Introduction to dynamical mean-field theory of randomly connected neural networks with bidirectionally correlated couplings

Dynamical mean-field theory is a powerful physics tool used to analyze the typical behavior of neural networks, where neurons can be recurrently connected, or multiple layers of neurons can be stacked. However, it is not easy for beginners to access the essence of this tool and the underlying physics. Here, we give a pedagogical introduction of this method in a particular example of random neural networks, where neurons are randomly and fully connected by correlated synapses and therefore the network exhibits rich emergent collective dynamics. We also review related past and recent important works applying this tool. In addition, a physically transparent and alternative method, namely the dynamical cavity method, is also introduced to derive exactly the same results. The numerical implementation of solving the integro-differential mean-field equations is also detailed, with an illustration of exploring the fluctuation dissipation theorem.Comment: 27 pages, 5 figures, 44 references, revised version for SciPost Physics Lecture Note

Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation

With the development of deep convolutional neural networks, medical image segmentation has achieved a series of breakthroughs in recent years. However, the higher-performance convolutional neural networks always mean numerous parameters and high computation costs, which will hinder the applications in clinical scenarios. Meanwhile, the scarceness of large-scale annotated medical image datasets further impedes the application of high-performance networks. To tackle these problems, we propose Graph Flow, a comprehensive knowledge distillation framework, for both network-efficiency and annotation-efficiency medical image segmentation. Specifically, our core Graph Flow Distillation transfer the essence of cross-layer variations from a well-trained cumbersome teacher network to a non-trained compact student network. In addition, an unsupervised Paraphraser Module is designed to purify the knowledge of the teacher network, which is also beneficial for the stabilization of training procedure. Furthermore, we build a unified distillation framework by integrating the adversarial distillation and the vanilla logits distillation, which can further refine the final predictions of the compact network. Extensive experiments conducted on Gastric Cancer Segmentation Dataset and Synapse Multi-organ Segmentation Dataset demonstrate the prominent ability of our method which achieves state-of-the-art performance on these different-modality and multi-category medical image datasets. Moreover, we demonstrate the effectiveness of our Graph Flow through a new semi-supervised paradigm for dual efficient medical image segmentation. Our code will be available at Graph Flow

ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction

Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It holds significant importance in numerous domains, including traffic flow prediction and weather forecasting. However, existing methods face challenges in handling spatiotemporal correlations, as they commonly adopt encoder and decoder architectures with identical receptive fields, which adversely affects prediction accuracy. This paper proposes an Asymmetric Receptive Field Autoencoder (ARFA) model to address this issue. Specifically, we design corresponding sizes of receptive field modules tailored to the distinct functionalities of the encoder and decoder. In the encoder, we introduce a large kernel module for global spatiotemporal feature extraction. In the decoder, we develop a small kernel module for local spatiotemporal information reconstruction. To address the scarcity of meteorological prediction data, we constructed the RainBench, a large-scale radar echo dataset specific to the unique precipitation characteristics of inland regions in China for precipitation prediction. Experimental results demonstrate that ARFA achieves consistent state-of-the-art performance on two mainstream spatiotemporal prediction datasets and our RainBench dataset, affirming the effectiveness of our approach. This work not only explores a novel method from the perspective of receptive fields but also provides data support for precipitation prediction, thereby advancing future research in spatiotemporal prediction.Comment: 0 pages, 5 figure

A universal strategy to prepare sulfur-containing polymer composites with desired morphologies for lithium−sulfur batteries

Lithium–sulfur (Li–S) batteries are probably the most promising candidates for the next-generation batteries owing to their high energy density. However, Li–S batteries face severe technical problems where the dissolution of intermediate polysulfides is the biggest problem because it leads to the degradation of the cathode and the lithium anode, and finally the fast capacity decay. Compared with the composites of elemental sulfur and other matrices, sulfur-containing polymers (SCPs) have strong chemical bonds to sulfur and therefore show low dissolution of polysulfides. Unfortunately, most SCPs have very low electron conductivity and their morphologies can hardly be controlled, which undoubtedly depress the battery performances of SCPs. To overcome these two weaknesses of SCPs, a new strategy was developed for preparing SCP composites with enhanced conductivity and desired morphologies. With this strategy, macroporous SCP composites were successfully prepared from hierarchical porous carbon. The composites displayed discharge/charge capacities up to 1218/1139, 949/922, and 796/785 mA h g–1 at the current rates of 5, 10, and 15 C, respectively. Considering the universality of this strategy and the numerous morphologies of carbon materials, this strategy opens many opportunities for making carbon/SCP composites with novel morphologies

High-performance supercapacitors based on hierarchically porous carbons with a three-dimensional conductive network structure

