March 2024 arXiv papers — page 28

Xusheng Cao, Haori Lu, Linlan Huang, Xialei Liu

In class-incremental learning (CIL) scenarios, the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models, we would explore replacing discriminativ

Peng-Jie Wong

In this article, we aim to establish a prototype result regarding lower bounds of (joint) distributions of central $L'$-values through extending a method of Radziwill and Soundararajan of proving conditional bounds for distributions of central $L$-values (via the one-level density of low-lying zeros of involving $L$-functions). To illustrate this, we give se

Dongfang Li, Zetian Sun, Baotian Hu, Zhenyu Liu

Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content. Recent works aim to reduce misinformation and hallucinations by resorting to attribution as a means to provide evidence (i.e., citations). However, current attribution methods usually focus on the retrieval stage and au

Abhijit Chatterjee

In multicomponent lattice problems, e.g., in alloys, and at crystalline surfaces and interfaces, atomic arrangements exhibit spatial correlations that dictate the kinetic and thermodynamic phase behavior. These correlations emerge from interparticle interactions and are frequently reported in terms of the short-range order (SRO) parameter. Expressed usually

Guangzai Ye, Li Feng, Jianlan Guo, Yuqiang Chen

Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for maintaining the safe and stable operation of rechargeable battery management systems. However, this task is often challenging due to the complex temporal dynamics involved. Recently, attention-based networks, such as Transformers and Informer, have been the popular

Victorita Dolean, Mark Fry, Matthias Langer

Wave propagation problems governed by the Helmholtz equation remain among the most challenging in scientific computing, due to their indefinite nature. Domain decomposition methods with spectral coarse spaces have emerged as some of the most effective preconditioners, yet their theoretical guarantees often lag behind practical performance. In this work, we i

Myeong-Hwan Mun, Eunja Ha, Yong-Beom Choi, Myung-Ki Cheoun

Recent experiments using advanced laser spectroscopy technique revealed that the charge radii of neutron-deficient gold (Au) isotopes exhibit significant changes in ground state deformation: odd-even shape staggering in the $N = 98 \sim 100$ region and abrupt change of charge radii from $N =$ 108. In this study, we examine the abnormal shape evolution of the

Carlos J. Pérez-del-Pulgar, Antonio López-Palomeque, Jesús Juli, Matteo Madi

The use of cubesat swarms is being proposed for different missions where cooperation between satellites is required. Commonly, the cube swarm requires formation flight and even rendezvous and docking, which are very challenging tasks since they required more energy and the use of advanced guidance, navigation and control techniques. In this paper, we propose

Natalie Lang, Alejandro Cohen, Nir Shlezinger

Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive

Marius Meyer, Tobias Kenter, Lucian Petrica, Kenneth O'Brien

Most FPGA boards in the HPC domain are well-suited for parallel scaling because of the direct integration of versatile and high-throughput network ports. However, the utilization of their network capabilities is often challenging and error-prone because the whole network stack and communication patterns have to be implemented and managed on the FPGAs. Also,

Changshun Wu, Weicheng He, Chih-Hong Cheng, Xiaowei Huang

Out-of-distribution (OoD) detection techniques for deep neural networks (DNNs) become crucial thanks to their filtering of abnormal inputs, especially when DNNs are used in safety-critical applications and interact with an open and dynamic environment. Nevertheless, integrating OoD detection into state-of-the-art (SOTA) object detection DNNs poses significan

Ryo Takahashi, Tomoki Ozawa

In this study, we discuss a new type of bulk-boundary correspondence which holds for topological insulators and superconductors when the parity-time ($PT$) and/or parity-particle-hole ($PC$) symmetry are present. In these systems, even when the bulk topology is nontrivial, the edge spectrum is generally gapped, and thus the conventional bulk-boundary corresp

Victor Daniel Reyes Dreke, Ygor Pereira Marca, Maurice Roes, Mircea Lazar

Well-designed current control is a key factor in ensuring the efficient and safe operation of modular multilevel converters (MMCs). Even though this control problem involves multiple control objectives, conventional current control schemes are comprised of independently designed decoupled controllers, e.g., proportional-integral (PI) or proportional-resonant

