The Entropy of Conditional Markov Trajectories

To quantify the randomness of Markov trajectories with fixed initial and final states, Ekroot and Cover proposed a closed-form expression for the entropy of trajectories of an irreducible finite state Markov chain. Numerous applications, including the study of random walks on graphs, require the computation of the entropy of Markov trajectories conditioned on a set of intermediate states. However, the expression of Ekroot and Cover does not allow for computing this quantity. In this paper, we propose a method to compute the entropy of conditional Markov trajectories through a transformation of the original Markov chain into a Markov chain that exhibits the desired conditional distribution of trajectories. Moreover, we express the entropy of Markov trajectories - a global quantity - as a linear combination of local entropies associated with the Markov chain states.Comment: Accepted for publication in IEEE Transactions on Information Theor

Describing and Understanding Neighborhood Characteristics through Online Social Media

Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e., tags) that specifically characterizes a region (e.g., neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital

Mining, Modeling and Predicting Mobility

Mobility is a central aspect of our life, and our movements reveal much more about us than simply our whereabouts. In this thesis, we are interested in mobility and study it from three different perspectives: the modeling perspective, the information-theoretic perspective, and the data mining perspective. For the modeling perspective, we represent mobility as a probabilistic process described by both observable and latent variables, and we introduce formally the notion of individual and collective dimensions in mobility models. Ideally, we should take advantage of both dimensions to learn accurate mobility models, but the nature of data might limit us. We take a data-driven approach to study three scenarios, which differ on the nature of mobility data, and present, for each scenario, a mobility model that is tailored for it. The first scenario is individual-specific as we have mobility data about individuals but are unable to cross reference data from them. In the second scenario, we introduce the collective model that we use to overcome the sparsity of individual traces, and for which we assume that individuals in the same group exhibit similar mobility patterns. Finally, we present the ideal scenario, for which we can take advantage of both the individual and collective dimensions, and analyze collective mobility patterns in order to create individual models. In the second part of the thesis, we take an information-theoretic approach in order to quantify mobility uncertainty and its evolution with location updates. We discretize the userâs world to obtain a map that we represent as a mobility graph. We model mobility as a random walk on this graph âequivalent to a Markov chain âand quantify trajectory uncertainty as the entropy of the distribution over possible trajectories. In this setting, a location update amounts to conditioning on a particular state of the Markov chain, which requires the computation of the entropy of conditional Markov trajectories. Our main result enables us to compute this entropy through a transformation of the original Markov chain. We apply our framework to real-world mobility datasets and show that the influence of intermediate locations on trajectory entropy depends on the nature of these locations. We build on this finding and design a segmentation algorithm that uncovers intermediate destinations along a trajectory. The final perspective from which we analyze mobility is the data mining perspective: we go beyond simple mobility and analyze geo-tagged data that is generated by online social medias and that describes the whole user experience. We postulate that mining geo-tagged data enables us to obtain a rich representation of the user experience and all that surrounds its mobility. We propose a hierarchical probabilistic model that enables us to uncover specific descriptions of geographical regions, by analyzing the geo-tagged content generated by online social medias. By applying our method to a dataset of 8 million geo-tagged photos, we are able to associate with each neighborhood the tags that describe it specifically, and to find the most unique neighborhoods in a city

Mitigating Epidemics through Mobile Micro-measures

Epidemics of infectious diseases are among the largest threats to the quality of life and the economic and social well-being of developing countries. The arsenal of measures against such epidemics is well-established, but costly and insufficient to mitigate their impact. In this paper, we argue that mobile technology adds a powerful weapon to this arsenal, because (a) mobile devices endow us with the unprecedented ability to measure and model the detailed behavioral patterns of the affected population, and (b) they enable the delivery of personalized behavioral recommendations to individuals in real time. We combine these two ideas and propose several strategies to generate such recommendations from mobility patterns. The goal of each strategy is a large reduction in infections, with a small impact on the normal course of daily life. We evaluate these strategies over the Orange D4D dataset and show the benefit of mobile micro-measures, even if only a fraction of the population participates. These preliminary results demonstrate the potential of mobile technology to complement other measures like vaccination and quarantines against disease epidemics.Comment: Presented at NetMob 2013, Bosto

