23 research outputs found
A Probabilistic Approach to Socio-Geographic Reality Mining
No full textAs we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio geographic data could be continuously captured by hundreds of millions of people around the world and promises to reveal important behavioral clues about humans in a manner never before possible. Mining patterns of human behavior from large-scale mobile phone data has deep potential impact on society. For example, by understanding a community's movements and interactions, appropriate measures may be put in place to prevent the threat of an epidemic. The study of such human-centric massive datasets requires advanced mathematical models and tools. In this thesis, we investigate probabilistic topic models as unsupervised machine learning tools for large-scale socio-geographic activity mining. We first investigate two types of probabilistic topic models for large-scale location-driven phone data mining. We propose a methodology based on Latent Dirichlet Allocation, followed by the Author Topic Model, for the discovery of dominant location routines mined from the MIT Reality Mining data set containing the activities of 97 individuals over the course of a 16 month period. We investigate the many possibilities of our proposed approach in terms of activity modeling, including differentiating users with high and low varying lifestyles and determining when a user's activities fluctuate from the norm over time. We then consider both location and interaction features from cell tower connections and Bluetooth, in single and multimodal forms for routine discovery, where the daily routines discovered contain information about the interactions of the day in addition to the locations visited. We also propose a method for the prediction of missing multimodal data based on Latent Dirichlet Allocation. We further consider a supervised approach for day type and student type classification using similar socio-geographic features. We then propose two new probabilistic approaches to alleviate some of the limitations of Latent Dirichlet Allocation for activity modeling. Large duration activities and varying time duration activities can not be modeled with the initially proposed methods due to problems with input and model parameter size explosion. We first propose a Multi-Level Topic Model as a method to incorporate multiple time duration sequences into a probabilistic generative topic model. We then propose the Pairwise-Distance Topic Model as an approach to address the problem of modelling long duration activities with topics. Finally, we consider an application of our work to the study of influencing factors in human opinion change with mobile sensor data. We consider the Social Evolution Project Reality Mining dataset, and investigate other mobile phone sensor features including communication logs. We consider the difference in behaviors of individuals who change political opinion and those who do not. We combine several types of data to form multimodal exposure features, which express the exposure of individuals to others' political opinions. We use the previously defined methodology based on Latent Dirichlet Allocation to define each group's behaviors in terms of their exposure to opinions, and determine statistically significant features which differentiate those who change opinions and those who do not. We also consider the difference in exposure features of individuals that increases their interest in politics versus those who do not. Overall, this thesis addresses several important issues in the recent body of work called Computational Social Science. Investigations principled on mathematical models and multiple types of mobile phone sensor data are performed to mine real life human activities in large-scale scenarios
What did you do today?: discovering daily routines from large-scale mobile data
No full textWe present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. Using 68 000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. The first studied model, Latent Dirichlet Allocation (LDA), automatically discovers characteristic routines for all individuals in the study, including "going to work at 10am", "leaving work at night", or "staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as "being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines.</p
Discovering routines from large-scale human locations using probabilistic topic models
No full textIn this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events
Role of Image-Guided Percutaneous Drainage in Pancreatic Collections
No full textAbstractAcute pancreatitis is one of the major gastrointestinal conditions that lead to around 300,000 hospital admissions per year in the United States. While mild inflammation of the pancreas is often managed conservatively, progression of the disease process to necrosis significantly increases the overall morbidity and mortality and often requires surgical or other interventional techniques for management. The purpose of this review is to describe the role of percutaneous drainage for the management of complicated pancreatitis.</jats:p
