Tootfinder

Centrist Democrats Rebuke Party's Left Wing: 'We Are Capitalist, Not Socialist' (Tim Balk/New York Times)
https://www.nytimes.com/2026/06/26/us/politics/moderate-democrats-capitalist-socialism.html
http://www.memeorandum.com/260626/p102#a260626p102

Volador 1.0: A Data-Driven Air-Sea Full-Coupling Regional Forecast Model with Submesoscale-Permitting Based on MOE-Swin-Transformer Framework
Yuhang Zhu, Jianxin Wang, Yu-kun Qian, Yineng Li, Yahui Liu, Yankun Gong, Shilin Tang, Shiqiu Peng, Tao Song
https://arxiv.org/abs/2605.24032 https://arxiv.org/pdf/2605.24032 https://arxiv.org/html/2605.24032
arXiv:2605.24032v1 Announce Type: new
Abstract: A data-driven air-sea full-coupling regional forecast model with submesoscale-permitting, named "Volador 1.0", is developed for the South China Sea (SCS). The model features a Swin-Transformer framework integrated with a Mixture-of-Experts (MoE) system, a latent space interaction architecture based on Cross-Grid Bidirectional Cross-Attention, and a fast-slow dual-branch architecture. Both the three-month hindcast test and the 15-day operational real-time forecasting demonstrate that Volador 1.0 has a very encouraging and promising performance in 0-72h forecasting of temperature and salinity in the 0-500m upper ocean as well as the sea surface height with root-mean-square-error (RMSE) or mean absolute error (MAE) smaller than or at least comparable to those from the reanalysis datasets REDOS V2.0 and GLORYS12 and the state-of-the-art regional numerical model Regional Ocean Modeling System (ROMS). In particular, Volador 1.0 demonstrates its capability of capturing/forecasting submesoscale processes including internal waves, with an energy spectrum well representing sub- to mesoscale energy cascade as expected by the classical turbulence theory. Further analysis based on ablation experiments shows that the air-sea full-coupling framework, which takes into account the dynamic exchanges of momentum and heat fluxes between the atmosphere and the ocean, indeed helps improve the model's performance compared to the non-full-coupling one. Volador 1.0, though still subject to refinement in the coming future with a large space for improvement, blazes a path for an accurate, fine and fast marine environment forecasting, and thus could help promote our capability of disaster prevention and mitigation in the SCS as well as in other coastal regions where these innovative techniques can be applied.
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“People are tired of hearing what government can’t do.
They want to hear what government can do,”
Lewis George said in an interview before the DC city’s primary,
where she defeated her Democratic opponents and positioned herself to win the general election in November in a city dominated by Democrats.
Lewis George’s victory signals a break with a quarter-century of centrist governance in Washington,
and it puts her in the vanguard of democratic socialists who h…

Democratic socialists are winning major mayor's races
Janeese Lewis George is making waves in Washington, D.C., with her progressive agenda as she aims for the mayor's office. Her plans include subsidized childcare and increased homebuyer assistance. She also promises to challenge President Donald Trump's influence. Her Democratic primary victory was a shift from centrist governance in the nation's capital and aligns with a broader trend of democratic socialists gaining traction in urban politics. While some cities still favor moderate candidates,…

Leaky Language Models: Stealing Architecture and Inference Optimizations via Per-Token Timing
Sadegh Majidi, Niloofar Mireshghallah, Kazem Taram
https://arxiv.org/abs/2607.20723 https://arxiv.org/pdf/2607.20723 https://arxiv.org/html/2607.20723
arXiv:2607.20723v1 Announce Type: new
Abstract: This work presents LeakyLMs, a set of attacks that leak proprietary model, architecture, and deployment information from production language models. LeakyLMs is the first to demonstrate that key model and deployment details can be inferred using only token generation timing, even when interacting through remote APIs. LeakyLMs introduces two core attacks. The first attack targets inference optimizations and deployment strategies. For example, our attack detects whether a provider uses speculative decoding, a widely deployed inference-time optimization, and further identifies the context length of the draft model used in the pipeline. Our measurements show that Google Gemini Flash 2.5 uses speculative decoding with a draft context window of approximately 128K tokens. The second attack recovers key architectural properties, including the number of transformer layers, hidden dimension size, and number of attention heads. To achieve this, LeakyLMs builds a detailed and accurate model of token-generation timing on modern NVIDIA GPUs, characterizing how latency scales with model configuration and hardware parameters. The attack then performs a search over the architecture space using this timing model. In experiments with Llama models, the near-correct architectural configuration appears in the top-10 guesses more than 90% of the time.
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Centrist Democrats are freaking out about progressives' winning streak (Politico)
https://www.politico.com/news/2026/06/25/progressive-moderate-democratic-party-battlegrounds-00975000
http://www.memeorandum.com/260625/p16#a260625p16

