Bounded Model Checking for Linear Time Temporal-Epistemic Logic

We present a novel approach to the verification of multi-agent systems using bounded model checking for specifications in LTLK, a linear time temporal-epistemic logic. The method is based on binary decision diagrams rather than the standard conversion to Boolean satisfiability. We apply the approach to two classes of interpreted systems: the standard, synchronous semantics and the interleaved semantics. We provide a symbolic algorithm for the verification of LTLK over models of multi-agent systems and evaluate its implementation against MCK, a competing model checker for knowledge. Our evaluation indicates that the interleaved semantics can often be preferable in the verification of LTLK

Evaluating Nuclei Concentration in Amyloid Fibrillation Reactions Using Back-Calculation Approach

Background: In spite of our extensive knowledge of the more than 20 proteins associated with different amyloid diseases, we do not know how amyloid toxicity occurs or how to block its action. Recent contradictory reports suggest that the fibrils and/or the oligomer precursors cause toxicity. An estimate of their temporal concentration may broaden understanding of the amyloid aggregation process. Methodology/Principal Findings: Assuming that conversion of folded protein to fibril is initiated by a nucleation event, we back-calculate the distribution of nuclei concentration. The temporal in vitro concentration of nuclei for the model hormone, recombinant human insulin, is estimated to be in the picomolar range. This is a conservative estimate since the back-calculation method is likely to overestimate the nuclei concentration because it does not take into consideration fibril fragmentation, which would lower the amount of nuclei Conclusions: Because of their propensity to form aggregates (non-ordered) and fibrils (ordered), this very low concentration could explain the difficulty in isolating and blocking oligomers or nuclei toxicity and the long onset time for amyloid diseases

A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Simple performance evaluation of pulsed spontaneous parametric down-conversion sources for quantum communications

Fast and complete characterization of pulsed spontaneous parametric down conversion (SPDC) sources is important for applications in quantum information processing and communications. We propose a simple method to perform this task, which only requires measuring the counts on the two output channels and the coincidences between them, as well as modeling the filter used to reduce the source bandwidth. The proposed method is experimentally tested and used for a complete evaluation of SPDC sources (pair emission probability, total losses, and fidelity) of different bandwidths. This method can find applications in the setting up of SPDC sources and in the continuous verification of the quality of quantum communication links

Image 100 procedures manual development: Applications system library definition and Image 100 software definition

An outline for an Image 100 procedures manual for Earth Resources Program image analysis was developed which sets forth guidelines that provide a basis for the preparation and updating of an Image 100 Procedures Manual. The scope of the outline was limited to definition of general features of a procedures manual together with special features of an interactive system. Computer programs were identified which should be implemented as part of an applications oriented library for the system

Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability. Recently, conversion of a trained deep neural network to an SNN has improved the accuracy of deep SNNs. However, most of the previous studies have not achieved satisfactory results in terms of inference speed and energy efficiency. In this paper, we propose a fast and energy-efficient information transmission method with burst spikes and hybrid neural coding scheme in deep SNNs. Our experimental results showed the proposed methods can improve inference energy efficiency and shorten the latency.Comment: Accepted to DAC 201