Accelerating junior-secondary mathematics

Unfortunately, in Australia there is a prevalence of mathematically underperforming junior-secondary students in low-socioeconomic status schools. This requires targeted intervention to develop the affected students’ requisite understanding in preparation for post-compulsory study and employment and, ultimately, to increase their life chances. To address this, the ongoing action research project presented in this paper is developing a curriculum of accelerated learning, informed by a lineage of cognitivist-based structural sequence theory building activity (e.g., Cooper & Warren, 2011). The project’s conceptual framework features three pillars: the vertically structured sequencing of concepts; pedagogy grounded in students’ reality and culture; and professional learning to support teachers’ implementation of the curriculum (Cooper, Nutchey, & Grant, 2013). Quantitative and qualitative data informs the ongoing refinement of the theory, the curriculum, and the teacher support.

Accelerating Pseudo-Marginal MCMC using Gaussian Processes

Pseudo-marginal methods such as the grouped independence Metropolis-Hastings (GIMH) and Markov chain within Metropolis (MCWM) algorithms have been introduced in the literature as an approach to perform Bayesian inference in latent variable models. These methods replace intractable likelihood calculations with unbiased estimates within Markov chain Monte Carlo algorithms. The GIMH method has the posterior of interest as its limiting distribution, but suffers from poor mixing if it is too computationally intensive to obtain high-precision likelihood estimates. The MCWM algorithm has better mixing properties, but less theoretical support. In this paper we propose to use Gaussian processes (GP) to accelerate the GIMH method, whilst using a short pilot run of MCWM to train the GP. Our new method, GP-GIMH, is illustrated on simulated data from a stochastic volatility and a gene network model.

Micropropagation of vegetatively propagated crops: accelerating release of new cultivars and providing an important source of clean planting material

Micropropagation is unequalled for the rapid clonal propagation of improved cultivars from several Australian breeding programmes. This has been particularly true of the pineapple breeding programme, but it has also found an important role in the strawberry breeding programme where high-health mother stock is of paramount concern. In the banana and ginger industries, while access to new cultivars has been of importance, micropropagation has been adopted by the industry to ensure that planting materials are free from serious pests and diseases. Bananas can be used as planting material as early as the first generation ex vitro and is responsible for the establishment of laboratories and nurseries specializing in the production of pathogen-tested plants. The ginger industry, on the other hand, has used micropropagated plants as a source of disease and pest-free stock to establish a clean 'seed' scheme based on the production of conventional planting material.

Accelerating Waves in Polar Coronal Holes as Seen by EIS and SUMER

We present EIS/Hinode and SUMER/SOHO observations of propagating disturbances detected in coronal lines in inter-plume and plume regions of a polar coronal hole. The observation was carried out on 2007 November 13 as part of the JOP196/HOP045 program. The SUMER spectroscopic observation gives information about fluctuations in radiance and on both resolved (Doppler shift) and unresolved (Doppler width) line-of-sight velocities, whereas EIS 40 `'wide slot images detect fluctuations only in radiance but maximize the probability of overlapping field of view between the two instruments. From distance-time radiance maps, we detect the presence of propagating waves in a polar inter-plume region with a period of 15-20 minutes and a propagation speed increasing from 130 +/- 14 km s(-1) just above the limb to 330 +/- 140 km s(-1) around 160 `' above the limb. These waves can be traced to originate from a bright region of the on-disk part of the coronal hole where the propagation speed is in the range of 25 +/- 1.3 to 38 +/- 4.5 km s(-1), with the same periodicity. These on-disk bright regions can be visualized as the base of the coronal funnels. The adjacent plume region also shows the presence of propagating disturbances with the same range of periodicity but with propagation speeds in the range of 135 +/- 18 to 165 +/- 43 km s(-1) only. A comparison between the distance-time radiance map of the two regions indicates that the waves within the plumes are not observable (may be getting dissipated) far off-limb, whereas this is not the case in the inter-plume region. A correlation analysis was also performed to find out the time delay between the oscillations at several heights in the off-limb region, finding results consistent with those from the analysis of the distance-timemaps. To our knowledge, this result provides first spectroscopic evidence of the acceleration of propagating disturbances in the polar region close to the Sun (within 1.2 R/R-circle dot), which provides clues to the understanding of the origin of these waves. We suggest that the waves are likely either Alfvenic or fast magnetoacoustic in the inter-plume region and slow magnetoacoustic in the plume region. This may lead to the conclusion that inter-plumes are a preferred channel for the acceleration of the fast solar wind.

