Nanoradian angular stabilization of x-ray optical components

An x-ray free electron laser oscillator (XFELO) has been recently proposed [K. Kim, Y. Shvyd'ko, and S. Reiche, Phys. Rev. Lett. 100, 244802 (2008)]. Angular orientation and position in space of Bragg mirrors of the XFELO optical cavity must be continuously adjusted to compensate instabilities and maximize the output intensity. An angular stability of about 10 nrad (rms) is required [K. Kim and Y. Shvyd'ko Phys. Rev. STAB 12, 030703 (2009)]. To approach this goal, a feedback loop based on a null-detection principle was designed and used for stabilization of a high energy resolution x-ray monochromator ($\Delta E/E \simeq 4 \times 10^{-8}$, $E$ = 23.7 keV) and a high heat load monochromator. Angular stability of about 13 nrad (rms) has been demonstrated for x-ray optical elements of the monochromators.Comment: 8 figure

Cognitive architectures as Lakatosian research programmes: two case studies

Cognitive architectures - task-general theories of the structure and function of the complete cognitive system - are sometimes argued to be more akin to frameworks or belief systems than scientific theories. The argument stems from the apparent non-falsifiability of existing cognitive architectures. Newell was aware of this criticism and argued that architectures should be viewed not as theories subject to Popperian falsification, but rather as Lakatosian research programs based on cumulative growth. Newell's argument is undermined because he failed to demonstrate that the development of Soar, his own candidate architecture, adhered to Lakatosian principles. This paper presents detailed case studies of the development of two cognitive architectures, Soar and ACT-R, from a Lakatosian perspective. It is demonstrated that both are broadly Lakatosian, but that in both cases there have been theoretical progressions that, according to Lakatosian criteria, are pseudo-scientific. Thus, Newell's defense of Soar as a scientific rather than pseudo-scientific theory is not supported in practice. The ACT series of architectures has fewer pseudo-scientific progressions than Soar, but it too is vulnerable to accusations of pseudo-science. From this analysis, it is argued that successive versions of theories of the human cognitive architecture must explicitly address five questions to maintain scientific credibility

Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on Artificial Intelligence and Mobile Services) on June 2

Decision Process in Human-Agent Interaction: Extending Jason Reasoning Cycle

The main characteristic of an agent is acting on behalf of humans. Then, agents are employed as modeling paradigms for complex systems and their implementation. Today we are witnessing a growing increase in systems complexity, mainly when the presence of human beings and their interactions with the system introduces a dynamic variable not easily manageable during design phases. Design and implementation of this type of systems highlight the problem of making the system able to decide in autonomy. In this work we propose an implementation, based on Jason, of a cognitive architecture whose modules allow structuring the decision-making process by the internal states of the agents, thus combining aspects of self-modeling and theory of the min

Reasoning deficits among illicit drug users are associated with aspects of cannabis use

Background. Deficits in deductive reasoning have been observed among ecstasy/polydrug users. The present study seeks to investigate dose-related effects of specific drugs and whether these vary with the cognitive demands of the task. Methods. One hundred and five participants (mean age 21.33, S.D. 3.14; 77 females, 28 males) attempted to generate solutions for eight one-model syllogisms and one syllogism for which there was no valid conclusion (NVC). All of the one model syllogisms generated at least one valid conclusion and six generated two valid conclusions. In these six cases one of the conclusions was classified as common and the other as non-common. Results. The number of valid common inferences was negatively associated with aspects of short term cannabis use and with measures of IQ. The outcomes observed were more than simple post intoxication effects since cannabis use in the 10 days immediately before testing was unrelated to reasoning performance. Following adjustment for multiple comparisons, the number of non-common valid inferences was not significantly associated with any of the drug use measures. Conclusions. Recent cannabis use appears to impair the processes associated with generating valid common inferences while not affecting the production of non-common inferences. It is possible, therefore, that the two types of inference may recruit different executive resources which may differ in their susceptibility to cannabis-related effects

Modeling Operator Behavior in the Safety Analysis of Collaborative Robotic Applications

Human-Robot Collaboration is increasingly prominent in peo- ple's lives and in the industrial domain, for example in manufacturing applications. The close proximity and frequent physical contacts between humans and robots in such applications make guaranteeing suitable levels of safety for human operators of the utmost importance. Formal veri- cation techniques can help in this regard through the exhaustive explo- ration of system models, which can identify unwanted situations early in the development process. This work extends our SAFER-HRC method- ology with a rich non-deterministic formal model of operator behaviors, which captures the hazardous situations resulting from human errors. The model allows safety engineers to rene their designs until all plausi- ble erroneous behaviors are considered and mitigated

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

Increased Rate of CD4+ T-Cell Decline and Faster Time to Antiretroviral Therapy in HIV-1 Subtype CRF01_AE Infected Seroconverters in Singapore

It remains controversial as to whether HIV-1 subtypes influence disease progression. Singapore offers a unique opportunity to address this issue due to the presence of co-circulating subtypes. We compared subtype CRF01_AE and non-CRF01_AE infected patients, with regards to estimated annual rate of CD4+ T-cell loss and time from estimated data of seroconversion (EDS) to antiretroviral therapy (ART).We recruited ART-naive patients with known dates of seroconversion between October 2002 and December 2007 at the Singapore Communicable Disease Centre, the national reference treatment centre. Multilevel mixed-effects models were used to analyse the rate of CD4+ T-cell decline. Time from EDS to ART was analyzed with the Kaplan-Meier survival method and compared with Cox proportional hazards models. viral load.Infecting subtype significantly impacted the rate of CD4+ T-cell loss and time to treatment in this cohort. Studies to understand the biological basis for this difference could further our understanding of HIV pathogenesis

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure