@denkbeteiligung@digitalcourage.socialDas Kabinett will die Reform des #EEG achnell angehen. Das ist grundsätzlich gut und allerhöchste Eisenbahn, aber ich hoffe, dass die #Bürgerenergie projekte die bereits in Entwickkung sind nicht abgewürgt werden indem die Bedingungen maßgeblich verschlechtert werden.
@tinoeberl@mastodon.onlineDie Debatte um das #EEG2027 dreht sich um Kosten, #Förderung und den Umbau der #Energienetze.
Diskutiert werden flexiblere Modelle, mehr Systemdienlichkeit und eine stärkere Marktinteg…
@arXiv_qbioNC_bot@mastoxiv.pageSSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
Taketo Akama, Akima Connelly, Shun Minamikawa, Natalia Polouliakh
https://arxiv.org/abs/2512.05500
@seeingwithsound@mas.toA low-latency neural inference framework for real-time handwriting recognition from #EEG signals on an edge device https://www.nature.com/articles/s41598-025-24972-y
@tinoeberl@mastodon.onlineHäufigkeitsverteilung der Bruttoleistung von #Balkonkraftwerken in #Deutschland mit Stand vom 03.12.2025.
Unplausible Inbetriebnahmedaten ersetzt, Sonderregelungen nach #Solarpaket…
@daehne@muenster.imIn Brandenburg müssen Betreiber von Windanlagen umliegende Gemeinden an ihren Gewinnen beteiligen. Mit dem Geld sollen Infrastruktur, kulturelle und soziale Angebote oder Aufklärung über Windenergie finanziert werden. Aber ob und in welchem Rahmen diese Zahlungen Gemeinden wirklich erreichen – und wofür diese verwendet werden – ist intransparent.
Ich habe mit #IFG eine Anfrage gestellt:
@tinoeberl@mastodon.online#Artikelupdate
Ich habe einen sehr alten Artikel überarbeitet. #Solarpaket I ist z.B. neu.
Von der #Direktvermarktung habe ich wenig vernommen. Gibt es dafür inzwischen bew…
@seeingwithsound@mas.toEEG changes associated with hallucinations caused by Charles Bonnet Syndrome https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1697094/full "reduced α power in occipital electrodes at the onset of the hallucinations&q…
@arXiv_csLG_bot@mastoxiv.pageReplaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
https://arxiv.org/abs/2506.10892 https://mastoxiv.page/@arXiv_csLG_bot/114675526577078472
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
https://arxiv.org/abs/2506.20494 https://mastoxiv.page/@arXiv_csLG_bot/114749113025183688
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
https://arxiv.org/abs/2506.20584 https://mastoxiv.page/@arXiv_csLG_bot/114749118923266356
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
https://arxiv.org/abs/2506.22376 https://mastoxiv.page/@arXiv_csLG_bot/114771735361664528
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
https://arxiv.org/abs/2507.18242 https://mastoxiv.page/@arXiv_csLG_bot/114913322736512937
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
https://arxiv.org/abs/2508.17702 https://mastoxiv.page/@arXiv_csLG_bot/115095014405732247
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
https://arxiv.org/abs/2508.19009 https://mastoxiv.page/@arXiv_csLG_bot/115100269482762688
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011 https://mastoxiv.page/@arXiv_csLG_bot/115100270137397046
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
https://arxiv.org/abs/2508.20705 https://mastoxiv.page/@arXiv_csLG_bot/115111565155687451
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
https://arxiv.org/abs/2509.25977 https://mastoxiv.page/@arXiv_csLG_bot/115298721327100391
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
https://arxiv.org/abs/2510.01384 https://mastoxiv.page/@arXiv_csLG_bot/115309690976554356
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
https://arxiv.org/abs/2510.09775 https://mastoxiv.page/@arXiv_csLG_bot/115372387779061015
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
https://arxiv.org/abs/2510.14386 https://mastoxiv.page/@arXiv_csLG_bot/115389079527543821
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
https://arxiv.org/abs/2510.16882 https://mastoxiv.page/@arXiv_csLG_bot/115412243355962887
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
https://arxiv.org/abs/2510.23117 https://mastoxiv.page/@arXiv_csLG_bot/115451891042176876
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
https://arxiv.org/abs/2510.23501 https://mastoxiv.page/@arXiv_csLG_bot/115451942159737549
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
https://arxiv.org/abs/2511.00040 https://mastoxiv.page/@arXiv_csLG_bot/115490555013124989
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
https://arxiv.org/abs/2511.04469 https://mastoxiv.page/@arXiv_csLG_bot/115507943827841017
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
https://arxiv.org/abs/2511.09902 https://mastoxiv.page/@arXiv_csLG_bot/115547587245365920
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
toXiv_bot_toot
@tinoeberl@mastodon.onlineDie UKA‑Gruppe will künftig jährlich #Windenergieanlagen mit einer Gesamtleistung von 1 GW an Land ans Netz bringen.
