No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

一种基于GN算法的动态图划分方法

随着图规模的急剧增长，对动态图进行实时处理的需求日益增加。大多现有的算法针对静态图划分是有效的，直接用其处理动态图会带来较大的通信开销。针对该问题，提出一种基于GN算法的动态图划分方法。首先收集一段时间内加入动态图中的顶点；然后，利用GN算法对这些新加入的顶点进行预划分，产生若干个内部联系紧密的社区；最后，将预划分产生的社区结果插入到已经划分好的当前图中。实验从交叉边数和负载均衡度两方面将该方法与传统流式划分方法进行比较，结果表明， 在公开数据集上，该方法的交叉边数降低了13%，负载均衡度减少了42.3%。由此可见，该方法的划分质量明显优于传统的流式划分方法。     

The three Rs of river ecosystem resilience: Resources,recruitment, and refugia

Resilience in river ecosystems requires that organisms must persist in the face of highly dynamic hydrological and geomorphological variations. Disturbance events such as floods and droughts are postulated to shape life history traits that support resilience, but river management and conservation would benefit from greater understanding of the emergent effects in communities of river organisms. We unify current knowledge of taxonomic‐, phylogenetic‐, and trait‐based aspects of river communities that might aid the identification and quantification of resilience mechanisms. Temporal variations in river productivity, physical connectivity, and environmental heterogeneity resulting from floods and droughts are highlighted as key characteristics that promote resilience in these dynamic ecosystems. Three community‐wide mechanisms that underlie resilience are (a) partitioning (competition/facilitation) of dynamically varying resources, (b) dispersal, recolonization, and recruitment promoted by connectivity, and (c) functional redundancy in communities promoted by resource heterogeneity and refugia. Along with taxonomic and phylogenetic identity, biological traits related to feeding specialization, dispersal ability, and habitat specialization mediate organism responses to disturbance. Measures of these factors might also enable assessment of the relative contributions of different mechanisms to community resilience. Interactions between abiotic drivers and biotic aspects of resource use, dispersal, and persistence have clear implications for river conservation and management. To support these management needs, we propose a set of taxonomic, phylogenetic, and life‐history trait metrics that might be used to measure resilience mechanisms. By identifying such indicators, our proposed framework can enable targeted management strategies to adapt river ecosystems to global change.     

Measurements and modeling of absorptive partitioning of volatile organic compounds to painted surfaces

Partitioning to surfaces is an important sink for volatile organic compounds (VOCs) indoors, but the mechanisms are not well understood or quantified. Here, a mass spectrometer was coupled to a portable surface reactor and a flow tube to measure partitioning of VOCs into paint films coated onto glass or wallboard, and their subsequent diffusion. A model was developed to extract values of the effective absorbing organic mass concentration of the film, C_w, which is a measure of absorption capacity, and VOC diffusion coefficients, D_f, from VOC time profiles measured during film passivation and depassivation. Values of C_w agreed well with the value estimated from the paint film mass and flow tube air volume, and D_f values (also measured using attenuated total reflectance-Fourier transform infrared spectroscopy) correlated well with VOC vapor saturation concentrations, C*, estimated using a group contribution method. The value of these relationships for estimating key parameters that control VOC partitioning into paint and the fate of VOCs indoors was demonstrated using a house model, which indicated that >50% of VOCs with C* ≤10⁸ μg/m³ (C* of octane, hexanone, and propanol) that contacted a paint film of typical thickness fully permeated the film regardless of emission duration.     

Altering the ratio of dietary palmitic,stearic, and oleic acids in diets with or without whole cottonseed affects nutrient digestibility,energy partitioning,and production responses of dairy cows

