1,249 research outputs found
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Extending the solicitation management system: PDF reporting and database support
The main purpose of this project is to develop new functionalities for the exisiting Solicitation Management System (SMS) to support Office of Technology Transfer and Commercialization (OTTC), California State University San Bernardino (CSUSB) and Center for the Commercialization of Advanced Technology (CCAT), San Diego State University (SDSU) for the 2008 solicitation on 28 January 2008 to improve its reporting capabilities and support the new functional requirements. The new functions include uploading announcements, instructions and proposal templates. The scope of this project includes creation of new PDF reports and functions to support the management of topics and classes
Weakly-Supervised Neural Text Classification
Deep neural networks are gaining increasing popularity for the classic text
classification task, due to their strong expressive power and less requirement
for feature engineering. Despite such attractiveness, neural text
classification models suffer from the lack of training data in many real-world
applications. Although many semi-supervised and weakly-supervised text
classification models exist, they cannot be easily applied to deep neural
models and meanwhile support limited supervision types. In this paper, we
propose a weakly-supervised method that addresses the lack of training data in
neural text classification. Our method consists of two modules: (1) a
pseudo-document generator that leverages seed information to generate
pseudo-labeled documents for model pre-training, and (2) a self-training module
that bootstraps on real unlabeled data for model refinement. Our method has the
flexibility to handle different types of weak supervision and can be easily
integrated into existing deep neural models for text classification. We have
performed extensive experiments on three real-world datasets from different
domains. The results demonstrate that our proposed method achieves inspiring
performance without requiring excessive training data and outperforms baseline
methods significantly.Comment: CIKM 2018 Full Pape
Optimal Distributed Beamforming for MISO Interference Channels
We consider the problem of quantifying the Pareto optimal boundary in the
achievable rate region over multiple-input single-output (MISO) interference
channels, where the problem boils down to solving a sequence of convex
feasibility problems after certain transformations. The feasibility problem is
solved by two new distributed optimal beamforming algorithms, where the first
one is to parallelize the computation based on the method of alternating
projections, and the second one is to localize the computation based on the
method of cyclic projections. Convergence proofs are established for both
algorithms.Comment: 7 Pages, 6 figures, extended version for the one in Proceeding of
Asilomar, CA, 201
Empirical Analysis of Patent Litigation: A Comparison Study between Japan and China
In this paper, we gathered the data from public sources for analyzing the outcomes of 531 cases and 785 cases decided respectively by Japanese and Chinese courts at the first trial of patent litigations between 2004 and 2016. Using these data, we implemented a comparison analysis on recent patent litigations between Japan and China. Moreover, combining with information from Patstat, a patent database, for the patents infringed, we did an empirical analysis on determinants of trail win rate and rewards in patent litigations both for Japan and China.
Our estimated results suggest that China has the determinants on rates of success and appeal which very similar to those in Japanese patent suits. On the other hand, however, for infringement awards, those that influence the outcome of the courts are quite different between the two countries
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
We present a discriminative nonparametric latent feature relational model
(LFRM) for link prediction to automatically infer the dimensionality of latent
features. Under the generic RegBayes (regularized Bayesian inference)
framework, we handily incorporate the prediction loss with probabilistic
inference of a Bayesian model; set distinct regularization parameters for
different types of links to handle the imbalance issue in real networks; and
unify the analysis of both the smooth logistic log-loss and the piecewise
linear hinge loss. For the nonconjugate posterior inference, we present a
simple Gibbs sampler via data augmentation, without making restricting
assumptions as done in variational methods. We further develop an approximate
sampler using stochastic gradient Langevin dynamics to handle large networks
with hundreds of thousands of entities and millions of links, orders of
magnitude larger than what existing LFRM models can process. Extensive studies
on various real networks show promising performance.Comment: Accepted by AAAI 201
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
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