December 2024 arXiv papers — page 144

Karyn Le Hur

We introduce a quantum spin Hall semimetal or Fermi liquid characterized with a Z2 topological invariant, measurable through circularly polarized light. We propose its engineering through two topological metallic band structures in crystals on the honeycomb lattice, with spin-orbit interaction, realizable through the interplay of a charge or spin density wav

Lance Calvin Lim Gamboa, Mark Lee

Bias studies on multilingual models confirm the presence of gender-related stereotypes in masked models processing languages with high NLP resources. We expand on this line of research by introducing Filipino CrowS-Pairs and Filipino WinoQueer: benchmarks that assess both sexist and anti-queer biases in pretrained language models (PLMs) handling texts in Fil

Jae-Young Yim, Jae-Young Sim

Large-scale 3D point clouds (LS3DPC) obtained by LiDAR scanners require huge storage space and transmission bandwidth due to a large amount of data. The existing methods of LS3DPC compression separately perform rule-based point sampling and learnable feature extraction, and hence achieve limited compression performance. In this paper, we propose a fully end-

Kentaro Imafuku

This paper critically analyzes the Y00 protocol, a quantum noise-based stream cipher proposed to enhance classical cryptographic methods through quantum mechanical properties. Despite its promise, we reveal a structural vulnerability that enables the leakage of secret information from measurement outcomes. To systematically evaluate its security, we first fo

Maxime Lavaud, Yosef Shokeeb, Juliette Lacherez, Yacine Amarouchene

Characterizing anomalous diffusion is crucial in order to understand the evolution of complex stochastic systems, from molecular interactions to cellular dynamics. In this work, we characterize the performances regarding such a task of Bi-Mamba, a novel state-space deep-learning architecture articulated with a bidirectional scan mechanism. Our implementation

Jiawei Chen, Wentao Chen, Jing Su, Jingjing Xu

Large language models (LLMs) have shown significant multilingual capabilities. However, the mechanisms underlying the development of these capabilities during pre-training are not well understood. In this paper, we use code LLMs as an experimental platform to explore the evolution of multilingual capabilities in LLMs during the pre-training process. Based on

Dylan King, Marcelo Sales, Bjarne Schülke

The study of uniform Tur\'an densities was initiated in the 1980s by Erd\H{o}s and S\'os. Given a $3$-graph $F$, the uniform Tur\'an density of $F$, $\pi_{\therefore}(F)$, is defined as the infimum $d\in[0,1]$ such that every $3$-graph $H$ in which every linearly sized $S\subseteq V(H)$ induces at least $(d+o(1))\binom{\vert S\vert}{3}$ edges must contain a

M T Hartman, N Wagner, S Seidelin, B Fang

Techniques in frequency stabilization of lasers to fixed-spacer optical cavities have advanced to the point where the ultimate frequency stabilities are limited by thermal noise in the cavity materials for standard cavity configurations at room temperature. The use of spectral-hole burning (SHB) in laser stabilization has produced promising results in early

Daniel Mutter, Diego A. Pantano, Christian Elsässer, Daniel F. Urban

Li containing transition metal oxides are known as good ionic conductors. Performing classical molecular dynamics simulations, the diffusion behavior of Li ions is investigated in crystalline and amorphous phases with the stoichiometries Li$_2$ZrO$_3$ and LiNbO$_3$. We first demonstrate the stability of the crystal structures for the used interatomic potenti

Faisal Etminan

The $ \Lambda{\text -} $ and $ \Xi{\text -}$triton(t) momentum correlation functions, to be measured in high-energy heavy-ion collisions, are explored. Mainly, STAR detector acquired data provides an opportunity to explore the $ \Lambda t $ correlation function. The $ \Lambda t $ correlation functions are calculated using an isle-type and spin-averaged $ \La

Philippe Gille, Vladimir Chernousov, Arturo Pianzola

Loop torsors over Laurent polynomial rings in characteristic 0 were originally introduced in relation to infinite dimensional Lie theory. Applications to other areas require a theory that can yields results in positive characteristic, and for group schemes that are not of finite type. The relation between loop and so-called toral torsors, is one of the centr

