Smart Cities: Inverse Design of 3D Urban Procedural Models with Traffic and Weather Simulation

Urbanization, the demographic transition from rural to urban, has changed how we envision and share the world. From just one-fourth of the population living in cities one hundred years ago, now more than half of the population does, and this ratio is expected to grow in the near future. Creating more sustainable, accessible, safe, and enjoyable cities has become an imperative

Characterization of ISP Traffic: Trends, User Habits, and Access Technology Impact

In the recent years, the research community has increased its focus on network monitoring which is seen as a key tool to understand the Internet and the Internet users. Several studies have presented a deep characterization of a particular application, or a particular network, considering the point of view of either the ISP, or the Internet user. In this paper, we take a different perspective. We focus on three European countries where we have been collecting traffic for more than a year and a half through 5 vantage points with different access technologies. This humongous amount of information allows us not only to provide precise, multiple, and quantitative measurements of "What the user do with the Internet" in each country but also to identify common/uncommon patterns and habits across different countries and nations. Considering different time scales, we start presenting the trend of application popularity; then we focus our attention to a one-month long period, and further drill into a typical daily characterization of users activity. Results depict an evolving scenario due to the consolidation of new services as Video Streaming and File Hosting and to the adoption of new P2P technologies. Despite the heterogeneity of the users, some common tendencies emerge that can be leveraged by the ISPs to improve their servic

BLADE: Filter Learning for General Purpose Computational Photography

The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization

Cost-aware multi data-center bulk transfers in the cloud from a customer-side perspective

Many cloud applications (e.g., data backup and replication, video distribution) require dissemination of large volumes of data from a source data-center to multiple geographically distributed data-centers. Given the high costs of wide-area bandwidth, the overall cost of inter-data-center communication is a major concern in such scenarios. While previous works have focused on optimizing the costs of bulk transfer, most of them use the charging models of Internet service providers, typically based on the 95th percentile of bandwidth consumption. However, public Cloud Service Providers (CSP) follow very different models to charge their customers. First, the cost for transmission is flat and depends on the location of the source and receiver data-centers. Second, CSPs offer discounts once customer transfers exceed certain volume thresholds per data-center. We present a systematic framework, CloudMPcast, that exploits these two aspects of cloud pricing schemes. CloudMPcast constructs overlay distribution trees for bulk-data transfer that both optimizes dollar costs of distribution, and ensures end-to-end data transfer times are not affected. CloudMPCast monitors TCP throughputs between data-centers and only proposes alternative trees that respect original transfer times. After an extensive measurement study, the cost savings range from 10 to 60 percent for both Azure and EC2 infrastructures, which potentially translates to millions of dollars a year assuming realistic demandsThis material is based upon work supported in part by the National Science Foundation (NSF) under Award No.1162333, . J. L. Garc ıa-Dorado is thankful for the financial support of the Jos e Castillejo Program (CAS12/00057

Gender effect on the relationship between talent identification tests and later world triathlon series performance

Background: We examined the explanatory power of the Spanish triathlon talent identification (TID) tests for later World Triathlon Series (WTS)-level racing performance as a function of gender. Methods: Youth TID (100 m and 1000 m swimming and 400 m and 1000 m running) test performance times for when they were 14–19 years old, and WTS performance data up to the end of 2017, were obtained for 29 female and 24 male “successful” Spanish triathletes. The relationships between the athletes’ test performances and their later best WTS ranking positions and performance times were modeled using multiple linear regression. Results: The swimming and running TID test data had greater explanatory power for best WTS ranking in the females and for best WTS position in the males (R2a = 0.34 and 0.37, respectively, p ≤ 0.009). The swimming TID times were better related to later race performance than were the running TID times. The predictive power of the TID tests for WTS performance was, however, low, irrespective of exercise mode and athlete gender. Conclusions: These results confirm that triathlon TID tests should not be based solely on swimming and running performance. Moreover, the predictive value of the individual tests within the Spanish TID battery is gender specific.Fundação para a Ciência e Tecnologia | Ref. UIDB / 00447/202