Programming by Demonstration on Riemannian Manifolds

This thesis presents a Riemannian approach to Programming by Demonstration (PbD). It generalizes an existing PbD method from Euclidean manifolds to Riemannian manifolds. In this abstract, we review the objectives, methods and contributions of the presented approach. OBJECTIVES PbD aims at providing a user-friendly method for skill transfer between human and robot. It enables a user to teach a robot new tasks using few demonstrations. In order to surpass simple record-and-replay, methods for PbD need to \u2018understand\u2019 what to imitate; they need to extract the functional goals of a task from the demonstration data. This is typically achieved through the application of statisticalmethods. The variety of data encountered in robotics is large. Typical manipulation tasks involve position, orientation, stiffness, force and torque data. These data are not solely Euclidean. Instead, they originate from a variety of manifolds, curved spaces that are only locally Euclidean. Elementary operations, such as summation, are not defined on manifolds. Consequently, standard statistical methods are not well suited to analyze demonstration data that originate fromnon-Euclidean manifolds. In order to effectively extract what-to-imitate, methods for PbD should take into account the underlying geometry of the demonstration manifold; they should be geometry-aware. Successful task execution does not solely depend on the control of individual task variables. By controlling variables individually, a task might fail when one is perturbed and the others do not respond. Task execution also relies on couplings among task variables. These couplings describe functional relations which are often called synergies. In order to understand what-to-imitate, PbDmethods should be able to extract and encode synergies; they should be synergetic. In unstructured environments, it is unlikely that tasks are found in the same scenario twice. The circumstances under which a task is executed\u2014the task context\u2014are more likely to differ each time it is executed. Task context does not only vary during task execution, it also varies while learning and recognizing tasks. To be effective, a robot should be able to learn, recognize and synthesize skills in a variety of familiar and unfamiliar contexts; this can be achieved when its skill representation is context-adaptive. THE RIEMANNIAN APPROACH In this thesis, we present a skill representation that is geometry-aware, synergetic and context-adaptive. The presented method is probabilistic; it assumes that demonstrations are samples from an unknown probability distribution. This distribution is approximated using a Riemannian GaussianMixtureModel (GMM). Instead of using the \u2018standard\u2019 Euclidean Gaussian, we rely on the Riemannian Gaussian\u2014 a distribution akin the Gaussian, but defined on a Riemannian manifold. A Riev mannian manifold is a manifold\u2014a curved space which is locally Euclidean\u2014that provides a notion of distance. This notion is essential for statistical methods as such methods rely on a distance measure. Examples of Riemannian manifolds in robotics are: the Euclidean spacewhich is used for spatial data, forces or torques; the spherical manifolds, which can be used for orientation data defined as unit quaternions; and Symmetric Positive Definite (SPD) manifolds, which can be used to represent stiffness and manipulability. The Riemannian Gaussian is intrinsically geometry-aware. Its definition is based on the geometry of the manifold, and therefore takes into account the manifold curvature. In robotics, the manifold structure is often known beforehand. In the case of PbD, it follows from the structure of the demonstration data. Like the Gaussian distribution, the Riemannian Gaussian is defined by a mean and covariance. The covariance describes the variance and correlation among the state variables. These can be interpreted as local functional couplings among state variables: synergies. This makes the Riemannian Gaussian synergetic. Furthermore, information encoded in multiple Riemannian Gaussians can be fused using the Riemannian product of Gaussians. This feature allows us to construct a probabilistic context-adaptive task representation. CONTRIBUTIONS In particular, this thesis presents a generalization of existing methods of PbD, namely GMM-GMR and TP-GMM. This generalization involves the definition ofMaximum Likelihood Estimate (MLE), Gaussian conditioning and Gaussian product for the Riemannian Gaussian, and the definition of ExpectationMaximization (EM) and GaussianMixture Regression (GMR) for the Riemannian GMM. In this generalization, we contributed by proposing to use parallel transport for Gaussian conditioning. Furthermore, we presented a unified approach to solve the aforementioned operations using aGauss-Newton algorithm. We demonstrated how synergies, encoded in a Riemannian Gaussian, can be transformed into synergetic control policies using standard methods for LinearQuadratic Regulator (LQR). This is achieved by formulating the LQR problem in a (Euclidean) tangent space of the Riemannian manifold. Finally, we demonstrated how the contextadaptive Task-Parameterized Gaussian Mixture Model (TP-GMM) can be used for context inference\u2014the ability to extract context from demonstration data of known tasks. Our approach is the first attempt of context inference in the light of TP-GMM. Although effective, we showed that it requires further improvements in terms of speed and reliability. The efficacy of the Riemannian approach is demonstrated in a variety of scenarios. In shared control, the Riemannian Gaussian is used to represent control intentions of a human operator and an assistive system. Doing so, the properties of the Gaussian can be employed to mix their control intentions. This yields shared-control systems that continuously re-evaluate and assign control authority based on input confidence. The context-adaptive TP-GMMis demonstrated in a Pick & Place task with changing pick and place locations, a box-taping task with changing box sizes, and a trajectory tracking task typically found in industr

