2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)最新文献_第5页

Deep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data 深度生成迁移学习从神经成像基因组学数据预测阿尔茨海默病的转化

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193683

Giorgio Dolci, M. A. Rahaman, I. Galazzo, F. Cruciani, A. Abrol, Jiayu Chen, Z. Fu, K. Duan, G. Menegaz, V. Calhoun

{"title":"Deep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data","authors":"Giorgio Dolci, M. A. Rahaman, I. Galazzo, F. Cruciani, A. Abrol, Jiayu Chen, Z. Fu, K. Duan, G. Menegaz, V. Calhoun","doi":"10.1109/ICASSPW59220.2023.10193683","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193683","url":null,"abstract":"Alzheimer’s disease (AD) represents nowadays the most common form of dementia affecting 46 million people worldwide. Important insights into the AD continuum are currently available thanks to functional and structural magnetic resonance imaging (fMRI/sMRI) techniques which allow to assess brain activity and cortical/subcortical atrophy, respectively, being now established biomarkers of the AD. In this paper we propose an imaging genetics framework which integrates sMRI/fMRI and genetics data with the aim of discriminating not only AD from healthy controls, but also mild cognitive impairment (MCI) subjects that converted to AD (MCIc) from those that remained stable over time (MCInc). This is one of the main challenges in the state-of-the-art owing to the prognostic and treatment impact. To this end, the model was first trained and tested for CN versus AD classification, and then transfer learning was exploited for assessing its performance in differentiating MCIc vs MCInc subjects in an unseen cohort. Experimental results showed that the proposed method allowed reaching competitive performance with respect to methods relying on the complete set of modalities for each subject, while outperforming the state-of-the-art in case of inclusion of subjects with missing views.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121643444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Confidence-Based Federated Distillation for Vision-Based Lane-Centering 基于置信度的基于视觉的车道定心联邦蒸馏

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193741

Yitao Chen, Dawei Chen, Haoxin Wang, Kyungtae Han, Mingbi Zhao

{"title":"Confidence-Based Federated Distillation for Vision-Based Lane-Centering","authors":"Yitao Chen, Dawei Chen, Haoxin Wang, Kyungtae Han, Mingbi Zhao","doi":"10.1109/ICASSPW59220.2023.10193741","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193741","url":null,"abstract":"A fundamental challenge of autonomous driving is maintaining the vehicle in the center of the lane by adjusting the steering angle. Recent advances leverage deep neural networks to predict steering decisions directly from images captured by the car cameras. Machine learning-based steering angle prediction needs to consider the vehicle’s limitation in uploading large amounts of potentially private data for model training. Federated learning can address these constraints by enabling multiple vehicles to collaboratively train a global model without sharing their private data, but it is difficult to achieve good accuracy as the data distribution is often non-i.i.d. across the vehicles. This paper presents a new confidence-based federated distillation method to improve the performance of federated learning for steering angle prediction. Specifically, it proposes the novel use of entropy to determine the predictive confidence of each local model, and then selects the most confident local model as the teacher to guide the learning of the global model. A comprehensive evaluation of vision-based lane centering shows that the proposed approach can outperform FedAvg and FedDF by 11.3% and 9%, respectively.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"38 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120856922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Momentum-Based Wireless Federated Learning Acceleration With Distributed Principle Decomposition

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193196

Yanjie Dong, Luya Wang, Yuanfang Chi, Xiping Hu, Haijun Zhang, F. Yu, Victor C. M. Leung

引用次数: 0

Semi-Distributed Hybrid Beamforming Design for Cooperative Cell-Free Dual-Function Radar-Communication Networks 协同无小区双功能雷达通信网络的半分布式混合波束形成设计

