A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network

In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and pooling layers, which can model a variety of compositions by the feature maps and choose the most informative compositions by the pooling layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list of candidate dependency parsing trees. The experiments show that RCNN is very effective to improve the state-of-the-art dependency parsing on both English and Chinese datasets

Practical Resource Allocation Algorithms for QoS in OFDMA-based Wireless Systems

In this work we propose an efficient resource allocation algorithm for OFDMA based wireless systems supporting heterogeneous traffic. The proposed algorithm provides proportionally fairness to data users and short term rate guarantees to real-time users. Based on the QoS requirements, buffer occupancy and channel conditions, we propose a scheme for rate requirement determination for delay constrained sessions. Then we formulate and solve the proportional fair rate allocation problem subject to those rate requirements and power/bandwidth constraints. Simulations results show that the proposed algorithm provides significant improvement with respect to the benchmark algorithm.Comment: To be presented at 2nd IEEE International Broadband Wireless Access Workshop. Las Vegas, Nevada USA Jan 12 200

Fast Adaptive S-ALOHA Scheme for Event-driven Machine-to-Machine Communications

Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for M2M communication systems with bursty traffic. The statistics of consecutive idle and collision slots, rather than the observation in a single slot, are used in FASA to accelerate the tracking process of network status. Furthermore, the fast convergence property of FASA is guaranteed by using drift analysis. Simulation results demonstrate that the proposed FASA scheme achieves near-optimal performance in reducing access delay, which outperforms that of traditional additive schemes such as PB-ALOHA. Moreover, compared to multiplicative schemes, FASA shows its robustness even under heavy traffic load in addition to better delay performance.Comment: 5 pages, 3 figures, accepted to IEEE VTC2012-Fal

Drug Discovery from Diverse Bacteria – Bioactivity-guided Isolation of Known and New Metabolites

Exploring bioactive natural products has contributed largely to clinically approved drugs we have been using over the last 100 years. Especially among the anti-infective drugs, around 70% of currently used antibiotics were discovered or derived from microbial secondary metabolites, among them compounds like vancomycin, chloramphenicol, and erythromycin. Facing the unavoidable fact of microbial drug resistance and low cure rate of cancers, exploring new drug leads is essential and urgent. Drug discovery from microbial sources has just scratched the surface: recent surveys have shown that bacterial genomes are filled with genes encoding for secondary metabolites, that have not been seen in the laboratory, and that unique environments and underexplored biodiverse niches can yield new bacterial species with unique chemotypes. Bioactivity-guided isolation with dereplication is still an efficient method used in the laboratory to discover new bioactive compounds. This thesis includes the details on isolation of bacterial strains from diverse environments, bioactivity-guided fractionation, and dereplication/characterization of isolated metabolites. Bacterial strain library, consisting ~400 bacteria, was established in Loesgen Lab. The protocols of bacterial isolation from terrestrial and marine sources, the workflow of methods for chemical and bioactivity screening, and dereplication methods are presented. Approximately 50% of the bacterial strains have been extracted, fractionated and tested for cytotoxicity against a colon cancer cell line. Projects were prioritized based on the chemical and bioactivity screening results. An investigation of 19 bacterial strains from Oregonian soils yielded twelve known metabolites and two new natural products, a new tetrapeptide from Streptomyces sp. and a new chromone from Paraburkholderia sp. Their absolute configuration was established via advanced Marfey’s analysis and X-ray crystallography. Besides, an unusual cytotoxic diterpenoid was discovered from Streptomyces flaveolus, featuring five chiral centers and two double bond geometries within a fused bicyclo[8.4.0]tetradecane macrocycle. The metabolite existed in two distinct ring-flipped conformers in solution and its absolute configuration was determined by Mosher ester analysis, J-based coupling analysis, and DFT modeling

On NIS-Apriori Based Data Mining in SQL

We have proposed a framework of Rough Non-deterministic Information Analysis (RNIA) for tables with non-deterministic information, and applied RNIA to analyzing tables with uncertainty. We have also developed the RNIA software tool in Prolog and getRNIA in Python, in addition to these two tools we newly consider the RNIA software tool in SQL for handling large size data sets. This paper reports the current state of the prototype named NIS-Apriori in SQL, which will afford us more convenient environment for data analysis.International Joint Conference on Rough Sets　(IJCRS 2016), October 7-11, 2016, Santiago, Chil

Fair Scheduling in OFDMA-based Wireless Systems with QoS Constraints

In this work we consider the problem of downlink resource allocation for proportional fairness of long term received rates of data users and quality of service for real time sessions in an OFDMA-based wireless system. The base station allocates available power and bandwidth to individual users based on long term average received rates, QoS based rate constraints and channel conditions. We solve the underlying constrained optimization problem and propose an algorithm that achieves the optimal allocation. Numerical evaluation results show that the proposed algorithm provides better QoS to voice and video sessions while providing more and fair rates to data users in comparison with existing schemes.Comment: Presented at International OFDM Workshop, Hamburg Germany on Aug. 30th 2007 (Inowo 07

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Physics-informed neural networks (PINNs) have shown to be an effective tool for solving forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network, and this PDE loss is evaluated at a set of scattered residual points. The distribution of these points are highly important to the performance of PINNs. However, in the existing studies on PINNs, only a few simple residual point sampling methods have mainly been used. Here, we present a comprehensive study of two categories of sampling: non-adaptive uniform sampling and adaptive nonuniform sampling. We consider six uniform sampling, including (1) equispaced uniform grid, (2) uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence, (5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling strategy for uniform sampling. To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training. Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling. We extensively tested the performance of these sampling methods for four forward problems and two inverse problems in many setups. Our numerical results presented in this study are summarized from more than 6000 simulations of PINNs. We show that the proposed adaptive sampling methods of RAD and RAR-D significantly improve the accuracy of PINNs with fewer residual points. The results obtained in this study can also be used as a practical guideline in choosing sampling methods