Luigi Sigillo, Riccardo Fosco Gramaccioni, Alessandro Nicolosi, Danilo Comminiello

In recent years, remarkable advancements have been achieved in the field of image generation, primarily driven by the escalating demand for high-quality outcomes across various image generation subtasks, such as inpainting, denoising, and super resolution. A major effort is devoted to exploring the application of super-resolution techniques to enhance the qu

Y. Navidtehrani, R. Duddu, E. Martínez-Pañeda

In this work, we describe our contribution to the Purdue-SANDIA-LLNL \emph{Damage Mechanics Challenge}. The phase field fracture model is adopted to blindly estimate the failure characteristics of the challenge test, an unconventional three-point bending experiment on an additively manufactured rock resembling a type of gypsum. The model is formulated in a v

Eyal Neuman, Sturmius Tuschmann

We generalize the characterization theorem going back to Mercer and Young, which states that a symmetric and continuous kernel is positive definite if and only if it is integrally positive definite, to matrix-valued kernels on separable metric spaces. We also demonstrate the applications of the generalized theorem to the field of convex optimization and othe

Florian Freye, Jie Lou, Christian Lanius, Tobias Gemmeke

Vector-matrix-multiplication (VMM) accel-erators have gained a lot of traction, especially due to therise of convolutional neural networks (CNNs) and the desireto compute them on the edge. Besides the classical digitalapproach, analog computing has gone through a renais-sance to push energy efficiency further. A more recent ap-proach is called time-domain (T

V. S. Beskin, T. I. Khalilov, E. E. Nokhrina, I. N. Pashchenko

At present, there is no doubt that relativistic jets observed in active galactic nuclei pass from highly magnetized to weakly magnetized stage, which is observed as a break in the dependence on their width $d_{\rm jet}(z)$ on the distance $z$ to the central engine. In this paper, we discuss the possibility of observing another break, which should be located

Haitao Li, Qingyao Ai, Jia Chen, Qian Dong

Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc. To address this issue, previous approaches either conduct continuous

Salwa Mostafa, Mateus P. Mota, Alvaro Valcarce, Mehdi Bennis

We investigate the problem of supporting Industrial Internet of Things user equipment (IIoT UEs) with intent (i.e., requested quality of service (QoS)) and random traffic arrival. A deep reinforcement learning (DRL) based centralized dynamic scheduler for time-frequency resources is proposed to learn how to schedule the available communication resources amon

Julia Sudhoff Santos, Lars Kroll

The safety of streets is difficult to quantify numerically. However, it is possible to sort streets regarding their safety into ordered categories, like safe, neutral and unsafe. In this paper we model the computation of safe bicycle routes as an optimization problem with ordinal coefficients. We describe an appropriate optimality concept for ordinal optimiz

Omar Bettinotti, Stéphane Guinard, Eric Véron, Pierre Gosselet

This paper presents results and convergence study of the Global--Local Iterative Coupling through the implementation in the commercial software Abaqus making use of the co-simulation engine. A hierarchical modeling and simulation approach is often required to alleviate modeling burdens. Particular focus has been devoted here on convergence acceleration and p

Khaled Hariz, Fernando Jiménez, Sina Ober-Blöbaum

Fractional dissipation is a powerful tool to study non-local physical phenomena such as damping models. The design of geometric, in particular, variational integrators for the numerical simulation of such systems relies on a variational formulation of the model. In [19], a new approach is proposed to deal with dissipative systems including fractionally dampe

Louise Robert, Quentin Luzurier, Anaïs Velcker, Emma Mathieu

ERIOS, is a collaborative project between Dedalus, a health software company, Montpellier University Hospital Center (CHU), and the University of Montpellier. This initiative aims to incorporate research and development (R\&D) directly within the hospital, focusing on co-creating components of the Electronic Health Record (EHR) alongside end-users. The proje

