Related papers: Smart Contract Adoption under Discrete Overdispers…

We study a sparse negative binomial regression (NBR) for count data by showing the non-asymptotic advantages of using the elastic-net estimator. Two types of oracle inequalities are derived for the NBR's elastic-net estimates by using the…

Non-orthogonal multiple access (NOMA) has emerged as a novel air interface technology for massive connectivity in sixth-generation (6G) era. The recent integration of NOMA in backscatter communication (BC) has triggered significant research…

Problem definition: We study a workforce redeployment problem in hospital networks, where clinical staff, such as nurses, are temporarily reassigned from overstaffed to understaffed sites to address short-term imbalances. This practice of…

Distribution shifts are ubiquitous in real-world machine learning applications, posing a challenge to the generalization of models trained on one data distribution to another. We focus on scenarios where data distributions vary across…

Large electricity customers (e.g., large data centers) can exhibit huge and variable electricity demands, which poses significant challenges for the electricity suppliers to plan for sufficient capacity. Thus, it is desirable to design…

This paper introduces a demand-side distributed and secured energy commitment framework and operations for a Power Producer and Supplier (PPS) in deregulated environment. Due to the diversity of geographical location as well as customers'…

In smart grid, the demand side management (DSM) techniques need to be designed to process a large number of controllable loads of several types. In this paper, we proposed a framework to study the demand side management in smart grid which…

This paper proposes methods for identification of large-scale networked systems with guarantees that the resulting model will be contracting -- a strong form of nonlinear stability -- and/or monotone, i.e. order relations between states are…

Capturing latent demand has a pivotal role in designing transit services as omitting these riders can lead to poor quality of service and/or additional costs. This paper explores this topic in the design of transit networks by considering…

Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large.…

Negative binomial regression is essential for analyzing over-dispersed count data in in comparative studies, but parameter estimation becomes computationally challenging in large screens requiring millions of comparisons. We investigate…

Ultra-narrowband (UNB) communications has become a signature feature for many emerging low-power wide-area (LPWA) networks. Specifically, using extremely narrowband signals helps the network connect more Internet-of-things (IoT) devices…

Bayesian Neural Networks (BNNs) provide principled estimates of model and data uncertainty by encoding parameters as distributions. This makes them key enablers for reliable AI that can be deployed on safety critical edge systems. These…

Making judicious channel access and transmission scheduling decisions is essential for improving performance as well as energy and spectral efficiency in multichannel wireless systems. This problem has been a subject of extensive study in…

Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popularity on resource-limited devices and has an outsize…

With the recent advent of Internet of Humans (IoH), wireless body-to-body networks (WBBNs) are emerging as the fundamental part of this new paradigm. In particular with reference to newly emerging applications, the research trends on data…

This paper proposes a novel distributed optimization framework that addresses time-varying optimization problems without requiring explicit derivative information of the objective functions. Traditional distributed methods often rely on…

Dynamical systems with high intrinsic dimensionality are often characterized by extreme events having the form of rare transitions several standard deviations away from the mean. For such systems, order-reduction methods through projection…

In the growing field of blockchain technology, smart contracts exist as transformative digital agreements that execute transactions autonomously in decentralised networks. However, these contracts face challenges in the form of security…

Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks. Herein, we propose a deep reinforcement learning based distributed RA scheme, entitled Neural Network-Based…