Tootfinder

In what environmental advocates describe as
“another gift to the fossil fuel industry,”
🆘the Trump administration plans to end rules limiting the release of planet-heating emissions from power plants that burn coal and gas.
🔥Power plants are considered the largest industrial source of greenhouse gas emissions in the US and account for about a quarter of the nation’s climate pollution, according to the Environmental Protection Agency (EPA).
In 2024, the EPA under the B…

‘Actively malevolent’: Trump EPA to eviscerate rules on planet-heating pollution from power plants
“This is full-throated climate denial while the climate crisis happens in real time and a shocking betrayal of the American public.”

The Impact of Circumgalactic Rotation on Ly$\alpha$ Radiative Transfer
Hee-Gyeong Kym, Seok-Jun Chang, Max Gronke, Kwang-Il Seon
https://arxiv.org/abs/2609.08396 https://arxiv.org/pdf/2609.08396 https://arxiv.org/html/2609.08396
arXiv:2609.08396v1 Announce Type: new
Abstract: Hydrogen Lyman-alpha (Ly$\alpha$) is a prominent emission line from the circumgalactic medium (CGM). Due to its resonant nature, Ly$\alpha$ carries imprints of the physical properties and kinematics of the cold CGM. In particular, CGM rotation can modify the Ly$\alpha$ peak separation, which is often interpreted as a tracer of H I column density. We present 3D Monte Carlo Ly$\alpha$ radiative-transfer simulations in a CGM-like rotating medium and examine how the emergent spectra depend on rotational velocity ($V_{\rm rot}$), H I column density ($N_{\rm HI}$), viewing angle, clumpiness, and intrinsic source width. We find that rotation broadens the integrated spectra and increases the peak separation, with the strongest viewing-angle dependence when rotational Doppler shifts dominate over frequency diffusion. At high $N_{\rm HI}$, numerous scatterings reduce the sensitivity of integrated spectra to rotation, producing a degeneracy between $V_{\rm rot}$ and $N_{\rm HI}$. Consequently, Ly$\alpha$ peak separation alone can overestimate $N_{\rm HI}$ in a rotating medium. Spatially resolved halo spectra provide a clearer diagnostic: opposite sides of the rotating medium show systematic redshifted and blueshifted asymmetries associated with the line-of-sight velocity of the last-scattering gas. Such rotation-driven signatures can also contribute to velocity-map patterns often interpreted in terms of inflow or outflow, highlighting the need to consider rotation in spatially resolved Ly$\alpha$ observations. We further show that the main signatures persist in simple clumpy media, while the halo signatures are largely insensitive to the intrinsic source width. Our results demonstrate that spatially resolved Ly$\alpha$ observation is essential for disentangling CGM rotation from radiative-transfer effects.
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Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra
Teng-Ruei Chen
https://arxiv.org/abs/2608.11693 https://arxiv.org/pdf/2608.11693 https://arxiv.org/html/2608.11693
arXiv:2608.11693v1 Announce Type: new
Abstract: NVIDIA's published specifications give the Blackwell Ultra GPU (B300) a dense-compute ratio of roughly 30:1 between FP8 and INT8 tensor-core throughput; its predecessors, H200 and B200, both provide 1:1. We audit what this deprioritization means in practice by tracing INT8 W8A8 support through four layers of the stack: the published specifications, the PTX ISA, NVIDIA's CUTLASS kernel library, and the two major open-source LLM serving engines (vLLM and SGLang). We find a consistent, layered withdrawal: (i) the PTX ISA never exposes the fifth-generation tensor-core integer path (tcgen05.mma with .kind::i8) on sm_103a, even though the same PTX revision extends the FP4 kinds to that target, leaving legacy warp-level IMMA as the only architecturally legal integer tensor-core path on B300; (ii) CUTLASS's kernel generator explicitly skips INT8 UMMA generation for any build targeting 103a, while generating FP8 unconditionally; (iii) vLLM ships no INT8 GEMM for Blackwell and fails with a hard runtime error at the first forward pass, after the model has loaded; and (iv) SGLang's ahead-of-time INT8 GEMM stops at Sm90, while its FP8 tuning configurations already cover B200. We document an escape hatch (rerouting vLLM's INT8 path to a JIT-compiled Triton backend via an environment variable), a false-negative trap in the obvious profiler methodology for detecting "native INT8" on sm_103, and the practical failure semantics that make naive testing expensive. Together, these findings show that a quantization format's availability is a property of the whole stack rather than of the model or the spec sheet. Four distinct layers, three of them NVIDIA's own, withdrew INT8 support in mutually consistent ways, and a format that is nominally present on the datasheet is, by default, undeployable on this hardware.
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Crosslisted article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/2]:
- Interpolation of Microscale Stress and Strain Fields Based on Mechanical Models
Wenzhe Shan, Udo Nackenhorst
https://arxiv.org/abs/2104.09749
- Joint discovery of governing partial differential equations from multi-source datasets by competi...
Hao Xu, Siyu Lou, Yuntian Chen, Dongxiao Zhang
https://arxiv.org/abs/2606.30699 https://mastoxiv.page/@arXiv_csLG_bot/116843565775934283
- LinApart3: efficient algorithm for multivariate partial fraction decomposition with linear denomi...
L. Fek\'esh\'azy, A. Kardos
