November 2024 arXiv papers — page 81

Pablo de Oliveira Castro, El-Mehdi El Arar, Eric Petit, Devan Sohier

The quality of numerical computations can be measured through their forward error, for which finding good error bounds is challenging in general. For several algorithms and using stochastic rounding (SR), probabilistic analysis has been shown to be an effective alternative for obtaining tight error bounds. This analysis considers the distribution of errors a

Olivier Fercoq

In this paper, we reinterpret quadratic Lyapunov functions as solutions to a performance estimation saddle point problem. This allows us to automatically detect the existence of such a Lyapunov function and thus numerically check that a given algorithm converges. The novelty of this work is that we show how to define the saddle point problem using the PEPit

Asuka Shiga

Let $E/\mathbb{Q}$ be an elliptic curve. We study the behavior of the Tate--Shafarevich group of $E$ under quadratic extensions $\mathbb{Q}(\sqrt{D})/\mathbb{Q}$. By analyzing the cokernel of the restriction map, without assuming the finiteness of the Tate--Shafarevich group, we prove that the ratio $\frac{\#\Sha(E/\mathbb{Q}(\sqrt{D}))[4]}{\#\Sha(E_D/\mathb

Christophe Profeta

In this note, we are interested in the probability that two independent squared Bessel processes do not cross for a long time. We show that this probability has a power decay which is given by the first zero of some hypergeometric function. We also compute along the way the distribution of the location where the crossing eventually occurs.

Clément Cosserat, Ben Gabrielson, Emilie Chouzenoux, Jean-Christophe Pesquet

Independent vector analysis (IVA) is an attractive solution to address the problem of joint blind source separation (JBSS), that is, the simultaneous extraction of latent sources from several datasets implicitly sharing some information. Among IVA approaches, we focus here on the celebrated IVA-G model, that describes observed data through the mixing of inde

Xiuyuan Guo, Chengqi Xu, Guinan Guo, Feiyu Zhu

Currently, training large-scale deep learning models is typically achieved through parallel training across multiple GPUs. However, due to the inherent communication overhead and synchronization delays in traditional model parallelism methods, seamless parallel training cannot be achieved, which, to some extent, affects overall training efficiency. To addres

Yu. A. Mammadov, H. I. Ahmadov

In this study, we investigate a mixed problem linked to a second-order parabolic equation, characterized by temporal dependencies and variable~coefficients, and constrained by non-local, non-self-adjoint boundary conditions. By defining precise conditions on the input data, we establish the unique solvability of the problem through a synthesis of the residue

Sabine Meunier, Dominique Habault, Emmanuel Friot, Philippe Lasaygues

The Laboratory of Mechanics and Acoustics in Marseilles (France) was created in 1941, under the name of Centre de Recherches Scientifiques, Industrielles et Maritimes (CRSIM). But it was actually issued from the French Naval Research Center created in Toulon by the French Navy to work on submarine detection during World War I. LMA is therefore the result of

Pete Rigas

We implement the quantum inverse scattering method for the 4-vertex model. In comparison to previous works of the author which examined the 6-vertex, and 20-vertex, models, the 4-vertex model exhibits different characteristics, ranging from L-operators expressed in terms of projectors and Pauli matrices to algebraic and combinatorial properties, including Po

Xiaohe Li, Feilong Huang, Zide Fan, Fangli Mou

Trajectory prediction for multi-agents in complex scenarios is crucial for applications like autonomous driving. However, existing methods often overlook environmental biases, which leads to poor generalization. Additionally, hardware constraints limit the use of large-scale data across environments, and continual learning settings exacerbate the challenge o

Hervé Bulou

Healthcare materials, whether they are natural or synthetic, are complex structures made up of simpler materials. Because of their intricate structure, composite materials are ideal for prosthetics because it is possible to tune their structure to get mechanical properties that are compatible with bone, thus encouraging biointegration. To be effective, impla

Ambreen Talib, Rabbya Rayan Shah, Rameen Atique, Hafiza Arshi Saeed

Chikungunya virus (CHIKV) is one of the most relevant arboviruses affecting public health today. It belongs to the Togaviridae family and alphavirus genus, causing an arthritogenic disease known as Chikungunya fever (CHIKF). This multifaceted disease is distinguished from other arbovirus infections by intense arthralgia, which can persist for months or even

