User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table

VisualGPTScore: Visio-Linguistic Reasoning with Multimodal Generative Pre-Training Scores

Vision-language models (VLMs) discriminatively pre-trained with contrastive image-text matching losses such as $P(\text{match}|\text{text}, \text{image})$ have been criticized for lacking compositional understanding. This means they might output similar scores even if the original caption is rearranged into a different semantic statement. To address this, we propose to use the ${\bf V}$isual ${\bf G}$enerative ${\bf P}$re-${\bf T}$raining Score (${\bf VisualGPTScore}$) of $P(\text{text}|\text{image})$, a $\textit{multimodal generative}$ score that captures the likelihood of a text caption conditioned on an image using an image-conditioned language model. Contrary to the belief that VLMs are mere bag-of-words models, our off-the-shelf VisualGPTScore demonstrates top-tier performance on recently proposed image-text retrieval benchmarks like ARO and Crepe that assess compositional reasoning. Furthermore, we factorize VisualGPTScore into a product of the $\textit{marginal}$ P(text) and the $\textit{Pointwise Mutual Information}$ (PMI). This helps to (a) diagnose datasets with strong language bias, and (b) debias results on other benchmarks like Winoground using an information-theoretic framework. VisualGPTScore provides valuable insights and serves as a strong baseline for future evaluation of visio-linguistic compositionality.Comment: Website: https://linzhiqiu.github.io/papers/visual_gpt_score/ Code: https://github.com/linzhiqiu/visual_gpt_score

Boosting API Recommendation with Implicit Feedback

Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of users' feedback into the recommendation loop. In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting users' feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.Comment: 15 pages, 4 figure

Initializing Models with Larger Ones

Weight initialization plays an important role in neural network training. Widely used initialization methods are proposed and evaluated for networks that are trained from scratch. However, the growing number of pretrained models now offers new opportunities for tackling this classical problem of weight initialization. In this work, we introduce weight selection, a method for initializing smaller models by selecting a subset of weights from a pretrained larger model. This enables the transfer of knowledge from pretrained weights to smaller models. Our experiments demonstrate that weight selection can significantly enhance the performance of small models and reduce their training time. Notably, it can also be used together with knowledge distillation. Weight selection offers a new approach to leverage the power of pretrained models in resource-constrained settings, and we hope it can be a useful tool for training small models in the large-model era. Code is available at https://github.com/OscarXZQ/weight-selection

Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset

To compare the impact of surface–atmosphere exchanges from rural and urban areas, fully vegetated areas (e.g. deciduous trees, evergreen trees and grass) commonly found adjacent to cities need to be modelled. Here we provide a general workflow to derive parameters for SUEWS (Surface Urban Energy and Water Balance Scheme), including those associated with vegetation phenology (via leaf area index, LAI), heat storage and surface conductance. As expected, attribution analysis of bias in SUEWS-modelled QE finds that surface conductance (gs) plays the dominant role; hence there is a need for more estimates of surface conductance parameters. The workflow is applied at 38 FLUXNET sites. The derived parameters vary between sites with the same plant functional type (PFT), demonstrating the challenge of using a single set of parameters for a PFT. SUEWS skill at simulating monthly and hourly latent heat flux (QE) is examined using the site-specific derived parameters, with the default NOAH surface conductance parameters (Chen et al., 1996). Overall evaluation for 2 years has similar metrics for both configurations: median hit rate between 0.6 and 0.7, median mean absolute error less than 25Wm-2, and median mean bias error ~5Wm-2. Performance differences are more evident at monthly and hourly scales, with larger mean bias error (monthly: ~40Wm-2; hourly ~30Wm-2) results using the NOAH-surface conductance parameters, suggesting that they should be used with caution. Assessment of sites with contrasting QE performance demonstrates how critical capturing the LAI dynamics is to the SUEWS prediction skills of gs and QE. Generally gs is poorest in cooler periods (more pronounced at night, when underestimated by ~3mms-1). Given the global LAI data availability and the workflow provided in this study, any site to be simulated should benefit

HIV Screening and Awareness Survey for Pregnant Women in a Remote Area in Xinjiang Uyghur Autonomous Region of China

Objective: The number of people infected with human immunodeficiency virus (HIV) in China has increased in recent years. HIV screening for pregnant women was performed in a remote area in Xinjiang, as an effort to promote universal HIV screening in pregnant women and to help prevention of mother-to-child transmission. Methods: Pregnant women in Burqin and Jeminay Counties in Xinjiang were offered free voluntary HIV screening. Local mid-level medical workers were trained to use Determine® HIV-1/2 kit for HIV screening. All the tested pregnant women signed a consent form, received HIV education material, and participated in an HIV knowledge survey. Results: All the 890 pregnant women receiving HIV test had negative result. Among these women, 67.6% were Kazakh and 40.9% were farmers. Survey of HIV knowledge showed that these women's awareness about mother-to-child transmission was limited. The levels of HIV knowledge were related with ethnic background, age, education and profession of the pregnant women. Conclusion: The results suggested that HIV infection had not become a significant problem among the pregnant women in this remote area of Xinjiang, but continued efforts to improve the awareness of HIV, especially the knowledge about mother-to-child transmission of HIV, in pregnant women were needed

SIVA1 directs the E3 ubiquitin ligase RAD18 for PCNA monoubiquitination

published_or_final_versio

Search for dark matter annihilation signals in the H.E.S.S. Inner galaxy survey

The central region of the Milky Way is one of the foremost locations to look for dark matter (DM) signatures. We report the first results on a search for DM particle annihilation signals using new observations from an unprecedented γ-ray survey of the Galactic Center (GC) region, i.e., the Inner Galaxy Survey, at very high energies (≳100  GeV) performed with the H.E.S.S. array of five ground-based Cherenkov telescopes. No significant γ-ray excess is found in the search region of the 2014-2020 dataset and a profile likelihood ratio analysis is carried out to set exclusion limits on the annihilation cross section ⟨σv⟩. Assuming Einasto and Navarro-Frenk-White (NFW) DM density profiles at the GC, these constraints are the strongest obtained so far in the TeV DM mass range. For the Einasto profile, the constraints reach ⟨σv⟩ values of 3.7×10^{-26}  cm^{3} s^{-1} for 1.5 TeV DM mass in the W^{+}W^{-} annihilation channel, and 1.2×10^{-26}  cm^{3} s^{-1} for 0.7 TeV DM mass in the τ^{+}τ^{-} annihilation channel. With the H.E.S.S. Inner Galaxy Survey, ground-based γ-ray observations thus probe ⟨σv⟩ values expected from thermal-relic annihilating TeV DM particles

HESS J1809−-193: a halo of escaped electrons around a pulsar wind nebula?

Context. HESS J1809$-$193 is an unassociated very-high-energy $\gamma$-ray source located on the Galactic plane. While it has been connected to the nebula of the energetic pulsar PSR J1809$-$1917, supernova remnants and molecular clouds present in the vicinity also constitute possible associations. Recently, the detection of $\gamma$-ray emission up to energies of $\sim$100 TeV with the HAWC observatory has led to renewed interest in HESS J1809$-$193. Aims. We aim to understand the origin of the $\gamma$-ray emission of HESS J1809$-$193. Methods. We analysed 93.2 h of data taken on HESS J1809$-$193 above 0.27 TeV with the High Energy Stereoscopic System (H.E.S.S.), using a multi-component, three-dimensional likelihood analysis. In addition, we provide a new analysis of 12.5 yr of Fermi-LAT data above 1 GeV within the region of HESS J1809$-$193. The obtained results are interpreted in a time-dependent modelling framework. Results. For the first time, we were able to resolve the emission detected with H.E.S.S. into two components: an extended component that exhibits a spectral cut-off at $\sim$13 TeV, and a compact component that is located close to PSR J1809$-$1917 and shows no clear spectral cut-off. The Fermi-LAT analysis also revealed extended $\gamma$-ray emission, on scales similar to that of the extended H.E.S.S. component. Conclusions. Our modelling indicates that based on its spectrum and spatial extent, the extended H.E.S.S. component is likely caused by inverse Compton emission from old electrons that form a halo around the pulsar wind nebula. The compact component could be connected to either the pulsar wind nebula or the supernova remnant and molecular clouds. Due to its comparatively steep spectrum, modelling the Fermi-LAT emission together with the H.E.S.S. components is not straightforward. (abridged)Comment: 14 pages, 10 figures. Accepted for publication in A&A. Corresponding authors: Vikas Joshi, Lars Mohrman