ran, Lushuang Wang, Tao Zhuo, Yinghui Xing

Semantic segmentation of remote sensing images is a challenging and hot issue due to the large amount of unlabeled data. Unsupervised domain adaptation (UDA) has proven to be advantageous in incorporating unclassified information from the target domain. However, independently fine-tuning UDA models on the source and target domains has a limited effect on the

Sébastien Verel, Sarah Thomson, Omar Rifki

The Quadratic Assignment Problem (QAP) is one of the major domains in the field of evolutionary computation, and more widely in combinatorial optimization. This paper studies the phase transition of the QAP, which can be described as a dramatic change in the problem's computational complexity and satisfiability, within a narrow range of the problem parameter

Kumaranage Ravindu Yasas Nagasinghe, Honglu Zhou, Malitha Gunawardhana, Martin Renqiang Min

In this paper, we explore the capability of an agent to construct a logical sequence of action steps, thereby assembling a strategic procedural plan. This plan is crucial for navigating from an initial visual observation to a target visual outcome, as depicted in real-life instructional videos. Existing works have attained partial success by extensively leve

Zheng Li, Xiang Li, Xinyi Fu, Xin Zhang

Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts, neglecting the potential of prompts as effective distillers for learning from larger teacher models. In this paper, we introduce an u

Keiyu Nosaka, Yamato Suetake, Yuichi Takano, Akiko Yoshise

Data Collaboration (DC) enables multiple parties to jointly train a model by sharing only linear projections of their private datasets. The core challenge in DC is to align the bases of these projections without revealing each party's secret basis. While existing theory suggests that any target basis spanning the common subspace should suffice, in practice,

Baptiste Devyver, Emmanuel Russ

This work is devoted to the study of so-called ``reverse Riesz'' inequalities and suitable variants in the context of some fractal-like cable systems. It was already proved by L. Chen, T. Coulhon, J. Feneuil and the second author that, in the Vicsek cable system, the inequality $\left\Vert \Delta^{1/2}f\right\Vert_p\lesssim \left\Vert \nabla f\right\Vert_p$

Sebastian Graf, Simon Peyton Jones, Sven Keidel

We explore denotational interpreters: denotational semantics that produce coinductive traces of a corresponding small-step operational semantics. By parameterising our denotational interpreter over the semantic domain and then varying it, we recover dynamic semantics with different evaluation strategies as well as summary-based static analyses such as type a

Sanaz Sadat Hosseini, Babak Rahimi Ardabili, Mona Azarbayjani, Srinivas Pulugurtha

Urban design significantly impacts sustainability, particularly in the context of public transit efficiency and carbon emissions reduction. This study explores two neighborhoods with distinct urban designs: South End, Charlotte, NC, featuring a dynamic mixed-use urban design pattern, and Avondale, Chattanooga, TN, with a residential suburban grid layout. Usi

Valentina Scarponi, Michel Duprez, Florent Nageotte, Stéphane Cotin

Purpose: The treatment of cardiovascular diseases requires complex and challenging navigation of a guidewire and catheter. This often leads to lengthy interventions during which the patient and clinician are exposed to X-ray radiation. Deep Reinforcement Learning approaches have shown promise in learning this task and may be the key to automating catheter na

Thomas Gawne, Zhandos A. Moldabekov, Oliver S. Humphries, Karen Appel

Using a novel ultrahigh resolution ($\Delta E \sim 0.1\,$eV) setup to measure electronic features in x-ray Thomson scattering (XRTS) experiments at the European XFEL in Germany, we have studied the collective plasmon excitation in aluminium at ambient conditions, which we can measure very accurately even at low momentum transfers. As a result, we can resolve

Hanlin Tang, Yifu Sun, Decheng Wu, Kai Liu

Large language models (LLMs) have proven to be very superior to conventional methods in various tasks. However, their expensive computations and high memory requirements are prohibitive for deployment. Model quantization is an effective method for reducing this overhead. The problem is that in most previous works, the quantized model was calibrated using few