Shima Salar Hosseini, Paeiz Azmi, Ali Nazari

Unmanned aerial vehicles (UAVs) have the potential for time-sensitive applications. Due to wireless channel variation, received data may have an expiration time, particularly in critical situations such as rescue operations, natural disasters, or the military. Age of Information (AoI) is a metric that measures the freshness of received packets to specify the

Yunhee Euh, Sinhwi Kim, Yuri Nikolayevsky, JeongHyeong Park

A Riemannian manifold is called \emph{weakly Einstein} if the tensor $R_{iabc}R_{j}^{~~abc}$ is a scalar multiple of the metric tensor $g_{ij}$. We consider weakly Einstein Lie groups with a left-invariant metric which are weakly Einstein. We prove that there exist no weakly Einstein non-abelian $2$-step nilpotent Lie groups and no weakly Einstein non-abelia

Nozomu Masuya, Sho Sakaino, Toshiaki Tsuji

Conventional methods of imitation learning for variable-speed motion have difficulty extrapolating speeds because they rely on learning models running at a constant sampling frequency. This study proposes variable-frequency imitation learning (VFIL), a novel method for imitation learning with learning models trained to run at variable sampling frequencies al

Hao Li, Yuanyuan Gao, Haosong Peng, Chenming Wu

Novel-view synthesis (NVS) approaches play a critical role in vast scene reconstruction. However, these methods rely heavily on dense image inputs and prolonged training times, making them unsuitable where computational resources are limited. Additionally, few-shot methods often struggle with poor reconstruction quality in vast environments. This paper prese

Christel Grimaud, Dominique Longin, Andreas Herzig

We present the architecture of a fully autonomous, bio-inspired cognitive agent built around a spiking neural network (SNN) implementing the agent's semantic memory. This agent explores its universe and learns concepts of objects/situations and of its own actions in a one-shot manner. While object/situation concepts are unary, action concepts are triples mad

Junhua Liu, Yong Keat Tan, Bin Fu, Kwan Hui Lim

Accurate multi-turn intent classification is essential for advancing conversational AI systems. However, challenges such as the scarcity of comprehensive datasets and the complexity of contextual dependencies across dialogue turns hinder progress. This paper presents two novel approaches leveraging Large Language Models (LLMs) to enhance scalability and redu

Jie Shao, Hanxiao Zhang, Jianxin Wu

In this work, we explore the quantization of diffusion models in extreme compression regimes to reduce model size while maintaining performance. We begin by investigating classical vector quantization but find that diffusion models are particularly susceptible to quantization error, with the codebook size limiting generation quality. To address this, we intr

R. Drebotiy, H. Shynkarenko

We provide the proof of convergence of the directional diffusion splitting scheme for two-dimensional parabolic and elliptic advection-diffusion-reaction problems with certain restrictions on problem data

Shuoling Liu, Gaoguo Jia, Yuhang Jiang, Liyuan Chen

Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been utilized in diverse fields, such as clinical research, where domain-specific models like Med-Palm have achieved human-level performance. Recently, researchers have employed advanced pr

Kazuya Horibe, Naoto Yoshida

We discuss the possibility of world models and active exploration as emergent properties of open-ended behavior optimization in autonomous agents. In discussing the source of the open-endedness of living things, we start from the perspective of biological systems as understood by the mechanistic approach of theoretical biology and artificial life. From this

Shamim Akhter, Kiyoshi Honda, Kento Aida, Amor V. M. Ines

Agriculture activity monitoring needs to deal with large amounts of data originating from various organizations (weather stations, agriculture repositories, field management, farm management, universities, etc.) and mass people. Therefore, a scalable environment with flexible information access, easy communication, and real-time collaboration from all types

Zhongling Huang, Long Liu, Shuxin Yang, Zhirui Wang

The disperse structure distributions (discreteness) and variant scattering characteristics (variability) of SAR airplane targets lead to special challenges of object detection and recognition. The current deep learning-based detectors encounter challenges in distinguishing fine-grained SAR airplanes against complex backgrounds. To address it, we propose a no

Ravi Kant, Sarthak Agarwal, Aakash Gupta, Abhishek Mishra

This article discusses the single Depot multiple Set Orienteering Problem (sDmSOP), a recently suggested generalization of the Set Orienteering Problem (SOP). This problem aims to discover a path for each traveler over a subset of vertices, where each vertex is associated with only one cluster, and the total profit made from the clusters visited is maximized

