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Atmospheric Methane Removal as a Third Climate Intervention: Termination Risks and Air Pollutant Effects

Physics > Atmospheric and Oceanic Physics [Submitted on 24 Jan 2026 (v1), last revised 29 May 2026 (this version, v3)] Title:Atmospheric Methane Removal as a Third Climate Intervention: Termination Risks and Air Pollutant Effects View PDFAbstract:Atmospheric Methane Removal (AMR) is a third class of climate intervention, along with Carbon Dioxide Removal (CDR) and Solar Radiation Management (SRM). We show that, unlike CDR, the avoided warming by AMR is not durable due to methane's short...

arXiv Physics 8d ago

Goldfish: Monolingual Language Models for 350 Languages

Announce Type: replace Abstract: For many low-resource languages, the only available language models are large multilingual models trained on many languages simultaneously. Despite state-of-the-art performance on reasoning tasks, we find that these models still struggle with basic grammatical text generation in many languages. First, large multilingual models perform worse than bigrams for many languages (e.g. 24% of languages in XGLM 4.5B; 43% in BLOOM 7.1B) using FLORES perplexity as an...

arXiv CS 9d ago

Gooey: A GPU-accelerated UI framework for Zig

A GPU-accelerated UI framework for Zig, targeting macOS (Metal), Linux (Vulkan/Wayland), and Browser (WASM/WebGPU). Join the Gooey discord Early Development: API is evolving. Example app built with Gooey — chat-zig, an Anthropic Claude client using the Zig 0.16 std.

Hacker News 6d ago

MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts

arXiv:2606.00967v3 Announce Type: replace Abstract: Generative models for volumetric medical images have found many applications in medical imaging, ranging from data augmentation to serving as priors for inverse problems. For these applications, generating high-resolution 3D images with strong controllability is essential but remains highly challenging. Existing approaches typically control generation either through radiology reports used as text prompts or through full image segmentation.

arXiv CS 1d ago

Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review

arXiv:2601.00990v3 Announce Type: replace-cross Abstract: Fetal ultrasound is the cornerstone of antenatal care, and accurate recognition of a small set of standard anatomical planes underpins biometry, growth surveillance, and detection of structural anomalies. Deep learning classifiers now match or exceed expert accuracy on curated benchmarks, but most remain opaque and miscalibrated, leaving clinicians without the calibrated confidence or faithful explanations needed for safe decision...

arXiv CS 7d ago

Hallucination Detection-Guided Preference Optimization for Clinical Summarization

arXiv:2605.28910v3 Announce Type: replace Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications. We introduce Hallucination Detection Guided Self-Refinement (HDSR), an inference-time method that leverages hallucination detectors to guide iterative summary revisions toward factual corrections. Building on this, we...

arXiv CS 7d ago

A Matter Wi-Fi Light Bulb in Rust on the Raspberry Pi Pico 2 W

This repository contains examples for the Raspberry Pi Pico 2 (RP2350) board, written in Rust using the Embassy async framework. cargo generate --git https://github.com/ImplFerris/pico2-template.git --name rust-rpico2-embassy-examples Board: Raspberry Pi Pico 2 - MCU: RP2350 (Dual-core Arm Cortex-M33 and RISC-V cores) - On-board peripherals: - LED on GPIO25 - I2C pins: - I2C0 SDA: GPIO4 - I2C0 SCL: GPIO5 - I2C1 SDA: GPIO2 - I2C1 SCL: GPIO3 - UART pins: - UART0 TX: GPIO0, UART0 RX: GPIO1 -...

Hacker News 2d ago

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models

Announce Type: replace Abstract: Learning-to-defer (L2D) routes each decision to a system's own predictor or to an external expert. Streaming time-series settings break the offline-L2D assumptions: the data are non-stationary, expert availability shifts over time, and the internal predictor is trained online. We propose L2D-SLDS, a one-stage online L2D framework based on a factorized switching linear-Gaussian state-space model over all potential residuals: a discrete regime, a shared global...

arXiv CS 9d ago

Simple is Better: Multiplication May Be All You Need for LLM Request Scheduling

Announce Type: replace Abstract: High-quality LLM request scheduling requires meeting two key objectives: ensuring the routed instance has KVCache to accelerate request execution, and ensuring that the workload is balanced across instances. Achieving both objectives is challenging because pursuing one may compromise the other. Current approaches use various combinators (e.g., linear combinations) to compute a scheduling score that combines indicators for the two objectives.

arXiv CS 2d ago