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@metacurity@infosec.exchange
2025-07-08 07:24:18

therecord.media/cyberattack-ru
“Russian developers behind a custom firmware used to convert consumer drones for military use in Ukraine have reported a cyberattack on their infrastructure, disrupting the system that distributes t…

Cyberattack deals blow to Russian firmware used to repurpose civilian drones for Ukraine war
The developers behind a custom firmware used to convert consumer drones for military use in Ukraine have reported a cyberattack disrupting the system that distributes the software.

@arXiv_csRO_bot@mastoxiv.page
2025-08-11 08:54:30

Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution
Zhanyi Sun, Shuran Song
arxiv.org/abs/2508.05941 arxi…

Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution
Visuomotor policies trained via behavior cloning are vulnerable to covariate shift, where small deviations from expert trajectories can compound into failure. Common strategies to mitigate this issue involve expanding the training distribution through human-in-the-loop corrections or synthetic data augmentation. However, these approaches are often labor-intensive, rely on strong task assumptions, or compromise the quality of imitation. We introduce Latent Policy Barrier, a framework for robust …

@arXiv_statML_bot@mastoxiv.page
2025-09-11 08:15:53

kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions
Parastoo Pashmchi, Jerome Benoit, Motonobu Kanagawa
arxiv.org/abs/2509.08366

kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions
We study a missing-value imputation method, termed kNNSampler, that imputes a given unit's missing response by randomly sampling from the observed responses of the $k$ most similar units to the given unit in terms of the observed covariates. This method can sample unknown missing values from their distributions, quantify the uncertainties of missing values, and be readily used for multiple imputation. Unlike popular kNNImputer, which estimates the conditional mean of a missing response given an…

@arXiv_eessSP_bot@mastoxiv.page
2025-07-10 08:23:22

Graph Learning for Cooperative Cell-Free ISAC Systems: From Optimization to Estimation
Peng Jiang, Ming Li, Rang Liu, Qian Liu
arxiv.org/abs/2507.06612

Graph Learning for Cooperative Cell-Free ISAC Systems: From Optimization to Estimation
Cell-free integrated sensing and communication (ISAC) systems have emerged as a promising paradigm for sixth-generation (6G) networks, enabling simultaneous high-rate data transmission and high-precision radar sensing through cooperative distributed access points (APs). Fully exploiting these capabilities requires a unified design that bridges system-level optimization with multi-target parameter estimation. This paper proposes an end-to-end graph learning approach to close this gap, modeling t…

@cybertailor@craba.cab
2025-07-09 18:23:48

Не ОРИ — комиссия по разработке XMPP оштрафована за отказ сотрудничать с властями РФ
roskomsvoboda.org/ru/post/russ

Не ОРИ — комиссия по разработке XMPP оштрафована за отказ сотрудничать с властями РФ
Независимая некоммерческая организация XMPP Standards Foundation оштрафована за неуплату административного штрафа, возложенного на неё за повторный отказ исполнять обязанности организатора распространения информации (ОРИ).

@StephenRees@mas.to
2025-09-08 19:07:41

Several days after announcing the state had joined in the West Coast Health Alliance – to set vaccine policy with Oregon, California, and Hawaii – Washington state announced that anyone over six months of age – including pregnant people – can receive a COVID vaccine without a prescription

Confused about your eligibility for the Covid shot? In Washington state, you’re covered
Thanks to a standing order signed by the state health officer Thursday, anyone in Washington state can get the Covid-19 vaccine without consulting their doctor.

