arXiv subjects

Jialu Zhang

Publications and source records attributed to Jialu Zhang.

40 records · Page 3Linked to original sources

arXiv2022

Nonperturbative Determination of Collins-Soper Kernel from Quasi Transverse-Momentum Dependent Wave Functions

In the framework of large-momentum effective theory at one-loop matching accuracy, we perform a lattice calculation of the Collins-Soper kernel which governs the rapidity evolution of transverse-momentum-dependent (TMD) distributions. We first obtain the quasi TMD wave functions at three different meson momenta on a lattice with valence clover quarks on a dynamical HISQ sea and lattice spacing $a=0.12$~fm from MILC, and renormalize the pertinent linear divergences using Wilson loops. Through one-loop matching to the light-cone wave functions, we determine the Collins-Soper kernel with transverse separation up to 0.6~fm. We study the systematic uncertainties from operator mixing and scale dependence, as well as the impact from higher power corrections. Our results potentially allow for a determination of the soft function and other transverse-momentum dependent quantities at one-loop accuracy.

hep-lat↗

arXiv2022

Spatial-context-aware deep neural network for multi-class image classification

Multi-label image classification is a fundamental but challenging task in computer vision. Over the past few decades, solutions exploring relationships between semantic labels have made great progress. However, the underlying spatial-contextual information of labels is under-exploited. To tackle this problem, a spatial-context-aware deep neural network is proposed to predict labels taking into account both semantic and spatial information. This proposed framework is evaluated on Microsoft COCO and PASCAL VOC, two widely used benchmark datasets for image multi-labelling. The results show that the proposed approach is superior to the state-of-the-art solutions on dealing with the multi-label image classification problem.

cs.CV↗

arXiv2021

Can Pre-trained Language Models be Used to Resolve Textual and Semantic Merge Conflicts?

Program merging is standard practice when developers integrate their individual changes to a common code base. When the merge algorithm fails, this is called a merge conflict. The conflict either manifests in textual merge conflicts where the merge fails to produce code, or semantic merge conflicts where the merged code results in compiler or test breaks. Resolving these conflicts for large code projects is expensive because it requires developers to manually identify the sources of conflict and correct them. In this paper, we explore the feasibility of automatically repairing merge conflicts (both textual and semantic) using k-shot learning with large neural language models (LM) such as GPT-3. One of the challenges in leveraging such language models is to fit the examples and the queries within a small prompt (2048 tokens). We evaluate LMs and k-shot learning for two broad applications: (a) textual and semantic merge conflicts for a divergent fork Microsoft Edge, and (b) textual merge conflicts for a large number of JavaScript projects in GitHub. Our results are mixed: one one-hand, LMs provide the state-of-the-art (SOTA) performance on semantic merge conflict resolution for Edge compared to earlier symbolic approaches; on the other hand, LMs do not yet obviate the benefits of fine-tuning neural models (when sufficient data is available) or the design of special purpose domain-specific languages (DSL) for restricted patterns for program synthesis.

cs.SE↗

arXiv2018

Statically Verifying Continuous Integration Configurations

Continuous Integration (CI) testing is a popular software development technique that allows developers to easily check that their code can build successfully and pass tests across various system environments. In order to use a CI platform, a developer must include a set of configuration files to a code repository for specifying build conditions. Incorrect configuration settings lead to CI build failures, which can take hours to run, wasting valuable developer time and delaying product release dates. Debugging CI configurations is challenging because users must manage configurations for the build across many system environments, to which they may not have local access. Thus, the only way to check a CI configuration is to push a commit and wait for the build result. To address this problem, we present the first approach, VeriCI, for statically checking for errors in a given CI configuration before the developer pushes a commit to build on the CI server. Our key insight is that the repositories in a CI environment contain lists of build histories which offer the time-aware repository build status. Driven by this insight, we introduce the Misclassification Guided Abstraction Refinement (MiGAR) loop that automates part of the learning process across the heterogeneous build environments in CI. We then use decision tree learning to generate constraints on the CI configuration that must hold for a build to succeed by training on a large history of continuous integration repository build results. We evaluate VeriCI on real-world data from GitHub and find that we have 83% accuracy of predicting a build failure.

cs.SE↗