Generating Test Sets from Non-Deterministic Stream X-Machines

X-machines were proposed by Holcombe as a possible specification language and since then a number of further investigations have demonstrated that the model is intuitive and easy to use as well as general enough to cater for a wide range of applications. In particular (generalised) stream X-machines have been found to be extremely useful as a specification method and most of the theory developed so far has concentrated on this particular class of X-machines. Furthermore, a method for testing systems specified by stream X-machines exists and is proved to detect all faults of the implementation provided that the system meets certain initial requirements. However, this method can only be used to generate test sequences from deterministic X-machine specifications. In this paper we present the theoretical basis for a method for generating test sets from non-deterministic generalised stream X-machines. Received November 1999 / Accepted in revised form September 2000     

Learning with "relevance": using a third factor to stabilize Hebbian learning

It is a well-known fact that Hebbian learning is inherently unstable because of its self-amplifying terms: the more a synapse grows, the stronger the postsynaptic activity, and therefore the faster the synaptic growth. This unwanted weight growth is driven by the autocorrelation term of Hebbian learning where the same synapse drives its own growth. On the other hand, the cross-correlation term performs actual learning where different inputs are correlated with each other. Consequently, we would like to minimize the autocorrelation and maximize the cross-correlation. Here we show that we can achieve this with a third factor that switches on learning when the autocorrelation is minimal or zero and the cross-correlation is maximal. The biological counterpart of such a third factor is a neuromodulator that switches on learning at a certain moment in time. We show in a behavioral experiment that our three-factor learning clearly outperforms classical Hebbian learning.     

Adapting computer data storage to user group behaviour

We have designed several interactive computer systems for financial analysts. In early designs we treated the analysts-computer interactive dialogue at a computer terminal as being isolated from group considerations. In a later system we were forced to make extensive technical changes to take account of data storage sharing. This sharing of data storage had to operate in a way which was consistent with existing group attitudes. The relevant features of this system are described, and reviewed after more than two years of operation. We conclude that many user-computer interactive dialogues cannot be designed as if they were 'private' to a single user, but must be considered in a wider context of the way in which members of a user group interact.     

Microglial Activation and Priming in Alzheimer’s Disease: State of the Art and Future Perspectives

Alzheimer’s Disease (AD) is the most common cause of dementia, having a remarkable social and healthcare burden worldwide. Amyloid β (Aβ) and protein Tau aggregates are disease hallmarks and key players in AD pathogenesis. However, it has been hypothesized that microglia can contribute to AD pathophysiology, as well. Microglia are CNS-resident immune cells belonging to the myeloid lineage of the innate arm of immunity. Under physiological conditions, microglia are in constant motion in order to carry on their housekeeping function, and they maintain an anti-inflammatory, quiescent state, with low expression of cytokines and no phagocytic activity. Upon various stimuli (debris, ATP, misfolded proteins, aggregates and pathogens), microglia acquire a phagocytic function and overexpress cytokine gene modules. This process is generally regarded as microglia activation and implies that the production of pro-inflammatory cytokines is counterbalanced by the synthesis and the release of anti-inflammatory molecules. This mechanism avoids excessive inflammatory response and inappropriate microglial activation, which causes tissue damage and brain homeostasis impairment. Once the pathogenic stimulus has been cleared, activated microglia return to the naïve, anti-inflammatory state. Upon repeated stimuli (as in the case of Aβ deposition in the early stage of AD), activated microglia shift toward a less protective, neurotoxic phenotype, known as “primed” microglia. The main characteristic of primed microglia is their lower capability to turn back toward the naïve, anti-inflammatory state, which makes these cells prone to chronic activation and favours chronic inflammation in the brain. Primed microglia have impaired defence capacity against injury and detrimental effects on the brain microenvironment. Additionally, priming has been associated with AD onset and progression and can represent a promising target for AD treatment strategies. Many factors (genetics, environmental factors, baseline inflammatory status of microglia, ageing) generate an aberrantly activated phenotype that undergoes priming easier and earlier than normally activated microglia do. Novel, promising targets for therapeutic strategies for AD have been sought in the field of microglia activation and, importantly, among those factors influencing the baseline status of these cells. The CX3CL1 pathway could be a valuable target treatment approach in AD, although preliminary findings from the studies in this field are controversial. The current review aims to summarize state of the art on the role of microglia dysfunction in AD pathogenesis and proposes biochemical pathways with possible targets for AD treatment.     

