arXiv subjects

A. Wong

Publications and source records attributed to A. Wong.

10 recordsLinked to original sources

arXiv2026

The science of compressional heating on the LM26 magnetized target fusion experiment

The Lawson Machine 26 (LM26) at General Fusion has demonstrated compressional heating of a spherical tokamak deuterium plasma as it was compressed by an imploding solid lithium liner. Results from the first 11 compression shots on LM26 are presented, the highest-performing of which show more than a 3x increase in $T_e$, a 10x increase in $n_e$, and a 10x increase in $B_{pol}$ within the plasma driven by 3x radial compression. The experimental device and instrumentation are reviewed in detail, followed by observations about the liner trajectory and evolution of plasma properties, including increases in emission of neutrons, X-rays, and visible radiation. Observations from fast-camera images during compression provide context for interpreting the spatial structure of plasma-wall interaction. Overviews of relevant models and analysis are presented. Diagnostic data are used to reconstruct the experimental equilibrium state in computational framework as a function of time. The results build confidence in the stability and transport analyses that support the primary conclusions. Trends across the full set of 11 compression shots are presented, and detailed examinations of the high-performance shots are given individually. The central conclusions of the integrated physics model specifically indicate that compressional heating was achieved in this set of experiments, as evidenced by the balance of heating power from compression, Ohmic heating from plasma current, and losses to the boundary needed to match the experimental data. A majority of the temperature rise is attributable to compressional heating. An increase in neutron flux is also observed during compression. The results provide a basis for planned improvements to the LM26 facility that will enable the compression of magnetized plasma to increasingly higher densities and temperatures.

physics.plasm-ph↗

arXiv2023

ALMA High-Level Data Products: Submillimetre counterparts of SDSS quasars in the ALMA footprint

The Atacama Large Millimetre/submillimetre Array (ALMA) is the world's most advanced radio interferometric facility, producing science data with an average rate of about 1 TB per day. After a process of calibration, imaging and quality assurance, the scientific data are stored in the ALMA Science Archive (ASA), along with the corresponding raw data, making the ASA an invaluable resource for original astronomical research. Due to their complexity, each ALMA data set has the potential for scientific results that go well beyond the ideas behind the original proposal that led to each observation. For this reason, the European ALMA Regional Centre initiated the High-Level Data Products initiative to develop science-oriented data products derived from data sets publicly available in the ASA, that go beyond the formal ALMA deliverables. The first instance of this initiative is the creation of a catalogue of submillimetre (submm) detections of Sloan Digital Sky Survey (SDSS) quasars from the SDSS Data Release 14 that lie in the aggregate ALMA footprint observed since ALMA Cycle 0. The ALMA fluxes are extracted in an automatic fashion, using the ALMA Data Mining Toolkit. All extractions above a signal-to-noise cut of 3.5 are considered, they have been visually inspected and the reliable detections are presented in a catalogue of 376 entries, corresponding to 275 unique quasars. Interesting targets found in the process, i.e. lensed or jetted quasars as well as quasars with nearby submm counterparts are highlighted, to facilitate further studies or potential follow up observations.

astro-ph.GA↗

arXiv2020

Square Kilometre Array Science Data Challenge 1: analysis and results

As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality datasets for testing and evaluating new techniques. In this paper we present a description and results from the first such Science Data Challenge (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560 MHz, 1400MHz and 9200 MHz) at three depths (8 h, 100 h and 1000 h), SDC1 asked participants to apply source detection, characterization and classification methods to simulated data. The challenge opened in November 2018, with nine teams submitting results by the deadline of April 2019. In this work we analyse the results for 8 of those teams, showcasing the variety of approaches that can be successfully used to find, characterise and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

astro-ph.IM↗

arXiv2017

Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions

In this work, we perform an exploratory study on synthesizing deep neural networks using biological synaptic strength distributions, and the potential influence of different distributions on modelling performance particularly for the scenario associated with small data sets. Surprisingly, a CNN with convolutional layer synaptic strengths drawn from biologically-inspired distributions such as log-normal or correlated center-surround distributions performed relatively well suggesting a possibility for designing deep neural network architectures that do not require many data samples to learn, and can sidestep current training procedures while maintaining or boosting modelling performance.

cs.NE↗

arXiv2016

NeRD: a Neural Response Divergence Approach to Visual Salience Detection

In this paper, a novel approach to visual salience detection via Neural Response Divergence (NeRD) is proposed, where synaptic portions of deep neural networks, previously trained for complex object recognition, are leveraged to compute low level cues that can be used to compute image region distinctiveness. Based on this concept , an efficient visual salience detection framework is proposed using deep convolutional StochasticNets. Experimental results using CSSD and MSRA10k natural image datasets show that the proposed NeRD approach can achieve improved performance when compared to state-of-the-art image saliency approaches, while the attaining low computational complexity necessary for near-real-time computer vision applications.

