178 research outputs found
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt
to understand proximity interactions among people and the diffusion, through
these interactions, of diseases, behaviors, and opinions. However, it remains a
challenge to track the proximity-based interactions of a whole community and
then model the social diffusion of diseases and behaviors starting from the
observations of a small fraction of the volunteer population. In this paper, we
propose a novel approach that tries to connect together these sparse
observations using a model of how individuals interact with each other and how
social interactions happen in terms of a sequence of proximity interactions. We
apply our approach to track the spreading of flu in the spatial-proximity
network of a 3000-people university campus by mobilizing 300 volunteers from
this population to monitor nearby mobile phones through Bluetooth scanning and
to daily report flu symptoms about and around them. Our aim is to predict the
likelihood for an individual to get flu based on how often her/his daily
routine intersects with those of the volunteers. Thus, we use the daily
routines of the volunteers to build a model of the volunteers as well as of the
non-volunteers. Our results show that we can predict flu infection two weeks
ahead of time with an average precision from 0.24 to 0.35 depending on the
amount of information. This precision is six to nine times higher than with a
random guess model. At the population level, we can predict infectious
population in a two-week window with an r-squared value of 0.95 (a random-guess
model obtains an r-squared value of 0.2). These results point to an innovative
approach for tracking individuals who have interacted with people showing
symptoms, allowing us to warn those in danger of infection and to inform health
researchers about the progression of contact-induced diseases
Framework for waveband switching in multigranular optical networks: part I-multigranular cross-connect architectures
Optical networks using wavelength-division multiplexing (WDM) are the foremost solution to the ever-increasing traffic in the Internet backbone. Rapid advances in WDM technology will enable each fiber to carry hundreds or even a thousand wavelengths (using dense-WDM, or DWDM, and ultra-DWDM) of traffic. This, coupled with worldwide fiber deployment, will bring about a tremendous increase in the size of the optical cross-connects, i.e., the number of ports of the wavelength switching elements. Waveband switching (WBS), wherein wavelengths are grouped into bands and switched as a single entity, can reduce the cost and control complexity of switching nodes by minimizing the port count. This paper presents a detailed study on recent advances and open research issues in WBS networks. In this study, we investigate in detail the architecture for various WBS cross-connects and compare them in terms of the number of ports and complexity and also in terms of how flexible they are in adjusting to dynamic traffic. We outline various techniques for grouping wavelengths into bands for the purpose of WBS and show how traditional wavelength routing is different from waveband routing and why techniques developed for wavelength-routed networks (WRNs) cannot be simply applied to WBS networks. We also outline how traffic grooming of subwavelength traffic can be done in WBS networks. In part II of this study [Cao , submitted to J. Opt. Netw.], we study the effect of wavelength conversion on the performance of WBS networks with reconfigurable MG-OXCs. We present an algorithm for waveband grouping in wavelength-convertible networks and evaluate its performance. We also investigate issues related to survivability in WBS networks and show how waveband and wavelength conversion can be used to recover from failures in WBS networks
S-QGPU: Shared Quantum Gate Processing Unit for Distributed Quantum Computing
We propose a distributed quantum computing (DQC) architecture in which
individual small-sized quantum computers are connected to a shared quantum gate
processing unit (S-QGPU). The S-QGPU comprises a collection of hybrid two-qubit
gate modules for remote gate operations. In contrast to conventional DQC
systems, where each quantum computer is equipped with dedicated communication
qubits, S-QGPU effectively pools the resources (e.g., the communication qubits)
together for remote gate operations, and thus significantly reduces the cost of
not only the local quantum computers but also the overall distributed system.
Moreover, S-QGPU's shared resources for remote gate operations enable efficient
resource utilization. When not all computing qubits in the system require
simultaneous remote gate operations, S-QGPU-based DQC architecture demands
fewer communication qubits, further decreasing the overall cost. Alternatively,
with the same number of communication qubits, it can support a larger number of
simultaneous remote gate operations more efficiently, especially when these
operations occur in a burst mode.Comment: 8 pages, 6 figure
Architectural Implications of GNN Aggregation Programming Abstractions
Graph neural networks (GNNs) have gained significant popularity due to the
powerful capability to extract useful representations from graph data. As the
need for efficient GNN computation intensifies, a variety of programming
abstractions designed for optimizing GNN Aggregation have emerged to facilitate
acceleration. However, there is no comprehensive evaluation and analysis upon
existing abstractions, thus no clear consensus on which approach is better. In
this letter, we classify existing programming abstractions for GNN Aggregation
by the dimension of data organization and propagation method. By constructing
these abstractions on a state-of-the-art GNN library, we perform a thorough and
detailed characterization study to compare their performance and efficiency,
and provide several insights on future GNN acceleration based on our analysis.Comment: 4 pages, to be published in IEEE Computer Architecture Letters (CAL
VehSense: Slippery Road Detection Using Smartphones
This paper investigates a new application of vehicular sensing: detecting and
reporting the slippery road conditions. We describe a system and associated
algorithm to monitor vehicle skidding events using smartphones and OBD-II (On
board Diagnostics) adaptors. This system, which we call the VehSense, gathers
data from smartphone inertial sensors and vehicle wheel speed sensors, and
processes the data to monitor slippery road conditions in real-time.
Specifically, two speed readings are collected: 1) ground speed, which is
estimated by vehicle acceleration and rotation, and 2) wheel speed, which is
retrieved from the OBD-II interface. The mismatch between these two speeds is
used to infer a skidding event. Without tapping into vehicle manufactures'
proprietary data (e.g., antilock braking system), VehSense is compatible with
most of the passenger vehicles, and thus can be easily deployed. We evaluate
our system on snow-covered roads at Buffalo, and show that it can detect
vehicle skidding effectively.Comment: 2017 IEEE 85th Vehicular Technology Conference (VTC2017-Spring
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