Human attributes from 3D pose tracking

It is well known that biological motion conveys a wealth of socially meaningful information. From even a brief exposure, biological motion cues enable the recognition of familiar people, and the inference of attributes such as gender, age, mental state, actions and intentions. In this paper we show that from the output of a video-based 3D human tracking algorithm we can infer physical attributes (e.g., gender and weight) and aspects of mental state (e.g., happiness or sadness). In particular, with 3D articulated tracking we avoid the need for view-based models, specific camera viewpoints, and constrained domains. The task is useful for man–machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods (vs. conventional Euclidean joint error metrics). We show results on a large corpus of motion capture data and on the output of a simple 3D pose tracker applied to videos of people walking.     

Metabolism of sitosteryl β-D-glucoside and its nutritional effects in rats

[4-¹⁴C]Sitosteryl β-D-glucoside, intragastrically administered to rats, was not absorbed by the intestinal mucosa. At three hr after the application, radioactivity was concentrated almost exclusively in the digesta of stomach, small intestine as well as cecum and colon, whereas only low proportions of radioactively labeled compounds were found in the various tissues of the gastrointestinal tract. Minor proportions of labeled metabolites of [4-¹⁴C]sitosteryl β-D-glucoside, such as sitosterol and sitosteryl esters, were formed in the small intestine in vivo and in slices of small intestine in vitro. In the tissues of cecum and colon as well as the digesta derived from them, high proportions of labeled coprositostanol, i.e. 24α-ethyl-5β-cholestan-3β-ol, that obviously had been formed by bacterial degradation of the substrate were detected. The feeding of sitosteryl β-D-glucoside (0.5 g/kg body weight×day) over a period of four weeks did not alter significantly body weights or organ weights of rats. Analyses of steryl lipids of the various organs and tissues confirmed the findings obtained with the radioactive substrate: neither sitosteryl β-D-glucoside nor sitosterol or sitosteryl esters derived therefrom had been transported in appreciable amounts to organs and tissues outside the alimentary canal during the feeding period. Minor proportions of unmetabolized sitosteryl β-D-glucoside were detected in the tissues of stomach and intestine, whereas large proportions of the substrate were found in feces of rats that had received the sitosteryl β-D-glucoside-containing diet; coprositostanol was found in feces of these animals in high proportions as well. Thus, the use of sitosteryl β-D-glucoside as emulsifier or preservative in food and feed does not appear to involve any risk. The systematic nomenclature of the sterols referred to by trivial names is, cholest-5-en-3β-ol (cholesterol); 5α-cholestan-3β-ol (5α-cholestanol); 5β-cholestan-3β-ol (5β-cholestanol, coprostanol); 24α-methylcholest-5-en-3β-ol (campesterol); 24α-methyl-5α-cholestan-3β-ol (5α-campestanol); 24α-methyl-5β-cholestan-3β-ol (5β-campestanol, coprocampestanol); 24α-methyl-cholesta-5,22-dien-3β-ol (brassicasterol); 24α-ethylcholest-5-en-3β-ol (sitosterol, β-sitosterol); 24α-ethyl-5α-cholestan-3β-ol (5α-sitostanol); 24α-ethyl-5β-cholestan-3β-cholestan-3β-ol (5β-sitostanol, coprositostanol); 24α-ethylcholesta-5,22-dien-3β-ol (stigmasterol).     

On the Black-Box Complexity of Sperner’s Lemma

We present several results on the complexity of various forms of Sperner’s Lemma in the black-box model of computing. We give a deterministic algorithm for Sperner problems over pseudo-manifolds of arbitrary dimension. The query complexity of our algorithm is linear in the separation number of the skeleton graph of the manifold and the size of its boundary. As a corollary we get an deterministic query algorithm for the black-box version of the problem 2D-SPERNER, a well studied member of Papadimitriou’s complexity class PPAD. This upper bound matches the deterministic lower bound of Crescenzi and Silvestri. The tightness of this bound was not known before. In another result we prove for the same problem an lower bound for its probabilistic, and an lower bound for its quantum query complexity, showing that all these measures are polynomially related. Research supported by the European Commission IST Integrated Project Qubit Application (QAP) 015848, the OTKA grants T42559 and T46234, and by the ANR Blanc AlgoQP grant of the French Research Ministry.     

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.     

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Distributed boundary coverage with a team of networked miniature robots using a robust market-based algorithm

We study distributed boundary coverage of known environments using a team of miniature robots. Distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection, cleaning, and painting among others. The proposed algorithm is robust to sensor and actuator noise, failure of individual robots, and communication loss. We use a market-based algorithm with known lower bounds on the performance to allocate the environmental objects of interest among the team of robots. The coverage time for systems subject to sensor and actuator noise is significantly shortended by on-line task re-allocation. The complexity and convergence properties of the algorithm are formally analyzed. The system performance is systematically analyzed at two different microscopic modeling levels, using agent-based, discrete-event and module-based, realistic simulators. Finally, results obtained in simulation are validated using a team of Alice miniature robots involved in a distributed inspection case study.     

3D periodic human motion reconstruction from 2D motion sequences

We present and evaluate a method of reconstructing three-dimensional (3D) periodic human motion from two-dimensional (2D) motion sequences. Using Fourier decomposition, we construct a compact representation for periodic human motion. A low-dimensional linear motion model is learned from a training set of 3D Fourier representations by means of principal components analysis. Two-dimensional test data are projected onto this model with two approaches: least-square minimization and calculation of a maximum a posteriori probability using the Bayes' rule. We present two different experiments in which both approaches are applied to 2D data obtained from 3D walking sequences projected onto a plane. In the first experiment, we assume the viewpoint is known. In the second experiment, the horizontal viewpoint is unknown and is recovered from the 2D motion data. The results demonstrate that by using the linear model, not only can missing motion data be reconstructed, but unknown view angles for 2D test data can also be retrieved.     

An analysis of mutative sigma-self-adaptation on linear fitness functions

This paper investigates sigma-self-adaptation for real valued evolutionary algorithms on linear fitness functions. We identify the step-size logarithm log sigma as a key quantity to understand strategy behavior. Knowing the bias of mutation, recombination, and selection on log sigma is sufficient to explain sigma-dynamics and strategy behavior in many cases, even from previously reported results on non-linear and/or noisy fitness functions. On a linear fitness function, if intermediate multi-recombination is applied on the object parameters, the i-th best and the i-th worst individual have the same sigma-distribution. Consequently, the correlation between fitness and step-size sigma is zero. Assuming additionally that sigma-changes due to mutation and recombination are unbiased, then sigma-self-adaptation enlarges sigma if and only if mu < lambda/2, given (mu, lambda)-truncation selection. Experiments show the relevance of the given assumptions.     

Just how insoluble are monoterpenes?

Prior generalizations about the ecological roles of monoterpenes may be misleading if based on the presumed insolubility of monoterpenes in water. We determined the aqueous solubility of 31 biologically active monoterpenes by gas chromatography. While hydrocarbons were of low solubility (< 35 ppm), oxygenated monoterpenes exhibited solubilities one or two orders of magnitude higher, with ranges of 155–6990 ppm for ketones and of 183–1360 ppm for alcohols. Many monoterpenes are phytotoxic in concentrations under 100 ppm, well below the saturated aqueous concentrations of oxygenated monoterpenes. Therefore, even dilute, unsaturated solutions of monoterpenes, occurring naturally in plant tissues and soil solutions, may act as potent biological inhibitors.