22,096 research outputs found
SANTO: Social Aerial NavigaTion in Outdoors
In recent years, the advances in remote connectivity, miniaturization of electronic components and computing power has led to the integration of these technologies in daily devices like cars or aerial vehicles. From these, a consumer-grade option that has gained popularity are the drones or unmanned aerial vehicles, namely quadrotors. Although until recently they have not been used for commercial applications, their inherent potential for a number of tasks where small and intelligent devices are needed is huge. However, although the integrated hardware has advanced exponentially, the refinement of software used for these applications has not beet yet exploited enough. Recently, this shift is visible in the improvement of common tasks in the field of robotics, such as object tracking or autonomous navigation. Moreover, these challenges can become bigger when taking into account the dynamic nature of the real world, where the insight about the current environment is constantly changing. These settings are considered in the improvement of robot-human interaction, where the potential use of these devices is clear, and algorithms are being developed to improve this situation. By the use of the latest advances in artificial intelligence, the human brain behavior is simulated by the so-called neural networks, in such a way that computing system performs as similar as possible as the human behavior. To this end, the system does learn by error which, in an akin way to the human learning, requires a set of previous experiences quite considerable, in order for the algorithm to retain the manners. Applying these technologies to robot-human interaction do narrow the gap. Even so, from a bird's eye, a noticeable time slot used for the application of these technologies is required for the curation of a high-quality dataset, in order to ensure that the learning process is optimal and no wrong actions are retained. Therefore, it is essential to have a development platform in place to ensure these principles are enforced throughout the whole process of creation and optimization of the algorithm. In this work, multiple already-existing handicaps found in pipelines of this computational gauge are exposed, approaching each of them in a independent and simple manner, in such a way that the solutions proposed can be leveraged by the maximum number of workflows. On one side, this project concentrates on reducing the number of bugs introduced by flawed data, as to help the researchers to focus on developing more sophisticated models. On the other side, the shortage of integrated development systems for this kind of pipelines is envisaged, and with special care those using simulated or controlled environments, with the goal of easing the continuous iteration of these pipelines.Thanks to the increasing popularity of drones, the research and development of autonomous capibilities has become easier. However, due to the challenge of integrating multiple technologies, the available software stack to engage this task is restricted. In this thesis, we accent the divergencies among unmanned-aerial-vehicle simulators and propose a platform to allow faster and in-depth prototyping of machine learning algorithms for this drones
Reimaginging Learning: A Big Bet on the Future of American Education
Today's young people are the most diverse, connected generation in history and have incredible aspirations for themselves. Educators all over the country are reimagining learning to better meet this generation's needs, rethinking classrooms and schools so they work better for students. It's an exciting time for innovation in education.At the same time, big bets are an increasingly popular concept in philanthropy. Several articles and papers in the last year have encouraged donors to consider them as a way of creating meaningful change, including in education. Big bets are usually defined as large grants to a specific issue or an individual organization.We're proposing something different.We've been working with partners across the country who are pursuing a common vision: reimagining learning with a broad set of outcomes in mind, so that every student finishes high school with an abundance of choices and the freedom to pursue them. Philanthropists have an opportunity to make a big bet on this shared vision.Most schools weren't designed with this vision in mind. But right now, all over the country, teams of educators are working to change this. They are partnering with families to create schools that speak to their hopes and honor their strengths. These schools prioritize rigorous academics and help students develop critical thinking skills, set important goals and create plans to reach them, and develop the mindsets and habits they need to take charge of their futures.Through deep engagement with our partners, we've thought concretely about how these ideas might spread and where existing momentum and early evidence might shine a light on a path forward. In September 2015, with our partners Summit Public Schools and Transcend, we released a paper entitled Dissatisfied Yet Optimistic (DYO), which made the case for reimagining learning. This new companion piece explores what it might take to strengthen and accelerate the momentum created by the early pioneers who are designing schools consistent with the ideas in DYO.What follows is a big idea for how $4 billion in philanthropy over 10 years could dramatically improve the performance of our schools by focusing on this emerging vision for how schools could produce much better and broader outcomes for students
Designing Traceability into Big Data Systems
Providing an appropriate level of accessibility and traceability to data or
process elements (so-called Items) in large volumes of data, often
Cloud-resident, is an essential requirement in the Big Data era.
