Computer Science

Most modern computers are equipped with powerful yet cost-effective Graphics Processing Units (GPUs) to accelerate graphics operations. Although programmable shaders on these GPUs were designed for the creation of 3-D rendering effects, they can also be used as generic processing units for vector data. This paper proposes a hardware renderer capable of executing motion compensation, reconstruction, and visualization entirely on the GPU by the use of vertex and pixel shaders. Our measurements show that a speedup of 297% can be achieved by relying on the processing power of the GPU, relative to the CPU. As an example, real-time playback of high-definition video (1080p) was achieved at 62.0 frames per second, consuming only 68.2% of all CPU cycles on a modern machine.

In this paper we tried to analyze the contribution of e-commerce and e-business in general. This paper holds valuable information that must be interesting for readers who are interested in terms e-commerce and e-business. Electronic commerce facilitates many way for consumers and businesses such as on-line shopping which applies ecommerce and easily can deliver the products to the markets unlike traditional commerce that is carried out physically with effort of a person to go and get products. In some cases e-commerce is secure and still there are challenges to improve the security and make the customers confident. The purpose of this research was to determine the current level of e-commerce in general and in some causes in developing countries. This paper is written by two persons. Actually, we both worked and studied together, but all the introduction and type of e-commerce is written by Foawziah Naseri and the explanation of the other terminologies are written by Huma Yari.

When we perceive the external world, our brain has to deal with the incompleteness and uncertainty associated with sensory inputs, memory and prior knowledge. In theoretical neuroscience probabilistic approaches have received a growing interest recently, as they account for the ability to reason with incomplete knowledge and to efficiently describe perceptive and behavioral tasks. How can the probability distributions that need to be estimated in these models be represented and processed in the brain, in particular at the single cell level? We consider the basic function carried out by photoreceptor cells which consists in detecting the presence or absence of light. We give a system-level understanding of the process of phototransduction based on a bayesian formalism: we show that the process of phototransduction is equivalent to a temporal probabilistic inference in a Hidden Markov Model (HMM), for estimating the presence or absence of light. Thus, the biochemical mechanisms of phototransduction underlie the estimation of the current state probability distribution of the presence of light. A classical descriptive model describes the interactions between the different molecular messengers, ions, enzymes and channel proteins occurring within the photoreceptor by a set of nonlinear coupled differential equations. In contrast, the probabilistic HMM model is described by a discrete recurrence equation. It appears that the binary HMM has a general solution in the case of constant input. This allows a detailed analysis of the dynamics of the system. The biochemical system and the HMM behave similarly under steady-state conditions. Consequently a formal equivalence can be found between the biochemical system and the HMM. Numerical simulations further extend the results to the dynamic case and to noisy input. All in all, we have derived a probabilistic model equivalent to a classical descriptive model of phototransduction, which has the additional advantage of assigning a function to phototransduction. The example of phototransduction shows how simple biochemical interactions underlie simple probabilistic inferences.

In this study we consider how probability distributions calculated in probabilistic cognitive models can be represented and processed in the brain. More exactly we show that a photoreceptor cell can compute a simple Bayesian inference in a binary Hidden Markov Model (HMM), based on the underlying biochemical interactions of this single cell. We derive, under steady-state conditions, a formal equivalence between the probablistic model and the molecular mechanisms, and show that the equivalence can be extended to the dynamic case. From the photoreceptor example we see that biochemical interactions can represent probability distributions and implement basic probabilistic reasoning.

Recent research suggests that novelty has an influence on reward-related learning. Here, we
showed that novel stimuli presented from a pre-familiarized category can accelerate or decelerate
learning of the most rewarding category, depending on the condition. The extent of this
influence depended on the individual trait of novelty seeking. Different reinforcement learning
models were developed to quantify subjects’ choices. We introduced a bias parameter to model
explorative behavior toward novel stimuli and characterize individual variation in novelty response.
The theoretical framework allowed us to test different assumptions, concerning the
motivational value of novelty. The best fitting-model combined all novelty components
and had a significant positive correlation with both the experimentally measured novelty bias
and the independent novelty-seeking trait. Altogether, we have not only shown that novelty by
itself enhances behavioral responses underlying reward processing, but also that novelty has a
direct influence on reward-dependent learning processes, consistently with computational
predictions.