Qihang Fan, Quanzeng You, Xiaotian Han, Yongfei Liu

This paper tackles a significant challenge faced by Vision Transformers (ViTs): their constrained scalability across different image resolutions. Typically, ViTs experience a performance decline when processing resolutions different from those seen during training. Our work introduces two key innovations to address this issue. Firstly, we propose a novel mod

Ba Hung Ngo, Nhat-Tuong Do-Tran, Tuan-Ngoc Nguyen, Hae-Gon Jeon

Most domain adaptation (DA) methods are based on either a convolutional neural networks (CNNs) or a vision transformers (ViTs). They align the distribution differences between domains as encoders without considering their unique characteristics. For instance, ViT excels in accuracy due to its superior ability to capture global representations, while CNN has

Indu K. Dihingia, Yosuke Mizuno

An essential factor in determining the flow characteristics of an accretion flow is its angular momentum. According to the angular momentum of the flow, semi-analytical analysis suggests various types of accretion solutions. It is critical to test it with numerical simulations using the most advanced framework available (general relativistic magnetohydrodyna

Sangwoo Jung, Hyesu Jang, Minwoo Jung, Ayoung Kim

The integration of sensor data is crucial in the field of robotics to take full advantage of the various sensors employed. One critical aspect of this integration is determining the extrinsic calibration parameters, such as the relative transformation, between each sensor. The use of data fusion between complementary sensors, such as radar and LiDAR, can pro

Mélisande Albert, Juliette Chevallier, Béatrice Laurent, Ousmane Sacko

We consider the problem of nonparametric density estimation under privacy constraints in an adversarial framework. To this end, we study minimax rates over Sobolev spaces under local differential privacy. We first obtain a lower bound which allows us to quantify the impact of privacy compared with the classical framework. Next, we introduce a new Coordinate

Keyu Wu, Lingchen Yang, Zhiyi Kuang, Yao Feng

Undoubtedly, high-fidelity 3D hair is crucial for achieving realism, artistic expression, and immersion in computer graphics. While existing 3D hair modeling methods have achieved impressive performance, the challenge of achieving high-quality hair reconstruction persists: they either require strict capture conditions, making practical applications difficult

Mitja Briscik, Gabriele Tazza, Marie-Agnes Dillies, László Vidács

Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite bein

Zheng Sun, Ting Li, Quan Wang, Shangbin Yang

Aims. Solar active regions (ARs), which are formed by flux emergence, serve as the primary sources of solar eruptions. However, the specific physical mechanism that governs the emergence process and its relationship with flare productivity remains to be thoroughly understood. Methods. We examined 136 emerging ARs, focusing on the evolution of their magnetic

Maia Tienstra, Sebastian Reich

Bayesian linear inverse problems aim to recover an unknown signal from noisy observations, incorporating prior knowledge. This paper analyses a data-dependent method to choose the scale parameter of a Gaussian prior. The method we study arises from early stopping methods, which have been successfully applied to a range of problems, such as statistical invers

Caroline Gora, Ana Dudas, Océane Vaugrente, Lucile Drobecq

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social interaction, communication, as well as restrained or stereotyped behaviors. The inherent heterogeneity within the autism spectrum poses challenges for developing effective pharmacological treatments targeting core features. Successful clinical tria

Mikolaj Cieslak, Umabharathi Govindarajan, Alejandro Garcia, Anuradha Chandrashekar

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and enviro

Ali Mahboub, Muhy Eddin Za'ter, Bashar Al-Rfooh, Yazan Estaitia

The latest advancements in machine learning and deep learning have brought forth the concept of semantic similarity, which has proven immensely beneficial in multiple applications and has largely replaced keyword search. However, evaluating semantic similarity and conducting searches for a specific query across various documents continue to be a complicated

Hongshen Xu, Zichen Zhu, Situo Zhang, Da Ma

Large Language Models (LLMs) often generate erroneous outputs, known as hallucinations, due to their limitations in discerning questions beyond their knowledge scope. While addressing hallucination has been a focal point in research, previous efforts primarily concentrate on enhancing correctness without giving due consideration to the significance of reject

Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai

Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items. Recent relation-aware sequential recommendation models have achieved promising performance by explicitly inco

Sanmoy Bandyopadhyay, Suman Kundu

The detection and analysis of the solar coronal holes (CHs) is an important field of study in the domain of solar physics. Mainly, it is required for the proper prediction of the geomagnetic storms which directly or indirectly affect various space and ground-based systems. For the detection of CHs till date, the solar scientist depends on manual hand-drawn a

Meiqi Chen, Yixin Cao, Yan Zhang, Chaochao Lu

Recent advancements in Large Language Models (LLMs) have facilitated the development of Multimodal LLMs (MLLMs). Despite their impressive capabilities, MLLMs often suffer from over-reliance on unimodal biases (e.g., language bias and vision bias), leading to incorrect answers or hallucinations in complex multimodal tasks. To investigate this issue, we propos

Klaus Hulek, Shigeyuki Kondo, Yota Maeda

All arithmetic non-compact ball quotients by Deligne-Mostow's unitary monodromy group arise as sub-ball quotients of either of two spaces called ancestral cases, corresponding to Gaussian or Eisenstein Hermitian forms respectively. In a previous paper, we investigated the compactifications of the Gaussian Deligne-Mostow variety. Here we work on the remaining

Mingxing Peng, Xusen Guo, Xianda Chen, Meixin Zhu

To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretabi

Johannes Emmert, Ronald Mendez, Houman Mirzaalian Dastjerdi, Christopher Syben

Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further, the increasing use of data-driven AI-methods in process models and industrial sensory often requires regular fine-tuning t

Yuekun Dai, Shangchen Zhou, Qinyue Li, Chongyi Li

Colorizing line art is a pivotal task in the production of hand-drawn cel animation. This typically involves digital painters using a paint bucket tool to manually color each segment enclosed by lines, based on RGB values predetermined by a color designer. This frame-by-frame process is both arduous and time-intensive. Current automated methods mainly focus

Xiusi Chen, Hongzhi Wen, Sreyashi Nag, Chen Luo

With the rapid development of large language models (LLMs), aligning LLMs with human values and societal norms to ensure their reliability and safety has become crucial. Reinforcement learning with human feedback (RLHF) and Constitutional AI (CAI) have been proposed for LLM alignment. However, these methods require either heavy human annotations or explicitl

Fabian Frank, Patrick Lederer

The metric distortion of a randomized social choice function (RSCF) quantifies its worst-case approximation ratio to the optimal social cost when the voters' costs for alternatives are given by distances in a metric space. This notion has recently attracted significant attention as numerous RSCFs that aim to minimize the metric distortion have been suggested

Jinpeng Lu, Jingyun Chen, Linghan Cai, Songhan Jiang

Positron emission tomography (PET) combined with computed tomography (CT) imaging is routinely used in cancer diagnosis and prognosis by providing complementary information. Automatically segmenting tumors in PET/CT images can significantly improve examination efficiency. Traditional multi-modal segmentation solutions mainly rely on concatenation operations

Christophe Servan, Sahar Ghannay, Sophie Rosset

Within the current trend of Pretained Language Models (PLM), emerge more and more criticisms about the ethical andecological impact of such models. In this article, considering these critical remarks, we propose to focus on smallermodels, such as compact models like ALBERT, which are more ecologically virtuous than these PLM. However,PLMs enable huge breakth

Johannes Rosenberger, Johannes Tlatlik, Sebastian Münstermann

To this date the safety assessment of materials, used for example in the nuclear power sector, commonly relies on a fracture mechanical analysis utilizing macroscopic concepts, where a global load quantity K or J is compared to the materials fracture toughness curve. Part of the experimental effort involved in these concepts is dedicated to the quantitative

Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin

User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse s

Cai Heng Li, Luyi Liu

This is the second of a series of papers which aim towards a classification of edge-transitive maps of which the Euler characteristic and the edge number are coprime. This one carries out the classification work for arc-transitive maps with non-solvable automorphism groups, which together with the first one completes a description of arc-transitive maps with