Ratanond Koonchanok, Khairi Reda

People often use visualizations not only to explore a dataset but also to draw generalizable conclusions about underlying models or phenomena. While previous research has viewed deviations from rational analysis as problematic, we hypothesize that human reliance on non-normative heuristics may be advantageous in certain situations. In this study, we investig

Weizhe Lin, Junxiao Shen

The rapid evolution of artificial intelligence, especially through multi-modal large language models, has redefined user interactions, enabling responses that are contextually rich and human-like. As AI becomes an integral part of daily life, a new frontier has emerged: developing systems that not only understand spatial and sensory data but also interpret t

Zhepu Yin, Weijun Gao, Chong Han

Terahertz (THz) band communication, ranging from 0.1 THz to 10 THz, is envisioned as a key enabling technology for next-generation networks and future applications such as inter-satellite communications and environmental sensing. The surging number of space debris in Low Earth Orbit poses a big threat to orbital infrastructure and the development of the spac

Yuanjing Zhang, Tao Shang, Kun Zhang, Chenyi Zhang

Quantum computing solutions are increasingly deployed in commercial environments through delegated computing, especially one of the most critical issues is to guarantee the confidentiality and proprietary of quantum implementations. Since the proposal of general-purpose indistinguishability obfuscation (iO) and functional encryption schemes, iO has emerged a

Weijun Gao, Chong Han

Terahertz (THz) band (0.1-10 THz) possesses multi-gigahertz continuous bandwidth resources, making it a promising frequency band for high-speed wireless communications and environment sensing. The interaction between the THz wave and the external environment has been studied for various scenarios. However, it has recently been revealed that the friction forc

Ivy Zhang, Robert Tibshirani

This paper proposes a sparse regression method that continuously interpolates between Forward Stepwise selection (FS) and the LASSO. When tuned appropriately, our solutions are much sparser than typical LASSO fits but, unlike FS fits, benefit from the stabilizing effect of shrinkage. Our method, Adaptive Forward Stepwise Regression (AFS) addresses this need

Alejandro Pardo, Jui-Hsien Wang, Bernard Ghanem, Josef Sivic

The objective of this work is to manipulate visual timelines (e.g. a video) through natural language instructions, making complex timeline editing tasks accessible to non-expert or potentially even disabled users. We call this task Instructed visual assembly. This task is challenging as it requires (i) identifying relevant visual content in the input timelin

Gerardo Flores, Mark W. Spong

This paper presents, for the first time, the soft planar vertical take-off and landing (Soft-PVTOL) aircraft. This concept captures the soft aerial vehicle's fundamental dynamics with a minimum number of states and inputs but retains the main features to consider when designing control laws. Unlike conventional PVTOL and multi-rotors, where altering position

Anupam Singh

The Standard Model of Elementary Particle Physics has global Family flavor symmetries corresponding to the 3 families in the Standard Model. It has been shown that the breaking of these symmetries at low energy produces Dark Energy which is the dominant component of the energy density of the Universe. It has also been shown that this model of Dark Energy not

Abolfazl Mohammadi Gorjaei, Mohammad Ali Nazari, Asghar Afshari, Saeed Farzad-Mohajeri

Introduction Speech is an integral component of human communication, requiring the coordinated efforts of various organs to produce sound (Titze & Alipour, 2006). The glottis region, a key player in voice production, assumes a crucial role in this intricate process. As air, emanating from the lungs in a confined space, interacts with the vocal folds (VFs) wi

Stefan Graser, Felix Kirschenlohr, Stephan Böhm

Due to technological development, Augmented Reality (AR) can be applied in different domains. However, innovative technologies refer to new interaction paradigms, thus creating a new experience for the user. This so-called User Experience (UX) is essential for developing and designing interactive products. Moreover, UX must be measured to get insights into t

Haowen Zheng, Yanyan Liang

Recent advancements in 3D diffusion-based semantic scene generation have gained attention. However, existing methods rely on unconditional generation and require multiple resampling steps when editing scenes, which significantly limits their controllability and flexibility. To this end, we propose SSEditor, a controllable Semantic Scene Editor that can gener

Stefan Graser, Anastasia Snimshchikova, Martin Schrepp, Stephan Böhm

User Experience (UX) Research covers various methods for gathering the users' subjective impressions of a product. For this, practitioners face different activities and tasks related to the research process. This includes processing a large amount of data based on qualitative and quantitative data. However, this can be very laborious in practice. Thus, the a

