Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florid

A formal proof of the Kepler conjecture

This article describes a formal proof of the Kepler conjecture on dense sphere packings in a combination of the HOL Light and Isabelle proof assistants. This paper constitutes the official published account of the now completed Flyspeck project

Associations Between Nutrition, Gut Microbiome, and Health in A Novel Nonhuman Primate Model.

Red-shanked doucs (Pygathrix nemaeus) are endangered, foregut-fermenting colobine primates which are difficult to maintain in captivity. There are critical gaps in our understanding of their natural lifestyle, including dietary habits such as consumption of leaves, unripe fruit, flowers, seeds, and other plant parts. There is also a lack of understanding of enteric adaptations, including their unique microflora. To address these knowledge gaps, we used the douc as a model to study relationships between gastrointestinal microbial community structure and lifestyle. We analyzed published fecal samples as well as detailed dietary history from doucs with four distinct lifestyles (wild, semi-wild, semi-captive, and captive) and determined gastrointestinal bacterial microbiome composition using 16S rRNA sequencing. A clear gradient of microbiome composition was revealed along an axis of natural lifestyle disruption, including significant associations with diet, biodiversity, and microbial function. We also identified potential microbial biomarkers of douc dysbiosis, including Bacteroides and Prevotella, which may be related to health. Our results suggest a gradient-like shift in captivity causes an attendant shift to severe gut dysbiosis, thereby resulting in gastrointestinal issues

Finding important people in large news video databases using multimodal and clustering analysis

The wide availability of large scale databases requires more efficient and scalable tools for data understanding and knowledge discovery. In this paper, we present a method to find important people who have appeared repeatedly in a certain time period from large news video databases. Specifically, we investigate two issues: how to group similar faces to find dominant groups and how to label these groups by the corresponding names for identification. These are challenging problems because firstly people can appear with large appearance variations such as hair styles, illumination conditions and poses that make comparing between similar faces more difficult; secondly, the number of people and their occurrence frequencies that are unknown make finding dominant and useful groups more complicated; and finally, the fact that in news video faces and names usually do not appear together can make troubles in aligning faces and names. To handle above problems, we propose using the relevant set correlation based clustering model which can efficiently handle dataset of millions of objects represented in thousands or even millions of dimensions to find groups of similar faces from the large and noisy face dataset. Then in order to identify faces in clusters, names extracted from the transcripts are filtered and used to find the best correspondences by using methods developed in the statistical machine translation literature. Experiments on large video datasets containing hundreds of hours showed that our system can efficiently find out important people by not only their appearance but also their identification. 1

The impact of institutional pressures on corporate social responsibility and green marketing adoption: an empirical approach in Vietnam banking industry

Purpose: Institutional pressure and corporate social responsibility (CSR) are gaining increasing recognition in scholarly works; however, there is an apparent and unsettled relationship between these concepts and the concept of green marketing adoption (GMA) that influences efforts to gain a relative competitive advantage (RCA). This study is aimed at examining the roles of institutional pressure and CSR on GMA and RCA and proposes recommendations for promoting green marketing management and CSC in the banking industry in Vietnam. Design/methodology/approach: In this study, partial least squares structural equation modeling is utilized to investigate the evolution of the structural model, while the hypotheses are evaluated using structural equation modeling (SEM). The data are scrutinized from 288 banking employees through an online survey. Findings: The results show that the components of institutional pressure exert a significant impact on GMA and RCA, but the level and type of this impact differ. Additionally, the mediating role of the CSR variable in this relationship is revealed. Under the influence of institutional pressure, companies tend to increase their implementation of CSR activities, thereby promoting their GMA and RCA. Originality/value: This study offers both theoretical and practical implications. Theoretically, this study adds to the extant evidence concerning the significance of CSR integration and institutional pressure to the advancement of GMA. In addition, maintaining a focus on fostering holistic GMA practices has enabled the banking industry in Vietnam to achieve an RCA

Skin Lesion Analysis Towards Melanoma Detection for ISIC 2018

In this paper we summarize our methods for the ISIC 2018 Competition: Skin Lesion Analysis Towards Melanoma Detection

Application of remote sensing and GIS technique to analyze the land-use change: the case of Phu Giao district, Binh Duong province

Digital change detection is a helpful technique using multi-temporal satellite image for analyzing landscape exchange. The objective of this study is an attempt to assess the land-use changes in Phu Giao district, Binh Duong province, Vietnam in the period of fifteen years, from 2001 to 2015. Landsat Thematic Mapper (TM) image data files of years from 2001 to 2015 were collected on website of United States Geological Survey (USGS). Then, the images supervised were classified into five classes including perennial plant, annual plant, barren and urban land, and water body using Maximum Likelihood classification method in ENVI 4.7, and mapped using ArcGIS. The results show that during fifteen years, perennial land and urban land have been increased by 39.83% and 10.32%, while annual land and water body have been decreased by 1.37% and 5.35% accordingly, respectively.Phát hiện thay đổi số hóa là một kỹ thuật hiệu quả sử dụng hình ảnh vệ tinh đa thời gian cho phân tích thay đổi cảnh quan. Bài viết này là một sự cố gắng nhằm đánh giá sự thay đổi đất sử dụng ở huyện Phú Giáo, tỉnh Bình Dương, Việt Nam trong khoảng thời gian mười lăm năm từ năm 2001 đến năm 2015. Các file dữ liệu ảnh Landsat TM của các năm từ 2001 đến 2015 đã được thu thập trên trang web nghiên cứu Địa chất Hoa Kỳ (USGS). Sau đó, các hình ảnh giám sát được phân thành năm lớp bao gồm cả cây trồng lâu năm, cây trồng hàng năm, đất đô thị cằn cỗi và vùng nước sử dụng phương pháp phân loại Maximum Likelihood trong ENVI 4.7, và lập bản đồ bằng sử dụng ArcGIS. Kết quả cho thấy rằng trong suốt mười lăm năm, diện tích đất trồng cây lâu năm, đất đô thị đã được tăng tương ứng là 39,83% và 10,32%, trong khi đất đai hàng năm và vùng nước giảm 1,37% và 5,35%

A new constraint programming model and a linear programming-based adaptive large neighborhood search for the vehicle routing problem with synchronization constraints

We consider a vehicle routing problem which seeks to minimize cost subject to time window and synchronization constraints. In this problem, the fleet of vehicles is categorized into regular and special vehicles. Some customers require both vehicles' services, whose starting service times at the customer are synchronized. Despite its important real-world application, this problem has rarely been studied in the literature. To solve the problem, we propose a Constraint Programming (CP) model and an Adaptive Large Neighborhood Search (ALNS) in which the design of insertion operators is based on solving linear programming (LP) models to check the insertion feasibility. A number of acceleration techniques is also proposed to significantly reduce the computational time. The computational experiments show that our new CP model finds better solutions than an existing CP-based ANLS, when used on small instances with 25 customers and with a much shorter running time. Our LP-based ALNS dominates the cp-ALNS, in terms of solution quality, when it provides solutions with better objective values, on average, for all instance classes. This demonstrates the advantage of using linear programming instead of constraint programming when dealing with a variant of vehicle routing problems with relatively tight constraints, which is often considered to be more favorable for CP-based methods