Mode selection mechanism in traveling and standing waves revealed by Min wave reconstituted in artificial cells

Reaction-diffusion coupling (RDc) generates spatiotemporal patterns, including two dynamic wave modes: traveling and standing waves. Although mode selection plays a significant role in the spatiotemporal organization of living cell molecules, the mechanism for selecting each wave mode remains elusive. Here, we investigated a wave mode selection mechanism using Min waves reconstituted in artificial cells, emerged by the RDc of MinD and MinE. Our experiments and theoretical analysis revealed that the balance of membrane binding and dissociation from the membrane of MinD determines the mode selection of the Min wave. We successfully demonstrated that the transition of the wave modes can be regulated by controlling this balance and found hysteresis characteristics in the wave mode transition. These findings highlight a novel role of the balance between activators and inhibitors as a determinant of the mode selection of waves by RDc and depict a novel mechanism in intracellular spatiotemporal pattern formations.

Accelerating DNN Training Through Selective Localized Learning

Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. We propose LoCal+SGD, a new algorithmic approach to accelerate DNN training by selectively combining localized or Hebbian learning within a Stochastic Gradient Descent (SGD) based training framework. Back-propagation is a computationally expensive process that requires 2 Generalized Matrix Multiply (GEMM) operations to compute the error and weight gradients for each layer. We alleviate this by selectively updating some layers' weights using localized learning rules that require only 1 GEMM operation per layer. Further, since localized weight updates are performed during the forward pass itself, the layer activations for such layers do not need to be stored until the backward pass, resulting in a reduced memory footprint. Localized updates can substantially boost training speed, but need to be used judiciously in order to preserve accuracy and convergence. We address this challenge through a Learning Mode Selection Algorithm, which gradually selects and moves layers to localized learning as training progresses. Specifically, for each epoch, the algorithm identifies a Localized→SGD transition layer that delineates the network into two regions. Layers before the transition layer use localized updates, while the transition layer and later layers use gradient-based updates. We propose both static and dynamic approaches to the design of the learning mode selection algorithm. The static algorithm utilizes a pre-defined scheduler function to identify the position of the transition layer, while the dynamic algorithm analyzes the dynamics of the weight updates made to the transition layer to determine how the boundary between SGD and localized updates is shifted in future epochs. We also propose a low-cost weak supervision mechanism that controls the learning rate of localized updates based on the overall training loss. We applied LoCal+SGD to 8 image recognition CNNs (including ResNet50 and MobileNetV2) across 3 datasets (Cifar10, Cifar100, and ImageNet). Our measurements on an Nvidia GTX 1080Ti GPU demonstrate upto 1.5× improvement in end-to-end training time with ~0.5% loss in Top-1 classification accuracy.

Device to Device Hybridization in 5G and Beyond: A Literature Review

Abstract- Device-to-Device (D2D) communication is one of the most promising technologies to enhance user experience in 5G and beyond. Despite the huge benefit anticipated, enabling D2D in cellular network has encounter some challenges, these challenges include peer discovery and synchronization, mode selection and interference management. However, resolving these challenges promises improved service delivery, spectrum efficiency and reduced latency amongst other gains. Attempts to enable D2D in both microwave and millimeter wave network gained some traction in recent years in a bid to enable wider coverage and utilization of the technology. Some of the research attempts, challenges and prosects are discussed in this paper.Keywords- Device-to-Device, Microwave, millimeter wave, Inter-cell Interference

The Choice of Multimodal Transport Mode of Agricultural By-Product Logistics in Land-Sea New Corridor in Western China Based on Big Data

The “new land-sea corridor” has brought cross-border facilitation and increased trade financing channels. It not only has long been a road-sea transportation corridor but also has been upgraded to a trade corridor. As one of the most inclusive cities, Chongqing, with the help of this channel, can bring more dividends and international resources to the entire western region. Through the logistics base of Singaporean-Chongqing cooperation in multimodal transport, Chongqing can play an important role as a channel operation center and an important logistics hub. Some international shipping resources will be extended to Chongqing, letting the whole western region share the agricultural by-products brought by Southeast Asian countries. Multimodal transport is a common mode of transport in international trade; it combines various modes of transport organically, brings into play the advantages of various modes of transport, and can reduce costs to a large extent. At present, multimodal transport is mostly used for importing and exporting goods; multimodal transport is not widely used in agricultural by-product logistics transportation. Multimodal combination will be used in the transportation of agricultural by-product logistics; it can avoid the shortcomings of simply using road transportation and make the logistics transportation cost of agricultural by-products lower and management more convenient. Based on the large data, this paper considers factors such as route factors, transfer mode selection, and window meeting time in the transfer process; a mathematical model and advanced colony ant algorithm can be used to solve the transfer optimization problem of a very large fleet of agricultural by-product logistics. This solution can provide instructions and suggestions for companies that should increase relevant scientific research.