The data volumes generated by theWidefield ASKAP L-band Legacy All-sky Blind surveY atomic hydrogen (Hi) survey using the Australian Square Kilometre Array Pathfinder (ASKAP) necessitate greater automation and reliable automation in the task of source finding and cataloguing. To this end, we introduce and explore a novel deep learning framework for detecting low signal-to-noise ratio (SNR) Hi sources in an automated fashion. Specifically, our proposed method provides an automated process for separating true Hi detections from false positives when used in combination with the source finding application output candidate catalogues. Leveraging the spatial and depth capabilities of 3D convolutional neural networks, our method is specifically designed to recognize patterns and features in three-dimensional space, making it uniquely suited for rejecting false-positive sources in low SNR scenarios generated by conventional linear methods. As a result, our approach is significantly more accurate in source detection and results in considerably fewer false detections compared to previous linear statistics-based source finding algorithms. Performance tests using mock galaxies injected into real ASKAP data cubes reveal our method’s capability to achieve near-100% completeness and reliability at a relatively low integrated SNR $\sim3-5$. An at-scale version of this tool will greatly maximise the science output from the upcoming widefield Hi surveys.

On September 27, 2018, Professor James G. March, a giant in our field, passed away at the age of 90 (1928–2018), just one month after his wife and high school sweetheart, Jayne, passed away. March's impact on the field of organization studies and beyond is profound and long-lasting. The advancement of the field is truly indebted to March's brilliance and dedication to the search of truth as a great scholar. March wrote the inaugural article for Management and Organization Review (MOR) (2005), ‘Parochialism in the Evolution of a Research Community: The Case of Organization Studies’. This article not only provided a critical foundation underlying the editorial structure and philosophy of MOR but also argued eloquently for the salience of indigenous Chinese management studies as a necessary condition for building both contextualized and universal knowledge.

We introduce a new class of Monte Carlo methods, which we call exact estimation algorithms. Such algorithms provide unbiased estimators for equilibrium expectations associated with real-valued functionals defined on a Markov chain. We provide easily implemented algorithms for the class of positive Harris recurrent Markov chains, and for chains that are contracting on average. We further argue that exact estimation in the Markov chain setting provides a significant theoretical relaxation relative to exact simulation methods.

Reactive ion etching of features down to 100 nm in linewidth in tungsten has been studied using an SF6 based chemistry. The studies were carried out in a PlasmaTherm 500 etcher operated at low pressure (2 mTorr) and power (100 mWatts/cm2). Key processing parameters have been identified to achieve the resolution and aspect ratio required for high contrast x-ray masks. The critical parameters include sample temperature, gas dilution and end point detection. However, even with all of these parameters optimized, additional sidewall passivation is required to obtain the necessary 6.5:1 aspect ratio. A novel method of achieving such passivation based on an intermittent etching process is described.