A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework

Runoff forecasting is of great importance for flood mitigation and power generation plan preparation. To explore the better application of time-frequency decomposition technology in runoff forecasting and improve the prediction accuracy, this research has developed a framework of runoff forecasting named Decomposition-Integration-Prediction (DIP) using parallel-input neural network, and proposed a novel runoff forecasting model with Variational Mode Decomposition (VMD), Gated Recurrent Unit (GRU), and Stochastic Fractal Search (SFS) algorithm under this framework. In this model, the observed runoff series is first decomposed into several sub-series via the VMD method to extract different frequency information. Secondly, the parallel layers in the parallel-input neural network based on GRU are trained to receive the input samples of each subcomponent and integrate their output adaptively through the concatenation layers. Finally, the output of concatenation layers is treated as the final runoff forecasting result. In this process, the SFS algorithm was adopted to optimize the structure of the neural network. The prediction performance of the proposed model was evaluated using the historical monthly runoff data at Pingshan and Yichang hydrological stations in the Upper Yangtze River Basin of China, and seven various single and decomposition-based hybrid models were developed for comparison. The results show that the proposed model has obvious advantages in overall prediction performance, model training time, and multi-step-ahead prediction compared to several comparative methods, which is a reasonable and more efficient monthly runoff forecasting method based on time series decomposition and neural networks.

Thalamus and claustrum control parallel layer 1 circuits in retrosplenial cortex

The granular retrosplenial cortex (RSG) is critical for both spatial and non-spatial behaviors, but the underlying neural codes remain poorly understood. Here, we use optogenetic circuit mapping in mice to reveal a double dissociation that allows parallel circuits in superficial RSG to process disparate inputs. The anterior thalamus and dorsal subiculum, sources of spatial information, strongly and selectively recruit small low-rheobase (LR) pyramidal cells in RSG. In contrast, neighboring regular-spiking (RS) cells are preferentially controlled by claustral and anterior cingulate inputs, sources of mostly non-spatial information. Precise sublaminar axonal and dendritic arborization within RSG layer 1, in particular, permits this parallel processing. Observed thalamocortical synaptic dynamics enable computational models of LR neurons to compute the speed of head rotation, despite receiving head direction inputs that do not explicitly encode speed. Thus, parallel input streams identify a distinct principal neuronal subtype ideally positioned to support spatial orientation computations in the RSG.

Energy harvesting optical modulators with sub-attojoule per bit electrical energy consumption

The light input to a semiconductor optical modulator can constitute an electrical energy supply through the photovoltaic effect, which is unexploited in conventional modulators. In this work, we leverage this effect to demonstrate a silicon modulator with sub-aJ/bit electrical energy consumption at sub-GHz speeds, relevant for massively parallel input/output systems such as neural interfaces. We use the parasitic photovoltaic current to self-charge the modulator and a single transistor to modulate the stored charge. This way, the electrical driver only needs to charge the nano-scale gate of the transistor, with attojoule-scale energy dissipation. We implement this ‘photovoltaic modulator’ in a monolithic CMOS platform. This work demonstrates how close integration and co-design of electronics and photonics offers a path to optical switching with as few as 500 injected electrons and electrical energy consumption as low as 20 zJ/bit, achieved only by recovering the absorbed optical energy that is wasted in conventional modulation.

Distributed Based Serial Regression Multiple Imputation for High Dimensional Multivariate Data in Multicore Environment of Cloud

Multiple imputations (MI) are predominantly applied in such processes that are involved in the transaction of huge chunks of missing data. Multivariate data that follow traditional statistical models undergoes great suffering for the inadequate availability of pertinent data. The field of distributed computing research faces the biggest hurdle in the form of insufficient high dimensional multivariate data. It mainly deals with the analysis of parallel input problems found in the cloud computing network in general and evaluation of high-performance computing in particular. In fact, it is a tough task to utilize parallel multiple input methods for accomplishing remarkable performance as well as allowing huge datasets achieves scale. In this regard, it is essential that a credible data system is developed and a decomposition strategy is used to partition workload in the entire process for minimum data dependence. Subsequently, a moderate synchronization and/or meager communication liability is followed for placing parallel impute methods for achieving scale as well as more processes. The present article proposes many novel applications for better efficiency. As the first step, this article suggests distributed-oriented serial regression multiple imputation for enhancing the efficiency of imputation task in high dimensional multivariate normal data. As the next step, the processes done in three diverse with parallel back ends viz. Multiple imputation that used the socket method to serve serial regression and the Fork Method to distribute work over workers, and also same work experiments in dynamic structure with a load balance mechanism. In the end, the set of distributed MI methods are used to experimentally analyze amplitude of imputation scores spanning across three probable scenarios in the range of 1:500. Further, the study makes an important observation that due to the efficiency of numerous imputation methods, the data is arranged proportionately in a missing range of 10% to 50%, low to high, while dealing with data between 1000 and 100,000 samples. The experiments are done in a cloud environment and demonstrate that it is possible to generate a decent speed by lessening the repetitive communication between processors.