Philipp Hess, Michael Aich, Baoxiang Pan, Niklas Boers

Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computationally too expensive to be run at sufficiently high spatial resolution. Recent machine learning approaches have shown promising results in downscaling ESM simulations, outperform

Hyesu Jang, Minwoo Jung, Myung-Hwan Jeon, Ayoung Kim

Maritime radars are prevalently adopted to capture the vessel's omnidirectional data as imagery. Nevertheless, inherent challenges persist with marine radars, including limited frequency, suboptimal resolution, and indeterminate detections. Additionally, the scarcity of discernible landmarks in the vast marine expanses remains a challenge, resulting in conse

Mark Karlov, Ali Abedi, Shehroz S. Khan

Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates. AI-driven virtual rehabilitation, which allows patients to independently complete exercises at home, utilizes AI algorithms to analyze exercise data, providing feedback to patients and updating clinicians on

Anna V. Guglielmi, Stefano Tomasin

Reconfigurable intelligent surfaces (RISs) are a promising solution to improve the coverage of cellular networks, thanks to their ability to steer impinging signals in desired directions. However, they introduce an overhead in the communication process since the optimal configuration of a RIS depends on the channels to and from the RIS, which must be estimat

Sonej Alam, Somasri Sen, Soumitra Sengupta

One of the surprising aspects of the present Universe, is the absence of any noticeable observable effects of higher-rank antisymmetric tensor fields in any natural phenomena. Here, we address the possible explanation of the absence of the higher rank antisymmetric tensor fields within the framework of a general class of $f(R)$ gravity represented by $f (R)

Yuya Matsumoto

We introduce an inseparable version of Kummer surfaces. It is defined as a supersingular K3 surface in characteristic 2 with 16 smooth rational curves forming a certain configuration and satisfying a suitable divisibility condition. The main result is that such a surface admits an inseparable double covering by a non-normal surface $A$ that is similar to abe

Yichen Yao, Zimo Jiang, Yujing Sun, Zhencai Zhu

Human-centric 3D scene understanding has recently drawn increasing attention, driven by its critical impact on robotics. However, human-centric real-life scenarios are extremely diverse and complicated, and humans have intricate motions and interactions. With limited labeled data, supervised methods are difficult to generalize to general scenarios, hindering

Alex Bellon, Miro Haller, Andrey Labunets, Enze Liu

Government investigatory and surveillance powers are important tools for examining crime and protecting public safety. However, since these tools must be employed in secret, it can be challenging to identify abuses or changes in use that could be of significant public interest. In this paper, we evaluate this phenomenon in the context of National Security Le

Cheng Huang, Shoudong Han, Mengyu He, Wenbo Zheng

Accurate data association is crucial in reducing confusion, such as ID switches and assignment errors, in multi-object tracking (MOT). However, existing advanced methods often overlook the diversity among trajectories and the ambiguity and conflicts present in motion and appearance cues, leading to confusion among detections, trajectories, and associations w

Vernon Cooray, Gerald Cooray, Farhad Rachidi, Marcos Rubinstein

Observations and theoretical principles suggest that electromagnetic waves, including light, travel more slowly in dielectric media than in vacuum. Maxwell's equations, incorporating material dependent permittivity and permeability, elegantly capture this effect. Previous studies indicate that the observed slower speed is due to interference effects, with th

Shrimon Mukherjee, Pulakesh Pramanik, Partha Basuchowdhuri, Santanu Bhattacharya

G-Quadruplexes are the four-stranded non-canonical nucleic acid secondary structures, formed by the stacking arrangement of the guanine tetramers. They are involved in a wide range of biological roles because of their exceptionally unique and distinct structural characteristics. After the completion of the human genome sequencing project, a lot of bioinforma

Gabriele Todeschi, Ludovic Métivier, Jean-Marie Mirebeau

Optimal transport has recently started to be successfully employed to define misfit or loss functions in inverse problems. However, it is a problem intrinsically defined for positive (probability) measures and therefore strategies are needed for its applications in more general settings of interest. In this paper we introduce an unbalanced optimal transport

