760 research outputs found
The call of the crowd: Event participation in location-based social services
Understanding the social and behavioral forces behind event participation is
not only interesting from the viewpoint of social science, but also has
important applications in the design of personalized event recommender systems.
This paper takes advantage of data from a widely used location-based social
network, Foursquare, to analyze event patterns in three metropolitan cities. We
put forward several hypotheses on the motivating factors of user participation
and confirm that social aspects play a major role in determining the likelihood
of a user to participate in an event. While an explicit social filtering signal
accounting for whether friends are attending dominates the factors, the
popularity of an event proves to also be a strong attractor. Further, we
capture an implicit social signal by performing random walks in a high
dimensional graph that encodes the place type preferences of friends and that
proves especially suited to identify relevant niche events for users. Our
findings on the extent to which the various temporal, spatial and social
aspects underlie users' event preferences lead us to further hypothesize that a
combination of factors better models users' event interests. We verify this
through a supervised learning framework. We show that for one in three users in
London and one in five users in New York and Chicago it identifies the exact
event the user would attend among the pool of suggestions.We acknowledge the support of Microsoft Research and EPSRC
through grant GALE (EP/K019392).This is the final published version. It's also available from AAAI at http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8068. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved
Local boundedness of weak solutions to elliptic equations with p, q−growth
This article is dedicated to Giuseppe Mingione for his 50th birthday, a leading expert in the regularity theory and in particular in the subject of this manuscript. In this paper we give conditions for the local boundedness of weak solutions to a class of nonlinear elliptic partial differential equations in divergence form of the type considered below in (1.1), under p, q-growth assumptions. The novelties with respect to the mathematical literature on this topic are the general growth conditions and the explicit dependence of the differential equation on u, other than on its gradient Du and on the x variable
ZOE: A cloud-less dialog-enabled continuous sensing wearable exploiting heterogeneous computation
The wearable revolution, as a mass-market phenomenon, has finally
arrived. As a result, the question of how wearables should evolve
over the next 5 to 10 years is assuming an increasing level of societal
and commercial importance. A range of open design and
system questions are emerging, for instance: How can wearables
shift from being largely health and fitness focused to tracking a
wider range of life events? What will become the dominant methods
through which users interact with wearables and consume the
data collected? Are wearables destined to be cloud and/or smartphone
dependent for their operation?
Towards building the critical mass of understanding and experience
necessary to tackle such questions, we have designed and
implemented ZOE – a match-box sized (49g) collar- or lapel-worn
sensor that pushes the boundary of wearables in an important set of
new directions. First, ZOE aims to perform multiple deep sensor
inferences that span key aspects of everyday life (viz. personal, social
and place information) on continuously sensed data; while also
offering this data not only within conventional analytics but also
through a speech dialog system that is able to answer impromptu
casual questions from users. (Am I more stressed this week than
normal?) Crucially, and unlike other rich-sensing or dialog supporting
wearables, ZOE achieves this without cloud or smartphone
support – this has important side-effects for privacy since all user
information can remain on the device. Second, ZOE incorporates
the latest innovations in system-on-a-chip technology together with
a custom daughter-board to realize a three-tier low-power processor
hierarchy. We pair this hardware design with software techniques
that manage system latency while still allowing ZOE to remain energy
efficient (with a typical lifespan of 30 hours), despite its high
sensing workload, small form-factor, and need to remain responsive to user dialog requests.This work was supported by Microsoft Research through its PhD
Scholarship Program. We would also like to thank the anonymous
reviewers and our shepherd, Jeremy Gummeson, for helping us improve
the paper.This is the author accepted manuscript. The final version is available from ACM at http://dl.acm.org/citation.cfm?doid=2742647.2742672
DSP.Ear: Leveraging co-processor support for continuous audio sensing on smartphones
The rapidly growing adoption of sensor-enabled smartphones has greatly fueled
the proliferation of applications that use phone sensors to monitor user
behavior. A central sensor among these is the microphone which enables, for
instance, the detection of valence in speech, or the identification of
speakers. Deploying multiple of these applications on a mobile device to
continuously monitor the audio environment allows for the acquisition of a
diverse range of sound-related contextual inferences. However, the cumulative
processing burden critically impacts the phone battery.