Comparison of clinical outcome between pyeloperfused versus non-pyeloperfused microwave ablation of renal cell carcinoma
No full textPurpose: We present the outcomes of microwave ablation (MWA) of renal cell carcinoma (RCC) with and without pyeloperfusion. Material and methods: A retrospective review of patients' records was undertaken to identify patients with RCC, who were treated with MWA with and without adjunctive pyeloperfusion. The distance between the tumour and ureter as well as the tumour size were measured on axial imaging. Pyeloperfusion was performed in nine patients in this series after placement of a ureteral stent and instilment of diluted contrast into the ureter. MWAs of the tumours were performed under computed tomography (CT) guidance. Hydrodissection was performed to displace at-risk organs. Creatinine was measured as renal function index after and before the procedure. A CT scan was performed at the end of the procedure and also after one, three, and six months, to identify the presence of residual disease and complications. Results: Eighteen biopsies of proven RCC were treated with 20 sessions of MWA. The average follow-up time for this study was 180 days. The average distance between the ureter and the tumour in axial CT view was 20.8 (± 2.9) mm. Primary efficacy was achieved in 88% of pyeloperfused patients and in 100% of the non-pyeloperfused patients. Two pyeloperfused patients required secondary procedure, and full secondary efficacy was achieved for both. There was only one grade 2 urological complication, which occurred in a patient who underwent pyeloperfusion. Creatinine was not significantly different after the procedure in this study (p-value 0.4). Conclusion: In this study MWAs of RCCs were successfully performed using pyeloperfusion as a protective measure against thermal injury to the ureter
Author Correction: Trading contact tracing efficiency for finding patient zero (Scientific Reports, (2022), 12, 1, (22582), 10.1038/s41598-022-26892-7)
No full textThe original version of this Article contained an error in Affiliation 1, which was incorrectly given as ‘New York University Abu Dhabi, Abu Dhabi, UAE’. The correct affiliation is listed below: Computer Science, Science Division, New York University Abu Dhabi, Abu Dhabi, UAE. The original Article and accompanying Supplementary Information file have been corrected.</p
Ureteral protection during microwave ablation of renal cell carcinoma: combined use of pyeloperfusion and hydrodissection
No full textA 56-year-old female with past medical history of thrombotic microangiopathy presented to her physician with nonspecific abdominal pain. A magnetic resonance imaging scan was obtained, which revealed a 3.1 cm mass arising from medial lower pole of the left kidney that was subsequently shown to be renal cell carcinoma by percutaneous biopsy. Because of her history of thrombotic microangiopathy and other comorbidities, she was deemed a nonsurgical candidate and was therefore referred to interventional radiology for thermal ablation. Computed tomography (CT)-guided microwave ablation was performed with the combined use of pyeloperfusion and hydrodissection for maximal ureteral protection. Follow-up unenhanced CT scan obtained one month after ablation showed a normal collecting system without evidence of hydronephrosis or urinoma
Microwave ablation of hepatocellular carcinoma metastatic to peritoneum : diagnostic value of hydrodissection
No full textSocio-inspired ICT: Towards a socially grounded society-ICT symbiosis
No full textModern ICT (Information and Communication Technology) has developed a vision where the “computer” is no longer associated with the concept of a single device or a network of devices, but rather the entirety of situated services originating in a digital world, which are perceived through the physical world. It is observed that services with explicit user input and output are becoming to be replaced by a computing landscape sensing the physical world via a huge variety of sensors, and controlling it via a plethora of actuators. The nature and appearance of computing devices is changing to be hidden in the fabric of everyday life, invisibly networked, and omnipresent, with applications greatly being based on the notions of context and knowledge. Interaction with such globe spanning, modern ICT systems will presumably be more implicit, at the periphery of human attention, rather than explicit, i.e. at the focus of human attention.Socio-inspired ICT assumes that future, globe scale ICT systems should be viewed as social systems. Such a view challenges research to identify and formalize the principles of interaction and adaptation in social systems, so as to be able to ground future ICT systems on those principles. This position paper therefore is concerned with the intersection of social behaviour and modern ICT, creating or recreating social conventions and social contexts through the use of pervasive, globe-spanning, omnipresent and participative ICTValues and TechnologyTechnology, Policy and Managemen
