Spiking Neural Networks (SNNs) have found increasing utility in designing safety-critical Cyber-Physical Systems (CPSs) such as implantable medical devices, autonomous vehicles, and space robotics due to their capability to operate on information represented in temporal coding and exhibit various behavioural modalities. Thus, there has been recent interest in formally verifying their timing behaviours and providing soundness guarantees of their diverse characteristics. However, beyond the simplistic Leaky Integrate and Fire (LIF) model, which only mimics 3 spiking behaviours, there is a lack of unifying methodology in literature to verify complex dynamics of biological neurons exhibiting 20 spiking behaviours as demonstrated by the pioneering work of Izhikevich. There is also a complete lack of formulation for the verification of SNN-based systems. This paper bridges these gaps by proposing a model-based approach for designing SNN-based controllers in CPS. We propose sound structural transformations translating any spiking neuron into networks of Timed Automata (TA), model the complex Izhikevich neural model and formally verify all 20 timing behaviours it exhibits for the first time. We then present two case studies that were modelled as SNNs using our approach: the PID controller, and the Car-Following controller, and subsequently attempt static model checking and statistical verification of their generated TA models for safety guarantees.

This paper explores a novel approach to dexterous manipulation, aimed at levels of speed, precision, robustness, and simplicity suitable for practical deployment. The enabling technology is a Direct-drive Hand (DDHand) comprising two fingers, two DOFs each, that exhibit high speed and a light touch. The test application is the dexterous manipulation of three small and irregular parts, moving them to a grasp suitable for a subsequent assembly operation, regardless of initial presentation. We employed four primitive behaviors that use ground contact as a “third finger”, prior to or during the grasp process: pushing, pivoting, toppling, and squeeze-grasping. In our experiments, each part was presented from 30 to 90 times randomly positioned in each stable pose. Success rates varied from 83% to 100%. The time to manipulate and grasp was 6.32 seconds on average, varying from 2.07 to 16 seconds. In some cases, performance was robust, precise, and fast enough for practical applications, but in other cases, pose uncertainty required time-consuming vision and arm motions. The paper concludes with a discussion of further improvements required to make the primitives robust, eliminate uncertainty, and reduce this dependence on vision and arm motion.

A method is presented that uses β-strand interactions at both the sequence and the atomic level, to predict the beta-structural motifs in protein sequences. A program called Wrap-and-Pack implements this method, and is shown to recognize β-trefoils, an important class of globular β-structures, in the Protein Data Bank with 92% specificity and 92.3% sensitivity in cross-validation. It is demonstrated that Wrap-and-Pack learns each of the ten known SCOP β-trefoil families, when trained primarily on β-structures that are not β-trefoils, together with 3D structures of known β-trefoils from outside the family. Wrap-and-Pack also predicts many proteins of unknown structure to be β-trefoils. The computational method used here may generalize to other β-structures for which strand topology and profiles of residue accessibility are well conserved.

A method is presented that uses β-strand interactions to predict the right-handed β-helix super-secondary structural motif in protein sequences. A program called BetaWrap implements this method, and is shown to score known β-helices above non-β-helices in the Protein Data Bank in cross-validation. It is demonstrated that BetaWrap learns each of the seven known SCOP β-helix families, when trained on the the known β-helices from outside the family. BetaWrap also predicts many bacterial proteins of unknown structure that play a role in human infectious disease to β-helices; in particular, these proteins serve as virulence factors, adhesins and toxins in bacterial pathogenesis, and include cell surface proteins from Chlamydia and the intestinal bacterium Helicobacter pylori. The computational method used here may generalize to other β structures for which strand topology and profiles of residue accessibility are well conserved.

This paper describes a natural language processing system implemented at Hewlett-Packard's Computer Research Center. The system's main components are: a Generalized Phrase Structure Grammar (GPSG); a top-down parser; a logic transducer that outputs a first-order logical representation; and a "disambiguator" that uses sortal information to convert "normal-form" first-order logical expressions into the query language for HIRE, a relational database hosted in the SPHERE system. We argue that theoretical developments in GPSG syntax and in Montague semantics have specific advantages to bring to this domain of computational linguistics. The syntax and semantics of the system are totally domain-independent, and thus, in principle, highly portable. We discuss the prospects for extending domain-independence to the lexical semantics as well, and thus to the logical semantic representations.