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Over the last decades, learning methods using kernels have become very popular. The main reason is that real data analysis often requires nonlinear methods to detect the dependencies that allow successful predictions of properties of interest. Gaussian kernels have been used in many studies such as learning algorithms and data analysis. Most of these studies have shown that the parameter chosen for a Gaussian kernel could have a huge impact on the desired results. Therefore, it is essential to understand this impact on a theoretical level. The main contribution of this paper is to study the effect of the Gaussian kernel bandwidth parameter on how well an empirical operator defined from data approximates its continuous counterpart. Some results in spectral approximations are provided as well as some examples.

Aiming at alleviate the detrimental effect of similar object interferences and target state changes in SiamRPN tracker, a Channel Positive and Negative Feedback Network (CPFN) is proposed, in which the Gaussian score map is generated by the feature channels selected by a Gaussian kernel, and the map is combined with the classification branches of SiamRPN. In this way, the feature channels are divided into positive feedback channels and interference channels, and these feature channels are effectively utilized. In addition, a channel weight update strategy is proposed to enhance the robustness of the tracker and avoid template pollution caused by inadequate template update. Extensive experiments on tracking benchmarks including VOT2016, VOT2018, VOT2019, OTB100, UAV123, LaSOT and GOT-10k show that the proposed CPFN outperforms the state-of-the-art methods based on small backbone network in terms of accuracy and achieves high-speed tracking.

Music is a heavenly way of expressing feelings about the world. The language of music has vast diversity. For centuries, people have indulged in debates to stratisfy between Western and Indian Classical Music. But through this paper, an understanding can be fabricated while differentiating the types of Indian Classical Music. Classical music is one of the essential characteristics of Indian Cultural Heritage. Indian Classical Music is divided into two major parts, i.e. Hindustani and Carnatic. Models have been sculptured and trained to classify between Hindustani and Carnatic Music. In this paper, two approaches are used to implement classification models. MFCCs are used as features and implemented models like DNN (1 Layer, 2 Layers, 3 Layers), CNN (1 Layer, 2 Layers, 3 Layers), RNN-LSTM, SVM (Sigmoid, Polynomial & Gaussian Kernel) as one approach. A 3 channels input is created by merging features like MFCC, Spectrogram and Scalogram and implemented models like VGG-16, CNN (1 Layer, 2 Layers, 3 Layers), ResNet-50 as another approach. 3 Layered CNN and RNN-LSTM model performed best among all the approaches.

A set-valued information system (SVIS) with missing values is known as an incomplete set-valued information system (ISVIS). This article focuses on studying uncertainty measurement for an ISVIS and the optimal selection of subsystems by means of Gaussian kernel. First, the distance between two information values on each attribute in an ISVIS is put forward. Second, the fuzzy $T_{cos}$ -equivalence relation induced by a given subsystem is proposed based on Gaussian kernel. Next, some tools are used to measure the uncertainty of an ISVIS. Moreover, effectiveness analysis is done from a statistical point of view. In the end, the optimal selection of subsystems based on $\delta $ -information granulation and $\delta $ -information amount is given. These results will help us comprehend nature of uncertainty in an ISVIS.

China's soybean supply and demand are seriously imbalanced. It is crucial to improve the level of soybean breeding. Hundred-grain weight is one of the most essential phenotypic parameters for crop breeding. Accurate soybean seed counting is a key step for 100-grain weight. There are several seed counting methods, which have their own limitations one way or the other. Among these, manual counting is time-consuming, electronic automatic seed counter devices are expensive and their counting speed is very slow, and the traditional digital image processing techniques are not suitable for seed counting based on individual pod images. This paper attempted to develop a method that would combine the density estimation-based methods and the convolution neural network (CNN)-based methods to accurately estimate the seed count from an individual soybean pod image with a single perspective. In this paper, we first introduced a new large-scale seed counting dataset, named Soybean-pod. The dataset contains 500 annotated pod images with a total of 32 126 seeds and is the largest annotated dataset for soybean seed counting so far. Simultaneously, we used annotation information to generate a ground-truth density map by convolving a Gaussian kernel and, then, devised a simple but effective method that would elucidate pod images to a seed density map using a two-column CNN (TCNN) and thus accomplish seed counting ultimately. We conducted relevant experiments from three aspects on the new dataset to verify the effectiveness of our model and method, which provided 13.21 mean absolute error (MAE) and 17.62 mean squared error (mse). In addition, our research results showed that deep learning techniques can be easily adapted to precision tasks for plant phenotyping and breeding purposes.

Aspect-level sentiment classification (ASC) is a research hotspot in natural language processing, which aims to infer the sentiment polarity of a particular aspect in an opinion sentence. There are three main influence factors in the aspect-level sentiment classification: the semantic information of the context; the interaction information of the context and aspect; the position information between the aspect and the context. Some researchers have proposed way to solve aspect-level sentiment classification. However, previous work mainly used the average vector of the aspect to calculate the attention score of the context, which introduced the influence of noise words. Moreover, these attention-based approaches simply used relative positions to calculate positional information for contextual and aspect terms and did not provided better semantic information. Based on these above questions, in this paper, we propose the PSRTN model. Firstly, obtaining the position-aware influence propagate between words and aspects by Gaussian kernel and generating the influence vector for each context word. Secondly, capturing global and local information of the context by the R-Transformer, and using the self-attention mechanism to obtain the keywords in the aspect. Finally, context representation of a particular aspect is generated for classification. In order to evaluate the validity of the model, we conduct experiments on SemEval2014 and Twitter. The results show that the accuracy of the PSRTN model can reach 83.8%, 80.9%, and 75.1% on three data sets, respectively.