Learning kinematic structure correspondences using multi-order similarities

We present a novel framework for finding the kinematic structure correspondences between two articulated objects in videos via hypergraph matching. In contrast to appearance and graph alignment based matching methods, which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Thus our method allows matching the structure of objects which have similar topologies or motions, or a combination of the two. Our main contributions are summarised as follows: (i)casting the kinematic structure correspondence problem into a hypergraph matching problem by incorporating multi-order similarities with normalising weights, (ii)introducing a structural topology similarity measure by aggregating topology constrained subgraph isomorphisms, (iii)measuring kinematic correlations between pairwise nodes, and (iv)proposing a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on synthetic and real data, showing that various other recent and state of the art methods are outperformed. Our method is not limited to a specific application nor sensor, and can be used as building block in applications such as action recognition, human motion retargeting to robots, and articulated object manipulation

Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML

Approximate standard errors (ASE) of variance components for random regression coefficients are calculated from the average information matrix obtained in a residual maximum likelihood procedure. Linear combinations of those coefficients define variance components for the additive genetic variance at given points of the trajectory. Therefore, ASE of these components and heritabilities derived from them can be calculated. In our example, the ASE were larger near the ends of the trajectory

Towards Anchoring Self-Learned Representations to Those of Other Agents

In the future, robots will support humans in their every day activities. One particular challenge that robots will face is understanding and reasoning about the actions of other agents in order to cooperate effectively with humans. We propose to tackle this using a developmental framework, where the robot incrementally acquires knowledge, and in particular 1) self-learns a mapping between motor commands and sensory consequences, 2) rapidly acquires primitives and complex actions by verbal descriptions and instructions from a human partner, 3) discovers correspondences between the robots body and other articulated objects and agents, and 4) employs these correspondences to transfer the knowledge acquired from the robots point of view to the viewpoint of the other agent. We show that our approach requires very little a-priori knowledge to achieve imitation learning, to find correspondent body parts of humans, and allows taking the perspective of another agent. This represents a step towards the emergence of a mirror neuron like system based on self-learned representations

Transferring visuomotor learning from simulation to the real world for robotics manipulation tasks

Hand-eye coordination is a requirement for many manipulation tasks including grasping and reaching. However, accurate hand-eye coordination has shown to be especially difficult to achieve in complex robots like the iCub humanoid. In this work, we solve the hand-eye coordination task using a visuomotor deep neural network predictor that estimates the arm's joint configuration given a stereo image pair of the arm and the underlying head configuration. As there are various unavoidable sources of sensing error on the physical robot, we train the predictor on images obtained from simulation. The images from simulation were modified to look realistic using an image-to-image translation approach. In various experiments, we first show that the visuomotor predictor provides accurate joint estimates of the iCub's hand in simulation. We then show that the predictor can be used to obtain the systematic error of the robot's joint measurements on the physical iCub robot. We demonstrate that a calibrator can be designed to automatically compensate this error. Finally, we validate that this enables accurate reaching of objects while circumventing manual fine-calibration of the robot

Children Show Highest Levels of Polybrominated Diphenyl Ethers in a California Family of Four: A Case Study

Polybrominated diphenyl ethers (PBDEs), a major class of flame retardants, are ubiquitous environmental contaminants with particularly high concentrations in humans from the United States. This study is a first attempt to report and compare PBDE concentrations in blood drawn from a family. Serum samples from family members collected at two sampling occasions 90 days apart were analyzed for PBDE congeners. Concentrations of the lower-brominated PBDEs were similar at the two sampling times for each family member, with children’s levels 2- to 5-fold higher than those of their parents. Concentrations of, for example, 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) varied from 32 ng/g lipid weight (lw) in the father to 60, 137, and 245 ng/g lw in the mother, child, and toddler, respectively. Decabromodiphenyl ether (BDE-209) concentrations differed significantly between the two samplings. September concentrations in the father, mother, child, and toddler were 23, 14, 143, and 233 ng/g lw, respectively. December concentrations (duplicate results from the laboratory) were 2 and 3, 4 and 4, 9 and 12, and 19 and 26 ng/g lw, respectively. Parents’ ∑PBDE concentrations approached U.S. median concentrations, with children’s concentrations near the maximum (top 5%) found in U.S. adults. The youngest child had the highest concentrations of all PBDE congeners, suggesting that younger children are more exposed to PBDEs than are adults. Our estimates indicate that house dust contributes to children’s higher PBDE levels. BDE-209 levels for all family members were 10-fold lower at the second sampling. The short half-life of BDE-209 (15 days) indicates that BDE-209 levels can decrease rapidly in response to decreased exposures. This case study suggests that children are at higher risk for PBDE exposures and, accordingly, face higher risks of PBDE-related health effects than adults

Service composition in stochastic settings

With the growth of the Internet-of-Things and online Web services, more services with more capabilities are available to us. The ability to generate new, more useful services from existing ones has been the focus of much research for over a decade. The goal is, given a specification of the behavior of the target service, to build a controller, known as an orchestrator, that uses existing services to satisfy the requirements of the target service. The model of services and requirements used in most work is that of a finite state machine. This implies that the specification can either be satisfied or not, with no middle ground. This is a major drawback, since often an exact solution cannot be obtained. In this paper we study a simple stochastic model for service composition: we annotate the tar- get service with probabilities describing the likelihood of requesting each action in a state, and rewards for being able to execute actions. We show how to solve the resulting problem by solving a certain Markov Decision Process (MDP) derived from the service and requirement specifications. The solution to this MDP induces an orchestrator that coincides with the exact solution if a composition exists. Otherwise it provides an approximate solution that maximizes the expected sum of values of user requests that can be serviced. The model studied although simple shades light on composition in stochastic settings and indeed we discuss several possible extensions

Eurythmy Therapy in clinical studies: a systematic literature review

<p>Abstract</p> <p>Background</p> <p>We aimed to overview the current literature on eurythmy therapy (EYT) which is an integral part of Anthroposophic Medicine. EYT can be described as a movement therapy in which speech movements are transposed into exercises which address the patient's capability to soul expression and strengthen his salutogenetic resources.</p> <p>Methods</p> <p>We searched several databases such as Cochrane, EMBASE, NCCAM, NLM, DIMDI, CAMbase, and Medline for case-control studies, cohort studies and randomised controlled trials on the treatment effects of EYT in a clinical setting. In a second search we included journal databases from Karger, Kluwer, Springer, Thieme, and Merkurstab archive.</p> <p>Results</p> <p>We found 8 citations which met the inclusion criterion: 4 publications referring to a prospective cohort study without control group (the AMOS study), and 4 articles referring to 2 explorative pre-post studies without control group, 1 prospective, non-randomized comparative study, and 1 descriptive study with a control group. The methodological quality of studies ranged in from poor to good, and in sample size from 5 to 898 patients. In most studies, EYT was used as an add-on, not as a mono-therapy. The studies described positive treatment effects with clinically relevant effect sizes in most cases.</p> <p>Conclusion</p> <p>Indications, study designs and the usage of additional treatments within the identified studies were quite heterogeneous. Despite of this, EYT can be regarded as a potentially relevant add-on in a therapeutic concept, although its specific relevance remains to be clarified. Well performed controlled studies on this unique treatment are highly recommended.</p