National Research University – Higher School of Economics

About: National Research University – Higher School of Economics is a education organization based out in Moscow, Russia. It is known for research contribution in the topics: Population & Politics. The organization has 12873 authors who have published 23376 publications receiving 256396 citations.

PSM and Turnover Intention in Public Organizations: Does Change-Oriented Organizational Citizenship Behavior Play a Role?

Abstract: Not all employees contribute equally to the performance of their organizations, and the highest performers may have a disproportionate impact on organizational success. It is thus crucial for publi...

Romantic Love and Family Organization: A Case for Romantic Love as a Biosocial Universal

Abstract: We propose that romantic love is a biosocial phenomenon that may well be a universal and that its cultural aspects are a product of social conditions. This position is unique because romantic love ...

Open data ecosystems: an international comparison

Abstract: Purpose In this paper, the authors compare the open government data (OGD) ecosystems of Mexico, Russia and the USA in an effort to extract some of the major points of similarity and differentiation between these countries and to trace how variations in these ecosystems may be related to context-specific historical problems and politics, particularly with regard to the possibility of sustained and institutionalized practice. Design/methodology/approach The authors take a comparative approach, using a set of concepts commonly applied to the three countries to analyze similarities and differences within this group. The authors gathered textual data and information, the searches for relevant documents guided by a set of concepts or criteria that are frequently used in studies of government’s open data readiness assessment. Findings The authors conclude by focusing on the very different national exigencies that have given rise to open data ecosystems in the three countries, the variations in policy vehicles and implementation schemes that have instantiated open data practices within the three ecosystems and the common challenges that each country faces in institutionalizing OGD programs beyond the tenures of their current executives. Originality/value OGD is an information policy with near global relevance and increasing application. Practitioners and scholars alike have used the concept of an “ecosystem” to guide their approach to implementing this policy and to theorizing its scope and benefits. The international comparison is original and adds to the current understanding of an ecosystem approach to OGD.

Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load

Abstract: The maintenance of items in working memory relies on persistent neural activity in a widespread network of brain areas. To investigate the influence of load on working memory, we asked human subjects to maintain sets of letters in memory while we recorded single neurons and intracranial encephalography (EEG) in the medial temporal lobe and scalp EEG. Along the periods of a trial, hippocampal neural firing differentiated between success and error trials during stimulus encoding, predicted workload during memory maintenance, and predicted the subjects’ behavior during retrieval. During maintenance, neuronal firing was synchronized with intracranial hippocampal EEG. On the network level, synchronization between hippocampal and scalp EEG in the theta-alpha frequency range showed workload dependent oscillatory coupling between hippocampus and cortex. Thus, we found that persistent neural activity in the hippocampus participated in working memory processing that is specific to memory maintenance, load sensitive and synchronized to the cortex.

Expressive power of recurrent neural networks

Abstract: Deep neural networks are surprisingly efficient at solving practical tasks, but the theory behind this phenomenon is only starting to catch up with the practice. Numerous works show that depth is the key to this efficiency. A certain class of deep convolutional networks -- namely those that correspond to the Hierarchical Tucker (HT) tensor decomposition -- has been proven to have exponentially higher expressive power than shallow networks. I.e. a shallow network of exponential width is required to realize the same score function as computed by the deep architecture. In this paper, we prove the expressive power theorem (an exponential lower bound on the width of the equivalent shallow network) for a class of recurrent neural networks -- ones that correspond to the Tensor Train (TT) decomposition. This means that even processing an image patch by patch with an RNN can be exponentially more efficient than a (shallow) convolutional network with one hidden layer. Using theoretical results on the relation between the tensor decompositions we compare expressive powers of the HT- and TT-Networks. We also implement the recurrent TT-Networks and provide numerical evidence of their expressivity.