The objective of this study was to evaluate the effects of varying the ratio of dietary palmitic (C16:0), stearic (C18:0), and oleic (cis-9 C18:1) acids in basal diets containing soyhulls or whole cottonseed on nutrient digestibility, energy partitioning, and production response of lactating dairy cows. Twenty-four mid-lactation multiparous Holstein cows were used in a split-plot Latin square design. Cows were allocated to a main plot receiving either a basal diet with soyhulls (SH, n = 12) or a basal diet with whole cottonseed (CS, n = 12) that was fed throughout the experiment. Within each plot a 4 × 4 Latin square arrangement of treatments was used in 4 consecutive 21-d periods. Treatments were (1) control (CON; no supplemental fat), (2) high C16:0 supplement [PA; fatty acid (FA) supplement blend provided ～80% C16:0], (3) C16:0 and C18:0 supplement (PA+SA; FA supplement blend provided ～40% C16:0 + ～40% C18:0), and (4) C16:0 and cis-9 C18:1 supplement (PA+OA; FA supplement blend provided ～45% C16:0 + ～35% cis-9 C18:1). Interactions between basal diets and FA treatments were observed for dry matter intake (DMI) and milk yield. Among the SH diets, PA and PA+SA increased DMI compared with CON and PA+OA treatments, whereas in the CS diets PA+OA decreased DMI compared with CON. The PA, PA+SA, and PA+OA treatments increased milk yield compared with CON in the SH diets. The CS diets increased milk fat yield compared with the SH diets due to the greater yield of de novo and preformed milk FA. The PA treatment increased milk fat yield compared with CON, PA+SA, and PA+OA due to the greater yield of mixed-source (16-carbon) milk FA. The PA treatment increased 3.5% fat-corrected milk compared with CON and tended to increase it compared with PA+SA and PA+OA. The CS diets increased body weight (BW) change compared with the SH diets. Additionally, PA+OA tended to increase BW change compared with CON and PA and increased it in comparison with PA+SA. The PA and PA+OA treatments increased dry matter and neutral detergent fiber digestibility compared with PA+SA and tended to increase them compared with CON. The PA+SA treatment reduced 16-carbon, 18-carbon, and total FA digestibility compared with the other treatments. The CS diets increased energy partitioning toward body reserves compared with the SH diets. The PA treatment increased energy partitioning toward milk compared with CON and PA+OA and tended to increase it compared with PA+SA. In contrast, PA+OA increased energy partitioned to body reserves compared with PA and PA+SA and tended to increase it compared with CON. In conclusion, milk yield responses to different combinations of FA were affected by the addition of whole cottonseed in the diet. Among the combinations of C16:0, C18:0, and cis-9 C18:1 evaluated, fat supplements with more C16:0 increased energy output in milk, whereas fat supplements with more cis-9 C18:1 increased energy storage in BW. The combination of C16:0 and C18:0 reduced nutrient digestibility, which most likely explains the lower performance observed compared with other treatments.     

Simplified partitioning model to simulate high pressure under-expanded jet flows impinging vertical obstacles

Various reduced-order models have been developed to quickly model high pressure underexpanded jets. One example is the two-layer partitioning model which was developed to model underexpanded jets, but it has not been evaluated for high pressure jets with obstacles in the jet flow region. This research describes an improved two-layer partitioning model based on the Abel-Noble equation of state that is applied here to model horizontal jet flows impacting a vertical obstacle with validations against high pressure gas experiments, full CFD simulations and a revised notional nozzle model based on the Abel-Noble equation of state. The improved two-layer partitioning model accurately predicts the gas concentrations on the obstacle for a 15 MPa underexpanded jet while consuming much less computational resources and time compared with the full CFD simulation.     

淬火配分处理对锻态Fe-0.2C-9Mn-3.5Al钢显微组织及力学行为的影响

分析了淬火配分处理对锻态Fe-0.2C-9Mn-3.5Al钢显微组织及力学行为的影响。结果表明,热处理态试验钢主要由块状δ-铁素体、马氏体和板条状残留奥氏体等多相构成;残留奥氏体的体积分数随等温淬火温度升高而增大,在310 ℃时达到峰值;310 ℃等温淬火后在400 ℃配分3 min时可以获得较优的综合力学性能,抗拉强度和断后伸长率分别为1175 MPa和21.50%,强塑积达到25.26 GPa·%;应力-应变曲线中存在着明显的“锯齿”状起伏,可能与亚稳态的残留奥氏体集中转变为马氏体有关。     

视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。     

中锰Q&P钢连续退火过程碳元素分配行为

采用扫描电镜、X射线衍射等研究了连续退火工艺中退火、淬火和配分等关键过程参数对中锰Q&P钢碳元素分配行为的影响,并分析了相应工艺条件下残留奥氏体量与碳含量的关系。结果表明:两相区退火温度的提高会导致奥氏体中的碳含量下降,微观组织表现为奥氏体含量增加,渗碳体量减少;退火时间10~60 s时,随着退火时间的延长,奥氏体含量和碳含量急剧增加,60 s后基本保持稳定;试验条件下淬火温度对残留奥氏体及碳含量的影响不显著;配分温度350~500℃时,随着配分温度的提高,奥氏体含量和碳含量呈现先增加后减小的趋势,配分温度450℃时均达到最大值;延长配分时间,残留奥氏体含量呈现先减少后增大再减少的趋势,残留奥氏体中的碳含量先减小后增加。