Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

For machine reading comprehension, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride of the noises is essential to improve its performance. Traditional attentive models attend to all words without explicit constraint, which results in inaccurate concentration on some dispensable words. In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanism for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the SAN from the original Transformer encoder through a dual contextual architecture for better linguistics inspired representation. To verify its effectiveness, the proposed SG-Net is applied to typical pre-trained language model BERT which is right based on a Transformer encoder. Extensive experiments on popular benchmarks including SQuAD 2.0 and RACE show that the proposed SG-Net design helps achieve substantial performance improvement over strong baselines.

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Abstract-Web applications increasingly integrate third-party services. The integration introduces new security challenges due to the complexity for an application to coordinate its internal states with those of the component services and the web client across the Internet. In this paper, we study the security implications of this problem to merchant websites that accept payments through third-party cashiers (e.g., PayPal, Amazon Payments and Google Checkout), which we refer to as Cashier-as-a-Service or CaaS. We found that leading merchant applications (e.g., NopCommerce and Interspire), popular online stores (e.g., Buy.com and JR.com) and a prestigious CaaS provider (Amazon Payments) all contain serious logic flaws that can be exploited to cause inconsistencies between the states of the CaaS and the merchant. As a result, a malicious shopper can purchase an item at an arbitrarily low price, shop for free after paying for one item, or even avoid payment. We reported our findings to the affected parties. They either updated their vulnerable software or continued to work on the fixes with high priorities. We further studied the complexity in finding this type of logic flaws in typical CaaS-based checkout systems, and gained a preliminary understanding of the effort that needs to be made to improve the security assurance of such systems during their development and testing processes.

The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process. Extensive experiments on a standard summarization dataset were conducted and the results show that the template-equipped BiSET model manages to improve the summarization performance significantly with a new state of the art.

IntroductionBioinformatics is a broad discipline in which one common denominator is the need to produce and/or use software that can be applied to biological data in different contexts. To enable and ensure the replicability and traceability of scientific claims, it is essential that the scientific publication, the corresponding datasets, and the data analysis are made publicly available [1,2]. All software used for the analysis should be either carefully documented (e.g., for commercial software) or, better yet, openly shared and directly accessible to others [3,4]. The rise of openly available software and source code alongside concomitant collaborative development is facilitated by the existence of several code repository services such as SourceForge, Bitbucket, GitLab, and GitHub, among others. These resources are also essential for collaborative software projects because they enable the organization and sharing of programming tasks between different remote contributors. Here, we introduce the main features of GitHub, a popular webbased platform that offers a free and integrated environment for hosting the source code, documentation, and project-related web content for open-source projects. GitHub also offers paid plans for private repositories (see Box 1) for individuals and businesses as well as free plans including private repositories for research and educational use.GitHub relies, at its core, on the well-known and open-source version control system Git, originally designed by Linus Torvalds for the development of the Linux kernel and now developed and maintained by the Git community. One reason for GitHub's success is that it offers more than a simple source code hosting service [5,6]. It provides developers and researchers with a dynamic and collaborative environment, often referred to as a social coding platform, that supports peer review, commenting, and discussion [7]. A diverse range of efforts, ranging from individual to large bioinformatics projects, laboratory repositories, as well as global PLOS Computational Biology |

Solution-processed indium-gallium-zinc oxide (IGZO) thin film transistors (TFTs) have become well known in recent decades for their promising commercial potential. However, the unsatisfactory performance of small-sized IGZO TFTs is limiting their applicability. To address this issue, this work introduces an interface engineering method of bi-functional acid modification to regulate the interfaces between electrodes and the channels of IGZO TFTs. This method increases the interface oxygen vacancy concentration and reduces the surface roughness, resulting in higher mobility and enhanced contact at the interfaces. The TFT devices thus treated display contact resistance reduction from 9.1 to 2.3 kΩmm, as measured by the gated four-probe method, as well as field-effect mobility increase from 1.5 to 4.5 cm 2 (V s) −1 . Additionally, a 12 × 12 organic light emitting diode display constructed using the acid modified IGZO TFTs as switching and driving elements demonstrate the applicability of these devices.