64,913 research outputs found
Augmenting human memory using personal lifelogs
Memory is a key human facility to support life activities, including social interactions, life management and problem solving. Unfortunately, our memory is not perfect. Normal individuals will have occasional memory problems which can be frustrating, while those with memory impairments can often experience a greatly reduced quality of life. Augmenting memory has the potential to make normal individuals more effective, and those with significant memory problems to have a higher general quality of life. Current technologies are now making it possible to automatically capture and store daily life experiences over an extended period, potentially even over a lifetime. This type of data collection, often referred to as a personal life log (PLL), can include data such as continuously captured pictures or videos from a first person perspective, scanned copies of archival material such as books, electronic documents read or created, and emails and SMS messages sent and received, along with context data of time of capture and access and location via GPS sensors.
PLLs offer the potential for memory augmentation. Existing work on PLLs has focused on the technologies of data capture and retrieval, but little work has been done to explore how these captured data and retrieval techniques can be applied to actual use by normal people in supporting their memory. In this paper, we explore the needs for augmenting human memory from normal people based on the psychology literature on mechanisms about memory problems, and discuss the possible functions that PLLs can provide to support these memory augmentation needs. Based on this, we also suggest guidelines for data for capture, retrieval needs and computer-based interface design. Finally we introduce our work-in-process prototype PLL search system in the iCLIPS project to give an example of augmenting human memory with PLLs and computer based interfaces
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
Enabling collaboration in virtual reality navigators
In this paper we characterize a feature superset for Collaborative
Virtual Reality Environments (CVRE), and derive a component
framework to transform stand-alone VR navigators into full-fledged
multithreaded collaborative environments. The contributions of our
approach rely on a cost-effective and extensible technique for
loading software components into separate POSIX threads for
rendering, user interaction and network communications, and adding a
top layer for managing session collaboration. The framework recasts
a VR navigator under a distributed peer-to-peer topology for scene
and object sharing, using callback hooks for broadcasting remote
events and multicamera perspective sharing with avatar interaction.
We validate the framework by applying it to our own ALICE VR
Navigator. Experimental results show that our approach has good
performance in the collaborative inspection of complex models.Postprint (published version
Wide-address spaces - exploring the design space
In a recent issue of Operating System Review, Hayter and McAuley [1991] argue that future high-performance systems trade a traditional, bus-based organization for one where all components are linked together by network switches (the Desk-Area Network). In this issue of Operating System Review, Leslie, McAuley and Mullender conclude that DAN-based architectures allow the exploitation of shared memory on a wider scale than just a single (multi)processor. In this paper, we will explore how emerging 64-bit processors can be used to implement shared address spaces spanning multiple machines
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