Analysis and Detection of Information Types of Open Source Software Issue Discussions

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering (ICSE2019

Tuning the properties of high-Tc superconductor & Sr2IrO4, and exploring transport through single nanocrystals

This thesis is composed of three projects including the AC magnetic susceptibility study of high-temperature superconductor YBa$_2$Cu$_3$O$_{7-\delta}$, the ionic-liquid gating study of the Mott insulator Sr$_2$IrO$_4$, and the single-electron study of quantum dot device with self-assembled nanocrystal PbS. Chapter 1 covers a general introduction to all three projects. The basic background and the motivation for each project are presented. Project I is covered in Chapter 2, Chapter 3, and Chapter 4. The first part of Chapter 2 is a theoretical introduction to the Bardeen–Cooper–Schrieffer theory of superconductivity with its main conclusions presented. This chapter builds a basis for the use of high pressure technique to YBa$_2$Cu$_3$O$_{7-\delta}$ in the later chapters. The rest of Chapter 2 reviews the work in the study of high-temperature superconductors, especially on YBa$_2$Cu$_3$O$_{7-\delta}$, on both experiments and theories and the possible applications of high-temperature superconductors. Chapter 3 introduces the YBa$_2$Cu$_3$O$_{7-\delta}$ sample preparation process and the characterisation. A dry cryomagnetic equipment was employed for the measurement. The results and the discussion are presented in Chapter 4. Project II is described in Chapter 5, Chapter 6, and Chapter 7. Chapter 5 firstly introduces the background knowledge of the gated material SrTiO$_3$ and the technical details of the ionic-liquid gating technique. Then the sample growth and the characterisation are presented. The fabrication process of Sr$_2$IrO$_4$ and SrTiO$_3$ (material for a control experiment) are described in Chapter 6. Chapter 7 covers the measurement and the result of the fabricated devices and related discussion. Project III ranges from Chapter 8, and Chapter 9. A literature review of quantum-dot devices and self-assembled nanocrystals is presented in Chapter 8. The experimental design of this nanocrystal quantum dot device is also included. Following it, the fabrication process of quantum-dot devices and the techniques used for fabrication are introduced in the start of Chapter 9. Chapter 9 also gives a description of the probe-station for measurements. The results and discussion of the measurements are covered in the last section of Chapter 9. Chapter 10 summarises and concludes the three projects stated above and gives some suggestions about the directions for future work

Targeting Axonal Transport: A New Therapeutic Avenue for ALS

Motor neurons have an extreme polarized morphology and heavily rely on efficient cargo transport along axons to maintain their neuronal connections and connections with muscles. Axonal transport deficits have been observed in almost all model systems of ALS. More and more studies have confirmed the close genetic and mechanistic linkage between axonal transport deficits with ALS pathogenesis. Moreover, several therapeutic approaches have been developed to target axonal transport deficits in ALS and showed promising effects in disease models. In this concise chapter, we summarize some major discoveries of axonal transport deficits in ALS pathogenesis and some related therapeutic strategies. We propose that targeting axonal transport may provide a potential therapeutic avenue for ALS

Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

The emerging 1mm resolution 3D data collection technology is capable of covering the entire pavement surface, and provides more data sets than traditional line-of-sight data collection systems. As a result, quantifying the impact of sample size including sample width and sample length on the calculation of pavement texture indicators is becoming possible. In this study, 1mm 3D texture data are collected and processed at seven test sites using the PaveVision3D Ultra system. Analysis of Variance (ANOVA) test and linear regression models are developed to investigate various sample length and width on the calculation of three widely used texture indicators: Mean Profile Depth (MPD), Mean Texture Depth (MTD) and Power Spectra Density (PSD). Since the current ASTM standards and other procedures cannot be directly applied to 3D surface for production due to a lack of definitions, the results from this research are beneficial in the process to standardize texture indicators’ computations with 1mm 3D surface data of pavements

Transfer Learning Based Traffic Sign Recognition Using Inception-v3 Model

Traffic sign recognition is critical for advanced driver assistant system and road infrastructure survey. Traditional traffic sign recognition algorithms can't efficiently recognize traffic signs due to its limitation, yet deep learning-based technique requires huge amount of training data before its use, which is time consuming and labor intensive. In this study, transfer learning-based method is introduced for traffic sign recognition and classification, which significantly reduces the amount of training data and alleviates computation expense using Inception-v3 model. In our experiment, Belgium Traffic Sign Database is chosen and augmented by data pre-processing technique. Subsequently the layer-wise features extracted using different convolution and pooling operations are compared and analyzed. Finally transfer learning-based model is repetitively retrained several times with fine-tuning parameters at different learning rate, and excellent reliability and repeatability are observed based on statistical analysis. The results show that transfer learning model can achieve a high-level recognition performance in traffic sign recognition, which is up to 99.18 % of recognition accuracy at 0.05 learning rate (average accuracy of 99.09 %). This study would be beneficial in other traffic infrastructure recognition such as road lane marking and roadside protection facilities, and so on

High-order rational-type solutions of the analogous (3+1)-dimensional Hirota-bilinear-like equation

In this article, a new dynamical system equation named the (3+1)-dimensional Hirota-bilinear-like equation (HBLE) was constructed. The generalized Hirota bilinear method was applied to obtain this new HBLE in (3+1) dimensions. This new HBLE possesses a similar bilinear form to the original (3+1)-dimensional Hirota bilinear equation, but with additional nonlinear terms. A set of high-order rational solutions is constructed for the given equation, generated from polynomial solutions to the associated generalized bilinear equation. The analyticity conditions of the resulting solutions were investigated and six groups of general solutions were derived. In addition, the shape and surface of the high-order rational function solutions and their dynamic behaviors were studied by utilizing Maple

Refined Qingkailing Protects MCAO Mice from Endoplasmic Reticulum Stress-Induced Apoptosis with a Broad Time Window

In the current study, we are investigating effect of refined QKL on ischemia-reperfusion-induced brain injury in mice. Methods. Mice were employed to induce ischemia-reperfusion injury of brain by middle cerebral artery occlusion (MCAO). RQKL solution was administered with different doses (0, 1.5, 3, and 6 mL/kg body weight) at the same time of onset of ischemia, and with the dose of 1.5 mL/kg at different time points (0, 1.5, 3, 6, and 9 h after MCAO). Neurological function and brain infarction were examined and cell apoptosis and ROS at prefrontal cortex were evaluated 24 h after MCAO, and western blot and intracellular calcium were also researched, respectively. Results. RQKL of all doses can improve neurological function and decrease brain infarction, and it performed significant effect in 0, 1.5, 3, and 6 h groups. Moreover, RQKL was able to reduce apoptotic process by reduction of caspase-3 expression, or restraint of eIF2a phosphorylation and caspase-12 activation. It was also able to reduce ROS and modulate intracellular calcium in the brain. Conclusion. RQKL can prevent ischemic-induced brain injury with a time window of 6 h, and its mechanism might be related to suppress ER stress-mediated apoptotic signaling