Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization

How intrinsic gene-regulatory networks interact with a cell's spatial environment to define its identity remains poorly understood. We developed an approach to distinguish between intrinsic and extrinsic effects on global gene expression by integrating analysis of sequencing-based and imaging-based single-cell transcriptomic profiles, using cross-platform cell type mapping combined with a hidden Markov random field model. We applied this approach to dissect the cell-type- and spatial-domain-associated heterogeneity in the mouse visual cortex region. Our analysis identified distinct spatially associated, cell-type-independent signatures in the glutamatergic and astrocyte cell compartments. Using these signatures to analyze single-cell RNA sequencing data, we identified previously unknown spatially associated subpopulations, which were validated by comparison with anatomical structures and Allen Brain Atlas images

Feature Representation for Online Signature Verification

Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information Forensics and Securit

Offline signature verification using classifier combination of HOG and LBP features

We present an offline signature verification system based on a signature’s local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP). The classification is performed using Support Vector Machines (SVMs), where two different approaches for training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s signature from others, whereas a single global SVM trained with difference vectors of query and reference signatures’ features of all users, learns how to weight dissimilarities. The global SVM classifier is trained using genuine and forgery signatures of subjects that are excluded from the test set, while userdependent SVMs are separately trained for each subject using genuine and random forgeries. The fusion of all classifiers (global and user-dependent classifiers trained with each feature type), achieves a 15.41% equal error rate in skilled forgery test, in the GPDS-160 signature database without using any skilled forgeries in training

Anonymous reputation based reservations in e-commerce (AMNESIC)

Online reservation systems have grown over the last recent years to facilitate the purchase of goods and services. Generally, reservation systems require that customers provide some personal data to make a reservation effective. With this data, service providers can check the consumer history and decide if the user is trustable enough to get the reserve. Although the reputation of a user is a good metric to implement the access control of the system, providing personal and sensitive data to the system presents high privacy risks, since the interests of a user are totally known and tracked by an external entity. In this paper we design an anonymous reservation protocol that uses reputations to profile the users and control their access to the offered services, but at the same time it preserves their privacy not only from the seller but the service provider

Modal logics for reasoning about object-based component composition

Component-oriented development of software supports the adaptability and maintainability of large systems, in particular if requirements change over time and parts of a system have to be modified or replaced. The software architecture in such systems can be described by components and their composition. In order to describe larger architectures, the composition concept becomes crucial. We will present a formal framework for component composition for object-based software development. The deployment of modal logics for defining components and component composition will allow us to reason about and prove properties of components and compositions

Researcher-led teaching:embodiment of academic practice

This paper explores the embodied practices of leading researchers(and/or leading scholars/practitioners), suggesting that distinctive‘researcher-led teaching’ depends on educators who are willing and able to be their research in the teaching setting. We advocate an approach to the development of higher education pedagogy which makes lead-researchers the objects of inquiry and we summarise case study analyses (in neuroscience and humanities) where the knowledge-making‘signatures’ of academic leaders are used to exhibit the otherwise hidden identities of research. We distinguish between learning readymade knowledge and the process of knowledge in the making and point towards the importance of inquiry in the flesh. We develop a view of higher education teaching that depends upon academic status a priori, but we argue that this stance is inclusive because it has the propensity to locate students as participants in academic culture