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Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation

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arXiv 2025年

作者： Kim, HyunGi Kim, Siwon Mok, Jisoo Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of AIIS ASRI INMC Seoul National University Korea Republic of

Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. Nonetheless, the non-stationarity of time series undermines the reliability of pre-trained source time series forecasters in mission-critical deployment settings. In this study, we introduce a pioneering test-time adaptation framework tailored for TSF (TSF-TTA). TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training. The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters. Experiments on diverse benchmark datasets and cutting-edge architectures demonstrate the efficacy and generality of TAFAS, especially in long-term forecasting scenarios that suffer from significant distribution shifts. The code is available at https://***/kimanki/TAFAS. © 2025, CC BY-NC-SA.

关键词： Deep neural networks

UAV-Based Real-Time Air Quality Monitoring and Prediction Using Machine Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Hsu, Huan-Chia Tarekegn, Getaneh Berie Getnet, Mastewal Endeshaw Yu, Kuo-Pin Tai, Li-Chia NYCU Department of Electrical and Computer Engineering Hsinchu30010 Taiwan NYCU Institute of Electrical and Control Engineering Hsinchu30010 Taiwan NYCU Institute of Environmental and Occupational Health Sciences Taipei112304 Taiwan NYCU Department of International Health Program Taipei112304 Taiwan

Globally, air pollution has become a major problem due to the growth of the human population and rapid urbanization, posing a serious threat to the environment and human health. Currently, most monitoring systems are based on fixed monitoring stations and are inefficient due to slow response time. An accurate and real-time air quality monitoring and prediction (AQMP) system is required to prevent health problems caused by air pollution. With the swift advancement of Internet of Things (IoT) technologies such as low-cost sensors and advanced wireless communication systems capable of sensing their environment, connecting with other systems, and interacting with users. To overcome the problems of existing systems, this paper aims to develop an unmanned aerial vehicle (UAV)-based real-time AQMP system by integrating artificial intelligence and data science technologies. The proposed UAV is equipped with lightweight air quality sensors including particulate matter (i.e., PM2.5 and PM10), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3), and meteorological sensors. The sensors are mounted on the UAV via the microcontroller, and the sensors reading data is transmitted to the web server via a message queueing telemetry transport (MQTT) protocol using a 5G mobile network module. Practical experiments were conducted in Hsinchu City and the industrial area of Yunlin County to collect real-time air pollutant data. Then, we employed a hybrid of attention mechanism, convolutional neural network (CNN) and gated recurrent unit (GRU) algorithms to predict future air quality levels. Experimental results show that the proposed system improves prediction performance compared with baseline time series algorithms. © 2014 IEEE.

关键词： Air quality

Effects of Incident Angle on Single-Event Leakage Current Degradation in Heavy-Ion Irradiated 1800 v and 4500 v SiC Power MOSFETs

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IEEE Transactions on Nuclear Science 2025年

作者： Sengupta, A. Martinella, C. Ball, D.R. Niskanen, K. Senarath, A.S. Islam, S. Harris, P.M. Hutson, J.M. Osheroff, J.M. Sternberg, A.L. Kettunen, H. Kosier, S.L. Grossner, U. Javanainen, A. Galloway, K.F. Schrimpf, R.D. Witulski, A.F. Zürich8092 Switzerland Vanderbilt University Interdisciplinary Materials Science Program NashvilleTN37235 United States Lipscomb University Electrical and Computer Engineering Department NashvilleTN37204 United States NASA/GSFC Code 561.4 GreenbeltMD20771 United States Vanderbilt University Electrical Engineering and Computer Science Department B 351825 NashvilleTN37235 United States University of Jyväskylä Department of Physics Jyväskylä40014 Finland

Experimental heavy-ion responses of two variants of SiC power MOSFETs are evaluated. The devices have similar epitaxial thickness but different doping. The higher doping in the epitaxial region results in lower breakdown voltage, and typically leads to a lower threshold drain voltage at which single-event leakage current (SELC) occurs. Furthermore, the heavy-ion-induced SELC degradation in the drain and gate currents of the SiC MOSFETs exhibit a strong dependence on the heavy-ion's angle of incidence. The devices' behavior significantly alters when irradiated with heavy-ions at different incident angles under the same fixed bias and total fluence, as also confirmed by 3-D Technology computer Aided Design (TCAD) simulations. © 1963-2012 IEEE.

