Facilitating Technology Transfer by Patent Knowledge Graph

Technologies are one of the most important driving forces of our societal development and realizing the value of technologies heavily depends on the transfer of technologies. Given the importance of technologies and technology transfer, an increasingly large amount of money has been invested to encourage technological innovation and technology transfer worldwide. However, while numerous innovative technologies are invented, most of them remain latent and un-transferred. The comprehension of technical documents and the identification of appropriate technologies for given needs are challenging problems in technology transfer due to information asymmetry and information overload problems. There is a lack of common knowledge base that can reveal the technical details of technical documents and assist with the identification of suitable technologies. To bridge this gap, this research proposes to construct knowledge graph for facilitating technology transfer. A case study is conducted to show the construction of a patent knowledge graph and to illustrate its benefit to finding relevant patents, the most common and important form of technologies

Quasi-Newton Solver for Robust Non-Rigid Registration

Imperfect data (noise, outliers and partial overlap) and high degrees of freedom make non-rigid registration a classical challenging problem in computer vision. Existing methods typically adopt the $\ell_{p}$ type robust estimator to regularize the fitting and smoothness, and the proximal operator is used to solve the resulting non-smooth problem. However, the slow convergence of these algorithms limits its wide applications. In this paper, we propose a formulation for robust non-rigid registration based on a globally smooth robust estimator for data fitting and regularization, which can handle outliers and partial overlaps. We apply the majorization-minimization algorithm to the problem, which reduces each iteration to solving a simple least-squares problem with L-BFGS. Extensive experiments demonstrate the effectiveness of our method for non-rigid alignment between two shapes with outliers and partial overlap, with quantitative evaluation showing that it outperforms state-of-the-art methods in terms of registration accuracy and computational speed. The source code is available at https://github.com/Juyong/Fast_RNRR.Comment: Accepted to CVPR2020. The source code is available at https://github.com/Juyong/Fast_RNR

Finite-time adaptive prescribed performance DSC for pure feedback nonlinear systems with input quantization and unmodeled dynamics

This paper presents a new prescribed performance-based finite-time adaptive tracking control scheme for a class of pure-feedback nonlinear systems with input quantization and dynamical uncertainties. To process the input signal, a new quantizer combining the advantages of a hysteresis quantizer and uniform quantizer has been used. Radial basis function neural networks have been utilized to approximate unknown nonlinear smooth functions. An auxiliary system has been employed to estimate unmodeled dynamics by producing a dynamic signal. By introducing a hyperbolic tangent function and performance function, the tracking error was made to fall within the prescribed time-varying constraints. Using modified dynamic surface control (DSC) technology and a finite-time control method, a novel finite-time controller has been designed, and the singularity problem of differentiating each virtual control scheme in the existing finite-time control scheme has been removed. Theoretical analysis shows that all signals in the closed-loop system are semi-globally practically finite-time stable, and that the tracking error converges to a prescribed time-varying region. Simulation results for two numerical examples have been provided to illustrate the validity of the proposed control method

Connecting Researchers with Companies for University-Industry Collaboration

Nowadays, companies are spending more time and money to enhance their innovation ability to respond to the increasing market competition. The pressure makes companies seek help from external knowledge, especially those from academia. Unfortunately, there is a gap between knowledge seekers (companies) and suppliers (researchers) due to the scattered and asymmetric information. To facilitate shared economy, various platforms are designed to connect the two parties. In this context, we design a researcher recommendation system to promote their collaboration (e.g. patent license, collaborative research, contract research and consultancy) based on a research social network with complete information about both researchers and companies. In the recommendation system, we evaluate researchers from three aspects, including expertise relevance, quality and trustworthiness. The experiment result shows that our system performs well in recommending suitable researchers for companies. The recommendation system has been implemented on an innovation platform, InnoCity.