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193721

Bowen Wang, Lingyun Xu, Ziyang Cheng, Zishu He

引用次数: 0

Rain Estimation Over a Region Using Cyclegan 使用Cyclegan估算一个地区的雨量

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10192962

Sergey Timinsky, H. Habi, J. Ostrometzky

{"title":"Rain Estimation Over a Region Using Cyclegan","authors":"Sergey Timinsky, H. Habi, J. Ostrometzky","doi":"10.1109/ICASSPW59220.2023.10192962","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10192962","url":null,"abstract":"In the last couple of years, supervised machine learning (ML) methods have shown state-of-the-art results for near-ground rain estimation. Information is usually obtained from two kinds of sensors - rain gauges, which measure rain rate, and commercial microwave links (CMLs) which measure attenuation. These data sources are paired to create a dataset on which a model is trained. The arising problem of such methods of training is in the need for the datasets to be constructed with a CML-rain gauge pairing relation. In this paper, we propose a novel approach for rain estimation using a training method that does not require a matching between a CML and a rain gauge. Our goal is to infer the relation between CML measurements to rain rate values, with a data-driven approach using an unpaired dataset. We achieve this by inducing two cycle-consistency losses that capture the intuition that if we translate from attenuation measurements to rain rate observations and back again - we should arrive at where we started. Moreover, we learn two mapping functions translating between A (attenuation) and R (rain-rate), denoted by $G: mathcal{A} rightarrow mathcal{R}$ and $F: mathcal{R} rightarrow mathcal{A}$. No information is provided as to which sample in, $mathcal{A}$ matches which sample in $mathcal{R}$. We demonstrate our results using estimated accumulated rain predictions and validate them with a nearby rain gauge station.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123948374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Study on the Impact of Virtual Reality on User Attention 虚拟现实对用户注意力的影响研究

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193220

Sara Baldoni, Mahmoud Z. A. Wahba, Marco Carli, F. Battisti

引用次数: 0

Copyright Page 版权页

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/icasspw59220.2023.10193134

引用次数: 0

Joint Waveform & Wavefront Engineering for Terahertz Communications in 6G 6G下太赫兹通信联合波形与波前工程

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193081

Duschia M. Bodet, J. Jornet

{"title":"Joint Waveform & Wavefront Engineering for Terahertz Communications in 6G","authors":"Duschia M. Bodet, J. Jornet","doi":"10.1109/ICASSPW59220.2023.10193081","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193081","url":null,"abstract":"Terahertz (THz) band communications are positioned to provide the broad available bandwidths needed to fulfill society’s increasing demand for faster data rates. However, due to the relatively small wavelength of THz signals and the need for high gain (i.e., electrically large compared to the wavelength) antennas, many devices that are anticipated to implement THz communications in the future will likely be operating in the near field. Of the exciting near-field solutions for THz communications, many rely on the ability to control the propagating wavefront with a thin programmable device such as an antenna array or a metasurface. However, when using a phased array or metasurface to manipulate a broadband beam, systems suffer from the well-known beam squint effect, which results in different frequencies within the signal spectrum being focused in slightly different directions. This effect can impart a frequency-selective response on a broadband signal observed by a receiver. This paper characterizes the frequency-selective response of a reconfigurable intelligent surface (RIS) and demonstrates that considering the waveform design (i.e., modulation scheme) can help improve the performance of a RIS operating on a broadband signal.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127313030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ICASSPW 2023 Cover Page ICASSPW 2023封面页

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/icasspw59220.2023.10193634

引用次数: 0

Modeling Lead-Lag Structure in Facial Expression Synchrony for Social-Psychological Outcome Prediction from Negotiation Interaction 面部表情同步性的先行-滞后结构建模:基于谈判互动的社会心理结果预测

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193002

Nobukatsu Hojo, Saki Mizuno, Satoshi Kobashikawa, Ryo Masumura

{"title":"Modeling Lead-Lag Structure in Facial Expression Synchrony for Social-Psychological Outcome Prediction from Negotiation Interaction","authors":"Nobukatsu Hojo, Saki Mizuno, Satoshi Kobashikawa, Ryo Masumura","doi":"10.1109/ICASSPW59220.2023.10193002","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193002","url":null,"abstract":"This study proposes introducing facial-expression synchrony features to machine learning to estimate a customer’s psychological information from online business negotiation dialogue data. It is important for synchrony features to model the information on who led the synchrony and who followed it, the lead-lag structure, because the psychology of the leader and follower can differ. However, conventional synchrony models cannot incorporate such lead-lag structure information because they are based on the assumption that synchrony involves the co-occurrence of features in the same frame. To solve this problem, we propose using synchrony features extracted on the basis of windowed time-lagged cross-correlation, which cuts out a short segment from each of the input sequences and computes the cross-correlation between the segments. Since this method measures the similarity of signals across different frames, it is suitable for modeling the lead-lag structure. We conducted experiments based on an audio visual corpus of business negotiation dialogue assessed with various psychological measurements. The results indicate that considering lead-lag information can improve the accuracy in estimating psychological information.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121770303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0