Tootfinder

Geometry-Aware Spiking Graph Neural Network
Bowen Zhang, Genan Dai, Hu Huang, Long Lan
https://arxiv.org/abs/2508.06793 https://arxiv.org/pdf/2508.06793

Geometry-Aware Spiking Graph Neural Network
Graph Neural Networks (GNNs) have demonstrated impressive capabilities in modeling graph-structured data, while Spiking Neural Networks (SNNs) offer high energy efficiency through sparse, event-driven computation. However, existing spiking GNNs predominantly operate in Euclidean space and rely on fixed geometric assumptions, limiting their capacity to model complex graph structures such as hierarchies and cycles. To overcome these limitations, we propose \method{}, a novel Geometry-Aware Spikin…

IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing
Zeyang Song, Shimin Zhang, Yuhong Chou, Jibin Wu, Haizhou Li
https://arxiv.org/abs/2507.07396

IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing
Spiking Neural Networks (SNNs), inspired by biological neural mechanisms, represent a promising neuromorphic computing paradigm that offers energy-efficient alternatives to traditional Artificial Neural Networks (ANNs). Despite proven effectiveness, SNN architectures have struggled to achieve competitive performance on large-scale speech processing task. Two key challenges hinder progress: (1) the high computational overhead during training caused by multi-timestep spike firing, and (2) the abs…

Nonlinear Photonic Neuromorphic Chips for Spiking Reinforcement Learning
Shuiying Xiang, Yonghang Chen, Haowen Zhao, Shangxuan Shi, Xintao Zeng, Yahui Zhang, Xingxing Guo, Yanan Han, Ye Tian, Yuechun Shi, Yue Hao
https://arxiv.org/abs/2508.06962

Nonlinear Photonic Neuromorphic Chips for Spiking Reinforcement Learning
Photonic computing chips have made significant progress in accelerating linear computations, but nonlinear computations are usually implemented in the digital domain, which introduces additional system latency and power consumption, and hinders the implementation of fully-functional photonic neural network chips. Here, we propose and fabricate a 16-channel programmable incoherent photonic neuromorphic computing chip by co-designing a simplified MZI mesh and distributed feedback lasers with satu…

Integration of Contrastive Predictive Coding and Spiking Neural Networks
Emirhan Bilgi\c{c}, Neslihan Serap \c{S}eng\"or, Nam{\i}k Berk Yalab{\i}k, Yavuz Selim \.I\c{s}ler, Aykut G\"orkem Gelen, Rahmi Elibol
https://arxiv.org/abs/2506.09194

Integration of Contrastive Predictive Coding and Spiking Neural Networks
This study examines the integration of Contrastive Predictive Coding (CPC) with Spiking Neural Networks (SNN). While CPC learns the predictive structure of data to generate meaningful representations, SNN mimics the computational processes of biological neural systems over time. In this study, the goal is to develop a predictive coding model with greater biological plausibility by processing inputs and outputs in a spike-based system. The proposed model was tested on the MNIST dataset and achie…

Testing time order and Leggett-Garg inequalities with noninvasive measurements on public quantum computers
Tomasz Rybotycki, Tomasz Bia{\l}ecki, Josep Batle, Bart{\l}omiej Zglinicki, Adam Szereszewski, Wolfgang Belzig, Adam Bednorz
https://arxiv.org/abs/2507.07904

Testing time order and Leggett-Garg inequalities with noninvasive measurements on public quantum computers
We present two protocols, with one and two two-qubit gates, to noninvasively measure two incompatible observables on a qubit. Both protocols are completed with a projective measurement. By gathering sufficiently large statistics, we have been able to violate Leggett-Garg inequality and time-order invariance. The detailed analysis of the data on 10 qubit sets from 5 devices available on IBM Quantum and one on IonQ reveals violations beyond 5 standard deviations in almost all cases. The noninvasi…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- Hespi: A pipeline for automatically detecting information from hebarium specimen sheets
Robert Turnbull, Emily Fitzgerald, Karen Thompson, Joanne L. Birch

SpikingBrain Technical Report: Spiking Brain-inspired Large Models
Yuqi Pan, Yupeng Feng, Jinghao Zhuang, Siyu Ding, Zehao Liu, Bohan Sun, Yuhong Chou, Han Xu, Xuerui Qiu, Anlin Deng, Anjie Hu, Peng Zhou, Man Yao, Jibin Wu, Jian Yang, Guoliang Sun, Bo Xu, Guoqi Li
https://arxiv.org/abs/2509.05276

SpikingBrain Technical Report: Spiking Brain-inspired Large Models
Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence length, and inference memory grows linearly, limiting long-context processing. Building large models on non-NVIDIA platforms also poses challenges for stable and efficient training. To address this, we introduce SpikingBrain, a family of brain-inspired models designed for efficient long-context training and inference. SpikingBrain leverages the MetaX GPU …

Cowboys' Jerry Jones hasn't spoken with Micah Parsons since trade request, not confident he'll play Week 1

https://www.cbssports.com/nfl/news/cowb…

STEEP -- An Alternative To Quantum Key Distribution
Yingbo Hua
https://arxiv.org/abs/2508.05882 https://arxiv.org/pdf/2508.05882

STEEP -- An Alternative To Quantum Key Distribution
Secret-message transmission by echoing encrypted probes (STEEP) is discussed as an alternative to quantum key distribution (QKD). The former only needs classic or non-quantum channels while the latter needs both quantum and classic channels for secret-key generation. STEEP is shown to yield a secrecy rate sufficient for one-time pads encryption in many practical situations including in-air channels or undersea optical cables. Other advantages of STEEP over QKD include cost, complexity, compatib…

Replaced article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation
Maximilian Baronig, Romain Ferrand, Silvester Sabathiel, Robert Legenstein