2025 erreichte das Unternehmen mit 350 MW ein Rekordergebnis. Aktuell befinden sich über 1,5 GW in Bau, weitere Projekte sind durch EEG-Zuschläge gesichert. UKA setzt auf Kooperation, um Projektrechte zuzukaufen und die Umsetzung trotz wachsendem Wettbewerb zu bes…
@seeingwithsound@mas.toFunctional plasticity in auditory and visual discrimination processing in patients with single-sided deafness: An EEG Study https://journals.sagepub.com/doi/10.1177/23312165251413850
@tinoeberl@mastodon.online#Windräder in der #Rureifel bringen den umliegenden #Kommunen über 450.000 Euro ein.
Der Projektierer REA aus Düren profitiert von einer EEG-Regelung, nach der bis zu 0,2 Cent pro erzeugt…
@arXiv_qbioNC_bot@mastoxiv.pageModeling, Segmenting and Statistics of Transient Spindles via Two-Dimensional Ornstein-Uhlenbeck Dynamics
C. Sun, D. Fettahoglu, D. Holcman
https://arxiv.org/abs/2512.10844 https://arxiv.org/pdf/2512.10844 https://arxiv.org/html/2512.10844
arXiv:2512.10844v1 Announce Type: new
Abstract: We develop here a stochastic framework for modeling and segmenting transient spindle- like oscillatory bursts in electroencephalogram (EEG) signals. At the modeling level, individ- ual spindles are represented as path realizations of a two-dimensional Ornstein{Uhlenbeck (OU) process with a stable focus, providing a low-dimensional stochastic dynamical sys- tem whose trajectories reproduce key morphological features of spindles, including their characteristic rise{decay amplitude envelopes. On the signal processing side, we propose a segmentation procedure based on Empirical Mode Decomposition (EMD) combined with the detection of a central extremum, which isolates single spindle events and yields a collection of oscillatory atoms. This construction enables a systematic statistical analysis of spindle features: we derive empirical laws for the distributions of amplitudes, inter-spindle intervals, and rise/decay durations, and show that these exhibit exponential tails consistent with the underlying OU dynamics. We further extend the model to a pair of weakly coupled OU processes with distinct natural frequencies, generating a stochastic mixture of slow, fast, and mixed spindles in random temporal order. The resulting framework provides a data- driven framework for the analysis of transient oscillations in EEG and, more generally, in nonstationary time series.
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@tinoeberl@mastodon.onlineHäufigkeitsverteilung der Bruttoleistung von #Balkonkraftwerken in #Deutschland mit Stand vom 22.12.2025.
Unplausible Inbetriebnahmedaten ersetzt, Sonderregelungen nach #Solarpaket…
@tinoeberl@mastodon.onlineHäufigkeitsverteilung der Bruttoleistung von #Balkonkraftwerken in #Deutschland mit Stand vom 08.01.2026.
Unplausible Inbetriebnahmedaten ersetzt, Sonderregelungen nach #Solarpaket…
@arXiv_qbioNC_bot@mastoxiv.pageNeuroSketch: 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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