Arman Ferdowsi, Matthias Függer, Josef Salzmann, Ulrich Schmid

Dynamic digital timing analysis is a less accurate but fast alternative to highly accurate but slow analog simulations of digital circuits. It relies on gate delay models, which allow the determination of input-to-output delays of a gate on a per-transition basis. Accurate delay models not only consider the effect of preceding output transitions here but als

Seun Osonuga, Frederic Wurtz, Benoit Delinchant

This data paper presents the dataset from a study on the use of electric vehicles (EVs). This dataset covers the first dataset collected in this study: the usage data from a Renault Zoe over 3 years. The process of collection of the dataset, its treatment, and descriptions of all the included variables are detailed. The collection of this dataset represents

Mancheon Han, Hyowon Park, Sangkook Choi

The recent development of logical quantum processors marks a pivotal transition from the noisy intermediate-scale quantum (NISQ) era to the fault-tolerant quantum computing (FTQC) era. These devices have the potential to address classically challenging problems with polynomial computational time using quantum properties. However, they remain susceptible to n

Andy C. Y. Li, Imanol Hernandez

Variational quantum algorithms are promising candidates for delivering practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware. However, optimizing the noisy cost functions associated with these algorithms is challenging for system sizes relevant to quantum advantage. In this work, we investigate the effect of noise on optimization by

Tigran Harutyunyan, Yuri Ashrafyan

The main issues of the spectral theory of Dirac operators are presented, namely: transformation operators, asymptotics of eigenvalues and eigenfunctions, description of symmetric and self-adjoint operators in Hilbert space, expansion in eigenfunctions, uniqueness theorems in inverse problems, constructive solution of inverse problems, description of isospect

Xiaonan Xu, Yichao Wu, Penghao Liang, Yuhang He

With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences. Although deep neural networks (DNNs) have made significant progress in improving recommendation systems by simulating the interaction between users and items and incorp

Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh

We present a survey of more than 90 recent papers that aim to study cultural representation and inclusion in large language models (LLMs). We observe that none of the studies explicitly define "culture, which is a complex, multifaceted concept; instead, they probe the models on some specially designed datasets which represent certain aspects of "culture". We

Junaid Tariq, M. U. Ashraf, Grigory Nigmatkulov

We report the investigation of the kinetic freeze-out properties of identified hadrons ($\pi^\pm$, $K^\pm$ and $p(\bar p)$) along with light (anti-)nuclei $d (\bar d)$, $t (\bar t)$ and ${}^{3}He$ in relativistic heavy-ion collisions at RHIC and LHC energies. A simultaneous fit is performed with the Blast-Wave (BW) model to the transverse momentum ({\ppt}) s

Rabah Labbas, Stéphane Maingot, Alexandre Thorel

After different variables and functions changes, the generalized dispersal problem, recalled in (1) below and considered in part I, see Labbas, Maingot and Thorel [14], leads us to consider, to study and to invert the sum of linear operators (4) below in a suitable Banach space by using two strategies: namely the theory of sums of operators in Banach spaces

Bo Wang, Tianxiang Sun, Hang Yan, Siyin Wang

The exploration of whether agents can align with their environment without relying on human-labeled data presents an intriguing research topic. Drawing inspiration from the alignment process observed in intelligent organisms, where declarative memory plays a pivotal role in summarizing past experiences, we propose a novel learning framework. The agents adept

Rui Wang, Fei Mi, Yi Chen, Boyang Xue

The growing interest in Large Language Models (LLMs) for specialized applications has revealed a significant challenge: when tailored to specific domains, LLMs tend to experience catastrophic forgetting, compromising their general capabilities and leading to a suboptimal user experience. Additionally, crafting a versatile model for multiple domains simultane

