Analysis of Dependence Tracking Algorithms for Task Dataflow Execution

Processor architectures has taken a turn towards many-core processors, which integrate multiple processing cores on a single chip to increase overall performance, and there are no signs that this trend will stop in the near future. Many-core processors are harder to program than multi-core and single-core processors due to the need of writing parallel or concurrent programs with high degrees of parallelism. Moreover, many-cores have to operate in a mode of strong scaling because of memory bandwidth constraints. In strong scaling increasingly finer-grain parallelism must be extracted in order to keep all processing cores busy.

Task dataflow programming models have a high potential to simplify parallel program- ming because they alleviate the programmer from identifying precisely all inter-task de- pendences when writing programs. Instead, the task dataflow runtime system detects and enforces inter-task dependences during execution based on the description of memory each task accesses. The runtime constructs a task dataflow graph that captures all tasks and their dependences. Tasks are scheduled to execute in parallel taking into account dependences specified in the task graph.

Several papers report important overheads for task dataflow systems, which severely limits the scalability and usability of such systems. In this paper we study efficient schemes to manage task graphs and analyze their scalability. We assume a programming model that supports input, output and in/out annotations on task arguments, as well as commutative in/out and reductions. We analyze the structure of task graphs and identify versions and generations as key concepts for efficient management of task graphs. Then, we present three schemes to manage task graphs building on graph representations, hypergraphs and lists. We also consider a fourth edge-less scheme that synchronizes tasks using integers. Analysis using micro-benchmarks shows that the graph representation is not always scalable and that the edge-less scheme introduces least overhead in nearly all situations.

A theoretical assessment of surface defect machining and hot machining of nanocrystalline silicon carbide

In this paper, a newly proposed machining method named “surface defect machining” (SDM) [Wear, 302, 2013 (1124-1135)] was explored for machining of nanocrystalline beta silicon carbide (3C-SiC) at 300K using MD simulation. The results were compared with isothermal high temperature machining at 1200K under the same machining parameters, emulating ductile mode micro laser assisted machining (µ-LAM) and with conventional cutting at 300 K. In the MD simulation, surface defects were generated on the top of the (010) surface of the 3C-SiC work piece prior to cutting, and the workpiece was then cut along the <100> direction using a single point diamond tool at a cutting speed of 10 m/sec. Cutting forces, sub-surface deformation layer depth, temperature in the shear zone, shear plane angle and friction coefficient were used to characterize the response of the workpiece. Simulation results showed that SDM provides a unique advantage of decreased shear plane angle which eases the shearing action. This in turn causes an increased value of average coefficient of friction in contrast to the isothermal cutting (carried at 1200 K) and normal cutting (carried at 300K). The increase of friction coefficient however was found to aid the cutting action of the tool due to an intermittent dropping in the cutting forces, lowering stresses on the cutting tool and reducing operational temperature. Analysis shows that the introduction of surface defects prior to conventional machining can be a viable choice for machining a wide range of ceramics, hard steels and composites compared to hot machining.

Generalized Laplacian Eigenmaps for Modeling and Tracking Human Motions

This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.

Tracking down the cause of proteinuria in primary care

Proteinuria originates from the kidney and occurs as a result of injury to either the glomerulus or the renal tubule or both. It is relatively common in the general population with reported point prevalence of up to 8% but the prevalence falls to around 2% on repeated testing. Chronic glomerular injury resulting in proteinuria may be secondary to prolonged duration of diabetes or hypertension. A tubular origin of proteinuria may be associated with inflammation of renal tubules triggered by prescribed drugs or ingested toxins. In the absence of obvious clues to the cause of persistent proteinuria on history or clinical examination it is worthwhile reviewing the patient's prescribed drugs to identify any potentially nephrotoxic agents e.g. NSAIDs. NICE guidelines recommend screening for proteinuria in individuals at higher risk for chronic kidney disease (CKD). These include patients with diabetes, hypertension, cardiovascular disease, connective tissue disorders, a family history of renal disease and those prescribed potentially nephrotoxic drugs. Patients with sudden onset of lower limb oedema and associated proteinuria should have a serum albumin level measured to exclude the nephrotic syndrome. Renal tract ultrasound will measure kidney size, and detect scarring associated with chronic pyelonephritis or prior renal stone disease which can cause proteinuria.

Adaptive Fusion Of Particle Filtering And Spatio-Temporal Motion Energy For Human Tracking

Object tracking is an active research area nowadays due to its importance in human computer interface, teleconferencing and video surveillance. However, reliable tracking of objects in the presence of occlusions, pose and illumination changes is still a challenging topic. In this paper, we introduce a novel tracking approach that fuses two cues namely colour and spatio-temporal motion energy within a particle filter based framework. We conduct a measure of coherent motion over two image frames, which reveals the spatio-temporal dynamics of the target. At the same time, the importance of both colour and motion energy cues is determined in the stage of reliability evaluation. This determination helps maintain the performance of the tracking system against abrupt appearance changes. Experimental results demonstrate that the proposed method outperforms the other state of the art techniques in the used test datasets.

