Predicting future technological convergence patterns based on machine learning using link prediction

Scientometrics - Technological convergence among different industries is an important source of innovation and economic growth. In this study, we propose a new framework for predicting patterns of...     

A multichannel subspace approach with signal presence probability for speech enhancement

Multidimensional Systems and Signal Processing - For the last few decades, speech enhancement based on microphone arrays has primarily utilized prior information about system models, e.g., array...     

What makes the first forward citation of a patent occur earlier?

Identifying the economic value of a patent is crucial to technology management. It also facilitates the commercialization and transactions of patents. As a proxy of patent value, forward citation counting is widely used, but it takes a long time for a patent to be sufficiently cited. In this context, we suggest the first citation lag, namely the time taken until the first citation, as a proxy of patent value, as it is positively correlated with the patent value and considers the fact that important patents with a high economic value tend to receive many citations in a short space of time. We explore the influential patent attributes related to the first citation lag to build a model to predict the patent value. By using the Cox proportional hazard model on green inventory patent data, we find that patents with a shorter technology cycle time and a larger number of IPC four-digit classes, claims, patent family, and backward citations are associated with the shorter first citation lag, while a patent’s science linkage has an inverse U-shaped relationship with the first citation lag. Further, patents having an unconventional technological core have a longer first citation lag and among patents having an unconventional technological core, the presence of a novel element makes the first citation earlier. Our study is expected to help patent evaluation in the early stage of technology transfer.     

A block-tridiagonal solver with two-level parallelization for finite element-spectral codes

Two-level parallelization is introduced to solve a massive block-tridiagonal matrix system. One-level is used for distributing blocks whose size is as large as the number of block rows due to the spectral basis, and the other level is used for parallelizing in the block row dimension. The purpose of the added parallelization dimension is to retard the saturation of the scaling due to communication overhead and inefficiencies in the single-level parallelization only distributing blocks. As a technique for parallelizing the tridiagonal matrix, the combined method of “Partitioned Thomas method” and “Cyclic Odd–Even Reduction” is implemented in an MPI-Fortran90 based finite element-spectral code (TORIC) that calculates the propagation of electromagnetic waves in a tokamak. The two-level parallel solver using thousands of processors shows more than 5 times improved computation speed with the optimized processor grid compared to the single-level parallel solver under the same conditions. Three-dimensional RF field reconstructions in a tokamak are shown as examples of the physics simulations that have been enabled by this algorithmic advance.