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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lew, Jaihyun Choi, Jooyoung Shin, Chaehun Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of AIIS ASRI and INMC Seoul National University Korea Republic of

ISBN: (纸本)157735897X

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Interpolation

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Microwave-Based Complementary Split-Ring Resonator for the Detection of Variations in Aqueous Media

Microwave-Based Complementary Split-Ring Resonator for the D...

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Microelectronics Technology and Devices (SBMicro)

作者： Reinaldo Velásquez Diego Tami Gilberto Medeiros-Ribeiro Cássio G. Rego Jhonattan C. Ramirez Graduate Program in Electrical Engineering UFMG Brazil Institute of Technological Sciences UNIFEI Brazil Department of Computer Science UFMG Brazil Department of Electronics Engineering UFMG Brazil

In this paper, we demonstrated a highly sensitive microwave-based resonator sensor capable of detecting variations in an aqueous medium of up to 0.94 mg/dL. In the presented device, the intrinsic resonant response (resonant amplitude, resonant frequency, and quality factor) is capacitively perturbed in reaction to the sample presence. To achieve higher sensitivity, the sample under testing generally is placed on the resonator surface, where the electric field is highly concentrated at the operating frequency. Thus, demonstrating excellent results in terms of sensitivity and reproducibility.

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A Preliminary Study on the Collaborative Playing Piano Automatic with Deep Learning

A Preliminary Study on the Collaborative Playing Piano Autom...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Varinya Phanichraksaphong Karuna Yampray Department of Electronic Engineering International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan College of Creative Design and Entertainment Technology Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

A great number of deep learning-based models have been recently proposed for automatic piano classification. In this paper, we describe our contribution to the challenge of automatic piano classification when the performer performs at the concert or stage. Among these models in deep learning, we use init-1D-WaveNet and init-2D-MLNet for comparison the accuracy in the piano beginning level of the Christmas song (Jingle bells). Our experimental results show that the assessment using the init-2D-MLNet still achieve high accuracy of 87.5%.

关键词： Deep learning Accuracy Instruments Collaboration Consumer electronics

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Towards cross-modal causal structure and representation learning 2

Towards cross-modal causal structure and representation lear...

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2nd Machine Learning for Health Symposium, ML4H 2022

作者： Mao, Haiyi Liu, Hongfu Xiaotian Dou, Jason Benos, Panayiotis V. Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh United States Michtom School of Computer Science Brandeis University United States Department of Electrical and Computer Engineering University of Pittsburgh United States Department of Epidemiology University of Florida United States

Does the SARS-CoV-2 virus cause patients' chest X-Rays ground-glass opacities? Does an IDH-mutation cause differences in patients' MRI images? Conventional causal discovery algorithms, although well developed to uncover the cause-effect relationships on structured data, cannot elucidate causal relations between unstructured images and structured scalar variables due to the complexity of the former. In this paper, we consider causal discovery between images and structured (scalar) variables. Specifically, we derive low dimensional image representations to analyze with structured variables. We propose a two-module amortized variational algorithm named Cross-Modal Variational Causal representation and structure Learning (CMCL). CMCL jointly learns identifiable representations given a set of independent structured variables and causal relations via formulating latent representations and structured variables into a direct acyclic graph. Moreover, we further enforce counterfactual invariance/variance onto representations. We demonstrate that CMCL outperforms other related methods on synthetic datasets and validate causal relations on semi-synthetic datasets by visualization. © 2022 P.N. Argaw, E. Healey & I.S. Kohane.

关键词： Coronavirus

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation

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Bayesian Parameter Estimation for Partially Observed McKean-Vlasov Diffusions Using Multilevel Markov chain Monte Carlo

arXiv

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arXiv 2025年

作者： Jasra, Ajay Wu, Amin School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this article we consider Bayesian estimation of static parameters for a class of partially observed McKean-Vlasov diffusion processes with discrete-time observations over a fixed time interval. This problem features several obstacles to its solution, which include that the posterior density is numerically intractable in continuous-time, even if the transition probabilities are available and even when one uses a time-discretization, the posterior still cannot be used by adopting well-known computational methods such as Markov chain Monte Carlo (MCMC). In this paper we provide a solution to this problem by using new MCMC algorithms which can solve the aforementioned issues. This MCMC algorithm is extended to use multilevel Monte Carlo (MLMC) methods. We prove convergence bounds on our parameter estimators and show that the MLMC-based MCMC algorithm reduces the computational cost to achieve a mean square error versus ordinary MCMC by an order of magnitude. We numerically illustrate our results on two models. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Communications Using Rate Splitting Multiple Access

Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Com...

