How to predict the global instantaneous feeling induced by a facial picture?

Picture selection is a time-consuming task for humans and a real challenge for machines, which have to retrieve complex and subjective information from image pixels. An automated system that infers human feelings from digital portraits would be of great help for profile picture selection, photo album creation or photo editing. In this work, two models of facial pictures evaluation are defined. The first one predicts the overall aesthetic quality of a facial image, and the second one answers the question “Among a set of facial pictures of a given person, on which picture does the person look like the most friendly?”. Aesthetic quality is evaluated by the computation of 15 features that encode low-level statistics in different image regions (face, eyes, and mouth). Relevant features are automatically selected by a feature ranking technique, and the outputs of 4 learning algorithms are fused in order to make a robust and accurate prediction of the image quality. Results are compared with recent works and the proposed algorithm obtains the best performance. The same pipeline is considered to evaluate the likability of a facial picture, with the difference that the estimation is based on high-level attributes such as gender, age, and smile. Performance of these attributes is compared with previous techniques that mostly rely on facial keypoint positions, and it is shown that it is possible to obtain likability predictions that are close to human perception. Finally, a combination of both models that selects a likable facial image of good quality for a given person is described.     

Quality assessment for color correction-based stitched images via bi-directional matching

With the deepening of social information, the panoramic image has drawn a significant interest of viewers and researchers as it can provide a very wide field of view (FoV). Since panoramic images are usually obtained by capturing images with the overlapping regions and then stitching them together, image stitching plays an important role in generating panoramic images. In order to effectively evaluate the quality of stitched images, a novel quality assessment method based on bi-directional matching is proposed for stitched images. Specifically, dense correspondences between the testing and benchmark stitched images are first established by bi-directional SIFT-flow matching. Then, color-aware, geometric-aware and structure-aware features are respectively extracted and fused via support vector regression (SVR) to obtain the final quality score. Experiments on our newly constructed database and ISIQA database demonstrate that the proposed method can achieve comparable performance compared with the conventional blind quality metrics and the quality metrics specially designed for stitched images.     

Melting thermodynamics of oligonucleic acids conjugated with relatively solvophobic linear polymers: A coarse‐grained molecular simulation study

Oligonucleic acids (ONAs), such as DNA and RNA, are used in various biotechnology and nanotechnology applications due to their ability to form a double helix that is stable at low temperature and melts at high temperatures. The melting temperature (T_m) of ONA duplexes can be tuned by the ONA composition, sequence, length and concentration, solvent quality, and salt concentration and by conjugation to other macromolecules. In this article, we use coarse‐grained (CG) molecular simulations to study ONAs conjugated with linear homopolymers that are relatively more solvophobic than the ONA. We study charged and stiff 8‐mer ONAs (e.g., DNA) and neutral and flexible 8‐mer ONAs (e.g., peptide nucleic acids or PNA), and vary the composition (or G‐C content) and sequence of ONA, conjugated homopolymer lengths and solvent quality for the polymer. For neutral and flexible ONAs, as the solvent quality worsens for the polymer, the ONA melting temperature increases from that of unconjugated ONA. The melting curves broaden with polymer length and worsening solvent quality, especially for ONAs with higher G‐C content. For charged and stiff ONAs, as the solvent quality worsens, the ONA melting temperature decreases compared to unconjugated ONA while the width of the melting curve remains the same. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019 , 57, 1196–1208     

QUANTIS: Data quality assessment tool by clustering analysis

Automatically generated kinetic networks are ideally validated against a large set of accurate, reproducible, and easy-to-model experimental data. However, although this might seem simple, it proves to be quite challenging. QUANTIS, a publicly available Python package, is specifically developed to evaluate both the precision and accuracy of experimental data and to ensure a uniform, quick processing, and storage strategy that enables automated comparison of developed kinetic models. The precision is investigated with two clustering techniques, PCA and t-SNE, whereas the accuracy is probed with checks for the conservation laws. First, the developed tool processes, evaluates, and stores experimental yield data automatically. All data belonging to a given experiment, both unprocessed and processed, are stored in the form of an HDF5 container. The demonstration of QUANTIS on three different pyrolysis cases showed that it can help in identifying and overcoming instabilities in experimental datasets, reduce mass and molar balance closure discrepancies, and, by evaluating the visualized correlation matrices, increase understanding in the underlying reaction pathways. Inclusion of all experimental data in the HDF5 file makes it possible to automate simulating the experiment with CHEMKIN. Because of the employed InChI string identifiers for molecules, it is possible to automate the comparison experiment/simulation. QUANTIS and the concepts demonstrated therein is a potentially useful tool for data quality assessment, kinetic model validation, and refinement.     

