Probabilistic Memory Model for Visual Images Categorization

During the past decades, numerous memory models have been proposed, which focused mainly on how spoken words are studied, whereas models on how visual images are studied are still limited. In this study, we propose a probabilistic memory model (PMM) for visual images categorization which is able to mimic the workings of the human brain during the image storage and retrieval. First, in the learning phase, the visual images are represented by the feature vectors extracted with convolutional neural network (CNN) and each feature component is assumed to conform to a Gaussian distribution and may be incompletely copied with a certain probability or randomly produced in accordance to an exponential distribution. Then, in the test phase, the likelihood ratio between the test image and each studied image is calculated based on the probabilistic inference theory, and an odd value in favor of an old item over a new one is obtained based on all likelihood values. Finally, if the odd value is above a certain threshold, the Bayesian decision rule is applied for image classification. Experimental results on two benchmark image datasets demonstrate that the proposed PMM can perform well on categorization tasks for both studied and non-studied images

Geochemical differences between subduction- and collision-related copper-bearing porphyries and implications for metallogenesis

Porphyry Cu (-Mo-Au) deposits occur not only in continental margin-arc settings (subduction-related porphyry Cu deposits, such as those along the eastern Pacific Rim (EPRIM)), but also in continent-continent collisional orogenic belts (collision-related porphyry Cu deposits, such as those in southern Tibet). These Cu-mineralized porphyries, which develop in contrasting tectonic settings, are characterized by some different trace element (e.g., Th, and Y) concentrations and their ratios (e.g., Sr/Y, and La/Yb), suggesting that their source magmas probably developed by different processes. Subduction-related porphyry Cu mineralization on the EPRIM is associated with intermediate to felsic calc-alkaline magmas derived from primitive basaltic magmas that pooled beneath the lower crust and underwent melting, assimilation, storage, and homogenization (MASH), whereas K-enriched collision-related porphyry Cu mineralization was associated with underplating of subduction-modified basaltic materials beneath the lower crust (with subsequent transformation into amphibolites and eclogite amphibolites), and resulted from partial melting of the newly formed thickened lower crust. These different processes led to the collision-related porphyry Cu deposits associated with adakitic magmas enriched by the addition of melts, and the subduction-related porphyry Cu deposits associated with magmas comprising all compositions between normal arc rocks and adakitic rocks, all of which were associated with fluid-dominated enrichment process. In subduction-related Cu porphyry magmas, the oxidation state (fO2), the concentrations of chalcophile metals, and other volatiles (e.g., S and Cl), and the abundance of water were directly controlled by the composition of the primary arc basaltic magma. In contrast, the high Cu concentrations and fO2 values of collision-related Cu porphyry magmas were indirectly derived from subduction modified magmas, and the large amount of water and other volatiles in these magmas were controlled in part by partial melting of amphibolite derived from arc basalts that were underplated beneath the lower crust, and in part by the contribution from the rising potassic and ultrapotassic magmas. Both subduction- and collision-related porphyries are enriched in potassium, and were associated with crustal thickening. Their high K2O contents were primarily as a result of the inheritance of enriched mantle components and/or mixing with contemporaneous ultrapotassic magmas

Saliency-Guided Remote Sensing Image Super-Resolution

Deep learning has recently attracted extensive attention and developed significantly in remote sensing image super-resolution. Although remote sensing images are composed of various scenes, most existing methods consider each part equally. These methods ignore the salient objects (e.g., buildings, airplanes, and vehicles) that have more complex structures and require more attention in recovery processing. This paper proposes a saliency-guided remote sensing image super-resolution (SG-GAN) method to alleviate the above issue while maintaining the merits of GAN-based methods for the generation of perceptual-pleasant details. More specifically, we exploit the salient maps of images to guide the recovery in two aspects: On the one hand, the saliency detection network in SG-GAN learns more high-resolution saliency maps to provide additional structure priors. On the other hand, the well-designed saliency loss imposes a second-order restriction on the super-resolution process, which helps SG-GAN concentrate more on the salient objects of remote sensing images. Experimental results show that SG-GAN achieves competitive PSNR and SSIM compared with the advanced super-resolution methods. Visual results demonstrate our superiority in restoring structures while generating remote sensing super-resolution images

Integrated Evaluation to High Yield and Water-saving of Winter Wheat in North China

Evaluation to high yield and water-saving for improving water use efficiency (WUE) of crops is becoming important in irrigated farming and dry farming. Field experiments with 7 winter wheat varieties under 2 levels of irrigation were conducted in Luancheng Experiment Station (37 53 LN C140 40 LE G 50m above the sea level) during 2001 to 2002. The growth and development of yield and its components, water use, WUE, photosynthesis rate, transpiration rate and stomata conductance were measured. The results showed that yield and drought resistance index was not significantly related with several measured agronomic traits. However, area of flag leaf and the leaf below flag leaf were significantly negatively related with WUE at yield level, specific leaf weight of flag leaf and the leaf below flag leaf was significantly positive related with WUE at yield level, transpiration rate and stomata conductance were significantly negatively related with WUE at leaf level. Based on leaf water potential ( t µ ), stomata resistance (Rs), transpiration rate (Tr), drought resistance coefficient (DC), maximum yield (Ym) and WUE at yield level, an evaluation index to high-yield, water saving and drought- resistance(IA) was established as follows : IA=0.4Ym + 0.2WUE + 0.1(Rs+ t µ +Tr) + 0.1DC.vokMyynti MTT tietopalvelu

