A novel culture system for modulating single cell geometry in 3D

Dedifferentiation of chondrocytes during in vitro expansion remains an unsolved challenge for repairing serious articular cartilage defects. In this study, a novel culture system was developed to modulate single cell geometry in 3D and investigate its effects on the chondrocyte phenotype. The approach uses 2D micropatterns followed by in situ hydrogel formation to constrain single cell shape and spreading. This enables independent control of cell geometry and extracellular matrix. Using collagen I matrix, we demonstrated the formation of a biomimetic collagenous “basket” enveloping individual chondrocytes cells. By quantitatively monitoring the production by single cells of chondrogenic matrix (e.g. collagen II and aggrecan) during 21-day cultures, we found that if the cell’s volume decreases, then so does its cell resistance to dedifferentiation (even if the cells remain spherical). Conversely, if the volume of spherical cells remains constant (after an initial decrease), then not only do the cells retain their differentiated status, but previously de-differentiated redifferentiate and regain a chondrocyte phenotype. The approach described here can be readily applied to pluripotent cells, offering a versatile platform in the search for niches toward either self-renewal or targeted differentiation

Credit risk evaluation by using nearest subspace method

AbstractIn this paper, a classification method named nearest subspace method is applied for credit risk evaluation. Virtually credit risk evaluation is a very typical classification problem to identify “good” and “bad” creditors. Currently some machine learning technologies, such as support vector machine (SVM), have been discussed widely in credit risk evaluation. But there are many effective classification methods in pattern recognition and artificial intelligence have not been tested for credit evaluation. This paper presents to use nearest subspace classification method, a successful face recognition method, for credit evaluation. The nearest subspace credit evaluation method use the subspaces spanned by the creditors in same class to extend the training set, and the Euclidean distance from a test creditor to the subspace is taken as the similarity measure for classification, then the test creditor belongs to the class of nearest subspace. Experiments on real world credit dataset show that the nearest subspace credit risk evaluation method is a competitive method

Text Categorization based on Clustering Feature Selection

AbstractIn this paper, we discuss a text categorization method based on k-means clustering feature selection. K-means is classical algorithm for data clustering in text mining, but it is seldom used for feature selection. For text data, the words that can express correct semantic in a class are usually good features. We use k-means method to capture several cluster centroids for each class, and then choose the high frequency words in centroids as the text features for categorization. The words extracted by k-means not only can represent each class clustering well, but also own high quality for semantic expression. On three normal text databases, classifiers based on our feature selection method exhibit better performances than original classifiers for text categorization

Single-Channel Speech Dereverberation using Subband Network with A Reverberation Time Shortening Target

This work proposes a subband network for single-channel speech dereverberation, and also a new learning target based on reverberation time shortening (RTS). In the time-frequency domain, we propose to use a subband network to perform dereverberation for different frequency bands independently. The time-domain convolution can be well decomposed to subband convolutions, thence it is reasonable to train the subband network to perform subband deconvolution. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training, and leads to a large prediction error. In this work, we propose a RTS learning target to suppress reverberation and meanwhile maintain the exponential decaying property of reverberation, which will ease the network training, and thus reduce the prediction error and signal distortions. Experiments show that the subband network can achieve outstanding dereverberation performance, and the proposed target has a smaller prediction error than the target of direct-path speech and early reflections.Comment: Submitted to INTERSPEECH 202

BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety violations while the diversity of the generated test cases is ignored, which may generate many redundant test cases and failures. Such redundant failures can reduce testing performance and increase failure analysis costs. In this paper, we present a novel behavior-guided fuzzing technique (BehAVExplor) to explore the different behaviors of the ego vehicle (i.e., the vehicle controlled by the ADS under test) and detect diverse violations. Specifically, we design an efficient unsupervised model, called BehaviorMiner, to characterize the behavior of the ego vehicle. BehaviorMiner extracts the temporal features from the given scenarios and performs a clustering-based abstraction to group behaviors with similar features into abstract states. A new test case will be added to the seed corpus if it triggers new behaviors (e.g., cover new abstract states). Due to the potential conflict between the behavior diversity and the general violation feedback, we further propose an energy mechanism to guide the seed selection and the mutation. The energy of a seed quantifies how good it is. We evaluated BehAVExplor on Apollo, an industrial-level ADS, and LGSVL simulation environment. Empirical evaluation results show that BehAVExplor can effectively find more diverse violations than the state-of-the-art

Empirical research on the evaluation model and method of sustainability of the open source ecosystem

The development of open source brings new thinking and production modes to software engineering and computer science, and establishes a software development method and ecological environment in which groups participate. Regardless of investors, developers, participants, and managers, they are most concerned about whether the Open Source Ecosystem can be sustainable to ensure that the ecosystem they choose will serve users for a long time. Moreover, the most important quality of the software ecosystem is sustainability, and it is also a research area in Symmetry. Therefore, it is significant to assess the sustainability of the Open Source Ecosystem. However, the current measurement of the sustainability of the Open Source Ecosystem lacks universal measurement indicators, as well as a method and a model. Therefore, this paper constructs an Evaluation Indicators System, which consists of three levels: The target level, the guideline level and the evaluation level, and takes openness, stability, activity, and extensibility as measurement indicators. On this basis, a weight calculation method, based on information contribution values and a Sustainability Assessment Model, is proposed. The models and methods are used to analyze the factors affecting the sustainability of Stack Overflow (SO) ecosystem. Through the analysis, we find that every indicator in the SO ecosystem is partaking in different development trends. The development trend of a single indicator does not represent the sustainable development trend of the whole ecosystem. It is necessary to consider all of the indicators to judge that ecosystem’s sustainability. The research on the sustainability of the Open Source Ecosystem is helpful for judging software health, measuring development efficiency and adjusting organizational structure. It also provides a reference for researchers who study the sustainability of software engineering

Social and Emotional Learning Difficulties of Refugee High School Students in an After-school Tutoring Program

School-aged children constitute a significant portion of the large number of refugees who have resettled in Canada in recent years. Due to the lack of cross-cultural competencies, a social justice focus, and transformative leadership skills, Canadian schools are often challenged to effectively address refugee students’ socio-psychological problems. Moreover, educational literature and policy, which specifically target Canadian refugee students, are scarce. To help with the issue, this study examined eight refugee high school students through an online after-school tutoring program and evaluated their performances in the five domains of social-emotional learning competencies: social awareness, self-management, relationship skills, responsible decision making, and social awareness. The two researchers participated in this study as tutors and adopted observation as the main approach. Findings of the study revealed that refugee students’ performances in these skills was not optimal, in general. Especially, there is a high demand in improving the refugee students’ self-awareness, self-management, and responsible decision-making. Most of them had good relationship skills as well as social awareness. Also, all the social-emotional learning skills connect closely with the refugee students’ academic success