Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features for each sample is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this paper, it is assumed that common features exist in both domains and that extra (new additional) features are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common features, the adaptation between the source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The experiments with simulated and real-world data show that our proposed algorithm is able to obtain better model for the target domain by considering the extra features given for the target domain.

Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this letter, it is assumed that common features exist in both domains and that extra (new additional) features are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common features, the adaptation between these source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The proposed algorithm is validated using both simulated and real-world data.

A large number of neurons form cell assemblies that process information in the brain. Recent developments in measurement technology, one of which is calcium imaging, have made it possible to study cell assemblies. In this study, we aim to extract cell assemblies from calcium imaging data. We propose a clustering approach based on non-negative matrix factorization (NMF). The proposed approach first obtains a similarity matrix between neurons by NMF and then performs spectral clustering on it. The application of NMF entails the problem of model selection. The number of bases in NMF affects the result considerably, and a suitable selection method is yet to be established. We attempt to resolve this problem by model averaging with a newly defined estimator based on NMF. Experiments on simulated data suggest that the proposed approach is superior to conventional correlation-based clustering methods over a wide range of sampling rates. We also analyzed calcium imaging data of sleeping/waking mice and the results suggest that the size of the cell assembly depends on the degree and spatial extent of slow wave generation in the cerebral cortex.

Single-molecule localization microscopy is a widely used technique in biological research for measuring the nanostructures of samples smaller than the diffraction limit.This study uses multifocal plane microscopy and addresses the three-dimensional (3D) single-molecule localization problem, where lateral and axial locations of molecules are estimated.However, when multifocal plane microscopy is used, the estimation accuracy of 3D localization is easily deteriorated by the small lateral drifts of camera positions.A 3D molecule localization problem was presented along with the lateral drift estimation as a compressed sensing problem.A deep neural network (DNN) was applied to solve this problem accurately and efficiently.The results show that the proposed method is robust to lateral drift and achieves an accuracy of 20 nm laterally and 50 nm axially without an explicit drift correction.

Introducing data tracking of patients, doctors, medicine, or medical equipment holds promise to optimize workflows within hospitals. However, placing tracking devices and collecting data does not automatically translate into new workflows; instead, it requires careful considerations, knowledge about the activities tracked, and technical knowledge. In this paper, we study the interdisciplinary work involved when data tracking techniques are used to construe workflows within a real life work setting: architectural design of hospitals. We provide an ethnographic account from a unique study where two types of data tracking techniques were utilized in an existing hospital with the aim of informing the architectural design of a future hospital. The paper makes three contributions. First, we characterize the work involved in construing workflows based upon data tracking. Second, we discuss surveillance as an empirical category, which emerged in our study. Finally, we argue that to be able to construe workflows based upon data tracking, balancing the seamless boundary of privacy in work and surveillance in cooperation with the practitioners is of critical importance. Without support from the practitioners, data quality is at risk, and the resulting workflows might turn out 'flawed'.