A Dual-view Attention Neural Network for Assigning Industrial Categories to Academic Patents

Industrial technology matching events are held by governmental institutions worldwide to promote patent transfer from universities to industries. When collecting academic patents for the matching events, governmental institutions lack professional knowledge for identifying academic patents suitable for various industries. Therefore, previous studies adopted International Patent Classification (IPC) codes assigned by patent examiners to represent patents and mined the industry-related cues through the mapping link between IPC codes and industry categories. However, IPC codes are too general to specifically represent the complex patents, leading to inaccurate tagging. The view of patent inventors (e.g., patent titles and abstracts) contains rich industry-related cues that benefit assigning industrial categories to academic patents. Therefore, we propose a dual-view attention neural network that learns low-dimensional patent representations from the views of patent examiners and inventors and merges the representations for classifying academic patents into suitable industrial categories. Experiments show that the proposed method outperforms benchmark methods

Polydopamine-based biofunctional substrate coating promotes mesenchymal stem cell migration

Rapid migration of mesenchymal stem cells (MSCs) on device surfaces could support in vivo tissue integration and might facilitate in vitro organoid formation. Here, polydopamine (PDA) is explored as a biofunctional coating to effectively promote MSC motility. It is hypothesized that PDA stimulates fibronectin deposition and in this way enhances integrin-mediated migration capability. The random and directional cell migration was investigated by time-lapse microscopy and gap closure assay respectively, and analysed with softwares as computational tools. A higher amount of deposited fibronectin was observed on PDA substrate, compared to the non-coated substrate. The integrin β1 activation and focal adhesion kinase (FAK) phosphorylation at Y397 were enhanced on PDA substrate, but the F-actin cytoskeleton was not altered, suggesting MSC migration on PDA was regulated by integrin initiated FAK signalling. This study strengthens the biofunctionality of PDA coating for regulating stem cells and offering a way of facilitating tissue integration of devices

BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction

Although deep pre-trained language models have shown promising benefit in a large set of industrial scenarios, including Click-Through-Rate (CTR) prediction, how to integrate pre-trained language models that handle only textual signals into a prediction pipeline with non-textual features is challenging. Up to now two directions have been explored to integrate multi-modal inputs in fine-tuning of pre-trained language models. One consists of fusing the outcome of language models and non-textual features through an aggregation layer, resulting into ensemble framework, where the cross-information between textual and non-textual inputs are only learned in the aggregation layer. The second one consists of splitting non-textual features into fine-grained fragments and transforming the fragments to new tokens combined with textual ones, so that they can be fed directly to transformer layers in language models. However, this approach increases the complexity of the learning and inference because of the numerous additional tokens. To address these limitations, we propose in this work a novel framework BERT4CTR, with the Uni-Attention mechanism that can benefit from the interactions between non-textual and textual features while maintaining low time-costs in training and inference through a dimensionality reduction. Comprehensive experiments on both public and commercial data demonstrate that BERT4CTR can outperform significantly the state-of-the-art frameworks to handle multi-modal inputs and be applicable to CTR prediction

Adaptive sliding mode fault-tolerant attitude control for flexible satellites based on T-S fuzzy disturbance modeling

This paper investigates the fault tolerance problem of flexible satellites subject to actuator faults and multiple disturbances. An adaptive sliding mode fault tolerant control (ASMFTC) approach based on Takagi-Sugeno (T-S) fuzzy disturbance observer (TSFDO) is presented for attitude control system (ACS) under loss of actuator effectiveness, environmental disturbance torque and elastic modal generated by flexible appendages. Compared with the traditional disturbance observer based control (DOBC) methods, the T-S fuzzy technology is applied to estimate the unknown nonlinear elastic modal. Then, the energy bounded disturbance is eliminated by designing an adaptive sliding mode controller. The proposed ASMFTC design can guarantee the sliding surface to approach zero. Finally, the effectiveness of the control method proposed in this paper is further verified by comparative simulation

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

© 2013 IEEE. As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very well for the type of tasks that requires continuous monitoring, e.g., WIFI signal sensing, where the WiFi signal may vary over time, and users are required to contribute continuous efforts. The incentive mechanism for continuous crowdsensing has yet to be investigated, where the corresponding tasks need continuous efforts of users, and the freshness of the sensed data is very important. In this paper, we design TIMCC, a privacy-preserving incentive mechanism for continuous crowdsensing. In contrast to most existing studies that treat tasks as one-shot tasks, we consider the tasks that require users to contribute continuous efforts, where the freshness of data is a key factor impacting the value of data, which further determines the rewards. We introduce a metric named age of data that is defined as the amount of time elapsed since the generation of the data to capture the freshness of data. We adopt the reverse auction framework to model the connection between the platform and the users. We prove that the proposed mechanism satisfies individual rationality, computational efficiency, and truthfulness. Simulation results further validate our theoretical analysis and the effectiveness of the proposed mechanism