Understanding the Prefrontal Cortex

The primate prefrontal cortex sits at the top of the sensory, motor, and outcome processing hierarchies of the neocortex. It transforms sensory inputs into motor outputs, determining the response that is appropriate given the current context and desired outcome. This transformation involves conditional rules. The dorsal prefrontal cortex supports the learning of behavioural sequences, where the next action is conditional on the previous one. The ventral prefrontal cortex supports associations between objects, where the choice of one object is conditional on the presence of another object. However, because hierarchical processing supports the extraction of abstract representations, the primate prefrontal cortex is able to represent conditional rules that are abstract, meaning that they apply irrespective of the specific inputs. The selective advantage is that by learning these rules, primates can solve new problems rapidly when they have the same conditional logic as prior problems. The human prefrontal cortex has the same fundamental organization as in other primates. The dorsal prefrontal cortex supports the understanding of sequences and the ventral prefrontal cortex supports the ability to learn semantic associations. Thus the human prefrontal cortex has co-opted and elaborated mechanisms that were present in ancestral primates. These mechanisms can be used for new ends. For example, words have been associated with objects so as to communicate with others. This means that to understand human intelligence it is necessary to take into account the fact that the abstract rules are transmitted verbally from one generation to another.

Incorporating Economic Decisions into Reservoir Simulation to Support Accurate and Efficient Optimization and Analysis of Field Development Strategies

Economic optimization of a reservoir can be extremely tedious and time consuming. It is particularly difficult with many wells, some of which can become non-economic within the simulated time period. These problems can be mitigated by: 1) analyzing the results of a simulation once it has run, or 2) applying injection or production constraints at the well level. An example of option 1 would be integration with a spreadsheet or economic simulation package after the simulation has run. An example of option 2 would be to set a maximum water cut, upon which the well constraints could be changed, or the well could be shut in within the simulation. Both of these methods have drawbacks. If the goal is to account for how changes in a well operating strategy affects other wells, then analysis after the fact requires many runs to sequentially identify and modify well constraints at the correct times and in the correct order. In contrast, applying injection and production constraints to wells is not the same as applying true economic constraints. The objective of this work was to develop an automated method which includes economic considerations within the simulator to decrease the amount of time optimizing a single model and allows more time to analyze uncertainty within the economic decision making process. This study developed automated methods and procedures to include economic calculations within the context of a standard reservoir simulation. The method utilized modifications to available conditional logic features to internally include and export key economic metrics to support appropriate automatic field development changes. This method was tested using synthetic models with different amounts of wells and operating conditions. It was validated using after the fact calculations on a well by well basis to confirm the process. People costs are always among the most significant associated with running a business. Therefore, it is imperative for people to be as efficient and productive as possible. The method presented in this study significantly reduces the amount of time and effort associated with tedious and manual manipulations of simulation models. These savings enable an organization to focus on more value-added activities including, but not limited to, accurately optimizing and estimating of uncertainty associated decisions supported by reservoir simulation.

This paper presents a two-dimensional histogram shifting technique for reversible data hiding algorithm. In order to avoid the distortion drift caused by hiding data into stereo H.264 video, we choose arbitrary embeddable blocks from 4×4 quantized discrete cosine transform luminance blocks which will not affect their adjacent blocks. Two coefficients in each embeddable block are chosen as a hiding coefficient pair. The selected coefficient pairs are classified into different sets on the basis of their values. Data could be hidden according to the set which the value of the coefficient pair belongs to. When the value of one coefficient may be changed by adding or subtracting 1, two data bits could be hidden by using the proposed method, whereas only one data bit could be embedded by employing the conventional histogram shifting. Experiments show that this two-dimensional histogram shifting method can be used to improve the hiding performance.

