亚洲男人的天堂2018av,欧美草比,久久久久久免费视频精选,国色天香在线看免费,久久久久亚洲av成人片仓井空

Background: The novel coronavirus, COVID-19, was first detected in the United States in January 2020. To curb the spread of the disease in mid-March, different states issued mandatory stay-at-home (SAH) orders. These nonpharmaceutical interventions were mandated based on prior experiences, such as the 1918 influenza epidemic. Hence, we decided to study the impact of restrictions on mobility on reducing COVID-19 transmission. Methods: We designed an ecological time series study with our exposure variable as Mobility patterns in the state of Maryland for March- December 2020 and our outcome variable as the COVID-19 hospitalizations for the same period. We built an Extreme Gradient Boosting (XGBoost) ensemble machine learning model and regressed the lagged COVID-19 hospitalizations with Mobility volume for different regions of Maryland. Results: We found an 18% increase in COVID-19 hospitalizations when mobility was increased by a factor of five, similarly a 43% increase when mobility was further increased by a factor of ten. Conclusion: The findings of our study demonstrated a positive linear relationship between mobility and the incidence of COVID-19 cases. These findings are partially consistent with other studies suggesting the benefits of mobility restrictions. Although more detailed approach is needed to precisely understand the benefits and limitations of mobility restrictions as part of a response to the COVID-19 pandemic.

Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training. However, this assumption may be violated in practice because of the dynamic nature of customer buying patterns, particularly due to unanticipated system shocks such as COVID-19. We study the changes in customer behavior for a major airline during the COVID-19 pandemic by framing it as a covariate shift and concept drift detection problem. We identify which customers changed their travel and purchase behavior and the attributes affecting that change using (i) Fast Generalized Subset Scanning and (ii) Causal Forests. In our experiments with simulated and real-world data, we present how these two techniques can be used through qualitative analysis.

The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policymakers have expressed the urgency to effectively evaluate the outcome of human restriction policies with the aid of big data and information technology. Thus, based on big human mobility data and city POI data, an interactive visual analytics system called Epidemic Mobility (EpiMob) was designed in this study. The system interactively simulates the changes in human mobility and infection status in response to the implementation of a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies. Then, the results reflecting the infection situation under different policies are dynamically displayed and can be flexibly compared and analyzed in depth. Multiple case studies consisting of interviews with domain experts were conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area) to demonstrate that the system can provide insight into the effects of different human mobility restriction policies for epidemic control, through measurements and comparisons.

Principal Component Analysis (PCA) is the workhorse tool for dimensionality reduction in this era of big data. While often overlooked, the purpose of PCA is not only to reduce data dimensionality, but also to yield features that are uncorrelated. Furthermore, the ever-increasing volume of data in the modern world often requires storage of data samples across multiple machines, which precludes the use of centralized PCA algorithms. This paper focuses on the dual objective of PCA, namely, dimensionality reduction and decorrelation of features, but in a distributed setting. This requires estimating the eigenvectors of the data covariance matrix, as opposed to only estimating the subspace spanned by the eigenvectors, when data is distributed across a network of machines. Although a few distributed solutions to the PCA problem have been proposed recently, convergence guarantees and/or communications overhead of these solutions remain a concern. With an eye towards communications efficiency, this paper introduces a feedforward neural network-based one time-scale distributed PCA algorithm termed Distributed Sanger's Algorithm (DSA) that estimates the eigenvectors of the data covariance matrix when data is distributed across an undirected and arbitrarily connected network of machines. Furthermore, the proposed algorithm is shown to converge linearly to a neighborhood of the true solution. Numerical results are also provided to demonstrate the efficacy of the proposed solution.

Detecting changes in COVID-19 disease transmission over time is a key indicator of epidemic growth.Near real-time monitoring of the pandemic growth is crucial for policy makers and public health officials who need to make informed decisions about whether to enforce lockdowns or allow certain activities. The effective reproduction number Rt is the standard index used in many countries for this goal. However, it is known that due to the delays between infection and case registration, its use for decision making is somewhat limited. In this paper a near real-time COVINDEX is proposed for monitoring the evolution of the pandemic. The index is computed from predictions obtained from a GAM beta regression for modelling the test positive rate as a function of time. The proposal is illustrated using data on COVID-19 pandemic in Italy and compared with Rt. A simple chart is also proposed for monitoring local and national outbreaks by policy makers and public health officials.

