Owing to the rising popularity of online social networking services (OSNs), studies on social influence and its diffusion have received significant attention from the research community. Prior research has mostly stud...
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Owing to the rising popularity of online social networking services (OSNs), studies on social influence and its diffusion have received significant attention from the research community. Prior research has mostly studied the impact of single-hop influence diffusion and multi-hop influence broadcast in online social networks. Very little research explores the idea of guiding (routing) multi-hop social influence towards a specific target. In this paper, we motivate the needs of timely routing social influence to formulate a new optimization problem, namely, Routing And Scheduling of Target-Oriented Social Influence Diffusion (RAS-TOSID). Accordingly, we propose the Efficient Routing And Scheduling with Social and Temporal decOmposition (ERASSTO) algorithm, which finds the optimal solution to RAS-TOSID in polynomial time. We carry out a user study by implementing ERASSTO in Facebook and conduct a comprehensive evaluation on ERASSTO and alternative approaches by simulation. The result shows that ERASSTO significantly outperforms other algorithms regarding solution quality and computational efficiency.
The rectilinear block packing problem is a problem of packing a set of rectilinear blocks into a larger rectangular container, where a rectilinear block is a polygonal block whose interior angles are either 90° o...
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ISBN:
(纸本)9781509036660
The rectilinear block packing problem is a problem of packing a set of rectilinear blocks into a larger rectangular container, where a rectilinear block is a polygonal block whose interior angles are either 90° or 270°. This problem has many applications, such as VLSI design and timber cutting. In this paper, we propose a new solution representation, based on an order of items, to decide a layout of rectilinear blocks whose x-coordinates are fixed. When the shapes of given items have certain characteristics, our representation guarantees optimality. We also generalize this result to the case of general shapes. We then propose an exact algorithm that iteratively generates x-coordinates of items and finds the corresponding optimal layout. The computational results show that our algorithm obtains five exact and one heuristic solutions for six inst.nces and it improves the running time of an existing algorithm.
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot...
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Misdosing medications with sensitive therapeutic windows, such as heparin, can place patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital resources. In this work, we present a cl...
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Misdosing medications with sensitive therapeutic windows, such as heparin, can place patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital resources. In this work, we present a clinician-in-the-loop sequential decision making framework, which provides an individualized dosing policy adapted to each patient's evolving clinical phenotype. We employed retrospective data from the publicly available MIMIC II intensive care unit database, and developed a deep reinforcement learning algorithm that learns an optimal heparin dosing policy from sample dosing trails and their associated outcomes in large electronic medical records. Using separate training and testing datasets, our model was observed to be effective in proposing heparin doses that resulted in better expected outcomes than the clinical guidelines. Our results demonstrate that a sequential modeling approach, learned from retrospective data, could potentially be used at the bedside to derive individualized patient dosing policies.
Cognitive aging in healthy adults exhibits significant and heterogeneous variability. In this study, we apply a robust unified learning framework to cluster subgroups using neuroimaging data (brain volume and white ma...
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Cognitive aging in healthy adults exhibits significant and heterogeneous variability. In this study, we apply a robust unified learning framework to cluster subgroups using neuroimaging data (brain volume and white matter), to identify neurological phenotypes that can sort out the heterogeneity in cognitive aging and help identify potential risk factors for suboptimal brain aging. Using machine learning analytics, results revealed two unique subgroups in healthy older adults with different patterns of white matter integrity and brain volumetric measures. The classification of phenotypical subgroups in healthy older adults may inform the understanding of the complexity of brain changes before the onset of clinical symptoms. The identified neuroimaging features that defined group classification are recognized as important structures that subserve cognitive performance. Further analysis of these potential biomarkers that help predict trajectory of cognitive decline in symptom free individuals could lead to the detection of early stages of neurodegenerative diseases.
Context. Extended and delayed emission around distant TeV sources induced by the effects of propagation of γ rays through the intergalactic medium can be used for the measurement of the intergalactic magnetic field (...
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MAXI J1820+070 is a low-mass X-ray binary with a black hole as a compact object. This binary underwent an exceptionally bright X-ray outburst from March to October 2018, showing evidence of a non-thermal particle popu...
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Mobile clients have been on the rise and Web sites are becoming more accessible by portable devices like tablets and smartphones. Mobile sites are built on standards such as HTML5, CSS3 and other modern web technologi...
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ISBN:
(纸本)9781450335515
Mobile clients have been on the rise and Web sites are becoming more accessible by portable devices like tablets and smartphones. Mobile sites are built on standards such as HTML5, CSS3 and other modern web technologies like JQuery Mobile, and they run on any mobile platform with a modern, standards-compliant web browser. However, the performance and usability of such sites are often insufficient. In this work we study the performance of jQuery Mobile web sites, investigate how the synergy with other technologies or techniques could be used to improve performance and conclude to a set of design patterns and coding practices, focused on jQuery Mobile framework, with emphasis on the architecture of the pages, navigation system and images loading techniques.
Programming languages form the interface between programmers (the users) and the computation that they desire the computer to execute. Although studies exist for some aspects of programming language design (such as co...
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