In this paper, we propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVANO for training Deep Neural Networks (DNNs) in resource-constrained environments. Despite its popularity, "classical" federated learning faces the increasingly difficult task of scaling synchronous communication over large wireless networks. Moreover, clients typically have different computing resources and therefore computing speed, which can lead to a significant bias (in favor of "fast" clients) when the updates are asynchronous. Therefore, practical deployment of FL requires to handle users with strongly varying computing speed in communication/resource constrained setting. Experimental results show that the FAVANO algorithm outperforms current methods on standard benchmarks.

Computational intensiveness of deep learning has motivated low-precision arithmetic designs. However, the current quantized/binarized training approaches are limited by: (1) significant performance loss due to arbitrary approximations of the latent weight gradient through its discretization/binarization function, and (2) training computational intensiveness due to the reliance on full-precision latent weights. This paper proposes a novel mathematical principle by introducing the notion of Boolean variation such that neurons made of Boolean weights and/or activations can be trained ---for the first time--- natively in Boolean domain instead of latent-weight gradient descent and real arithmetic. We explore its convergence, conduct extensively experimental benchmarking, and provide consistent complexity evaluation by considering chip architecture, memory hierarchy, dataflow, and arithmetic precision. Our approach achieves baseline full-precision accuracy in ImageNet classification and surpasses state-of-the-art results in semantic segmentation, with notable performance in image super-resolution, and natural language understanding with transformer-based models. Moreover, it significantly reduces energy consumption during both training and inference.

Accurate identification of DNA promoter sequences is of crucial importance in unraveling the underlying mechanisms that regulate gene transcription. Initiation of transcription is controlled through regulatory transcription factors binding to promoter core regions in the DNA sequence. Detection of promoter regions is necessary if we are to build genetic regulatory networks for biomedical and clinical applications, and for identification of rarely expressed genes. We propose a novel ensemble learning technique using deep recurrent neural networks with convolutional feature extraction and hard negative pattern mining to detect several types of promoter sequences, including promoter sequences with the TATA-box and without the TATA-box, within DNA sequences of four different species. Using extensive independent tests and previously published results, we demonstrate that our method sets a new state-of-the-art of over 98% Matthews correlation coefficient in all eight organism categories for recognizing the stretch of base pairs that code for the promoter region within DNA sequences.

The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation and economic instability contributing to a rise in suicidal behavior. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression, Post-Traumatic Stress Disorder (PTSD), Attention-Deficit/Hyperactivity Disorder (ADHD), anxiety disorders, and bipolar disorders. As these conditions develop, signs of suicidal ideation may manifest in social media interactions. Analyzing social media data using artificial intelligence (AI) techniques can help identify patterns of suicidal behavior, providing invaluable insights for suicide prevention agencies, professionals, and broader community awareness initiatives. Machine learning algorithms for this purpose require large volumes of accurately labeled data. Previous research has not fully explored the potential of incorporating explanations in analyzing and labeling longitudinal social media data. In this study, we employed a model explanation method, Layer Integrated Gradients, on top of a fine-tuned state-of-the-art language model, to assign each token from Reddit users' posts an attribution score for predicting suicidal ideation. By extracting and analyzing attributions of tokens from the data, we propose a methodology for preliminary screening of social media posts for suicidal ideation without using large language models during inference.

Network densification is a promising cellular deployment technique that leverages spatial reuse to enhance coverage and throughput. Recent work has identified that at some point, ultra-densification will no longer be able to deliver significant throughput gains. In this paper, we provide a unified treatment of the performance limits of network densification. We develop a general framework, which incorporates multi-slope pathloss and the entire space of shadowing and small scale fading distributions, under strongest cell association in a Poisson field of interferers. First, our results show that there are three scaling regimes for the downlink signal-to-interference-plus-noise ratio, coverage probability, and average per-user rate. Specifically, depending on the near-field pathloss and the fading distribution, the user performance of 5G ultra dense networks (UDNs) would either monotonically increase, saturate, or decay with increasing network density. Second, we show that network performance in terms of coverage density and area spectral efficiency can scale with the network density better than the user performance does. Furthermore, we provide ordering results for both coverage and average rate as a means to qualitatively compare different transmission techniques that may exhibit the same performance scaling. Our results, which are verified by simulations, provide succinct insights and valuable design guidelines for the deployment of 5G UDNs.

