May 2024 arXiv papers — page 88

Shubham Paliwal, Arushi Jain, Monika Sharma, Vikram Jamwal

Textual image generation spans diverse fields like advertising, education, product packaging, social media, information visualization, and branding. Despite recent strides in language-guided image synthesis using diffusion models, current models excel in image generation but struggle with accurate text rendering and offer limited control over font attributes

Xiaoyan Ma, Haixia Zhang, Xianhao Chen, Yuguang Fangmand Dongfeng Yuan

Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Existing multi-RIS works mainly focus on link-level optimization with predetermined transmission paths, which cannot be directly extended to system-level management, since they neith

Atila Poro, Mehmet Tanriver, Ahmet Keskin, Ahmet Bulut

This study presented the first light curve analysis of the OP Boo and V0511 Cam binary stars, which was conducted in the frame of the Binary Systems of South and North (BSN) Project. Photometric ground-based observations were conducted with standard filters at two observatories in the Czech Republic. We computed a new ephemeris for each of the systems using

Yao Yao, Zuchao Li, Hai Zhao

As Large Language Models (LLMs) become increasingly prevalent in various domains, their ability to process inputs of any length and maintain a degree of memory becomes essential. However, the one-off input of overly long texts is limited, as studies have shown that when input lengths exceed the LLMs' pre-trained text length, there is a dramatic decline in te

Cheng Ma, Min Chen, Yu Xie, Qiang Xu

Finite-temperature orbital-free density functional theory (FT-OFDFT) holds significant promise for simulating warm dense matter due to its favorable scaling with both system size and temperature. However, the lack of the numerically accurate and transferable noninteracting free energy functionals results in a limit on the application of FT-OFDFT for warm den

Srimanta Maity, Garima Arora

High-field terahertz (THz) pulse generation is investigated through the interaction of an intense single-color CO2 laser pulse with helium (He) gas targets. Employing multi-dimensional Particle-In-Cell (PIC) simulations, this study reveals a substantial enhancement in THz generation efficiency, even with a single-color laser pulse interacting with gas target

Dane C. Lacey, Christie L. Alappat, Florian Lange, Georg Hager

Sparse matrix-vector products (SpMVs) are a bottleneck in many scientific codes. Due to the heavy strain on the main memory interface from loading the sparse matrix and the possibly irregular memory access pattern, SpMV typically exhibits low arithmetic intensity. Repeating these products multiple times with the same matrix is required in many algorithms. Th

Zhitao Zhu, Chuanfu Xiao, Kejun Tang, Jizu Huang

Solving the Boltzmann-BGK equation with traditional numerical methods suffers from high computational and memory costs due to the curse of dimensionality. In this paper, we propose a novel accuracy-preserved tensor-train (APTT) method to efficiently solve the Boltzmann-BGK equation. A second-order finite difference scheme is applied to discretize the Boltzma

Jiaqi Li, Qianshan Wei, Chuanyi Zhang, Guilin Qi

Machine unlearning empowers individuals with the `right to be forgotten' by removing their private or sensitive information encoded in machine learning models. However, it remains uncertain whether MU can be effectively applied to Multimodal Large Language Models (MLLMs), particularly in scenarios of forgetting the leaked visual data of concepts. To overcome

Mohammad Amir Fallah, Mehdi Monemi, Mehdi Rasti, Matti Latva-Aho

Three-dimensional (3D) spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within a very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for eff

Charles O'Neill, Thang Bui

This paper introduces an efficient and robust method for discovering interpretable circuits in large language models using discrete sparse autoencoders. Our approach addresses key limitations of existing techniques, namely computational complexity and sensitivity to hyperparameters. We propose training sparse autoencoders on carefully designed positive and n

Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang

Graph Neural Networks (GNNs) are deep-learning architectures designed for graph-type data, where understanding relationships among individual observations is crucial. However, achieving promising GNN performance, especially on unseen data, requires comprehensive hyperparameter tuning and meticulous training. Unfortunately, these processes come with high comp

Eliza Kosoy, Soojin Jeong, Anoop Sinha, Alison Gopnik

In this work we investigate how children ages 5-12 perceive, understand, and use generative AI models such as a text-based LLMs ChatGPT and a visual-based model DALL-E. Generative AI is newly being used widely since chatGPT. Children are also building mental models of generative AI. Those haven't been studied before and it is also the case that the children'

