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The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually based on statistical computation, which can no longer meet the processing needs of massive data and do not take into account the hidden characteristics of data, which can easily lead to the problem of information scarcity. The paper combines recurrent convolutional neural network and traditional human resource allocation algorithm and designs a double recurrent neural network job matching recommendation algorithm applicable to the human resource field, which can improve the traditional algorithm data training quality problem. In the experimental part of the algorithm, the arithmetic F 1 value in the paper is 0.823, which is 20.1% and 7.4% higher than the other two algorithms, respectively, indicating that the algorithm can improve the hidden layer features of the data and then improve the training quality of the data and improve the job matching and recommendation accuracy., Competing Interests: The authors declare that they have no conflicts of interest regarding this work., (Copyright © 2021 Qi Feng et al.)

Photosynthesis response to carbon dioxide concentration can provide data on a number of important parameters related to leaf physiology. The genetic algorithm (GA), which is a robust stochastic evolutionary computational algorithm inspired by both natural selection and natural genetics, is proposed to simultaneously estimate the parameters [including maximum carboxylation rate allowed by ribulose 1.5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation rate (V(cmax)), potential light-saturated electron transport rate (J(max)), triose-phosphate utilization (TPU), leaf dark respiration in the light (R(d)) and mesophyll conductance (g(m))] of the photosynthesis models presented by Farquhar, von Caemmerer and Berry, and Ethier and Livingston. The results show that by properly constraining the parameter bounds the GA-based estimate methods can effectively and efficiently obtain globally (or, at least near globally) optimal solutions, which are as good as or better than those obtained by non-linear curve fitting methods used in previous studies. More complicated problems such as taking the g(m) variation response to CO(2) into account can be easily formulated and solved by using GA. The influence of the crossover probability (P(c)), mutation probability (P(m)), population size and generation on the performance of GA was also investigated.

We propose a deep signature/log-signature FBSDE algorithm to solve forward-backward stochastic differential equations (FBSDEs) with state and path dependent features. By incorporating the deep signature/log-signature transformation into the recurrent neural network (RNN) model, our algorithm shortens the training time, improves the accuracy, and extends the time horizon comparing to methods in the existing literature. Moreover, our algorithms can be applied to a wide range of applications such as state and path dependent option pricing involving high-frequency data, model ambiguity, and stochastic games, which are linked to parabolic partial differential equations (PDEs), and path-dependent PDEs (PPDEs). Lastly, we also derive the convergence analysis of the deep signature/log-signature FBSDE algorithm. [ABSTRACT FROM AUTHOR]

Covert communication is designed for hiding the subliminal communication which takes place between both of the speakers and their relationship. The traditional covert communication utilizes the centralized channel and the third-party central node or authority to distribute messages which leads to a lack of undetectability, antitraceability, and robustness. In recent years, there have been attempts to apply the blockchain to covert communication solutions, for the characteristics of blockchain such as decentralization, openness, and trustworthiness. In this paper, based on the analysis of the literature and the classification according to the hiding position, we identify four kinds of covert communication: Address Channel, Value Channel, DSA (Digital Signature Algorithm) Channel, and Script Channel, which will help inform future research agenda. [ABSTRACT FROM AUTHOR]

With the convenience brought by Location-based service (LBS), users' requirements for indoor positioning accuracy are getting higher than ever. However, many traditional indoor WiFi positioning methods may result in limited positioning accuracy because of the limited information of Received Signal Strength (RSS) of WiFi signal. This paper proposed a context-aware assisted WiFi positioning method (CAA-PM), which uses context information (i.e., light and sound) to assist WiFi-RSS for indoor positioning and uses an improved variable weight dynamic KNN fingerprint identification algorithm (VWD-KNN). Finally, experiments are carried out by using the dataset collected in both a closed laboratory and an open long corridor, and it is shown that the proposed algorithm substantially improves the localization accuracy comparing with other three classical algorithms. [ABSTRACT FROM AUTHOR]

The new radio (NR) system is recently proposed to deploy alongside the long term evolution (LTE) system to improve the wireless communication capacity. The Vandermonde-subspace frequency division multiplexing (VFDM) is an efficient technique for such a two tier network, whereby the NR transmitter can reuse the frequency spectrum of LTE system meanwhile guaranteeing interference free to LTE. In this paper, we first investigate the robust rate-maximization problem in the VFDM system. In the condition of imperfect channel state information (CSI), the precoder is optimized to satisfy both non-interference to LTE system and rate-maximization in the NR system. To tackle the challenge in solving such a non-convex problem, an iterative algorithm is proposed based on the block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Finally, simulation results demonstrate the superiority of the proposed algorithm in terms of robustness and achievable rate over the conventional SVD-based VFDM technique. [ABSTRACT FROM AUTHOR]