https://arxiv.org/abs/2606.30708 https://mastoxiv.page/@arXiv_hepph_bot/116843629511193799
- Introducing AuriGLOBES: the effect of compressive tides, compact object-induced mass loss, and si...
Pablo Contreras Guerra, Robert J. J. Grand, Marta Reina-Campos, Claudio Dalla Vecchia
https://arxiv.org/abs/2606.30746 https://mastoxiv.page/@arXiv_astrophGA_bot/116843732310368458
- Time-dependent adaptive mesh refinement solver for the Gross-Pitaevskii-Poisson equations
Iv\'an \'Alvarez-Rios
https://arxiv.org/abs/2606.30827 https://mastoxiv.page/@arXiv_astrophGA_bot/116843767701812779
- Computed materials proposals depart from the structural memory of experimental discovery
Dan Nguyen, Karen Cao, Brian Chu, Nick Lemoff, Paul Kienzle, William Ratcliff II
https://arxiv.org/abs/2606.30967 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/116843665854969922
- An Enhanced RPA-LDA Model for Ion Stopping Power from Cold Matter to High-Energy Density Plasmas:...
Thomas A. Mehlhorn, Ming Feng Gu, Igor Golovkin
https://arxiv.org/abs/2606.30978 https://mastoxiv.page/@arXiv_physicsplasmph_bot/116843597824839239
- Full-Wave Green's-Function Modeling of Collective Single-Photon Emission in Non-Markovian Open-Sy...
Hyunwoo Choi, Jisang Seo, Junwoo Gim, Bowoo Jang, Weng C. Chew, Dong-Yeop Na
https://arxiv.org/abs/2606.31317 https://mastoxiv.page/@arXiv_quantph_bot/116843791099390803
- Side-Chain Tuning of Thermal-Expansion Crossover in Metal-Organic Frameworks
Wei Qiu, Penghua Ying
https://arxiv.org/abs/2606.31417 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/116843748631678308
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Deepfake News Detection: A Multimodal Framework Integrating LipNet, DeepSpeech and ResNET for Enhanced Audio-Visual Analysis
Ameena Khan, Muhammad Ahsan Aziz, Muhammad Junaid Asif, Naeem Akhter, Rana Fayyaz Ahmad
https://arxiv.org/abs/2607.20579 https://arxiv.org/pdf/2607.20579 https://arxiv.org/html/2607.20579
arXiv:2607.20579v1 Announce Type: new
Abstract: Deepfake news refers to AI-generated (or AI ma-nipulated) multimedia content intentionally generated to deceive audiences by manipulating the facial expressions, or speech while maintaining the realistic appearance. The rapid progress of generative AI has made the synthesis of highly realistic fake videos and cloned voices widely accessible, posing a serious threat to the authenticity of digital news media. This paper presents a multi-modal framework that discerns the authenticity of video content by jointly exploiting audio and visual cues, thereby addressing the challenge of detecting the deepfake videos. We proposed a framework that involves features extraction from lip movements, audio content and video frames. Lip movements and speech content are encoded using the LipNet and DeepSpeech2 models, while facial features are extracted by leveraging the use of BlazeFace and represented with ResNet18. The extracted feature vectors are concatenated into a holistic video representation and classified with an ensemble of machine learning and deep learning models, including Random Forest (RF), Multi-layer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks. Exten-sive experiments performed on the FakeAVCeleb dataset shows that the proposed approach attains an accuracy of 94% using augmented audio features, outperforming a state-of-the-art multi-modal ensemble baseline. The results confirm the robustness and practical potential of the proposed framework for deepfake news detection.
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Learning Latent Memory States from Longitudinal Athlete Monitoring Data
Dae-Jin Lee
https://arxiv.org/abs/2608.06290 https://arxiv.org/pdf/2608.06290 https://arxiv.org/html/2608.06290
arXiv:2608.06290v1 Announce Type: new
Abstract: We propose a new unit of analysis for longitudinal data: the Latent Memory Table. The scientific contribution is not the encoder. It is that table, treated as a reusable statistical object on the same footing as a matrix of principal-component scores, a table of estimated random effects, or a table of predicted probabilities. We estimate a statistical table that summarizes recent longitudinal history and is intended to be stored, queried, analysed and reused throughout the statistical workflow. A memory operator maps each masked windowed history to a finite-dimensional state; collecting those states with uncertainty yields the Latent Memory Table. Validation is organized around six properties---recoverability, personalization, temporal coherence, interpretability, stability and reusability---summarized by a composite quality index \(Q\); the Transformer, the SoccerMon case study and the simulations exist to argue that this table deserves that status. Classical exponentially weighted moving averages and related short- and long-horizon scalar summaries arise as restricted, typically univariate special cases of the same operator class. A simulation study with known memory mechanisms shows that \(Q\) and rotation-invariant recovery scores discriminate genuine multivariate or personalized memory from negative controls and from misspecified windows, whereas regime classification accuracy alone does not. SoccerMon serves as an empirical case study: a constructed Latent Memory Table attains \(Q\approx 0.73\) versus about \(0.40\) for classical and lagged principal-component baselines, with incremental held-out value for some wellness targets and Procrustes ensembles for row-wise reliability.
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