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FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data

Forest attributes are essential for national-scale resource monitoring. Airborne LiDAR metrics are among the auxiliary variables most strongly correlated with forest attributes used in National Forest Inventory (NFI) estimates. However, producing wall-to-wall predictions remains challenging when LiDAR data are acquired under heterogeneous conditions. As national LiDAR programs expand across Europe, variability in sensors, flight parameters, seasons, and scan angles limits the robustness of existing models, which are often calibrated for local conditions. We present FLORA (Forest LiDAR Octree R

Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions

The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional optimal transport along random projections. Standard estimators of the SW distance rely on Monte Carlo averages of one-dimensional Wasserstein distances computed via quantile functions, which require sorting projected samples and access to full datasets. In this work, we introduce a new class of estimators for the Sliced Wasserstein distance based on cumulative distribution functions (CDFs) of projected measures, that avoid sorting and scale via

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems

Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution during testing differs from the one assumed during training. Distributionally robust optimization (DRO) addresses this by optimizing against the worst-case distribution within a prescribed ambiguity set, but standard Wasserstein DRO perturbs the full joint distribution uniformly, which can be overly conservative and ignores the physics of the measurement process. We develop a structured DRO framework in which the ambiguity set is restricted to struc

A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families. Traditional single-model detection mechanisms often fail to generalize across such diverse data, leading to degraded performance, particularly on obfuscated and rare malware samples. In this work, we propose a unified multi-task malware analysis framework based on Mixture of Experts (MoE) architectures. The proposed system evaluates performance across two different input representations, i.e., high-dimensional EMBER feature s

Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters

Chain-of-thought (CoT) prompting improves LLM reasoning, but the source is contested: do the intermediate steps help because they carry useful semantic content, or because conditioning on more tokens buys extra computation before the model commits to an answer? We bring two lines of evidence to bear. First, in distribution: we repeatedly sample each model on the same question and pair a shorter with a longer of its own natural generations that follow the same reasoning plan, so nothing is rewritten and both traces are genuinely in-distribution. Across 25 models the extra tokens leave accuracy

Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads

In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them. Identifying which attention heads perform this synthesis matters for interpreting long-context model behavior. Yet existing detectors miss these heads by construction: they reward heads whose attended token matches the generated token, a literal-copy criterion that captures where a head reads but not what it writes through its output-value (OV) circuit, the very mechanism that carries non-literal retrieval. We introduce Logit-Contribution

Optimizing Visual Generative Models via Distribution-wise Rewards

Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degrades image diversity and introduces visual anomalies. To address these limitations, we present a novel framework that finetunes generative models using distribution-wise rewards, ensuring better alignment with real-world data distributions. Unlike rewards that evaluate samples individually, distribution-wise reward accounts for the data distribution of the samples, mitigating the mode collapse problem that occurs whe

MARVEL: Margin-Aware Robust von Mises-Fischer Expert Learning for Long-Tailed Out-of-Distribution Detection

For clinical deployment, it is essential that automated diagnostic systems remain reliable when confronted with previously unseen cases, yet deep models routinely misclassify out-of-distribution (OOD) inputs with high confidence, underscoring the need for more robust OOD detection methods. Although substantial effort has been devoted to improving model robustness, most of the existing literature assumes balanced datasets, evaluates OOD detection on coarse or non-clinical OOD sources, or lacks comprehensive assessment across diverse OOD scenarios. To address the gaps, we propose a novel methodo

Godot’s New Contributing Policy Adds Barriers For AI Slop - Hackaday

<a href="https://news.google.com/rss/articles/CBMikwFBVV95cUxNbmd3SUw1ZmZFWDJQV3hia1NSRThueVdLbm9tMzAtVkI3R3NTZFQxQU5kdmdJc1Z4SmxpaUp1MW51bmNsN3pVUENKQnJCS3BSU2p0Ty0wbTFXX3Z2bVlzNFg2b0JndGpfYm11XzBjaGtnSHZtTGJWdGxZeHhMaFY0OTZBby1WcksxZy1UQmpVd2QzalU?oc=5" target="_blank">Godot’s New Contributing Policy Adds Barriers For AI Slop</a>&nbsp;&nbsp;<font color="#6f6f6f">Hackaday</font>

Godot will no longer accept AI-authored code contributions

<p>Article URL: <a href="https://www.pcgamer.com/gaming-industry/open-source-game-engine-godot-will-no-longer-accept-ai-authored-code-contributions-we-cant-trust-heavy-users-of-ai-to-understand-their-code-enough-to-fix-it/">https://www.pcgamer.com/gaming-industry/open-source-game-engine-godot-will-no-longer-accept-ai-authored-code-contributions-we-cant-trust-heavy-users-of-ai-to-understand-their-code-enough-to-fix-it/</a></p> <p>Comments URL: <a href="https://news.ycombinator.com/item?id=4874347

DiScoFormer: One transformer for density and score, across distributions

Godot Tightens Contribution Policy to Restrict AI Code - Let's Data Science

<a href="https://news.google.com/rss/articles/CBMinAFBVV95cUxQRzR6b0dXb1hFeV9iS04ybzFpQzQ1Rkw3Z1NFNkpGWHM0THBSUEQ1TG41bzgxLWM3UHZKZndOOWNZa2VmR2lIQ3BxVmY4LVc2WjhJM3BoVTJpVi1Cc2hHME5KZmZGRkRTR01PZFBocDlGSGhsOXFZaDVwSjF5VlBEOHdTZFZ3SjhwM0tuVkhIRjUxc2tzNlc3LXZIdnk?oc=5" target="_blank">Godot Tightens Contribution Policy to Restrict AI Code</a>&nbsp;&nbsp;<font color="#6f6f6f">Let's Data Science</font>

toolset_distributions

Dependency/tool package detected from repository manifests (toolset_distributions).