Robust Nonnegative Matrix Factorization via L1L_1 Norm Regularization

Nonnegative Matrix Factorization (NMF) is a widely used technique in many applications such as face recognition, motion segmentation, etc. It approximates the nonnegative data in an original high dimensional space with a linear representation in a low dimensional space by using the product of two nonnegative matrices. In many applications data are often partially corrupted with large additive noise. When the positions of noise are known, some existing variants of NMF can be applied by treating these corrupted entries as missing values. However, the positions are often unknown in many real world applications, which prevents the usage of traditional NMF or other existing variants of NMF. This paper proposes a Robust Nonnegative Matrix Factorization (RobustNMF) algorithm that explicitly models the partial corruption as large additive noise without requiring the information of positions of noise. In practice, large additive noise can be used to model outliers. In particular, the proposed method jointly approximates the clean data matrix with the product of two nonnegative matrices and estimates the positions and values of outliers/noise. An efficient iterative optimization algorithm with a solid theoretical justification has been proposed to learn the desired matrix factorization. Experimental results demonstrate the advantages of the proposed algorithm

Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity

Over the past decade, a growing number of studies have investigated the relationship between the structure and function of human brain by predicting the resting-state functional connectivity (rsFC) from structural connectivity (SC). Yet how the whole-brain patterns of FC emerge from SC still remains incompletely understood. Unlike previous studies, here we propose an alternative approach for addressing this issue by predicting SC from rsFC. We first hypothesize that the functional couplings among brain areas at rest are shaped at least in three phases temporally: the initial direct interplay between brain areas, the communications within and between network modules, and followed by the indirect interactions ascribed to indirect structural pathways. We then introduce a network deconvolution (ND) algorithm inspired from the mechanism of cell differentiation, named CDA, to distinguish the direct dependencies from the functional network followed by a weight trimming algorithm based on Euclidean distance kernel function for shrinking the modular effects. Finally, we keep those region pairs with shorter shortest path length (SPL) together with shorter Euclidean distance as the structural connections. We apply the model and the algorithms to three intensively studied group averaged empirical connectome datasets with different parcellation resolutions and the results demonstrate that the predicted intrahemispheric structural connections and the weights distribution are highly consistent with the empirical SC derived from diffusion magnetic resonance imaging (dMRI) and probabilistic tractography, thus strongly supporting the model and algorithms proposed

Root growth, available soil water, and water-use efficiency of winter wheat under different irrigation regimes applied at different growth stages in North China

Field experiments were conducted at the Luancheng Agro-Ecosystem Experimental Station of the Chinese Academy of Sciences during the winter wheat growing seasons in 2006-2007 and 2007-2008. Experiments involving winter wheat with 1, 2, and 3 irrigation applications at jointing, heading, or milking were conducted, and the total irrigation water supplied was maintained at 120 mm. The results indicated that irrigation during the later part of the winter wheat growing season and increase in irrigation frequency decreased the available soil water; this result was mainly due to the changes in the vertical distribution of root length density. In 30-cm-deep soil profiles, 1 time irrigation at jointing resulted in the highest root length density. With regard to evapotranspiration (ET), there was no significant (LSD, P Root length density Available soil water Water-use efficiency Winter wheat Deficit irrigation

CSN: Component-Supervised Network for Few-Shot Classification

The few-shot classification (FSC) task has been a hot research topic in recent years. It aims to address the classification problem with insufficient labeled data on a cross-category basis. Typically, researchers pre-train a feature extractor with base data, then use it to extract the features of novel data and recognize them. Notably, the novel set only has a few annotated samples and has entirely different categories from the base set, which leads to that the pre-trained feature extractor can not adapt to the novel data flawlessly. We dub this problem as Feature-Extractor-Maladaptive (FEM) problem. Starting from the root cause of this problem, this paper presents a new scheme, Component-Supervised Network (CSN), to improve the performance of FSC. We believe that although the categories of base and novel sets are different, the composition of the sample's components is similar. For example, both cat and dog contain leg and head components. Actually, such entity components are intra-class stable. They have fine cross-category versatility and new category generalization. Therefore, we refer to WordNet, a dictionary commonly used in natural language processing, to collect component information of samples and construct a component-based auxiliary task to improve the adaptability of the feature extractor. We conduct experiments on two benchmark datasets (mini-ImageNet and tiered-ImageNet), the improvements of $0.9\%$-$5.8\%$ compared with state-of-the-arts have evaluated the efficiency of our CSN

Theoretical and numerical investigation into brush seal hysteresis without pressure differential

Brush seal is a novel type contact seal, and it is well-known due to its excellent performance. However, there are many intrinsic drawbacks, such as hysteresis, which need to be solved. This article focused on modeling hysteresis in both numerical way and analytic way without pressure differential. The numerical simulation was solved by the finite element method. General contact method was used to model the inter-bristle contact, bristle-rotor contact, and bristle-backplate contact. Bristle deformation caused by both vertical and axial tip force was used to validate the numerical model together with reaction force. An analytic model in respect of the strain energy was created. The influence of structure parameters on the hysteresis ratio, with the emphasis on the derivation of hysteresis ratio formula for brush seals, was also presented. Both numerical model and analytic model presented that cant angle is the most influential factor. The aim of the article is to provide a useful theoretical and numerical method to analyze and predict the hysteresis. This work contributes the basis for future hysteresis investigation with pressure differential.open access</p