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NeuroSketch: An Effective Framework for Neural Decoding via Systematic Architectural Optimization
Gaorui Zhang, Zhizhang Yuan, Jialan Yang, Junru Chen, Li Meng, Yang Yang
https://arxiv.org/abs/2512.09524 https://arxiv.org/pdf/2512.09524 https://arxiv.org/html/2512.09524
arXiv:2512.09524v1 Announce Type: new
Abstract: Neural decoding, a critical component of Brain-Computer Interface (BCI), has recently attracted increasing research interest. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, the in-depth exploration of model architectures remains underexplored, despite its proven effectiveness in other tasks such as energy forecasting and image classification. In this study, we propose NeuroSketch, an effective framework for neural decoding via systematic architecture optimization. Starting with the basic architecture study, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this, we optimize the architecture from macro- to micro-level, achieving improvements in performance at each step. The exploration process and model validations take over 5,000 experiments spanning three distinct modalities (visual, auditory, and speech), three types of brain signals (EEG, SEEG, and ECoG), and eight diverse decoding tasks. Experimental results indicate that NeuroSketch achieves state-of-the-art (SOTA) performance across all evaluated datasets, positioning it as a powerful tool for neural decoding. Our code and scripts are available at https://github.com/Galaxy-Dawn/NeuroSketch.
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Just don’t use the internet and DHS won’t stalk you.
“What opportunities are available for individuals to consent to uses, decline…or opt out?
Any individual may decline or opt out of having their information gathered…by not posting material online or limiting access to who can view their
posts.”
PDF: <…

Köyhyyden poistaminen on halvempaa kuin sen ylläpitäminen.
it's cheaper to end poverty than to maintain it. The Social Dividend. An Actuarial Case for Higher Income Support https://mandalapartners.com/uploads/the-social-dividend.pdf

Certain results on selection principles associated with bornological structure in topological spaces
Debraj Chandra, Subhankar Das, Nur Alam
https://arxiv.org/abs/2511.04038 https://arxiv.org/pdf/2511.04038 https://arxiv.org/html/2511.04038
arXiv:2511.04038v1 Announce Type: new
Abstract: We study selection principles related to bornological covers in a topological space $X$ following the work of Aurichi et al., 2019, where selection principles have been investigated in the function space $C_\mathfrak{B}(X)$ endowed with the topology $\tau_\mathfrak{B}$ of uniform convergence on bornology $\mathfrak{B}$. We show equivalences among certain selection principles and present some game theoretic observations involving bornological covers. We investigate selection principles on the product space $X^n$ equipped with the product bornolgy $\mathfrak{B}^n$, $n\in \omega$. Considering the cardinal invariants such as the unbounding number ($\mathfrak{b}$), dominating numbers ($\mathfrak{d}$), pseudointersection numbers ($\mathfrak{p}$) etc., we establish connections between the cardinality of base of a bornology with certain selection principles. Finally, we investigate some variations of the tightness properties of $C_\mathfrak{B}(X)$ and present their characterizations in terms of selective bornological covering properties of $X$.
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Dzisiejsza demonstracja pod Ministerstwem Aktywów Państwowych ws. embarga na dostawy materiałów wybuchowych do USA.
18 listopada br. organizacja Palestinian Youth Movement opublikowała opracowanie, które opisało rolę naszego kraju - a konkretniej firmy Nitro-Chem z Bydgoszczy - w dostawach trotylu do produkcji broni wykorzystywanej przez Izrael w Gazie. Od ponad dekady firma ta jest czołowym producentem i eksporterem TNT w NATO i UE i odpowiada za 90% eksportu tego materiału do USA. A ten kraj z kolei wykorzystuje go do produkcji bomb i amunicji dostarczanej izraelskiej armii i wykorzystywanej w Gazie.
Cytując opracowanie: “Bez trotylu (TNT) wyprodukowanego w Polsce niemożliwa byłaby bezprecedensowa skala i intensywność bombardowań z powietrza, które pochłonęły życie dziesiątek tysięcy Palestyńczyków i zniszczyły warunki życia w Strefie Gazy”.
Link do PDFa do poczytania jest gdzieś tutaj:
https://www.embargoforpalestine.com/reports

High-Resolution Optical Correlation-Domain Reflectometry with 100-km Measurement Range
Takaki Kiyozumi, Soshi Yoshida, Yuta Higa, Keisuke Motoda, Sze Yun Set, Shinji Yamashita, Yosuke Mizuno
https://arxiv.org/abs/2511.18400 https://arxiv.org/pdf/2511.18400 https://arxiv.org/html/2511.18400
arXiv:2511.18400v1 Announce Type: new
Abstract: In the maintenance of optical fiber networks, there is a growing demand for high-precision measurement of optical loss distribution and fault locations over long distances. In this study, we propose an OCDR method incorporating periodic pseudo-random modulation (PPRM), and demonstrate that it enables the acquisition of loss distribution based on Rayleigh scattering and the positions of reflection points in an approximately 100-km optical fiber, with a spatial resolution of about 19 cm and a measurement time of about 20 seconds.
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MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design
S. Rodionov, A. Burguete-Lopez, M. Makarenko, Q. Wang, F. Getman, A. Fratalocchi
https://arxiv.org/abs/2511.18980 https://arxiv.org/pdf/2511.18980 https://arxiv.org/html/2511.18980
arXiv:2511.18980v1 Announce Type: new
Abstract: Foundation models (FM) are transforming artificial intelligence by enabling generalizable, data-efficient solutions across different domains for a broad range of applications. However, the lack of large and diverse datasets limits the development of FM in nanophotonics. This work presents MOCLIP (Metasurface Optics Contrastive Learning Pretrained), a nanophotonic foundation model that integrates metasurface geometry and spectra within a shared latent space. MOCLIP employs contrastive learning to align geometry and spectral representations using an experimentally acquired dataset with a sample density comparable to ImageNet-1K. The study demonstrates MOCLIP inverse design capabilities for high-throughput zero-shot prediction at a rate of 0.2 million samples per second, enabling the design of a full 4-inch wafer populated with high-density metasurfaces in minutes. It also shows generative latent-space optimization reaching 97 percent accuracy. Finally, we introduce an optical information storage concept that uses MOCLIP to achieve a density of 0.1 Gbit per square millimeter at the resolution limit, exceeding commercial optical media by a factor of six. These results position MOCLIP as a scalable and versatile platform for next-generation photonic design and data-driven applications.
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