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The year in rainforests 2025: Deforestation fell; the risks did not https://news.mongabay.com/2025/12/the-year-in-rainforests-2025-deforestation-fell-the-risks-did-not/

The year in rainforests 2025: Deforestation fell; the risks did not
The story of the world’s tropical forests in 2025 was not one of dramatic reversal, but one shaped by accumulated pressure. In several regions, deforestation slowed. In others, loss continued in less visible forms, shaped by fire, degradation, and political choices not limited to large-scale clearing alone. Governments continued to speak the language of protection, […]

"Two years later, the same BYTE Magazine asked on its January 1997 cover: “Can Java Replace Windows?” (Spoiler alert 2: it did not, and… seriously?) Reflecting the myopic insanity of the warlords commanding troops from the comfort of their bunkers, Scott McNealy’s interview on page 40 is just a puzzling diatribe against Microsoft PowerPoint instead of an actual argument in favor of Java."

Aftermath Of The Kernel Wars
During the 1980s and 90s, computer companies waged an open war for supremacy. We are witnesses of an age that could be described as post-apocalyptic. The dust has settled for the past 20 years, with a small ignition of hostilities around 2010, between members of the mobile duopoly and some late challengers. But to a large degree, we can say that the Kernel Wars are over, and we are privileged observers of their aftermath. But everyone was surprised to discover the winner emerging from the ashes…

Testing the Impact of Planet-stirring, Self-stirring, and Mixed-stirring on Debris Disc Architecture: A Case Study of HD 16743
Jonathan P. Marshall, Marco A. Mu\~noz-Guti\'errez, Antranik A. Sefilian, Antonio Peimbert
https://arxiv.org/abs/2509.22822

Testing the Impact of Planet-stirring, Self-stirring, and Mixed-stirring on Debris Disc Architecture: A Case Study of HD 16743
Dynamical interactions between planets and debris discs can excite the orbits of embedded planetesimals to such a degree that a collisional cascade is triggered, generating detectable amounts of dust. Millimetre wavelength observations are sensitive to emission from large and cold dust grains, which are unperturbed by radiation forces and act as a proxy for the location of the planetesimals. The influence of unseen planetary companions on debris discs can be inferred with high-resolution imagin…

Trace Element Behavior during Shock Transformation of Zircon to Reidite
A. A. Shiryaev, A. N. Zhukov, V. V. Yakushev, A. A. Averin, V. O. Yapaskurt, A. Yu. Borisova, A. Yu. Bychkov, O. G. Safonov, I. V. Lomonosov
https://arxiv.org/abs/2509.23446

Trace Element Behavior during Shock Transformation of Zircon to Reidite
Large single crystals of natural zircon were shock-loaded at 13.6 and 51.3 GPa in planar geometry. No structural changes were observed after loading at 13.6 GPa. Loading to 51.3 GPa resulted in zircon transformation to a denser scheelite-structured phase, reidite. The investigation of reidite samples by X-ray diffraction, Raman, photo- and cathodoluminescence spectroscopies revealed segregation of some trace cations (such as REE) on planar defects during the transformation. The segregation has …

Beyond the Clinic: A Large-Scale Evaluation of Augmenting EHR with Wearable Data for Diverse Health Prediction
Will Ke Wang, Rui Yang, Chao Pang, Karthik Natarajan, Nan Liu, Daniel McDuff, David Slotwiner, Fei Wang, Xuhai Orson Xu
https://arxiv.org/abs/2509.22920

Beyond the Clinic: A Large-Scale Evaluation of Augmenting EHR with Wearable Data for Diverse Health Prediction
Electronic health records (EHRs) provide a powerful basis for predicting the onset of health outcomes. Yet EHRs primarily capture in-clinic events and miss aspects of daily behavior and lifestyle containing rich health information. Consumer wearables, by contrast, continuously measure activity, heart rate, and sleep, and more, offering complementary signals that can fill this gap. Despite this potential, there has been little systematic evaluation of the benefit that wearable data can bring to …

GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures
Jackson Loth, Pedro Sarmento, Saurjya Sarkar, Zixun Guo, Mathieu Barthet, Mark Sandler
https://arxiv.org/abs/2509.22655

GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures
In recent years, the guitar has received increased attention from the music information retrieval (MIR) community driven by the challenges posed by its diverse playing techniques and sonic characteristics. Mainly fueled by deep learning approaches, progress has been limited by the scarcity and limited annotations of datasets. To address this, we present the Guitar On Audio and Tablatures (GOAT) dataset, comprising 5.9 hours of unique high-quality direct input audio recordings of electric guitar…

SolContractEval: A Benchmark for Evaluating Contract-Level Solidity Code Generation
Zhifan Ye, Jiachi Chen, Zhenzhe Shao, Lingfeng Bao, Xiaohu Yang, Zhongxin Liu
https://arxiv.org/abs/2509.23824

SolContractEval: A Benchmark for Evaluating Contract-Level Solidity Code Generation
The rise of blockchain has brought smart contracts into mainstream use, creating a demand for smart contract generation tools. While large language models (LLMs) excel at generating code in general-purpose languages, their effectiveness on Solidity, the primary language for smart contracts, remains underexplored. Solidity constitutes only a small portion of typical LLM training data and differs from general-purpose languages in its version-sensitive syntax and limited flexibility. These factors…

Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective
Maryam Babazadeh, Naim Bajcinca
https://arxiv.org/abs/2509.24598 https://arxi…

Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective
This paper presents a new method for achieving dynamic consensus in linear discrete-time homogeneous multi-agent systems (MAS) with marginally stable or unstable dynamics. The guarantee of consensus in this setting involves a set of constraints based on the graph's spectral properties, complicating the design of the coupling gains. This challenge intensifies for large-scale systems with diverse graph Laplacian spectra. The proposed approach reformulates the dynamic consensus problem with a pres…

StrucADT: Generating Structure-controlled 3D Point Clouds with Adjacency Diffusion Transformer
Zhenyu Shu, Jiajun Shen, Zhongui Chen, Xiaoguang Han, Shiqing Xin
https://arxiv.org/abs/2509.23709

StrucADT: Generating Structure-controlled 3D Point Clouds with Adjacency Diffusion Transformer
In the field of 3D point cloud generation, numerous 3D generative models have demonstrated the ability to generate diverse and realistic 3D shapes. However, the majority of these approaches struggle to generate controllable 3D point cloud shapes that meet user-specific requirements, hindering the large-scale application of 3D point cloud generation. To address the challenge of lacking control in 3D point cloud generation, we are the first to propose controlling the generation of point clouds by…

Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM
Biyao Zhang, Mingkai Zheng, Debargha Ganguly, Xuecen Zhang, Vikash Singh, Vipin Chaudhary, Zhao Zhang
https://arxiv.org/abs/2509.22832

Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM
Training Large Language Models(LLMs) is one of the most compute-intensive tasks in high-performance computing. Predicting end-to-end training time for multi-billion parameter models distributed across hundreds of GPUs remains challenging due to complex interactions between transformer components, parallelism strategies(data, model, pipeline, tensor), and multi-tier communication. Learned models require costly sampling, while analytical models often struggle with real-world network and hardware …