The extraction of SAR information from structurally diverse compound data sets is a challenging task. One of the focal points of systematic SAR analysis is the search for activity cliffs, that is, structurally similar compounds having large potency differences, from which SAR determinants can be deduced. The assessment of SAR information is usually based on pairwise similarity and potency comparisons of data set compounds. As a consequence, activity cliffs are mostly evaluated at a compound pair level. Here, we present an extension of the activity cliff concept by introducing "activity ridges" that are formed by overlapping "combinatorial" activity cliffs between participating compounds, giving rise to ridge-like structures in activity landscapes. Activity ridges are rich in SAR information. In a systematic analysis of 242 compound data sets, we have identified well-defined activity ridges in 71 different sets. In addition, an information-theoretic approach has been devised to characterize the structural composition of activity ridges. Taken together, our results show that activity ridges frequently occur in sets of active compounds and that different categories of ridges can be distinguished on the basis of their structural content. The computational identification of activity ridges provides access to compound subsets having high priority for SAR analysis.

Multiword expressions (MWEs) have been proved useful for many natural language processing tasks. However, how to use them to improve performance of statistical machine translation (SMT) is not well studied. This paper presents a simple yet effective strategy to extract domain bilingual multiword expressions. In addition, we implement three methods to integrate bilingual MWEs to Moses, the state-ofthe-art phrase-based machine translation system. Experiments show that bilingual MWEs could improve translation performance significantly.

Complex skill mastery requires not only acquiring individual basic component skills, but also practicing integrating such basic skills. However, traditional approaches to knowledge modeling, such as Bayesian knowledge tracing, only trace knowledge of each decomposed basic component skill. This risks early assertion of mastery or ine ective remediation failing to address skill integration. We introduce a novel integration-level approach to model learners' knowledge and provide ne-grained diagnosis: a Bayesian network based on a new kind of knowledge graph with progressive integration skills. We assess the value of such a model from multifaceted aspects: performance prediction, parameter plausibility, expected instructional e ectiveness, and real-world recommendation helpfulness. Our experiments based on a Java programming tutor show that proposed model signi cantly improves two popular multipleskill knowledge tracing models on all these four aspects.

Fabric defect recognition is an important measure for quality control in a textile factory. This article utilizes a deep convolutional neural network to recognize defects in fabrics that have complicated textures. Although convolutional neural networks are very powerful, a large number of parameters consume considerable computation time and memory bandwidth. In real-world applications, however, the fabric defect recognition task needs to be carried out in a timely fashion on a computation-limited platform. To optimize a deep convolutional neural network, a novel method is introduced to reveal the input pattern that originally caused a specific activation in the network feature maps. Using this visualization technique, this study visualizes the features in a fully trained convolutional model and attempts to change the architecture of original neural network to reduce computational load. After a series of improvements, a new convolutional network is acquired that is more efficient to the fabric image feature extraction, and the computation load and the total number of parameters in the new network is 23% and 8.9%, respectively, of the original model. The proposed neural network is specifically tailored for fabric defect recognition in resource-constrained environments. All of the source code and pretrained models are available online at https://github.com/ZCmeteor .

The partial crystallization properties of Ge1Sb2Te4 phase-change optical disks are studied using two methods. The first one involves annealing of the samples in a vacuum oven while controlling annealing time and temperature, while the second one involves the use of a static tester. A difference in reflectivity was observed, indicating that different crystallization fractions give rise to different reflection levels. The optical constants of amorphous, partial and full crystaline states were measured by spectroscopic ellipsometry. The optical constant of the partial crystalline state was calculated under the assumption that the partial crystalline state is a combination of full crystalline and amorphous states. The crystallization fraction was determined by simulating the refractive index of the partial crystalline state. The stability of the partially crystalized disk was measured for more than 200 days demonstrating that the partially crystallied disk is very stable. A possible recording strategy using the multilevel reflection to realize multi-level reflection modulation recording in write-once media is discussed.