Moral dilemmas are used to study the situations in which there is a conflict between two moral rules: e.g. is it permissible to kill one person in order to save more people. In standard moral dilemmas the protagonist is a human. However, the recent progress in robotics leads to the question of how artificial cognitive agents should act in situations involving moral dilemmas. Here, we study moral judgments when the protagonist in the dilemma is an artificial cognitive agent -a humanoid robot or an automated system -and compare them to moral judgments for the same action taken by a human agent. Participants are asked to choose the appropriate protagonist action, to evaluate the rightness and the moral permissibility of the utilitarian action, and the blameworthiness of the agent. We also investigate the role of the instrumentality of the inflicted harm. The main results are that participants rate the utilitarian actions of a humanoid robot or of an automated system as more morally permissible than the same actions of a human. The act of killing undertaken by a humanoid robot is rated as less blameworthy than the action done by a human or by an automated system. The results are interpreted and discussed in terms of responsibility and intentionality as characteristics of moral agency.

In this study we consider how probability distributions calculated in probabilistic cognitive models can be represented and processed in the brain. More exactly we show that a photoreceptor cell can compute a simple Bayesian inference in a binary Hidden Markov Model (HMM), based on the underlying biochemical interactions of this single cell. We derive, under steady-state conditions, a formal equivalence between the probablistic model and the molecular mechanisms, and show that the equivalence can be extended to the dynamic case. From the photoreceptor example we see that biochemical interactions can represent probability distributions and implement basic probabilistic reasoning.

1 Abstract To account for the ability of living organisms to reason with uncertain and incomplete information , it has been recently proposed that the brain is a probabilistic inference machine , evaluating subjective probabilistic models over cognitively relevant variables. A number of such Bayesian models have been shown to account efficiently for perceptive and behavioral tasks. However , little is known about the way these subjective probabilities are represented and processed in the brain. Several theoretical proposals have been made , from large populations of neurons to specialized cortical microcircuits or individual neurons as potential substrates for such subjective probabilistic inferences. In contrast , we propose in this paper that at a subcellular level , biochemical cascades of cell signaling can perform the necessary probabilistic computations. Specifically , we propose that macromolecular assemblies (receptors , ionic channels , and allosteric enzymes) coupled throu...

In this study we consider how probability distributions calculated in probabilistic cognitive models can be represented and processed in the brain. More exactly we show that a photoreceptor cell can compute a simple Bayesian inference in a binary Hidden Markov Model (HMM), based on the underlying biochemical interactions of this single cell. We derive, under steady-state conditions, a formal equivalence between the probablistic model and the molecular mechanisms, and show that the equivalence can be extended to the dynamic case. From the photoreceptor example we see that biochemical interactions can represent probability distributions and implement basic probabilistic reasoning.

During the ongoing development of software the implemen-tation drifts away from the initial design due to the changing requirements (known as moving targets), lack of communica-tion and unforeseen difficulties. This drift is unacceptable as the design represents the common picture and is the basis for further development and maintenance of the software, and should therefore only be changed after communication took place which updates this common picture. Automating con-tinuous comparison of design and implementation (resulting in model differences) during the development phase increases the quality of a software project. Checking continuously for model differences and propagating them appropriately will increase the communication between the roles engaged in software projects leading to less misunderstandings and de-creasing the amount of failed software projects. Our approach detects differences between architecture and implementation in a non-invasive manner. It initiates com-munication between architects and engineers so that both the implementation and the architectural artefact keep up-to-date.