Jun Zhang, Wenxuan Ao, Junbo Yan, Can Rong

In the research of Intelligent Transportation Systems (ITS), traffic simulation is a key procedure for the evaluation of new methods and optimization of strategies. However, existing traffic simulation systems face two challenges. First, how to balance simulation scale with realism is a dilemma. Second, it is hard to simulate realistic results, which require

Yuwen Qian, Shuchi Wu, Kang Wei, Ming Ding

Federated self-supervised learning (FSSL) has recently emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data while preserving data privacy. While FSSL offers advantages, its susceptibility to backdoor attacks, a concern identified in traditional federated supervised learning (FSL), has not been investigated.

Zhaoning Yu, Hongyang Gao

Graph Neural Networks (GNNs) have shown remarkable success in molecular tasks, yet their interpretability remains challenging. Traditional model-level explanation methods like XGNN and GNNInterpreter often fail to identify valid substructures like rings, leading to questionable interpretability. This limitation stems from XGNN's atom-by-atom approach and GNN

Yanping Yang, Shihan Liu, Yong Geng, Huashun Wen

Cavity ring-down is an essential test to measure ultra-high quality factor (UHQ) optical cavities, which is, however, frequently misinterpreted due to lacking of a specified analysis guideline. Here we clarify the basic property of cavity ring down and present a step-by-step method that enables extraction of the overall quality factor, as well as the intrins

Roland Bacon, Vincenzo Maineiri, Sofia Randich, Andrea Cimatti

In this paper, we describe the wide-field spectroscopic survey telescope (WST) project. WST is a 12-metre wide-field spectroscopic survey telescope with simultaneous operation of a large field-of-view (3 sq. degree), high-multiplex (20,000) multi-object spectrograph (MOS), with both a low and high-resolution modes, and a giant 3x3 arcmin2 integral field spec

Reimi Irokawa

We study degenerating families of hyperbolic dynamics over complex K3 surfaces by means of the theory of hybrid spaces by Boucksom, Favre, and Jonsson. For an analytic family of hyperbolic automorphisms $\{f_t: X_t\to X_t\}_{t\in\mathbb{D}^*}$ over K3 surfaces $X_t$ that is possibly meromorphically degenerating at the origin, we consider the family of invari

Ruby Varshney, Kaustubh Manchanda, Haider Hasan Jafri

We study the effect of network topology on the collective dynamics of an oscillator ensemble. Specifically, we explore explosive synchronization in a system of interacting star networks. Explosive synchronization is characterized by an abrupt transition from an incoherent state to a coherent state. In this study, we couple multiple star networks through thei

Supriti Laha, Lakshmi Kanta Dey

Hyre-Ulam stability of functional equation in single variable is studied in non-triangular metric spaces. We derive it as applications of some fixed point results developed on the said structure. A general version of Baker's theorem is also deduced as a consequence.

Pat Pataranutaporn, Kavin Winson, Peggy Yin, Auttasak Lapapirojn

We introduce "Future You," an interactive, brief, single-session, digital chat intervention designed to improve future self-continuity--the degree of connection an individual feels with a temporally distant future self--a characteristic that is positively related to mental health and wellbeing. Our system allows users to chat with a relatable yet AI-powered

Sai Dhawal Phaye, Gregory J. Duck, Roland H. C. Yap, Trevor E. Carlson

Memory errors continue to be a critical concern for programs written in low-level programming languages such as C and C++. Many different memory error defenses have been proposed, each with varying trade-offs in terms of overhead, compatibility, and attack resistance. Some defenses are highly compatible but only provide minimal protection, and can be easily

Yuhao Cheng, Siru Zhang, Yiqiang Yan

Optical flow estimation is one of the fundamental tasks in low-level computer vision, which describes the pixel-wise displacement and can be used in many other tasks. From the apparent aspect, the optical flow can be viewed as the correlation between the pixels in consecutive frames, so continuously refining the correlation volume can achieve an outstanding

