@primonatura@mstdn.social2026-07-19 17:00:43
@frankel@mastodon.top2026-07-19 16:55:05
@Kingu@sakurajima.moe2026-06-21 10:39:15
Meanwhile, in the US of A :floofSad:
https://aflegal.org/press-release/america-first-legal-files-formal-complaints-with-the-tr…
@cdarwin@c.im2026-06-21 04:08:47
U.S. Ambassador to France,
Charles Kushner,
whose son Jared is married to Trump’s daughter Ivanka,
clashed with his French hosts weeks after arriving last July
when he accused them of not doing enough to combat antisemitism.
Nearly a year later, he contends the problem has gotten worse
— so much so that he has suggested the Trump administration grant refugee status to French Jews, even as it otherwise scales back asylum provision.
“It would be an obje…
@cosmos4u@scicomm.xyz2026-06-17 17:07:30
On the Energy Distribution of the #GalacticCenter Excess' Sources: #DarkMatter in the Center of the #MilkyWay Not Ruled Out: https://www.univie.ac.at/en/news/press-room/press-releases/detail/dark-matter-in-the-center-of-the-milky-way-not-ruled-out
@cdarwin@c.im2026-07-21 14:38:33
Firefighters rescued a small dog that had been missing for weeks after finding it swimming into the San Francisco Bay.
The dog had gone missing over the Fourth of July weekend, according to the Menlo Park fire protection district.
But someone called 911 on 16 July to say they had seen a dog enter the bay near the Dumbarton Bridge in East Palo Alto.
The dog “just kept swimming”, according to a Menlo Park fire district Instagram post.
The Menlo Park fire protection distri…
@arXiv_mathST_bot@mastoxiv.page2026-08-14 08:41:49
Crosslisted article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- Stable convergence of partial sum processes towards discontinuous limits
Johannes Brutsche
https://arxiv.org/abs/2608.12740 https://mastoxiv.page/@arXiv_mathPR_bot/117092840163122403
- Bayesian Inference Procedures for A/B Testing: An Overview
M{\aa}rten Schultzberg, Mattias Fr{\aa}nberg
https://arxiv.org/abs/2608.12949 https://mastoxiv.page/@arXiv_statME_bot/117092799825341949
- Estimation of distribution functions, their jumps and interval probabilities under measurement error
Kairat Mynbaev, Carlos Martins-Filho, Chad Brown
https://arxiv.org/abs/2608.13152 https://mastoxiv.page/@arXiv_econEM_bot/117092724945077876
- Extreme principal minors of Wishart and deformed GOE matrices
Zhanrui Dong, Tiefeng Jiang, Tuan Pham, Jianfeng Yao
https://arxiv.org/abs/2608.13154 https://mastoxiv.page/@arXiv_mathPR_bot/117092881421641444
- Sinkhorn Linearization and the Spectral Proxy: Unifying the Statistical and Algorithmic Theory of...
Han Dong, Jiaming Li, Yongqiang Gong, Ruixi Li, Yin Liu
https://arxiv.org/abs/2608.13201 https://mastoxiv.page/@arXiv_statML_bot/117092803789923153
- Weighted cumulative past inaccuracy and Kullback-Leibler divergence based on extropy: properties,...
