A 2D based Partition Strategy for Solving Ranking under Team Context (RTP)

In this paper, we propose a 2D based partition method for solving the problem of Ranking under Team Context(RTC) on datasets without a priori. We first map the data into 2D space using its minimum and maximum value among all dimensions. Then we construct window queries with consideration of current team context. Besides, during the query mapping procedure, we can pre-prune some tuples which are not top ranked ones. This pre-classified step will defer processing those tuples and can save cost while providing solutions for the problem. Experiments show that our algorithm performs well especially on large datasets with correctness

Environmental and Social Disclosure in Australian Mining Industry

The purpose of this paper is to examine how Australian mining industry performs in environmental and social disclosure. There is growing public consensus that corporations should take responsibility for their environmental and social activities, which have already produced a massive amount of social problems such as wastes or pollutions. In response to the increasing public demand, environmental and social disclosure has developed rapidly during last decades. The paper tries to attribute to the literature by examining how the Australian mining industry performs in environmental and social disclosure. Content analysis will be conducted through twenty largest Australian mining companies and the result will be compared with previous literature when is appropriate. This analysis confirms the leading position mining industry has obtained over other industries. The relative emphasis placed on different disclosure categories will be discussed and the size relationship will be examined. The result suggests that larger mining companies are now tending to make the environmental and social disclosure in a separated report rather than annual report, which makes it necessary to extend the scope of disclosure analysis in the future

Which Channel to Ask My Question? Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning

Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers' requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers' satisfaction. To achieve the optimal tradeoff between resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers' satisfaction are optimal.Comment: 13 pages, 7 figure

Iron and zinc binding activity of Escherichia coli topoisomerase I homolog YrdD

YrdD, a homolog of the C-terminal zinc-binding region of Escherichia coli topoisomerase I, is highly conserved among proteobacteria and enterobacteria. However, the function of YrdD remains elusive. Here we report that YrdD purified from E. coli cells grown in LB media contains both zinc and iron. Supplement of exogenous zinc in the medium abolishes the iron binding of YrdD in E. coli cells, indicating that iron and zinc may compete for the same metal binding sites in the protein. While the zinc-bound YrdD is able to bind single-stranded (ss) DNA and protect ssDNA from the DNase I digestion in vitro, the iron-bound YrdD has very little or no binding activity for ssDNA, suggesting that the zinc-bound YrdD may have an important role in DNA repair by interacting with ssDNA in cells. © 2014 Springer Science+Business Media

Electrochemical Reducation of TiO2/Al2O3/C to Ti3AlC2 and Its Derived Two-Dimensional (2D) Carbides

Ti3AlC2 has been directly synthesized from TiO2/Al2O3/C mixture precursors (3TiO2/0.5Al2O3/1.5C and 2TiO2/0.5Al2O3/C) by a molten salt electrolysis process at 900?C and 3.2 V in molten CaCl2. The influence of initial carbon content on the electrosynthesized products has been investigated. The result shows that the main phase of the electrosynthesized products changes from Ti3AlC to Ti2AlC and then to Ti3AlC2 with the increasing carbon content, and the electrosynthesized Ti3AlC2 is carbon deficient. The morphology observation shows that the electrosynthesized Ti3AlC2 particles possess smooth surfaces and dense flake-like microstructure. The reaction mechanism of the electroreduction of TiO2/Al2O3/C mixture precursor has been discussed based on the time- and position-dependent phase constitution analysis. In addition, two-dimensional (2D) Ti3AlC2-derived carbides, i.e., Ti3C2Tx and TiCx have been successfully prepared from the electrosynthesized Ti3AlC2 by a chemical etching process and an electrochemical etching process, respectively. Both derived carbides exhibit the similar layered structure, in which single layer carbides are composed of plentiful nanometer carbides. It is suggested that the molten salt electrolysis process has a great potential to be used for the facile synthesis of Mn+1AXn phases (such as Ti3AlC2) from their oxides precursors, and the synthesized Mn+1AXn phases can be further converted into 2D carbidesauthorsversionPeer reviewe

A benchmark and an algorithm for detecting germline transposon insertions and measuring de novo transposon insertion frequencies

Transposons are genomic parasites, and their new insertions can cause instability and spur the evolution of their host genomes. Rapid accumulation of short-read whole-genome sequencing data provides a great opportunity for studying new transposon insertions and their impacts on the host genome. Although many algorithms are available for detecting transposon insertions, the task remains challenging and existing tools are not designed for identifying de novo insertions. Here, we present a new benchmark fly dataset based on PacBio long-read sequencing and a new method TEMP2 for detecting germline insertions and measuring de novo \u27singleton\u27 insertion frequencies in eukaryotic genomes. TEMP2 achieves high sensitivity and precision for detecting germline insertions when compared with existing tools using both simulated data in fly and experimental data in fly and human. Furthermore, TEMP2 can accurately assess the frequencies of de novo transposon insertions even with high levels of chimeric reads in simulated datasets; such chimeric reads often occur during the construction of short-read sequencing libraries. By applying TEMP2 to published data on hybrid dysgenic flies inflicted by de-repressed P-elements, we confirmed the continuous new insertions of P-elements in dysgenic offspring before they regain piRNAs for P-element repression. TEMP2 is freely available at Github: https://github.com/weng-lab/TEMP2