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We develop a self-supervised ensemble learning (SSEL) method to accurately classify distinct types of phase transitions by analyzing the fluctuation properties of machine learning outputs. Employing the 2D Potts model and the 2D Clock model as benchmarks, we demonstrate the capability of SSEL in discerning first-order, second-order, and Berezinskii-Kosterlitz-Thouless transitions, using in-situ spin configurations as the input features. Furthermore, we show that the SSEL approach can also be applied to investigate quantum phase transitions in 1D Ising and 1D XXZ models upon incorporating quantum sampling. We argue that the SSEL model simulates a special state function with higher-order correlations between physical quantities, and hence provides richer information than previous machine learning methods. Consequently, our SSEL method can be generally applied to the identification/classification of phase transitions even without explicit knowledge of the underlying theoretical models.

We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of ultracold atoms. Different from the conventional supervised learning approach, where the predicted phase transition point is very sensitive to the training region and data labeling, our self-supervised learning approach identifies the phase transition point by the largest deviation of the predicted results from the known system parameters and by the highest confidence through a systematic shift of the training regions. We demonstrate the robust application of this approach results in various 1D and 2D exactly solvable models, using different input features (time-of-flight images, spatial correlation function or density-density correlation function). As a result, our self-supervised approach should be a very general and reliable method for many condensed matter or solid-state systems to observe new states of matters solely based on experimental measurements, even without a priori knowledge of the phase transition models., Comment: 13 pages and 7 figures

Accurate measurements of statistical properties, such as the star formation rate and the lifetime of young stellar objects (YSOs) in different stages, is essential for constraining star formation theories. However, it is a difficult task to separate galaxies and YSOs based on spectral energy distributions (SEDs) alone, because they contain both thermal emission from stars and dust around them and no reliable theories can be applied to distinguish them. Here we compare different machine learning algorithms and develop the Spectrum Classifier of Astronomical Objects (SCAO), based on Fully Connected Neural Network (FCN), to classify regular stars, galaxies, and YSOs. Superior to previous classifiers, SCAO is solely trained by high quality data labeled in Molecular Cores to Planet-forming Disks (c2d) catalog without a priori theoretical knowledge, and provides excellent results with high precision (>96%) and recall (>98%) for YSOs when only eight bands are included. We systematically investigate the effects of observation errors and distance effects, and show that high accuracy performance is still maintained even when using fluxes of only three bands (IRAC 3, IRAC 4, and MIPS 1) in the long wavelengths regime, because the silicate absorption feature is automatically detected by SCAO. Finally, we apply SCAO to Spitzer Enhanced Imaging Products (SEIP), the most complete catalog of Spitzer observations, and found 129219 YSO candidates. The website from SCAO is available at http://scao.astr.nthu.edu.tw., Comment: 15 pages, 5 figures, Submitted to Astronomy and Computing on 19 February 2021, Accepted 26 April 2021, Available online 28 April 2021

In this paper, a practical micromachining technology was applied for the fabrication of a micro probe using a complex nontraditional machining process. A series process was combined to machine tungsten carbide rods from original dimension. The original dimension of tungsten carbide rods was 3mm ; the rods were ground to a fixed-dimension of 50 micrometers using precision grinding machine in first step. And then, the rod could be machined to a middle-dimension of 20 micrometers by electrolysis. A final desired micro dimension can be achieved using supersonic aided electrolysis. High-aspect-ratio of micro tungsten carbide rod was easily obtained by this process. Surface roughness of the sample with supersonic aided agitation was compared with that with no agitation in electrolysis. The machined surface of the sample is very smooth due to ionized particles of anode could be removed by supersonic aided agitation during electrolysis. Deep micro holes can also be achieved by the machined high-aspect-rati tungsten carbide rod using EDM process. A micro probe of a ball shape at the end was processed by proposed supersonic aided electrolysis machining process., Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/handle/2042/16838)