cs.CV↗

arXiv2016

Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification

The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random Projection (LaRP) framework, where we model the linear kernels and nonlinearity separately for increased training efficiency. The proposed LaRP framework was assessed using the MNIST hand-written digits database and the COIL-100 object database, and showed notable improvement in object classification performance relative to other state-of-the-art random projection methods.

cs.CV↗

arXiv2009

An imaging time-of-propagation system for charged particle identification at a super B factory

Super B factories that will further probe the flavor sector of the Standard Model and physics beyond will demand excellent charged particle identification (PID), particularly K/pi separation, for momenta up to 4 GeV/c, as well as the ability to operate under beam backgrounds significantly higher than current B factory experiments. We describe an Imaging Time-of-Propagation (iTOP) detector which shows significant potential to meet these requirements. Photons emitted from charged particle interactions in a Cerenkov radiator bar are internally reflected to the end of the bar, where they are collected on a compact image plane using photodetectors with fine spatial segmentation in two dimensions. Precision measurements of photon arrival time are used to enhance the two dimensional imaging, allowing the system to provide excellent PID capabilities within a reduced detector envelope. Results of the ongoing optimization of the geometric and physical properties of such a detector are presented, as well as simulated PID performance. Validation of simulations is being performed using a prototype in a cosmic ray test stand at the University of Hawaii.

physics.ins-det↗

arXiv2008

Higher Accuracy for Bayesian and Frequentist Inference: Large Sample Theory for Small Sample Likelihood

Recent likelihood theory produces $p$-values that have remarkable accuracy and wide applicability. The calculations use familiar tools such as maximum likelihood values (MLEs), observed information and parameter rescaling. The usual evaluation of such $p$-values is by simulations, and such simulations do verify that the global distribution of the $p$-values is uniform(0, 1), to high accuracy in repeated sampling. The derivation of the $p$-values, however, asserts a stronger statement, that they have a uniform(0, 1) distribution conditionally, given identified precision information provided by the data. We take a simple regression example that involves exact precision information and use large sample techniques to extract highly accurate information as to the statistical position of the data point with respect to the parameter: specifically, we examine various $p$-values and Bayesian posterior survivor $s$-values for validity. With observed data we numerically evaluate the various $p$-values and $s$-values, and we also record the related general formulas. We then assess the numerical values for accuracy using Markov chain Monte Carlo (McMC) methods. We also propose some third-order likelihood-based procedures for obtaining means and variances of Bayesian posterior distributions, again followed by McMC assessment. Finally we propose some adaptive McMC methods to improve the simulation acceptance rates. All these methods are based on asymptotic analysis that derives from the effect of additional data. And the methods use simple calculations based on familiar maximizing values and related informations. The example illustrates the general formulas and the ease of calculations, while the McMC assessments demonstrate the numerical validity of the $p$-values as percentage position of a data point. The example, however, is very simple and transparent, and thus gives little indication that in a wide generality of models the formulas do accurately separate information for almost any parameter of interest, and then do give accurate $p$-value determinations from that information. As illustration an enigmatic problem in the literature is discussed and simulations are recorded; various examples in the literature are cited.

stat.ME↗

arXiv1999

Formal Modeling in a Commercial Setting: A Case Study

This paper describes a case study conducted in collaboration with Nortel to demonstrate the feasibility of applying formal modeling techniques to telecommunication systems. A formal description language, SDL, was chosen by our qualitative CASE tool evaluation to model a multimedia-messaging system described by an 80-page natural language specification. Our model was used to identify errors in the software requirements document and to derive test suites, shadowing the existing development process and keeping track of a variety of productivity data.

cs.SE↗

arXiv1997

Muon-Spin Rotation Measurements of the Magnetic Field Dependence of the Vortex-Core Radius and Magnetic Penetration Depth in NbSe2

Muon-spin rotation spectroscopy has been used to measure the internal magnetic field distribution in NbSe2 for Hc1 << H < 0.25 Hc2. The deduced profiles of the supercurrent density indicate that the vortex-core radius in the bulk decreases sharply with increasing magnetic field. This effect, which is attributed to increased vortex-vortex interactions, does not agree with the dirty-limit microscopic theory. A simple phenomenological equation in which the core radius depends on the intervortex spacing is used to model this behaviour. In addition, we find for the first time that the in-plane magnetic penetration depth increases linearly with H in the vortex state of a conventional superconductor.

cond-mat.supr-con↗