Toshiya Yura, Ashkan Mirzaei, Igor Gilitschenski

We introduce a method for using event camera data in novel view synthesis via Gaussian Splatting. Event cameras offer exceptional temporal resolution and a high dynamic range. Leveraging these capabilities allows us to effectively address the novel view synthesis challenge in the presence of fast camera motion. For initialization of the optimization process,

Fuhai Chen, Pengpeng Huang, Xuri Ge, Jie Huang

With the rapid development of multimedia, the shift from unimodal textual sentiment analysis to multimodal image-text sentiment analysis has obtained academic and industrial attention in recent years. However, multimodal sentiment analysis is affected by unimodal data bias, e.g., text sentiment is misleading due to explicit sentiment semantic, leading to low

Ralph Silva, Pharnam Bakhshinezhad, Fabien Clivaz

The preparation of quantum states, especially cooling, is a fundamental technology for nanoscale devices. The past decade has seen important results related to both the limits of state transformation and the limits to their efficiency -- the quantum versions of the third and second law of thermodynamics. The limiting cases always involve an infinite resource

Mahendra Paipuri

With the rapid acceleration of ML/AI research in the last couple of years, the energy consumption of the Information and Communication Technology (ICT) domain has rapidly increased. As a major part of this energy consumption is due to users' workloads, it is evident that users need to be aware of the energy footprint of their applications. Compute Energy and

Yongqi Li, Xin Miao, Shen Zhou, Mayi Xu

Despite the rapid progress that existing automated feedback methods have made in correcting the output of large language models (LLMs), these methods cannot be well applied to the relation extraction (RE) task due to their designated feedback objectives and correction manner. To address this problem, we propose a novel automated feedback framework for RE, wh

Isabela M. Yepes, Manasvi Goyal

This study investigates the applicability of Singular Value Decomposition for the image classification of specific breeds of cats and dogs using fur color as the primary identifying feature. Sequential Quadratic Programming (SQP) is employed to construct optimally weighted templates. The proposed method achieves 69% accuracy using the Frobenius norm at rank

Eric Persson, Florian Bernlochner

We present a model selection framework for the extraction of the CKM matrix element $|V_{cb}|$ from exclusive $B \to D^* l \nu$ decays. By framing the truncation of the Boyd-Grinstein-Lebed (BGL) parameterization as a model selection task, we apply the Akaike Information Criterion (AIC) to choose the optimal truncation order. We demonstrate the performance o

Mohammad Farhat, Pierre Auclair-Desrotour, Gwenaël Boué, Tim Lichtenberg

Understanding the physics of planetary magma oceans has been the subject of growing efforts, in light of the increasing abundance of Solar system samples and extrasolar surveys. A rocky planet harboring such an ocean is likely to interact tidally with its host star, planetary companions, or satellites. To date, however, models of the tidal response and heat

Kristen C. Dage, Evangelia Tremou, Bolivia Cuevas Otahola, Eric W. Koch

Intermediate mass black holes (10^2 < M_BH< 10^5 Msun) are an open question in our understanding of black hole evolution and growth. They have long been linked to dense star cluster environments thanks to cluster dynamics, but there are a limited number of secure detections. We leverage existing X-ray observations from Chandra X-ray Observatory and optical c

Jeaheang Bang, Changfeng Gui, Hao Liu, Yun Wang

This paper is concerned with self-similar solutions of the steady Navier-Stokes system in a two-dimensional sector with the no-slip boundary condition. We give necessary and sufficient conditions in terms of the angle of the sector and the flux to guarantee the existence of self-similar solutions of a given type. We also investigate the uniqueness and non-un

Romain Storaï, Seung-won Hwang

This paper aims to improve the performance of large language models by addressing the variable computational demands in inference steps, where some tokens require more computational resources than others. We present HARP, a simple modification to "off-the-shelf" Transformer forward pass. Drawing from hesitation and the framing effect in decision-making, HARP

S. Bonzio, P. A. García-Sánchez

We characterize numerical semigroups for which the poset of its ideal class monoid is a lattice, and study the irreducible elements of such a lattice with respect to union, intersection, infimum and supremum.

