Facial expression is widely used to evaluate emotional impairment in neuropsychiatric disorders. Ekman and Friesen’s Facial Action Coding System (FACS) encodes movements of individual facial muscles from distinct momentary changes in facial appearance. Unlike facial expression ratings based on categorization of expressions into prototypical emotions (happiness, sadness, anger, fear, disgust, etc.), FACS can encode ambiguous and subtle expressions, and therefore is potentially more suitable for analyzing the small differences in facial affect. However, FACS rating requires extensive training, and is time consuming and subjective thus prone to bias. To overcome these limitations, we developed an automated FACS based on advanced computer science technology. The system automatically tracks faces in a video, extracts geometric and texture features, and produces temporal profiles of each facial muscle movement. These profiles are quantified to compute frequencies of single and combined Action Units (AUs) in videos, which can facilitate statistical study of large populations in disorders affecting facial expression. We derived quantitative measures of flat and inappropriate facial affect automatically from temporal AU profiles. Applicability of the automated FACS was illustrated in a pilot study, by applying it to data of videos from eight schizophrenia patients and controls. We created temporal AU profiles that provided rich information on the dynamics of facial muscle movements for each subject. The quantitative measures of flatness and inappropriateness showed clear differences between patients and the controls, highlighting their potential in automatic and objective quantification of symptom severity.

Medical image registration is a challenging problem, especially when there is large anatomical variation in the anatomies. Geodesic registration methods have been proposed to solve the large deformation registration problem. However, analytically defined geodesic paths may not coincide with biologically plausible paths of registration, since the manifold of diffeomorphisms is immensely broader than the manifold spanned by diffeomorphisms between real anatomies. In this paper, we propose a novel framework for large deformation registration using the learned manifold of anatomical variation in the data. In this framework, a large deformation between two images is decomposed into a series of small deformations along the shortest path on an empirical manifold that represents anatomical variation. Using a manifold learning technique, the major variation of the data can be visualized by a low dimensional embedding, and the optimal group template is chosen as the geodesic mean on the manifold. We demonstrate the advantages of the proposed framework over direct registration with both simulated and real databases of brain images.

Abstract. Geodesic registration methods have been used to solve the large deformation registration problems, which are hard to solve with conventional registration methods. However, analytically defined geodesics may not coincide with anatomically optimal paths of registration. In this paper we propose a novel and efficient method for large deformation registration by learning the underlying structure of the data using a manifold learning technique. In this method a large deformation between two images is decomposed into a series of small deformations along the shortest path on the graph that approximates the metric structure of data. Furthermore, the graph representation allows us to estimate the optimal group template by minimizing geodesic distances. We demonstrate the advantages of the proposed method with synthetic 2D images and real 3D mice brain volumes.

Abstract-Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowdsensing data with differential privacy guarantees. Crowd-ML endows a crowdsensing system with an ability to learn classifiers or predictors online from crowdsensing data privately with minimal computational overheads on devices and servers, suitable for a practical and large-scale employment of the framework. We analyze the performance and the scalability of Crowd-ML, and implement the system with off-the-shelf smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML with real and simulated experiments under various conditions.

Pervasive internet and sensor technologies promise to revolutionize psychological science. However, the data collected using these technologies are often very personal—indeed, the value of the data is often directly related to how personal they are. At the same time, driven by the replication crisis, there is a sustained push to publish data to open repositories. These movements are in fundamental conflict. In this article, we propose a way to navigate this issue. We argue that there are significant advantages to be gained by ceding the ownership of data to the participants who generate the data. We then provide desiderata for a privacy-preserving platform. In particular, we suggest that researchers should use an interface to perform experiments and run analyses, rather than observing the stimuli themselves. We argue that this method not only improves privacy but will also encourage greater compliance with good research practices than is possible through open repositories.