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MARBLE: interpretable representations of neural population dynamics using geometric deep learning
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation learning method, MARBLE, which decomposes on-manifold dynamics into local flow fields and maps them into a common latent space using unsupervised geometric deep learning. In simulated nonlinear dynamical systems, recurrent neural networks and experimental single-neuron recordings from primates and rodents, we discover emergent low-dimensional latent representations that parametrize high-dimensional neural dynamics during gain modulation, decision-making and changes in the internal state. These representations are consistent across neural networks and animals, enabling the robust comparison of cognitive computations. Extensive benchmarking demonstrates state-of-the-art within- and across-animal decoding accuracy of MARBLE compared to current representation learning approaches, with minimal user input. Our results suggest that a manifold structure provides a powerful inductive bias to develop decoding algorithms and assimilate data across experiments.
Towards next-gen smart manufacturing systems: the explainability revolution
The paper shares the author’s perspectives on the role of explainable-AI in the evolving landscape of AI-driven smart manufacturing decisions. First, critical perspectives on the reasons for the slow adoption of explainable-AI in manufacturing are shared, leading to a discussion on its role and relevance in inspiring scientific understanding and discoveries towards achieving complete autonomy. Finally, to standardize explainability quantification, a new Transparency–Cohesion–Comprehensibility (TCC) evaluation framework is proposed and demonstrated.
Memristors based on two-dimensional h-BN materials: synthesis, mechanism, optimization and application
Memristors offer vast application opportunities in storage, logic devices, and computation due to their nonvolatility, low power consumption, and fast operational speeds. Two-dimensional materials, characterized by their novel mechanisms, ultra-thin channels, high mechanical flexibility, and superior electrical properties, demonstrate immense potential in the domain of high-density, fast, and energy-efficient memristors. Hexagonal boron nitride (h-BN), as a new two-dimensional material, has the characteristics of high thermal conductivity, flexibility, and low power consumption, and has a significant application prospect in the field of memristor. In this paper, the recent research progress of the h-BN memristor is reviewed from the aspects of device fabrication, resistance mechanism, and application prospect.
Advanced 3D printing accelerates electromagnetic wave absorption from ceramic materials to structures
As 3D printing technology and ceramic material advance, significant progress has been achieved in the field of 3D-printed ceramic materials for electromagnetic wave absorption (EMWA), transitioning from simple material fabrication to complex structure creation. This review summarizes the key advancements in ceramic materials and structures fabricated by 3D printing for EMWA. Despite significant progress, the limitations that remain in 3D-printed ceramic materials and structures for EMWA are highlighted, and future development tendencies are also identified. This review aims to motivate further development and application of 3D-printed ceramic materials and structures for EMWA.
Surface-hydroxylated single-atom catalyst with an isolated Co-O-Zn configuration achieves high selectivity in regulating active species
Single-atom catalysts (SACs) are emerging as potent tools for the selective regulation of active species, offering substantial promise for green and sustainable Fenton catalysis. However, current SACs face limitations due to the specificity of their supports, which only allow selective regulation within certain oxidant systems. This constraint makes targeted regulation across different systems challenging. In response, this study designs a SAC, termed CoSAs-ZnO, featuring surface hydroxylation and an isolated asymmetric Co-O-Zn configuration. This SAC can realize a nearly 100% selective generation of sulfate radicals (SO4•−) and singlet oxygen (1O2) in peroxymonosulfate (PMS) and peracetic acid (PAA) systems, respectively. Moreover, the PMS-activated system can efficiently treat electron-deficient-dominated and refractory benzoic acid wastewater, achieving 100.0% removal in multiple consecutive pilot-scale experiments. The PAA-activated system facilitates the rapid conversion of benzyl alcohol to benzaldehyde, with a high selectivity of 89.0%. Detailed DFT calculations reveal that the surface hydroxyl groups on ZnO play a critical role in modulating the adsorption configurations of the oxidants, thus enabling the selective generation of specific active species in each system. This study provides insights into the design of SACs for multifunctional applications and paves the way for their deployment in wastewater treatment and high-value chemical conversion.
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