Haixiao Gao, Mengying Sun, Xiaodong Xu, Bingxuan Xu

In this paper, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose

Stefan Graser, Martin Schrepp, Stephan Böhm

Measuring User Experience (UX) with questionnaires is essential for developing and improving products. However, no domain-specific standardized UX questionnaire exists for Augmented Reality (AR) in Corporate Training (CT). Thus, this study introduces the UXAR-CT questionnaire - an AR-specific UX questionnaire for CT environments. We describe the construction

Dongyoung Go, Taesun Whang, Chanhee Lee, Hwa-Yeon Kim

The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language Models (MLLMs) has revolutionized information retrieval and expanded the practical applications of AI. However, current systems struggle in accurately interpreting user intent, employing diverse retrieval strategies, and effectively filtering unintended or inappropriate re

Teli Ma, Zifan Wang, Jiaming Zhou, Mengmeng Wang

Inferring affordable (i.e., graspable) parts of arbitrary objects based on human specifications is essential for robots advancing toward open-vocabulary manipulation. Current grasp planners, however, are hindered by limited vision-language comprehension and time-consuming 3D radiance modeling, restricting real-time, open-vocabulary interactions with objects.

Tomona Kinugawa, Tetsuo Hyodo

Recent observations of exotic hadrons have stimulated the theoretical investigation of the internal structure of hadrons. While all hadrons are ultimately composed of quarks and gluons bound by the strong interaction, quark clustering phenomena can generate hadronic molecules -- weakly bound systems of hadrons -- which are expected to emerge near two-hadron

Sunday Amatare, Gaurav Singh, Raul Shakya, Aavash Kharel

Autonomous system navigation is a well-researched and evolving field. Recent advancements in improving robot navigation have sparked increased interest among researchers and practitioners, especially in the use of sensing data. However, this heightened focus has also raised significant privacy concerns, particularly for robots that rely on cameras and LiDAR

Qian-Hao Guo, Yang Zhang, Xiao-Huan Wan, Li-Yang Zheng

Here, we propose an isospectral reduction (IR) approach for the mapping of a trimer Su-Schrieffer-Heeger (SSH3) lattice into a simplified two-site model, whose coupling dynamics ingeniously results in a precise bulk-edge correspondence of the original lattice. The isospectrally-reduced model has inter-cell couplings with dynamic response to the eigenstate en

Myeong-Hwan Mun, Eunja Ha, Myung-Ki Cheoun, Yusuke Tanimura

We study the evolution of the total binding energy (TBE) and pairing energy of Pb, Hg and Ar isotopes, as a function of the nuclear deformation. As for the nuclear model, we exploit a deformed relativistic Hartree-Bogoliubov theory in the continuum (DRHBc), and a deformed Skyrme Hartree-Fock plus BCS model. It is found that the dependence of pairing energy o

Dennis Bonatsos, Andriana Martinou, S. K. Peroulis, D. Petrellis

The proxy-SU(3) symmetry predicts, in a parameter-free way, based only on the Pauli principle and the short-range nature of the nucleon-nucleon interaction, non-vanishing values of the collective variable gamma almost everywhere across the nuclear chart. Substantial triaxiality with gamma between 15 and 45 degrees is proved to be expected along horizontal an

Siyu Liu, Tongqi Wen, Beilin Ye, Zhuoyuan Li

Efficient and accurate prediction of material properties is critical for advancing materials design and applications. The rapid-evolution of large language models (LLMs) presents a new opportunity for material property predictions, complementing experimental measurements and multi-scale computational methods. We focus on predicting the elastic constant tenso

Ziyang Zong, Guanying Chen, Zhaohuan Zhan, Fengcheng Yu

This paper proposes a two-stage text-to-floorplan generation framework that combines the reasoning capability of Large Language Models (LLMs) with the generative power of diffusion models. In the first stage, we leverage a Chain-of-Thought (CoT) prompting strategy to guide an LLM in generating an initial layout (Layout-Init) from natural language description

Zheng Gong, Zhuo Deng, Weihao Gao, Wenda Zhou

Cataract is one of the most common blinding eye diseases and can be treated by surgery. However, because cataract patients may also suffer from other blinding eye diseases, ophthalmologists must diagnose them before surgery. The cloudy lens of cataract patients forms a hazy degeneration in the fundus images, making it challenging to observe the patient's fun