Bighneswar Sahoo, Suchandan Kayal
https://arxiv.org/abs/2608.13363 https://mastoxiv.page/@arXiv_statME_bot/117092858417590746
- Nearly sharp comparison results for sliced and max-sliced Wasserstein distances
Jonathan Niles-Weed, Jacob Shkrob
https://arxiv.org/abs/2608.13374 https://mastoxiv.page/@arXiv_mathPR_bot/117092901477246948
- Theoretical Properties of Covariate-Adaptive Randomization with a Diverging Number of Covariates
Yuhang Tao, Li-Xin Zhang
https://arxiv.org/abs/2608.13442 https://mastoxiv.page/@arXiv_statME_bot/117092865494823071
- The data geometry of masking diffusion: Certified-optimal schedules via unmasking growth complexity
Martin J. Wainwright
https://arxiv.org/abs/2608.13520 https://mastoxiv.page/@arXiv_csLG_bot/117092942960850473
toXiv_bot_toot
@arXiv_physicsmedph_bot@mastoxiv.page2026-07-21 07:52:56
Sensitivity of silicon-to-water dose conversion and Bragg-peak metrics to stopping-power datasets in proton dosimetry
F. Matias, J. M. B. Shorto, P. de Vera, R. Garcia-Molina, I. Abril, T. F. Silva, J. Pereira, H. Yoriyaz
https://arxiv.org/abs/2607.16441 https://arxiv.org/pdf/2607.16441 https://arxiv.org/html/2607.16441
arXiv:2607.16441v1 Announce Type: new
Abstract: Accurate proton dosimetry requires consistent stopping-power data for detector-to-water conversion and Monte Carlo radiation transport. We quantify the sensitivity of water-to-silicon stopping-power ratios, converted dose-to-water distributions, and Bragg-peak metrics to the stopping-power dataset for a 67.5 MeV pristine proton beam in water. PHITS simulations used SRIM-2013, PSTAR/NIST, ATIMA, and TDDFT-Penn stopping powers up to 10 MeV, together with a common SBETHE-based extension at higher energies. The water-to-silicon stopping-power ratio showed relative entrance-to-distal variations of 17.40%$-$22.10%. Conversion of normalized PTW silicon-diode percentage-depth-ionization data yielded relative percentage-depth-dose curves with a common maximum at 36.86 mm, consistent with the experimental Bragg-peak depth of (36.81 $\pm$ 0.15) mm. The converted curves were approximately 3% below the reference throughout the entrance and plateau regions, while deviations of up to 2.1% occurred near the Bragg peak. Direct PHITS calculations predicted Bragg-peak depths of 36.575$-$36.675 mm; only the TDDFT-Penn result lay within the quoted experimental uncertainty. These results show that stopping-power treatment affects the magnitude of the converted dose more strongly than the Bragg-peak position does, and that it should be harmonized or explicitly included in uncertainty budgets for proton-beam calibration, commissioning, quality assurance, and Monte Carlo validation.
toXiv_bot_toot
@arXiv_qfinTR_bot@mastoxiv.page2026-07-21 07:49:46
Uniform-Loss Automated Market Making for Prediction Markets
Ciamac C. Moallemi, Dan Robinson, Brian Zhu
https://arxiv.org/abs/2607.17428 https://arxiv.org/pdf/2607.17428 https://arxiv.org/html/2607.17428
arXiv:2607.17428v1 Announce Type: new
Abstract: Automated market makers (AMMs) for prediction markets descend from market scoring rules, where a mechanism operator subsidizes a market to aggregate beliefs about uncertain events. The existing literature has focused on bounding the total worst-case loss to the subsidizer, but has not addressed how that loss is distributed across price states or over time. We use the framework of loss-versus-rebalancing (LVR) to study this distribution and introduce \textit{uniform AMMs}, defined by the property that instantaneous LVR is proportional to pool value and independent of the current token price. In a static setting, we show that for a broad class of \textit{win-martingales} -- processes that converge to 0 or 1 at a fixed resolution time -- there exists a pricing function that achieves uniform LVR under that process, and conversely, that any sufficiently regular pricing function induces a win-martingale under which it is uniform. We then extend the framework to dynamic liquidity management, showing that liquidity levels can be adjusted over time to implement a prescribed target expected cumulative loss schedule. This theory is illustrated with canonical examples of win-martingales and pricing functions. Our results can inform AMM designers and liquidity providers on how the inevitable cost of subsidizing price discovery can be shaped and controlled across both price and time.
toXiv_bot_toot
@cdarwin@c.im2026-06-16 18:13:31
According to Iran, the "peace deal" calls for a permanent and immediate cessation of hostilities on all fronts
-- including in Lebanon.
But Israel, which is not a party to the agreement, says it plans to keep troops in parts of southern Lebanon.
“The Israelis are trying to destroy this deal,
and they will continue to try,”
says Trita Parsi, executive vice president of the Quincy Institute for Responsible Statecraft.
“It will require persistent, co…
