Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Structural Change in an Open Economy

We develop a tractable, three-sector model to study structural change in an open economy. The model features an endogenous pattern of trade dictated by comparative advantage. We derive an intuitive expression linking sectoral employment shares to sectoral expenditure shares and to sectoral net export shares of total GDP. Changes in productivity and in trade barriers affect expenditure and net export shares, and thus, employment shares, across sectors. We show how these driving forces can generate the "hump" pattern that characterizes the manufacturing employment share as a country develops, even when manufacturing is the sector with the highest productivity growth.structural transformation, international trade, sectoral labor reallocation

Structural change in an open economy

We develop a tractable, three-sector model to study structural change in a two-country world. The model features an endogenous pattern of trade dictated by comparative advantage. We derive an intuitive expression linking sectoral employment shares to sectoral expenditure shares and to sectoral net export shares of total GDP. Changes in productivity and in trade barriers affect expenditure and net export shares, and thus, employment shares, across sectors. We show how these driving forces can generate the "hump" pattern that characterizes the manufacturing employment share as a country develops, even when manufacturing is the sector with the highest productivity growth.

Regulation Of Cardiovascular Homeostasis By Autophagy

Macroautophagy (hereafter autophagy) is a fundamental cellular process that removes unnecessary or dysfunctional components. It allows the orderly degradation and recycling of cellular components. Mitophagy refers to the selective removal of damaged mitochondria via autophagy pathway. In addition to utilizing core autophagic machinery components, mitophagy exploits a variety of molecules, such as PTEN-induced putative kinase protein 1 (PINK1) and Parkin, to identify and eliminate damaged or superfluous mitochondria. Dysregulation of autophagy and mitophagy contributes to a variety of human disorders, including cardiovascular diseases, such as atherosclerosis and diabetic cardiomyopathy. Vascular smooth muscle cells (VSMCs) are a major component of the vascular media, and are vital for maintaining vessel homeostasis. Migration of VSMCs from the media to intima occurs during the development of atherosclerosis. Although alterations in autophagy activity have been reported in atherosclerosis, further investigation is required to delineate the mechanism by which autophagy regulates microtubule stability and cell migration. Diabetic cardiomyopathy, which develops in the absence of traditional risk factors, is a major cause of heart failure in Type 2 diabetic patients. Although multiple factors may collectively contribute to the development of diabetic cardiomyopathy, there is an urgent need to determine the role of autophagy in the development of diabetic cardiomyopathy. This dissertation has explored the role of autophagy and mitophagy in regulating VSMCs migration as well as in the development of diabetic cardiomyopathy, using comprehensive physiological, pathophysiological, molecular, and genetic approaches. We show that activation of autophagy selectively degrades KAT2A/GCN5, a histone acetyltransferase that acetylates α-tubulin in VSMCs, leading to microtubule instability and promotion of VSMC migration. In diabetic heart, defective autophagy and PINK1/Parkin-mediated mitophagy are regulated by bromodomain-containing protein 4 (BRD4), a bromodomain and extra-terminal domain (BET) family of proteins. Administration of JQ1, one of the BET bromodomain inhibitors, restores PINK1/Parkin-mediated mitophagy and prevents high-fat-diet induced diabetic cardiomyopathy. Collectively, our work suggests that autophagy suppression in VSMCs is an important therapeutic target for atherosclerosis and that suppression of BRD4 may be a new therapeutic approach for diabetic cardiomyopathy

Fitting magnetic field gradient with Heisenberg-scaling accuracy

We propose a quantum fitting scheme to estimate the magnetic field gradient with $N$-atom spins preparing in W state, which attains the Heisenberg-scaling accuracy. Our scheme combines the quantum multi-parameter estimation and the least square linear fitting method to achieve the quantum Cram\'{e}r-Rao bound (QCRB). We show that the estimated quantity achieves the Heisenberg-scaling accuracy. In single parameter estimation with assumption that the magnetic field is strictly linear, two optimal measurements can achieve the identical Heisenberg-scaling accuracy. Proper interpretation of the super-Heisenberg-scaling accuracy is presented. The scheme of quantum metrology combined with data fitting provides a new method in fast high precision measurements.Comment: 7 pages, 2 figure

Minimal sets determining universal and phase-covariant quantum cloning

We study the minimal input sets which can determine completely the universal and the phase-covariant quantum cloning machines. We find that the universal quantum cloning machine, which can copy arbitrary input qubit equally well, however can be determined completely by only four input states located at the four vertices of a tetrahedron. The phase-covariant quantum cloning machine, which can copy all qubits located on the equator of the Bloch sphere, can be determined by three equatorial qubits with equal angular distance. These results sharpen further the well-known results that BB84 states and six-states used in quantum cryptography can determine completely the phase-covariant and universal quantum cloning machines. This concludes the study of the power of universal and phase-covariant quantum cloning, i.e., from minimal input sets necessarily to full input sets by definition. This can simplify dramatically the testing of whether the quantum clone machines are successful or not, we only need to check that the minimal input sets can be cloned optimally.Comment: 7 pages, 4 figure

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

New Thoughts on College Teachers’ Training

With the continuous improvement and development of teaching quality in colleges, college teachers’ training has achieved fast development and been strengthened significantly. However, conditions are various at different times and problems are complicated. Concerning the issue of college teachers’ training, the authors put forward several new thoughts, i.e., enriching the training contents, enhancing the diversity of training methods, improving teachers’ initiatives and enthusiasm, and adopting modern training tools