@leftsidestory@mstdn.social
2025-08-08 00:30:10

City Snaps - Chengdu / A Rain in Park ☔️
城市抓拍 - 成都 / 雨中的公园 ☔️
📷 Nikon FE
🎞️Ilford Pan 400
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Ilford Pan 400 (FF) **English:** A serene black-and-white photograph of a small, traditional boat with a covered structure, gently floating on a calm pond. The boat is situated near the edge of the water, surrounded by lush trees and foliage. The scene is tranquil, with a backdrop of tall trees and faint outlines of buildings in the distance, suggesting an urban park setting. **Chinese:** 一张宁静的黑白照片，展示了一艘带有遮盖结构的传统小船，轻轻漂浮在平静的池塘上。小船位于水边，周围环绕着茂密的树木和植被。场景宁静祥和，背景是高大的树木和远处建筑物的淡淡轮廓，暗示这是一个城市公园的环境。
Ilford Pan 400 (FF) **English:** A close-up black-and-white photograph of a pond's surface, showing ripples and reflections of light. The water appears slightly disturbed, creating a dynamic texture. The edges of the pond are lined with aquatic plants and reeds, adding to the natural ambiance of the scene. **Chinese:** 一张黑白照片的特写，展示了池塘的水面，显示出涟漪和光线的反射。水面看起来略有波动，形成了动态的纹理。池塘的边缘排列着水生植物和芦苇，为场景增添了自然的氛围。
Ilford Pan 400 (FF) **English:** A black-and-white image of a pond partially covered with lily pads. The lily pads are scattered across the surface, creating a sense of natural beauty and tranquility. The edges of the pond are bordered by tall grasses and reeds, enhancing the serene and untouched feel of the environment. **Chinese:** 一张黑白照片，展示了一个部分被睡莲覆盖的池塘。睡莲散布在水面上，营造出一种自然美感和宁静感。池塘的边缘由高高的草和芦苇围绕，增强了环境的宁静和原始感。
Ilford Pan 400 (FF) **English:** A black-and-white image capturing a small boat with a covered structure floating on a pond. The boat is centrally positioned in the frame, with the pond's surface reflecting light. The surrounding area is lush with trees and greenery, creating a peaceful atmosphere. The background includes more trees and some distant structures, indicating the setting might be within a park. **Chinese:** 一张黑白照片，捕捉到一艘带有遮盖结构的小船漂浮在池塘上。小船位于画面中央，池塘的水面反射着光线。周围地区郁郁葱葱，树木和绿地营造出宁静的氛围。背景…
@arXiv_statML_bot@mastoxiv.page
2025-07-10 08:23:25

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
Zhiyi Dong, Zixuan Liu, Yongyi Mao
arxiv.org/abs/2507.06552

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
This paper studies the hardness of unsupervised domain adaptation (UDA) under covariate shift. We model the uncertainty that the learner faces by a distribution $π$ in the ground-truth triples $(p, q, f)$ -- which we call a UDA class -- where $(p, q)$ is the source -- target distribution pair and $f$ is the classifier. We define the performance of a learner as the overall target domain risk, averaged over the randomness of the ground-truth triple. This formulation couples the source distributi…

@arXiv_statML_bot@mastoxiv.page
2025-07-10 08:44:31

Distribution-free inference for LightGBM and GLM with Tweedie loss
Alokesh Manna, Aditya Vikram Sett, Dipak K. Dey, Yuwen Gu, Elizabeth D. Schifano, Jichao He
arxiv.org/abs/2507.06921

Distribution-free inference for LightGBM and GLM with Tweedie loss
Prediction uncertainty quantification is a key research topic in recent years scientific and business problems. In insurance industries (\cite{parodi2023pricing}), assessing the range of possible claim costs for individual drivers improves premium pricing accuracy. It also enables insurers to manage risk more effectively by accounting for uncertainty in accident likelihood and severity. In the presence of covariates, a variety of regression-type models are often used for modeling insurance clai…

@arXiv_statML_bot@mastoxiv.page
2025-09-08 08:14:30

Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shift
Ren-Rui Liu, Zheng-Chu Guo
arxiv.org/abs/2509.05106 arxiv…

Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shift
This paper investigates the convergence properties of spectral algorithms -- a class of regularization methods originating from inverse problems -- under covariate shift. In this setting, the marginal distributions of inputs differ between source and target domains, while the conditional distribution of outputs given inputs remains unchanged. To address this distributional mismatch, we incorporate importance weights, defined as the ratio of target to source densities, into the learning framewor…