The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search mitigates this problem by encouraging exploration in all interesting directions by replacing the performance objective with a reward for novel behaviors. This reward for novel behaviors has traditionally required a human-crafted, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network (DNN) that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a DNN-based novelty search in the image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images (e.g., churches, mosques, obelisks, etc.). Here, we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm’s key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: for example, producing intelligent software, robot controllers, optimized physical components, and art.

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

The B cell antigen receptor (BCR) plays an essential role in all phases of B cell development. Here, we show the extracellular domains of murine and human Igβ form an I-set immunoglobulin-like structure with an inter-chain disulfide between cysteines on their G-strands. Structural and sequence analysis suggests that Igα displays similar fold as Igβ. An Igαβ heterodimer model was generated based on the unique disulfide bonded Igβ dimer. Solution binding studies showed that the extracellular domains of Igαβ preferentially recognized the constant region of BCR with the μ-chain specificity, suggesting a role for Igαβ to enhance the BCRμ-chain signaling. Cluster mutations on Igα, Igβ and mIgM based on the structural model identified distinct area of potential contacts involving charged residues on both subunits of the co-receptor and the Cμ4 domain of mIgM. These studies provide the first structural model for understanding BCR function.

A commercial coating (epoxy-polyaminoamide waterborne paint) deposited on a 2024 aluminium alloy was characterized by impedance measurements, first in dry conditions and then as a function of the immersion time in NaCl solutions (wet conditions). The behaviour of the dry coating was close to that of an ideal capacitor and could be accurately modelled with the power-law model corresponding to a constant phase element (CPE) behaviour. Upon immersion in NaCl solutions, the behaviour of the wet coating became progressively less ideal, i.e. farther from a capacitive behaviour. This result provided support to the hypothesis that an inhomogeneous uptake of the electrolyte solution was the cause of the often observed non-ideal responses of wet coatings. The experimental EIS data recorded for immersion times up to 504 hours were compared with models assuming either a power-law or an exponential variation of the coating resistivity along its thickness, respectively implying a phase angle independent of frequency or slightly dependent on it.

2016-11-03T14:11:40

A number of metabolic disturbances occur in response to the consumption of a high fat Western diet. Such metabolic disturbances can include the progressive development of hyperglycemia, hyperinsulemia, obesity, metabolic syndrome, and diabetes. Cumulatively, diet-induced disturbance in metabolism are known to promote increased morbidity and negatively impact life expectancy through a variety of mechanisms. While the impact of metabolic disturbances on the hepatic, endocrine, and cardiovascular systems are well established there remains a noticeable void in understanding the basis by which the central nervous system (CNS) becomes altered in response to diet-induced metabolic dysfunction. In particular, it remains to be fully elucidated which established features of diet-induced pathogenesis (observed in non-CNS tissues) are recapitulated in the brain, and identification as to whether the observed changes in the brain are a direct or indirect effect of peripheral metabolic disturbances. This review will focus on each of these key issues and identify some critical experimental questions which remain to be elucidated experimentally, as well as provide an outline of our current understanding for how diet-induced alterations in metabolism may impact the brain during aging and age-related diseases of the nervous system.

No abstract

2017-08-25T16:31:22