Joint Optimization of Energy Consumption and Completion Time in Federated Learning

Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.Comment: This paper appears in the Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS) 2022. Please feel free to contact us for questions or remark

GeohashTile: Vector Geographic Data Display Method Based on Geohash

© 2020 MDPI AG. All rights reserved. In the development of geographic information-based applications for mobile devices, achieving better access speed and visual effects is the main research aim. In this paper, we propose a new geographic data display method based on Geohash, namely GeohashTile, to improve the performance of traditional geographic data display methods in data indexing, data compression, and the projection of different granularities. First, we use the Geohash encoding system to represent coordinates, as well as to partition and index large-scale geographic data. The data compression and tile encoding is accomplished by Geohash. Second, to realize a direct conversion between Geohash and screen-pixel coordinates, we adopt the relative position projection method. Finally, we improve the calculation and rendering efficiency by using the intermediate result caching method. To evaluate the GeohashTile method, we have implemented the client and the server of the GeohashTile system, which is also evaluated in a real-world environment. The results show that Geohash encoding can accurately represent latitude and longitude coordinates in vector maps, while the GeohashTile framework has obvious advantages when requesting data volume and average load time compared to the state-of-the-art GeoTile system

LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks

In this paper, an LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machine-type communication (MTC) networks, where massive MTC (mMTC) devices and ultra-reliable low latency communications (URLLC) devices coexist. In the proposed LSTMH-RA scheme, mMTC devices access the network via a timing advance (TA)-aided four-step procedure to meet massive access requirement, while the access procedure of the URLLC devices is completed in two steps coupled with the mMTC devices' access procedure to reduce latency. Furthermore, we propose an attention-based LSTM prediction model to predict the number of active URLLC devices, thereby determining the parameters of the multi-user detection algorithm to guarantee the latency and reliability access requirements of URLLC devices.We analyze the successful access probability of the LSTMH-RA scheme. Numerical results show that, compared with the benchmark schemes, the proposed LSTMH-RA scheme can significantly improve the successful access probability, and thus satisfy the diverse QoS requirements of URLLC and mMTC devices

Macrophage polarization states in atherosclerosis

Atherosclerosis, a chronic inflammatory condition primarily affecting large and medium arteries, is the main cause of cardiovascular diseases. Macrophages are key mediators of inflammatory responses. They are involved in all stages of atherosclerosis development and progression, from plaque formation to transition into vulnerable plaques, and are considered important therapeutic targets. Increasing evidence suggests that the modulation of macrophage polarization can effectively control the progression of atherosclerosis. Herein, we explore the role of macrophage polarization in the progression of atherosclerosis and summarize emerging therapies for the regulation of macrophage polarization. Thus, the aim is to inspire new avenues of research in disease mechanisms and clinical prevention and treatment of atherosclerosis

Interleukin 18 function in atherosclerosis is mediated by the interleukin 18 receptor and the Na-Cl co-transporter

Interleukin-18 (IL18) participates in atherogenesis through several putative mechanisms1, 2. Interruption of IL18 action reduces atherosclerosis in mice3, 4. Here, we show that absence of the IL18 receptor (IL18r) does not affect atherosclerosis in apolipoprotein E–deficient (Apoe−/−) mice, nor does it affect IL18 cell surface binding to or signaling in endothelial cells. As identified initially by co-immunoprecipitation with IL18, we found that IL18 interacts with the Na-Cl co-transporter (NCC; also known as SLC12A3), a 12-transmembrane-domain ion transporter protein preferentially expressed in the kidney5. NCC is expressed in atherosclerotic lesions, where it colocalizes with IL18r. In Apoe−/− mice, combined deficiency of IL18r and NCC, but not single deficiency of either protein, protects mice from atherosclerosis. Peritoneal macrophages from Apoe−/− mice or from Apoe−/− mice lacking IL18r or NCC show IL18 binding and induction of cell signaling and cytokine and chemokine expression, but macrophages from Apoe−/− mice with combined deficiency of IL18r and NCC have a blunted response. An interaction between NCC and IL18r on macrophages was detected by co-immunoprecipitation. IL18 binds to the cell surface of NCC-transfected COS-7 cells, which do not express IL18r, and induces cell signaling and cytokine expression. This study identifies NCC as an IL18-binding protein that collaborates with IL18r in cell signaling, inflammatory molecule expression, and experimental atherogenesis

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Novel Nanocrystal Floating Gate Memory

This work is devoted to investigating the feasibility of engineering nanocrystals and tunnel oxide layer with a novel structure. Several novel devices are demonstrated to improve the performance of the novel nanocrystal memories.A novel TiSi2 nanocrystal memory was demonstrated. TiSi2 nanocrystals were synthesized on SiO2 by annealing Ti covered Si nanocrystals. Compared to the reference Si nanocrystal memory, both experiment and simulation results show that TiSi2 nanocrystal memory exhibits larger memory window, faster writing and erasing, and longer retention lifetime as a result of the metallic property of the silicide nanocrystals. Due to thermally stable, CMOS compatible properties, TiSi2 nanocrystals are highly promising for nonvolatile memory device application. Metal/high-k dielectric core-shell nanocrystal memory capacitors were proposed. This kind of MOS memory shows good performance in charge storage capacity, programming and erasing speed. A self-assembled di-block co-polymer is used to align the NCs to improve the scalability of the overall sample. An ordered Co/Al2O3 core-shell nanocrystal (NC) nonvolatile memory device was also fabricated. Self-assembled di-block co-polymer process aligned the NCs with uniform size. Co/Al2O3 core-shell NCs were formed using atomic layer deposition of Al2O3 before and after the ordered Co NC formation. Compared to Co NC memory, Co/Al2O3 core-shell NC memory shows improved retention performance without sacrificing writing and erasing speeds.Another new discrete NiSi nanocrystals (NCs) were synthesized by rapid thermal oxygen annealing (RTO) of very thin Si/Ni/Si films on SiO2 tunneling layer. The RTO process resulted in smooth surface of the NC floating layer, in turn, uniform thickness of subsequent control oxide layer. Metal-oxide-semiconductor capacitor memory was fabricated. Electrical properties of the memory device such as programming, erasing and retention were characterized and good performance was achieved, which is due to the reduction of the leakage paths in the smooth device structure. Therefore, it is concluded that metallic nanocrystal with aligned core-shell structure memory is a very promising candidate to replace Si nanocrystal for future generation nonvolatile flash memory devices