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642 papers
This study examined whether introductory Qiskit homework could remain autogradable while requiring students to run, review, and discuss results rather than banning AI.
A descriptive model of agent-documentation interaction is derived as a two-lobed cycle rather than a linear journey, and it is shown that two widely assumed properties of"agent-friendly"documentation - actionability and verifiability - lack consistent behavioural support.
This work presents a modern extraction of deeply virtual Compton scattering (DVCS) amplitudes off the proton and off the neutron through a global analysis of experimental data. These amplitudes serve as a crucial intermediate quantity linking generalized parton distributions (GPDs) to experimental observables. The analysis relies on a novel extraction framework recently integrated into the PARTONS software ecosystem. We employ two distinct modelling approaches for the DVCS amplitudes, both utilizing machine learning techniques: one model-agnostic and the other theory-augmented. For the first time, we incorporate the extraction of helicity-flip amplitudes, which are particularly sensitive to higher-twist effects. Furthermore, we compare our extracted amplitudes to timelike Compton scattering (TCS) data using the established relations between DVCS and TCS, serving as an important test of universality of GPDs. We also extract the DVCS subtraction constant, for which ab initio predictions from lattice QCD have been obtained. This allows us to confront these predictions with DVCS data for the first time, initiating a new virtuous cycle between first-principles theory and phenomenology.
Accuracy and intolerance to interference of GNSS receivers depend on the efficiency of tracking modules. To achieve better characteristics, designers should implement various issues, affecting the process of tracking in these modules: integration interval, spacing and configuration parameters of PLL and DLL. The article discusses practical issues concerning implementation of tracking modules. Using the obtained results, it is possible to improve transient characteristics of tracking modules. These issues were tested in an experimental navigation receiver. The results were obtained using the simulation software, developed by the authors, as well as Vivado simulator and experimental testing.
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This work proposes BreakGuard, an approach that generates a test suite to detect breaking changes in clients and successfully detected BCs from different library categories, but finds LLM-generated tests to be more reliable for detecting crash-type breaking changes as opposed to behavioural BCs.
To address fatigue failure, weld cracking and resonance in steel-frame pedestals caused by mechanical vibration, this study proposes a composite steel frame fabricated by integrating concave hexagonal negative-Poisson’s-ratio honeycomb structures with I-shaped steel, aiming to achieve the integrated structural function of load bearing and vibration damping. Bending and vibration tests are carried out on both conventional and composite steel frames to compare their static load-bearing characteristics and dynamic vibration-damping effects. Meanwhile, a finite-element model is established based on the Abaqus software platform to explore the regulation mechanism of honeycomb geometric parameters on the composite frame’s performance. Results show the composite frame realises ‘load-bearing–vibration-damping’ synergy, with yield strength basically consistent with conventional frames and excellent low-frequency vibration energy dissipation capacity. Honeycomb arrangement position and cell thickness mainly regulate load-bearing performance, while layer number and cell thickness have a significant impact on damping. After parameter optimisation, the maximum vibration level difference of the composite frame peaks at 62.81 dB, providing new design ideas and technical support for steel frame performance optimisation under low-frequency vibration environments.
Repo0 is presented, a continuous structural evolution framework for zero-to-all code generation that maintains an explicit architectural state instantiated as a Dual-Directed-Acyclic-Graph (Dual-DAG), consisting of a requirement-level DAG, a component-level DAG, and their alignment relation.
This work presents an end-to-end, deployment-aware testing pipeline for IoT-based automotive applications that combines requirement-driven test and code generation with large language model (LLM) and vision-language model (VLM) assistance, and human-in-the-loop curation to reduce manual effort and improve consistency.
An audit-and-placebo protocol is proposed that separates verifier artifacts, interaction scaffolding, and grounded feedback credit in evaluations of self-evolving test generators in evaluations of self-evolving test generators.
In his latest book, Professor Eugene Fitzgerald examines the forces that turn breakthroughs into value — and why innovation resists simple formulas.
MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
Our latest voice model has improved precision and lower latency to make voice interactions more fluid, natural and precise.
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