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We devise a pattern-aware feedback framework for representing the forced climate response using a suite of Green's function experiments with solar radiation perturbations. By considering the column energy balance, a comprehensive linear response function (CLRF) forimportant climate variables and feedback quantities such as moist static energy, sea surface temperature, albedo, cloud optical depth, and lapse rate is learned from Green's function data. The learned CLRF delineates the effects of the energy diffusion in both the ocean and atmosphere and the pattern-aware feedbacks from the aforementioned radiatively active processes. The CLRF can then be decomposed into forcing-response mode pairs which are in turn used to construct a reduced-order model (ROM) describing the dominant dynamics of climate responses. These mode pairs capture nonlocal effects and teleconnections in the climate and thus, make the ROM apt for capturing regional features of climate change response. A key observation is that the CLRF captures the polar amplified response as the most excitable mode of the climate system and this mode is explainable in the data-learned pattern-aware feedback framework. The ROM can be used for predicting the response for a given forcing and for reconstructing the forcing from a given response; we demonstrate these capabilities for independent forcing pattern.

Nitrogen-vacancy (NV) centers, atomic spin defects in diamond, represent an active contender for advancing transformative quantum information science (QIS) and innovations. One of the major challenges for designing NV-based hybrid systems for QIS applications results from the difficulty of realizing local control of individual NV spin qubits in a scalable and energyefficient way. To address this bottleneck, we introduce magnetic tunnel junction (MTJ) devices to establish coherent driving of an NV center by a resonant MTJ with voltage controlled magnetic anisotropy. We show that the oscillating magnetic stray field produced by a resonant micromagnet can be utilized to effectively modify and drive NV spin rotations when the NV frequency matches the corresponding resonance conditions of the MTJ. Our results present a new pathway to achieve all-electric control of an NV spin qubit with reduced power consumption and improved solid-state scalability for implementing cutting-edge QIS technological applications.

Data-serialization libraries are essential tools in software development, responsible for converting between programmable data structures and data persistence formats. Among them, JSON is the most popular choice for exchanging data between different systems and programming languages, while JSON libraries serve as the programming toolkit for this task. Despite their widespread use, bugs in JSON libraries can cause severe issues such as data inconsistencies and security vulnerabilities. Unit test generation techniques are widely adopted to identify bugs in various libraries. However, there is limited systematic testing effort specifically for exposing bugs within JSON libraries in industrial practice. In this paper, we propose JSONTestGen, an approach leveraging large language models (LLMs) to generate unit tests for fastjson2, a popular open source JSON library from Alibaba. Pre-trained on billions of open-source text and code corpora, LLMs have demonstrated remarkable abilities in programming tasks. Based on historical bug-triggering unit tests, we utilize LLMs to generate more diverse test cases by incorporating JSON domain-specific mutation rules. To systematically and efficiently identify potential bugs, we adopt differential testing on the results of the generated unit tests. Our evaluation shows that JSONTestGen outperforms existing test generation tools in unknown defect detection. With JSONTestGen, we found 34 real bugs in fastjson2, 30 of which have already been fixed, including 12 non-crashing bugs. While manual inspection reveals that LLM-generated tests can be erroneous, particularly with self-contradictory assertions, we demonstrate that LLMs have the potential for classifying false-positive test failures. This suggests a promising direction for improved test oracle automation in the future.

Twist engineering of magnetic van der Waals (vdW) moir\'e superlattices provides an attractive way to achieve precise nanoscale control over the spin degree of freedom on two-dimensional flatland. Despite the very recent demonstrations of moir\'e magnetism featuring exotic phases with noncollinear spin order in twisted vdW magnet chromium triiodide CrI3, the local magnetic interactions, spin dynamics, and magnetic phase transitions within and across individual moir\'e supercells remain elusive. Taking advantage of a scanning single-spin magnetometry platform, here we report observation of two distinct magnetic phase transitions with separate critical temperatures within a moir\'e supercell of small-angle twisted double trilayer CrI3. By measuring temperature dependent spin fluctuations at the coexisting ferromagnetic and antiferromagnetic regions in twisted CrI3, we explicitly show that the Curie temperature of the ferromagnetic state is higher than the N\'eel temperature of the antiferromagnetic one by ~10 K. Our mean-field calculations attribute such a spatial and thermodynamic phase separation to the stacking order modulated interlayer exchange coupling at the twisted interface of the moir\'e superlattices. The presented results highlight twist engineering as a promising tuning knob to realize on-demand control of not only the nanoscale spin order of moir\'e quantum matter but also its dynamic magnetic responses, which may find relevant applications in developing transformative vdW electronic and magnetic devices.