Natural rubber (Hevea Brasiliensis)-based quasi-solid electrolyte as a potential candidate for arresting recombination and improving performance in aqueous dye-sensitized solar cells

Journal of Materials Science: Materials in Electronics - We successfully fabricated dye sensitized solar cells (DSSCs) employing a quasi-solid state electrolyte based on pristine insulator natural...     

A Novel Trajectory Generation Method for Robot Control

This paper presents a novel trajectory generator based on Dynamic Movement Primitives (DMP). The key ideas from the original DMP formalism are extracted, reformulated and extended from a control theoretical viewpoint. This method can generate smooth trajectories, satisfy position- and velocity boundary conditions at start- and endpoint with high precision, and follow accurately geometrical paths as desired. Paths can be complex and processed as a whole, and smooth transitions can be generated automatically. Performance is analyzed for several cases and a comparison with a spline-based trajectory generation method is provided. Results are comparable and, thus, this novel trajectory generating technology appears to be a viable alternative to the existing solutions not only for service robotics but possibly also in industry.     

Polysilazane derived micro/nano Si3N4/SiC composites

The pyrolised polysilazanes poly(hydridomethyl)silazane NCP 200 and poly(urea)silazane CERASET derived Si–C–N amorphous powders were used for preparation of micro/nano Si₃N₄/SiC composites by hot pressing. Y₂O₃–Al₂O₃ and Y₂O₃–Yb₂O₃ were used, as sintering aids. The resulting ceramic composites of all compositions were dense and polycrystalline with fine microstructure of average grain size <1 μm of both Si₃N₄ and SiC phases. The fine SiC nano-inclusions were identified within the Si₃N₄ micrograins. Phase composition of both composites consist of , β modifications of Si₃N₄ and SiC. High weight loss was observed during the hot pressing cycle, 12 and 19 wt.% for NCP 200 and CERASET precursors, respectively. The fracture toughness of both nanocomposites (NCP 2000 and CERASET derived) was not different. Indentation method measured values are from 5 to 6 MPa m^1/2, with respect to the sintering additive system. Fracture toughness is slightly sensitive to the SiC content of the nanocomposite. Hardness increases with the content of SiC in the nanocomposite. The highest hardness was achieved for pyrolysed CERASET precursor with 2 wt.% Y₂O₃ and 6 wt.% Yb₂O₃, HV 23 GPa. This is a consequence of the highest SiC content as well as the chemical composition of additives.     

An empirical evaluation of P system testing techniques

This paper presents the existing techniques for P system testing and performs an empirical evaluation of their fault-detection efficiency. The comparison is performed using mutation testing and, based on the results obtained, some improved testing methodologies are proposed.     

Isotropic sequence order learning

In this article, we present an isotropic unsupervised algorithm for temporal sequence learning. No special reward signal is used such that all inputs are completely isotropic. All input signals are bandpass filtered before converging onto a linear output neuron. All synaptic weights change according to the correlation of bandpass-filtered inputs with the derivative of the output. We investigate the algorithm in an open- and a closed-loop condition, the latter being defined by embedding the learning system into a behavioral feedback loop. In the open-loop condition, we find that the linear structure of the algorithm allows analytically calculating the shape of the weight change, which is strictly heterosynaptic and follows the shape of the weight change curves found in spike-time-dependent plasticity. Furthermore, we show that synaptic weights stabilize automatically when no more temporal differences exist between the inputs without additional normalizing measures. In the second part of this study, the algorithm is is placed in an environment that leads to closed sensor-motor loop. To this end, a robot is programmed with a prewired retraction reflex reaction in response to collisions. Through isotropic sequence order (ISO) learning, the robot achieves collision avoidance by learning the correlation between his early range-finder signals and the later occurring collision signal. Synaptic weights stabilize at the end of learning as theoretically predicted. Finally, we discuss the relation of ISO learning with other drive reinforcement models and with the commonly used temporal difference learning algorithm. This study is followed up by a mathematical analysis of the closed-loop situation in the companion article in this issue, "ISO Learning Approximates a Solution to the Inverse-Controller Problem in an Unsupervised Behavioral Paradigm" (pp. 865-884).     

ISO learning approximates a solution to the inverse-controller problem in an unsupervised behavioral paradigm

In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed reflex reaction, which has the objective disadvantage that it can react only after a disturbance has occurred. ISO learning eliminates this disadvantage by replacing the reflex-loop reactions with earlier anticipatory actions. In this article, we analytically demonstrate that this process can be understood in terms of control theory, showing that the system learns the inverse controller of its own reflex. Thereby, this system is able to learn a simple form of feedforward motor control.