Towards an Adaptive Skeleton Framework for Performance Portability

The proliferation of widely available, but very different, parallel architectures makes the ability to deliver good parallel performance on a range of architectures, or performance portability, highly desirable. Irregularly-parallel problems, where the number and size of tasks is unpredictable, are particularly challenging and require dynamic coordination. The paper outlines a novel approach to delivering portable parallel performance for irregularly parallel programs. The approach combines declarative parallelism with JIT technology, dynamic scheduling, and dynamic transformation. We present the design of an adaptive skeleton library, with a task graph implementation, JIT trace costing, and adaptive transformations. We outline the architecture of the protoype adaptive skeleton execution framework in Pycket, describing tasks, serialisation, and the current scheduler.We report a preliminary evaluation of the prototype framework using 4 micro-benchmarks and a small case study on two NUMA servers (24 and 96 cores) and a small cluster (17 hosts, 272 cores). Key results include Pycket delivering good sequential performance e.g. almost as fast as C for some benchmarks; good absolute speedups on all architectures (up to 120 on 128 cores for sumEuler); and that the adaptive transformations do improve performance

DIY Human Action Data Set Generation

The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in the field, training data itself plays a significant role in the machine learning process, especially deep learning approaches which are data hungry. In order to solve each new problem and get a decent performance, a large amount of data needs to be captured which may in many cases pose logistical difficulties. Therefore, the ability to generate de novo data or expand an existing data set, however small, in order to satisfy data requirement of current networks may be invaluable. Herein, we introduce a novel way to partition an action video clip into action, subject and context. Each part is manipulated separately and reassembled with our proposed video generation technique. Furthermore, our novel human skeleton trajectory generation along with our proposed video generation technique, enables us to generate unlimited action recognition training data. These techniques enables us to generate video action clips from an small set without costly and time-consuming data acquisition. Lastly, we prove through extensive set of experiments on two small human action recognition data sets, that this new data generation technique can improve the performance of current action recognition neural nets

Coral skeleton P/Ca proxy for seawater phosphate: Multi-colony calibration with a contemporaneous seawater phosphate record

A geochemical proxy for surface ocean nutrient concentrations recorded in coral skeleton could provide new insight into the connections between sub-seasonal to centennial scale nutrient dynamics, ocean physics, and primary production in the past. Previous work showed that coralline P/Ca, a novel seawater phosphate proxy, varies synchronously with annual upwelling-driven cycles in surface water phosphate concentration. However, paired contemporaneous seawater phosphate time-series data, needed for rigorous calibration of the new proxy, were lacking. Here we present further development of the P/Ca proxy in Porites lutea and Montastrea sp. corals, showing that skeletal P/Ca in colonies from geographically distinct oceanic nutrient regimes is a linear function of seawater phosphate (PO4 SW) concentration. Further, high-resolution P/Ca records in multiple colonies of Pavona gigantea and Porites lobata corals grown at the same upwelling location in the Gulf of Panama were strongly correlated to a contemporaneous time-series record of surface water PO4 SW at this site (r2 = 0.7–0.9). This study supports application of the following multi-colony calibration equations to down-core records from comparable upwelling sites, resulting in ±0.2 and ±0.1 lmol/kg uncertainties in PO4 SW reconstructions from P. lobata and P. gigantea, respectively.P/Ca Porites lobata (lmol/mol) = (21.1 ? 2.4)PO4 SW (lmol/kg) + (14.3 ? 3.8)P/Ca Pavona gigantea (lmol/mol) = (29.2 ? 1.4)PO4 SW (lmol/kg) + (33.4 ? 2.7)Inter-colony agreement in P/Ca response to PO4 SW was good (±5–12% about mean calibration slope), suggesting that species-specific calibration slopes can be applied to new coral P/Ca records to reconstruct past changes in surface ocean phosphate. However, offsets in the y-intercepts of calibration regressions among co-located individuals and taxa suggest that biologically-regulated “vital effects” and/or skeletal extension rate may also affect skeletal P incorporation. Quantification of the effect of skeletal extension rate on P/Ca could lead to corrected calibration equations and improved inter-colony P/Ca agreement. Nevertheless, the efficacy of the P/Ca proxy is thus supported by both broad scale correlation to mean surface water phosphate and regional calibration against documented local seawater phosphate variations

Which One is Me?: Identifying Oneself on Public Displays

While user representations are extensively used on public displays, it remains unclear how well users can recognize their own representation among those of surrounding users. We study the most widely used representations: abstract objects, skeletons, silhouettes and mirrors. In a prestudy (N=12), we identify five strategies that users follow to recognize themselves on public displays. In a second study (N=19), we quantify the users' recognition time and accuracy with respect to each representation type. Our findings suggest that there is a significant effect of (1) the representation type, (2) the strategies performed by users, and (3) the combination of both on recognition time and accuracy. We discuss the suitability of each representation for different settings and provide specific recommendations as to how user representations should be applied in multi-user scenarios. These recommendations guide practitioners and researchers in selecting the representation that optimizes the most for the deployment's requirements, and for the user strategies that are feasible in that environment