Gongsheng Yuan, Yuxing Chen, Jiaheng Lu, Sai Wu

In the decades, the general field of quantum computing has experienced remarkable progress since its inception. A plethora of researchers not only proposed quantum algorithms showing the power of quantum computing but also constructed the prototype of quantum computers, making it walk into our tangible reality. Those remarkable advancements in quantum comput

Kamaljeet Singh, Jayanta Dey, Raghunath Sahoo

Relativistic heavy-ion collisions produce quark-gluon plasma (QGP), which is locally thermalized. Due to electrically charged particles (quarks), QGP exhibits interesting thermoelectric phenomena during its evolution, resulting in an electromagnetic (EM) field in the medium. In this study, for the first time, we estimate the induced electric field in QGP due

Dejie Yang, Yang Liu

Accurately detecting active objects undergoing state changes is essential for comprehending human interactions and facilitating decision-making. The existing methods for active object detection (AOD) primarily rely on visual appearance of the objects within input, such as changes in size, shape and relationship with hands. However, these visual changes can b

Iu-Iong Ng

In this paper, we calculate the upper bound on quantum space complexity of the quantum algorithms proposed by Biasse and Song (SODA'16) for solving class group computation and the principal ideal problem using the reductions to $S$-unit group computation. We follow the approach of Barbulescu and Poulalion (AFRICACRYPT'23) and the framework given by de Boer,

Pawel K. Radtke, Tobias Weinzierl

The C programming language and its cousins such as C++ stipulate the static storage of sets of structured data: Developers have to commit to one, invariant data model -- typically a structure-of-arrays (SoA) or an array-of-structs (AoS) -- unless they manually rearrange, i.e.~convert it throughout the computation. Whether AoS or SoA is favourable depends on

Peng Gao

We establish upper bounds for moments of zeta sums using results on shifted moments of the Riemann zeta function under the Riemann hypothesis.

Hongsheng Wang, Nanjie Yao, Xinrui Zhou, Shengyu Zhang

In the animation industry, 3D modelers typically rely on front and back non-overlapped concept designs to guide the 3D modeling of anime characters. However, there is currently a lack of automated approaches for generating anime characters directly from these 2D designs. In light of this, we explore a novel task of reconstructing anime characters from non-ov

Xin-Bo Huang, Xiang-Gao Wang, Long Li, Li-Ping Xin

We present photometric and spectroscopic observations and analysis of the type IIb supernova (SN) SN 2019tua, which exhibits multiple bumps in its declining light curves between 40 and 65 days after discovery. SN 2019tua shows a time to peak of about 25 days similar to other type IIb SNe. Our observations indicate a decrease in its brightness of about 1 magn

Mitra Darvishi, Malihe Heydari-Fard, Morteza Mohseni

In this paper, we investigate the effects of the {\ae}ther field and the electric charge on the observed shadow of two types of charged black holes in the Einstein-Maxwell-{\AE}ther theory. By considering that the Einstein-Maxwell-{\AE}ther black holes surrounded by the static/infalling spherical accretion flows, as well as an optically and geometrically thi

Weiqing Qi, Guoyang Zhao, Fulong Ma, Linwei Zheng

Road lanes are integral components of the visual perception systems in intelligent vehicles, playing a pivotal role in safe navigation. In lane detection tasks, balancing accuracy with real-time performance is essential, yet existing methods often sacrifice one for the other. To address this trade-off, we introduce CLRKDNet, a streamlined model that balances

Yihong Huang, Yuang Zhang, Liping Wang, Fan Zhang

Unsupervised Outlier Detection (UOD) is an important data mining task. With the advance of deep learning, deep Outlier Detection (OD) has received broad interest. Most deep UOD models are trained exclusively on clean datasets to learn the distribution of the normal data, which requires huge manual efforts to clean the real-world data if possible. Instead of

M. Martínez-Marín, K. Glazebrook, T. Nanayakkara, C. Jacobs

We present a sample of 22 massive galaxies with stellar masses $>10^{10} M_{\odot}$ at $3<z<4$ with deep H and K-band high resolution spectra (R=3500-3000) from Keck/MOSFIRE and VLT/KMOS near-infrared spectrographs. We find a large fraction have strong [OIII]5007 and H$\beta$ emission lines with large line widths ($\sigma$ 100 -- 450 km/s). We measure the si