Yan Sun, Yeping Wang, Zhaohui Li, Shihao Yang

The capture of changes in dynamic systems, especially ordinary differential equations (ODEs), is an important and challenging task, with multiple applications in biomedical research and other scientific areas. This article proposes a fast and mathematically rigorous online method, called ODE-informed MAnifold-constrained Gaussian process Inference for Change

Nai-Xuan Ye, Tan-Ha Mai, Hsiu-Hsuan Wang, Wei-I Lin

Complementary-label learning (CLL) is a weakly supervised learning paradigm for multiclass classification, where only complementary labels -- indicating classes an instance does not belong to -- are provided to the learning algorithm. Despite CLL's increasing popularity, previous studies highlight two main challenges: (1) inconsistent results arising from va

Huzaifa Sidhpurwala, Garth Mollett, Emily Fox, Mark Bestavros

This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their potential risks and vulnerabilities is crucial. We review the current security and safety scenarios while highlighting challenges such as tra

Mohammad Loni, Fatemeh Poursalim, Mehdi Asadi, Arash Gharehbaghi

This paper presents the results of a novel scoping review on the practical models for generating three different types of synthetic health records (SHRs): medical text, time series, and longitudinal data. The innovative aspects of the review, which incorporate study objectives, data modality, and research methodology of the reviewed studies, uncover the impo

Zheng Gong, Zhuo Deng, Run Gan, Zhiyuan Niu

The retinal fundus images are utilized extensively in the diagnosis, and their quality can directly affect the diagnosis results. However, due to the insufficient dataset and algorithm application, current fundus image quality assessment (FIQA) methods are not powerful enough to meet ophthalmologists` demands. In this paper, we address the limitations of dat

Hidekazu Yoshioka

Stochastic processes with long memories, known as long memory processes, are ubiquitous in various science and engineering problems. Superposing Markovian stochastic processes generates a non-Markovian long memory process serving as powerful tools in several research fields, including physics, mathematical economics, and environmental engineering. We formula

Bohan Li, Dawei Li, Ming Fu, Shaowei Cai

Leveraging the flexible expressive ability of (Max)SMT and the powerful solving ability of SMT solvers, we propose a novel layout model named SMT-Layout. SMT-Layout is the first constraint-based layout model that can support real-time interaction for real-world GUI layout adapting to various screen sizes with only one specification. Previous works neglect th

Maheswar Bora, Saurabh Atreya, Aritra Mukherjee, Abhijit Das

In this work, we attempted to extend the thought and showcase a way forward for the Self-supervised Learning (SSL) learning paradigm by combining contrastive learning, self-distillation (knowledge distillation) and masked data modelling, the three major SSL frameworks, to learn a joint and coordinated representation. The proposed technique of SSL learns by t

A. Murphy, E. T. Whelan, F. Bacciotti, D. Coffey

Characterising stellar jet asymmetries is key to setting robust constraints on jet launching models and improving our understanding of the underlying mechanisms behind jet launching. We aim to characterise the asymmetric properties of the bipolar jet coming from the Classical T Tauri Star Th 28. We combined data from integral field spectroscopy with VLT/MUSE

Hui-Peng Du, Ye-Xin Lu, Yang Ai, Zhen-Hua Ling

This paper proposes a novel neural denoising vocoder that can generate clean speech waveforms from noisy mel-spectrograms. The proposed neural denoising vocoder consists of two components, i.e., a spectrum predictor and a enhancement module. The spectrum predictor first predicts the noisy amplitude and phase spectra from the input noisy mel-spectrogram, and

Vincent Laheurte

We consider the one-dimensional Burgers' equation linearized at a stationary shock, and investigate its null-controllability cost with a control at the left endpoint. We give an upper and a lower bound on the control time required for this cost to remain bounded in the vanishing viscosity limit, and construct an admissible control with an explicit limit beha

Aditi Sengupta, Abhijeet Guha

Natural laminar flow airfoils are essential technologies designed to reduce drag and significantly enhance aerodynamic performance. A notable example is the SHM1 airfoil, created to meet the requirements of the small-business Honda jet. This airfoil has undergone extensive testing across various operational conditions, including low-speed wind tunnel tests a

Gianluca Cena, Gabriele Formis, Matteo Rosani, Stefano Scanzio

The radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in next-generation Wi-Fi 8. All these appr