‘Agressie in de tram’:Het perspectief van trambestuurders

In this article we focus on how tram drivers react when confronted with a situation of aggression in the tram. Their role in these situations has hardly been explored in recent Dutch studies on this topic. In our explorative research, which consisted of observation and interviews in The Hague, our aim is to gain the perspective of tram drivers on aggressive passengers. Through their eyes we describe a range of strategies which they use to restore public order. Our research shows that in order to understand the reaction of tram drivers a) this has to be placed in the sequential development of an aggressive interaction and b) this has to be related to the ways they give meaning to the aggression of passengers and their work context. Our research shows that there are two perspectives among tram drivers: a) a ‘business’ perspective, with an emphasis of tram drivers as employees who avoid risk situations and call for support in situations they cannot control and b) a personal perspective, in which tram drivers tend to follow more personal guidelines, in which masculinity plays a central role. Those who use the later perspective become more involved in violent and physical interactions

Clinical application of biomarkers in colon cancer : studies on apoptosis, proliferation and the immune system

Colon cancer is a major contributor to can- cer-related mortality worldwide. Death from colon cancer occurs in the majority of cases from widespread metastatic disease. Only 15% of stage II colon cancer patients that develop metastasis will benefit from adjuvant chemotherapy, all of them will suffer from treatment-related toxicity. This makes it essential for the clinician to precisely identify the patient cohort at risk for metastasis. Prognostic biomarkers might improve current staging criteria. In this thesis a tumor biology-directed approach is applied to study and develop bio- markers focusing on tumor proliferation and evasion of apoptosis and immune destructionBoehringer Ingelheim, Covidien, Roche, Novartis Oncology, Erbe, Chipsoft, ABN Amro, ISA Pharmaceuticals, Olympus Nederland BV., ConvatecUBL - phd migration 201

Élaboration d'un système d'alerte aux étiages à l'échelle du bassin versant de la rivière Yamaska au Québec

Diverses études hydrologiques suggèrent que les changements climatiques anticipés entraîneront une diminution des débits d'étiage pour une majorité des rivières du sud du Québec. Cette situation risque de devenir particulièrement préoccupante dans le bassin versant de la rivière Yamaska, car son débit estival est déjà faible. Puisque cette rivière est la principale source d'approvisionnement en eau pour différentes municipalités et les industries qu'elles desservent, des actions doivent être entreprises avant d'en arriver à des situations critiques. Un système d'alerte basé sur des débits seuils jumelé à une prévision météorologique constituerait un outil de gestion puissant. L'élaboration d'un tel système d'alerte nécessite une bonne connaissance de la rivière. Des analyses statistiques des différents épisodes d'étiage survenus dans les années passées permettent d'en faire ressortir les débits historiques ainsi que d'en comprendre le comportement en tarissement. Une revue bibliographique et l'analyse de différents modèles de prévision des débits permettent de déterminer les modèles qui s'appliquent le mieux au bassin de la rivière Yamaska. Des mesures de restriction pour chacun des différents niveaux du système d'alerte proposé doivent être déterminées en s'inspirant des différentes mesures de restriction présentes dans différents pays, tout en tenant compte des facteurs économiques et politiques. Aussi, les critères écologiques et fonctionnels doivent être pris en compte lors de l'établissement de ces débits seuils. Sur la base d'un suivi d'indicateurs et de prévisions associées, ce mémoire propose donc un système d'alerte qui indiquera le moment où des mesures restrictives de prélèvement et de consommation doivent commencer à être appliquées. En plus de constituer un outil puissant pour le bassin versant de la rivière Yamaska, ce prototype de système d'alerte aux étiages pourra être adapté à d'autres bassins versants du Québec enclins aux étiages sévères

