Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 6 de 6
Filter
1.
Journal of Financial Data Science ; 4(2):37-49, 2022.
Article in English | Scopus | ID: covidwho-1911790

ABSTRACT

Recent natural language processing (NLP) breakthroughs have proven effective for addressing many language-directed tasks, such as completing sentences and addressing search queries. This technology has been successfully implemented by tech firms including Google and others. An important element consists of language embeddings linked to pretraining systems. This ar ticle describes NLP concepts and their application to por tfolio models via a modern version of sentiment analysis. The authors demonstrate the advantages of employing information from Twitter along with the NLP for constructing a portfolio of stocks, especially during unusual events such as the COVID-19 pandemic. © 2022 With Intelligence Ltd.

2.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; : 8163-8167, 2021.
Article in English | Web of Science | ID: covidwho-1532690

ABSTRACT

The spread of COVID-19 has been among the most devastating events affecting the health and well-being of humans worldwide since World War II. A key scientific goal concerning COVID-19 is to develop mathematical models that help us to understand and predict its spreading behavior, as well as to provide guidelines on what can be done to limit its spread. In this paper, we discuss how our recent work on a multiple-strain spreading model with mutations can help address some key questions concerning the spread of COVID-19. We highlight the recent reports on a mutation of SARS-CoV-2 that is thought to be more transmissible than the original strain and discuss the importance of incorporating mutation and evolutionary adaptations (together with the network structure) in epidemic models. We also demonstrate how the multiplestrain transmission model can be used to assess the effectiveness of mask-wearing in limiting the spread of COVID19. Finally, we present simulation results to demonstrate our ideas and the utility of the multiple-strain model in the context of COVID-19.

3.
Journal of Communications and Networks ; 23(5):314-325, 2021.
Article in English | Web of Science | ID: covidwho-1524836

ABSTRACT

The coronavirus pandemic has been declared a world health emergency by the World Health Organization, which has raised the importance of an accurate epidemiological model to predict the evolution of COVID-19. In this paper, we propose mean field evolutionary dynamics (MFEDs), inspired by optimal transport theory and mean field games on graphs, to model the evolution of COVID-19. In the MFEDs, we derive the payoff functions for different individual states from the commonly used replicator dynamics (RDs) and employ them to govern the evolution of epidemics. We also compare epidemic modeling based on MFEDs with that based on RDs through numerical experiments. Moreover, we show the efficiency of the proposed MFED-based model by fitting it to the COVID-19 statistics of Wuhan, China. Finally, we analyze the effects of one-time social distancing as well as the seasonality of COVID19 through the post-pandemic period.

4.
American Control Conference (ACC) ; : 3132-3137, 2021.
Article in English | Web of Science | ID: covidwho-1486012

ABSTRACT

Masks are used as part of a comprehensive strategy of measures to limit transmission and save lives during the COVID-19 pandemic. Research about the impact of mask-wearing in the COVID-19 pandemic has raised formidable interest across multiple disciplines. In this paper, we investigate the impact of mask-wearing in spreading processes over complex networks. This is done by studying a heterogeneous bond percolation process over a multi-type network model, where nodes can be one of two types (mask-wearing, and not-mask-wearing). We provide analytical results that accurately predict the expected epidemic size and probability of emergence as functions of the characteristics of the spreading process (e.g., transmission probabilities, inward and outward efficiency of the masks, etc.), the proportion of mask-wearers in the population, and the structure of the underlying contact network. In addition to the theoretical analysis, we also conduct extensive simulations on random networks. We also comment on the analogy between the mask-model studied here and the multiple-strain viral spreading model with mutations studied recently by Eletreby et al.

5.
Ieee Journal on Selected Areas in Communications ; 39(11):3306-3320, 2021.
Article in English | Web of Science | ID: covidwho-1483753

ABSTRACT

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

6.
IEEE Globecom Workshops, GC Wkshps - Proc. ; 2020.
Article in English | Scopus | ID: covidwho-1153355
SELECTION OF CITATIONS
SEARCH DETAIL