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In this paper, we investigate LLM-assisted code generation using architectural documentation as a first-class input. Building on prior work that guides LLMs through Attribute-Driven Design (ADD), we propose a workflow in which the LLM synthesizes an implementation plan from requirements and an architecture document, and then generates code with human oversight. Using a ReAct-style agent as a case study, we perform three experiments and evaluate architectural conformance and functional correctness of a primary use case. In addition we capture size and modularity metrics using the DV8 tool. Our results show that incorporating architectural documentation substantially improves conformance and that adding an explicit implementation plan further enhances functional correctness and modularity. 

As one of the most primitive operators in graph algorithms, such as the triangle counting, maximal clique enumeration, and subgraph listing, a set intersection operator returns common vertices between any two given sets of vertices in data graphs. It is therefore very important to accelerate the set intersection, which will benefit a bunch of tasks that take it as a built-in block. Existing works on the set intersection usually followed the merge intersection or galloping-search framework, and most optimization research focused on how to leverage the SIMD hardware instructions. In this paper, we propose a novel multi-level set intersection framework, namely hierarchical set partitioning and join (HERO), by using our well-designed set intersection bitmap tree (SIB-tree) index, which is independent of SIMD instructions and completely orthogonal to the merge intersection framework. We recursively decompose the set intersection task into small-sized subtasks and solve each subtask using bitmap and boolean AND operations. To sufficiently achieve the acceleration brought by our proposed intersection approach, we formulate a graph reordering problem, prove its NP-hardness, and then develop a heuristic algorithm to tackle this problem. Extensive experiments on real-world graphs have been conducted to confirm the efficiency and effectiveness of our HERO approach. The speedup over classic merge intersection achieves up to 188x and 176x for triangle counting and maximal clique enumeration, respectively. 

Aims.We present optical, ultraviolet, and X-ray observations of supernova (SN) 2024iss, a Type IIb SN that shows a prominent double-peaked light curve. Methods.We modeled the first peak with a semianalytical shock-cooling model and the X-ray emission with a free-free model. We also compared the envelope radius and mass-loss rate with those of other Type IIb SNe to explore the relationships between the progenitor envelope and the circumstellar material. Results.The shock-cooling peak in theV-band light curve reachedM_V = −17.33 ± 0.26 mag, while the⁵⁶Ni-powered second peak attainedM_V = −17.43 ± 0.26 mag. Early spectra show a photospheric velocity of approximately 19 400 km s⁻¹at 3.82 days from the HαP Cygni profile. The Balmer lines persist for at least more than 87 days after the explosion, which is characteristic of hydrogen-rich ejecta. Modeling the first light-curve peak with the shock-cooling model suggests an extended hydrogen envelope with a mass of 0.11 ± 0.04 M_⊙and a radius of 244 ± 43 R_⊙. Fitting the second light-curve peak with an Arnett-like model indicates a typical⁵⁶Ni mass of 0.117 ± 0.013 M_⊙and a relatively low ejecta mass of 1.27 ± 0.34 M_⊙. X-ray observations revealed bright thermal bremsstrahlung emission and indicate a mass-loss rate of 1.6 × 10⁻⁵M_⊙yr⁻¹, which is similar to that of SN 1993J. Conclusions.Supernova 2024iss occupies a transitional position between the two subclasses of extended and compact Type IIb SNe. Its envelope radius and preexplosion mass-loss rate appear to be consistent with the correlation observed in the broader sample. The observational properties of SN 2024iss are compatible with a binary-interaction scenario being the dominant mechanism for envelope stripping. 

Numerous applications in machine learning and data analytics can be formulated as equilibrium computation over Riemannian manifolds. Despite the extensive investigation of their Euclidean counterparts, the performance of Riemannian gradient-based algorithms remain opaque and poorly understood. We revisit the original scheme of Riemannian gradient descent (RGD) and analyze it under a geodesic monotonicity assumption, which includes the well-studied geodesically convex-concave min-max optimization problem as a special case. Our main contribution is to show that, despite the phenomenon of distance distortion, the RGD scheme, with a step size that is agnostic to the manifold's curvature, achieves a curvature-independent and linear last-iterate convergence rate in the geodesically strongly monotone setting. To the best of our knowledge, the possibility of curvature-independent rates and/or last-iterate convergence in the Riemannian setting has not been considered before. 

ABSTRACT Atmospheric methane (CH₄) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. 

Abstract The plate tectonic cycle produces chemically distinct mid-ocean ridge basalts and arc volcanics, with the latter enriched in elements such as Ba, Rb, Th, Sr and Pb and depleted in Nb owing to the water-rich flux from the subducted slab. Basalts from back-arc basins, with intermediate compositions, show that such a slab flux can be transported behind the volcanic front of the arc and incorporated into mantle flow. Hence it is puzzling why melts of subduction-modified mantle have rarely been recognized in mid-ocean ridge basalts. Here we report the first mid-ocean ridge basalt samples with distinct arc signatures, akin to back-arc basin basalts, from the Arctic Gakkel Ridge. A new high precision dataset for 576 Gakkel samples suggests a pervasive subduction influence in this region. This influence can also be identified in Atlantic and Indian mid-ocean ridge basalts but is nearly absent in Pacific mid-ocean ridge basalts. Such a hemispheric-scale upper mantle heterogeneity reflects subduction modification of the asthenospheric mantle which is incorporated into mantle flow, and whose geographical distribution is controlled dominantly by a “subduction shield” that has surrounded the Pacific Ocean for 180 Myr. Simple modeling suggests that a slab flux equivalent to ~13% of the output at arcs is incorporated into the convecting upper mantle.