Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling

Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subjects and objects are in a long distance. In this paper, we propose to learn more robust relation representations from the shortest dependency path through a convolution neural network. We further propose a straightforward negative sampling strategy to improve the assignment of subjects and objects. Experimental results show that our method outperforms the state-of-the-art methods on the SemEval-2010 Task 8 dataset

Keyword Search on RDF Graphs - A Query Graph Assembly Approach

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently. Based on the input keywords, we first obtain the elementary query graph building blocks, such as entity/class vertices and predicate edges. Then, we formally define the query graph assembly (QGA) problem. Unfortunately, we prove theoretically that QGA is a NP-complete problem. In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm. The algorithm's time complexity is $O(k^{2l} \cdot l^{3l})$, where $l$ is the number of the keywords and $k$ is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword. Although QGA is intractable, both $l$ and $k$ are small in practice. Furthermore, the algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs. Experiments on DBpedia and Freebase confirm the superiority of our system on both effectiveness and efficiency

Learning to Predict Charges for Criminal Cases with Legal Basis

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.Comment: 10 pages, accepted by EMNLP 201

vHaul: Towards Optimal Scheduling of Live Multi-VM Migration for Multi-tier Applications

Abstract—Live virtual machine (VM) migration enables seamless movement of an online server from one location to another to achieve failure recovery, load balancing, and system maintenance. Beyond single VM migration, a multi-tier application involves a group of correlated VMs and its live mi-gration will require careful scheduling of the migrations of the member VMs. Our observations from extensive experiments using a variety of multi-tier applications suggest that, in a dedicated data center with dedicated migration links, different migration strategies result in distinct performance impacts on a multi-tier application. The root cause of the problem is the inter-dependence between functional components of a multi-tier application. We leverage these observations in vHaul, a system that coordinates multi-VM migration to approximate the optimal scheduling. Our evaluation of a vHaul prototype on Xen suggests that vHaul yields the optimal multi-VM live migra-tion schedules. Further, our application-level evaluation using Apache Olio, a web 2.0 cloud application, shows that the optimal migration schedule produced by vHaul outperforms the worst-case schedule by 43 % in application throughput. Moreover, the optimal schedule significantly reduces service latency during migration by up to 70%

Efficiency Droop in III-nitride LEDs

To dominate the illumination market, applications of high-power, group III-nitride light-emitting diodes (LEDs) with lower cost and higher efficiency at high injection current density must prevail. In this chapter, three possible origins of efficiency droop (including electron leakage, poor hole injection, and delocalization of carriers) in III-nitride LEDs are systematically summarized. To seek a more comprehensive understanding of the efficiency droop, experimental results based on commercialized LEDs are obtained to explain the physical mechanisms. Proposals for droop mitigation, such as (1) improving hole injection, and (2) increasing effective optical volume or reducing carrier density in the active region, are introduced. Finally, a simple expression for the effects of V-shaped pits on the droop is demonstrated