To address this problem, we propose DSP.Ear - an integrated sensing system
that takes advantage of the latest low-power DSP co-processor technology in
commodity mobile devices to enable the continuous and simultaneous operation of
multiple established algorithms that perform complex audio inferences. The
system extracts emotions from voice, estimates the number of people in a room,
identifies the speakers, and detects commonly found ambient sounds, while
critically incurring little overhead to the device battery. This is achieved
through a series of pipeline optimizations that allow the computation to remain
largely on the DSP. Through detailed evaluation of our prototype implementation
we show that, by exploiting a smartphone's co-processor, DSP.Ear achieves a 3
to 7 times increase in the battery lifetime compared to a solution that uses
only the phone's main processor. In addition, DSP.Ear is 2 to 3 times more
power efficient than a naive DSP solution without optimizations. We further
analyze a large-scale dataset from 1320 Android users to show that in about
80-90% of the daily usage instances DSP.Ear is able to sustain a full day of
operation (even in the presence of other smartphone workloads) with a single
battery charge.This work was supported by Microsoft Research through its PhD Scholarship Program.This is the author's accepted manuscript. The final version is available from ACM in the proceedings of the ACM Conference on Embedded Networked Sensor Systems: http://dl.acm.org/citation.cfm?id=2668349
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Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations
Continuous audio analysis from embedded and mobile devices is an increasingly important application domain. More and more, appliances like the Amazon Echo, along with smartphones and watches, and even research prototypes seek to perform multiple discriminative tasks simultaneously from ambient audio; for example, monitoring background sound classes (e.g., music or conversation), recognizing certain keywords (‘Hey Siri’ or ‘Alexa’), or identifying the user and her emotion from speech. The use of deep learning algorithms typically provides state-of-the-art model performances for such general audio tasks. However, the large computational demands of deep learning models are at odds with the limited processing, energy and memory resources of mobile, embedded and IoT devices.
In this paper, we propose and evaluate a novel deep learning modeling and optimization framework that speci cally targets this category of embedded audio sensing tasks. Although the supported tasks are simpler than the task of speech recognition, this framework aims at maintaining accuracies in predictions while minimizing the overall processor resource footprint. The proposed model is grounded in multi-task learning principles to train shared deep layers and exploits, as input layer, only statistical summaries of audio lter banks to further lower computations.
We nd that for embedded audio sensing tasks our framework is able to maintain similar accuracies, which are observed in comparable deep architectures that use single-task learning and typically more complex input layers. Most importantly, on an average, this approach provides almost a 2.1⇥ reduction in runtime, energy, and memory for four separate audio sensing tasks, assuming a variety of task combinations.Microsoft Researc
Measuring urban social diversity using interconnected geo-social networks
Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.This work was supported by the Project LASAGNE, Contract No. 318132 (STREP), funded by the European Commission and EPSRC through Grant GALE (EP/K019392).This is the author accepted manuscript. The final version is available from the Association for Computing Machinery via http://dx.doi.org/10.1145/2872427.288306
Lipschitz regularity for degenerate elliptic integrals with p, q-growth
We establish the local Lipschitz continuity and the higher differentiability of vector-valued local minimizers of a class of energy integrals of the Calculus of Variations. The main novelty is that we deal with possibly degenerate energy densities with respect to the x -variable
Waterhouse Friderichsen Syndrome: Medico-legal issues
The Waterhouse-Friderichsen Syndrome (WFS) is a pediatric emergency characterized by high mortality due to the combination of bilateral adrenal haemorrhage, meningococcal infection and cutaneous purpura. WFS often raises medico-legal problems related to missed or delayed diagnosis mainly related to the short clinical course, the sudden onset of symptoms and unexpected death. We report the death of a 2-year-old child who had no other pathologies. Death occurred quickly about 20 h after the first care visit. The forensic autopsy was ordered following the parental complaint for diagnostic delay in primary care. Clinical data, autopsy and histological findings were consistent for WFS by Neisseria meningitidis (NM) serotype B. Medical malpractice was excluded. WFS has a rapid clinical course. By the time fever and purpura are reported, it may be too late as thrombotic and bleeding complications may already be present
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Monitoring a large construction site using wireless sensor networks
Despite the significant advances made by wireless sensor network research, deployments of such networks in real application environments are fraught with significant difficulties and challenges that include robust topology design, network diagnostics and maintenance. Based on our experience of a six-month-long wireless sensor network deployment in a large construction site, we highlight these challenges and argue the need for new tools and enhancements to current protocols to address these challenges.This research has been funded by the EPSRC Innovation and Knowledge Centre for Smart Infrastructure and Construction project (EP/K000314/1). We would like to thank Costain-Skanska Joint Venture (CSJV) and our industrial partner Crossrail for allowing access and instrumentation of the Paddington site. We would also like to thank Dr Munenori Shibata from Japan Railway Technical Research Institute for his assistance with network deployment.This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2820990.2820997 Data supporting this paper is available from https://www.repository.cam.ac.uk/handle/1810/250538
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