@wraithe@mastodon.socialThis is what social media (and the internet) exists for.
(Also glad that Rami has bridging turned on, so I can repost it here easily! 😁) https://fed.brid.gy/r/https://bsky.app/profile/did:plc:jye22xkea3jqsabskhfec347/post/3mf3ngx67rs2k
@timjan@social.linux.pizzaUhm, no thank you, I don't think I'll bother with that "bridging" to "bluesky" that you want me to do.
@Techmeme@techhub.socialLI.FI, which provides businesses with price comparisons of crypto exchange rates and bridging fees, raised $29M, bringing its total funding to ~$52M (Carlos Garcia/Fortune)
https://fortune.com/2025/12/11/exclusive-crypto-startup-li-fi-raises-29-million/
@emd@cosocial.caIs there a “quick” to follow all the folks i follow on bluesky, over here? I know about the bridging bot, but is there a way to automate that?
Related, is there a way to automate reaching out to bsky folks that I follow that aren’t bridged with that bridging bot?
@niklaskorz@rheinneckar.socialI'll be in the #fosdem translations dev room this afternoon, speaking about something completely unrelated to my usual topics.
Don't expect a ready to use project though, it's more about sharing a story of creative problem solving. :blobcatartist:
@lysander07@sigmoid.socialWe are happy to welcome @… from RWTH in today's #nfdicore playground talking about "Bridging the Gap from Biomedical to Domain-Agnostic Semantics".
Besides others, he is demonstrating that our
@brichapman@mastodon.socialClimate action is finally moving from promises to practice. The Global Implementation Accelerator is bridging the gap between national climate plans and real-world results.
Here's what's working: Private capital restoring Brazilian forests. Unilever switching to renewables. H&M and IKEA modernizing Vietnam's grid. Companies in Taiwan pooling demand for clean energy.
The breakthrough? Aligning business needs with national climate goals creates wins on both sides.
@chris@mstdn.chrisalemany.caMinimum step… blocking ICE on Bluesky.
If you are bridging from a Mastodon/Fediverse account to Bluesky through BridgyFed just send a Private Message to the bridgyfed account @… with the command:
"block @icegov.bsky.social”
And the block will be in place for your account. (you can do this with any account of course)
#ICE #Bluesky #NoFascists
@arXiv_qbioNC_bot@mastoxiv.pageAllometric scaling of brain activity explained by avalanche criticality
Tiago S. A. N. Sim\~oes, Jos\'e S. Andrade Jr., Hans J. Herrmann, Stefano Zapperi, Lucilla de Arcangelis
https://arxiv.org/abs/2512.10834 https://arxiv.org/pdf/2512.10834 https://arxiv.org/html/2512.10834
arXiv:2512.10834v1 Announce Type: new
Abstract: Allometric scaling laws, such as Kleiber's law for metabolic rate, highlight how efficiency emerges with size across living systems. The brain, with its characteristic sublinear scaling of activity, has long posed a puzzle: why do larger brains operate with disproportionately lower firing rates? Here we show that this economy of scale is a universal outcome of avalanche dynamics. We derive analytical scaling laws directly from avalanche statistics, establishing that any system governed by critical avalanches must exhibit sublinear activity-size relations. This theoretical prediction is then verified in integrate-and-fire neuronal networks at criticality and in classical self-organized criticality models, demonstrating that the effect is not model-specific but generic. The predicted exponents align with experimental observations across mammal species, bridging dynamical criticality with the allometry of brain metabolism. Our results reveal avalanche criticality as a fundamental mechanism underlying Kleiber-like scaling in the brain.