Been There, Done That: What Your Mobility Traces Reveal about Your Behavior

Mobility is a central aspect of our life; the locations we visit reflect our tastes and lifestyle and shape our social relationships. The ability to foresee the places a user will visit is therefore beneficial to numerous applications, ranging from forecasting the dynamics of crowds to improving the relevance of location-based recommendations. To solve the Next Place Prediction task of the Nokia Mobile Data Challenge, we developed several mobility predictors, based on graphical models, neural networks, and decision trees, and explain some of the challenges that we faced. Then, we combine these predictors using different blending strategies, which improve the prediction accuracy over any individual predictor

Traveling Salesman in Reverse: Conditional Markov Entropy for Trajectory Segmentation

We are interested in inferring the set of waypoints (or intermediate destinations) of a mobility trajectory in the absence of timing information. We find that, by mining a dataset of real mobility traces, computing the entropy of conditional Markov trajectory enables us to uncover waypoints, even though no timing information nor absolute geographic location is provided. We build on this observation and design an efficient algorithm for trajectory segmentation. Our empirical evaluation demonstrates that the entropy-based heuristic used by our segmentation algorithm outperforms alternative approaches as it is 43% more accurate than a geometric approach and 20% more accurate than path-stretch based approach. We further explore the link between trajectory entropy, mobility predictability and the nature of intermediate locations using a route choice model on real city maps

Postoperative outcomes in oesophagectomy with trainee involvement

BACKGROUND: The complexity of oesophageal surgery and the significant risk of morbidity necessitates that oesophagectomy is predominantly performed by a consultant surgeon, or a senior trainee under their supervision. The aim of this study was to determine the impact of trainee involvement in oesophagectomy on postoperative outcomes in an international multicentre setting. METHODS: Data from the multicentre Oesophago-Gastric Anastomosis Study Group (OGAA) cohort study were analysed, which comprised prospectively collected data from patients undergoing oesophagectomy for oesophageal cancer between April 2018 and December 2018. Procedures were grouped by the level of trainee involvement, and univariable and multivariable analyses were performed to compare patient outcomes across groups. RESULTS: Of 2232 oesophagectomies from 137 centres in 41 countries, trainees were involved in 29.1 per cent of them (n = 650), performing only the abdominal phase in 230, only the chest and/or neck phases in 130, and all phases in 315 procedures. For procedures with a chest anastomosis, those with trainee involvement had similar 90-day mortality, complication and reoperation rates to consultant-performed oesophagectomies (P = 0.451, P = 0.318, and P = 0.382, respectively), while anastomotic leak rates were significantly lower in the trainee groups (P = 0.030). Procedures with a neck anastomosis had equivalent complication, anastomotic leak, and reoperation rates (P = 0.150, P = 0.430, and P = 0.632, respectively) in trainee-involved versus consultant-performed oesophagectomies, with significantly lower 90-day mortality in the trainee groups (P = 0.005). CONCLUSION: Trainee involvement was not found to be associated with significantly inferior postoperative outcomes for selected patients undergoing oesophagectomy. The results support continued supervised trainee involvement in oesophageal cancer surgery

Where to go from here? Mobility prediction from instantaneous information

We present the work that allowed us to win the Next-Place Prediction task of the Nokia Mobile Data Challenge. Using data collected from the smartphones of 80 users, we explore the characteristics of their mobility traces. We then develop three families of predictors, including tailored models and generic algorithms, to predict, based on instantaneous information only, the next place a user will visit. These predictors are enhanced with aging techniques that allow them to adapt quickly to the users' changes of habit. Finally, we devise various strategies to blend predictors together and take advantage of their diversity, leading to relative improvements of up to 4%. (C) 2013 Elsevier B.V. All rights reserved