Evolutionary psychology and the generation of culture,part II: Case study: A computational theory of social exchange

Models of the various adaptive specializations that have evolved in the human psyche could become the building blocks of a scientific theory of culture. The first step in creating such models is the derivation of a so-called “computational theory” of the adaptive problem each psychological specialization has evolved to solve. In Part II, as a case study, a sketch of a computational theory of social exchange (cooperation for mutual benefit) is developed. The dynamics of natural selection in Pleistocene ecological conditions define adaptive information processing problems that humans must be able to solve in order to participate in social exchange: individual recognition, memory for one's history of interaction, value communication, value modeling, and a shared grammar of social contracts that specifies representational structure and inferential procedures. The nature of these adaptive information processing problems places constraints on the class of cognitive programs capable of solving them; this allows one to make empirical predictions about how the cognitive processes involved in attention, communication, memory, learning, and reasoning are mobilized in situations of social exchange. Once the cognitive programs specialized for regulating social exchange are mapped, the variation and invariances in social exchange within and between cultures can be meaningfully discussed.     

An adaptive human–machine control system based on multiple fuzzy predictive models of operator functional state

Under the framework of adaptive Human–Machine (HM) systems, it has been proposed that human operators’ task level should be dynamically adjusted according to his/her functional state. The construction of models that can reliably predict the operator functional state (OFS) becomes critical to accomplish such adjustments. However, most of the existing models that evaluate the current OFS by using operators’ current physiological data are static and are of no real predictive capability. Thus, when they are used in adaptive HM systems, the resultant task allocation between operators and machines would be time-delayed. To overcome this problem, a one-step-ahead predictive model concept for OFS computation is proposed. Meanwhile, multiple fuzzy models are developed by using the Wang–Mendel method. These models are able to increase the accuracy of the OFS breakdown prediction, as well as to reduce the model training time. In addition, an adaptive task allocation strategy is designed to validate the proposed models. The results demonstrate that, compared to the conventional HM systems, a 6.7% OFS increment and a 57.1% OFS breakdown decrement can be obtained in the multiple models based adaptive HM systems. The multiple predictive models and the adaptive task allocation strategy would pave the way for future implementations of real-time adaptive HM systems.     

Adaptive properties of living beings: proposal for a generic mechanism. (Self-programming machines III)

Living systems are capable to have appropriate responses to unpredictable environment. This kind of self-organization seems to operate as a self-programming machine, i.e. an organization able to modify itself. Until now the models of self-organization of living beings proposed are functions solutions of differential systems or transition functions of automata. These functions are fixed and these models are therefore unable to modify their organization. On the other hand, computer science propose a lot of models having the properties of adaptive systems of living beings, but all these models depend on the comparison between a goal and the results and ingenious choices of parameters by programmers, whereas there are no programmer's intention nor choice in the living systems. From two best known examples of adaptive systems of living beings, nervous system and immune system that have in common that the external signals modify the rewriting of their organization and therefore work as self-organizing machines, we devised machines with a finite set of inputs, based upon a recurrence, are able to rewrite their organization (Self-programming machines or m(sp)) whenever external conditions vary and have striking properties of adaptation. M(sp) have similar properties whatever the operation defining the recurrence maybe. These results bring us to make the following statement: adaptive properties of living systems can be explained by their ability to rewrite their organization whenever external conditions vary under the only assumption that the rewriting mechanism be a deterministic constant recurrence in a finite state set.     

Individual-based modelling of bacterial ecologies and evolution

This paper presents two approaches to the individual-based modelling of bacterial ecologies and evolution using computational tools. The first approach is a fine-grained model that is based on networks of interactivity between computational objects representing genes and proteins. The second approach is a coarser-grained, agent-based model, which is designed to explore the evolvability of adaptive behavioural strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of these computational models is discussed, and some results from simulation experiments are presented. Finally, the potential applications of the proposed models to the solution of real-world computational problems, and their use in improving our understanding of the mechanisms of evolution, are briefly outlined.     

Decomposition methods for scheduling semiconductor testing facilities

We present decomposition procedures for scheduling semiconductor testing facilities. These facilities are characterized by the presence of different types of work centers, some of which have sequence-dependent setup times and some parallel identical machines. We exploit the structure of the routings in semiconductor testing to develop tailored decomposition procedures that decompose the shop into a number of work centers that are scheduled using specialized procedures. Extensive computational experiments show that these procedures significantly outperform existing methods in reasonable CPU times. These results indicate that decomposition methods can be successfully applied to complex scheduling problems of the type addressed in this paper, as well as the classical job shop problems addressed in previous research.     