Yves Achdou, Le Bris Claude

We study homogenization of a class of bidimensional stationary Hamilton-Jacobi equations where the Hamiltonian is obtained by perturbing near a half-line of the state space a Hamiltonian that either does not have fast variations with respect to the state variable, or depends on the latter in a periodic manner. We prove that the limiting problem belongs to th

O. Castelló, Sofía M. López Baptista, K. Watanabe, T. Taniguchi

In recent years, graphene Field-Effect-Transistors (GFETs) have demonstrated an outstanding potential for Terahertz (THz) photodetection due to their fast response and high-sensitivity. Such features are essential to enable emerging THz applications, including 6G wireless communications, quantum information, bioimaging and security. However, the overall perf

Yosuke Miyanishi, Keita Mitani

The alignment between human objectives and machine learning models built on these objectives is a crucial yet challenging problem for achieving Trustworthy AI, particularly when preparing for superintelligence (SI). First, given that SI does not exist today, empirical analysis for direct evidence is difficult. Second, SI is assumed to be more intelligent tha

Yi Yu, Song Xia, Xun Lin, Wenhan Yang

No-Reference Image Quality Assessment (NR-IQA), responsible for assessing the quality of a single input image without using any reference, plays a critical role in evaluating and optimizing computer vision systems, e.g., low-light enhancement. Recent research indicates that NR-IQA models are susceptible to adversarial attacks, which can significantly alter p

Mengyu Wang, Yi Duan, Qin Li, Luying Lin

When a waverider flies at hypersonic speed, the thermodynamic properties of the surrounding gas change because of the rapid increase in temperature, so it is reasonable to consider real-gas effects in the vehicle design. In addition, a hypersonic waverider usually travels at varying speed during flight, and deviating from the default speed designed in terms

Keyi Liu, Yufeng Chen, Liyan Xu, Xiao Zhang

Balancing human development with conservation necessitates ecological policies that optimize outcomes within limited budgets, highlighting the importance of cost-efficiency and local impact analysis. This study employs the Socio-Econ-Ecosystem Multipurpose Simulator (SEEMS), an Agent-Based Model (ABM) designed for simulating small-scale Coupled Human and Nat

Aixuan Li, Jing Zhang, Jiawei Shi, Yiran Zhong

We find that the well-trained victim models (VMs), against which the attacks are generated, serve as fundamental prerequisites for adversarial attacks, i.e. a segmentation VM is needed to generate attacks for segmentation. In this context, the victim model is assumed to be robust to achieve effective adversarial perturbation generation. Instead of focusing o

Junwei Su, Shan Wu

Temporal Graph Neural Networks (TGNNs) are a family of graph neural networks designed to model and learn dynamic information from temporal graphs. Given their substantial empirical success, there is an escalating interest in TGNNs within the research community. However, the majority of these efforts have been channelled towards algorithm and system design, w

Hirotaka Yarimoto, Masamune Oguri

The information about lensed gravitational waves is encapsulated by an amplification factor, which is calculated by an integration of an oscillatory function. The Born approximation, which has been studied in terms of wave optics in gravitational lensing, may provide a means of overcoming the difficulty in evaluating the oscillating function and better under

Yoshihiko Hasegawa

Enhancing the precision of a thermodynamic process inevitably necessitates a thermodynamic cost. This notion was recently formulated as the thermodynamic uncertainty relation, which states that the lower bound on the relative variance of thermodynamic currents decreases as entropy production increases. From another viewpoint, the thermodynamic uncertainty re

Hairong Su, Shibo Wang, Shusen Yang, Tianchi Huang

Bitrate adaptation (also known as ABR) is a crucial technique to improve the quality of experience (QoE) for video streaming applications. However, existing ABR algorithms suffer from severe traffic wastage, which refers to the traffic cost of downloading the video segments that users do not finally consume, for example, due to early departure or video skipp

Lingnan Xie, Linning Peng, Junqing Zhang

Radio frequency fingerprint identification (RFFI) is a promising device authentication approach by exploiting the unique hardware impairments as device identifiers. Because the hardware features are extracted from the received waveform, they are twisted with the channel propagation effect. Hence, channel elimination is critical for a robust RFFI system. In t