Earth-observing satellite instruments obtain a massive number of observations every day. For example, tens of millions of sea surface temperature (SST) observations on a global scale are collected daily by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Despite their size, such datasets are incomplete and noisy, necessitating spatial statistical inference to obtain complete, high-resolution fields with quantified uncertainties. Such inference is challenging due to the high computational cost, the nonstationary behavior of environmental processes on a global scale, and land barriers affecting the dependence of SST. In this work, we develop a multi-resolution approximation (M-RA) of a Gaussian process (GP) whose nonstationary, global covariance function is obtained using local fits. The M-RA requires domain partitioning, which can be set up application-specifically. In the SST case, we partition the domain purposefully to account for and weaken dependence across land barriers. Our M-RA implementation is tailored to distributed-memory computation in high-performance-computing environments. We analyze a MODIS SST dataset consisting of more than 43 million observations, to our knowledge the largest dataset ever analyzed using a probabilistic GP model. We show that our nonstationary model based on local fits provides substantially improved predictive performance relative to a stationary approach.

Data protection law, including the General Data Protection Regulation (GDPR), usually requires a privacy policy before data can be collected from individuals. We analysed 15,145 privacy policies from 26,910 mobile apps in May 2019 (about one year after the GDPR came into force), finding that only opening the policy webpages shares data with third-parties for 48.5% of policies, potentially violating the GDPR. We compare this data sharing across countries, payment models (free, in-app-purchases, paid) and platforms (Google Play Store, Apple App Store). We further contacted 52 developers of apps, which did not provide a privacy policy, and asked them about their data practices. Despite being legally required to answer such queries, 12 developers (23%) failed to respond.

COVID-19 vaccines have been rolled out in many countries and with them a number of vaccination certificates. For instance, the EU is utilizing a digital certificate in the form of a QR-code that is digitally signed and can be easily validated throughout all EU countries. In this paper, we investigate the current state of the COVID-19 vaccination certificate market in the darkweb with a focus on the EU Digital Green Certificate (DGC). We investigate $17$ marketplaces and $10$ vendor shops, that include vaccination certificates in their listings. Our results suggest that a multitude of sellers in both types of platforms are advertising selling capabilities. According to their claims, it is possible to buy fake vaccination certificates issued in most countries worldwide. We demonstrate some examples of such sellers, including how they advertise their capabilities, and the methods they claim to be using to provide their services. We highlight two particular cases of vendor shops, with one of them showing an elevated degree of professionalism, showcasing forged valid certificates, the validity of which we verify using two different national mobile COVID-19 applications.

There is a growing concern about consolidation trends in Internet services, with, for instance, a large fraction of popular websites depending on a handful of third-party service providers. In this paper, we report on a large-scale study of third-party dependencies around the world, using vantage points from 50 countries, from all inhabited continents, and regional top-500 popular websites.This broad perspective shows that dependencies vary widely around the world. We find that between 15% and as much as 80% of websites, across all countries, depend on a DNS, CDN or CA third-party provider.Sites critical dependencies, while lower, are equally spread ranging from 9% and 61% (CDN and DNS in China, respectively).Despite this high variability, our results suggest a highly concentrated market of third-party providers: three third-party providers across all countries serve an average of 91.2% and Google, by itself, serves an average of 72% of the surveyed websites. We explore various factors that may help explain the differences and similarities in degrees of third-party dependency across countries, including economic conditions, Internet development, language, and economic trading partners.

We present an analysis of embeddings extracted from different pre-trained models for content-based image retrieval. Specifically, we study embeddings from image classification and object detection models. We discover that even with additional human annotations such as bounding boxes and segmentation masks, the discriminative power of the embeddings based on modern object detection models is significantly worse than their classification counterparts for the retrieval task. At the same time, our analysis also unearths that object detection model can help retrieval task by acting as a hard attention module for extracting object embeddings that focus on salient region from the convolutional feature map. In order to efficiently extract object embeddings, we introduce a simple guided student-teacher training paradigm for learning discriminative embeddings within the object detection framework. We support our findings with strong experimental results.