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@arXiv_csDS_bot@mastoxiv.pageReplaced article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time
Vladimir Braverman, Prathamesh Dharangutte, Shreyas Pai, Vihan Shah, Chen Wang
https://arxiv.org/abs/2411.09979 https://mastoxiv.page/@arXiv_csDS_bot/113502653187863544
- A Simple and Combinatorial Approach to Proving Chernoff Bounds and Their Generalizations
William Kuszmaul
https://arxiv.org/abs/2501.03488 https://mastoxiv.page/@arXiv_csDS_bot/113791396712128907
- The Structural Complexity of Matrix-Vector Multiplication
Emile Anand, Jan van den Brand, Rose McCarty
https://arxiv.org/abs/2502.21240 https://mastoxiv.page/@arXiv_csDS_bot/114097340825270885
- Clustering under Constraints: Efficient Parameterized Approximation Schemes
Sujoy Bhore, Ameet Gadekar, Tanmay Inamdar
https://arxiv.org/abs/2504.06980 https://mastoxiv.page/@arXiv_csDS_bot/114312444050875805
- Minimizing Envy and Maximizing Happiness in Graphical House Allocation
Anubhav Dhar, Ashlesha Hota, Palash Dey, Sudeshna Kolay
https://arxiv.org/abs/2505.00296 https://mastoxiv.page/@arXiv_csDS_bot/114437013364446063
- Fast and Simple Densest Subgraph with Predictions
Thai Bui, Luan Nguyen, Hoa T. Vu
https://arxiv.org/abs/2505.12600 https://mastoxiv.page/@arXiv_csDS_bot/114538936921930134
- Compressing Suffix Trees by Path Decompositions
Becker, Cenzato, Gagie, Kim, Koerkamp, Manzini, Prezza
https://arxiv.org/abs/2506.14734 https://mastoxiv.page/@arXiv_csDS_bot/114703384646892523
- Improved sampling algorithms and functional inequalities for non-log-concave distributions
Yuchen He, Zhehan Lei, Jianan Shao, Chihao Zhang
https://arxiv.org/abs/2507.11236 https://mastoxiv.page/@arXiv_csDS_bot/114862112197588124
- Deterministic Lower Bounds for $k$-Edge Connectivity in the Distributed Sketching Model
Peter Robinson, Ming Ming Tan
https://arxiv.org/abs/2507.11257 https://mastoxiv.page/@arXiv_csDS_bot/114862223634372292
- Optimally detecting uniformly-distributed $\ell_2$ heavy hitters in data streams
Santhoshini Velusamy, Huacheng Yu
https://arxiv.org/abs/2509.07286 https://mastoxiv.page/@arXiv_csDS_bot/115178875220889588
- Uncrossed Multiflows and Applications to Disjoint Paths
Chandra Chekuri, Guyslain Naves, Joseph Poremba, F. Bruce Shepherd
https://arxiv.org/abs/2511.00254 https://mastoxiv.page/@arXiv_csDS_bot/115490402963680492
- Dynamic Matroids: Base Packing and Covering
Tijn de Vos, Mara Grilnberger
https://arxiv.org/abs/2511.15460 https://mastoxiv.page/@arXiv_csDS_bot/115580946319285096
- Branch-width of connectivity functions is fixed-parameter tractable
Tuukka Korhonen, Sang-il Oum
https://arxiv.org/abs/2601.04756 https://mastoxiv.page/@arXiv_csDS_bot/115864074799755995
- CoinPress: Practical Private Mean and Covariance Estimation
Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan Ullman
https://arxiv.org/abs/2006.06618
- The Ideal Membership Problem and Abelian Groups
Andrei A. Bulatov, Akbar Rafiey
https://arxiv.org/abs/2201.05218
- Bridging Classical and Quantum: Group-Theoretic Approach to Quantum Circuit Simulation
Daksh Shami
https://arxiv.org/abs/2407.19575 https://mastoxiv.page/@arXiv_quantph_bot/112874282709517475
- Young domination on Hamming rectangles
Janko Gravner, Matja\v{z} Krnc, Martin Milani\v{c}, Jean-Florent Raymond
https://arxiv.org/abs/2501.03788 https://mastoxiv.page/@arXiv_mathCO_bot/113791421814248215
- On the Space Complexity of Online Convolution
Joel Daniel Andersson, Amir Yehudayoff
https://arxiv.org/abs/2505.00181 https://mastoxiv.page/@arXiv_csCC_bot/114437005955255553
- Universal Solvability for Robot Motion Planning on Graphs
Anubhav Dhar, Pranav Nyati, Tanishq Prasad, Ashlesha Hota, Sudeshna Kolay
https://arxiv.org/abs/2506.18755 https://mastoxiv.page/@arXiv_csCC_bot/114737342714568702
- Colorful Minors
Evangelos Protopapas, Dimitrios M. Thilikos, Sebastian Wiederrecht
https://arxiv.org/abs/2507.10467
- Learning fermionic linear optics with Heisenberg scaling and physical operations
Aria Christensen, Andrew Zhao
https://arxiv.org/abs/2602.05058
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@arXiv_csOS_bot@mastoxiv.pageProphetKV: User-Query-Driven Selective Recomputation for Efficient KV Cache Reuse in Retrieval-Augmented Generation
Shihao Wang, Jiahao Chen, Yanqi Pan, Hao Huang, Yichen Hao, Xiangyu Zou, Wen Xia, Wentao Zhang, Haitao Wang, Junhong Li, Chongyang Qiu, Pengfei Wang
https://arxiv.org/abs/2602.02579 https://arxiv.org/pdf/2602.02579 https://arxiv.org/html/2602.02579
arXiv:2602.02579v1 Announce Type: new
Abstract: The prefill stage of long-context Retrieval-Augmented Generation (RAG) is severely bottlenecked by computational overhead. To mitigate this, recent methods assemble pre-calculated KV caches of retrieved RAG documents (by a user query) and reprocess selected tokens to recover cross-attention between these pre-calculated KV caches. However, we identify a fundamental "crowding-out effect" in current token selection criteria: globally salient but user-query-irrelevant tokens saturate the limited recomputation budget, displacing the tokens truly essential for answering the user query and degrading inference accuracy.
