Effective Bug Triage based on Historical Bug-Fix Information

International audienceFor complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this pa-per, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called BugFixer, which recommends developers for a new bug report based on historical bug-fix in-formation. BugFixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship be-tween the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, BugFixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate BugFixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects

InstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence Generation

Empowering models to dynamically accomplish tasks specified through natural language instructions represents a promising path toward more capable and general artificial intelligence. In this work, we introduce InstructSeq, an instruction-conditioned multi-modal modeling framework that unifies diverse vision tasks through flexible natural language control and handling of both visual and textual data. InstructSeq employs a multimodal transformer architecture encompassing visual, language, and sequential modeling. We utilize a visual encoder to extract image features and a text encoder to encode instructions. An autoregressive transformer fuses the representations and generates sequential task outputs. By training with LLM-generated natural language instructions, InstructSeq acquires a strong comprehension of free-form instructions for specifying visual tasks. This provides an intuitive interface for directing capabilities using flexible natural instructions. Without any task-specific tuning, InstructSeq achieves compelling performance on semantic segmentation, referring expression segmentation/comprehension, and image captioning. The flexible control and multi-task unification empower the model with more human-like versatility and generalizability for computer vision. The code will be released soon at https://github.com/rongyaofang/InstructSeq.Comment: 10 page

Searching for new globular clusters in M 31 with Gaia EDR3

We found 50 new globular cluster (GC) candidates around M\,31 with Gaia Early Data Release 3 (EDR3), with the help from Pan-STARRS1 DR1 magnitudes and Pan-Andromeda Archaeological Survey (PAndAS) images. Based on the latest Revised Bologna Catalog and \textit{simbad}, we trained 2 Random Forest (RF) classifiers, the first one to distinguish extended sources from point sources and the second one to further select GCs from extended sources. From 1.85 million sources of $16^m{<}g{<}19.5^m$ and within a large area of $\sim$392\,deg$^2$ around M\,31, we selected 20,658 extended sources and 1,934 initial GC candidates. After visual inspection of the PAndAS images to eliminate the contamination of non-cluster sources, particularly galaxies, we finally got 50 candidates. These candidates are divided into 3 types (\textbf{a}, \textbf{b}, \textbf{c}) according to their projected distance $D$ to the center of M\,31 and their probability to be a true GC, $P_{GC}$, which is calculated by our second RF classifier. Among these candidates, 14 are found to be associated (in projection) with the large-scale structures within the halo of M\,31. We also provided several simple parameter criteria for selecting extended sources effectively from the Gaia EDR3, which can reach a completeness of 92.1\% with a contamination fraction lower than 10\%

Advances in targeted therapy for acute myeloid leukemia.

Acute myeloid leukemia (AML) is a clonal malignancy characterized by genetic heterogeneity due to recurrent gene mutations. Treatment with cytotoxic chemotherapy has been the standard of care for more than half of a century. Although much progress has been made toward improving treatment related mortality rate in the past few decades, long term overall survival has stagnated. Exciting developments of gene mutation-targeted therapeutic agents are now changing the landscape in AML treatment. New agents offer more clinical options for patients and also confer a more promising outcome. Since Midostaurin, a FLT3 inhibitor, was first approved by US FDA in 2017 as the first gene mutation-targeted therapeutic agent, an array of new gene mutation-targeted agents are now available for AML treatment. In this review, we will summarize the recent advances in gene mutation-targeted therapies for patients with AML

Back to the Starting Point: on the Simulation of Initial Magnetic Fields and Spin Periods of Non-accretion Pulsars

Neutron stars (NSs) play essential roles in modern astrophysics. Magnetic fields and spin periods of newborn (zero age) NSs have large impact on the further evolution of NSs, which are however poorly explored in observation due to the difficulty of finding newborn NSs. In this work, we aim to infer the magnetic fields and spin periods (Bi and Pi) of zero-age NSs from the observed properties of NS population. We select non-accretion NSs (NANSs) whose evolution is solely determined by magnetic dipole radiation. We find that both Bi and Pi can be described by log-normal distribution and the fitting sensitively depends on our parameters.Comment: 8 pages, 5 figures, accepted for publication in Ap

Ghost in the Minecraft: Generally Capable Agents for Open-World Enviroments via Large Language Models with Text-based Knowledge and Memory