Biomolecular computing: Is it ready to take off?

Biomolecular computing is an emerging field at the interface of computer science, biological science and engineering. It uses DNA and other biological materials as the building blocks for construction of living computational machines to solve difficult combinatorial problems. In this article, notable advances in the biomolecular computing are reviewed and challenges associated with this multidisciplinary research are addressed. Finally, several perspectives are given based on the review of biomolecular computing.     

Punitive sentiment as an anti-free rider psychological device

Those who contribute to a public good sometimes experience punitive sentiments toward others. But is the system that produces these sentiments an adaptation and, if so, which collective action problem was it designed to solve? Prior results from experimental economics show that acts of free riding are sometimes punished, that punishment deters free riding, and that the risk or actuality of punishment recruits higher levels of cooperation in a joint effort. This suggests that one function of punitive sentiments could be to recruit labor for collective actions. However, adaptations designed to cause participation in collective actions could not have evolved unless there were some mechanism that protected those who participated from having lower fitness than nonparticipating free riders. Therefore, a second possible function of punishment could be to eliminate or reverse fitness differentials that favor free rider designs over participant designs. To map the computational structure of this motivational adaptation (and hence identify its specific function) requires data that relate an individual's circumstances to his or her desire to punish. Herein, we report such data. The results indicate that the computational system that regulates one's level of punitive sentiment in collective action contexts is functionally specialized for removing the fitness advantage enjoyed by free riders rather than for labor recruitment or other functions. Results also support the hypothesis that a separate pro-reward motivational system exists that appears designed to handle the problem of labor recruitment. Rational choice counterexplanations for punitive sentiments were considered but eliminated on the basis of the evidence.     

Understanding the functional consequences of synaptic specialization: insight from the Drosophila antennal lobe

Synapses exhibit diverse functional properties, and it seems likely that these properties are specialized to perform specific computations. The Drosophila antennal lobe provides a useful experimental preparation for exploring the relationship between synaptic physiology and neural computations. This review summarizes recent progress in describing synaptic properties in the Drosophila antennal lobe. These studies reveal that several types of synapses in this circuit are highly specialized, and that these specializations are in some cases under tight regulatory control. These synaptic specializations can be understood in terms of the computational features they confer on the circuit. Specifically, many of these properties appear to promote odor detection when odor concentrations are low, while promoting adaptive gain control when odor concentrations are high.     

Building multiclass classifiers for remote homology detection and fold recognition

Background  

Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems.     

Analysis of twin data using SAS

Summary .  Twin studies are essential for assessing disease inheritance. Data generated from twin studies are traditionally analyzed using specialized computational programs. For many researchers, especially those who are new to twin studies, understanding and using those specialized computational programs can be a daunting task. Given that SAS (Statistical Analysis Software) is the most popular software for statistical analysis, we suggest that the use of SAS procedures for twin data may be a helpful alternative and demonstrate that we can obtain similar results from SAS to those produced by specialized computational programs. This numerical validation is practically useful, because a natural concern with general statistical software is whether it can deal with data that are generated from special study designs such as twin studies and if it can test a particular hypothesis. We concluded through our extensive simulation that SAS procedures can be used easily as a very convenient alternative to specialized programs for twin data analysis.     

Membrane fusion

The process of membrane fusion in the case of lipid bilayers, as well as induced by influenza virus is reviewed shortly. The methods of studying fusion kinetics in pure lipid and lipid-protein systems are described. The main theories of molecular fusion machines are presented. Open questions and unsolved problems are discussed in details. In conclusion, possible ways to solve the remaining problems are suggested.     

Biomimetic Sensors: Active Electrolocation of Weakly Electric Fish as a Model for Active Sensing in Technical Systems

