From text to graph: a general transition-based AMR parsing using neural network

Neural Computing and Applications - Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing,...     

Proactive replication for rare objects in unstructured peer-to-peer networks

Unstructured peer-to-peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. The search efficiency problem in unstructured P2P networks has not been adequately addressed so far, especially concerning search for rare objects. In this paper, we propose a proactive replication strategy to improve search efficiency for rare objects. It uses an object-probing technique for peers to decide whether or not to establish replications for their objects when they join the network. This strategy can effectively increase the popularity of rare objects in order to enhance search efficiency. We also present a rare object search algorithm to reduce the overhead caused by the replication strategy. When a peer forwards a search request, the forward probability is calculated according to its neighbors' degrees and the number of neighbors' objects. Therefore, the search request is forwarded to the peers more likely containing target objects. Simulations show that our proactive replication strategy greatly improves search efficiency for rare objects with moderate communication overhead. The rare object search algorithm not only improves search efficiency for rare objects, but also achieves load balance in search.     

Fuzzy local maximal marginal embedding for feature extraction

In graph-based linear dimensionality reduction algorithms, it is crucial to construct a neighbor graph that can correctly reflect the relationship between samples. This paper presents an improved algorithm called fuzzy local maximal marginal embedding (FLMME) for linear dimensionality reduction. Significantly differing from the existing graph-based algorithms is that two novel fuzzy gradual graphs are constructed in FLMME, which help to pull the near neighbor samples in same class nearer and nearer and repel the far neighbor samples of margin between different classes farther and farther when they are projected to feature subspace. Through the fuzzy gradual graphs, FLMME algorithm has lower sensitivities to the sample variations caused by varying illumination, expression, viewing conditions and shapes. The proposed FLMME algorithm is evaluated through experiments by using the WINE database, the Yale and ORL face image databases and the USPS handwriting digital databases. The results show that the FLMME outperforms PCA, LDA, LPP and local maximal marginal embedding.     

Collapsing and Separating Completeness Notions Under Average-Case and Worst-Case Hypotheses

This paper presents the following results on sets that are complete for NP.

1. If there is a problem in NP that requires $2^{n^{\Omega(1)}}$ time at almost all lengths, then every many-one NP-complete set is complete under length-increasing reductions that are computed by polynomial-size circuits.
2. If there is a problem in co-NP that cannot be solved by polynomial-size nondeterministic circuits, then every many-one NP-complete set is complete under length-increasing reductions that are computed by polynomial-size circuits.
3. If there exist a one-way permutation that is secure against subexponential-size circuits and there is a hard tally language in NP??co-NP, then there is a Turing complete language for NP that is not many-one complete.

Our first two results use worst-case hardness hypotheses whereas earlier work that showed similar results relied on average-case or almost-everywhere hardness assumptions. The use of average-case and worst-case hypotheses in the last result is unique as previous results obtaining the same consequence relied on almost-everywhere hardness results.     

A Distributed Workflow Management System with Case Study of Real-life Scientific Applications on Grids

Next-generation scientific applications feature complex workflows comprised of many computing modules with intricate inter-module dependencies. Supporting such scientific workflows in wide-area networks especially Grids and optimizing their performance are crucial to the success of collaborative scientific discovery. We develop a Scientific Workflow Automation and Management Platform (SWAMP), which enables scientists to conveniently assemble, execute, monitor, control, and steer computing workflows in distributed environments via a unified web-based user interface. The SWAMP architecture is built entirely on a seamless composition of web services: the functionalities of its own are provided and its interactions with other tools or systems are enabled through web services for easy access over standard Internet protocols while being independent of different platforms and programming languages. SWAMP also incorporates a class of efficient workflow mapping schemes to achieve optimal end-to-end performance based on rigorous performance modeling and algorithm design. The performance superiority of SWAMP over existing workflow mapping schemes is justified by extensive simulations, and the system efficacy is illustrated by large-scale experiments on real-life scientific workflows for climate modeling through effective system implementation, deployment, and testing on the Open Science Grid.     

基于视差与分层渲染的立体显示效果评价研究

本文在充分分析国内外立体显示技术评价方法的基础上,提出了利用视差和分层渲染的方法进行立体显示效果的评价,结果表明其对于自由立体显示评价可以得到较明确的分析结果,最后对研究前景进行了必要的探讨。     

DH-1型型砂水分自动测控系统

介绍了一种新型的型砂水分测控系统。它以单片机为主控机，采用自行 研制的电容式集成水分传感器对型砂水分进行测控。该系统应用先进的数据采集和自动控制技术，具有控制精度高、运行可靠、安装操作方便等特点，实际应用试验表明本系统较好地符合生产实际要求。     

巨磁电阻材料的研究与应用

介绍了一种新型的磁性功能材料－巨磁电阻材料，综述了其研究进展情况。并就它在巨磁电阻传感器、高密度磁记录读出磁头、巨磁电阻随机存储器以及自旋晶体管等磁电子元器件上的进行了论述。     

锌铝超塑性合金超塑压缩试验研究

对Ｚｎ－２２％Ａｌ超塑性合金进行了等温超塑压缩试验。在相同的温度、变形速率条件下，研究了不同的变形强度与流动应力及硬度，以及在相同的温度、变形程度条件下，不同的变形速率与流动应力等的关系。     

Modulation on the electrical and optical properties of Na and Rh co-doped Ruddlesden-Popper layered Ca3Ti2O7

Layered perovskite Ca_2.91Na_0.09Ti_2-xRh_xO₇ (x?=?0.00, 0.02, 0.04, 0.06) were synthesized by a conventional solid-state reaction. Room temperature ferroelectricity has been confirmed. The remanent polarization increases with an increase of Rh content, which is due to a larger oxygen octahedral distortion by Rh doping. The coercive field increases with Rh doping as the pinning effect of oxygen vacancies reduce the mobility of domain wall. Remanent polarization and coercive field are caused by different mechanisms, so it is possible to modulate them independently to meet the requirement of application in ferroelectric field. The concentration of oxygen vacancy increased with Rh doping, leading to the significant increase of leakage current density. The bandgap of samples doped with Rh drastically decrease and the visible light response of the sample was improved by Rh doping due to the formation of impurity energy levels within the band gap.