We propose ProphetKV, a user-query-driven KV Cache reuse method for RAG scenarios. ProphetKV dynamically prioritizes tokens based on their semantic relevance to the user query and employs a dual-stage recomputation pipeline to fuse layer-wise attention metrics into a high-utility set. By ensuring the recomputation budget is dedicated to bridging the informational gap between retrieved context and the user query, ProphetKV achieves high-fidelity attention recovery with minimal overhead. Our extensive evaluation results show that ProphetKV retains 96%-101% of full-prefill accuracy with only a 20% recomputation ratio, while achieving accuracy improvements of 8.8%-24.9% on RULER and 18.6%-50.9% on LongBench over the state-of-the-art approaches (e.g., CacheBlend, EPIC, and KVShare).
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@arXiv_csGR_bot@mastoxiv.pageCrosslisted article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- Optimization and Mobile Deployment for Anthropocene Neural Style Transfer
Po-Hsun Chen, Ivan C. H. Liu
https://arxiv.org/abs/2601.21141 https://mastoxiv.page/@arXiv_csHC_bot/115983012560898066
- HiFi-Mesh: High-Fidelity Efficient 3D Mesh Generation via Compact Autoregressive Dependence
Yanfeng Li, Tao Tan, Qingquan Gao, Zhiwen Cao, Xiaohong liu, Yue Sun
https://arxiv.org/abs/2601.21314 https://mastoxiv.page/@arXiv_csCV_bot/115983484680840673
- Synthetic-to-Real Domain Bridging for Single-View 3D Reconstruction of Ships for Maritime Monitoring
Borja Carrillo-Perez, Felix Sattler, Angel Bueno Rodriguez, Maurice Stephan, Sarah Barnes
https://arxiv.org/abs/2601.21786 https://mastoxiv.page/@arXiv_csCV_bot/115983653113440181
- EditYourself: Audio-Driven Generation and Manipulation of Talking Head Videos with Diffusion Tran...
Flynn, Paier, Dinev, Nguyen, Poghosyan, Toribio, Banerjee, Gafni
https://arxiv.org/abs/2601.22127 https://mastoxiv.page/@arXiv_csCV_bot/115983720617201966
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@arXiv_physicsoptics_bot@mastoxiv.pageRoadmap: Emerging Platforms and Applications of Optical Frequency Combs and Dissipative Solitons
Dmitry Skryabin, Arne Kordts, Richard Zeltner, Ronald Holzwarth, Victor Torres-Company, Tobias Herr, Fuchuan Lei, Qi-Fan Yang, Camille-Sophie Br\`es, John F. Donegan, Hai-Zhong Weng, Delphine Marris-Morini, Adel Bousseksou, Markku Vainio, Thomas Bunel, Matteo Conforti, Arnaud Mussot, Erwan Lucas, Julien Fatome, Yuk Shan Cheng, Derryck T. Reid, Alessia Pasquazi, Marco Peccianti, M. Giudici, M. Marconi, A. Bartolo, N. Vigne, B. Chomet, A. Garnache, G. Beaudoin, I. Sagnes, Richard Burguete, Sarah Hammer, Jonathan Silver
https://arxiv.org/abs/2511.18231 https://arxiv.org/pdf/2511.18231 https://arxiv.org/html/2511.18231
arXiv:2511.18231v1 Announce Type: new
Abstract: The discovery of optical frequency combs (OFCs) has revolutionised science and technology by bridging electronics and photonics, driving major advances in precision measurements, atomic clocks, spectroscopy, telecommunications, and astronomy. However, current OFC systems still require further development to enable broader adoption in fields such as communication, aerospace, defence, and healthcare. There is a growing need for compact, portable OFCs that deliver high output power, robust self-referencing, and application-specific spectral coverage. On the conceptual side, progress toward such systems is hindered by an incomplete understanding of the fundamental principles governing OFC generation in emerging devices and materials, as well as evolving insights into the interplay between soliton and mode-locking effects. This roadmap presents the vision of a diverse group of academic and industry researchers and educators from Europe, along with their collaborators, on the current status and future directions of OFC science. It highlights a multidisciplinary approach that integrates novel physics, engineering innovation, and advanced researcher training. Topics include advances in soliton science as it relates to OFCs, the extension of OFC spectra into the visible and mid-infrared ranges, metrology applications and noise performance of integrated OFC sources, new fibre-based OFC modules, OFC lasers and OFC applications in astronomy.
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@arXiv_csLG_bot@mastoxiv.pageSpatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
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