Joint Optimization for Secure and Reliable Communications in Finite Blocklength Regime

To realize ultra-reliable low latency communications with high spectral efficiency and security, we investigate a joint optimization problem for downlink communications with multiple users and eavesdroppers in the finite blocklength (FBL) regime. We formulate a multi-objective optimization problem to maximize a sum secrecy rate by developing a secure precoder and to minimize a maximum error probability and information leakage rate. The main challenges arise from the complicated multi-objective problem, non-tractable back-off factors from the FBL assumption, non-convexity and non-smoothness of the secrecy rate, and the intertwined optimization variables. To address these challenges, we adopt an alternating optimization approach by decomposing the problem into two phases: secure precoding design, and maximum error probability and information leakage rate minimization. In the first phase, we obtain a lower bound of the secrecy rate and derive a first-order Karush-Kuhn-Tucker (KKT) condition to identify local optimal solutions with respect to the precoders. Interpreting the condition as a generalized eigenvalue problem, we solve the problem by using a power iteration-based method. In the second phase, we adopt a weighted-sum approach and derive KKT conditions in terms of the error probabilities and leakage rates for given precoders. Simulations validate the proposed algorithm.Comment: 30 pages, 8 figure

FedFwd: Federated Learning without Backpropagation

In federated learning (FL), clients with limited resources can disrupt the training efficiency. A potential solution to this problem is to leverage a new learning procedure that does not rely on backpropagation (BP). We present a novel approach to FL called FedFwd that employs a recent BP-free method by Hinton (2022), namely the Forward Forward algorithm, in the local training process. FedFwd can reduce a significant amount of computations for updating parameters by performing layer-wise local updates, and therefore, there is no need to store all intermediate activation values during training. We conduct various experiments to evaluate FedFwd on standard datasets including MNIST and CIFAR-10, and show that it works competitively to other BP-dependent FL methods.Comment: ICML 2023 Workshop (Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities

Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems

The use of low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) significantly benefits energy efficiency (EE) at the cost of high quantization noise for massive multiple-input multiple-output (MIMO) systems. This paper considers a precoding optimization problem for maximizing EE in quantized downlink massive MIMO systems. To this end, we jointly optimize an active antenna set, precoding vectors, and allocated power; yet acquiring such joint optimal solution is challenging. To resolve this challenge, we decompose the problem into precoding direction and power optimization problems. For precoding direction, we characterize the first-order optimality condition, which entails the effects of quantization distortion and antenna selection. We cast the derived condition as a functional eigenvalue problem, wherein finding the principal eigenvector attains the best local optimal point. To this end, we propose generalized power iteration based algorithm. To optimize precoding power for given precoding direction, we adopt a gradient descent algorithm for the EE maximization. Alternating these two methods, our algorithm identifies a joint solution of the active antenna set, the precoding direction, and allocated power. In simulations, the proposed methods provide considerable performance gains. Our results suggest that a few-bit DACs are sufficient for achieving high EE in massive MIMO systems

Effective data reduction algorithm for topological data analysis

One of the most interesting tools that have recently entered the data science toolbox is topological data analysis (TDA). With the explosion of available data sizes and dimensions, identifying and extracting the underlying structure of a given dataset is a fundamental challenge in data science, and TDA provides a methodology for analyzing the shape of a dataset using tools and prospects from algebraic topology. However, the computational complexity makes it quickly infeasible to process large datasets, especially those with high dimensions. Here, we introduce a preprocessing strategy called the Characteristic Lattice Algorithm (CLA), which allows users to reduce the size of a given dataset as desired while maintaining geometric and topological features in order to make the computation of TDA feasible or to shorten its computation time. In addition, we derive a stability theorem and an upper bound of the barcode errors for CLA based on the bottleneck distance.Comment: 13 pages, 10 figures, 2 table

Effect of Regular Plyometric Training on Growth-related Factors in Obesity Female Teenager

OBJECTIVES This study aimed to investigate the effect of regular plyometric training on growth-related factors in obese female teenager. METHODS The subjects of the study consisted of elementary school students group (EG, n=5) and middle school students group(MG, n=6), and overweight or obese experimenters were selected based on the ‘2017 Child and Adolescent Growth Chart Age Body Mass’ index. Exercise was conducted for 12 weeks. All measurements were carried out before and after exercise. The data processing was verified using the SPSS 26.0 statistical program to verify the correlation between paired t-test and Pearson in the 12-week pretraining and post-training groups. RESULTS After 12 weeks of plyometric training, there were significant differences in height(p=.002), ASIS(p=.003), body fat percentage(p=.018), and muscle mass(p=.014) among body composition of EG. There was a significant difference in height(p=.015) in body composition of MG. In the evaluation of muscle function, in muscle strength(60°/sec), (R)-FLE PT/bw(p=.011), (L)-FLE PT/bw(p=.017) in EG and muscle power(180°/sec), (R)-FLE PT/bw(p=.024), (L)-EXT PT/bw(p=.001), (R)-FLE TW/bw(p=.004) and (L)-EXT TW/bw(p=.012) showed a statistically significant difference. In terms of correlation, significant relationships were found between EG body fat mass and IGF-1(p<.05), and between body fat mass and IGF-1/IGF-BP3(p<.05). CONCLUSIONS Regular plyometric training had a positive effect on growth-related factors in obese female teenager