Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets

Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets

Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion

There is increasing interest in the use of multiple measurement types, including indirect (geophysical) methods, to constrain hydrologic interpretations. To date, most examples integrating geophysical measurements in hydrology have followed a three-step, uncoupled inverse approach. This approach begins with independent geophysical inversion to infer the spatial and/or temporal distribution of a geophysical property (e.g. electrical conductivity). The geophysical property is then converted to a hydrologic property (e.g. water content) through a petrophysical relation. The inferred hydrologic property is then used either independently or together with direct hydrologic observations to constrain a hydrologic inversion. We present an alternative approach, coupled inversion, which relies on direct coupling of hydrologic models and geophysical models during inversion. We compare the abilities of coupled and uncoupled inversion using a synthetic example where surface-based electrical conductivity surveys are used to monitor one-dimensional infiltration and redistribution

Amelioration of non-motor dysfunctions after transplantation of human dopamine neurons in a model of Parkinson's disease

Background Patients suffering from Parkinson's disease (PD) display cognitive and neuropsychiatric dysfunctions, especially with disease progression. Although these impairments have been reported to impact more heavily upon a patient's quality of life than any motor dysfunctions, there are currently no interventions capable of adequately targeting these non-motor deficits. Objectives Utilizing a rodent model of PD, we investigated whether cell replacement therapy, using intrastriatal transplants of human-derived ventral mesencephalic (hVM) grafts, could alleviate cognitive and neuropsychiatric, as well as motor, dysfunctions. Methods Rats with unilateral 6-hydroxydopamine lesions to the medial forebrain bundle were tested on a complex operant task that dissociates motivational, visuospatial and motor impairments sensitive to the loss of dopamine. A subset of lesioned rats received intrastriatal hVM grafts of ~ 9 weeks gestation. Post-graft, rats underwent repeated drug-induced rotation tests and were tested on two versions of the complex operant task, before post-mortem analysis of the hVM tissue grafts. Results Post-graft behavioural testing revealed that hVM grafts improved non-motor aspects of task performance, specifically visuospatial function and motivational processing, as well as alleviating motor dysfunctions. Conclusions We report the first evidence of human VM cell grafts alleviating both non-motor and motor dysfunctions in an animal model of PD. This intervention, therefore, is the first to improve cognitive and neuropsychiatric symptoms long-term in a model of PD

Sequential approach to joint flow-seismic inversion for improved characterization of fractured media

Seismic interpretation of subsurface structures is traditionally performed without any account of flow behavior. Here we present a methodology for characterizing fractured geologic reservoirs by integrating flow and seismic data. The key element of the proposed approach is the identification—within the inversion—of the intimate relation between fracture compliance and fracture transmissivity, which determine the acoustic and flow responses of a fractured reservoir, respectively. Owing to the strong (but highly uncertain) dependence of fracture transmissivity on fracture compliance, the modeled flow response in a fractured reservoir is highly sensitive to the geophysical interpretation. By means of synthetic models, we show that by incorporating flow data (well pressures and tracer breakthrough curves) into the inversion workflow, we can simultaneously reduce the error in the seismic interpretation and improve predictions of the reservoir flow dynamics. While the inversion results are robust with respect to noise in the data for this synthetic example, the applicability of the methodology remains to be tested for more complex synthetic models and field cases.Eni-MIT Energy Initiative Founding Member ProgramKorea (South). Ministry of Land, Transportation and Maritime Affairs (15AWMP-B066761-03