Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth

Federated Learning for Medical Image Analysis: A Survey

Machine learning in medical imaging often faces a fundamental dilemma, namely the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/datasets to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We first introduce the background and motivation of federated learning for dealing with privacy protection and collaborative learning issues in medical imaging. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges and potential research opportunities in this promising research field.Comment: 19 pages, 6 figure

Decomposition methods for machine learning with small, incomplete or noisy datasets

In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Sole Casals, Jordi. Center for Advanced Intelligence; JapónFil: Marti Puig, Pere. University of Catalonia; EspañaFil: Sun, Zhe. RIKEN; JapónFil: Tanaka,Toshihisa. Tokyo University of Agriculture and Technology; Japó

Developing effective child death review : a study of ‘early starter’ child death overview panels in England

Aim This qualitative study of a small number of child death overview panels aimed to observe and describe their experience in implementing new child death review processes, and making prevention recommendations. Methods Nine sites reflecting a geographic and demographic spread were selected from Local Safeguarding Children Boards across England. Data were collected through a combination of questionnaires, interviews, structured observations, and evaluation of documents. Data were subjected to qualitative analysis. Results Data analysis revealed a number of themes within two overarching domains: the systems and structures in place to support the process; and the process and function of the panels. The data emphasised the importance of child death review being a multidisciplinary process involving senior professionals; that the process was resource and time intensive; that effective review requires both quantitative and qualitative information, and is best achieved through a structured analytic framework; and that the focus should be on learning lessons, not on trying to apportion blame. In 17 of the 24 cases discussed by the panels, issues were raised that may have indicated preventable factors. A number of examples of recommendations relating to injury prevention were observed including public awareness campaigns, community safety initiatives, training of professionals, development of protocols, and lobbying of politicians. Conclusions The results of this study have helped to inform the subsequent establishment of child death overview panels across England. To operate effectively, panels need a clear remit and purpose, robust structures and processes, and committed personnel. A multiagency approach contributes to a broader understanding of and response to children’s deaths

Toward empirical force fields that match experimental observables

Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics simulations on larger systems achieving ergodic sampling is paving the way to directly using such simulations along with solution experiments obtained on macromolecular systems. Recently, a number of methods have been introduced to automatize this approach. Here, we review these methods, highlight their relationship with machine learning methods, and discuss the open challenges in the field

Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research