Telesurgery QoS improvement over SDN based on a Type‐2 fuzzy system and enhanced cuckoo optimization algorithm

Telemedicine is a new area based on the information and communication technology for collecting, storing, organizing, retrieving and exchanging medical information. One of the most important applications of telemedicine is indeed telesurgery in which an efficient telecommunication infrastructure between the surgery room and remote surgeons need to be established. One of the most important issues to be tackled in telesurgery is to find favorable links for routing as well as providing high Quality of Service (QoS). In this paper, an efficient model based on the hybridization of Type‐2 Fuzzy System (T2FS) and Cuckoo Optimization Algorithm (COA) over the Software Defined Networks (SDN) is proposed in order to achieve optimal and reliable routes for telesurgery application. Using T2FS, the fitness of the links is determined; then, a COA is conducted over the Constraint Shortest Path (CSP) problem to find the best routes. Delay is considered as a CSP problem which is satisfied by trying to find the paths with minimum cost. Due to the NP‐completeness of the CSP problem, an Enhanced COA (so‐called E‐COA) is proposed and utilized as a metaheuristic solver. To the best of our knowledge, this paper is the first SDN‐based communication model that applies both T2FS and E‐COA for assigning proper costs to the network's links, and solves the consequence CSP problem according to the QoS requirement for telesurgery. The model also recognizes and preserves the second‐best routes in order to keep the reliability for such a critical application. In addition to the simulations, the performance evaluation is also conducted on a real experimental scenario. Many comparisons are carried out between the proposed model and other conventional methods, and the evaluation study shows the superiority of the proposed model on all the three QoS‐related metrics, i.e. average end‐to‐end delay, packet loss ratio and PSNR.     

基于火箭遥测的地面站接收信号情况预估方法

火箭尾焰造成的信号衰减是地面测量点位选择重点考虑的问题,通常采用传统β角确定火箭尾焰对遥测地面站接收信号的影响程度。针对新型固体火箭发射中β角分析法对遥测地面站接收信号情况预测不准确的问题,文中在分析影响遥测地面站信号接收因素的基础上,提出了一种采用信号衰减方向性预估遥测地面站接收信号情况的模型。通过分析火箭遥测实测飞行姿态、位置以及地面遥测设备实际跟踪信号情况确定了模型参数,并通过实例计算证明了该方法优的优越性。     

3种现场快速水质检测仪的性价对比

选择了市场上口碑好、占有率高的3款便携式水质现场应急检测设备,通过准确度、精密度、检测时长及价格等指标,以权重积分法对3款仪器进行了比选,结果表明德国MN公司的PF-12型便携式多参数分光光度计的综合性能最优.     

户外LED显示屏图像质量主观评价初探

当前户外LED显示屏的图像质量主要靠业内人士进行简单的主观评价，不确定因素多，尚未形成规范的评价标准。本文参考通用的图像质量主观评价方法，针对LED显示屏显示特性，探讨了主观评价中的各个环境因素的控制方法，并定义了一系列主观评价测试图像的类型，能对显示屏的均匀性、对比度、色彩还原性等显示性能进行更规范更全面的评价。     

Comparison of the chromatographic fingerprint,multicomponent quantitation and antioxidant activity of Salvia miltiorrhiza Bge. between sweating and nonsweating

Salvia miltiorrhiza Bge. is a traditional Chinese medicine applied in the treatment of various diseases in clinical practice. In the course of its processing, S. miltiorrhiza Bge. is usually processed by sweating. This study employed 10‐component contents determination coupled with high‐performance liquid chromatography (HPLC) fingerprint and antioxidant activity to investigate the effect of sweating on S. miltiorrhiza Bge. so as to evaluate the quality of S. miltiorrhiza Bge. The HPLC method was performed using C₁₈ and 0.05% phosphoric acid aqueous solution–acetonitrile with a gradient elution system. It was validated for linearity, precision, repeatability, stability and recovery. Similarity analysis, principal components analysis and antioxidant activity assays were used to compare sweated S. miltiorrhiza Bge. (SSM) and nonsweated S. miltiorrhiza Bge. (NSSM). SSM and NSSM showed good similarities in HPLC fingerprint (>0.9), but principal components analysis could classify the HPLC fingerprint and 10‐component quantitation analysis. Meanwhile, the antioxidant activity of SSM was significantly higher than that of NSSM (p < 0.01). The results of this study indicated that sweating could alter the content of chemical constituents in S. miltiorrhiza Bge., and could also improve its antioxidant activity. In addition, the method not only affords a viable strategy for comparing SSM and NSSM and assessing the quality of S. miltiorrhiza Bge., but also provides a reference for other herbal medicine that suffers from sweating.     

Geographical discrimination of Glechomae Herba based on fifteen phenolic constituents determined by LC–MS/MS method combined with chemometric methods

Glechomae Herba (GH) is rich in bioactive phenolic constituents and is widely used for treatment of cholelithiasis, urolithiasis and dropsy. The simultaneous determination of phenolic constituents in GH from different geographical origins is significant for authentication and quality control purposes. In this study, we developed a strategy integrating targeted analysis and chemometric methods for quality evaluation and discrimination of GH from different geographical origins. Firstly, an accurate and reliable liquid chromatography–tandem mass spectrometry method was developed for simultaneous quantification of 15 phenolic constituents in GH from different geographical origins. The established method was well validated in terms of desirable specificity, linearity, precision and accuracy. Secondly, the quantitative data were subjected to principal component analysis and orthogonal partial least squares discriminant analysis. Thirdly, a heatmap visualization was employed for clarifying the distribution of 15 phenolic compounds in GH from different geographical origins. These results indicated that GH samples from Shandong province obviously differ from those from other provinces in the content of bioactive phenolic constituents. Collectively, the proposed platform might be a suitable tool for quality evaluation and discrimination of GH from different geographical origins, providing promising perspectives in tracking the formulation processes of traditional Chinese medicine products.