Noé Hernández, Rafael Morales, Luis A. Pineda

The entropic associative memory (EAM) is a computational model of natural memory incorporating some of its putative properties of being associative, distributed, declarative, abstractive and constructive. Previous experiments satisfactorily tested the model on structured, homogeneous and conventional data: images of manuscripts digits and letters, images of

Edgar Torres-Teutle, Francisco J. Mendoza-Torres, Maria G. Morales-Macias

This work proves pointwise convergence of the truncated Fourier double integral of non-Lebesgue integrable bounded variation functions. This leads to the Dirichlet-Jordan theorem proof for non-Lebesgue integrable functions, which has not been sufficiently studied. Note that recent contributions regarding this subject consider Lebesgue integrable functions, [

Tonghua Liu, Xinyi Zhong, Jieci Wang, Marek Biesiada

The sound horizon is a key theoretical prediction of the cosmological model that depends on the speed of sound and the rate of expansion in the early universe, before matter and radiation decoupled. The standard ruler for low redshift calibration of baryon acoustic oscillations (BAOs) is a direct measurement that would exist even if the standard cosmological

Jianyang Gao, Cheng Long

Searching for approximate nearest neighbors (ANN) in the high-dimensional Euclidean space is a pivotal problem. Recently, with the help of fast SIMD-based implementations, Product Quantization (PQ) and its variants can often efficiently and accurately estimate the distances between the vectors and have achieved great success in the in-memory ANN search. Desp

Xiaoyi Shen, Dongyuan Shi, Zhengding Luo, Junwei Ji

Active Noise Control (ANC) is a widely adopted technology for reducing environmental noise across various scenarios. This paper focuses on enhancing noise reduction performance, particularly through the refinement of signal quality fed into ANC systems. We discuss the main wireless technique integrated into the ANC system, equipped with some innovative algor

Wen-Shu Fan, Xin-Chun Li, De-Chuan Zhan

Knowledge Distillation (KD) could transfer the ``dark knowledge" of a well-performed yet large neural network to a weaker but lightweight one. From the view of output logits and softened probabilities, this paper goes deeper into the dark knowledge provided by teachers with different capacities. Two fundamental observations are: (1) a larger teacher tends to

Li-Xin Zhang

The randomized play-the-winner rule (RPW) is a response-adaptive design proposed by Wei and Durham (1978) for sequentially randomizing patients to treatments in a two-treatment clinical trial so that more patients are assigned to the better treatment as the clinical trial goes on. The elephant random walk (ERW) proposed by Schutz and Trimper (2004) is a non-

Mithün Paul, Genevieve Bartlett, Jelena Mirkovic, Marjorie Freedman

Enterprise security is increasingly being threatened by social engineering attacks, such as phishing, which deceive employees into giving access to enterprise data. To protect both the users themselves and enterprise data, more and more organizations provide cyber security training that seeks to teach employees/customers to identify and report suspicious con

Yichu Xu, Xin-Chun Li, Lan Li, De-Chuan Zhan

The loss landscape of deep neural networks (DNNs) is commonly considered complex and wildly fluctuated. However, an interesting observation is that the loss surfaces plotted along Gaussian noise directions are almost v-basin ones with the perturbed model lying on the basin. This motivates us to rethink whether the 1D or 2D subspace could cover more complex l

Tao Wen, Songzhan Chen, BenZhong Dai

With great advance of ground-based extensive air shower array, such as LHAASO and HAWC, many very high energy (VHE) gamma-ray sources have been discovered and are been monitored regardless of the day and the night. Hence, the Sun and Moon would have some compact on the observation of gamma-ray sources, which have not been taken into account in previous analy

Xu Wen, Wanling Gao, Lei Wang, Jianfeng Zhan

The rapid development of domain-specific frameworks has presented us with a significant challenge: The current approach of implementing solutions on a case-by-case basis incurs a theoretical complexity of O(M*N), thereby increasing the cost of porting applications to different hardware platforms. To address these challenges, we propose a systematic methodolo

Jinshu Chen, Bingchuan Li, Miao Hua, Panpan Xu

Existing solutions to image editing tasks suffer from several issues. Though achieving remarkably satisfying generated results, some supervised methods require huge amounts of paired training data, which greatly limits their usages. The other unsupervised methods take full advantage of large-scale pre-trained priors, thus being strictly restricted to the dom

