Sameep Mehta

TL;DR: In this paper, a method and system for planning a workforce headcount for a given business process is described, which comprises the steps of providing as inputs, i) productivity rampups to model the level of experience and to measure the performance of both new hires and current employees, and ii) industry/market attrition rates for employees; and performing an evaluation, using said inputs, of at least one given management objective.

TL;DR: Experimental results suggest that these discussion threads tend to evolve more strongly over geographically finer granularities: they evolve more at city levels compared to country levels, and more at country levelsCompared to globally.

TL;DR: This article presents trajectory representation algorithms for tangible features found in temporally varying scientific datasets based on motion and shape parameters including linear velocity, angular velocity, etc, which are used to segment the trajectory instead of relying on the geometry of the trajectory.

TL;DR: This paper proposes a deep quadlet network to learn the feature embedding using the quadlet loss function, and presents an extensive evaluation of the proposed ranking model against state-of-the-art baselines on three datasets with fine-grained categorization.

TL;DR: The authors identify event-specific social discussion threads by identifying a spatial relationship and one or more additional relationships across two or more topical clusters derived from a text source, extracting one or several temporally evolving discussion sequences across the two or multiple topical clusters, identifying at least one social discussion thread across the topical clusters by identifying the correlation between the one or multiple additional relationships and the temporally changing discussion sequences, and identifying a geographically-constrained social topic discussion thread among the identified social topics.