Accelerating frequency-domain diffuse optical tomographic image reconstruction using graphics processing units

Diffuse optical tomographic image reconstruction uses advanced numerical models that are computationally costly to be implemented in the real time. The graphics processing units (GPUs) offer desktop massive parallelization that can accelerate these computations. An open-source GPU-accelerated linear algebra library package is used to compute the most intensive matrix-matrix calculations and matrix decompositions that are used in solving the system of linear equations. These open-source functions were integrated into the existing frequency-domain diffuse optical image reconstruction algorithms to evaluate the acceleration capability of the GPUs (NVIDIA Tesla C 1060) with increasing reconstruction problem sizes. These studies indicate that single precision computations are sufficient for diffuse optical tomographic image reconstruction. The acceleration per iteration can be up to 40, using GPUs compared to traditional CPUs in case of three-dimensional reconstruction, where the reconstruction problem is more underdetermined, making the GPUs more attractive in the clinical settings. The current limitation of these GPUs in the available onboard memory (4 GB) that restricts the reconstruction of a large set of optical parameters, more than 13, 377. (C) 2010 Society of Photo-Optical Instrumentation Engineers. DOI: 10.1117/1.3506216]

Accelerating multi-core simulators

Simulation is an important means of evaluating new microarchitectures. With the invention of multi-core (CMP) platforms, simulators are becoming larger and more complex. However, with the availability of CMPs with larger caches and higher operating frequency, the wall clock time required for simulating an application has become comparatively shorter. Reducing this simulation time further is a great challenge, especially in the case of multi-threaded workload due to indeterminacy introduced due to simultaneously executing various threads. In this paper, we propose a technique for speeding multi-core simulation. The model of the processor core and cache are replaced with functional models, to achieve speedup. A timed Petri net model is used to estimate the execution time of the processor and the memory access latencies are estimated using hit/miss information obtained from the functional model of the cache. This model can be used to predict performance of data parallel applications or multiprogramming workload on CMP platform with various cache hierarchies and shared bus interconnect. The error in estimation of the execution time of an application is within 6%. The speedup achieved ranges between an average of 2x--4x over the cycle accurate simulator.

Accelerating Numerical Linear Algebra Kernels on a Scalable Run Time Reconfigurable Platform

Numerical Linear Algebra (NLA) kernels are at the heart of all computational problems. These kernels require hardware acceleration for increased throughput. NLA Solvers for dense and sparse matrices differ in the way the matrices are stored and operated upon although they exhibit similar computational properties. While ASIC solutions for NLA Solvers can deliver high performance, they are not scalable, and hence are not commercially viable. In this paper, we show how NLA kernels can be accelerated on REDEFINE, a scalable runtime reconfigurable hardware platform. Compared to a software implementation, Direct Solver (Modified Faddeev's algorithm) on REDEFINE shows a 29X improvement on an average and Iterative Solver (Conjugate Gradient algorithm) shows a 15-20% improvement. We further show that solution on REDEFINE is scalable over larger problem sizes without any notable degradation in performance.

Accelerating Reduction for Enabling Fast Multiplication over Large Binary Fields

In this paper we present a hardware-software hybrid technique for modular multiplication over large binary fields. The technique involves application of Karatsuba-Ofman algorithm for polynomial multiplication and a novel technique for reduction. The proposed reduction technique is based on the popular repeated multiplication technique and Barrett reduction. We propose a new design of a parallel polynomial multiplier that serves as a hardware accelerator for large field multiplications. We show that the proposed reduction technique, accelerated using the modified polynomial multiplier, achieves significantly higher performance compared to a purely software technique and other hybrid techniques. We also show that the hybrid accelerated approach to modular field multiplication is significantly faster than the Montgomery algorithm based integrated multiplication approach.

Accelerating Information Diffusion in Social Networks Under the Susceptible-Infected-Susceptible Epidemic Model

Standard Susceptible-Infected-Susceptible (SIS) epidemic models assume that a message spreads from the infected to the susceptible nodes due to only susceptible-infected epidemic contact. We modify the standard SIS epidemic model to include direct recruitment of susceptible individuals to the infected class at a constant rate (independent of epidemic contacts), to accelerate information spreading in a social network. Such recruitment can be carried out by placing advertisements in the media. We provide a closed form analytical solution for system evolution in the proposed model and use it to study campaigning in two different scenarios. In the first, the net cost function is a linear combination of the reward due to extent of information diffusion and the cost due to application of control. In the second, the campaign budget is fixed. Results reveal the effectiveness of the proposed system in accelerating and improving the extent of information diffusion. Our work is useful for devising effective strategies for product marketing and political/social-awareness/crowd-funding campaigns that target individuals in a social network.