Xin-Chun Li, Jin-Lin Tang, Bo Zhang, Lan Li

Exploring the loss landscape offers insights into the inherent principles of deep neural networks (DNNs). Recent work suggests an additional asymmetry of the valley beyond the flat and sharp ones, yet without thoroughly examining its causes or implications. Our study methodically explores the factors affecting the symmetry of DNN valleys, encompassing (1) th

Super-Kamiokande, T2K collaborations, :, S. Abe

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of

Chiman Salavati, Shannon Song, Willmar Sosa Diaz, Scott A. Hale

Biased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce BRICC, a firstin-class initiative that seeks to mitigate medical bisinformation using machine learning to systematically identify and flag text with potential biases, for subsequent review in an e

Yan He, Bing Tu, Bo Liu, Jun Li

Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional Neural Networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies. However, these architectures suffer from limited receptive fields and quadratic computational complexity, r

Hao Jiang, Chuanzhen Li, Mingxiao An

Large Language Models (LLMs) have revolutionized text comprehension, leading to State-of-the-Art (SOTA) news recommendation models that utilize LLMs for in-depth news understanding. Despite this, accurately modeling user preferences remains challenging due to the inherent uncertainty of click behaviors. Techniques like multi-head attention in Transformers se

Francesca Fernandes, Matilde Marcolli

We present a categorical formalism for context-free languages with morphisms given by correspondences obtained from rational transductions. We show that D0L-systems are a special case of the correspondences that define morphisms in this category. We construct a functorial mapping to aperiodic spin chains. We then generalize this construction to a class of mi

Chun Yuan, Haoyang Shi, Lei Lan, Yuxing Qiu

This paper presents volumetric homogenization, a spatially varying homogenization scheme for knitwear simulation. We are motivated by the observation that macro-scale fabric dynamics is strongly correlated with its underlying knitting patterns. Therefore, homogenization towards a single material is less effective when the knitting is complex and non-repetiti

Ru Wang, Yue Dai, Jinsong Cheng, Ruoyu Wang

Inversion symmetry eliminates the second-order nonlinear responses in materials commonly used in silicon photonics with electric-dipole approximation. The lack of effective methods to induce the second-order nonlinearity in silicon photonic materials prevents their applications in second-order nonlinear integrated photonics. Here, we experimentally demonstra

Byoung S. Ham

Coherence spectroscopy has been intensively studied over the last several decades for various applications in science and engineering. The Rayleigh criterion defines the resolution limit of an interferometer, where many-wave interference beats the resolution limit of a two-slit system. On the other hand, the diffraction angle in a slit is reduced by the Kth

Yusuke Shibasaki

The fundamental properties of 2-dimensional (2D) Ising system were formulated using the Loewner theory. We focus on the role of the complexity measure of the 2D geometry, referred to as the Loewner entropy, to derive the statistical-mechanical relations of the 2D Ising system by analyzing the structure of the interface (i.e., the phase separation line). For

Kaixin Ji, Sachin Pathiyan Cherumanal, Johanne R. Trippas, Danula Hettiachchi

Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines. However, user-system interactions in audio-only channels -- as is the case for Spoken Conversational Search (SCS) -- are harder to characterize, given the lack of instruments to effectively and precisely capture interactions. Furthe

W. Brent Lindquist, Svetlozar T. Rachev, Jagdish Gnawali, Frank J. Fabozzi

We present a unified, market-complete model that integrates both the Bachelier and Black-Scholes-Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless rates. In contrast to classical Black-Scholes-Merton, we

Minghao Han, Jingshi Yao, Adrian Wing-Keung Law, Xunyuan Yin

Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational performance of the water treatment facilities. In this study, we propose a data-driven economic predictive control approach within the Koopman modeling framework. First, we propose a dee

Hongsheng Wang, Weiyue Zhang, Sihao Liu, Xinrui Zhou

Although 3D Gaussian Splatting (3DGS) has recently made progress in 3D human reconstruction, it primarily relies on 2D pixel-level supervision, overlooking the geometric complexity and topological relationships of different body parts. To address this gap, we introduce the Hierarchical Graph Human Gaussian Control (HUGS) framework for achieving high-fidelity