Zining Wnag, Jinyang Guo, Ruihao Gong, Yang Yong

With the increased attention to model efficiency, post-training sparsity (PTS) has become more and more prevalent because of its effectiveness and efficiency. However, there remain questions on better practice of PTS algorithms and the sparsification ability of models, which hinders the further development of this area. Therefore, a benchmark to comprehensiv

Zihan Huang, Tong Li, Yong Li

Mobile app usage behavior reveals human patterns and is crucial for stakeholders, but data collection is costly and raises privacy issues. Data synthesis can address this by generating artificial datasets that mirror real-world data. In this paper, we propose AppGen, an autoregressive generative model designed to generate app usage behavior based on users' m

Shilei Ji, Jianping Yang, Li Gao, Xing'ao Li

In valleytronic devices, the valley transport of electrons can carry not only charge but also spin angular momentum (SAM) and orbital angular momentum (OAM). However, investigations on thermoelectric transport of OAM manipulated by valley degrees of freedom remain limited. Here, using the ferromagnetic transition metal dichalcogenides RuCl$_2$ as an example,

Kesen Wang, Minwoo Kim, Stefano Castruccio, Marc G. Genton

In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an exclusive use of oil to a reliance on renewable energy, and, in particular, wind. Modeling wind for assessing potential energy output in a country

Salvatore Calcagno, Isaak Kavasidis, Simone Palazzo, Marco Brondi

Understanding complex animal behaviors hinges on deciphering the neural activity patterns within brain circuits, making the ability to forecast neural activity crucial for developing predictive models of brain dynamics. This capability holds immense value for neuroscience, particularly in applications such as real-time optogenetic interventions. While tradit

Constantia Alexandrou, Giannis Koutsou, Yan Li, Marcus Petschlies

We investigate an improved method to extract nucleon matrix elements from lattice 3-point functions using a generalized eigenvalue problem (GEVP) with nucleon and pion-nucleon interpolating fields. Our method avoids the computation of the costly three-point functions that have pion-nucleon interpolators at both source and sink. We demonstrate that excited st

Ziyao Yi, Diego Valsesia, Tiziano Bianchi, Enrico Magli

The rise of portable Lidar instruments, including their adoption in smartphones, opens the door to novel computational imaging techniques. Being an active sensing instrument, Lidar can provide complementary data to passive optical sensors, particularly in situations like low-light imaging where motion blur can affect photos. In this paper, we study if the de

Zhenpeng Wu, Jian Lou, Zibin Zheng, Chuan Chen

Large language models (LLMs) have been shown to memorize and reproduce content from their training data, raising significant privacy concerns, especially with web-scale datasets. Existing methods for detecting memorization are primarily sample-specific, relying on manually crafted or discretely optimized memory-inducing prompts generated on a per-sample basi

Peipeng Yu, Hui Gao, Jianwei Fei, Zhitao Huang

Recent advances in deepfake forensics have primarily focused on improving the classification accuracy and generalization performance. Despite enormous progress in detection accuracy across a wide variety of forgery algorithms, existing algorithms lack intuitive interpretability and identity traceability to help with forensic investigation. In this paper, we

Joanna Jasińska, Balázs Ráth

We introduce and study a new random permutation model that generalizes the $k$-card minimum model defined by Travers and the Mallows model. We calculate the permuton limit of such a sequence of random permutations. As a corollary, we deduce the law of large numbers for pattern densities. Moreover, we prove a universality result about the band structure of th

Yilong Cui, Liang Hou, Kenan Guo, Yue Jiang

Metasurfaces have provided new opportunities for the realization of flat lenses, among which tunable metalenses have garnered considerable attention due to their flexible functionalities. In this paper, we present a continuously tunable metalens based on the phase-change material Sb$_{2}$S$_{3}$, which enables precise and continuous focal length control thro

Yuzhi Zhao, Lai-Man Po, Xin Ye, Yongzhe Xu

Image degradation caused by noise and blur remains a persistent challenge in imaging systems, stemming from limitations in both hardware and methodology. Single-image solutions face an inherent tradeoff between noise reduction and motion blur. While short exposures can capture clear motion, they suffer from noise amplification. Long exposures reduce noise bu

