2,133 research outputs found
Correlating Pedestrian Flows and Search Engine Queries
An important challenge for ubiquitous computing is the development of
techniques that can characterize a location vis-a-vis the richness and
diversity of urban settings. In this paper we report our work on correlating
urban pedestrian flows with Google search queries. Using longitudinal data we
show pedestrian flows at particular locations can be correlated with the
frequency of Google search terms that are semantically relevant to those
locations. Our approach can identify relevant content, media, and
advertisements for particular locations.Comment: 4 pages, 1 figure, 1 tabl
Semantics, sensors, and the social web: The live social semantics experiments
The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data and technologies from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities at conferences. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes this application, and discusses and evaluates the results of its two deployment
Handling oversampling in dynamic networks using link prediction
Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.Comment: ECML/PKDD 201
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Patterns of Individual Shopping Behavior
Much of economic theory is built on observations of aggregate, rather than
individual, behavior. Here, we present novel findings on human shopping
patterns at the resolution of a single purchase. Our results suggest that much
of our seemingly elective activity is actually driven by simple routines. While
the interleaving of shopping events creates randomness at the small scale, on
the whole consumer behavior is largely predictable. We also examine
income-dependent differences in how people shop, and find that wealthy
individuals are more likely to bundle shopping trips. These results validate
previous work on mobility from cell phone data, while describing the
unpredictability of behavior at higher resolution.Comment: 4 pages, 5 figure
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
High resolution dynamical mapping of social interactions with active RFID
In this paper we present an experimental framework to gather data on
face-to-face social interactions between individuals, with a high spatial and
temporal resolution. We use active Radio Frequency Identification (RFID)
devices that assess contacts with one another by exchanging low-power radio
packets. When individuals wear the beacons as a badge, a persistent radio
contact between the RFID devices can be used as a proxy for a social
interaction between individuals. We present the results of a pilot study
recently performed during a conference, and a subsequent preliminary data
analysis, that provides an assessment of our method and highlights its
versatility and applicability in many areas concerned with human dynamics
LINKING PROTEOME AND GENOME FOR BIOMARKER DISCOVERY IN CLL-ESTABLISHMENT OF A CLL PROTEIN DATABASE
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
A Review Evaluating Intravascular Access for High Volume Resuscitation: Can You Keep Up?
Anesthetists and anesthesiologists are frequently in the unique position of administering high-volume resuscitation in the setting of hemorrhage, hypovolemia, or vasodilatory shock. The ability to rapidly infuse intravenous (IV) fluid solutions differs vastly for different types and sizes of IV access. In patients that may require rapid large volume resuscitation, it is critical to understand the capacity of existing IV devices. Selecting the most appropriate IV access for patients can be paramount in preventing hypotension, end organ dysfunction, and even death. This article objectively reviews and compares the flow rates of commonly used central and peripheral intravenous devices to demonstrate the influence of catheter length and radius.  
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