Arc, one virus-like gene, crucial for learning and memory, was dis-covered by researchers in neurological disorders fields, Arc mRNA’s single directed path and allowing protein binding regional restric-tively is a potential investigation on helping shuttle toxic proteins responsible for some diseases related to memory deficiency. Mean time to switching (MTS) is calculated explicitly quantifying the switching process in statistical methods combining Hamiltonian Markov Chain(HMC). The model derived from predator and prey with typeII functional response studies the mechanism of normals with intrin-sic rate of increase and the persisters with the instantaneous discovery rate and converting coefficients. During solving the results, since the numeric method is applied for the 2D approximation of Hamiltonion with intrinsic noise induced switching combining geometric minimum action method. In the application of Hamiltonian Markov Chain, the behavior of the convertion (between mRNA and proteins through 6 states from off to on ) is described with probabilistic conditional logic formula and the final concentration is computed with both Continuous and Discret Time Markov Chain(CTMC/DTMC) through Embedding and Switching Diffusion. The MTS, trajectories and Hamiltonian dynamics demonstrate the practical and robust advantages of our model on interpreting the switching process of genes (IGFs, Hax Arcs and etc.) with respects to memory deficiency in aging process which can be useful in further drug efficiency test and disease curing. Coincidentally, the Hamiltonian is also well used in describing quantum mechanics and convenient for computation with time and position information using quantum bits while in the second model we construct, switching between excitatory and inhibitory neurons, similarity of qubit and neuron is an interesting object as well. Especially with the interactions operated with phase gates, the excitation from the ground state to excitation state is a well analogue to the neuron excitation. Not only on theoretical aspect, the experimental methods in neuron switching model is also inspiring to quantum computing. Most basic one is as stimulate hippocampus can be identical to spontaneous neural excitation(|g>|e>), pi-pulse is utilized to drive the ground state to the higher state. There thus exists prosperous potential to study the transfer between states with our switch models both classical and quantum computationally.

Motion Primitive Approach to Rotorcraft Regime Recognition

This paper describes a novel approach to regime recognition based on the notion of motion primitives. Originally developed for path planning, motion primitives decompose a vehicle trajectory into maneuver and trim segments. In a regime recognition context, this decomposition can be used to improve component life tracking through separate classification of trim segments and maneuver segments. The proposed algorithm functions in three major steps. The first step consists of classifying the flight data into trim and maneuver segments. The second step leverages the information in the trim state and control vectors to classify each trim segment as a particular trim regime based on conditional logic. The final step makes use of dynamic time warping for the classification of each maneuver segment (flown between two trim segments) as a particular maneuver regime. Accuracy of the proposed algorithm is evaluated using simulated flight data for the SH-60B, and advantages of the proposed method compared to a threshold-based algorithm are assessed. The algorithm is also applied to actual flight data from a generic utility helicopter to demonstrate operation of the algorithm using real-world data.

The Creature as a Figure of Unrestitutability, or Monsters in Paradise not Allowed: Benevolence and Restitution in Mary Shelley’s Frankenstein

This chapter brings Mary Shelley’s Frankenstein in conversation with two moral sentiment philosophers of the 18th century, Joseph Butler and David Hume. It focuses on the connection between the modern restitutive trope and reparation as premised on shared humanity. The ‘problem’ that the Creature from Frankenstein illuminates is the conditional logic of restitution, which is open only to those who are already included in human society; animals, monsters, and other non-humans do not partake in restitution. By showing that the concept of benevolence has a central place in the construction of prelapsarian desires in Shelley’s novel, the chapter argues that the Creature represents for the other protagonists the humanity’s ‘radical outside’; he is both excluded from the benevolent society and divested of restitutive possibilities. The Creature is a figure of ‘unrestitutability’ because the possibilities of return, undoing and repair are barred from him by the virtue of his constitutive exclusion from humanity.

Completeness by Modal Definitions. Application to the Epistemic Logic With Hypotheses

We investigate the variant of epistemic logic S5 for reasoning about knowledge under hypotheses. The logic is equipped with a modal operator of necessity that can be parameterized with a hypothesis representing background assumptions. The modal operator can be described as relative necessity and the resulting logic turns out to be a variant of Chellas’ Conditional Logic. We present an axiomatization of the logic and its extension with the common knowledge operator and distributed knowledge operator. We show that the logics are decidable, complete w.r.t. Kripke as well as topological structures. The topological completeness results are obtained by utilizing the Alexandroff connection between preorders and Alexandroff spaces.