Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

Hydraulic reactivity and cement formation of baghdadite

In this study, the hydraulic reactivity and cement formation of baghdadite (Ca₃ZrSi₂O₉) was investigated. The material was synthesized by sintering a mixture of CaCO₃, SiO₂, and ZrO₂ and then mechanically activated using a planetary mill. This leads to a decrease in particle and crystallite size and a partial amorphization of baghdadite as shown by X-ray powder diffraction (XRD) and laser diffraction measurements. Baghdadite cements were formed by the addition of water at a powder to liquid ratio of 2.0 g/ml. Maximum compressive strengths were found to be ~2 MPa after 3-day setting for a 24-h ground material. Inductively coupled plasma mass spectrometry (ICP-MS) measurements showed an incongruent dissolution profile of set cements with a preferred dissolution of calcium and only marginal release of zirconium ions. Cement formation occurs under alkaline conditions, whereas the unground raw powder leads to a pH of 11.9 during setting, while prolonged grinding increased pH values to approximately 12.3.     

Categorical observers for metamerism

A method is proposed to generate categorical colour observer functions (individual colour matching functions) for any field size based on the CIE 2006 system of physiological observer functions. The method combines proposed categorical observer techniques of Sarkar et al with a physiologically-based individual observer model of Asano et al and a clustering technique to produce the optimal set of categorical observers. The number of required categorical observers varies depending on an application with as many as 50 required to predict individual observers' matches when a laser projector is viewed. However, 10 categorical observers are sufficient to represent colour-normal populations for personalized colour imaging. The proposed and recommended categorical observers represent a robust and inclusive technique to examine and quantify observer metamerism in any application of colorimetry.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

胡萝卜丁中维生素C在热风辅助射频干燥过程中降解的动力学模型

目的研究热风辅助射频(hot air-assisted radio frequency,HA-RF)干燥过程中维生素C随温度变化的降解动力学模型。方法在6.5 cm极板间距和60℃热风系统条件下进行HA-RF干燥,重点研究胡萝卜丁中维生素C在干燥过程中的降解动力学,包括脱氢抗坏血酸(dehydroascorbic acid,DHAA)和抗坏血酸(ascorbic acid,AA)。总维生素C含量采用2,4-硝基苯肼分光光度法测定,AA采用2,6-二氯吲哚酚滴定法测定。结果HA-RF干燥过程中维生素C的降解规律符合一级可逆模型,其降解活化能为40.54 kJ/mol。DHAA活化能为35.83 kJ/mol,表明DHAA的稳定性低于AA。结论本研究使用的干燥温度为62.5~77.5℃,较高的干燥温度下维生素C降解速率较高,较低处理温度下的降解过程具有更好的模型适应度(R2>0.98)。     

Preparation of α-cordierite through mechanochemical activation of MgO–Al2O3–SiO2 ternary system

Samples of the ternary system MgO–Al₂O₃–SiO₂ with stoichiometric composition in relation to α-cordierite (Mg₂A_l4Si₅O₁₈), consisting of 22.2 mol% MgO, 22.2 mol% Al₂O₃, and 55.6 mol% SiO₂, were activated in a low energy mill with a constant speed of 100 rpm, in an aqueous medium. The precursors used were corundum (Al₂O₃), silica gel HF254 type 60 (SiO₂), and periclase (MgO). The objective of the present study was to evaluate the effect of mechanochemical activation on the solid-state synthesis of α-cordierite, using a low energy ball mill. Another objective was to shed light on the effect of mechanochemical activation on the steps of α-cordierite formation. For this end several grinding conditions were evaluated, varying the time and mass ratio of precursors/grinding elements, as well as calcination at different temperatures between 950 °C and 1350 °C for 2 h. The samples were analyzed for the determination of the formed phases by Infrared (IR) and X-ray Diffraction (XRD). The phases identified in uncalcined samples were brucite (Mg(OH)₂), forsterite (Mg₂SiO₄), enstatite (MgSiO₃), spinel (MgAl₂O₃), amorphous silica (SiO₂), corundum (α-Al₂O₃), and zirconia (monoclinic and tetragonal ZrO₂). The lowest temperature corresponding to the formation of α-cordierite (α-Mg₂Al₄Si₅O₁₈) was 1150 °C and a considerable amount of this phase (16.2%) was observed at this temperature, for the sample with the higher mechanochemical activation. In a solid-state reaction, α-cordierite is normally obtained at around 1400 °C, therefore, the formation of this phase at 1150 °C confirms that the mechanochemical activation method, using a low-cost ball mill, is efficient in reducing the solid-state reaction temperature.     

On one boundary problem for nonlinear stationary controlled system

The object of study is nonlinear stationary controlled system of ordinary differential equations with constant disturbance in the right part. The problem of constructing the synthesising control function providing the transfer of this system from the initial state to the origin is considered. The sufficiently simple for numerical implementation algorithm of solution of the above-mentioned problem is obtained. It is shown that for local null controllability of the considered system, it is sufficient that the conditions of the Kalman's type were satisfied. In addition, the estimates restricting the choice of initial conditions and external disturbances under which the transfer is guaranteed are obtained. The main idea of the method of construction of the desired control function consists in reducing the original problem to stabilisation of a special kind linear non-stationary system and solving the Cauchy problem for an auxiliary system of ordinary differential equations closed by stabilising control. The simplicity of the realisation of this algorithm is determined by the construction of the auxiliary system and its stabilisation that could be obtained by analytical methods. The effectiveness of the method is illustrated by solving the problem of crane control and its numerical simulation.     

Risk factors associated with online game addiction: A hierarchical model

Online gaming addiction has been increasingly recognized as a mental disorder. However, the predictive factors that lead to online gaming addiction are not well established. The aim of this study was to identify factors that may influence the development of online gaming addiction. A total of 263 patients with problematic online gaming addiction (255 males (97%) and 8 females (3%), age: mean = 20.4 ± 5.8 years) and153 healthy comparison subjects (118 males (77%) and 35 females (23%), age: 21.2 ± 5.5 years, range) were recruited for participation in the current study. Hierarchical logistic regression analyses among each set of variables were conducted. Individual factors (sex and age), cognitive factors (IQ and perseverative errors), psychopathological conditions (ADHD, depression, anxiety, and impulsivity), and social interaction factors (family environment, social anxiety, and self-esteem) were evaluated in a stepwise fashion. All four factors were associated with online gaming addiction, with psychopathological conditions being the strongest risk factors for the addiction. Individual factors, psychological factors, and social interactions were associated with the development of pure online gaming addiction. As before, psychological factors (attention, mood, anxiety and impulsivity) were the strongest risk factors for online gaming addiction in patients with pure online gaming addiction. Psychopathologies, including ADHD and depression, were the strongest factors associated with the development of online gaming addiction in individuals.     

Word length and lexical activation: Longer is better.

Many models of spoken word recognition posit the existence of lexical and sublexical representations, with excitatory and inhibitory mechanisms used to affect the activation levels of such representations. Bottom-up evidence provides excitatory input, and inhibition from phonetically similar representations leads to lexical competition. In such a system, long words should produce stronger lexical activation than short words, for 2 reasons: Long words provide more bottom-up evidence than short words, and short words are subject to greater inhibition due to the existence of more similar words. Four experiments provide evidence for this view. In addition, reaction-time-based partitioning of the data shows that long words generate greater activation that is available both earlier and for a longer time than is the case for short words. As a result, lexical influences on phoneme identification are extremely robust for long words but are quite fragile and condition-dependent for short words. Models of word recognition must consider words of all lengths to capture the true dynamics of lexical activation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)