Qinhong Lin, Linna Zhou, Zhongliang Yang, Yuang Cai

Large Language Models (LLMs) display formidable capabilities in generative tasks but also pose potential risks due to their tendency to generate hallucinatory responses. Uncertainty Quantification (UQ), the evaluation of model output reliability, is crucial for ensuring the safety and robustness of AI systems. Recent studies have concentrated on model uncert

Hong Duc Nguyen

In this paper, we establish a Mather-Yau theorem for higher Nash blowup algebras, demonstrating that the isomorphism type of the local ring of any hypersurface singularity, defined over an arbitrary field, is fully determined by its higher Nash blowup algebras. The classical Mather-Yau theorem (1982) asserts that for isolated complex hypersurface singulariti

Chaoqun Li, Zhuodong Liu, Huanqian Yan, Hang Su

Adversarial patches, often used to provide physical stealth protection for critical assets and assess perception algorithm robustness, usually neglect the need for visual harmony with the background environment, making them easily noticeable. Moreover, existing methods primarily concentrate on improving attack performance, disregarding the intricate dynamics

Yiming Zhou, Yifei Cheng, Linli Xu, Enhong Chen

Statistical diversity is a property of data distribution and can hinder the optimization of a decentralized network. However, the theoretical limitations of the Push-SUM protocol reduce the performance in handling the statistical diversity of optimization algorithms based on it. In this paper, we theoretically and empirically mitigate the negative impact of

Xiaonan Wang, Jinyoung Yeo, Joon-Ho Lim, Hansaem Kim

Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess o

Brahyam Ríos-Sánchez, Santiago F. Caballero-Benítez

Ultracold atomic systems confined in optical cavities have been demonstrated as a laboratory for the control of quantum matter properties and analog quantum simulation. Often neglected, but soon amenable to manipulation in a new generation of experiments, we show that atomic many-body interactions allow additional control in the cavity driven self-organizati

Xin Zhao, Xiaojun Chen, Yuexin Xuan, Zhendong Zhao

The rise of deep learning models in the digital era has raised substantial concerns regarding the generation of Not-Safe-for-Work (NSFW) content. Existing defense methods primarily involve model fine-tuning and post-hoc content moderation. Nevertheless, these approaches largely lack scalability in eliminating harmful content, degrade the quality of benign im

Sergi Liesegang, Alessio Zappone, Olga Muñoz, Antonio Pascual-Iserte

Reconfigurable intelligent surfaces (RISs) have become a promising candidate for the development of future mobile systems. In the context of massive machine-type communications (mMTC), a RIS can be used to support the transmission from a group of sensors to a collector node. Due to the short data packets, we focus on the design of the RIS for maximizing the

Jiahan Li, Zhiqi Li, Tong Lu

This technical report describes the methods we employed for the Driving with Language track of the CVPR 2024 Autonomous Grand Challenge. We utilized a powerful open-source multimodal model, InternVL-1.5, and conducted a full-parameter fine-tuning on the competition dataset, DriveLM-nuScenes. To effectively handle the multi-view images of nuScenes and seamles

M. A. Amirabadi, S. A. Nezamalhosseini, M. H. Kahaei, Lawrence R. Chen

The ever-growing complexity of optical communication systems and networks demands sophisticated methodologies to extract meaningful insights from vast amounts of heterogeneous data. Machine learning (ML) and deep learning (DL) have emerged as frontrunners in this domain, offering a transformative approach to data analysis and enabling automated self-configur

Ruiheng Liu, Jinyu Zhang, Yanqi Song, Yu Zhang

Continual Semantic Parsing (CSP) aims to train parsers to convert natural language questions into SQL across tasks with limited annotated examples, adapting to the real-world scenario of dynamically updated databases. Previous studies mitigate this challenge by replaying historical data or employing parameter-efficient tuning (PET), but they often violate da

Mateen Ashraf, Anna Gaydamaka, Dmitri Moltchanov, John Thompson

Dual functional radar and communication (DFRC) systems are a viable approach to extend the services of future communication systems. Most studies designing DFRC systems assume that the target direction is known. In our paper, we address a critical scenario where this information is not exactly known. For such a system, a signal-to-clutter-plus-noise ratio (S

