Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

An Analysis of the Changing Competitive Landscape in the Hotel Industry Regarding Airbnb

This thesis analyzes the competition between hotels and Airbnb in San Francisco. Airbnb is an internet platform that allows hosts to rent shared space, private rooms or homes to tourists. This study identifies the affect Airbnb has on hotels, workers, renters, neighborhoods, and tax revenue. Interviews and research was engaged with travel industry professionals. Hoteliers were found to be apathetic about the competition between hotels and Airbnb. Airbnb can be a meaningful experience between hosts and tourists. Budget travelers might not travel if not for low Airbnb rates. Airbnb rooms supplement hotel inventory during extraordinary events. This utopian view of Airbnb seems to overcome the dark sides; evidenced by rising apartment rental rates and declining inventory. Pressures are placed on working class neighborhoods driving out the local workforce for high rate tourists. To date, Airbnb has defeated efforts to be effectively regulated. Unregulated conversions of residential to hotel use is a safety concern. San Francisco Ordinance 218-14 was passed to legalize and regulate Airbnb; however 218-14 is unenforceable. California Senator McGuire authored SB 593: The Thriving Communities and Sharing Economy Act to empower regulation of Airbnb. SB 593 has not been passed yet by the California Senate. Until tax payments, legal, regulatory, safety codes, and compliance issues are addressed the majority of Airbnb will be operating illegally with an unfair competitive advantage over hotels

Dynamic phase and group detection in pedestrian crowd data using multiplex visibility graphs

AbstractWe study pedestrian crowd dynamics and the detection of groups in a scene. We propose a novel method to analyse pedestrian trajectories by translating them to multiplex networks, whose properties can be studied using the tools of graph theory. Our results show that simple measures on the resulting multiplex graphs accurately reflect both the global dynamics and local clustering within scenes

kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data

The k-Nearest Neighbors classifier is a simple yet effective widely renowned method in data mining. The actual application of this model in the big data domain is not feasible due to time and memory restrictions. Several distributed alternatives based on MapReduce have been proposed to enable this method to handle large-scale data. However, their performance can be further improved with new designs that fit with newly arising technologies. In this work we provide a new solution to perform an exact k-nearest neighbor classification based on Spark. We take advantage of its in-memory operations to classify big amounts of unseen cases against a big training dataset. The map phase computes the k-nearest neighbors in different training data splits. Afterwards, multiple reducers process the definitive neighbors from the list obtained in the map phase. The key point of this proposal lies on the management of the test set, keeping it in memory when possible. Otherwise, it is split into a minimum number of pieces, applying a MapReduce per chunk, using the caching skills of Spark to reuse the previously partitioned training set. In our experiments we study the differences between Hadoop and Spark implementations with datasets up to 11 million instances, showing the scaling-up capabilities of the proposed approach. As a result of this work an open-source Spark package is available

Extension of a task-based model to functional programming

Recently, efforts have been made to bring together the areas of high-performance computing (HPC) and massive data processing (Big Data). Traditional HPC frameworks, like COMPSs, are mostly task-based, while popular big-data environments, like Spark, are based on functional programming principles. The earlier are know for their good performance for regular, matrix-based computations; on the other hand, for fine-grained, data-parallel workloads, the later has often been considered more successful. In this paper we present our experience with the integration of some dataflow techniques into COMPSs, a task-based framework, in an effort to bring together the best aspects of both worlds. We present our API, called DDF, which provides a new data abstraction that addresses the challenges of integrating Big Data application scenarios into COMPSs. DDF has a functional-based interface, similar to many Data Science tools, that allows us to use dynamic evaluation to adapt the task execution in runtime. Besides the performance optimization it provides, the API facilitates the development of applications by experts in the application domain. In this paper we evaluate DDF's effectiveness by comparing the resulting programs to their original versions in COMPSs and Spark. The results show that DDF can improve COMPSs execution time and even outperform Spark in many use cases.This work was partially supported by CAPES, CNPq, Fapemig and NIC.BR, and by projects Atmosphere (H2020-EU.2.1.1 777154) and INCT-Cyber.Peer ReviewedPostprint (author's final draft

A review on artificial intelligence in high-speed rail

High-speed rail (HSR) has brought a number of social and economic benefits, such as shorter trip times for journeys of between one and five hours; safety, security, comfort and on-time commuting for passengers; energy saving and environmental protection; job creation; and encouraging sustainable use of renewable energy and land. The recent development in HSR has seen the pervasive applications of artificial intelligence (AI). This paper first briefly reviews the related disciplines in HSR where AI may play an important role, such as civil engineering, mechanical engineering, electrical engineering and signalling and control. Then, an overview of current AI techniques is presented in the context of smart planning, intelligent control and intelligent maintenance of HSR systems. Finally, a framework of future HSR systems where AI is expected to play a key role is provided

Spartan Daily, March 3, 1997

Volume 108, Issue 27https://scholarworks.sjsu.edu/spartandaily/9103/thumbnail.jp