Shuai Xiang, Pieter M. Blok, James Burridge, Haozhou Wang

Object detection has wide applications in agriculture, but domain shifts of diverse environments limit the broader use of the trained models. Existing domain adaptation methods usually require retraining the model for new domains, which is impractical for agricultural applications due to constantly changing environments. In this paper, we propose DODA ($D$if

Ahmed Almheiri, Akash Goel, Xu-Yao Hu

We consider a simplified model of double scaled SYK (DSSYK) in which the Hamiltonian is the position operator of the Harmonic oscillator. This model captures the high temperature limit of DSSYK but could also be defined as a quantum theory in its own right. We study properties of the emergent geometry including its dynamics in response to inserting matter pa

Ekta Yadav, Anand Nivedan, Sunil Kumar

Co2MnGe (CMG) has been demonstrated recently as a half-metallic ferromagnetic Heusler alloy which possesses a topologically non-trivial band structure. This behavior is unique to such systems and hence warrants extensive experimental exploration for potential spintronic and chirality sensitive optoelectonic applications. Here, we demonstrate that an epitaxia

Zhe Sun, Qixuan Liang, Meng Wang, Zhenliang Zhang

Human-computer symbiosis is a crucial direction for the development of artificial intelligence. As intelligent systems become increasingly prevalent in our work and personal lives, it is important to develop strategies to support users across physical and virtual environments. While technological advances in personal digital devices, such as personal compute

Ting-Kang Yen, Igor Morawski, Shusil Dangi, Kai He

Event-based object detection has recently garnered attention in the computer vision community due to the exceptional properties of event cameras, such as high dynamic range and no motion blur. However, feature asynchronism and sparsity cause invisible objects due to no relative motion to the camera, posing a significant challenge in the task. Prior works hav

Raushan Kant, Rahul Kumar Gupta, Harsh Soni, A K Sood

We present experiments, supported by mechanically detailed simulations, establishing bulk vapor-liquid condensation of a hard-bead fluid by a tiny population of orientable motile grains that self-assembles into a moving polarized monolayer. In a quasi-1D geometry two such layers, oppositely aligned, immobilize the condensed non-motile component. We account f

Lisa Anita De Santi, Jörg Schlötterer, Michael Scheschenja, Joel Wessendorf

Information from neuroimaging examinations is increasingly used to support diagnoses of dementia, e.g., Alzheimer's disease. While current clinical practice is mainly based on visual inspection and feature engineering, Deep Learning approaches can be used to automate the analysis and to discover new image-biomarkers. Part-prototype neural networks (PP-NN) ar

Rushang Karia, Daniel Bramblett, Daksh Dobhal, Pulkit Verma

This paper presents $\forall$uto$\exists$val, a new approach for scaling LLM assessment in translating formal syntax -- such as first-order logic, regular expressions, etc -- to natural language (interpretation) or vice versa (compilation), thereby facilitating their use in applications such as generating/explaining logic and control flow for programs etc. E

Ehsan Lari, Reza Arablouei, Stefan Werner

Nonnegative matrix factorization (NMF) is an effective data representation tool with numerous applications in signal processing and machine learning. However, deploying NMF in a decentralized manner over ad-hoc networks introduces privacy concerns due to the conventional approach of sharing raw data among network agents. To address this, we propose a privacy

Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang

Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance. Nevertheless, the knowledge graphs used in previous work, namely metadata-based knowledge graphs, are usually constructed based on the attributes of items and co-occurring rela

Ronen Shekel, Ohad Lib, Yaron Bromberg

Entangled photons provide transformative new paths in the fields of communication, sensing, and computing. However, when entangled photons propagate through a complex medium such as a biological tissue or a turbulent atmosphere, their correlations are scrambled. Using wavefront shaping to compensate for the scattering and retrieve the two-photon correlations

