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Temporal logic inference for interpretable fault diagnosis of bearings via sparse and structured neural attention.
ISA Trans
January 2025
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation.
View Article and Find Full Text PDFLack of context modulation in human single neuron responses in the medial temporal lobe.
Cell Rep
January 2025
Centre for Systems Neuroscience, University of Leicester, Leicester, UK; Hospital Del Mar Medical Research Institute (IMIM), Barcelona, Spain; Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain; Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address:
In subjects implanted with intracranial electrodes, we use two different stories involving the same person (or place) to evaluate whether and to what extent context modulates human single-neuron responses. Nearly all neurons (97% during encoding and 100% during recall) initially responding to a person/place do not modulate their response with context. Likewise, nearly none (<1%) of the initially non-responsive neurons show conjunctive coding, responding to particular persons/places in a particular context during the tasks.
View Article and Find Full Text PDFScalable and energy-efficient task allocation in industry 4.0: Leveraging distributed auction and IBPSO.
PLoS One
January 2025
Department of Construction and Quality Management, School of Science and Technology, Hong Kong Metropolitan University, Homantin Kowloon, Hong Kong SAR, China.
Industry 4.0 has transformed manufacturing with the integration of cutting-edge technology, posing crucial issues in the efficient task assignment to multi-tasking robots within smart factories. The paper outlines a unique method of decentralizing auctions to handle basic tasks.
View Article and Find Full Text PDFDynamic planning in hierarchical active inference.
Neural Netw
January 2025
Institute of Cognitive Sciences and Technologies, National Research Council, Padova, Italy. Electronic address:
By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past years, many studies have shown how human and animal behaviors could be explained in terms of active inference - either as discrete decision-making or continuous motor control - inspiring innovative solutions in robotics and artificial intelligence.
View Article and Find Full Text PDFRefreshable memristor via dynamic allocation of ferro-ionic phase for neural reuse.
Nat Commun
January 2025
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Neural reuse can drive organisms to generalize knowledge across various tasks during learning. However, existing devices mostly focus on architectures rather than network functions, lacking the mimic capabilities of neural reuse. Here, we demonstrate a rational device designed based on ferroionic CuInPS, to accomplish the neural reuse function, enabled by dynamic allocation of the ferro-ionic phase.
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