Tracking magnetic bright point motions through the solar atmosphere

High-cadence, multiwavelength observations and simulations are employed for the analysis of solar photospheric magnetic bright points (MBPs) in the quiet Sun. The observations were obtained with the Rapid Oscillations in the Solar Atmosphere (ROSA) imager and the Interferometric Bidimensional Spectrometer at the Dunn Solar Telescope. Our analysis reveals that photospheric MBPs have an average transverse velocity of approximately 1 km s-1, whereas their chromospheric counterparts have a slightly higher average velocity of 1.4 km s-1. Additionally, chromospheric MBPs were found to be around 63 per cent larger than the equivalent photospheric MBPs. These velocity values were compared with the output of numerical simulations generated using the muram code. The simulated results were similar, but slightly elevated, when compared to the observed data. An average velocity of 1.3 km s-1 was found in the simulated G-band images and an average of 1.8 km s-1 seen in the velocity domain at a height of 500 km above the continuum formation layer. Delays in the change of velocities were also analysed. Average delays of ˜4 s between layers of the simulated data set were established and values of ˜29 s observed between G-band and Ca ii K ROSA observations. The delays in the simulations are likely to be the result of oblique granular shock waves, whereas those found in the observations are possibly the result of a semi-rigid flux tube.

Dense Multiperson Tracking with Robust Hierarchical Linear Assignment

We introduce a novel dual-stage algorithm for online multi-target tracking in realistic conditions. In the first stage, the problem of data association between tracklets and detections, given partial occlusion, is addressed using a novel occlusion robust appearance similarity method. This is used to robustly link tracklets with detections without requiring explicit knowledge of the occluded regions. In the second stage, tracklets are linked using a novel method of constraining the linking process that removes the need for ad-hoc tracklet linking rules. In this method, links between tracklets are permitted based on their agreement with optical flow evidence. Tests of this new tracking system have been performed using several public datasets.

Adaptive NormalHedge for robust visual tracking

In this paper, we propose a novel visual tracking framework, based on a decision-theoretic online learning algorithm namely NormalHedge. To make NormalHedge more robust against noise, we propose an adaptive NormalHedge algorithm, which exploits the historic information of each expert to perform more accurate prediction than the standard NormalHedge. Technically, we use a set of weighted experts to predict the state of the target to be tracked over time. The weight of each expert is online learned by pushing the cumulative regret of the learner towards that of the expert. Our simulation experiments demonstrate the effectiveness of the proposed adaptive NormalHedge, compared to the standard NormalHedge method. Furthermore, the experimental results of several challenging video sequences show that the proposed tracking method outperforms several state-of-the-art methods.

Enabling ultra high precision on hard steels using surface defect machining

This paper is an extension to an idea coined during the 13th EUSPEN Conference (P6.23) named "surface defect machining" (SDM). The objective of this work was to demonstrate how a conventional CNC turret lathe can be used to obtain ultra high precision machined surface finish on hard steels without recourse to a sophisticated ultra precision machine tool. An AISI 4340 hard steel (69 HRC) workpiece was machined using a CBN cutting tool with and without SDM. Post-machining measurements by a Form Talysurf and a Scanning Electron Microscope (FEI Quanta 3D) revealed that SDM culminates to several key advantages (i) provides better quality of the machined surface integrity and offers (ii) lowering feed rate to 5μm/rev to obtain a machined surface roughness of 30 nm (optical quality).

Efficient tracking of human poses using a manifold hierarchy

In this paper a 3D human pose tracking framework is presented. A new dimensionality reduction method (Hierarchical Temporal Laplacian Eigenmaps) is introduced to represent activities in hierarchies of low dimensional spaces. Such a hierarchy provides increasing independence between limbs, allowing higher flexibility and adaptability that result in improved accuracy. Moreover, a novel deterministic optimisation method (Hierarchical Manifold Search) is applied to estimate efficiently the position of the corresponding body parts. Finally, evaluation on public datasets such as HumanEva demonstrates that our approach achieves a 62.5mm-65mm average joint error for the walking activity and outperforms state-of-the-art methods in terms of accuracy and computational cost.

Vehicle-barrier tracking of a scaled crash test for roadside barrier design

In this paper the tracking system used to perform a scaled vehicle-barrier crash test is reported. The scaled crash test was performed as part of a wider project aimed at designing a new safety barrier making use of natural building materials. The scaled crash test was designed and performed as a proof of concept of the new mass-based safety barriers and the study was composed of two parts: the scaling technique and of a series of performed scaled crash tests. The scaling method was used for 1) setting the scaled test impact velocity so that energy dissipation and momentum transferring, from the car to the barrier, can be reproduced and 2) predicting the acceleration, velocity and displacement values occurring in the full-scale impact from the results obtained in a scaled test. To achieve this goal the vehicle and barrier displacements were to be recorded together with the vehicle accelerations and angular velocities. These quantities were measured during the tests using acceleration sensors and a tracking system. The tracking system was composed of a high speed camera and a set of targets to measure the vehicle linear and angular velocities. A code was developed to extract the target velocities from the videos and the velocities obtained were then compared with those obtained integrating the accelerations provided by the sensors to check the reliability of the method.