Fragment shaders for agent animation using finite state machines

In a previous paper we generated animated agents and their behavior using a combination of XML and images. The behavior of agents was specified as a finite state machine (FSM) in XML. We used images to determine properties of the world that agents react to. While this approach is very flexible, it can be made much faster by using the power available in modern GPUs. In this paper we implement FSMs as fragment shaders using three kinds of images: world space images, agent space images and FSM table images. We show a simple example and compare performance of CPU and GPU implementations. Then we examine a more complex example involving more maps and two types of agents (predator–prey). Furthermore we explore how to render agents in 3D more efficiently by using a variation on pseudoinstancing.     

Challenges and solutions for recruiting pregnant smokers into a nicotine replacement therapy trial.

Improvements in smoking cessation interventions for pregnant smokers are needed. One major step is to examine the potential effectiveness of nicotine replacement therapy (NRT). The potential benefits of providing pregnant women with NRT to help them quit smoking are still unknown; early interventions to test the effectiveness and efficacy are vital to advancing the field. This paper describes recruitment efforts for a multiclinic trial to test the effectiveness of NRT use in addition to behavioral therapy in promoting cessation during pregnancy. The biggest challenge is recruiting sufficient numbers of pregnant women. This paper discusses specific obstacles for recruitment and solutions. Knowing the potential pitfalls to recruiting pregnant women into these trials can lead to better studies and thus improved outcomes.     

Prospects for Bose-Einstein condensation in atomic hydrogen and other gases

Current experimental efforts to observe Bose-Einstein condensation in the laboratory are reviewed. New experiments in spin-polarized hydrogen at high density or very low temperatures offer promise of achieving this goal. The possibility of BEC in other elements, H₂,⁴He, and cesium are discussed.     

Transient central hypothyroidism as a cause of failure to thrive in newborns and infants

The course of two neonates and one 4-month-old infant with laboratory and clinical evidence of central hypothyroidism is described. All three presented with failure to thrive and improved after L-T4 therapy. Early recognition and treatment of newborns and infants with central hypothyroidism is important to maximize the potential for growth and development. Two of the three infants have been documented to have transient central hypothyroidism of hypothalamic origin, not previously reported.     

Fair attribution of functional contribution in artificial and biological networks

This letter presents the multi-perturbation Shapley value analysis (MSA), an axiomatic, scalable, and rigorous method for deducing causal function localization from multiple perturbations data. The MSA, based on fundamental concepts from game theory, accurately quantifies the contributions of network elements and their interactions, overcoming several shortcomings of previous function localization approaches. Its successful operation is demonstrated in both the analysis of a neurophysiological model and of reversible deactivation data. The MSA has a wide range of potential applications, including the analysis of reversible deactivation experiments, neuronal laser ablations, and transcranial magnetic stimulation "virtual lesions," as well as in providing insight into the inner workings of computational models of neurophysiological systems.     

Localization of function via lesion analysis

This article presents a general approach for employing lesion analysis to address the fundamental challenge of localizing functions in a neural system. We describe functional contribution analysis (FCA), which assigns contribution values to the elements of the network such that the ability to predict the network's performance in response to multilesions is maximized. The approach is thoroughly examined on neurocontroller networks of evolved autonomous agents. The FCA portrays a stable set of neuronal contributions and accurate multilesion predictions that are significantly better than those obtained based on the classical single lesion approach. It is also used for a detailed synaptic analysis of the neurocontroller connectivity network, delineating its main functional backbone. The FCA provides a quantitative way of measuring how the network functions are localized and distributed among its elements. Our results question the adequacy of the classical single lesion analysis traditionally used in neuroscience and show that using lesioning experiments to decipher even simple neuronal systems requires a more rigorous multilesion analysis.     

Moiré patterns between aperiodic layers: quantitative analysis and synthesis

Moiré effects that occur in the superposition of aperiodic layers such as random dot screens are known as Glass patterns. Unlike classical moiré effects between periodic layers, which are periodically repeated throughout the superposition, a Glass pattern is concentrated around a certain point in the superposition, and farther away from this point it fades out and disappears. I show that Glass patterns between aperiodic layers can be analyzed by using an extension of the Fourier-based theory that governs the classical moiré patterns between periodic layers. Surprisingly, even spectral-domain considerations can be extended in a natural way to aperiodic cases, with some straightforward adaptations. These new results allow us to predict quantitatively the intensity profile of Glass patterns; furthermore, they open the way to the synthesis of Glass patterns that have any desired shapes and intensity profiles.     

Controlled analysis of neurocontrollers with informational lesioning

How does one aim to understand neural information processing? One of the difficult first challenges is to identify the roles of the network's elements. To this end a functional contribution analysis (FCA) method has been developed and applied for studying the neurocontrollers of evolutionary autonomous agents (EAAs). The FCA processes data composed of multiple lesion experiments and the corresponding performance levels that the agent obtains under these lesions. It calculates the contribution values (CVs) of the network's elements such that the ability to predict the agent's performance under new, unseen lesions is maximized. Previous analysis has found a strong dependence of the CVs and the prediction error on the specific type of lesioning method used, i.e. on the way in which the activity of lesioned neurons is disrupted. We present a new, informational lesioning method (ILM), which views a lesion as a noisy channel and applies a controlled lesion to the network by varying the lesioning level from large to arbitrarily small magnitudes. Studying the ILM within the FCA framework, our main results are threefold: first, that lower lesioning levels permit more accurate FCA predictions; second, that the usage of minute ILM lesioning levels can uncover the long-term effects of elements on the network's functioning; and third, that as the lesioning level decreases, the CVs tend to approach limit values, reflecting the importance of these elements in the intact, normal-functioning neurocontroller.