Enterprise-wide data systems need to be designed from the outset to support
usage of such Items across the spectrum of business use rather than from any
specific application view. The design philosophy advocated in this paper is to
drive the design process using a so-called description-driven approach which
enriches models with meta-data and description and focuses the design process
on Item re-use, thereby promoting traceability. Details are given of the
description-driven design of big data systems at CERN, in health informatics
and in business process management. Evidence is presented that the approach
leads to design simplicity and consequent ease of management thanks to loose
typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International
Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore
July 2015. arXiv admin note: text overlap with arXiv:1402.5764,
arXiv:1402.575
Nanopore Sequencing Technology and Tools for Genome Assembly: Computational Analysis of the Current State, Bottlenecks and Future Directions
Nanopore sequencing technology has the potential to render other sequencing
technologies obsolete with its ability to generate long reads and provide
portability. However, high error rates of the technology pose a challenge while
generating accurate genome assemblies. The tools used for nanopore sequence
analysis are of critical importance as they should overcome the high error
rates of the technology. Our goal in this work is to comprehensively analyze
current publicly available tools for nanopore sequence analysis to understand
their advantages, disadvantages, and performance bottlenecks. It is important
to understand where the current tools do not perform well to develop better
tools. To this end, we 1) analyze the multiple steps and the associated tools
in the genome assembly pipeline using nanopore sequence data, and 2) provide
guidelines for determining the appropriate tools for each step. We analyze
various combinations of different tools and expose the tradeoffs between
accuracy, performance, memory usage and scalability. We conclude that our
observations can guide researchers and practitioners in making conscious and
effective choices for each step of the genome assembly pipeline using nanopore
sequence data. Also, with the help of bottlenecks we have found, developers can
improve the current tools or build new ones that are both accurate and fast, in
order to overcome the high error rates of the nanopore sequencing technology.Comment: To appear in Briefings in Bioinformatics (BIB), 201
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Deep neural networks (DNNs) have become core computation components within
low latency Function as a Service (FaaS) prediction pipelines: including image
recognition, object detection, natural language processing, speech synthesis,
and personalized recommendation pipelines. Cloud computing, as the de-facto
backbone of modern computing infrastructure for both enterprise and consumer
applications, has to be able to handle user-defined pipelines of diverse DNN
inference workloads while maintaining isolation and latency guarantees, and
minimizing resource waste. The current solution for guaranteeing isolation
within FaaS is suboptimal -- suffering from "cold start" latency. A major cause
of such inefficiency is the need to move large amount of model data within and
across servers. We propose TrIMS as a novel solution to address these issues.
Our proposed solution consists of a persistent model store across the GPU, CPU,
local storage, and cloud storage hierarchy, an efficient resource management
layer that provides isolation, and a succinct set of application APIs and
container technologies for easy and transparent integration with FaaS, Deep
Learning (DL) frameworks, and user code. We demonstrate our solution by
interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x
speedup in latency for image classification models and up to 210x speedup for
large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201
PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development
This paper describes PlinyCompute, a system for development of
high-performance, data-intensive, distributed computing tools and libraries. In
the large, PlinyCompute presents the programmer with a very high-level,
declarative interface, relying on automatic, relational-database style
optimization to figure out how to stage distributed computations. However, in
the small, PlinyCompute presents the capable systems programmer with a
persistent object data model and API (the "PC object model") and associated
memory management system that has been designed from the ground-up for high
performance, distributed, data-intensive computing. This contrasts with most
other Big Data systems, which are constructed on top of the Java Virtual
Machine (JVM), and hence must at least partially cede performance-critical
concerns such as memory management (including layout and de/allocation) and
virtual method/function dispatch to the JVM. This hybrid approach---declarative
in the large, trusting the programmer's ability to utilize PC object model
efficiently in the small---results in a system that is ideal for the
development of reusable, data-intensive tools and libraries. Through extensive
benchmarking, we show that implementing complex objects manipulation and
non-trivial, library-style computations on top of PlinyCompute can result in a
speedup of 2x to more than 50x or more compared to equivalent implementations
on Spark.Comment: 48 pages, including references and Appendi
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