Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data

The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i.e., separating points drawn from a union of subspaces). In this paper, we revisit the SIM and reveal its connections to several recent subspace clustering methods. Our analysis lets us derive a simple, yet effective algorithm to robustify the SIM and make it applicable to realistic scenarios where the data is corrupted by noise. We justify our method by intuitive examples and the matrix perturbation theory. We then show how this approach can be extended to handle missing data, thus yielding an efficient and general subspace clustering algorithm. We demonstrate the benefits of our approach over state-of-the-art subspace clustering methods on several challenging motion segmentation and face clustering problems, where the data includes corrupted and missing measurements.Comment: This is an extended version of our iccv15 pape

When the Periphery Meets the Core of a Party-Press System: Remember Comrade Lei Feng in China's Shifting Media Kaleidscope

At the epicenter of China's reform, media adapt their propagandist role to different extents. They present distinct images about China's Communist past. Against the backdrop, we examine media reconstructions of Lei Feng, a Communist icon CCP created in the 1960s. Drawing on media reform and collective memory literature, we find party-organ newspapers draw on altruism and loyalty in his original image to promote social stability and economic development for present purposes. Meanwhile, user generated contents in cyberspace question the authenticity of Lei Feng's official records. Different reconstructions collide in online discussions. When commemoration is linked to chronicling, Lei Feng becomes a demoralizing lie; when not, a symbol for much-needed virtues in the present. Implications for understanding China's media reform and for China's collective memorization of revolutionary heroes are discussed

Remarks on the SS-wave masses of singly heavy mesons

Based on the study of the string model methods of singly heavy mesons and singly heavy baryons, we calculate the mass spectrum of $1S$-and $2S$-wave for both charm and bottom mesons$(D/D_{s}, B/B_{s})$. Experimentally, there are most masses spectra of $1S$-wave have been found, while the masses part of the $2S$-state is not determined. In this paper, we will use singly light quark or diquark model images and Regge trajectory models, combined with perturbation processing methods, to analyze and study the observed singly heavy mesons, further predict the unobserved mesons masses and their corresponding spin-parity quantum numbers.Comment: 11 pages, 8 figure

Service quality of front-line staff in the South African hotel industry

ThesisThe hotel industry is a developing, dynamic, diverse and complex industry. As competition becomes tougher, service quality becomes an increasingly important issue for hoteliers. It is perceived to be the means by which a hotel can gain a competitive edge in the marketplace, differentiate itself from competitors, retain existing customers and attract new ones. The overall goal of the study is to investigate the factors that influence the quality of service rendered by front-line staff in the Free State and Northem Cape hotels. The population for this survey includes all graded hotels, motels, guesthouses and guest farms within the central tourist region (Free State and Northern Cape). A sampling frame was selected with the assistance of SATOUR and a detailed address list of fifty-six graded hotels was obtained from SATOUR. A postal survey was completed by means of a questionnaire that was mailed to each one of the fifty-six hotels. The response rate was 51 .98%. The service performance of the front-line staff in the hotel industry is the key factor when assessing their customers' perception of quality. Hotel managers should stress that everyone is part of a team and that the success of the hotel depends on the performance of everyone involved. Effective performances are influenced by work opportunities, motivation and the working environment. The key to competitive advantage in the hotel industry is largely a superior plan that must fit the particular circumstances of front-line departments and prevent the occurrence of poor service quality. Otherwise the service will lack direction and the quality of performance will vary considerably both between hotels and also between individual staff members within hotels. Dimensions of service quality can be quantified by obtaining measures of expectations and perceptions of service standards. Analysing different perspectives of the measurement will be conducive to the improvement of service quality. The control of service quality is a management function to ensure that the hotel's goals and standards are met. Both dimensions and control of service quality can guarantee standardised service performance. In-service training can be a key instrument in maintaining optimal level of performance in a hotel. Staff cannot be expected to render high-quality service unless they know what is expected of them. Training programs should be designed to enable all the staff members to perform their service well, and fully develop their capability. The training must be interesting, detailed, and frequently reinforced. The training methods should be unique and effective in producing results under different circumstances. Continuous improvement of service quality is a vital task for both hotel managers and staff. Market competition means that innovation is always required in all of the hotel's activities, and all the components of service operations must be taken into consideration to achieve this improvement. The vast majority of the respondents indicated that personal service, and not material service, is the most important aspect in the hotel industry. Managers and front-line staff see a positive attitude towards customers as the most important service quality factor. From the survey it is clear that managers prefer their front-line staff to "tackle difficult situations", while also caring for the basic characteristics, such as "accuracy". The managers of the participating hotels see promotion to a higher position and money as the most important methods to motivate their staff. Helping staff to become aware of their goals and letting the staff know where they fit in best are the most important motivation factors, according to the front-line staff. Cleanliness, value for money and comfort are key elements in measuring service quality in participating hotels. The main techniques to assess service quality are cleanliness check-lists and customer comment forms. Hotel managers and front-line staff indicated that on-the-job training is the most important training method used in the participating hotels. It is also clear that front-line staff need more formal training courses to improve their knowledge and skills. According to the respondents, improved staff performance and productivity are the most important results of effective training. Service quality is one of the key issues for survival in the hotel industry, and is a major factor for achieving commercial success. Faced with rapidly increasing competition, the South African hotel industry simply has no choice but to improve quality through a well-designed training program

Neural Collaborative Subspace Clustering

We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to previous attempts, our model runs without the aid of spectral clustering. This makes our algorithm one of the kinds that can gracefully scale to large datasets. At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. Essential to our model is the construction of two affinity matrices, one from the classifier and the other from a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. We thoroughly assess and contrast the performance of our model against various state-of-the-art clustering algorithms including deep subspace-based ones.Comment: Accepted to ICML 201