Empirical support for the vascular apathy hypothesis:A structured review

Objectives: A systematic review of the relationship between subclinical small vessel disease (SSVD) in the general population and apathy to examine the hypothesis that apathy has a vascular basis. Methods: We searched for studies on associations between apathy and SSVD, operationalized as white matter hyperintensities (WMH) or white matter diffusivity changes, lacunar infarcts, cerebral microbleeds, decreasing cortical thickness, and perivascular spaces, while also peripheral proxies for SSVD were considered, operationalized as ankle brachial pressure index (ABI), intima media thickness, arterial stiffness, cardio-femoral pulse wave velocity, hypertension, or cardiovascular disease. Only eligible retrospective and prospective observational studies conducted in the general population were included. Results: The 14 studies eligible for review examined the associations between apathy and hypertension (3), ABI (1), arterial stiffness (1), cardiovascular disease (2), WMH (3), white matter diffusivity (2), cerebral microbleeds (1), or cortical thickness (3). Arterial stiffness and white matter diffusivity were not related to apathy, while the associations with cortical thickness were contradictory. Cross-sectional studies in the general population did find evidence of apathy being associated with WMH, CM, cardiovascular disease, hypertension, and ABI, and cardiovascular disease was prospectively associated with apathy. The methodologies of the studies reviewed were too heterogeneous to perform meta-analyses. Conclusions: Although more prospective evidence is needed and vascular depression needs to be controlled for, cardiovascular disease, hypertension, and ABI as proxies for SSVD, and WMH and cerebral microbleeds as direct measures of SSVD have been found to be associated with apathy in the general population, supporting the hypothesis of vascular apathy

Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease.

OBJECTIVE: To determine whether MRI markers, including diffusion tensor imaging (DTI), can predict cognitive decline and dementia in patients with cerebral small vessel disease (SVD). METHODS: In the prospective St George's Cognition and Neuroimaging in Stroke study, multimodal MRI was performed annually for 3 years and cognitive assessments annually for 5 years in a cohort of 99 patients with SVD, defined as symptomatic lacunar stroke and confluent white matter hyperintensities (WMH). Progression to dementia was determined in all patients. Progression of WMH, brain volume, lacunes, cerebral microbleeds, and a DTI measure (the normalized peak height of the mean diffusivity histogram distribution) as a marker of white matter microstructural damage were determined. RESULTS: Over 5 years of follow-up, 18 patients (18.2%) progressed to dementia. A significant change in all MRI markers, representing deterioration, was observed. The presence of new lacunes, and rate of increase in white matter microstructural damage on DTI, correlated with both decline in executive function and global functioning. Growth of WMH and deterioration of white matter microstructure on DTI predicted progression to dementia. A model including change in MRI variables together with their baseline values correctly classified progression to dementia with a C statistic of 0.85. CONCLUSIONS: This longitudinal prospective study provides evidence that change in MRI measures including DTI, over time durations during which cognitive change is not detectable, predicts cognitive decline and progression to dementia. It supports the use of MRI measures, including DTI, as useful surrogate biomarkers to monitor disease and assess therapeutic interventions

Learning task-space synergies using Riemannian geometry

In the context of robotic control, synergies can form elementary units of behavior. By specifying taskdependent coordination behaviors at a low control level, one can achieve task-specific disturbance rejection. In this work we present an approach to learn the parameters of such lowlevel controllers by demonstration. We identify a synergy by extracting covariance information from demonstration data. The extracted synergy is used to derive a time-invariant state feedback controller through optimal control. To cope with the non-Euclidean nature of robot poses, we utilize Riemannian geometry, where both estimation of the covariance and the associated controller take into account the geometry of the pose manifold. We demonstrate the efficacy of the approach experimentally in a bimanual manipulation task

Pattern and Rate of Cognitive Decline in Cerebral Small Vessel Disease: A Prospective Study.

