A low-order model of biological neural networks

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.     

The relationship between online game playing motivation and selection of online game characters – the case of Taiwan

The alarming growth rate in the online game industry calls for a need of understanding the determinants of online gamers’ behaviour. Investigating how online gamers choose different online characters assists online game vendors to design characters that match the needs of different online gamers. Recently, numerous online game designers have introduced celebrity figures as characters for certain online games. The popularity of those celebrities has resulted in greater awareness of these games, and hopefully further increases the playability of online games. While different online gamers are motivated by different desires for playing, it is insufficiently researched whether these online celebrity characters have actually altered players’ choice in giving preference to these games. Thus, this study conducts an experimental design to study the reasons behind players’ choice of characters by studying how online games perceive the interpersonal attractiveness of celebrity online characters, and how online gamers with different playing motivations display different behaviours in their choice of online game characters. Results from the survey filled out by participants after the experiments show that the attractiveness of celebrity online characters is significantly higher than that of non-celebrity characters. Further, online game players with a social playing motivation tend to choose celebrities as their online game characters.     

Using ontologies to perform threat analysis and develop defensive strategies for mobile security

Existing studies on the detection of mobile malware have focused mainly on static analyses performed to examine the code-structure signature of viruses, rather than the dynamic behavioral aspects. By contrast, the unidentified behavior of new mobile viruses using the self-modification, polymorphic, and mutation techniques for variants have largely been ignored. The problem of precision regarding malware variant detection has become one of the key concerns in mobile security. Accordingly, the present study proposed a threat risk analysis model for mobile viruses, using a heuristic approach incorporating both malware behavior analysis and code analysis to generate a virus behavior ontology associated with the Protégé platform. The proposed model can not only explicitly identify an attack profile in accordance with structural signature of mobile viruses, but also overcome the uncertainty regarding the probability of an attack being successful. This model is able to achieve this by extending frequent episode rules to investigate the attack profile of a given malware, using specific event sequences associated with the sandbox technique for mobile applications (apps) and hosts. For probabilistic analysis, defense evaluation metrics for each node were used to simulate the results of an attack. The simulations focused specifically on the attack profile of a botnet to assess the threat risk. The validity of the proposed approach was demonstrated numerically by using two malware cyber-attack examples. Overall, the results presented in this paper prove that the proposed scheme offers an effective countermeasure, evaluated using a set of security metrics, for mitigating network threats by considering the interaction between the attack profiles and defense needs.     

Electroclinical manifestations elicited by intracerebral electric stimulation "shocks" in temporal lobe epilepsy

In patients with severe drug-resistant partial epilepsy, undergoing Stereo-EEG investigations, spatial definition of the "epileptogenic area" is mainly based on spontaneous seizures recordings, but also on seizures induced by intracerebral electrical stimulation (ES). Only "trains" ES (TES, 50 pps) are currently used with this aim; "shocks" ES (SES, 1 pps) are principally applied to localize motor pathways. We have shown, during a prospective study concerning 10 temporal lobe epileptic patients, that SES could frequently induce seizures, especially when stimulation is applied in the anterior part of the Ammon's horn. Even if its efficacy seems lower than by TES, this kind of stimulation, in the majority of the cases, does reproduce isolated ictal subjective symptomatology, allowing the visualization of the progressive organisation of ictal electrical discharges, and avoids "unexpected" ("false positive"?) clinical responses.     

Learning extended FSA from software: An empirical assessment

A number of techniques that infer finite state automata from execution traces have been used to support test and analysis activities. Some of these techniques can produce automata that integrate information about the data-flow, that is, they also represent how data values affect the operations executed by programs.The integration of information about operation sequences and data values into a unique model is indeed conceptually useful to accurately represent the behavior of a program. However, it is still unclear whether handling heterogeneous types of information, such as operation sequences and data values, necessarily produces higher quality models or not.In this paper, we present an empirical comparative study between techniques that infer simple automata and techniques that infer automata extended with information about data-flow. We investigate the effectiveness of these techniques when applied to traces with different levels of sparseness, produced by different software systems. To the best of our knowledge this is the first work that quantifies both the effect of adding data-flow information within automata and the effectiveness of the techniques when varying sparseness of traces.     

Prediction of time-varying metrology delay for dEWMA and RLS-LT controllers

This paper investigates how to adaptively predict the time-varying metrology delay that can realistically occur in the semiconductor manufacturing practice. In the presence of metrology delays, the expected asymptotic double exponentially weighted moving average (dEWMA) control output, by using the EWMA and recursive least squares prediction methods, is derived. It has been found that the relationships between the expected control output and target in both estimation methods are equivalent, and six cases are addressed. Within the context of time-varying metrology delay, a new time update scheme to the recursive least squares-linear trend (RLS-LT) controller, combined with zone tests and the moving average (MA) control chart, is proposed. Simulated single input–single output (SISO) run-to-run processes subject to two time-varying metrology delay scenarios are used to assess the effectiveness of the proposed controller.     

A general bayes rule and its application to nonlinear estimation

This paper consists of two main results, a general Bayes rule, and a general Bucy representation theorem. The general Bayes rule is a natural generalization of the elementary Bayes rule:

$\text{P(A}\text{B)}\text{P(A)}\text{=}\text{P(B}\text{A)}\text{P(B)}$

. The general Bucy representation theorem plays a central role in nonlinear estimation theory as does the Bucy theorem in nonlinear filtering. A simple and direct proof of the general Bucy representation theorem is obtained by the application of the general Bayes rule.     

Simulation of interconnect inductive impact in the presence of process variations in 90 nm and beyond

The on-chip inductive impact on signal integrity has been a problem for designs in deep-submicrometer technologies. The inductive impact increases the clock skew, max timing, and noise of bus signals. In this letter, circuit simulations using silicon-validated macromodels show that there is a significant inductive impact on the signal max timing (/spl sim/ 10% pushout versus RC delay) and noise (/spl sim/2/spl times/RC noise). In nanometer technologies, process variations have become a concern. Results show that device and interconnect process variations add /spl sim/ 3% to the RLC max-timing impact. However, their impact on the RLC signal noise is not appreciable. Finally, inductive impact in 65- and 45-nm technologies is investigated, which indicates that the inductance impact will not diminish as technology scales.