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IEEE Conference on Vehicular Technology (VTC)

作者： Aamer Mohamed Huroon Yu-Chih Huang Li-Chun Wang Department of Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Institute of Communications Engineering National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University

In this paper, we study the transmission strategy of a ground-based beyond diagonal reconfigurable intelligent surface (BD-RIS), a.k.a RIS 2.0, in a network where multiple unmanned aerial vehicles (UAVs) simultaneously transmit signals to the respective groups of users. It is assumed that each group is assigned subcarriers orthogonal to those assigned to other groups and rate splitting multiple access (RSMA) is adopted within each group. A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders. To solve the problem, we propose using generalized benders decomposition (GBD) augmented with a manifold-based algorithm. GBD splits the MINLP problem into two sub-problems, namely the primal and the relaxed master problem, which are solved alternately and iteratively. In the primal problem, we apply block coordinate descent (BCD) to manage the coupling of variables effectively. Moreover, we recognize the manifold structure in the phase rotation constraint of BD-RIS, enabling the Riemannian conjugate gradient (RCG). Simulation results demonstrate the effectiveness of the proposed approach in maximizing spectral efficiency.

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Multi-Domain Emotion Recognition Enhancement: A Novel Domain Adaptation Technique for Speech-Emotion Recognition

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 528-541页

作者： Ammar Amjad Sucharita Khuntia Hsien-Tsung Chang Li-Chia Tai Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan Taiwan Department of Physical Medicine and Rehabilitation Chang Gung Memorial Hospital Taoyuan Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

As artificial intelligence advances, speech-emotion recognition (SER) has become a critical research area. Traditional SER methods typically rely on homogeneous domain data for training and testing. This practice requires adaptation when confronted with real-world data's heterogeneous linguistic, methodological, and speaker-related attributes. These variances can degrade the accuracy and generalization of SER models. To address this gap, we introduce a novel domain adaptation technique, multi domain emotion recognition enhancement (MDERE), which utilizes a non-negative matrix to reduce the inflexibility of the conventional binary label matrix for source domain data. This process yields a label matrix that better adapts to the nuances of the source labels while preserving their original structure. This framework refines SER methods by fine-tuning a transformation matrix for enhanced emotion discrimination. Elastic net regularization, which combines L1 and L2 penalties, enriches the transformation matrix, selectively emphasizing relevant features to enhance the robustness of emotion detection. The framework constructs customized similarity and dissimilarity graphs to reconcile the differences between source and target domains, enabling nuanced cross-domain data analysis. Extensive testing on multiple cross-domain SER tasks has shown that MDERE substantially improves recognition accuracy, surpassing several state-of-the-art algorithms. These results demonstrate MDERE's ability to effectively align domain variations enhances the generalizability of SER systems.

关键词： Emotion recognition Speech processing Speech recognition Training Adaptation models Testing Sparse matrices Linear regression Accuracy Vectors

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Real-time Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video in Compressed Domain

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APSIPA Transactions on Signal and Information Processing 2023年第4期12卷

作者： Ardianto, Sandy Hang, Hsueh-Ming Cheng, Wen-Huang Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Our goal is to develop real-time vehicle detection and tracking schemes for fisheye traffic monitoring video using the temporal information in the compressed domain without decoding the entire video. Two algorithms are proposed. The first algorithm starts with a conventional single-frame detector, but we introduce a multi-frame information fusion stage to improve the final detection and tracking accuracy, which is implemented using multi-modal bi-directional LSTM (MM bi-LSTM) network. The second algorithm first constructs multi-frame motion trail image, and then a single-image multi-head detector is designed to produce bounding boxes of an individual frame. The first scheme can be viewed as a detect-to-track design, and the second scheme is track-to-detect. We tested our proposals on the ICIP2020 VIP Cup dataset in H.265 video format. The aforementioned algorithms are applied to the motion fields and residual images in the H.265 compressed data set. It turns out that their detection and tracking performances are on par with their pixel-domain counterparts, and they can achieve the state-of-the-art accuracy of conventional video object detectors and trackers. If the decoding process for video compression is not counted, their computational complexities are much lower than the conventional pixel-domain video object detectors and trackers. © 2023 S. Ardianto, H.-M. Hang and W.-H. Cheng.

关键词： Image compression

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A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORING IL-6 BIOMARKER 25

A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORIN...

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25th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2021

作者： Laliberte, Kaitlyn E. Scott, Patrick Khan, Niazul I. Mahmud, Md Shaad Song, Edward Department of Electrical and Computer Engineering University of New Hampshire United States Materials Science Program University of New Hampshire United States

ISBN: (纸本)9781733419031

Graphene field-effect transistor (GFET) is becoming an increasingly popular biosensing platform for monitoring health conditions through biomarker detection. Moreover, the graphene's 2-dimensional geometry makes it ideal for implementing flexible wearable devices. In this work, we present a printable graphene transistor-based biosensor that can be used as a wearable sensor to detect a protein biomarker Interleukin-6 (IL-6). Our device contains a voltage control, a current meter, and a power source all integrated into a single miniaturized platform making it a truly skin-mounted wearable device. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Graphene transistors

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