Johannes Ebert

For a bundle of oriented closed smooth $n$-manifolds $\pi: E \to X$, the tautological class $\kappa_{\mathcal{L}_k} (E) \in H^{4k-n}(X;\mathbb{Q})$ is defined by fibre integration of the Hirzebruch class $\mathcal{L}_k (T_v E)$ of the vertical tangent bundle. More generally, given a discrete group $G$, a class $u \in H^p(B G;\mathbb{Q})$ and a map $f:E \to B

Zixuan Li, Lizi Liao, Tat-Seng Chua

Product search plays an essential role in eCommerce. It was treated as a special type of information retrieval problem. Most existing works make use of historical data to improve the search performance, which do not take the opportunity to ask for user's current interest directly. Some session-aware methods take the user's clicks within the session as implic

Chihiro Nakatani, Hiroaki Kawashima, Norimichi Ukita

This paper proposes Group Activity Feature (GAF) learning in which features of multi-person activity are learned as a compact latent vector. Unlike prior work in which the manual annotation of group activities is required for supervised learning, our method learns the GAF through person attribute prediction without group activity annotations. By learning the

Sam Yu-Te Lee, Kwan-Liu Ma

Sensemaking on a large collection of documents (corpus) is a challenging task often found in fields such as market research, legal studies, intelligence analysis, political science, computational linguistics, etc. Previous works approach this problem either from a topic- or entity-based perspective, but they lack interpretability and trust due to poor model

Timothy Chen, Ola Shorinwa, Joseph Bruno, Aiden Swann

We present Splat-Nav, a real-time robot navigation pipeline for Gaussian Splatting (GSplat) scenes, a powerful new 3D scene representation. Splat-Nav consists of two components: 1) Splat-Plan, a safe planning module, and 2) Splat-Loc, a robust vision-based pose estimation module. Splat-Plan builds a safe-by-construction polytope corridor through the map base

Suraj Bhute, Subhamoy Mandal, Debashree Guha

Ultrasound is a widely used medical tool for non-invasive diagnosis, but its images often contain speckle noise which can lower their resolution and contrast-to-noise ratio. This can make it more difficult to extract, recognize, and analyze features in the images, as well as impair the accuracy of computer-assisted diagnostic techniques and the ability of do

Zixuan Liu, Giulio Chiribella

Quantum theory is in principle compatible with processes that violate causal inequalities, an analogue of Bell inequalities that constrain the correlations observed by sets of parties operating in a definite causal order. Since the introduction of causal inequalities, determining their maximum quantum violation, analogue to Tsirelson's bound for Bell inequal

Nishu Jain, M. Bhuyan, Raj Kumar

The $\alpha$-decay half-lives of superheavy nuclei with $100 \leq Z \leq 120$ are comprehensively analyzed using the axially deformed relativistic mean field (RMF) formalism for the NL3$^*$ parameter set. We employ RMF binding energies to determine the $\alpha$-decay energies and make a comparison with both the available experimental data and the theoretical

Xin Wang, Yucheng Kuang, Chaojun Wang, Hongyuan Di

While neural networks have been successfully applied to the full-spectrum k-distribution (FSCK) method at a large range of thermodynamics with k-values predicted by a trained multilayer perceptron (MLP) model, the required a-values still need to be calculated on-the-fly, which theoretically degrades the FSCK method and may lead to errors. On the other hand,

Jiaxing Zhao, Shuzhe Shi

The inverse power method is a numerical algorithm to obtain the eigenvectors of a matrix. In this work, we develop an iteration algorithm, based on the inverse power method, to numerically solve the Schr\"odinger equation that couples an arbitrary number of components. Such an algorithm can also be applied to the multi-body systems. To show the power and acc

Zhuohong Li, Wei He, Jiepan Li, Fangxiao Lu

Large-scale high-resolution (HR) land-cover mapping is a vital task to survey the Earth's surface and resolve many challenges facing humanity. However, it is still a non-trivial task hindered by complex ground details, various landforms, and the scarcity of accurate training labels over a wide-span geographic area. In this paper, we propose an efficient, wea

Ameur Dhahri, Chul Ki Ko, Hyun Jae Yoo

We discuss the martingales in relevance with $G$-strongly quasi-invariant states on a $C^*$-algebra $\mathcal A$, where $G$ is a separable locally compact group of $*$-automorphisms of $\mathcal A$. In the von Neumann algebra $\mathfrak A$ of the GNS representation, we define a unitary representation of the group and define a group $\hat G$ of $*$-automorphi

Son The Nguyen, Niranjan Uma Naresh, Theja Tulabandhula

This paper addresses the challenges of aligning large language models (LLMs) with human values via preference learning (PL), focusing on incomplete and corrupted data in preference datasets. We propose a novel method for robustly and completely recalibrating values within these datasets to enhance LLMs' resilience against the issues. In particular, we devise

Naoto Watanabe, Taku Yamazaki, Takumi Miyoshi, Ryo Yamamoto

With the growth of internet of things (IoT) devices, cyberattacks, such as distributed denial of service, that exploit vulnerable devices infected with malware have increased. Therefore, vendors and users must keep their device firmware updated to eliminate vulnerabilities and quickly handle unknown cyberattacks. However, it is difficult for both vendors and

Hongyuan Di, Chaojun Wang, Chuanlong Hu, Xiao Liu

Radiative heat transfer has been proven to be important during the ignition process in gas turbine. Those radiating gases (CO2, H2O, CO) generated during combustion may display strong spectral, or nongray behavior, which is difficult to both characterize and calculate. In this work, both the full-spectrum k-distribution (FSK) and weighted-sum-of-gray-gases (

Zhonghai Wang, Jie Jiang, Yibing Zhan, Bohao Zhou

Medical large language models (LLMs) have gained popularity recently due to their significant practical utility. However, most existing research focuses on general medicine, and there is a need for in-depth study of LLMs in specific fields like anesthesiology. To fill the gap, we introduce Hypnos, a Chinese Anesthesia model built upon existing LLMs, e.g., Ll

Mukesh Ghimire, Lei Zhang, Zhe Xu, Yi Ren

We study zero-sum differential games with state constraints and one-sided information, where the informed player (Player 1) has a categorical payoff type unknown to the uninformed player (Player 2). The goal of Player 1 is to minimize his payoff without violating the constraints, while that of Player 2 is to violate the state constraints if possible, or to m

Ju Han, Xiaojie Chen, Attila Szolnoki

The public goods game is a broadly used paradigm for studying the evolution of cooperation in structured populations. According to the basic assumption, the interaction graph determines the connections of a player where the focal actor forms a common venture with the nearest neighbors. In reality, however, not all of our partners are involved in every games.

Arman Ferdowsi, Matthias Függer, Thomas Nowak, Michael Drmota

Thresholded hybrid systems are restricted dynamical systems, where the current mode, and hence the ODE system describing its behavior, is solely determined by externally supplied digital input signals and where the only output signals are digital ones generated by comparing an internal state variable to a threshold value. An attractive feature of such system

N. Jain, M. Bhuyan, Raj Kumar

In the present work, we have incorporated the microscopic relativistic nuclear potential obtained from recently developed relativistic R3Y NN potential in the coupled channels code CCFULL to study the fusion dynamics. The R3Y NN-potential and the densities of interacting nuclei are obtained for the relativistic mean-field approach for the NL3$^*$ parameter s

Congzhi Zhang, Linhai Zhang, Jialong Wu, Yulan He

Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on the model training stage, including approaches based on data augmentation and reweighting, yet they struggle w

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

Fractional Differential Equations (FDEs) are essential tools for modelling complex systems in science and engineering. They extend the traditional concepts of differentiation and integration to non-integer orders, enabling a more precise representation of processes characterised by non-local and memory-dependent behaviours. This property is useful in systems

Akram Zaytar, Caleb Robinson, Gilles Q. Hacheme, Girmaw A. Tadesse

Rare object detection is a fundamental task in applied geospatial machine learning, however is often challenging due to large amounts of high-resolution satellite or aerial imagery and few or no labeled positive samples to start with. This paper addresses the problem of bootstrapping such a rare object detection task assuming there is no labeled data and no

