Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Computational analysis of a plant receptor interaction network

Trabajo fin de máster en Bioinformática y Biología ComputacionalIn all organisms, complex protein-protein interactions (PPI) networks control major biological functions yet studying their structural features presents a major analytical challenge. In plants, leucine-rich-repeat receptor kinases (LRR-RKs) are key in sensing and transmitting non-self as well as self-signals from the cell surface. As such, LRR-RKs have both developmental and immune functions that allow plants to make the most of their environments. In the model organism in plant molecular biology, Arabidopsis thaliana, most LRR-RKs are still represented by biochemically and genetically uncharacterized receptors. To fix this an LRR-based Cell Surface Interaction (CSI LRR ) network was obtained in 2018, a protein-protein interaction network of the extracellular domain of 170 LRR-RKs that contains 567 bidirectional interactions. Several network analyses have been performed with CSI LRR . However, these analyses have so far not considered the spatial and temporal expression of its proteins. Neither has it been characterized in detail the role of the extracellular domain (ECD) size in the network structure. Because of that, the objective of the present work is to continue with more in depth analyses with the CSI LRR network. This would provide important insights that will facilitate LRR-RKs function characterization. The first aim of this work is to test out the fit of the CSI LRR network to a scale-free topology. To accomplish that, the degree distribution of the CSI LRR network was compared with the degree distribution of the known network models of scale-free and random. Additionally, three network attack algorithms were implemented and applied to these two network models and the CSI LRR network to compare their behavior. However, since the CSI LRR interaction data comes from an in vitro screening, there is no direct evidence whether its protein-protein interactions occur inside the plant cells. To gain insight on how the network composition changes depending on the transcriptional regulation, the interaction data of the CSI LRR was integrated with 4 different RNA-Seq datasets related with the network biological functions. To automatize this task a Python script was written. Furthermore, it was evaluated the role of the LRR-RKs in the network structure depending on the size of their extracellular domain (large or small). For that, centrality parameters were measured, and size-targeted attacks performed. Finally, gene regulatory information was integrated into the CSI LRR to classify the different network proteins according to the function of the transcription factors that regulate its expression. The results were that CSI LRR fits a power law degree distribution and approximates a scale- free topology. Moreover, CSI LRR displays high resistance to random attacks and reduced resistance to hub/bottleneck-directed attacks, similarly to scale-free network model. Also, the integration of CSI LRR interaction data and RNA-Seq data suggests that the transcriptional regulation of the network is more relevant for developmental programs than for defense responses. Another result was that the LRR-RKs with a small ECD size have a major role in the maintenance of the CSI LRR integrity. Lastly, it was hypothesized that the integration of CSI LRR interaction data with predicted gene regulatory networks could shed light upon the functioning of growth-immunity signaling crosstalk

DiviML: A Module-based Heuristic for Mapping Neural Networks onto Heterogeneous Platforms

Datacenters are increasingly becoming heterogeneous, and are starting to include specialized hardware for networking, video processing, and especially deep learning. To leverage the heterogeneous compute capability of modern datacenters, we develop an approach for compiler-level partitioning of deep neural networks (DNNs) onto multiple interconnected hardware devices. We present a general framework for heterogeneous DNN compilation, offering automatic partitioning and device mapping. Our scheduler integrates both an exact solver, through a mixed integer linear programming (MILP) formulation, and a modularity-based heuristic for scalability. Furthermore, we propose a theoretical lower bound formula for the optimal solution, which enables the assessment of the heuristic solutions' quality. We evaluate our scheduler in optimizing both conventional DNNs and randomly-wired neural networks, subject to latency and throughput constraints, on a heterogeneous system comprised of a CPU and two distinct GPUs. Compared to na\"ively running DNNs on the fastest GPU, he proposed framework can achieve more than 3$\times$ times lower latency and up to 2.9$\times$ higher throughput by automatically leveraging both data and model parallelism to deploy DNNs on our sample heterogeneous server node. Moreover, our modularity-based "splitting" heuristic improves the solution runtime up to 395$\times$ without noticeably sacrificing solution quality compared to an exact MILP solution, and outperforms all other heuristics by 30-60% solution quality. Finally, our case study shows how we can extend our framework to schedule large language models across multiple heterogeneous servers by exploiting symmetry in the hardware setup. Our code can be easily plugged in to existing frameworks, and is available at https://github.com/abdelfattah-lab/diviml.Comment: accepted at ICCAD'2

The Flood Mitigation Problem in a Road Network

Natural disasters are highly complex and unpredictable. However, long-term planning and preparedness activities can help to mitigate the consequences and reduce the damage. For example, in cities with a high risk of flooding, appropriate roadway mitigation can help reduce the impact of floods or high waters on transportation systems. Such communities could benefit from a comprehensive assessment of mitigation on road networks and identification of the best subset of roads to mitigate. In this study, we address a pre-disaster planning problem that seeks to strengthen a road network against flooding. We develop a network design problem that maximizes the improvement in accessibility and travel times between population centers and healthcare facilities subject to a given budget. We provide techniques for reducing the problem size to help make the problem tractable. We use cities in the state of Iowa in our computational experiments.Comment: 40 pages, 8 figures, 21 table

A Generalist Neural Algorithmic Learner

The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused on building specialist models. Specialist models are capable of learning to neurally execute either only one algorithm or a collection of algorithms with identical control-flow backbone. Here, instead, we focus on constructing a generalist neural algorithmic learner -- a single graph neural network processor capable of learning to execute a wide range of algorithms, such as sorting, searching, dynamic programming, path-finding and geometry. We leverage the CLRS benchmark to empirically show that, much like recent successes in the domain of perception, generalist algorithmic learners can be built by "incorporating" knowledge. That is, it is possible to effectively learn algorithms in a multi-task manner, so long as we can learn to execute them well in a single-task regime. Motivated by this, we present a series of improvements to the input representation, training regime and processor architecture over CLRS, improving average single-task performance by over 20% from prior art. We then conduct a thorough ablation of multi-task learners leveraging these improvements. Our results demonstrate a generalist learner that effectively incorporates knowledge captured by specialist models.Comment: 20 pages, 10 figure