Serzhan Ossenov

Traditional metrics like accuracy, F1-score, and precision are frequently used to evaluate machine learning models, however they may not be sufficient for evaluating performance on tiny, unbalanced, or high-dimensional datasets. A dataset-adaptive, normalized metric that incorporates dataset characteristics like size, feature dimensionality, class imbalance,

Biswadeep Chakraborty, Harshit Kumar, Saibal Mukhopadhyay

Oversmoothing in Graph Neural Networks (GNNs) poses a significant challenge as network depth increases, leading to homogenized node representations and a loss of expressiveness. In this work, we approach the oversmoothing problem from a dynamical systems perspective, providing a deeper understanding of the stability and convergence behavior of GNNs. Leveragi

Nikos Tsikouras, Constantine Caramanis, Christos Tzamos

Embeddings play a pivotal role across various disciplines, offering compact representations of complex data structures. Randomized methods like Johnson-Lindenstrauss (JL) provide state-of-the-art and essentially unimprovable theoretical guarantees for achieving such representations. These guarantees are worst-case and in particular, neither the analysis, nor

Fatma Yamac Sagirli, Xiaopeng Zhao, Zhenbo Wang

The increasing rates of global urbanization and vehicle usage are leading to a shift of mobility to the third dimension-through Advanced Air Mobility (AAM)-offering a promising solution for faster, safer, cleaner, and more efficient transportation. As air transportation continues to evolve with more automated and autonomous systems, advancements in AAM requi

Jakub Matousek, Jindrich Dunik, Marek Brandner

This paper deals with the state estimation of stochastic models with continuous dynamics. The aim is to incorporate spectral differentiation methods into the solution to the Fokker-Planck equation in grid-based state estimation routine, while taking into account the specifics of the field, such as probability density function (PDF) features, moving grid, zer

Jindrich Dunik, Jakub Matousek, Ondrej Straka, Erik Blasch

This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective mu

Hanlin Wu, Zhenguang G. Cai

The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and proposes an integrative model of language and speaker processing that integrates distinct mechanistic perspectives. We argue that speaker effects arise from the interplay between bottom-up perception-based proces

Jiayi Su, Youhe Feng, Zheng Li, Jinhua Song

This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limited to using predefined structures or retrieving shapes from static datasets. To address these challenges, we parameterize an articulated object as a tree of tokens and employ a tra

Jiquan Wang, Sha Zhao, Zhiling Luo, Yangxuan Zhou

Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the success of large language models, there is

Mario Bucev, Samuel Chassot, Simon Felix, Filip Schramka

We propose a verified executable Scala backend for ASN1SCC, a compiler for ASN.1/ACN. ASN.1 is a language for describing data structures widely employed in ground and space telecommunications. ACN can be used along ASN.1 to describe complex binary formats and legacy protocols. To avoid error-prone and time-consuming manual writing of serializers, we show how

Mohammad R. Garousi

It is well-established that compactifying type I string theory on a circle \( S^{(1)} \) transforms the theory under T-duality into type I' theory, the compactification of type IIA string theory on the orbifold \( \tilde{S}^{(1)}/\mathbb{Z}_2 \), where the \( \mathbb{Z}_2 \) action combines worldsheet parity with spacetime reflection along the dual circle \(

Kun Li, Xinge Peng, Dan Guo, Xun Yang

Repetitive Action Counting (RAC) aims to count the number of repetitive actions occurring in videos. In the real world, repetitive actions have great diversity and bring numerous challenges (e.g., viewpoint changes, non-uniform periods, and action interruptions). Existing methods based on the temporal self-similarity matrix (TSSM) for RAC are trapped in the

Ben Wooding, Abolfazl Lavaei

This work is concerned with synthesizing safety controllers for discrete-time nonlinear systems beyond polynomials with unknown mathematical models using the notion of k-inductive control barrier certificates (k-CBCs). Conventional CBC conditions (with k=1) for ensuring safety over dynamical systems are often restrictive, as they require the CBCs to be non-i

Lubin Meng, Xue Jiang, Xiaoqing Chen, Wenzhong Liu

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recent studies have shown that machine learni

Hui Deng, Jiawei Shi, Zhen Qin, Yiran Zhong

Non-Rigid Structure-from-Motion (NRSfM) is a classic 3D vision problem, where a 2D sequence is taken as input to estimate the corresponding 3D sequence. Recently, the deep neural networks have greatly advanced the task of NRSfM. However, existing deep NRSfM methods still have limitations in handling the inherent sequence property and motion ambiguity associa

