Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks

Scoring patent documents is very useful for technology management. However, conventional methods are based on static models and, thus, do not reflect the growth potential of the technology cluster of the patent. Because even if the cluster of a patent has no hope of growing, we recognize the patent is important if PageRank or other ranking score is high. Therefore, there arises a necessity of developing citation network clustering and prediction of future citations. In our research, clustering of patent citation networks by Stochastic Block Model was done with the aim of enabling corporate managers and investors to evaluate the scale and life cycle of technology. As a result, we confirmed nested SBM is appropriate for graph clustering of patent citation networks. Also, a high MAPE value was obtained and the direction accuracy achieved a value greater than 50% when predicting growth potential for each cluster by using LSTM.Comment: arXiv admin note: substantial text overlap with arXiv:1904.1204

Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author

Novel insights into the intraepithelial spread of extrahepatic cholangiocarcinoma: clinicopathological study of 382 cases on extrahepatic cholangiocarcinoma

BackgroundExtrahepatic cholangiocarcinoma (eCCA) is a rare and aggressive disease and consisted of conventional eCCA and intraductal papillary neoplasm of the bile duct (IPNB). Intraepithelial spread (IES) of cancer cells beyond the invasive area is often observed in IPNBs; however, the prevalence of IES remains to be examined in conventional eCCAs. Here, we evaluated the clinicopathological features of eCCAs according to tumor location, with a focus on the presence of IES. The IES extension was also compared among biliary tract cancers (BTCs).MethodsWe examined the prevalence and clinicopathological significance of IES in eCCAs (n=382) and the IES extension of BTCs, including gallbladder (n=172), cystic duct (n=20), and ampullary cancers (n=102).ResultsAmong the invasive eCCAs, IPNB had a higher rate of IES (89.2%) than conventional eCCAs (57.0%). Among conventional eCCAs, distal eCCAs (75.4%) had a significantly higher prevalence of IES than perihilar eCCAs (41.3%). The presence of IES was associated with a significantly higher survival rate in patients with distal eCCAs (P=0.030). Extension of the IES into the cystic duct (CyD) in distal eCCAs that cancer cells reached the junction of the CyD was a favorable prognostic factor (P<0.001). The association of survival with IES, either on the extrahepatic bile duct or on the CyD, differed depending on the tumor location and type of eCCA. The extension properties of IES were also dependent on different types of tumors among BTCs; usually, the IES incidence became higher than 50% in the tissues that the tumor developed, whereas IES extension to other tissues decreased the incidence.ConclusionThus, eCCAs have different clinicopathological characteristics depending on the tumor location and type

Effects of revisions to the health insurance system on the recovery-phase rehabilitation ward

In the present study, we investigated the effects of revisions to the medical fee system made in April 2006 on the recovery-phase rehabilitation ward of our hospital. Subjects were patients admitted to the recovery-phase rehabilitation ward of our hospital between April 1, 2005 and September 30, 2006, and were discharged. Patients admitted between April 1, 2005 and March 31, 2006 were allocated to the pre-revision group and those admitted between April 1, 2006 and September 30, 2006 to the post-revision group. Their medical charts were investigated for comparison of the mean age, duration of hospitalization, and outcome.A total of 126 patients were allocated to the pre-revision group, and 72 to the post-revision group. The number of days from onset to admission to the recovery-phase rehabilitation ward was 41.3 days in the pre-revision group and 26.1 days in the post-revision group, while the duration of hospitalization was 71.4 days in the former group and 41.9 days in the latter. The outcomes were transfer to homecare/discharge to home in 84 patients (67%) and transfer to another department in our hospital in six patients (5%) in the pre-revision group, and 43 patients (60%) and 14 patients (19%), respectively, in the post-revision group. No significant differences in FIM were found between the two groups.The effects of the medical fee system revisions made in April 2006 on the recovery-phase rehabilitation ward of our hospital included shortening of the number of days between onset and admission, duration of hospitalization, increased transfer to other departments, and decreased rates of transfer to homecare/discharge to home. These findings indicate the importance of systemic management and team-based approaches for enabling more efficient rehabilitation

Flow estimation solely from image data through persistent homology analysis

Abstract: Topological data analysis is an emerging concept of data analysis for characterizing shapes. A state-of-the-art tool in topological data analysis is persistent homology, which is expected to summarize quantified topological and geometric features. Although persistent homology is useful for revealing the topological and geometric information, it is difficult to interpret the parameters of persistent homology themselves and difficult to directly relate the parameters to physical properties. In this study, we focus on connectivity and apertures of flow channels detected from persistent homology analysis. We propose a method to estimate permeability in fracture networks from parameters of persistent homology. Synthetic 3D fracture network patterns and their direct flow simulations are used for the validation. The results suggest that the persistent homology can estimate fluid flow in fracture network based on the image data. This method can easily derive the flow phenomena based on the information of the structure

Pressure-induced anomalous valence crossover in cubic YbCu5-based compounds

A pressure-induced anomalous valence crossover without structural phase transition is observed in archetypal cubic YbCu5 based heavy Fermion systems. The Yb valence is found to decrease with increasing pressure, indicating a pressure-induced crossover from a localized 4f (13) state to the valence fluctuation regime, which is not expected for Yb systems with conventional c-f hybridization. This result further highlights the remarkable singularity of the valence behavior in compressed YbCu5-based compounds. The intermetallics Yb2Pd2Sn, which shows two quantum critical points (QCP) under pressure and has been proposed as a potential candidate for a reentrant Yb(2+) state at high pressure, was also studied for comparison. In this compound, the Yb valence monotonically increases with pressure, disproving a scenario of a reentrant non-magnetic Yb(2+) state at the second QCP