Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page

Endpoint Temperature and Tenderness Variability in Pork and Beef Cooked Using Sous-Vide Style Immersion Heaters and Grills

The objective was to determine endpoint temperature and Warner-Bratzler shear force (WBSF) variability (range, coefficient of variation) differences in both pork and beef cooked using grilling and sous vide. Four 2.54-cm steaks were cut from 10 beef eye-of-round (semitendinosus) Choice-grade muscles (n = 40) and aged for 21 d. Four 2.54-cm chops were cut from 51 pork loins (n = 204) sourced from standard commercial pigs and aged for 7 d. Steaks and chops were randomly allotted within whole muscle to 4 treatments: grilled to 63°C, sous vide to 63°C, grilled to 71°C, and sous vide to 71°C. Four cores measuring 1.25 cm in diameter were excised parallel to the muscle fibers of each chop and steak respectively, and analyzed for WBSF. Temperature accuracy was defined as how close thermometer readings were to the targeted cooked temperature. Temperature precision was defined as how similar 2 thermometer readings within a single cut were to each other. WBSF accuracy was defined as how close individual core values were to the cut average. WBSF precision was defined as how similar individual core values were to each other. In both pork and beef, sous vide was more accurate (P < 0.01) and precise (P < 0.01) in achieving target endpoint temperature at both 63°C and 71°C. At 63°C, chops cooked using sous vide were more tender than grilled (P < 0.01), but at 71°C, chops cooked using sous vide were less tender than grilled (P < 0.01). Steaks cooked to 71°C using sous vide had the lowest core coefficient of variation, whereas other treatments were not different. Cooking method had no effect on average WBSF within target endpoint temperature. Overall, these data indicate that sous vide is more precise and accurate in reaching target temperature but may decrease tenderness when used at 71°C in pork

Unsupervised Personalized Feature Selection

Feature selection is effective in preparing high-dimensional data for a variety of learning tasks such as classification, clustering and anomaly detection. A vast majority of existing feature selection methods assume that all instances share some common patterns manifested in a subset of shared features. However, this assumption is not necessarily true in many domains where data instances could show high individuality. For example, in the medical domain, we need to capture the heterogeneous nature of patients for personalized predictive modeling, which could be characterized by a subset of instance-specific features. Motivated by this, we propose to study a novel problem of personalized feature selection. In particular, we investigate the problem in an unsupervised scenario as label information is usually hard to obtain in practice. To be specific, we present a novel unsupervised personalized feature selection framework UPFS to find some shared features by all instances and instance-specific features tailored to each instance. We formulate the problem into a principled optimization framework and provide an effective algorithm to solve it. Experimental results on real-world datasets verify the effectiveness of the proposed UPFS framework