Zhe Zhang, Marc St-Hilaire, Xin Wei, Haiwei Dong

With the continuous evolution of networking technologies, multi-modal services that involve video, audio, and haptic contents are expected to become the dominant multimedia service in the near future. Edge caching is a key technology that can significantly reduce network load and content transmission latency, which is critical for the delivery of multi-modal

Abraham Itzhak Weinberg, Alessio Faccia

Financial crimes fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The st

E. Martel, R. Lazcano, J. Lopez, D. Madroñal

Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer from their computationally demanding nature, becoming advisable for their implementation onto h

Zhenting Luan, Defeng Sun, Haoning Wang, Liping Zhang

Real-time prediction plays a vital role in various control systems, such as traffic congestion control and wireless channel resource allocation. In these scenarios, the predictor usually needs to track the evolution of the latent statistical patterns in the modern high-dimensional streaming time series continuously and quickly, which presents new challenges

Gourab Pal, Subhasish Dutta Gupta, Saptarishi Chaudhuri

We report the observation of two additional sub-natural line width quantum interference in the $D_2$ manifold of $^{39}K$ vapor, in addition to the usual single Electromagnetically induced transparency peak. The other two features appear exclusively because $^{39}K$ ground hyperfine splitting is smaller than the Doppler broadened absorption profile. This all

Tian Ye, Rajgopal Kannan, Viktor Prasanna, Carl Busart

Adversarial attacks have demonstrated the vulnerability of Machine Learning (ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems. An adversarial attack can deceive the classifier into making incorrect predictions by perturbing the input SAR images, for example, with a few scatterers attached to the on-ground obj

Jiayu Li, Peijie Sun, Chumeng Jiang, Weizhi Ma

When users interact with Recommender Systems (RecSys), current situations, such as time, location, and environment, significantly influence their preferences. Situations serve as the background for interactions, where relationships between users and items evolve with situation changes. However, existing RecSys treat situations, users, and items on the same l

Fabian Baldenweg, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova

Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities. While prior works have successfully leveraged multiple modalities in supervised settings, we apply advanced self-supervised multi-modal contrastive learning techniques to ICU data, specifically focusing on clinical notes and time-series for clini

Niels Charlier, Hans Vernaeve

A new and extensive formalism is developed for monads and galaxies in non-standard enlargements. It is shown that monads and galaxies can be manipulated using order-preserving and order-reversing set-to-set maps, and that set properties associated with these maps can be extended not only to internal sets but to all monads and galaxies. An abstract theory of

Yunze Xiao, Houda Bouamor, Wajdi Zaghouani

Despite the considerable efforts being made to monitor and regulate user-generated content on social media platforms, the pervasiveness of offensive language, such as hate speech or cyberbullying, in the digital space remains a significant challenge. Given the importance of maintaining a civilized and respectful online environment, there is an urgent and gro

Takuhiro Fujiie, Ryota Abe, Masahiro Hino, Mayu Hishida

Specular reflection of neutrons on material surfaces has been demonstrated in the energy range of 0.09-0.7 eV. The results suggest that the applicable energy range of reflective neutron optics can be extended to the near-epithermal region by using existing techniques.

Bogumił Pilecki

We present a progress report of our project aiming to increase the number of known Cepheids in double-lined binary (SB2) systems from six to 100 or more. This will allow us, among other goals, to accurately measure masses for a large sample of Cepheids. Currently, only six accurate Cepheid masses are available, which hinders our understanding of their physic

Sung Joon Maeng, İsmail Güvenç

Unmanned aerial vehicle (UAV) corridors are sky lanes where UAVs fly through safely between their origin and destination. To ensure the successful operation of UAV corridors, beyond visual line of sight (BVLOS) wireless connectivity within the corridor is crucial. One promising solution to support this is the use of cellular-connected UAV (C-UAV) networks, w

Betim Bahtiri, Behrouz Arash, Sven Scheffler, Maximilian Jux

This work proposes a physics-informed deep learning (PIDL)-based constitutive model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticle-filled epoxies under various ambient conditions. The deep-learning model is trained to enforce thermodynamic principles, leading to a thermodynamically consistent constitutive mode