关键词： Power MOSFET

Evaluating the Systematic Reasoning Abilities of Large Language Models through Graph Coloring

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arXiv 2025年

作者： Heyman, Alex Zylberberg, Joel Department of Electrical Engineering and Computer Science York University TorontoON Canada Jules Stein Eye Institute University of California Los AngelesCA United States Learning in Machines and Brains Program CIFAR TorontoON Canada

Contemporary large language models are powerful problem-solving tools, but they exhibit weaknesses in their reasoning abilities which ongoing research seeks to mitigate. We investigate graph coloring as a means of evaluating an LLM’s capacities for systematic step-by-step reasoning and possibility space exploration, as well as effects of semantic problem framing. We test Claude 3.5 Sonnet, Llama 3.1 405B, Gemini 1.5 Pro, GPT-4o, o1-mini, and DeepSeek-R1 on a dataset of k-coloring problems with 2 ≤ k ≤ 4 and vertex count 4 ≤ n ≤ 8, using partial algorithmic solvers to further categorize problems by difficulty. In addition to substantial but varying framing effects, we find that all models except o1-mini and R1 exhibit > 60% error rates on difficult problem types in all frames (> 15% for o1-mini and > 10% for R1), and no model achieves perfect accuracy even in the simple domain of 2-coloring 4-vertex graphs. Our results highlight both the considerable recent progress in LLM systematic reasoning and the limits of its reliability, especially in relation to increasing computational costs. We expect that more complex graph coloring problems, and procedural generation of arbitrary-complexity reasoning problems more broadly, offer further untapped potential for LLM benchmarking. © 2025, CC BY.

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Alignment-Free Estimation of Read to Genome Distances and Its Applications 29th

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Alignment-Free Estimation of Read to Genome Distances and...

29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Şapcı, Ali Osman Berk Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering UC San Diego San DiegoCA92093 United States

ISBN: (纸本)9783031902512

Searching reads from unknown origins in a reference database and finding evolutionarily similar genomes is central to many applications. Quantifying the similarity by estimating the distance between each read and matching references could further help downstream analyses such as taxonomic characterization or even placing the read on a reference phylogeny. Such distances can be computed using alignment. Since alignment becomes impractical for ultra-large reference databases, many use k-mer-based tools for downstream tasks, but these methods do not compute distances. We introduce krepp to compute the distance between a read and a genome using a maximum likelihood framework built around k-mers. We further introduce algorithms to place reads on a reference tree. Empirical evaluations show that krepp accurately estimates distances, scales well with large databases, and can place short reads coming from any part of the genome on a reference phylogeny. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Average nucleotide identity Metagenomics Phylogenetic placement

Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025, CC BY.

关键词： Mobile edge computing

Porous silicon on paper sensors: Factors affecting biosensing performance 17

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Porous silicon on paper sensors: Factors affecting biosensin...

Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： An, Huijin Laibinis, Paul E. Locke, Andrea K. Weiss, Sharon M. Interdisciplinary Material Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemistry Vanderbilt University NashvilleTN37235 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States

ISBN: (纸本)9781510684249

A paper-based biosensor integrating a functionalized porous silicon (PSi) membrane as the active sensing element for quantifiable protein detection has been developed. For similar short-time exposures to an analyte, improved molecular transport in PSi membranes when on paper leads to larger signal changes compared to traditional PSi films that remain on a silicon substrate. In this work, we discuss controlling the incubation time of the analyte and the overall testing time of the sensor by incorporating different combinations of wicking and absorbent paper beneath the PSi membrane. With this control, the PSi-on-paper sensor platform has the potential to serve as an effective low-cost rapid diagnostic test with highly sensitive, quantitative readout for a wide range of analytes. © 2025 SPIE.

关键词： Porous silicon

Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes

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arXiv 2025年

作者： Rego, Brenner S. Raffo, Guilherme V. Terra, Marco H. Scott, Joseph K. Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Electronics Engineering the Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm. © 2025, CC BY-NC-ND.

关键词： Numerical methods

A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization