Self-Supervised Ensemble Learning: A Universal Method for Phase Transition Classification of Many-Body Systems

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

Heterogenous scaling in the inter-event time of on-line bookmarking

In this paper, we study the statistical properties of bookmarking behaviors in Delicious.com. We find that the inter-event time (τ) distributions of bookmarking decay in a power-like manner as τ increases at both individual and population levels. Remarkably, we observe a significant change in the exponent when the inter-event time increases from the intra-day range to the inter-day range. In addition, the dependence of the exponent on individual activity is found to be different in the two ranges. Instead of monotonically increasing with activity, the inter-day exponent peaks around 3. These results suggest that the mechanisms driving human actions are different in the intra-day and inter-day ranges. We further show that the global distributions of less active users are closer to an exponential distribution than those of more active users. Moreover, a universal behavior in the inter-day range is observed by considering the rescaled variable τ/left angle bracketτright-pointing angle bracket. Finally, the possible causes of these phenomena are discussed

In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing

Liquid Metal Jet Printing (LMJP) is a revolutionary three-dimensional (3D) printing technique in fast but low-cost additive manufacturing. The driving force is produced by magneto-hydrodynamic property of liquid metal in an alternating magnetic field. Due to its integrated melting and ink-jetting process, it can achieve 10x faster speed at 1/10th of the cost as compared to current metal 3D printing techniques. However, the jetting process is influenced by many uncertain factors, which impose a significant challenge to its process stability and product quality. To address this challenge, we present a closed-loop control framework by seamlessly integrating vision-based technique and neural network tool to inspect droplet behaviours and accordingly stabilize the printing process. This system automatically tunes the drive voltage applied to compensate the uncertain influence based on vision inspection result. To realize this, we first extract multiple features and properties from images to capture the droplet behaviour. Second, we use a neural network together with PID control process to determine how the drive voltage should be adjusted. We test this system on a piezoelectric-based ink-jetting emulator, which has a very similar jetting mechanism to the LMJP. Results show that significantly more stable jetting behavior can be obtained in real-time. This system can also be applied to other droplet related applications owing to its universally applicable characteristics

Techno-economic analysis of geopolymer production from the coal fly ash with high iron oxide and calcium oxide contents

In this work, we firstly examined the technical feasibility of geopolymer synthesis from the coal fly ash with high iron oxide (48.84 wt.%) and calcium oxide (22.15 wt.%) contents. The heat resistance of geopolymer was represented by the dry weight loss which ranged from 2.5 to 4.9% and was better than that (11.7%) of OPC. However, the high iron oxide content made the acid resistance (13–14%) of geopolymer inferior to OPC. The economics of geopolymer production changes significantly upon the variation in the arrangement of material use and geopolymer price. The costs of Na2SiO3 and NaOH and the benefit of geopolymer selling were the major factors affecting the economic feasibility of geopolymer production. When the Na2SiO3 price was around 400 USD/ton, the geopolymer production will be profitable even if the geopolymer price was as low as 50 USD/ton. It is possible to improve the economics of geopolymer production by varying the arrangement of material use while not impairing the performance of geopolymer

Factors that influence survival in colorectal cancer with synchronous distant metastasis

AbstractBackgroundTreatments for the purposes of curing or more effectively managing metastatic colorectal cancer (CRC) are evolving. Our study focused on patients with primary CRC with synchronous distant metastasis, and we analyzed the factors influencing patient survival.MethodsData review was conducted retrospectively. Clinicopathological parameters included age, sex, site of primary cancer, tumor cell differentiation, number of liver metastasis, presence of extrahepatic metastasis, treatment of liver metastasis, pre-treatment carcinoembryonic antigen (CEA) level, status of treatment response, salvage treatment and survival.ResultsA total of 420 patients were identified and considered for our study. Of those, 275 patients (65.4%) had liver-only metastasis, 100 patients (23.8%) had concomitant lung metastasis, and 40 patients (9.5%) had other metastases. Additionally, 145 patients (34.5%) had liver-directed treatment including surgical resection (28.5%), radiofrequency ablation (RFA) (10.6%) and transcatheter arterial chemoembolization (TAE) (1.2%). There were 80 patients (19%) with CEA levels < 10, 135 patients (32.1%) with CEA 10–100, and 165 patients (39.2%) with CEA > 100. There were 200 patients (47.6%) who had received chemotherapy, 130 patients (30.9%) with target therapy, and 40 patients (9.5%) who had not undergone any salvage treatment. Three significant factors were identified, including treatment of liver metastasis (p=0.027), pre-treatment CEA (p=0.04), and salvage treatment (p=0.005).ConclusionWe demonstrated three factors influencing patient survival including treatment of liver metastasis, pre-treatment CEA level, and salvage treatment. Aggressive treatment of liver metastasis including surgical resection or RFA combined with chemotherapeutic agents appear to provide an increased rate of survival to patients