Using a speeded lexical decision task, event-related potentials (ERPs), and minimum norm current source estimates, we investigated early spatiotemporal aspects of cortical activation elicited by words and pseudo-words that varied in their orthographic typicality, that is, in the frequency of their component letter pairs (bi-grams) and triplets (tri-grams). At around 100 msec after stimulus onse...

Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A ...

The quantum-dot cellular automata (QCA) is considered as an alternative tocomplementary metal oxide semiconductor (CMOS) technology based on physicalphenomena like Coulomb interaction to overcome the physical limitations of thistechnology. The decoder is one of the important components in digital circuits, whichcan be used in more comprehensive circuits such as full adde...

In this letter, a deep neural network (DNN) aided joint source-channel (JSCC) decoding scheme is proposed for polar codes. the scheme, an integrated factor graph with unfolded structure first designed. Then DNN flooding belief propagation (FBP) algorithm based on factor, in which both source and channel scaling parameters BP are optimized better performance. Experimental results show that, FBP ...

Logical metonymy resolution (begin a book → begin reading a book or begin writing a book) has traditionally been explained either through complex lexical entries (qualia structures) or through the integration of the implicit event via post-lexical access to world knowledge. We propose that recent work within the words-as-cues paradigm can provide a more dynamic model of logical metonymy, accoun...

The concept of typicality refers to properties holding for the “vast majority” of cases and is a fundamental idea of the qualitative approach to dynamical problems. We argue that measure-theoretical typicality would be the adequate viewpoint of the role of probability in classical statistical mechanics, particularly in understanding the micro to macroscopic change of levels

Typicality degrees were defined in supervised learning as a tool to build characteristic representatives for data categories. In this paper, an extension of these typicality degrees to unsupervised learning is proposed to perform clustering. The proposed algorithm constitutes a GustafsonKessel variant and makes it possible to identify ellipsoidal clusters with robustness as regards outliers.