Wan Jiang, He Wang, Xin Zhang, Dan Guo

Score-based Generative Models (SGMs) have demonstrated remarkable generalization abilities, e.g. generating unseen, but natural data. However, the greater the generalization power, the more likely the unintended generalization, and the more dangerous the abuse. Research on moderated generalization in SGMs remains limited. To fill this gap, we first examine t

Siyang Li, Ziwei Wang, Hanbin Luo, Lieyun Ding

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (

Henk Pijls, Quan Le Phuong

In this paper, we propose a closed-form solution to the inverse problem in interpolation with periodic uniform B-spline curves. This solution is obtained by modifying the one we have established to a similar problem with relaxed uniform B-spline curves. Then we use these solutions to determine the maximum curvature of a B\'{e}zier-spline curve. Our computati

Yingfan Wang, Guoliang Kang

Domain Generalization (DG) aims to learn a model from multiple source domains to achieve satisfactory performance on unseen target domains. Recent works introduce CLIP to DG tasks due to its superior image-text alignment and zeros-shot performance. Previous methods either utilize full fine-tuning or prompt-learning paradigms to harness CLIP for DG tasks. Tho

Yuhan He, Yuchun He

Image restoration represents a fundamental challenge in low-level vision, focusing on reconstructing high-quality images from their degraded counterparts. With the rapid advancement of deep learning technologies, transformer-based methods with pyramid structures have advanced the field by capturing long-range cross-scale spatial interaction. Despite its popu

Weizhen Bian, Yubo Zhou, Yuanhang Luo, Ming Mo

The interplay between cognition and gaming, notably through educational games enhancing cognitive skills, has garnered significant attention in recent years. This research introduces the CogSimulator, a novel algorithm for simulating user cognition in small-group settings with minimal data, as the educational game Wordle exemplifies. The CogSimulator employs

Joohyung Lee, Jungchan Cho, Wonjun Lee, Mohamed Seif

To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advantages of federated and split learning. However, recent studies have largely overlooked competitive situations. In this framework, the SFL model owner can choose the cut layer to bal

Wesley Chung, Lynn Cherif, David Meger, Doina Precup

Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequences of tasks -- all referring to the increased difficulty in training on new tasks. We propose to use Parseval regularization, which maintains orthogonality of weight matrices, to preserve useful optimization properties a

Zong Ke, Jingyu Xu, Zizhou Zhang, Yu Cheng

This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility,Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network and genetic algorithm to predict future

Yuchun He, Yuhan He

Single image super-resolution (SR) has long posed a challenge in the field of computer vision. While the advent of deep learning has led to the emergence of numerous methods aimed at tackling this persistent issue, the current methodologies still encounter challenges in modeling long sequence information, leading to limitations in effectively capturing the g

Gouri Ginde

Generative Artificial Intelligence (GenAI) advances have led to new technologies capable of generating high-quality code, natural language, and images. The next step is to integrate GenAI technology into various aspects while conducting research or other related areas, a task typically conducted by researchers. Such research outcomes always come with a certa

Bo Li, Di Liang, Zixin Zhang

The Transformer-based model have made significant strides in semantic matching tasks by capturing connections between phrase pairs. However, to assess the relevance of sentence pairs, it is insufficient to just examine the general similarity between the sentences. It is crucial to also consider the tiny subtleties that differentiate them from each other. Reg

Kohei Fukuda, Hiroaki Aizawa

Out-of-distribution (OOD) detection is the task of identifying data sampled from distributions that were not used during training. This task is essential for reliable machine learning and a better understanding of their generalization capabilities. Among OOD detection methods, Outlier Exposure (OE) significantly enhances OOD detection performance and general

Kenji Sugisaki, Shu Kanno, Toshinari Itoko, Rei Sakuma

Quantum-selected configuration interaction (QSCI) is an approach for quantum chemical calculations using current quantum computers. In conventional QSCI, Slater determinants used for the wave function expansion are sampled by iteratively performing approximate wave function preparation and subsequent measurement in the computational basis, and then the subsp