OBJECTIVES: Cognitive impairment, predominantly affecting processing speed and executive function, is an important consequence of cerebral small vessel disease (SVD). To date, few longitudinal studies of cognition in SVD have been conducted. We determined the pattern and rate of cognitive decline in SVD and used the results to determine sample size calculations for clinical trials of interventions reducing cognitive decline. METHODS: 121 patients with MRI confirmed lacunar stroke and leukoaraiosis were enrolled into the prospective St George's Cognition And Neuroimaging in Stroke (SCANS) study. Patients attended one baseline and three annual cognitive assessments providing 36 month follow-up data. Neuropsychological assessment comprised a battery of tests assessing working memory, long-term (episodic) memory, processing speed and executive function. We calculated annualized change in cognition for the 98 patients who completed at least two time-points. RESULTS: Task performance was heterogeneous, but significant cognitive decline was found for the executive function index (p<0.007). Working memory and processing speed decreased numerically, but not significantly. The executive function composite score would require the smallest samples sizes for a treatment trial with an aim of halting decline, but this would still require over 2,000 patients per arm to detect a 30% difference with power of 0.8 over a three year follow-up. CONCLUSIONS: The pattern of cognitive decline seen in SVD over three years is consistent with the pattern of impairments at baseline. Rates of decline were slow and sample sizes would need to be large for clinical trials aimed at halting decline beyond initial diagnosis using cognitive scores as an outcome measure. This emphasizes the importance of more sensitive surrogate markers in this disease.This work was supported by the Wellcome Trust [grant number 081589] and Alzheimer's Research UK [grant number ARUK-PG2013-2].This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.013552

Longitudinal patterns of leukoaraiosis and brain atrophy in symptomatic small vessel disease.

Cerebral small vessel disease is a common condition associated with lacunar stroke, cognitive impairment and significant functional morbidity. White matter hyperintensities and brain atrophy, seen on magnetic resonance imaging, are correlated with increasing disease severity. However, how the two are related remains an open question. To better define the relationship between white matter hyperintensity growth and brain atrophy, we applied a semi-automated magnetic resonance imaging segmentation analysis pipeline to a 3-year longitudinal cohort of 99 subjects with symptomatic small vessel disease, who were followed-up for ≥1 years. Using a novel two-stage warping pipeline with tissue repair step, voxel-by-voxel rate of change maps were calculated for each tissue class (grey matter, white matter, white matter hyperintensities and lacunes) for each individual. These maps capture both the distribution of disease and spatial information showing local rates of growth and atrophy. These were analysed to answer three primary questions: first, is there a relationship between whole brain atrophy and magnetic resonance imaging markers of small vessel disease (white matter hyperintensities or lacune volume)? Second, is there regional variation within the cerebral white matter in the rate of white matter hyperintensity progression? Finally, are there regionally specific relationships between the rates of white matter hyperintensity progression and cortical grey matter atrophy? We demonstrate that the rates of white matter hyperintensity expansion and grey matter atrophy are strongly correlated (Pearson's R = -0.69, P < 1 × 10(-7)), and significant grey matter loss and whole brain atrophy occurs annually (P < 0.05). Additionally, the rate of white matter hyperintensity growth was heterogeneous, occurring more rapidly within long association fasciculi. Using voxel-based quantification (family-wise error corrected P < 0.05), we show the rate of white matter hyperintensity progression is associated with increases in cortical grey matter atrophy rates, in the medial-frontal, orbito-frontal, parietal and occipital regions. Conversely, increased rates of global grey matter atrophy are significantly associated with faster white matter hyperintensity growth in the frontal and parietal regions. Together, these results link the progression of white matter hyperintensities with increasing rates of regional grey matter atrophy, and demonstrate that grey matter atrophy is the major contributor to whole brain atrophy in symptomatic cerebral small vessel disease. These measures provide novel insights into the longitudinal pathogenesis of small vessel disease, and imply that therapies aimed at reducing progression of white matter hyperintensities via end-arteriole damage may protect against secondary brain atrophy and consequent functional morbidity

Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change.

Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period. 98 patients (aged 43-89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials. Changes in brain structure described by DSEG θ were related to change in EF and IPS (p < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models (p = 0.002). Change in DSEG θ was also related to change in all other MRI markers (p < 0.017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity. DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i.e. DTIs)