JAX-SCM v1.0: a modern atmospheric single-column model for boundary layer research
Maximilian Pierzyna
https://arxiv.org/abs/2605.24544 https://arxiv.org/pdf/2605.24544 https://arxiv.org/html/2605.24544
arXiv:2605.24544v1 Announce Type: new
Abstract: We present JAX-SCM v1.0, an open-source atmospheric single-column model for boundary layer research, implemented in Python using the JAX computing library. The model solves for horizontal wind, potential temperature, and specific humidity, combined with prognostic turbulent kinetic energy and turbulent statistics parameterized by the Mellor-Yamada-Nakanishi-Niino level-2.5 (MYNN-2.5) turbulence closure. We verify the implementation against three well-established benchmark cases covering neutral (turbulent Ekman layer), stable (GABLS1), and convective (Wangara Day 33) conditions. Close agreement with reference solutions is demonstrated across all regimes. By building on JAX, the model benefits from just-in-time compilation and native GPU support. While JAX-SCM is not yet fully differentiable, basing it on JAX also lays the foundation for future integration with machine learning components. The model is designed for simplicity and modularity, lowering the barrier to entry for users and developers alike.
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Tomorrow! Join Grist Labs co-founder Stan for a demo of Grist's new MCP server.
We'll start small and then show off a complex workflow that leverages multiple integrations, so there's something for everyone.
Connect Claude, ChatGPT, Gemini, or your own local models to your docs over the Model Context Protocol, then list and query tables, read and write rows, and build schema, all via OAuth with scoped access.
Register:

Elastic Trapped States at Dislocation Defects in Scaled Coupling and Hofstadter Models
Yangkai Liu, Cheng Lin, Yuan Liu, Jiao Shen, Yifan Zhu, Haiyan Fan, Hui Zhang
https://arxiv.org/abs/2607.19890 https://arxiv.org/pdf/2607.19890 https://arxiv.org/html/2607.19890
arXiv:2607.19890v1 Announce Type: new
Abstract: Elastic topological dislocations provide a pathway for trapping elastic wave energy at internal defects, rather than being confined solely to external boundaries or corners, which are typically associated with topological insulators (TIs). However, two practical constraints persist. First, highly confined dislocation states based on conventional Su-Schrieffer-Heeger (SSH) dimerization usually require a large coupling contrast and a correspondingly enlarged bandgap, which may be challenging to realize. Second, some Hamiltonians with richer topological physics often contain complex hopping terms, synthetic gauge fields or nonlocal couplings, which substantially increase the geometric complexity of experimental samples. Here, dislocation-induced trapped states are demonstrated in both a scaled coupling (SC) model and a Hofstadter model (HM) within an elastic platform. In the SC model, the trapped mode is treated as a higher localized state in the continuum rather than an in-gap mode in the SSH model. Consequently, the SC-induced dislocation can trap an enhanced mode without the requirement of an enlarged bandgap. For the HM, Householder tridiagonalization is used to map the original tight-binding Hamiltonian with complex hopping terms onto a tridiagonal matrix with only positive-real-valued nearest-neighbour (NN) hopping terms. Truncation at a weak-hopping position preserves the topological phenomena and allows a dislocation defect to be constructed from the shortened aperiodic chain. The results establish a practical route for designing highly localized modes without relying solely on bandgap enlargement or complex couplings, which advance the topological physics of elastic wave systems and promise enhanced possibilities for elastic functional devices.
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Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
Zachary James, Joseph Guinness, Arthur DeGaetano
https://arxiv.org/abs/2605.24009 https://arxiv.org/pdf/2605.24009 https://arxiv.org/html/2605.24009
arXiv:2605.24009v1 Announce Type: new
Abstract: Convective available potential energy (CAPE) is an important variable for forecasting severe weather and understanding deep convection and precipitation. The latest versions of the Global Forecast System (GFS) and related Global Ensemble Forecast System (GEFS) have exhibited a bias towards underestimating CAPE values during the summertime. We train an artificial intelligence (AI) diffusion model to improve the skill and uncertainty quantification of afternoon 6-hour lead time ensemble forecasts over the United States. Our model takes a GFS CAPE forecast as input and outputs an ensemble that significantly outperforms both GFS and GEFS 6-hour forecasts on root mean square error, continuous ranked probability score, and Brier score. We propose a two-stage training pipeline to leverage both a larger historical GFS forecast dataset and a smaller historical GEFS dataset, despite the two using initialization and parameterization schemes that vary over time. We also show that classifier-free guidance can be used to control the skill and spread of the forecasts. We then demonstrate the versatility of our framework by adding aerosol optical depths (AODs) of black carbon, organic carbon, dust, sea salt, and sulfates as additional input features. Aerosols can invigorate or suppress convection depending on atmospheric conditions. Our AI models effectively incorporate aerosols to produce improved CAPE forecasts. We interpret the model components by using permutation feature importance to rank the influence of the different AODs and find that black carbon, organic carbon, and sulfate aerosols have a greater impact on the model's CAPE predictions than sea salt and dust aerosols.
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Democratic Leaders Want the Party to Moderate. Its Base Has Other Ideas. (New York Times)
https://www.nytimes.com/2026/06/24/us/politics/democratic-progressive-primary-candidates-mamdani.html
http://www.memeorandum.com/260624/p144#a260624p144