Youngbin Lee, Yejin Kim, Javier Sanz-Cruzado, Richard McCreadie

Recommender systems can be helpful for individuals to make well-informed decisions in complex financial markets. While many studies have focused on predicting stock prices, even advanced models fall short of accurately forecasting them. Additionally, previous studies indicate that individual investors often disregard established investment theories, favoring

Yejin Kim, Youngbin Lee, Minyoung Choe, Sungju Oh

This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We present a comprehensive framework that leverages TGN, capable of capturing dynamic changes in edges within financial networks, for fraud detection. Our study compares TGN's perfor

Bao Gia Doan, Dang Quang Nguyen, Paul Montague, Tamas Abraham

The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at the cost of sacrificing model performance for robustness. We hypothesize that adversarial malware exploits the

Maja Muftic Dedovic, Samir Avdakovic, Adnan Mujezinovic, Nedis Dautbasic

This paper introduces and compares the various techniques for identification and analysis of low frequency oscillations in a power system. Inter-area electromechanical oscillations are the focus of this paper. After multiresolution decomposition of characteristic signals, physical characteristics of system oscillations in signal components are identified and

Nemanja Stefan Perović, Le-Nam Tran

In the context of emerging stacked intelligent metasurface (SIM)-based holographic MIMO (HMIMO) systems, a fundamental problem is to study the mutual information (MI) between transmitted and received signals to establish their capacity. However, direct optimization or analytical evaluation of the MI, particularly for discrete signaling, is often intractable.

Zhixin Guo, Tao Wang, Chaoyang Wang, Jianping Zhou

The rare earth elements Sm and Nd significantly address fundamental questions about crustal growth, such as its spatiotemporal evolution and the interplay between orogenesis and crustal accretion. Their relative immobility during high-grade metamorphism makes the Sm-Nd isotopic system crucial for inferring crustal formation times. Historically, data have bee

Minjoo Choi, Seonmi Kim, Yejin Kim, Youngbin Lee

Recommender systems have been actively studied and applied in various domains to deal with information overload. Although there are numerous studies on recommender systems for movies, music, and e-commerce, comparatively less attention has been paid to the recommender system for NFTs despite the continuous growth of the NFT market. This paper presents a reco

Mingzhou Xu, Xuhang Kong

In this article, the complete moment convergence for the partial sum of moving average processes $\{X_n=\sum_{i=-\infty}^{\infty}a_iY_{i+n},n\ge 1\}$ is estabished under some proper conditions, where $\{Y_i,-\infty<i<\infty\}$ is a sequence of $m$-widely acceptable ($m$-WA) random variables, which is stochastically dominated by a random variable $Y$ in sub-l

Jan Ertl, Michael Marquardt, Moritz Schumacher, Patric Rommel

Excitons, as bound states of electrons and holes, embody the solid state analogue of the hydrogen atom, whose quantum spectrum is explained within a classical framework by the Bohr-Sommerfeld atomic model. In a first hydrogenlike approximation the spectra of excitons are also well described by a Rydberg series, however, due to the surrounding crystal environ

Chunhui Xu, Jason T. L. Wang, Haimin Wang, Haodi Jiang

Image super-resolution has been an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network (CNN) for solar image super-resolution. Our method, named SolarCNN, aims to enhance the quality of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (

Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta

The rise in internet usage has led to the generation of massive amounts of data, resulting in the adoption of various supervised and semi-supervised machine learning algorithms, which can effectively utilize the colossal amount of data to train models. However, before deploying these models in the real world, these must be strictly evaluated on performance m

Siyuan Zhao, Yanqi Wu, Zheng Wang

Byzantine consensus protocols are essential in blockchain technology. The widely recognized HotStuff protocol uses cryptographic measures for efficient view changes and reduced communication complexity. Recently, the main authors of HotStuff introduced an advanced iteration named HotStuff-2. This paper aims to compare the principles and analyze the effective