Yehonatan Fridman, Guy Tamir, Uri Steinitz, Gal Oren

Monte Carlo (MC) simulations play a pivotal role in diverse scientific and engineering domains, with applications ranging from nuclear physics to materials science. Harnessing the computational power of high-performance computing (HPC) systems, especially Graphics Processing Units (GPUs), has become essential for accelerating MC simulations. This paper focus

Donghan Kim, Byungmin Sohn, Yeonjae Lee, Jeongkeun Song

Quantum phases of matter such as superconducting, ferromagnetic and Wigner crystal states are often driven by the two-dimensionality (2D) of correlated systems. Meanwhile, spin-orbit coupling (SOC) is a fundamental element leading to nontrivial topology which gives rise to quantum phenomena such as the large anomalous Hall effect and nontrivial superconducti

Frederik F. Flöther

The last few years have seen rapid progress in transitioning quantum computing from lab to industry. In healthcare and life sciences, more than 40 proof-of-concept experiments and studies have been conducted; an increasing number of these are even run on real quantum hardware. Major investments have been made with hundreds of millions of dollars already allo

Busra Aris, Nenad Teofanov, Serap Oztop

We extend dilation properties of Wiener amalgam spaces when the local and global componenets are Lebesgue spaces to a more general setting of Orlicz spaces. We recover the result of Cordero and Nicola when restricted to Lebesgue spaces. In addition, we prove continuity of the Zak transform on Wiener amalgam spaces with Orlicz spaces as their local components

Nobuhito Maru, Ryujiro Nago

We discuss a possibility whether a model of grand gauge-Higgs unification incorporating family unification in higher dimensions can be constructed. We first extend a five dimensional $SU(6)$ grand gauge-Higgs unification model to a five dimensional $SU(7)$ grand gauge-Higgs unification model compactified on an orbifold $S^1/Z_2$ to obtain three generations o

Praveen K. Yadav, Raj Kumar, M. Bhuyan

In this theoretical study, we have derived a simplified analytical expression for the binding energy per nucleon as a function of density and isospin asymmetry within the relativistic mean-field model. We have generated a new parameterization for the density-dependent DD-ME2 parameter set using the Relativistic-Hartree-Bogoliubov approach. Moreover, this wor

J. Y. Süngü, N. Er

We study the $\Lambda(1405)$ resonance with $I(J^{P})=0(1/2^{-}) $ in the context of the pentaquark hypothesis in the nuclear medium. For the investigation of the influence of the nuclear medium on the physical parameters of $\Lambda(1405)$, we propose a molecular-type structure involving $K^{-}p$ and $\bar{K}^{0}n$ admixtures, correlated with the nuclear ma

Sijie Ji, Xinzhe Zheng, Chenshu Wu

There is an ongoing debate regarding the potential of Large Language Models (LLMs) as foundational models seamlessly integrated with Cyber-Physical Systems (CPS) for interpreting the physical world. In this paper, we carry out a case study to answer the following question: Are LLMs capable of zero-shot human activity recognition (HAR). Our study, HARGPT, pre

Mi Zhou, Vibhanshu Abhishek, Timothy Derdenger, Jaymo Kim

This study analyzed images generated by three popular generative artificial intelligence (AI) tools - Midjourney, Stable Diffusion, and DALLE 2 - representing various occupations to investigate potential bias in AI generators. Our analysis revealed two overarching areas of concern in these AI generators, including (1) systematic gender and racial biases, and

Jagadheep D. Pandian, Rwitika Chatterjee, Timea Csengeri, Jonathan P. Williams

The mass assembly in star forming regions arises from the hierarchical structure in molecular clouds in tandem with fragmentation at different scales. In this paper, we present a study of the fragmentation of massive clumps covering a range of evolutionary states, selected from the ATLASGAL survey, using the compact configuration of the Submillimeter Array.