Driven by new types of wireless devices and the proliferation of bandwidth-intensive applications, data traffic and the corresponding network load are increasing dramatically. Network densification has been recognized as a promising and efficient way to provide higher network capacity and enhanced coverage. Most prior work on performance analysis of ultra-dense networks (UDNs) has focused on random spatial deployment with idealized singular path loss models and Rayleigh fading. In this paper, we consider a more precise and general model, which incorporates multi-slope path loss and general fading distributions. We derive the tail behavior and scaling laws for the coverage probability and the capacity considering strongest base station association in a Poisson field network. Our analytical results identify the regimes in which the signal-to-interference-plus-noise ratio (SINR) either asymptotically grows, saturates, or decreases with increasing network density. We establish general results on when UDNs lead to worse or even zero SINR coverage and capacity, and we provide crisp insights on the fundamental limits of wireless network densification.

Handover measurement is responsible for finding a handover target and directly decides the performance of mobility management. It is governed by a complex combination of parameters dealing with multicell scenarios and system dynamics. A network design has to offer an appropriate handover measurement procedure in such a multiconstraint problem. This paper proposes a unified framework for the network analysis and optimization. The exposition focuses on the stochastic modeling and addresses its key probabilistic events, namely: suitable handover target found; service failure; handover measurement triggering; and handover measurement withdrawal. We derive their closed-form expressions and provide a generalized setup for the analysis of handover measurement failure and target cell quality by the best signal quality and level crossing properties. Finally, we show its application and effectiveness in today's 3GPP-LTE cellular networks.

Small cell network is designed to provide mobile services to hot spots by deploying a large number of small access points (APs). As traditional network deployment requires costly AP location acquisition, cost-effective network deployment is necessary for small cell networks. We investigate this question by studying the network performance in terms of spatial outage and throughput of a completely random topology in comparison to that of a perfectly regular topology. Using a stochastic geometry model of user SINR in a random topology, our results show that the performance gap in terms of user SINR guarantee becomes narrow when the network density increases during the network densification. By a massive deployment, the loss is about 1 dB. Besides, it is at about 18% loss in user average throughput. These comparative results would provide helpful information to choose an appropriate deployment. In particular, as far as this relatively small performance loss can be compensated by other network control algorithms, the massive random deployment of a small cell network becomes attractive considering the cost reduction by the given deployment freedom.

This paper deals with the handover measurement in cellular networks. The work is dedicated to network modeling and performance evaluation. The exposition concentrates on temporal evolution of the scanning process with key events: (i) a suitable handover target found, (ii) call drop or service failure, and (iii) scan withdrawal. By observations on the stochastic properties of best signal quality and minimum-duration outage, we determine the probability of the key elements and provide a generalized framework for network design and analysis such as in scanning failure probability and target cell expected quality. Results obtained with LTE have also shown its effectiveness.

The neighbour cell list (NCL) in cellular networks has an important impact on the number of dropped calls and is traditionally optimized manually with the help of planning tools. In this paper, a method for automatically optimizing a NCL is presented, which consists of an initialization using a self-configuration phase, followed by a self-optimization phase that further refines the NCL based on measurements provided by mobile stations during the network operation. The performance of the proposed methods is evaluated for different user speeds and different NCL sizes. Besides, the convergence speed of the proposed self-optimization method is evaluated. It is shown that when about 6000 measurements are reported by mobile stations, the proposed self-optimization method attains a stable maximum performance about 99% of success rate.