Multi-Granular Optical Cross-Connect: Design, Analysis, and Demonstration

A fundamental issue in all-optical switching is to offer efficient and cost-effective transport services for a wide range of bandwidth granularities. This paper presents multi-granular optical cross-connect (MG-OXC) architectures that combine slow (ms regime) and fast (ns regime) switch elements, in order to support optical circuit switching (OCS), optical burst switching (OBS), and even optical packet switching (OPS). The MG-OXC architectures are designed to provide a cost-effective approach, while offering the flexibility and reconfigurability to deal with dynamic requirements of different applications. All proposed MG-OXC designs are analyzed and compared in terms of dimensionality, flexibility/reconfigurability, and scalability. Furthermore, node level simulations are conducted to evaluate the performance of MG-OXCs under different traffic regimes. Finally, the feasibility of the proposed architectures is demonstrated on an application-aware, multi-bit-rate (10 and 40 Gbps), end-to-end OBS testbed

Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics

First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049

The Contribution of Thalamocortical Core and Matrix Pathways to Sleep Spindles.

Sleep spindles arise from the interaction of thalamic and cortical neurons. Neurons in the thalamic reticular nucleus (TRN) inhibit thalamocortical neurons, which in turn excite the TRN and cortical neurons. A fundamental principle of anatomical organization of the thalamocortical projections is the presence of two pathways: the diffuse matrix pathway and the spatially selective core pathway. Cortical layers are differentially targeted by these two pathways with matrix projections synapsing in superficial layers and core projections impinging on middle layers. Based on this anatomical observation, we propose that spindles can be classified into two classes, those arising from the core pathway and those arising from the matrix pathway, although this does not exclude the fact that some spindles might combine both pathways at the same time. We find evidence for this hypothesis in EEG/MEG studies, intracranial recordings, and computational models that incorporate this difference. This distinction will prove useful in accounting for the multiple functions attributed to spindles, in that spindles of different types might act on local and widespread spatial scales. Because spindle mechanisms are often hijacked in epilepsy and schizophrenia, the classification proposed in this review might provide valuable information in defining which pathways have gone awry in these neurological disorders

Microscopic Inner Retinal Hyper-reflective Phenotypes in Retinal and Neurologic Disease

Purpose. We surveyed inner retinal microscopic features in retinal and neurologic disease using a reflectance confocal adaptive optics scanning light ophthalmoscope (AOSLO). Methods. Inner retinal images from 101 subjects affected by one of 38 retinal or neurologic conditions and 11 subjects with no known eye disease were examined for the presence of hyper-reflective features other than vasculature, retinal nerve fiber layer, and foveal pit reflex. The hyper-reflective features in the AOSLO images were grouped based on size, location, and subjective texture. Clinical imaging, including optical coherence tomography (OCT), scanning laser ophthalmoscopy, and fundus photography was analyzed for comparison. Results. Seven categories of hyper-reflective inner retinal structures were identified, namely punctate reflectivity, nummular (disc-shaped) reflectivity, granular membrane, waxy membrane, vessel-associated membrane, microcysts, and striate reflectivity. Punctate and nummular reflectivity also was found commonly in normal volunteers, but the features in the remaining five categories were found only in subjects with retinal or neurologic disease. Some of the features were found to change substantially between follow up imaging months apart. Conclusions. Confocal reflectance AOSLO imaging revealed a diverse spectrum of normal and pathologic hyper-reflective inner and epiretinal features, some of which were previously unreported. Notably, these features were not disease-specific, suggesting that they might correspond to common mechanisms of degeneration or repair in pathologic states. Although prospective studies with larger and better characterized populations, along with imaging of more extensive retinal areas are needed, the hyper-reflective structures reported here could be used as disease biomarkers, provided their specificity is studied further

The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity

Due to the complexity of the traffic flow dynamics in urban road networks, most quantitative descriptions of city traffic so far are based on computer simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation approach, which facilitates a simple simulation of congestion spreading in cities. First, we show that a quantization of the macroscopic turning flows into units of single vehicles is necessary to obtain realistic fluctuations in the traffic variables, and how this can be implemented in a fluid-dynamic model. Then, we propose a new method to simulate destination flows without the requirement of individual route assignments. Combining both methods allows us to study a variety of different simulation scenarios. These reveal fundamental relationships between the average flow, the average density, and the variability of the vehicle densities. Considering the inhomogeneity of traffic as an independent variable can eliminate the scattering of congested flow measurements. The variability also turns out to be a key variable of urban traffic performance. Our results can be explained through the number of full links of the road network, and approximated by a simple analytical formula

Economic Effects of Lifting the Spring Load Restriction Policy in Minnesota

Spring load restrictions (SLR) regulate the weight per axle carried by heavy trucks during the spring thaw period. This policy aims to reduce pavement damage caused by heavy vehicles and extend the useful life of roads, but it also imposes costs on the trucking industry due to detouring or increased number of truckloads. Although the policies have been implemented for many years, their resulting economic effect has been unclear. The Minnesota Local Road Research Board (LRRB) and the Minnesota Department of Transportation (Mn/DOT) sponsored a cost/benefit study of spring load restrictions in Minnesota. The study, based on the results of surveys of industry costs, a pavement performance model, and a freight demand model, concludes that the benefits of lifting the existing SLR policy outweigh the additional costs. Roadways operating at 5-tons require additional study; however, current analysis warrants repealing SLR and keeping roadways operating year-round at 9-tons. The cost of additional damage should be recovered from those who benefit from the change in policy.

Using ontology in query answering systems: Scenarios, requirements and challenges

Equipped with the ultimate query answering system, computers would finally be in a position to address all our information needs in a natural way. In this paper, we describe how Language and Computing nv (L&C), a developer of ontology-based natural language understanding systems for the healthcare domain, is working towards the ultimate Question Answering (QA) System for healthcare workers. L&C’s company strategy in this area is to design in a step-by-step fashion the essential components of such a system, each component being designed to solve some one part of the total problem and at the same time reflect well-defined needs on the prat of our customers. We compare our strategy with the research roadmap proposed by the Question Answering Committee of the National Institute of Standards and Technology (NIST), paying special attention to the role of ontology