Wenyu Guo, Xuan Liu, Ronggang Shi

The aim of this paper is to study the product of $n$ linear forms over function fields. We calculate the maximum value of the minima of the forms with determinant one when $n$ is small. The value is equal to the natural bound given by algebraic number theory. Our proof is based on a reduction theory of diagonal group orbits on homogeneous spaces. We also sho

Jixuan Ruan, Xiang Fang, Hezi Zhang, Ang Li

Neutral atom-based quantum computers (NAQCs) have recently emerged as promising candidates for scalable quantum computing, largely due to their advanced hardware capabilities, particularly qubit movement and the zoned architecture (ZA). However, fully leveraging these features poses significant compiler challenges, as it requires addressing complexities acro

Raphael Merx, Adérito José Guterres Correia, Hanna Suominen, Ekaterina Vylomova

Low-resource machine translation (MT) presents a diversity of community needs and application challenges that remain poorly understood. To complement surveys and focus groups, which tend to rely on small samples of respondents, we propose an observational study on actual usage patterns of tetun$.$org, a specialized MT service for the Tetun language, which is

Luke Myers, Nigel Hew, Shun-Li Shang, Zi-Kui Liu

The magnetic ground-state configuration of iron selenide FeSe has been a topic of debate, with experimental evidence suggesting the stripe spin fluctuations as predominant at low temperatures, while density functional theory (DFT) calculations using exchange-correlation (XC) functional of the Generalized Gradient Approximation (GGA) have historically predict

Na Wang, Zhong Shen, Wang Luo, Hua-Kai Li

Noncollinear dipole textures greatly extend the scientific merits and application perspective of ferroic materials. In fact, noncollinear spin textures have been well recognized as one of the core issues of condensed matter, e.g. cycloidal/conical magnets with multiferroicity and magnetic skyrmions with topological properties. However, the counterparts in el

Baoquan Zhang, Shanshan Feng, Bingqi Shan, Xutao Li

Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then performing class prediction via a cosine classifier with mean-based prototypes. Nevertheless, due to the data scarcity, the mean-based prototypes are usu

Madhurima Panja, Tanujit Chakraborty, Anubhab Biswas, Soudeep Deb

Modeling and forecasting air quality is crucial for effective air pollution management and protecting public health. Air quality data, characterized by nonlinearity, nonstationarity, and spatiotemporal correlations, often include extreme pollutant levels in severely polluted cities (e.g., Delhi, the capital of India). This is ignored by various geometric dee

J. D. Soler, S. Molinari, S. C. O. Glover, R. J. Smith

We present a reconstruction of the line-of-sight motions of the local interstellar medium (ISM) based on the combination of a model of the three-dimensional dust density distribution within 1.25 kpc from the Sun and the HI and CO line emission within Galactic latitudes $|b| < 5^{\circ}$. We used the histogram of oriented gradient (HOG) method, a computer vis

Ding Ning, Varvara Vetrova, Yun Sing Koh, Karin R. Bryan

Marine heatwaves (MHWs), an extreme climate phenomenon, pose significant challenges to marine ecosystems and industries, with their frequency and intensity increasing due to climate change. This study introduces an integrated deep learning approach to forecast short-to-long-term MHWs on a global scale. The approach combines graph representation for modeling

Honghua Zhang, Benjie Wang, Marcelo Arenas, Guy Van den Broeck

Probabilistic circuits (PCs) are a unifying representation for probabilistic models that support tractable inference. Numerous applications of PCs like controllable text generation depend on the ability to efficiently multiply two circuits. Existing multiplication algorithms require that the circuits respect the same structure, i.e. variable scopes decompose

Hiroshi Sato, Masashi Konosu, Sho Sakaino, Toshiaki Tsuji

In recent years, imitation learning using neural networks has enabled robots to perform flexible tasks. However, since neural networks operate in a feedforward structure, they do not possess a mechanism to compensate for output errors. To address this limitation, we developed a feedback mechanism to correct these errors. By employing a hierarchical structure

Daeyong Kwon, SeungHeon Doh, Juhan Nam

Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains, it has not received much attention in the music domain. In this paper, we investigate intent classification models for music discovery conversation, focusing on pre-trained languag

Xiang-kun Shao, Xue-song Li, Nan-jing Huang, Donal O'Regan

This paper investigates the initial boundary value problem of a finitely degenerate semilinear pseudo-parabolic equation associated with H\"{o}rmander's operator. Based on the global existence of solutions in previous literature, the exponential decay estimate of the energy functional is obtained. Moreover, by developing some novel estimates about solutions