Weizhen Liu, Jiayu Tan, Guangyu Lan, Ao Li

Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduc

Zhenwei Wang, Ruibin Bai, Fazlullah Khan, Ender Ozcan

Learning-based methods have become increasingly popular for solving vehicle routing problems due to their near-optimal performance and fast inference speed. Among them, the combination of deep reinforcement learning and graph representation allows for the abstraction of node topology structures and features in an encoder-decoder style. Such an approach makes

Keke Huang, Yu Guang Wang, Ming Li, and Pietro Liò

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these

Mingjia Yin, Hao Wang, Wei Guo, Yong Liu

Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains. While significant endeavors have been made, they primarily concentrated on developing advanced transfer modules and aligning user representations using self-supervised learning techniques. However, the problem of al

Govind Ramesh, Yao Dou, Wei Xu

Research on jailbreaking has been valuable for testing and understanding the safety and security issues of large language models (LLMs). In this paper, we introduce Iterative Refinement Induced Self-Jailbreak (IRIS), a novel approach that leverages the reflective capabilities of LLMs for jailbreaking with only black-box access. Unlike previous methods, IRIS

Ruichen Zhang, Hongyang Du, Dusit Niyato, Jiawen Kang

In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is becoming increasingly important. Motivated by this, this article studies the contrasting and converging of MAS and MoE in AIGC-enabled networking

Qiaozhi Xu, Hasan Siddiquee, Shannon Gould, Jiahui Althena Zhu

Kondo lattice systems are recognized for potentially hosting a variety of rich topological phases. Several pioneering studies have demonstrated significant anomalous Hall and anomalous Nernst effects in these systems, attributed to the Berry curvature of the hybridization bands. In this study, we investigate UBiTe, a ferromagnetic Kondo lattice system. Our f

Liwei Qiu, Lih-King Lim, Xin Wan

We explore the time evolution of a topological system when the system undergoes a sudden quantum quench within the same nontrivial phase. Using Haldane's honeycomb model as an example, we show that equilibrium states in a topological phase can be distinguished by geometrical features, such as the characteristic momentum at which the half-occupied edge modes

Zirui Neil Zhao, Adam Morrison, Christopher W. Fletcher, Josep Torrellas

Last-level cache side-channel attacks have been mostly demonstrated in highly-controlled, quiescent local environments. Hence, it is unclear whether such attacks are feasible in a production cloud environment. In the cloud, side channels are flooded with noise from activities of other tenants and, in Function-as-a-Service (FaaS) workloads, the attacker has a

James D. Finch, Jinho D. Choi

We demonstrate substantial performance gains in zero-shot dialogue state tracking (DST) by enhancing training data diversity through synthetic data generation. Existing DST datasets are severely limited in the number of application domains and slot types they cover due to the high costs of data collection, restricting their adaptability to new domains. This

Yu Hao, Hiroyuki Kasahara

This paper extends the work of Arcidiacono and Miller (2011, 2019) by introducing a novel characterization of finite dependence within dynamic discrete choice models, demonstrating that numerous models display 2-period finite dependence. We recast finite dependence as a problem of sequentially searching for weights and introduce a computationally efficient m

Jose Carlos Pelayo, Thomás Fogarty, Thomas Busch, Simeon I. Mistakidis

We unravel the ground state properties and emergent non-equilibrium dynamics of a mixture consisting of a few spin-polarized fermions embedded in a two-dimensional bosonic quantum droplet. For an increasingly attractive droplet-fermion interaction we find a transition from a spatially delocalized fermion configuration to a state where the fermions are highly

Yusuke Yamazaki, Ali Harandi, Mayu Muramatsu, Alexandre Viardin

We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (PDEs). The proposed framework employs a loss function inspired by the finite element method (FEM) with the implicit Euler time integration scheme. A transient thermal conduction pr

Haoji Liu, Fatemeh Jahedinia, Zeyu Mu, B. Brian Park

Cooperative on-ramp merging control for connected automated vehicles (CAVs) has been extensively investigated. However, they did neglect the connected vehicle identification process, which is a must for CAV cooperations. In this paper, we introduced a connected vehicle identification system (VIS) into the on-ramp merging control process for the first time an

