A Multilateral Attention-enhanced Deep Neural Network for Disease Outbreak Forecasting: A Case Study on COVID-19

The worldwide impact of the recent COVID-19 pandemic has been substantial, necessitating the development of accurate forecasting models to predict the spread and course of a pandemic. Previous methods for outbreak forecasting have faced limitations by not utilizing multiple sources of input and yielding suboptimal performance due to the limited availability of data. In this study, we propose a novel approach to address the challenges of infectious disease forecasting. We introduce a Multilateral Attention-enhanced GRU model that leverages information from multiple sources, thus enabling a comprehensive analysis of factors influencing the spread of a pandemic. By incorporating attention mechanisms within a GRU framework, our model can effectively capture complex relationships and temporal dependencies in the data, leading to improved forecasting performance. Further, we have curated a well-structured multi-source dataset for the recent COVID-19 pandemic that the research community can utilize as a great resource to conduct experiments and analysis on time-series forecasting. We evaluated the proposed model on our COVID-19 dataset and reported the output in terms of RMSE and MAE. The experimental results provide evidence that our proposed model surpasses existing techniques in terms of performance. We also performed performance gain and qualitative analysis on our dataset to evaluate the impact of the attention mechanism and show that the proposed model closely follows the trajectory of the pandemic.

New Closed-Form ASER Expressions for Dual-Hop Mixed THz-RF Cooperative Relay Networks

In this paper, we consider a dual-hop mixed THz-RF system model for backhaul-fronthaul applications where the link between source and destination is established only through the relay node in which decode-and-forward relaying protocol is used. The THz link suffers from the joint impact of antenna misalignment and stochastic characteristics of wireless channels, including the effect of environmental conditions such as pressure, humidity, and temperature. The envelope of THz link in the first hop follows a generalized $\alpha-\mu$ distribution, and for the RF end, the Nakagami-$m$ distribution is considered. In this context, we obtain new closed-form expressions of the cumulative density function and the moment-generating function of the end-to-end signal-to-noise ratio. Further, we derive the average symbol error rate expressions for coherent rectangular quadrature amplitude modulation (RQAM) and coherent hexagonal QAM (HQAM), as well as the non-coherent modulation scheme. The asymptotic behavior is also discussed to examine the system's diversity. Furthermore, the impact of several parameters, such as fading coefficients of individual links and antenna misalignment, as well as the distance between nodes, are also highlighted in the system's performance. Moreover, Monte Carlo simulations are used to validate the presented analytical framework. Finally, the presented numerical insights aid in the extraction of practical design principles.

Correlation and Class Based Block Formation for Improved Structured Dictionary Learning

In recent years, the creation of block-structured dictionary has attracted a lot of interest. Learning such dictionaries involve two step process: block formation and dictionary update. Both these steps are important in producing an effective dictionary. The existing works mostly assume that the block structure is known a priori while learning the dictionary. For finding the unknown block structure given a dictionary commonly sparse agglomerative clustering (SAC) is used. It groups atoms based on their consistency in sparse coding with respect to the unstructured dictionary. This paper explores two innovations towards improving the reconstruction as well as the classification ability achieved with the block-structured dictionary. First, we propose a novel block structuring approach that makes use of the correlation among dictionary atoms. Unlike the SAC approach, which groups diverse atoms, in the proposed approach the blocks are formed by grouping the top most correlated atoms in the dictionary. The proposed block clustering approach is noted to yield significant reductions in redundancy as well as provides a direct control on the block size when compared with the existing SAC-based block structuring. Later, motivated by works using supervised \emph{a priori} known block structure, we also explore the incorporation of class information in the proposed block formation approach to further enhance the classification ability of the block dictionary. For assessment of the reconstruction ability with proposed innovations is done on synthetic data while the classification ability has been evaluated in large variability speaker verification task.