Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems

The purpose of this work is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides python utilities to simplify common recommender-related data science work as well as example Jupyter notebooks that demonstrate use of the algorithms and tools under various environments.Comment: pages: 2; submitted to: RecSys '1

Multiple Classifier Fusion using k-Nearest Localized Templates

Abstract. This paper presents a method for combining classifiers that uses knearest localized templates. The localized templates are estimated from a training set using C-means clustering algorithm, and matched to the decision profile of a new incoming sample by a similarity measure. The sample is assigned to the class which is most frequently represented among the k most similar templates. The appropriate value of k is determined according to the characteristics of the given data set. Experimental results on real and artificial data sets show that the proposed method performs better than the conventional fusion methods

Customer process management A framework for using customer-related data to create customer value

Purpose The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services. Design/methodology/approach This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature. Findings The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature. Originality/value "Using data to advance service" is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company's use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data

Symptoms to Use for Diagnostic Criteria of Hwa-Byung, an Anger Syndrome

ObjectiveaaThe aim of this study was to identify the characteristic symptoms which can be used for the diagnosis of hwa-byung, a culture-related anger syndrome in Korea. MethodsaaThe symptoms of the Hwa-byung Scale were correlated with the Korean versions of the Hamilton Depression Rating Scale (K-HDRS) and the State and Trait Anger Inventory (K-STAXI) in 89 patients, who were diagnosed as having major depressive disorder, dysthymic disorder, anxiety disorders, somatoform disorders, or adjustment disorder according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria and who had self-labeled hwa-byung. Also, the symptoms of the Hwa-byung Scale were correlated with each other. ResultsaaThe symptoms of the Hwa-byung Scale which were significantly correlated with the state anger of the K-STAXI but not with the depressive mood (item 1 of K-HDRS) included feelings of unfairness, subjective anger, external anger, heat sensation, pushing-up in the chest, dry mouth, and sighing. The symptoms which were significantly correlated with state anger and depressed mood included respiratory stuffiness, “haan ” and hate. The symptoms which were not significantly correlated with depressed mood and state anger include

CD44 Disruption Attenuates Murine Hepatic Ischemia/Reperfusion Injury

Neutrophil adhesion and migration are critical in hepatic ischemia/reperfusion (I/R) injury. Despite very strong preclinical data, recent clinical trials failed to show a protective effect of anti-adhesion therapy in reperfusion injury. Therefore, the aim of this study was to assess the role of CD44 in neutrophil infiltration and liver injury from hepatic I/R. In this study, using a partial hepatic ischemic model in vivo, we determined the potential role of CD44 in neutrophil infiltration and liver injury from I/R. Reperfusion caused significant hepatocellular injury as it was determined by plasma ALT levels and liver histopathology. The injury was associated with a marked neutrophil recruitment and CD44 expression into the ischemic livers. Administration of anti-CD44 antibody to mice reduced the infiltration of neutrophil into the ischemic tissue, associated with liver function preservation. These results support crucial roles of CD44 in neutrophil recruitment and infiltration leading to liver damage in hepatic I/R injury. Moreover, they provide the rationale for targeting to CD44 as a potential therapeutic approach in liver I/R injury

Anderson transition of in-gap quasiparticles in a quasi-two-dimensional disordered superconductor

The Anderson transition of Bogoliubov-de Gennes (BdG) quasiparticles in superconducting state has been studied theoretically for last three decades. However, its experimental proof is lacking. In particular, the relationship of the superconducting order-parameter fluctuations and the Anderson transition of BdG quasiparticles have not been well understood. Our study, based on scanning tunneling microscopy measurements, investigates how BdG quasiparticles become Anderson-localized and delocalized as a function of energy and applied magnetic field in a quasi-two-dimensional Fe-based superconductor with sufficient zero-bias BdG quasiparticles. The anomalous multifractal spectra based on the spatial distributions of the pairing gaps and the coherent peak heights suggest that superconducting fluctuations play a key role in the delocalization of in-gap BdG quasiparticles. Our real-space Hartree-Fock-BCS-Anderson simulations and renormalization group analysis with pairing fluctuations support quasiparticle localization and suggest that enhanced pairing fluctuations lead to delocalization of BdG quasiparticles and "weak localization" of phase-fluctuating Cooper pairs in quasi-two-dimensional disordered superconductors. The present study proposes that the 10-fold way classification scheme has to be generalized to take order-parameter fluctuations in actual quantum matter. Also, it shed light on how ac energy loss due to quasiparticles at Fermi level can be controlled in a quasi-2d superconductor with sufficient pairing fluctuation

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.Comment: 21 pages, 4 figure