Improved subspace clustering via exploitation of spatial constraints

We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso regression and suggest numerical algorithms so solve the proposed formulation. The experiments on the benchmark Johns Hopkins 155 dataset demonstrate that exploiting spatial constraints significantly improves motion segmentation.

Internet traffic clustering with constraints

Due to the limitations of the traditional port-based and payload-based traffic classification approaches, the past decade has seen extensive work on utilizing machine learning techniques to classify network traffic based on packet and flow level features. In particular, previous studies have shown that the unsupervised clustering approach is both accurate and capable of discovering previously unknown application classes. In this paper, we explore the utility of side information in the process of traffic clustering. Specifically, we focus on the flow correlation information that can be efficiently extracted from packet headers and expressed as instance-level constraints, which indicate that particular sets of flows are using the same application and thus should be put into the same cluster. To incorporate the constraints, we propose a modified constrained K-Means algorithm. A variety of real-world traffic traces are used to show that the constraints are widely available. The experimental results indicate that the constrained approach not only improves the quality of the resulted clusters, but also speeds up the convergence of the clustering process.

Minimizing bounded uncertainty impact on scheduling with earliest start and due-date constraints via interval computation

Bounded uncertainty is a major challenge to real life scheduling as it increases the risk and cost depending on the objective function. Bounded uncertainty provides limited information about its nature. It provides only the upper and the lower bounds without information in between, in contrast to probability distributions and fuzzymembership functions. Bratley algorithm is usually used for scheduling with the constraints of earliest start and due-date. It is formulated as . The proposed research uses interval computation to minimize the impact of bounded uncertainty of processing times on Bratley’s algorithm. It minimizes the uncertainty of the estimate of the objective function. The proposed concept is to do the calculations on the interval values and approximate the end result instead of approximating each interval then doing numerical calculations. This methodology gives a more certain estimate of the objective function.

Multiple emitter localization using range only measurements considering geometrical constraints

This paper proposes a constrained optimization approach to improve the accuracy of a Time-of-Arrival (ToA) based multiple target localization system. Instead of using an overdetermined measurement system, this paper uses local distance measurements between the targets/emitters as the geometric constraint.Computer simulations are used to evaluate the performance of the geometrically constrained optimization method.

Gender differences in recreational and transport cycling : a cross-sectional mixed-methods comparison of cycling patterns, motivators, and constraints

Background
Gender differences in cycling are well-documented. However, most analyses of gender differences make broad comparisons, with few studies modeling male and female cycling patterns separately for recreational and transport cycling. This modeling is important, in order to improve our efforts to promote cycling to women and men in countries like Australia with low rates of transport cycling. The main aim of this study was to examine gender differences in cycling patterns and in motivators and constraints to cycling, separately for recreational and transport cycling.

Methods
Adult members of a Queensland, Australia, community bicycling organization completed an online survey about their cycling patterns; cycling purposes; and personal, social and perceived environmental motivators and constraints (47% response rate). Closed and open-end questions were completed. Using the quantitative data, multivariable linear, logistic and ordinal regression models were used to examine associations between gender and cycling patterns, motivators and constraints. The qualitative data were thematically analyzed to expand upon the quantitative findings.

Results
In this sample of 1862 bicyclists, men were more likely than women to cycle for recreation and for transport, and they cycled for longer. Most transport cycling was for commuting, with men more likely than women to commute by bicycle. Men were more likely to cycle on-road, and women off-road. However, most men and women did not prefer to cycle on-road without designed bicycle lanes, and qualitative data indicated a strong preference by men and women for bicycle-only off-road paths. Both genders reported personal factors (health and enjoyment related) as motivators for cycling, although women were more likely to agree that other personal, social and environmental factors were also motivating. The main constraints for both genders and both cycling purposes were perceived environmental factors related to traffic conditions, motorist aggression and safety. Women, however, reported more constraints, and were more likely to report as constraints other environmental factors and personal factors.

Conclusion
Differences found in men’s and women’s cycling patterns, motivators and constraints should be considered in efforts to promote cycling, particularly in efforts to increase cycling for transport.

A new hybrid method for Bayesian network learning With dependency constraints

A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation information from statistical tests: Given a sample d, search for a structure G that maximizes score(G, d), over the set of structures G that satisfy the dependencies detected in d. We rely on the statistical test only to accept conditional dependencies, not conditional independencies. We show how to adapt local search algorithms to accommodate the observed dependencies. Simulation studies with GES search and the BDeu/BIC scores provide evidence that the additional dependency information leads to Bayes nets that better fit the target model in distribution and structure.

The effect of financial constraints, investment policy, product market competition and corporate governance on the value of cash holdings

This study empirically investigates the value shareholders place on excess cash holdings and how shareholders’ valuation of cash holdings is associated with financial constraints, firm growth, cash-flow uncertainty and product market competition for Australian firms from 1990 to 2007. Our results indicate that the marginal value of cash holdings to shareholders declines with larger cash holdings and higher leverage. However, firms that are more financially constrained, that have higher growth rates and that face greater uncertainty exhibit a higher marginal value of cash holdings. These findings are consistent with the explanation that excess cash holdings are not necessarily detrimental to firm value. Firms with costly external financing and that also save more cash for current operating and future investing needs find that the market values these cash hoarding policies favourably. Finally, there is limited evidence of an association between various corporate governance measures and the value of cash holdings for a shorter sample period.

Overcoming data constraints to create meaningful ecological models

Many techniques used to model ecosystems cannot be meaningfully applied to large-scale ecological problems due to data constraints. Disparate collection methods, data types and incomplete data sets, or limited theoretical understanding mean that a wide range of modelling techniques used to model physical processes or for problems specific to species or populations cannot be used at an ecosystem scale. In developing an ecological response model for the Coorong, a South Australian hypersaline estuary, we combined several flexible modelling approaches in a statistical framework to develop an approach we call ‘ecosystem states’. This model uses simulated hydrodynamic conditions as input to predict one of a suite of states per space and time, allowing prediction of likely ecological conditions under a variety of scenarios. Each ecosystem state has defined sets of biota and physico-chemical parameters. The existing model is limited in that its predictions have yet to be tested and, as yet, no spatial or temporal connectivity has been incorporated into simulated time series of ecosystem states. This approach can be used in a wide range of ecosystems, where enough data are available to model ecosystem states. We are in the process of applying the technique to a nearby lake system. This has been more difficult than for the Coorong as there is little overlap in the spatial and temporal coverage of biological data sets for that region. The approach is robust to low-quality biological data and missing environmental data, so should suit situations where community or management monitoring programs have occurred through time.

Constraints to park visitation: a meta-analysis of North American studies

The constraints that curb visitation to parks have received considerable research attention and remain an important issue. Constraints to outdoor leisure in parks include structural barriers, such as cost of entry, interpersonal barriers, for example, lack of a partner to visit with, or intrapersonal barriers, including lack of interest. Using a meta-analysis approach, this article integrates the findings of 22 North American studies with 541 estimates, conducted over a 30-year period, to determine the key constraints to park visitation based on various socio-demographic factors. The findings highlight the varying roles that constraints play in limiting the visitation of people according to race, age, gender, education and income. Gaps in the literature are identified, with implications for encouraging visitation for more constrained groups discussed. An agenda for future research is presented.