In this paper, a three-component decomposition algorithm is proposed for processing compact polarimetric SAR images. By using the correspondence between the covariance matrix and the Stokes vector, three-component scattering models for CTLR and DCP modes are established. The explicit expression of decomposition results is then derived by setting the contribution of volume scattering as a free parameter. The degree of depolarization is taken as the upper bound of the free parameter, for the constraint that the weighting factor of each scattering component should be nonnegative. Several methods are investigated to estimate the free parameter suitable for decomposition. The feasibility of this algorithm is validated by AIRSAR data over San Francisco and RADARSAT-2 data over Flevoland. [ABSTRACT FROM AUTHOR]

This work presents a distributed time-slot assignment algorithm which adopts TDMA as Medium Access Control, specially suited to support applications with strict delay, jitter, and throughput requirements characterized by convergecast traffic pattern in sensor networks. (e.g. wireless video surveillance sensor networks). The proposed algorithm has three characteristics: (1) every node is guaranteed a path to the base station for its data delivery. In the path, sufficient resource is reserved and weighted fairness can be achieved. (2) It uses cascading time-slot assignment and jitter minimization algorithm in each node to minimize jitter and end-to-end delay. (3) Nodes are only active during their scheduled slots and sleep otherwise. This offers energy saving by reducing idle listening and avoiding overhearing. The performance of the proposed algorithm is evaluated over simulations and analyzed theoretically in comparison with existing slot assignment algorithm. The results show that our algorithm provides lower end-to-end delay, jitter, and higher throughput. Copyright © 2010 John Wiley & Sons, Ltd. In this paper, we introduce a time slot assignment algorithm for real time applications in wireless sensor networks. The algorithm has three characteristics: (1) every node is guaranteed a path to the base station for its data delivery. In the path, sufficient resource is reserved and weighted fairness can be achieved. (2) Cascading time slot assignment and jitter minimization algorithm are proposed to minimize jitter and transmission delay. (3) Nodes are only active during their scheduled slots and sleep otherwise. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Although 2-adic complexity was proposed more than ten years ago, even for m -sequences which are thought of as the most important linear recurring sequences, no theoretical results about their 2-adic complexity has been presented. In this paper, it is shown that for a binary m-sequence, its 2-adic complexity attains the maximum, which implies that no feedback with carry shift registers (FCSRs) with connection integer less than 22n-1 can generate m-sequences of order n. [ABSTRACT FROM AUTHOR]

Abstract  In the note, a simple proof is provided for the logarithmic convexity of extended mean values. [ABSTRACT FROM AUTHOR]

Simultaneously focusing multi-wavelength light through scattering medium is of great significance. However, due to the incoherent superposition of multiple uncorrelated speckle patterns produced by different frequency components, the inability to better balance high intensity and good uniformity of focus points with different wavelengths has always been an important factor restricting the focusing effect. Here we employ a multi-objective iterative algorithm to feedback-based wavefront shaping and experimentally realize controllable multi-point focusing of different wavelengths through scattering medium. The experimental result shows that even for focus points with different wavelengths, the range of the coefficients of variation among the focus points is lower than 5%. Our work verifies that a uniform intensity and a relatively high enhancement can be better balanced in multi-wavelength focusing process. The powerful optical control ability of our method in uniform intensity focusing will further promote more practical applications based on multi-wavelength focusing of wavefront shaping, such as nanophotonics, optical microscopy and optogenetics. [ABSTRACT FROM AUTHOR]

Let a be an l-sequence generated by a feedback-with-carry shift register with connection integer pϵ, where p is an odd prime and e ≥ 1. Goresky and Klapper conjectured that when pϵ E {5, 9, 11, 13}, all decimations of, a are cyclically distinct. When e = 1 and p > 13, they showed that the set of distinct decimations is large and, in some cases, all decimations are distinct. In this article, we further show that when e ≥ 2 and pϵ ≠ 9, all decimations of a are also cyclically distinct. [ABSTRACT FROM AUTHOR]

Spatial data mining techniques have been investigated to extract valuable knowledge from natural resource maps. One challenge in applying general-purpose data mining algorithms to knowledge discovery from these maps is that the unavoidable existence of errors in traditional area-class resource maps introduces noise to the process and impacts the accuracy of the extracted knowledge. This paper examines the effect of data preprocessing in knowledge discovery from area-class resource maps. Presented here is a sampling method designed to obtain samples that exclude noise and are representative of the central concepts of the mapped classes. The method is based on selecting only the pixels that fall in modes of environmental histograms for each individual class. A case study in extracting knowledge from soil maps shows that data preprocessing plays an important role in the knowledge discovery process, and that the sampling method based on environmental histograms significantly improves the knowledge discovery performance. [ABSTRACT FROM AUTHOR]