Accelerating method of moments based package-board 3D parasitic extraction using FPGA

In this article, a Field Programmable Gate Array (FPGA)-based hardware accelerator for 3D electromagnetic extraction, using Method of Moments (MoM) is presented. As the number of nets or ports in a system increases, leading to a corresponding increase in the number of right-hand-side (RHS) vectors, the computational cost for multiple matrix-vector products presents a time bottleneck in a linear-complexity fast solver framework. In this work, an FPGA-based hardware implementation is proposed toward a two-level parallelization scheme: (i) matrix level parallelization for single RHS and (ii) pipelining for multiple-RHS. The method is applied to accelerate electrostatic parasitic capacitance extraction of multiple nets in a Ball Grid Array (BGA) package. The acceleration is shown to be linearly scalable with FPGA resources and speed-ups over 10x against equivalent software implementation on a 2.4GHz Intel Core i5 processor is achieved using a Virtex-6 XC6VLX240T FPGA on Xilinx's ML605 board with the implemented design operating at 200MHz clock frequency. (c) 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:776-783, 2016

Load-Unload Response Ratio and Accelerating Moment/Energy Release Critical Region Scaling and Earthquake Prediction

The main idea of the Load-Unload Response Ratio (LURR) is that when a system is stable, its response to loading corresponds to its response to unloading, whereas when the system is approaching an unstable state, the response to loading and unloading becomes quite different. High LURR values and observations of Accelerating Moment/Energy Release (AMR/AER) prior to large earthquakes have led different research groups to suggest intermediate-term earthquake prediction is possible and imply that the LURR and AMR/AER observations may have a similar physical origin. To study this possibility, we conducted a retrospective examination of several Australian and Chinese earthquakes with magnitudes ranging from 5.0 to 7.9, including Australia's deadly Newcastle earthquake and the devastating Tangshan earthquake. Both LURR values and best-fit power-law time-to-failure functions were computed using data within a range of distances from the epicenter. Like the best-fit power-law fits in AMR/AER, the LURR value was optimal using data within a certain epicentral distance implying a critical region for LURR. Furthermore, LURR critical region size scales with mainshock magnitude and is similar to the AMR/AER critical region size. These results suggest a common physical origin for both the AMR/AER and LURR observations. Further research may provide clues that yield an understanding of this mechanism and help lead to a solid foundation for intermediate-term earthquake prediction.

High-speed streams from coronal holes and the accelerating mechanism of the solar-wind

In this paper, the general Mach number equation is derived, and the influence of typical energy forms in the solar wind is analysed in detail. It shows that the accelerating process of the solar wind is influenced critically by the form of heating in the corona, and that the transonic mechanism is mainly the result of the adjustment of the variation of the crosssection of flowing tubes and the heat source term.The accelerating mechanism for both the high-speed stream from the coronal hole and the normal solar wind is similar. But, the temperature is low in the lower level of the coronal hole and more heat energy supply in the outside is required, hence the high speed of the solar wind; while the case with the ordinary coronal region is just the opposite, and the velocity of the solar wind is therefore lower. The accelerating process for various typical parameters is calculated, and it is found that the high-speed stream may reach 800 km/sec.

Spatio-Temporal Scanning and Statistical Test of the Accelerating Moment Release (AMR) Model Using Australian Earthquake Data

The Accelerating Moment Release (AMR) preceding earthquakes with magnitude above 5 in Australia that occurred during the last 20 years was analyzed to test the Critical Point Hypothesis. Twelve earthquakes in the catalog were chosen based on a criterion for the number of nearby events. Results show that seven sequences with numerous events recorded leading up to the main earthquake exhibited accelerating moment release. Two occurred near in time and space to other earthquakes preceded by AM R. The remaining three sequences had very few events in the catalog so the lack of AMR detected in the analysis may be related to catalog incompleteness. Spatio-temporal scanning of AMR parameters shows that 80% of the areas in which AMR occurred experienced large events. In areas of similar background seismicity with no large events, 10 out of 12 cases exhibit no AMR, and two others are false alarms where AMR was observed but no large event followed. The relationship between AMR and Load-Unload Response Ratio (LURR) was studied. Both methods predict similar critical region sizes, however, the critical point time using AMR is slightly earlier than the time of the critical point LURR anomaly.