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Abstract An important discovery of MESSENGER is the occurrence of dayside disappearing magnetosphere (DDM) events that occur when the solar wind dynamic pressure is extremely high and the interplanetary magnetic field (IMF) is both intense and southward. In this study, we investigate the DDM events at Mercury under extreme solar wind conditions using a three‐dimensional (3‐D) global hybrid simulation model. Our results show that when the solar wind dynamic pressure is 107 nPa and the magnitude of the purely southward IMF is 50 nT, most of the dayside magnetosphere disappears within 10 s after the interaction between the solar wind and the planetary magnetic field starts. During the DDM event, the ion flux is significantly enhanced at most of the planetary dayside surface and reaches its maximum value of about 10¹⁰ cm⁻² s⁻¹at the low‐latitude surface, which is much larger than that under normal solar wind conditions. During the DDM events, the dayside bow shock mostly disappears for about 9 s and then reappears. Moreover, the time evolution of magnetopause standoff distance under different solar wind conditions is also studied. When the solar wind dynamic pressure exceeds 25 nPa and the IMF is purely southward, a part of the dayside magnetosphere disappears. Under the same IMF, the higher the solar wind dynamic pressure, the faster the magnetopause standoff distance reaches the planetary surface. When the solar wind conditions are normal (with a dynamic pressure of 8 nPa) or the IMF is purely northward, the dayside magnetosphere does not disappear. The results provide a clear physical image of DDM events from a 3‐D perspective. 

The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impedes model accountability and inadvertently abets inappropriate or misuse of models. Recently, model cards, a proposal for model documentation, have attracted notable attention, but their impact on the actual practice is unclear. In this work, we systematically study the model documentation in the field and investigate how to encourage more responsible and accountable documentation practice. Our analysis of publicly available model cards reveals a substantial gap between the proposal and the practice. We then design a tool named DocML aiming to (1) nudge the data scientists to comply with the model cards proposal during the model development, especially the sections related to ethics, and (2) assess and manage the documentation quality. A lab study reveals the benefit of our tool towards long-term documentation quality and accountability. 

Early euarthropod evolution involved a major transition from lobopodian-like taxa to organisms featuring a segmented, well-sclerotized trunk (arthrodization) and limbs (arthropodization). However, the precise origin of a completely arthrodized trunk and arthropodized ventral biramous appendages remain controversial, as well as the early onset of anterior–posterior limb differentiation in stem-group euarthropods. New fossil material and micro-computed tomography inform the detailed morphology of the arthropodized biramous appendages in the carapace-bearing euarthropod Isoxys curvirostratus from the early Cambrian Chengjiang biota. In addition to well-developed grasping frontal appendages, I. curvirostratus possesses two batches of morphologically and functionally distinct biramous limbs. The first batch consists of four pairs of short cephalic appendages with robust endites with a feeding function, whereas the second batch has more elongate trunk appendages for locomotion. Critically, our new material shows that the trunk of I. curvirostratus was not arthrodized. The results of our phylogenetic analyses recover isoxyids as some of the earliest branching sclerotized euarthropods, and strengthens the hypothesis that arthropodized biramous appendages evolved before full body arthrodization. 

Data scientists reportedly spend 60 to 80 percent of their time in their daily routines on data wrangling, i.e. cleaning data and extracting features. However, data wrangling code is often repetitive and error-prone to write. Moreover, it is easy to introduce subtle bugs when reusing and adopting existing code, which result not in crashes but reduce model quality. To support data scientists with data wrangling, we present a technique to generate interactive documentation for data wrangling code. We use (1) program synthesis techniques to automatically summarize data transformations and (2) test case selection techniques to purposefully select representative examples from the data based on execution information collected with tailored dynamic program analysis. We demonstrate that a JupyterLab extension with our technique can provide documentation for many cells in popular notebooks and find in a user study that users with our plugin are faster and more effective at finding realistic bugs in data wrangling code. 

Software projects produce large quantities of data such as feature requests, requirements, design artifacts, source code, tests, safety cases, release plans, and bug reports. If leveraged effectively, this data can be used to provide project intelligence that supports diverse software engineering activities such as release planning, impact analysis, and software analytics. However, project stakeholders often lack skills to formulate complex queries needed to retrieve, manipulate, and display the data in meaningful ways. To address these challenges we introduce TiQi, a natural language interface, which allows users to express software-related queries verbally or written in natural language. TiQi is a web-based tool. It visualizes available project data as a prompt to the user, accepts Natural Language (NL) queries, transforms those queries into SQL, and then executes the queries against a centralized or distributed database. Raw data is stored either directly in the database or retrieved dynamically at runtime from case tools and repositories such as Github and Jira. The transformed query is visualized back to the user as SQL and augmented UML, and raw data results are returned. Our tool demo can be found on YouTube at the following link:http://tinyurl.com/TIQIDemo.