Shiba Biswas, P. S. Burada, G. P. Raja Sekhar

We investigate the low Reynolds number hydrodynamics of a spherical swimmer with a predominantly hydrophobic surface, except for a hydrophilic active patch. This active patch covers a portion of the surface and exhibits chiral activity that varies as a function of $\theta$ and $\phi$. Our study considers two types of active patches: (i) a symmetric active pa

César Galindo, Simon Lentner, Sven Möller

We explicitly construct nondegenerate braided $\mathbb{Z}_2$-crossed tensor categories of the form $\operatorname{Vect}_Γ\oplus\operatorname{Vect}_{Γ/2Γ}$. They are $\mathbb{Z}_2$-crossed extensions, in the sense of arXiv:0909.3140, of the braided tensor category $\operatorname{Vect}_Γ$ with $\mathbb{Z}_2$-action given by $-\mathrm{id}$ on the finite, abelia

Xiao Jiang, Fei Zhou, Jiongzhi Lin

Event cameras operate fundamentally differently from traditional Active Pixel Sensor (APS) cameras, offering significant advantages. Recent research has developed simulators to convert video frames into events, addressing the shortage of real event datasets. Current simulators primarily focus on the logical behavior of event cameras. However, the fundamental

Ziyang Gao, Emmanuel Ullmo

In this paper, we prove the following result advocating the importance of monomial quadratic relations between holomorphic CM periods. For any simple CM abelian variety $A$, we can construct a CM abelian variety $B$ such that all non-trivial Hodge relations between the holomorphic periods of the product $A\times B$ are generated by monomial quadratic ones wh

Zhanqiang Guo, Jiamin Wu, Yonghao Song, Jiahui Bu

Human's perception of the visual world is shaped by the stereo processing of 3D information. Understanding how the brain perceives and processes 3D visual stimuli in the real world has been a longstanding endeavor in neuroscience. Towards this goal, we introduce a new neuroscience task: decoding 3D visual perception from EEG signals, a neuroimaging technique

XueGuang Zhang

In this manuscript, an oversimplified model is proposed for the first time to explain the different variability trends in the observed broad H$\alpha$ emission line luminosity $L_{H\alpha}(t)$ and in the TDE model determined bolometric luminosity $L_{bol}(t)$ in the known TDE ASASSN-14li. Considering broad emission line regions (BLRs) lying into central accr

David Ge, Hao Ji

Self-organizing systems consist of autonomous agents that can perform complex tasks and adapt to dynamic environments without a central controller. Prior research often relies on reinforcement learning to enable agents to gain the skills needed for task completion, such as in the box-pushing environment. However, when agents push from opposing directions dur

Yasaman Saadati, M. Hadi Amini

Federated Learning (FL) is a decentralized learning approach that protects sensitive information by utilizing local model parameters rather than sharing clients' raw datasets. While this privacy-preserving method is widely employed across various applications, it still requires significant development and optimization. Automated Machine Learning (Auto-ML) ha

Tianyu Zhan, Zheqi Lv, Shengyu Zhang, Jiwei Li

This paper explores the application and effectiveness of Test-Time Training (TTT) layers in improving the performance of recommendation systems. We developed a model, TTT4Rec, utilizing TTT-Linear as the feature extraction layer. Our tests across multiple datasets indicate that TTT4Rec, as a base model, performs comparably or even surpasses other baseline mo

Jungbae Yoon, Jugyeong Jeong, Hyunjun Jang, Jinsu Jung

We experimentally demonstrate magnetic steganography using wide field quantum microscopy based on diamond nitrogen vacancy centers. The method offers magnetic imaging capable of revealing concealed information otherwise invisible with conventional optical measurements. For a proof of principle demonstration of the magnetic steganography, micrometer structure

Aditi Sengupta

The wake-induced transition on the suction surface of a T106A low-pressure turbine (LPT) blade is investigated through a series of implicit large eddy simulations, solving the two-dimensional (2D) compressible Navier-Stokes equations (NSE). The impact of the incoming Gaussian wake amplitude on the blade's profile loss and associated boundary layer parameters

