This paper proposes CANC, a Co-teaching Active Noise Cancellation method, applied in spatial computing to address deep learning trained with extreme noisy labels. Deep learning algorithms have been successful in spatial computing for land or building footprint recognition. However a lot of noise exists in ground truth labels due to how labels are collected in spatial computing and satellite imagery. Existing methods to deal with extreme label noise conduct clean sample selection and do not utilize the remaining samples. Such techniques can be wasteful due to the cost of data retrieval. Our proposed CANC algorithm not only conserves high-cost training samples but also provides active label correction to better improve robust deep learning with extreme noisy labels. We demonstrate the effectiveness of CANC for building footprint recognition for spatial computing.
Deep Active Speech Cancellation with Mamba-Masking Network
Blavatnik School of Computer Science Tel Aviv University（塔尔医学院计算机科学学院）；School of Electrical and Computer Engineering Ben-Gurion University of the Negev（本·古里安大学电气与计算机工程学院）
Active noise cancellation on open-ear smart glasses
Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）；Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg（奥尔登堡大学医学物理与声学系）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Deep learning (DL) is revolutionizing the scientific computing community. To reduce the data gap, active learning has been identified as a promising solution for DL in the scientific computing community. However, the deep active learning (DAL) literature is dominated by image classification problems and pool-based methods. Here we investigate the robustness of pool-based DAL methods for scientific computing problems (dominated by regression) where DNNs are increasingly used. We show that modern pool-based DAL methods all share an untunable hyperparameter, termed the pool ratio, denoted $γ$, wh…
Harnessing Synthetic Active Particles for Physical Reservoir Computing
Peter Debye Institute for Soft Matter Physics, Leipzig University（莱比锡大学彼得·德拜软物质物理研究所）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig（德累斯顿/莱比锡可扩展数据分析与人工智能中心（ScaDS.AI））
Cloud computing provides ubiquitous and on-demand access to vast reconfigurable resources that can meet any computational need. Many service models are available, but the Infrastructure as a Service (IaaS) model is particularly suited to operate as a high performance computing (HPC) platform, by networking large numbers of cloud computing nodes. We used the Pacific Northwest National Laboratory (PNNL) cloud computing environment to perform our experiments. A number of cloud computing providers such as Amazon Web Services, Microsoft Azure, or IBM Cloud, offer flexible and scalable computing res…
Industry 4.0 becomes possible through the convergence between Operational and Information Technologies. All the requirements to realize the convergence is integrated on the Fog Platform. Fog Platform is introduced between the cloud server and edge devices when the unprecedented generation of data causes the burden of the cloud server, leading the ineligible latency. In this new paradigm, we divide the computation tasks and push it down to edge devices. Furthermore, local computing (at edge side) may improve privacy and trust. To address these problems, we present a new method, in which we deco…
Side-Channel Attacks Survive Noise Cancellation in 3D Printers
Department of Computer Science, California Polytechnic State University（计算机科学系，加州大学Polytechnic州立大学）；Beacom College of Computer and Cyber Sciences, Dakota State University（计算机与网络科学学院，达科他州立大学）；Idaho National Laboratory（爱达荷国家实验室）；Department of Computational Data Science and Engineering, North Carolina A&T State University（计算数据科学与工程系，北卡罗来纳A&T州立大学）；Department of Engineering, South Carolina State University（工程系，南卡罗来纳州立大学）
Toward Optimal ANC: Establishing Mutual Information Lower Bound
School of Electrical and Computer Engineering（电气与计算机工程系）；Ben-Gurion University of the Negev（内盖夫本· Gurion大学）；Blavatnik School of Computer Science（Blavatnik计算机科学学院）；Tel Aviv University（特拉维夫大学）
Institute of Theoretical Physics, Center for Soft Nanoscience, University of Münster（理论物理研究所、软纳米科学中心、乌姆斯泰特大学）；Department of Physics, RWTH Aachen University（物理系、亚琛RWTH大学）；DWI -- Leibniz Institute for Interactive Materials（互动材料莱比锡研究所）
Huazhong University of Science and Technology（华中科技大学）
Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging. Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far fe…