The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states. [ABSTRACT FROM AUTHOR]

Modelling workflow for BRF-LSTM and BRF-GRU predictive models. [Display omitted] • Deep learning hybrid predictive model (BRF-LSTM) is built for streamflow forecasting. • Climate indices, rainfall and periodicity are factored for accurate predictions. • Adopts feature algorithm (i.e. Boruta) with long short-term memory and gated recurrent unit. • Six gauging sites tested; BRF-LSTM yields > 98% errors in ± 0.015 m, ~1.30% relative error. • The proposed model is useful for hydrological and strategic water resources planning. Long-term forecasting of any hydrologic phenomena is essential for strategic environmental planning, hydrologic and other forms of structural design, agriculture, and water resources management. Climate mode indices, utilising machine learning methods, are frequently considered as predictor variables in order to forecast several different hydrological variables. In this study, a feature selection algorithm based on two different deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capability of streamflow water levels at six gauging stations in the Murray Darling Basin of Australia. This paper therefore aggregates the significant antecedent lag memory of climate mode indices, rainfall, and the monthly factor based on the periodicity as the predictor variables to attain significantly accurate stream water level forecasts. This novel method identifies an improved relationship between the stream water level and climate mode indices through the aggregation of the significant lagged datasets capturing the historical features to predict the future streamflow water level. The boruta feature selection algorithm (BRF) was then applied in a two phase process before and after attaining the significant lagged inputs to screen the optimum predictor variables. The merits of the forecast models were evaluated through different performance evaluation criteria. The results show that the accumulated significant lagged inputs based on climate mode indices, along with the rainfall and periodicity factors are seen to provide improved forecasting of the SWL over the non-BRF deep learning approaches where no prior feature selection was applied. The hybrid LSTM method (i.e., BRF-LSTM model) achieved a unique advantage in terms of SWL forecasting, particularly attaining over 98% of the predictive errors lying within a band of +/-0.015 m with relatively low relative errors (RRMSE ≈1.30% and RMAE ≈ 0.882%), outperforming all of the benchmark models. It is also found that the periodicity factor has a potential influence on the accuracy of the forecast models for the four monitored study stations. This study concludes that the newly developed hybrid deep learning approaches, coupled with the BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can therefore be used to provide a strong provide predictive response algorithm for the hydrological variables that were influenced by the low-frequency variability of the climate model indices in respect to streamflow water level. [ABSTRACT FROM AUTHOR]

Many semi-distributed models that simulate pollutants' losses from watersheds do not handle well detailed spatially distributed and temporal data with which to identify accurate and cost-effective strategies for controlling pollutants issuing from non-point sources. Such models commonly overlook the flow pathways of pollutants across the landscape. This work aims at closing such knowledge gap by developing a Spatially and Temporally Distributed Empirical model for Phosphorus Management (STEM-P) that simulates the daily phosphorus loss from source areas to receiving waters on a spatially-distributed grid-cell basis. STEM-P bypasses the use of complex mechanistic algorithms by representing the phosphorus mobilization and delivery processes with zero-order mobilization and first-order delivery, respectively. STEM-P was applied to a 217 km 2 watershed with mixed forest and agricultural land uses situated in southwestern China. The STEM-P simulation of phosphorus concentration at the watershed outlet approximated the observed data closely: the percent bias (P bias ) was − 7.1%, with a Nash-Sutcliffe coefficient (E NS ) of 0.80 on a monthly scale for the calibration period. The P bias was 18.1%, with a monthly E NS equal to 0.72 for validation. The simulation results showed that 76% of the phosphorus load was transported with surface runoff, 25.2% of which came from 3.4% of the watershed area (classified as standard A critical source areas), and 55.3% of which originated from 17.1% of the watershed area (classified as standard B critical source areas). The standard A critical source areas were composed of 51% residences, 27% orchards, 18% dry fields, and 4% paddy fields. The standard B critical source areas were mainly paddy fields (81%). The calculated spatial and temporal patterns of phosphorus loss and recorded flow pathways identified with the STEM-P simulations revealed the field-scale critical source areas and guides the design and placement of effective practices for non-point source pollution control and water quality conservation. [ABSTRACT FROM AUTHOR]