As the importance of comprehensive evaluation in workshop courses increases, there is a growing demand for efficient and fair assessment methods that reduce the workload for faculty members. This paper presents an evaluation conducted with Large Language Models (LLMs) using actual student essays in three scenarios: 1) without providing guidance such as rubrics, 2) with pre-specified rubrics, and 3) through pairwise comparison of essays. Quantitative analysis of the results revealed a strong correlation between LLM and faculty member assessments in the pairwise comparison scenario with pre-specified rubrics, although concerns about the quality and stability of evaluations remained. Therefore, we conducted a qualitative analysis of LLM assessment comments, showing that: 1) LLMs can match the assessment capabilities of faculty members, 2) variations in LLM assessments should be interpreted as diversity rather than confusion, and 3) assessments by humans and LLMs can differ and complement each other. In conclusion, this paper suggests that LLMs should not be seen merely as assistants to faculty members but as partners in evaluation committees and outlines directions for further research.

Workshop courses designed to foster creativity are gaining popularity. However, even experienced faculty teams find it challenging to realize a holistic evaluation that accommodates diverse perspectives. Adequate deliberation is essential to integrate varied assessments, but faculty often lack the time for such exchanges. Deriving an average score without discussion undermines the purpose of a holistic evaluation. Therefore, this paper explores the use of a Large Language Model (LLM) as a facilitator to integrate diverse faculty assessments. Scenario-based experiments were conducted to determine if the LLM could integrate diverse evaluations and explain the underlying pedagogical theories to faculty. The results were noteworthy, showing that the LLM can effectively facilitate faculty discussions. Additionally, the LLM demonstrated the capability to create evaluation criteria by generalizing a single scenario-based experiment, leveraging its already acquired pedagogical domain knowledge., Comment: The final version appears in the proceedings of the 32nd International Conference on Computers in Education (ICCE 2024)

Optically active spin defects in wide band-gap semiconductors serve as a local sensor of multiple degrees of freedom in a variety of "hard" and "soft" condensed matter systems. Taking advantage of the recent progress on quantum sensing using van der Waals (vdW) quantum materials, here we report direct measurements of spin waves excited in magnetic insulator Y3Fe5O12 (YIG) by boron vacancy $V_B^-$ spin defects contained in few-layer thick hexagonal boron nitride nanoflakes. We show that the ferromagnetic resonance and parametric spin excitations can be effectively detected by $V_B^-$ spin defects under various experimental conditions through optically detected magnetic resonance measurements. The off-resonant dipole interaction between YIG magnons and $V_B^-$ spin defects is mediated by multi-magnon scattering processes, which may find relevant applications in a range of emerging quantum sensing, computing, and metrology technologies. Our results also highlight the opportunities offered by quantum spin defects in layered two-dimensional vdW materials for investigating local spin dynamic behaviors in magnetic solid-state matters.

Government development projects vary significantly from private sector initiatives in scope, stakeholder complexity, and regulatory requirements. There is a lack of empirical studies focusing on requirements engineering (RE) activities specifically for government projects. We addressed this gap by conducting a series of semi-structured interviews with 12 professional software practitioners working on government projects. These interviewees are employed by two types of companies, each serving different government departments. Our findings uncover differences in the requirements elicitation phase between government projects, particularly for data visualization aspects, and other software projects, such as stakeholders and policy requirements. Additionally, we explore the coverage of human and social aspects in requirements elicitation, finding that culture, team dynamics, and policy implications are critical considerations. Our findings also pinpoint the main challenges encountered during the requirements elicitation phase for government projects. Our findings highlight future research work that is important to bridge the gap in RE activities for government software projects.

Aerosol-cloud interactions (ACI) pose the largest uncertainty for climate projections. Among many challenges of understanding ACI, the question of whether ACI is deterministic or stochastic has not been explicitly formulated and asked. Here we attempt to answer this question by predicting cloud droplet number concentration Nc from aerosol number concentration Na and ambient conditions. We use aerosol properties, vertical velocity fluctuation w', and meteorological states (temperature T and water vapor mixing ratio q_v) from the ACTIVATE field observations (2020 to 2022) as predictor variables to estimate Nc. We show that the climatological Nc can be successfully predicted using a machine learning model despite the strongly nonlinear and multi-scale nature of ACI. However, the observation-trained machine learning model fails to predict Nc in individual cases while it successfully predicts Nc of randomly selected data points that cover a broad spatiotemporal scale, suggesting the stochastic nature of ACI at fine spatiotemporal scales.