Zhi-Hao Liu, Xun-Wei Xu

Photon blockade enhancement is an exciting and promising subject that has been well studied for photons in cavities. However, whether photon blockade can be enhanced in the output fields remains largely unexplored. We show that photon blockade can be greatly enhanced in the mixing output field of a nonlinear cavity and an auxiliary (linear) cavity, where no

Guillermo Vazquez, Daniel Sauceda, Raymundo Arróyave

The Cluster Expansion (CE) Method encounters significant computational challenges in multicomponent systems due to the computational expense of generating training data through density functional theory (DFT) calculations. This work aims to refine the cluster and structure selection processes to mitigate these challenges. We introduce a novel method that sig

Steven Campbell, Yuchong Zhang

We introduce a mean field game for a family of filtering problems related to the classic sequential testing of the drift of a Brownian motion. To the best of our knowledge this work presents the first treatment of mean field filtering games with stopping and an unobserved common noise in the literature. We show that the game is well-posed, characterize the s

Chunhang Zheng, Kechao Cai

Traditional approaches to semantic communication tasks rely on the knowledge of the signal-to-noise ratio (SNR) to mitigate channel noise. Moreover, these methods necessitate training under specific SNR conditions, entailing considerable time and computational resources. In this paper, we propose GeNet, a Graph Neural Network (GNN)-based paradigm for semanti

Yongwei Zhou, Tiejun Zhao

Recent advancements highlight the success of instruction tuning with large language models (LLMs) utilizing Chain-of-Thought (CoT) data for mathematical reasoning tasks. Despite the fine-tuned LLMs, challenges persist, such as incorrect, missing, and redundant steps in CoT generation leading to inaccuracies in answer predictions. To alleviate this problem, w

Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu

Convolutional Neural Networks (CNNs) have advanced significantly in visual representation learning and recognition. However, they face notable challenges in performance and computational efficiency when dealing with real-world, multi-scale image inputs. Conventional methods rescale all input images into a fixed size, wherein a larger fixed size favors perfor

Adilbek Karmanov, Dayan Guan, Shijian Lu, Abdulmotaleb El Saddik

Test-time adaptation with pre-trained vision-language models has attracted increasing attention for tackling distribution shifts during the test time. Though prior studies have achieved very promising performance, they involve intensive computation which is severely unaligned with test-time adaptation. We design TDA, a training-free dynamic adapter that enab

Xiao Hu, Zhi-Yun Li, Jaehan Bae, Zhaohuan Zhu

Recent high-angular resolution ALMA observations have revealed rich information about protoplanetary disks, including ubiquitous substructures and three-dimensional gas kinematics at different emission layers. One interpretation of these observations is embedded planets. Previous 3-D planet-disk interaction studies are either based on viscous simulations, or

Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu

Deep neural networks perform remarkably well in close-world scenarios. However, novel classes emerged continually in real applications, making it necessary to learn incrementally. Class-incremental learning (CIL) aims to gradually recognize new classes while maintaining the discriminability of old ones. Existing CIL methods have two limitations: a heavy reli

Kyung-Won Suh

In this study, we explore the characteristics of carbon stars within our Galaxy through a comprehensive analysis of observational data spanning visual and infrared (IR) bands. Leveraging datasets from IRAS, ISO, Akari, MSX, 2MASS, WISE, Gaia DR3, AAVSO, and the SIMBAD object database, we conduct a detailed comparison between the observational data and theore

Abolhassan Mohammadi

Loop quantum cosmology is a symmetry-reduced application of loop quantum gravity. The theory predicts a bounce for the universe at the Planck scale and resolves the singularity of standard cosmology. The dynamics is also governed by an effective Hamiltonian, which predicts a modified Friedmann equation containing the quadratic terms of the energy density. Th

In Woo Park, Hiroyuki Sako, Kazuya Aoki, Philipp Gubler

Observing the mass shifts of chiral partners will provide invaluable insight into the role of chiral symmetry breaking in the generation of hadron masses. Because both the $K^*$ and $K_1$ mesons have vacuum widths smaller than 100 MeV, they are ideal candidates for realizing mass shift measurements. On the other hand, the different momentum dependence of the