The Component Diagnosability of General Networks

The processor failures in a multiprocessor system have a negative impact on its distributed computing efficiency. Because of the rapid expansion of multiprocessor systems, the importance of fault diagnosis is becoming increasingly prominent. The [Formula: see text]-component diagnosability of [Formula: see text], denoted by [Formula: see text], is the maximum number of nodes of the faulty set [Formula: see text] that is correctly identified in a system, and the number of components in [Formula: see text] is at least [Formula: see text]. In this paper, we determine the [Formula: see text]-component diagnosability of general networks under the PMC model and MM[Formula: see text] model. As applications, the component diagnosability is explored for some well-known networks, including complete cubic networks, hierarchical cubic networks, generalized exchanged hypercubes, dual-cube-like networks, hierarchical hypercubes, Cayley graphs generated by transposition trees (except star graphs), and DQcube as well. Furthermore, we provide some comparison results between the component diagnosability and other fault diagnosabilities.

Domain-specific and domain-general neural network engagement during human-robot interactions

To what extent do domain-general and domain-specific neural networks generalise across interactions with human and artificial agents? In this exploratory study, we analysed a publicly available fMRI dataset (n = 22; Rauchbauer, et al., 2019) to probe the similarities and dissimilarities in neural architecture while participants conversed with another person or a robot. Incorporating trial-by-trial dynamics of the interactions, listening and speaking, we used whole-brain, region-of-interest, and functional connectivity analyses to test response profiles within and across social or non-social, domain-specific and domain-general networks, i.e., the person perception, theory-of-mind, object-specific, language, multiple-demand networks. Listening to a robot compared to a human resulted in higher activation in the language network, especially in areas associated with listening comprehension, and in the person perception network. No differences in activity of the theory-of-mind network were found. Results from the functional connectivity analysis showed no difference between interactions with a human or robot in within- and between-network connectivity. Together, these results suggest that while similar regions are activated during communication regardless of the type of conversational agent, activity profiles during listening point to a dissociation at a lower-level or perceptual level, but not higher-order cognitive level.

Distinct but cooperating brain networks supporting semantic cognition

Semantic cognition is a complex brain function involving multiple processes from sensory systems, semantic systems, to domain-general cognitive systems, reflecting its multifaceted nature. However, it remain unclear how these systems cooperate with each other to achieve effective semantic cognition. Here, we investigated the neural networks involved in semantic cognition using independent component analysis (ICA). We used a semantic judgement task and a pattern matching task as a control task with two levels of difficulty to disentangle task-specific networks from domain-general networks and to delineate task-specific involvement of these networks. ICA revealed that semantic processing recruited two task-specific networks (semantic network [SN] and extended semantic network [ESN]) as well as domain general networks including the frontoparietal network (FPN) and default mode network (DMN). Specifically, two distinct semantic networks were differently modulated by task difficulty. The SN was coupled with the extended semantic network and FPN but decoupled with the DMN, whereas the ESN was synchronised with the FPN and DMN. Furthermore, the degree of decoupling between the SN and DMN was associated with semantic performance. Our findings suggest that human higher cognition is achieved by the neural dynamics of brain networks, serving distinct and shared cognitive functions depending on task demands.

Is the Letter ‘t’ in the Word ‘gourmet’? Disruption in Task-Evoked Connectivity Networks in Adults with Impaired Literacy Skills

Much work has been done to characterize domain-specific brain networks associated with reading, but very little work has been done with respect to spelling. Our aim was to characterize domain-specific spelling networks (SpNs) and domain-general resting state networks (RSNs) in adults with and without literacy impairments. Skilled and impaired adults were recruited from the University of Alberta. Participants completed three conditions of an in-scanner spelling task called a letter probe task (LPT). We found highly connected SpNs for both groups of individuals, albeit comparatively more connections for skilled (50) vs. impaired (43) readers. Notably, the SpNs did not correlate with spelling behaviour for either group. We also found relationships between SpNs and RSNs for both groups of individuals, this time with comparatively fewer connections for skilled (36) vs. impaired (53) readers. Finally, the RSNs did predict spelling performance in a limited manner for the skilled readers. These results advance our understanding of brain networks associated with spelling and add to the growing body of literature that describes the important and intricate connections between domain-specific networks and domain-general networks (i.e., resting states) in individuals with and without developmental disorders.

High-accuracy Ising machine using Kerr-nonlinear parametric oscillators with local four-body interactions

A two-dimensional array of Kerr-nonlinear parametric oscillators (KPOs) with local four-body interactions is a promising candidate for realizing an Ising machine with all-to-all spin couplings, based on adiabatic quantum computation in the Lechner–Hauke–Zoller (LHZ) scheme. However, its performance has been evaluated only for a symmetric network of three KPOs, and thus it has been unclear whether such an Ising machine works in general cases with asymmetric networks. By numerically simulating an asymmetric network of more KPOs in the LHZ scheme, we find that the asymmetry in the four-body interactions causes inhomogeneity in photon numbers and hence degrades the performance. We then propose a method for reducing the inhomogeneity, where the discrepancies of the photon numbers are corrected by tuning the detunings of KPOs depending on their positions, without monitoring their states during adiabatic time evolution. Our simulation results show that the performance can be dramatically improved by this method. The proposed method, which is based on the understanding of the asymmetry, is expected to be useful for general networks of KPOs in the LHZ scheme and thus for their large-scale implementation.

Fuzzy Ranking Network DEA with General Structure

This paper extends two fuzzy ranking data envelopment analysis (DEA) approaches to the case of general networks of processes. The first approach provides an efficiency score for each possibility level which requires solving one linear program for each possibility level. The second approach is even simpler and provides an overall efficiency score solving just one linear program. The proposed approaches are tested on two datasets from the literature and compared with other fuzzy network DEA approaches. The results show that the two methods provide very highly correlated efficiency estimates which are also consistent with those of other fuzzy network DEA approaches.