Effective control and readout of qubits form the technical foundation of next-generation, transformative quantum information sciences and technologies. The nitrogen-vacancy (NV) center, an intrinsic three-level spin system, is naturally relevant in this context due to its excellent quantum coherence, high fidelity of operations, and remarkable functionality over a broad range of experimental conditions. It is an active contender for the development and implementation of cutting-edge quantum technologies. Here, we report magnetic domain wall motion driven local control and measurements of NV spin properties. By engineering the local magnetic field environment of an NV center via nanoscale reconfigurable domain wall motions, we show that NV photoluminescence, spin level energies, and coherence time can be reliably controlled and correlated to the magneto-transport response of a magnetic device. Our results highlight the electrically tunable dipole interaction between NV centers and nanoscale magnetic structures, providing an attractive platform to realize interactive information transfer between spin qubits and non-volatile magnetic memory in hybrid quantum spintronic systems., Comment: 13 pages, 5 figures

van der Waals (vdW) magnets, an emerging family of two-dimensional (2D) materials, have received tremendous attention due to their rich fundamental physics and significant potential for cutting-edge technological applications. In contrast to the conventional bulk counterparts, vdW magnets exhibit significant tunability of local material properties, such as stacking engineered interlayer coupling and layer-number dependent magnetic and electronic interactions, which promise to deliver previously unavailable merits to develop multifunctional microelectronic devices. As a further ingredient of this emerging topic, here we report nanoscale quantum sensing and imaging of atomically thin vdW magnet chromium thiophosphate CrPS4, revealing its characteristic layer-dependent 2D static magnetism and dynamic spin fluctuations. We also show a large tunneling magnetoresistance in CrPS4-based spin filter vdW heterostructures. The excellent material stability, robust strategy against environmental degradation, in combination with tailored magnetic properties highlight the potential of CrPS4 in developing state-of-the-art 2D spintronic devices for next-generation information technologies.

Moir\'e magnetism featured by stacking engineered atomic registry and lattice interactions has recently emerged as an appealing quantum state of matter at the forefront condensed matter physics research. Nanoscale imaging of moir\'e magnets is highly desirable and serves as a prerequisite to investigate a broad range of intriguing physics underlying the interplay between topology, electronic correlations, and unconventional nanomagnetism. Here we report spin defect-based wide-field imaging of magnetic domains and spin fluctuations in twisted double trilayer (tDT) chromium triiodide CrI3. We explicitly show that intrinsic moir\'e domains of opposite magnetizations appear over arrays of moir\'e supercells in low-twist-angle tDT CrI3. In contrast, spin fluctuations measured in tDT CrI3 manifest little spatial variations on the same mesoscopic length scale due to the dominant driving force of intralayer exchange interaction. Our results enrich the current understanding of exotic magnetic phases sustained by moir\'e magnetism and highlight the opportunities provided by quantum spin sensors in probing microscopic spin related phenomena on two-dimensional flatland.

Personas are crucial in software development processes, particularly in agile settings. However, no effective tools are available for generating personas from user feedback in agile software development processes. To fill this gap, we propose a novel tool that uses the GPT-4 model and knowledge graph to generate persona templates from well-processed user feedback, facilitating requirement analysis in agile software development processes. We developed a tool called PersonaGen. We evaluated PersonaGen using qualitative feedback from a small-scale user study involving student software projects. The results were mixed, highlighting challenges in persona-based educational practice and addressing non-functional requirements.

An earlier study evaluating the dust life cycle in the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1) has revealed that the simulated global mean dust lifetime is substantially shorter when higher vertical resolution is used, primarily due to significant strengthening of dust dry removal in source regions. This paper demonstrates that the sequential splitting of aerosol emissions, dry removal, and turbulent mixing in the model's time integration loop, especially the calculation of dry removal after surface emissions and before turbulent mixing, is the primary reason for the vertical resolution sensitivity reported in that earlier study. Based on this reasoning, we propose a simple revision to the numerical process coupling scheme, which moves the application of the surface emissions to after dry removal and before turbulent mixing. The revised scheme allows newly emitted particles to be transported aloft by turbulence before being removed from the atmosphere, and hence better resembles the dust life cycle in the real world. Sensitivity experiments are conducted and analyzed to evaluate the impact of the revised coupling on the simulated aerosol climatology in EAMv1.