Evaluation of the absolute affinity of neuraminidase inhibitor using steered molecular dynamics simulations

The absolute free energy difference of binding (ΔG) between neuraminidase and its inhibitor was evaluated using fast pulling of ligand (FPL) method over steered molecular dynamics (SMD) simulations. The metric was computed through linear interaction approximation. Binding nature was described by free energy differences of electrostatic and van der Waals (vdW) interactions. The finding indicates that vdW metric is dominant over electrostatics in binding process. The computed values are in good agreement with experimental data with a correlation coefficient of R = 0.82 and error of σΔG_exp = 2.2 kcal/mol. The results were observed using Amber99SB-ILDN force field in comparison with CHARMM27 and GROMOS96 43a1 force fields. Obtained results may stimulate the search for an Influenza therapy.     

Fast super-resolution algorithm using rotation-invariant ELBP classifier and hierarchical pattern matching

This paper proposes a fast super-resolution (SR) algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters based on a rotation-invariant classifier. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, we cluster a sufficient number of low-resolution (LR) and high-resolution (HR) patch pairs into a specific number of groups using the rotation-invariant classifier, and choose a specific number of dominant clusters. Then, we compute the optimal 2D FIR filter(s) to synthesize a high-quality HR patch from an LR patch per cluster, and finally store the patch-adaptive 2D FIR filters in a dictionary. Also, we present a smart hierarchical addressing method for effective dictionary exploration in the inference stage. In the inference stage, the ELBP of each input LR patch is extracted in the same way as the learning stage, and the best matched FIR filter(s) to the input LR patch is found from the dictionary by the hierarchical addressing. Finally, we synthesize the HR patch by using the optimal 2D FIR filter. The experimental results show that the proposed algorithm produces better HR images than the existing SR methods, while providing fast running time.     

A noun-based approach to feature location using time-aware term-weighting

ContextFeature location aims to identify the source code location corresponding to the implementation of a software feature. Many existing feature location methods apply text retrieval to determine the relevancy of the features to the text data extracted from the software repositories. One of the preprocessing activities in text retrieval is term-weighting, which is used to adjust the importance of a term within a document or corpus. Common term-weighting techniques may not be optimal to deal with text data from software repositories due to the origin of term-weighting techniques from a natural language context.ObjectiveThis paper describes how the consideration of when the terms were used in the repositories, under the condition of weighting only the noun terms, can improve a feature location approach.MethodWe propose a feature location approach using a new term-weighting technique that takes into account how recently a term has been used in the repositories. In this approach, only the noun terms are weighted to reduce the dataset volume and avoid dealing with dimensionality reduction.ResultsAn empirical evaluation of the approach on four open-source projects reveals improvements to the accuracy, effectiveness and performance up to 50%, 17%, and 13%, respectively, when compared to the commonly-used Vector Space Model approach. The comparison of the proposed term-weighting technique with the Term Frequency-Inverse Document Frequency technique shows accuracy, effectiveness, and performance improvements as much as 15%, 10%, and 40%, respectively. The investigation of using only noun terms, instead of using all terms, in the proposed approach also indicates improvements up to 28%, 21%, and 58% on accuracy, effectiveness, and performance, respectively.ConclusionIn general, the use of time in the weighting of terms, along with the use of only the noun terms, makes significant improvements to a feature location approach that relies on textual information.     

Gaussian-Hermite moment-based depth estimation from single still image for stereo vision

Depth information of objects plays a significant role in image-based rendering. Traditional depth estimation techniques use different visual cues including the disparity, motion, geometry, and defocus of objects. This paper presents a novel approach of focus cue-based depth estimation for still images using the Gaussian-Hermite moments (GHMs) of local neighboring pixels. The GHMs are chosen due to their superior reconstruction ability and invariance properties to intensity and geometric distortions of objects as compared to other moments. Since depths of local neighboring pixels are significantly correlated, the Laplacian matting is employed to obtain final depth map from the moment-based focus map. Experiments are conducted on images of indoor and outdoor scenes having objects with varying natures of resolution, edge, occlusion, and blur contents. Experimental results reveal that the depth estimated from GHMs can provide anaglyph images with stereo quality better than that provided by existing methods using traditional visual cues.     

Synergic predator-prey optimization for economic thermal power dispatch problem

This paper introduces a synergic predator-prey optimization (SPPO) algorithm to solve economic load dispatch (ELD) problem for thermal units with practical aspects. The basic PPO model comprises prey and predator as essential components. SPPO uses collaborative decision for movement and direction of prey and maintains diversity in the swarm due to fear factor of predator, which acts as the baffled state of preys’ mind. In the SPPO, the decision making of prey is bifurcated into corroborative and impeded parts. It comprises four behaviors namely inertial, cognitive, collective swarm intelligence, and prey's individual and neighborhood concern of predator. The prey particle memorizes its best and not-best positions as experiences. In this research work, to improve the quality of prey swarm, which influence convergence rate, opposition based initialization is used. To verify robustness of proposed algorithm general benchmark problems and small, medium, and large power generation test power system are simulated. These test systems have non-linear behavior due to multi-fuel options and practical constraints. The constraints of prohibited operating zone and ramp rate limits of power generators’ are handled using heuristics. Newton–Raphson procedure is exploited to attain the transmission losses using load flow analysis. The outcomes of SPPO are compared with the results described in literature and are found satisfactory.     

最小状态变元平均奖赏的强化学习方法

针对采用折扣奖赏作为评价目标的Q学习无法体现对后续动作的影响问题,提出将平均奖赏和Q学习相结合的AR-Q-Learning算法,并进行收敛性证明.针对学习参数个数随着状态变量维数呈几何级增长的"维数灾"问题,提出最小状态变元的思想.将最小变元思想和平均奖赏用于积木世界的强化学习中,试验结果表明,该方法更具有后效性,加快算法的收敛速度,同时在一定程度上解决积木世界中的"维数灾"问题.     

Recompression effects in iris recognition

Rating a compression algorithms' performance is usually done in experimental studies, where researchers have frequently used JPEG pre-compressed data. It is not clear yet, if results of such compression experiments are reliable when conducted on pre-compressed data. To investigate this issue, we first study the impact of using pre-compressed data in iris segmentation and evaluate the relation between iris segmentation performance and general image quality metrics. In this context we propose a method to overcome potential problems in case using pre-compressed data sets cannot be avoided. As the second step, we conduct experimentation on the entire iris recognition pipeline. We find that overall, recognition accuracy results might not be entirely reliable in case of applying JPEG XR or JPEG2000 to JPEG pre-compressed data.     

Tribological properties of Ni-based self-lubricating composites with addition of silver and molybdenum disulfide

Tribological properties of Ni-based composites containing silver and molybdenum disulfide were studied from room temperature to 700 °C. The composites were prepared by powder metallurgy technique and the phase composition and microstructure were addressed according to the characterization by SEM and XRD. The results show that the composites were composed of Ni, Cr₂O₃, Ag and Cr_xS_y phases after sintering. The best tribological properties were obtained when the silver and molybdenum disulfide contents were 20 wt% and 8.5 wt%, respectively. The tribo-chemical reaction occurred between Ag and Mo oxide during the rubbing process, which led to the formation of silver molybdate lubricating film. The improved tribological properties were attributed to the formation of silver molybdate lubricating tribolayers.