Eric M. Osterkamp, Dominik Köppl

Cartesian tree matching is a form of generalized pattern matching where a substring of the text matches with the pattern if they share the same Cartesian tree. This form of matching finds application for time series of stock prices and can be of interest for melody matching between musical scores. For the indexing problem, the state-of-the-art data structure

S. Tamang, D. J. Bora

Large Language Models (LLMs) based on transformer architectures have revolutionized a variety of domains, with tokenization playing a pivotal role in their pre-processing and fine-tuning stages. In multilingual models, particularly those tailored for Indic languages, effective tokenization is crucial for optimizing performance. This paper presents a comprehe

Anusree Rajan, Kushagra Parmeshwar, Pavankumar Tallapragada

This paper proposes an event-triggered parameterized control method using a control Lyapunov function approach for discrete time linear systems with external disturbances. In this control method, each control input to the plant is a linear combination of a fixed set of linearly independent scalar functions. The controller updates the coefficients of the para

Alan B McMillan

Background: Advances in artificial intelligence, particularly large language models (LLMs), have the potential to enhance technical expertise in magnetic resonance imaging (MRI), regardless of operator skill or geographic location. Methods: We assessed the accuracy of several LLMs in answering 570 technical MRI questions derived from a standardized review bo

Xia Wang, Donglei Yang, Fan Yang, Haotian Yang

In the paper, we focus on embedding clique immersions and subdivisions within sparse expanders, and we derive the following main results: (1) For any $0< \eta< 1/2$, there exists $K>0$ such that for sufficiently large $n$, every $(n,d,\lambda)$-graph $G$ contains a $K_{(1-5\eta)d}$-immersion when $d\geq K\lambda$. (2) For any $\varepsilon>0$ and $0<\eta <1/2

Yun-Chih Liao, Ben J. Powell, Thomas M. Stace

Superconducting circuit quantisation conventionally starts from classical Euler-Lagrange circuit equations-of-motion. Invoking the correspondence principle yields a canonically quantised circuit description of circuit dynamics over a bosonic Hilbert space. This process has been very successful for describing experiments, but implicitly starts from the classi

Zongmeng Zhang, Jinhua Zhu, Wengang Zhou, Xiang Qi

Dense retrieval, which aims to encode the semantic information of arbitrary text into dense vector representations or embeddings, has emerged as an effective and efficient paradigm for text retrieval, consequently becoming an essential component in various natural language processing systems. These systems typically focus on optimizing the embedding space by

Fumiya Okazaki, Takayuki Yamada

In this paper, we apply the framework of optimal transport to the formulation of optimal design problems. By considering the Wasserstein space as a set of design variables, we associate each probability measure with a shape configuration of a material in some ways. In particular, we focus on connections between differentials on the Wasserstein space and sens

Samuel Lai, Nithyanandan Thyagarajan, O. Ivy Wong, Foivos Diakogiannis

Interferometric closure invariants, constructed from triangular loops of mixed Fourier components, capture calibration-independent information on source morphology. While a complete set of closure invariants is directly obtainable from measured visibilities, the inverse transformation from closure invariants to the source intensity distribution is not establ

Zongrong Li, Junhao Xu, Siqin Wang, Yifan Wu

Geospatial predictions are crucial for diverse fields such as disaster management, urban planning, and public health. Traditional machine learning methods often face limitations when handling unstructured or multi-modal data like street view imagery. To address these challenges, we propose StreetViewLLM, a novel framework that integrates a large language mod

Yuhui Chen, Michael C. Dallaston

We consider a two-component reaction-diffusion system that has previously been developed to model invasion of cells into a resident cell population. The system is an idealised version of models of tumour growth in which tumour cells degrade the surrounding tissue by increasing the acidity of the local environment. By numerically computing families of travell

Kaibao Fan, Mengzhu Shi, Houpu Li, Ziji Xiang

Transition metal dichalcogenides exhibit many unexpected properties including two-dimensional (2D) superconductivity as the interlayer coupling being weakened upon either layer-number reduction or chemical intercalation. Here we report the realization of 2D superconductivity in the newly-synthesized niobium dichalcogenides-based bulk superlattices Ba$_{0.75}

Junghoon Kim, Junmo Lee, Yeonjun In, Kanghoon Yoon

Social graph-based fake news detection aims to identify news articles containing false information by utilizing social contexts, e.g., user information, tweets and comments. However, conventional methods are evaluated under less realistic scenarios, where the model has access to future knowledge on article-related and context-related data during training. In