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

Unified Modeling and Rate Coverage Analysis for Satellite-Terrestrial Integrated Networks: Coverage Extension or Data Offloading?

With the growing interest in satellite networks, satellite-terrestrial integrated networks (STINs) have gained significant attention because of their potential benefits. However, due to the lack of a tractable network model for the STIN architecture, analytical studies allowing one to investigate the performance of such networks are not yet available. In this work, we propose a unified network model that jointly captures satellite and terrestrial networks into one analytical framework. Our key idea is based on Poisson point processes distributed on concentric spheres, assigning a random height to each point as a mark. This allows one to consider each point as a source of desired signal or a source of interference while ensuring visibility to the typical user. Thanks to this model, we derive the probability of coverage of STINs as a function of major system parameters, chiefly path-loss exponent, satellites and terrestrial base stations' height distributions and density, transmit power and biasing factors. Leveraging the analysis, we concretely explore two benefits that STINs provide: i) coverage extension in remote rural areas and ii) data offloading in dense urban areas.Comment: submitted to IEEE journa

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

Analysis of Thin Film Parylene-Metal-Parylene Device Based on Mechanical Tensile Strength Measurement

International audienceThis paper presents an FEM analysis and experiment of parylene-metal-parylene flexible substrate for implantable medical devices. Tensile strength measurement of the parylene-metal-parylene has been carried out and corresponding FEM modeling and simulation has been done to understand its mechanical behaviour. Besides, frequently encountered metal delamination on parylene substrate has been studied based on cohesive zone model of interface between the two materials

Joint unsupervised and supervised learning for context-aware language identification

Language identification (LID) recognizes the language of a spoken utterance automatically. According to recent studies, LID models trained with an automatic speech recognition (ASR) task perform better than those trained with a LID task only. However, we need additional text labels to train the model to recognize speech, and acquiring the text labels is a cost high. In order to overcome this problem, we propose context-aware language identification using a combination of unsupervised and supervised learning without any text labels. The proposed method learns the context of speech through masked language modeling (MLM) loss and simultaneously trains to determine the language of the utterance with supervised learning loss. The proposed joint learning was found to reduce the error rate by 15.6% compared to the same structure model trained by supervised-only learning on a subset of the VoxLingua107 dataset consisting of sub-three-second utterances in 11 languages.Comment: Accepted by ICASSP 202

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

Out-of-school STEM Program for Students with Visual Impairments: Adaptations and Outcomes During the COVID-19 Pandemic

Although previous research exists on making adaptations for students with visual impairments in online settings, there is limited research on the teaching and learning dynamics of students with visual impairments during the COVID-19 pandemic. Since responses to the pandemic made it difficult for students with visual impairments to participate in educational opportunities that require hands-on experiences, gaps have been identified in gaining access to educational opportunities. The current project was originally planned with programs based on in-person modes, aimed at increasing motivation and awareness of science, technology, engineering, and math of students with visual impairments. Due to limitations of in-person participation, substantial modifications and adaptations were required for the programs to be meaningful and effective when delivered in online environments. It was found that proficiency in the use of technology options, specific instruction and guidance for access of information, and program planning in advance were the three key elements for successful implementation of the programs. This article documents 1) existing research on the impacts of the pandemic, 2) meaningful adaptations and modifications, 3) essential elements for developing online programs in STEM, and 4) identified strategies in program transition for students with visual impairments. Some skills may not be most efficiently taught through online interactions, however participation of family members, careful modifications of existing activities, and sufficient level of technology support allows many skills to be acquired through online learning. Most importantly, strong collaboration of participating team members makes it possible for students with visual impairments to participate equitably in online environments

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

Relationship between Physical Disability and Depression by Gender:A Panel Regression Model

Background Depression in persons with physical disabilities may be more common than in the general population. The purpose of this study was to examine the relationship between physical disability and depression by gender among adults, using a large, nationally representative sample. Methods This study used data from the Korean Longitudinal Study of Aging, Wave one through four, and ran a series of random effect panel regression models to test the relationship between physical disability status and depression by gender. We tested the moderating effect of gender on the relationship between disability status and depression level by examining the significance of the cross-product term between disability status and gender. Results After controlling for self-rated health, marital status, employment status, education, and age, subjects who were female or diagnosed as having any disability presented higher levels of depression scores. Further, the difference in terms of their depression level measured by Center for Epidemiologic Studies Short Depression Scale (CES-D 10) scores between those who were diagnosed as having any disability and those who were not was greater for females than for their male counterparts. Conclusion This study reaffirmed that disability is the risk factor of depression, using longitudinal data. In addition, female gender is the effect modifier rather than the risk factor. The effect of gender in the non-disability group, mostly composed of older persons, is limited. On the contrary, the female disability group showed more depressive symptoms than the male disability group. The gender difference in the disability group and the role of culture on these differences need further research