Aniket Bhanderi, Raj Bhatnagar

The problem of analyzing data streams of very large volumes is important and is very desirable for many application domains. In this paper we present and demonstrate effective working of an algorithm to find clusters and anomalous data points in a streaming datasets. Entropy minimization is used as a criterion for defining and updating clusters formed from a

Jingjing Xue, Sheng Sun, Min Liu, Yuwei Wang

To effectively manage and utilize massive distributed data at the network edge, Federated Learning (FL) has emerged as a promising edge computing paradigm across data silos. However, FL still faces two challenges: system heterogeneity (i.e., the diversity of hardware resources across edge devices) and statistical heterogeneity (i.e., non-IID data). Although

Feng Yan, Fanfan Liu, Liming Zheng, Yufeng Zhong

Recently, robotics has advanced significantly through the integration of larger models and large-scale datasets. However, challenges remain in applying these models to 3D spatial interactions and managing data collection costs. To address these issues, we propose the multimodal robotic manipulation model RoboTron-Mani and the comprehensive dataset RoboData.

Jun-Peng Zhu, Boyan Niu, Peng Cai, Zheming Ni

Exploratory data analysis (EDA), coupled with SQL, is essential for data analysts involved in data exploration and analysis. However, data analysts often encounter two primary challenges: (1) the need to craft SQL queries skillfully, and (2) the requirement to generate suitable visualization types that enhance the interpretation of query results. Due to its

Weizhen Bian, Siyan Liu, Yubo Zhou, Dezhi Chen

Faced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized intelligent academic literature management platform to address these challenges. This platform integrates a Large L

Wenjian Hao, Zehui Lu, Devesh Upadhyay, Shaoshuai Mou

This paper proposes a distributed data-driven framework for dynamics learning, termed distributed deep Koopman learning using partial trajectories (DDKL-PT). In this framework, each agent in a multi-agent system is assigned a partial trajectory offline and locally approximates the unknown dynamics using a deep neural network within the Koopman operator frame

Teng Ee Yap, Konstantin Batygin, François L. H. Tissot

Early Solar System (SS) planetesimals constitute the parent bodies of most meteorites investigated today. Nucleosynthetic isotope anomalies of bulk meteorites have revealed a dichotomy between non-carbonaceous (NC) and carbonaceous (CC) groups. Planetesimals sampling NC and CC isotopic signatures are conventionally thought to originate from the "dry" inner d

Jialiang Cheng, Ning Gao, Yun Yue, Zhiling Ye

Distributed training methods are crucial for large language models (LLMs). However, existing distributed training methods often suffer from communication bottlenecks, stragglers, and limited elasticity, particularly in heterogeneous or large-scale environments. Local SGD methods have been proposed to address these issues, but their effectiveness remains limi

Carlo Silvestri, Justin Widjaja, Austin Lin, C. Martijn de Sterke

We present a general theory of multicolor soliton microcombs. These frequency combs require specially engineered dispersion and have an optical spectrum consisting of multiple spectral windows, centered at distinct frequencies. Our theory is based on a multiple-scale approach applied to the Lugiato-Lefever equation, and provides a framework to investigate di

Fangfang Zhang, Jinyi Ge, Cuimei Jiang, Han Bao

The Sine-Cosine function, which is widely adopted in mathematics and physics, has attracted our attention due to its unique properties. By delving into the coupling effect of the Sine-Cosine function, we discover a previously unreported class of nonlinear systems, namely the Sine-Cosine Nonlinear System Family (SCNSF). This discovery is motivated by the need

Saaduddin Mahmud, Mason Nakamura, Shlomo Zilberstein

The advent of large language models (LLMs) has sparked significant interest in using natural language for preference learning. However, existing methods often suffer from high computational burdens, taxing human supervision, and lack of interpretability. To address these issues, we introduce MAPLE, a framework for large language model-guided Bayesian active

Marvin Jans, Gabriel J. Lord, Mariya Ptashnyk

We consider the numerical approximation of the stochastic complex Ginzburg-Landau equation with additive noise on the one dimensional torus. The complex nature of the equation means that many of the standard approaches developed for stochastic partial differential equations can not be directly applied. We use an energy approach to prove an existence and uniq