Georgiy A. Bondar, Robert Gifford, Linh Thi Xuan Phan, Abhishek Halder

We propose to learn the time-varying stochastic computational resource usage of software as a graph structured Schr\"odinger bridge problem. In general, learning the computational resource usage from data is challenging because resources such as the number of CPU instructions and the number of last level cache requests are both time-varying and statistically

Zhicheng Zhang, Yong Wang, Shaoqi Tan, Bowei Xia

Recently, Transformer-based models for long sequence time series forecasting have demonstrated promising results. The self-attention mechanism as the core component of these Transformer-based models exhibits great potential in capturing various dependencies among data points. Despite these advancements, it has been a subject of concern to improve the efficie

Changmao Chen, Yuren Cong, Zhen Kan

Affordance grounding aims to localize the interaction regions for the manipulated objects in the scene image according to given instructions. A critical challenge in affordance grounding is that the embodied agent should understand human instructions and analyze which tools in the environment can be used, as well as how to use these tools to accomplish the i

Jianan Li, Tao Huang, Qingxu Zhu, Tien-Tsin Wong

Creating scenes for captured motions that achieve realistic human-scene interaction is crucial for 3D animation in movies or video games. As character motion is often captured in a blue-screened studio without real furniture or objects in place, there may be a discrepancy between the planned motion and the captured one. This gives rise to the need for automa

Zeyu Zhou, Yan Lin, Haomin Wen, Qisen Xu

Spatio-temporal trajectories are crucial in various data mining tasks. It is important to develop a versatile trajectory learning method that performs different tasks with high accuracy. This involves effectively extracting two core aspects of information--movement patterns and travel purposes--from trajectories. However, this is challenging due to limitatio

Anna Kawakami, Amanda Coston, Hoda Heidari, Kenneth Holstein

As public sector agencies rapidly introduce new AI tools in high-stakes domains like social services, it becomes critical to understand how decisions to adopt these tools are made in practice. We borrow from the anthropological practice to ``study up'' those in positions of power, and reorient our study of public sector AI around those who have the power and

Yu Wang, Shubin Liu, Hao Zhuang, Zhengwu Ding

The demand for a large number of readout channels has been a limiting factor for the application of Micro-pattern Gaseous Detectors (MPGDs) in achieving higher spatial resolution and larger detection areas. This challenge is further compounded by issues related to system integration, power consumption, and cost efficiency. To address these challenges, this s

Jinxin Xu, Kaixian Xu, Yue Wang, Qinyan Shen

Financial market risk forecasting involves applying mathematical models, historical data analysis and statistical methods to estimate the impact of future market movements on investments. This process is crucial for investors to develop strategies, financial institutions to manage assets and regulators to formulate policy. In today's society, there are probl

Hadi Hadizadeh, S. Faegheh Yeganli, Bahador Rashidi, Ivan V. Bajić

In recent years, there has been a significant increase in applications of multimodal signal processing and analysis, largely driven by the increased availability of multimodal datasets and the rapid progress in multimodal learning systems. Well-known examples include autonomous vehicles, audiovisual generative systems, vision-language systems, and so on. Suc

Vladislav V. Serov

We develop a time-dependent multi-configurational numerical technique for calculating ionization by short laser pulses of many-electron molecules. The method is based on the expansion of the wave function of a molecule into the eigenstates of the molecular ion. We classify this method as time-dependent convergent close coupling (TDCCC) because it uses the sa

Ting Wang, Yu Wang, Zhihang Yao, Yulin Liu

Muon imaging technology is an innovative imaging technique that can be applied in volcano imaging, heavy nuclear material detection, and archaeological research. The Micromegas detector is a promising choice for muon imaging due to its high spatial resolution and large area. However, the large number of readout channels poses a challenge for electronics. In

Hanwen Huang

Generating samples from a probability distribution is a fundamental task in machine learning and statistics. This article proposes a novel scheme for sampling from a distribution for which the probability density $\mu({\bf x})$ for ${\bf x}\in{\mathbb{R}}^d$ is unknown, but finite independent samples are given. We focus on constructing a Schr\"odinger Bridge

