Tootfinder

Scenario-conditioned flow matching for probabilistic generation of three-component ground-motion waveforms
Yi Ding, Jinjun Hu, Su Chen, Xianwei Liu, Zhongxiang Zhang, Zongchao Li, Xiaojun Li, Lili Xie
https://arxiv.org/abs/2606.31340 https://arxiv.org/pdf/2606.31340 https://arxiv.org/html/2606.31340
arXiv:2606.31340v1 Announce Type: new
Abstract: Performance-based seismic risk assessment requires three-component acceleration histories compatible with specified source, path, and site conditions. Conventional ground-motion prediction equations provide scalar intensity measures, while many generative waveform models learn amplitude and waveform shape within a single high-dimensional target. We present WaveFlowGMM, a two-stage probabilistic ground-motion model that uses peak ground acceleration (PGA) as an amplitude interface between scenario conditioning and waveform generation. The amplitude stage uses physics-informed symbolic learning to estimate component-wise PGA medians and a full cross-component covariance. The waveform stage uses few-step AlphaFlow in an invertible wavelet-packet coefficient space to generate normalised three-component histories that are rescaled by sampled PGA. Tests on an event-level NGA-West2 holdout set show that the generated motions recover the main magnitude, distance, and site scaling, keep peak and spectral residuals close to zero, preserve three-component amplitude dependence, and yield velocity and displacement histories without systematic drift after integration of the generated three-component acceleration histories. The framework provides an interpretable and computationally efficient candidate component for waveform-level seismic hazard and risk analysis.
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Walking out of one of Hong Kong’s last remaining independent bookstores,
hands behind her back but head held high,
the woman’s T-shirt was emblazoned with a simple slogan:
💥“I am a bookstore employee”.💥
Escorted by police,
the woman was among five people arrested during raids on two bookstores last week,
suspected of displaying and selling publications with “seditious” content,
a national security crime which carries a maximum sentence of 10 years.…

‘A statement of freedom’: T-shirt slogan goes viral and book sales rocket in Taiwan after Hong Kong raids
Taiwanese people look on with concern at fate of Hong Kong’s independent booksellers, after series of arrests in recent months

RTP-LLM: High-Performance Alibaba LLM Inference Engine
Boyu Tan, Jiarui Guo, Zongwei Lv, Hanbo Sun, Tong Yang, Kan Liu, Xinfei Shi, Zetao Hu, Yaxin Yu, Chi Zhang, Jianning Zhang, Xi Yang, Wei Zhang, Bo Cai, Silu Zhou, Xiyu Wang, Na He, Yinghao Yu, Wending Bao, Guiyang Huang, Yuxing Yuan, Juncheng Yin, Nan Wang, Lin Yang, Zechao Zhang, Lu Chen, Guoding Li, Tao Lan, Lin Qu
https://arxiv.org/abs/2605.29639 https://arxiv.org/pdf/2605.29639 https://arxiv.org/html/2605.29639
arXiv:2605.29639v1 Announce Type: new
Abstract: Large Language Models (LLMs) have revolutionized AI applications, but deploying them at scale presents significant challenges. We present RTP-LLM, a high-performance inference engine for industrial-scale LLM deployment, successfully deployed across Alibaba Group serving over 100 million users. RTP-LLM addresses fundamental bottlenecks through integrated design. It optimizes model loading via file-order-driven I/O and parallel I/O-communication overlapping. The Prefill-Decode Disaggregation architecture decouples compute-intensive prefill from memory-bound decode phases, combined with hierarchical multi-tiered KV cache management enabling efficient cache reuse. In addition, RTP-LLM incorporates modular speculative decoding supporting multiple algorithms, adaptive KV cache quantization, and decoupled multimodal processing, with support for multi-level parallelism.
Comprehensive evaluations across diverse model architectures (8B-235B parameters) have been conducted, where both controlled benchmarks and real production workloads are used. The results demonstrate RTP-LLM's superior performance against vLLM and SGLang: 4.7x-6.3x model loading speedup, 35-37% TTFT P95 latency reduction with 215% cache reuse improvement in production traffic scheduling, 1.12x-2.48x and 1.86x-2.52x throughput improvements in speculative decoding and multimodal inference, respectively, and 35-40% batch latency reduction with 1.9x-3.0x TTFT improvement in quantized inference. RTP-LLM's production-proven architecture and open-source availability make it a comprehensive solution for industrial LLM deployment.
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How Paul Dacre broke new ground to sell readers an angry perspective at the Daily Mail and helped radicalize the UK's rightwing press, now run by his disciples (Andy Beckett/The Guardian)
https://www.theguardian.com…

The Dacre dynasty: how Britain’s rightwing press was radicalised
The long read: Paul Dacre broke new ground in selling readers an angry rightwing perspective. Today, most of Fleet Street is run by his disciples

About 10 months on and the displaymanager issue on Kalpa (used to be SDDM, should be Plasma Login manager by now) has still not been solved. You're thrown into the cli.
Probably abandonware by now. It used to be good, but I guess Suse is too busy selling itself.

Crosslisted article(s) found for physics.app-ph. https://arxiv.org/list/physics.app-ph/new
[1/1]:
- Hydrogen-induced lattice cohesion weakening favors atomic displacement
Gao, Mao, Wilde, Yi, Li, Wang, Schwarz-Selinger, Coenen, Kembleton, Brezinsek, Linsmeier, Lu