Single step optimal block matched motion estimation with motion vectors having arbitrary pixel precisions

This paper proposes a non-linear block matched motion model and solves the motion vectors with arbitrary pixel precisions in a single step. As the optimal motion vector which minimizes the mean square error is solved analytically in a single step, the computational complexity of our proposed algorithm is lower than that of conventional quarter pixel search algorithms. Also, our proposed algorithm can be regarded as a generalization of conventional half pixel search algorithms and quarter pixel search algorithms because our proposed algorithm could achieve motion vectors with arbitrary pixel precisions

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

Single step optimal block matched motion estimation with motion vectors having arbitrary pixel precisions

This paper proposes a non-linear block matched motion model with motion vectors having arbitrary pixel precisions. The optimal motion vector which minimizes the mean square error is solved analytically in a single step. Our proposed algorithm can be regarded as a generalization of conventional half pixel search algorithms and quarter pixel search algorithms because our proposed algorithm could achieve motion vectors with arbitrary pixel precisions. Also, the computational effort of our proposed algorithm is lower than that of conventional quarter pixel search algorithms because our proposed algorithm could achieve motion vectors in a single step

Apply adsorption technology to solve the UV sensor instability of dynamic control on periodic counter current purification system

Cell culture media are a source of major nutrients that provide cell growth and synthesis of proteins. Researchers and suppliers use combine media components test continually in an attempt to find more suitable cell culture media for use in continuous cell culture processes. Commercial media components are a black box for downstream purification researchers who cannot predict the effects of their constituents on resin affinity, absorbability, even for interference with UV instrument. To more effectively utilize in dynamic continuous purification system, the loading product percentage of resin absorbability is typically controlled at 40-60% maximum binding capacity. It is often five times more than traditional batch purification. Therefore, the swing of UV percentage caused by color material in the bulk harvest is the key process parameter in the periodic counter current purification system. We compared different commercial quaternary ammonium adsorbent matrix (such as Stabilized Regenerated Cellulose, Agarose and Styrene Divinyl Benzene Copolymer) to observe their effects on the swing of the UV amplitude. The results show that agarose group has better de-coloration efficacy than the other two commercial quaternary ammonium groups. Please click Additional Files below to see the full abstract

Right ventricular exclusion for hepatocellular carcinoma metastatic to the heart

We used for the first time a right ventricular exclusion procedure for the treatment of hepatocellular carcinoma metastatic to the right ventricle. Our case report shows that this surgical option can be effective as rescue therapy for right ventricular outflow tract obstruction secondary to myocardial metastasis in critically ill patients. Most notably, this technique can prevent inadvertent dislodgement of tumor cells

Selecting the Best K Features for Predicting Student Participation in Generic Competency Development Activities in Higher Education

Generic competency (GC) is an essential but often overlooked aspect of developing students in higher education. While there is much research about using technologies to develop discipline- specific skills for students, the use of technologies in GC development is insufficient. In particular, more research is needed on using technologies to predict student participation in GC development activities (GCDAs). Machine learning (ML) can use student characteristics, known as features, to predict their involvement in GCDAs. However, too many features will slow down the prediction process and reduce the ability to pinpoint the best features for prediction. This study explored an effective way to identify the minimal number of features essential for predicting student participation in GCDAs. The findings help educators develop recommendation systems to help students select the most beneficial GCDA for their holistic development. We collected 98 features from 9570 students from a community college. Then, we applied the Principal Component Analysis and SelectKBest algorithms to reduce the number of features from 98 to 8. Finally, we compared the accuracy of predictions using KNN and ANN based on the all-feature dataset with those based on the reduced-feature dataset. The results showed that the reduced-feature dataset maintained good prediction accuracy and enabled the educator to recommend the GCDAs to students. The findings could drive further research and development in applying machine learning technologies to enhance the recommendations for GCDAs for higher-education students