Junfeng Hu, Xu Liu, Zhencheng Fan, Yifang Yin

Spatio-temporal graph neural networks have proven efficacy in capturing complex dependencies for urban computing tasks such as forecasting and kriging. Yet, their performance is constrained by the reliance on extensive data for training on a specific task, thereby limiting their adaptability to new urban domains with varied task demands. Although transfer le

C. Flores, M. S. Connelley, B. Reipurth, A. Boogert

We perform the first magnetic field strength survey of Class I and Flat Spectrum (FS) sources using $K$-band observations with iSHELL. We obtained new observations of 42 Class I and FS sources and additionally included 10 sources from the archive. We detect photospheric lines in 44 of the sources, in addition to Br$\gamma$, H$_2$, and CO emission in several

S. Zhang, S. Wang, H. Miao, H. Chen

Multivariant time series (MTS) data are usually incomplete in real scenarios, and imputing the incomplete MTS is practically important to facilitate various time series mining tasks. Recently, diffusion model-based MTS imputation methods have achieved promising results by utilizing CNN or attention mechanisms for temporal feature learning. However, it is har

Seif Abukhalaf, Mohammad Hamdaqa, Foutse Khomh

The rapid progress of AI-powered programming assistants, such as GitHub Copilot, has facilitated the development of software applications. These assistants rely on large language models (LLMs), which are foundation models (FMs) that support a wide range of tasks related to understanding and generating language. LLMs have demonstrated their ability to express

R. R. S. Oliveira

In this paper, we study the relativistic energy spectrum for Dirac fermions under rainbow gravity effects in the $(3+1)$-dimensional Bonnor-Melvin-Lambda spacetime, where we work with the curved Dirac equation in cylindrical coordinates. Using the tetrads formalism of General Relativity and considering a first-order approximation for the trigonometric functi

Daria Pidhorodetska, Emily A. Gilbert, Stephen R. Kane, Thomas Barclay

Exoplanet discoveries have revealed a dramatic diversity of planet sizes across a vast array of orbital architectures. Sub-Neptunes are of particular interest; due to their absence in our own solar system, we rely on demographics of exoplanets to better understand their bulk composition and formation scenarios. Here, we present the discovery and characteriza

Weijia Fan, Jiajun Wen, Xi Jia, Linlin Shen

Prototype learning is widely used in face recognition, which takes the row vectors of coefficient matrix in the last linear layer of the feature extraction model as the prototypes for each class. When the prototypes are updated using the facial sample feature gradients in the model training, they are prone to being pulled away from the class center by the ha

Maxime Murray, J. D. Mireles James

This work develops a functional analytic framework for making computer assisted arguments involving transverse heteroclinic connecting orbits between hyperbolic periodic solutions of ordinary differential equations. We exploit a Fourier-Taylor approximation of the local stable/unstable manifold of the periodic orbit, combined with a numerical method for solv

Sergey V. Buldyrev, Thomas J. Longo, Frederic Caupin, Mikhail A. Anisimov

The blinking-checkers model [F. Caupin and M. A. Anisimov, Phys. Rev. Lett, 127,185701 (2021)] is a minimal lattice model which has demonstrated that, in the meanfield approximation, it can reproduce the phenomenon of fluid polyamorphism. This model is a binary lattice-gas, in which each site has three possible states: empty, occupied with particles of type

Mazen Elsaadany, Hamid R. Ossareh, Mads R. Almassalkhi

Aggregations of thermostatically controlled loads (TCLs), such as air conditioners, offer valuable flexibility to the power grid. The aggregate power consumption of a TCL fleet can be controlled by adjusting thermostat setpoints. An \textit{ex-ante} quantification of the flexibility that results from such setpoint change can inform grid operator decisions. T

Ali Karkehabadi, Houman Homayoun, Avesta Sasan

The Forward-Forward Learning (FFL) algorithm is a recently proposed solution for training neural networks without needing memory-intensive backpropagation. During training, labels accompany input data, classifying them as positive or negative inputs. Each layer learns its response to these inputs independently. In this study, we enhance the FFL with the foll

Qingyao Li, Wei Xia, Kounianhua Du, Qiji Zhang

Concept recommendation aims to suggest the next concept for learners to study based on their knowledge states and the human knowledge system. While knowledge states can be predicted using knowledge tracing models, previous approaches have not effectively integrated the human knowledge system into the process of designing these educational models. In the era