Clews of polymer nanobelts (CsPNBs) have the advantages of inexpensive raw materials, simple synthesis and large output. Novel clews of carbon nanobelts (CsCNBs) have been successfully prepared by carbonizing CsPNBs and by KOH activation subsequently. From the optimized process, CsCNBs*4, with a specific surface area of 2291 m2 g−1 and a pore volume of up to 1.29 cm3 g−1, has been obtained. Fundamentally, the CsCNBs possess a three-dimensional conductive network structure, a hierarchically porous framework, and excellent hydrophilicity, which enable fast ion diffusion through channels and a large enough ion adsorption/desorption surface to improve electrochemical performance of supercapacitors. The product exhibits a high specific capacitance of 327.5 F g−1 at a current density of 0.5 A g−1 in a three-electrode system. The results also reveal a high-rate capacitance (72.2% capacitance retention at 500 mV s−1) and stable cycling lifetime (95% of initial capacitance after 15 000 cycles). Moreover, CsCNBs*4 provides a high energy density of 29.8 W h kg−1 at a power density of 345.4 W kg−1 in 1 M tetraethylammonium tetrafluoroborate/acetonitrile (TEABF4/AN) electrolyte. These inspiring results imply that this carbon material with a three-dimensional conductive network structure possesses excellent potential for energy storage

Optimized synthesis of ultrahigh-surface-area and oxygen-doped carbon nanobelts for high cycle-stability lithium-sulfur batteries

Hierarchical clews of carbon nanobelts (CsCNBs) with ultrahigh specific surface area (2300 m2 g−1) and large pore volume (up to 1.29 cm3 g−1) has been successfully fabricated through carbonization and KOH activation of phenolic resin based nanobelts. The product possesses hierarchically porous structure, three-dimensional conductive network framework, and polar oxygen-rich groups, which are very befitting to load sulfur leading to excellent cycling stability of lithium-sulfur batteries. The composites of CsCNBs/sulfur exhibit an ultrahigh initial discharge capacity of 1245 mA h g−1 and ultralow capacity decay rate as low as 0.162% per cycle after 200 cycles at 0.1 C. Even at high current rate of 4 C, the cells still display a high initial discharge capacity (621 mA h g−1) and ultralow capacity decay rate (only 0.039% per cycle) after 1000 cycles. These encouraging results indicate that polar oxygen-containing functional groups are important for improving the electrochemical performance of carbons. The oxygen-doped carbon nanobelts have excellent energy storage potential in the field of energy storage

Ultrahigh-content nitrogen-decorated nanoporous carbon derived from metal organic frameworks and its application in supercapacitors

Single electric double-layer capacitors cannot meet the growing demand for energy due to their insufficient energy density. Generally speaking, the supercapacitors introduced with pseudo-capacitance by doping heteroatoms (N, O) in porous carbon materials can obtain much higher capacitance than electric double-layer capacitors. In view of above merits, in this study, nanoporous carbon materials with ultrahigh N enrichment (14.23 wt%) and high specific surface area (942 m2 g−1) by in situ introduction of N-doped MOF (ZTIF-1, Organic ligands 5-methyltetrazole/C2H4N4) were produced. It was found that as supercapacitors' electrode materials, these nanoporous carbons exhibit a capacitance as high as 272 F g-1 at 0.1 A g−1, and an excellent cycle life (almost no attenuation after 10,000 cycles.). Moreover, the symmetric supercapacitors were assembled to further investigate the actual capacitive performance, and the capacitance shows up to 154 F g-1 at 0.1 A g−1. Such excellent properties may be attributed to a combination of a high specific surface area, ultrahigh nitrogen content and hierarchically porous structure. The results shown in this study fully demonstrate that the nanoporous carbon materials containing ultrahigh nitrogen content can be used as a potential electrode material in supercapacitors

Facile synthesis of ultrahigh-surface-area hollow carbon nanospheres and their application in lithium-sulfur batteries

Hollow carbon nanospheres (HCNs) with specific surface areas up to 2949 m2 g−1 and pore volume up to 2.9 cm3 g−1 were successfully synthesized from polyaniline‐co‐polypyrrole hollow nanospheres by carbonization and CO2 activation. The cavity diameter and wall thickness of HCNs can be easily controlled by activation time. Owing to their large inner cavity and enclosed structure, HCNs are desirable carriers for encapsulating sulfur. To better understand the effects of pore characteristics and sulfur contents on the performances of lithium‐sulfur batteries, three composites of HCNs and sulfur are prepared and studied in detail. The composites of HCNs with moderate specific surface areas and suitable sulfur content present a better performance. The first discharge capacity of this composite reaches 1401 mAh g−1 at 0.2 C. Even after 200 cycles, the discharge capacity remains at 626 mAh g−1