The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-world environments. However, the current research landscape predominantly focuses on specific objectives, such as the popular "ObtainDiamond" task, and has not yet shown effective generalization to a broader spectrum of tasks. Furthermore, the current leading success rate for the "ObtainDiamond" task stands at around 20%, highlighting the limitations of Reinforcement Learning (RL) based controllers used in existing methods. To tackle these challenges, we introduce Ghost in the Minecraft (GITM), a novel framework integrates Large Language Models (LLMs) with text-based knowledge and memory, aiming to create Generally Capable Agents (GCAs) in Minecraft. These agents, equipped with the logic and common sense capabilities of LLMs, can skillfully navigate complex, sparse-reward environments with text-based interactions. We develop a set of structured actions and leverage LLMs to generate action plans for the agents to execute. The resulting LLM-based agent markedly surpasses previous methods, achieving a remarkable improvement of +47.5% in success rate on the "ObtainDiamond" task, demonstrating superior robustness compared to traditional RL-based controllers. Notably, our agent is the first to procure all items in the Minecraft Overworld technology tree, demonstrating its extensive capabilities. GITM does not need any GPU for training, but a single CPU node with 32 CPU cores is enough. This research shows the potential of LLMs in developing capable agents for handling long-horizon, complex tasks and adapting to uncertainties in open-world environments. See the project website at https://github.com/OpenGVLab/GITM

The study of GPX3 methylation in patients with Kashin-Beck Disease and its mechanism in chondrocyte apoptosis

Objective Selenium deficiency is a risk factor for Kashin-Beck Disease (KBD), an endemic osteoarthropathy. Although promoter hypermethylation of glutathione peroxidase 3 (GPX3) (a selenoprotein) has been identified in several cancers, little is known about promoter methylation and expression of GPX3 and their relation to selenium in KBD. The present study was thus conducted to investigate this research question. Methods Methylation and expressions of GPX3 in whole blood drawn from 288 KBD patients and 362 healthy controls and in chondrocyte cell line were evaluated using methylation-specific PCR and qRT-PCR, respectively. The protein levels of PI3K/Akt/c-fos signaling in the whole blood and chondrocyte cell line were determined with Western blotting. Chondrocytes apoptosis were detected by Hoechst 33342 and Annexin V-FITC/PI staining. Results GPX3 methylation was increased, GPX3 mRNA was decreased, and protein levels in the PI3K/Akt/c-fos signaling pathway were up-regulated in the whole blood collected from KBD patients as compared with healthy controls. Similar results were obtained for chondrocytes injured by oxidative stress. There was a significant, decreasing trend in GPX3 expression across groups of unmethylation, partial methylation, and complete methylation for GPX3, in sequence. Compared with unmethylation group, protein levels in PI3K/Akt/c-fos pathway were enhanced in partial and complete methylation groups. Treatment of chondrocytes with sodium selenite resulted in reduced methylation and increased expression of GPX3 as well as down-regulated level of PI3K/Akt/c-fos proteins. Conclusions The methylation and expression of GPX3 and expression of PI3K/Akt/c-fos pathway are altered in KBD and these changes are reversible by selenium supplementation

Progress in biological and medical research in the deep underground: an update

As the growing population of individuals residing or working in deep underground spaces for prolonged periods, it has become imperative to understand the influence of factors in the deep underground environment (DUGE) on living systems. Heping Xie has conceptualized the concept of deep underground medicine to identify factors in the DUGE that can have either detrimental or beneficial effects on human health. Over the past few years, an increasing number of studies have explored the molecular mechanisms that underlie the biological impacts of factors in the DUGE on model organisms and humans. Here, we present a summary of the present landscape of biological and medical research conducted in deep underground laboratories and propose promising avenues for future investigations in this field. Most research demonstrates that low background radiation can trigger a stress response and affect the growth, organelles, oxidative stress, defense capacity, and metabolism of cells. Studies show that residing and/or working in the DUGE has detrimental effects on human health. Employees working in deep mines suffer from intense discomfort caused by high temperature and humidity, which increase with depth, and experience fatigue and sleep disturbance. The negative impacts of the DUGE on human health may be induced by changes in the metabolism of specific amino acids; however, the cellular pathways remain to be elucidated. Biological and medical research must continue in deep underground laboratories and mines to guarantee the safe probing of uncharted depths as humans utilize the deep underground space