Instead of vision, many nocturnal animals use alternative senses for navigation and object detection in their dark environment. For this purpose, weakly electric mormyrid fish employ active electrolocation, during which they discharge a specialized electric organ in their tail which discharges electrical pulses. Each discharge builds up an electrical field around the fish, which is sensed by cutaneous electroreceptor organs that are distributed over most of the body surface of the fish. Nearby objects distort this electrical field and cause a local alteration in current flow in those electroreceptors that are closest to the object. By constantly monitoring responses of its electroreceptor organs, a fish can detect, localize, and identify environmental objects.Inspired by the remarkable capabilities of weakly electric fish in detecting and recognizing objects, we designed technical sensor systems that can solve similar problems of remote object sensing. We applied the principles of active electrolocation to technical systems by building devices that produce electrical current pulses in a conducting medium (water or ionized gases) and simultaneously sense local current density. Depending on the specific task a sensor was designed for devices could (i) detect an object, (ii) localize it in space, (iii) determine its distance, and (iv) measure properties such as material properties, thickness, or material faults. Our systems proved to be relatively insensitive to environmental disturbances such as heat, pressure, or turbidity. They have a wide range of applications including material identification, quality control, non-contact distance measurements, medical applications and many more. Despite their astonishing capacities, our sensors still lag far behind what electric fish are able to achieve during active electrolocation. The understanding of the neural principles governing electric fish sensory physiology and the corresponding optimization of our sensors to solve certain technical tasks therefore remain ongoing goals of our research.     

Biomimetic Sensors： Active Electrolocation of Weakly Electric Fish as a Model for Active Sensing in Technical Systems

Instead of vision, many nocturnal animals use alternative senses for navigation and object detection in their dark environment. For this purpose, weakly electric mormyrid fish employ active electrolocation, during which they discharge a specialized electric organ in their tail which discharges electrical pulses. Each discharge builds up an electrical field around the fish, which is sensed by cutaneous electroreceptor organs that are distributed over most of the body surface of the fish. Nearby objects distort this electrical field and cause a local alteration in current flow in those electroreceptors that are closest to the object. By constantly monitoring responses of its electroreceptor organs, a fish can detect, localize, and identify environmental objects.Inspired by the remarkable capabilities of weakly electric fish in detecting and recognizing objects, we designed technical sensor systems that can solve similar problems of remote object sensing. We applied the principles of active electrolocation to technical systems by building devices that produce electrical current pulses in a conducting medium (water or ionized gases) and simultaneously sense local current density. Depending on the specific task a sensor was designed for devices could (i) detect an object, (ii) localize it in space, (iii) determine its distance, and (iv) measure properties such as material properties, thickness, or material faults. Our systems proved to be relatively insensitive to environmental disturbances such as heat, pressure, or turbidity. They have a wide range of applications including material identification, quality control, non-contact distance measurements, medical applications and many more. Despite their astonishing capacities, our sensors still lag far behind what electric fish are able to achieve during active electrolocation. The understanding of the neural principles governing electric fish sensory physiology and the corresponding optimization of our sensors to solve certain technical tasks therefore remain ongoing goals of our research.     

A heuristic approach to minimize expected makespan in open shops subject to stochastic processing times and failures

Many real world situations exist where job scheduling is required. This is the case of some entities, machines, or workers who have to execute certain jobs as soon as possible. Frequently what happens is that several workers or machines are not available to perform their activities during some time periods, due to different circumstances. This paper deals with these situations, and considers stochastic scheduling models to study these problems. When scheduling models are used in practice, they have to take into account that some machines may not be working. That temporal lack of machine availability is known as breakdowns, which happen randomly at any time. The times required to repair those machines are also random variables. The jobs have operations with stochastic processing times, their own release times, and there is no precedence between them. Each job is divided into operations and each operation is performed on the corresponding specialized machine. In addition, in the problems considered, the order in which the operations of each job are done is irrelevant. We develop a heuristic approach to solve these stochastic open-shop scheduling problems where random machine breakdowns can happen. The proposed approach is general and it does not depend on the distribution types of the considered random input data. It provides solutions to minimize the expected makespan. Computational experiences are also reported. The results show that the proposed approach gives a solid performance, finding suitable solutions with short CPU times.     

Self-Programming Machines (II): Network of Self-Programming Machines Driving an Ashby Homeostat

The progress in artificial intelligence enables us to conceive adaptive systems whose characteristics are nearer and nearer to those of living beings. These characteristics though depend on ingenious choices by the designer of these systems: Initial conditions, parameters, optimisation functions, gradient and measure of fitness within the environment. Nevertheless, in living systems which are non-finalist, there are no programmers or designers to conceive of such ingenious choices. Our paper "Self-Programming Machines (I)" presents a non-finalist model since initial states and functions are randomly chosen at the beginning and once and for all. In spite of the fact that they are non-finalist, these machines always stabilise at fixed points when they are connected to an external process. This paper studies the dynamics of a mono-layered network of self-programming machines driving a real device. "the Ashby homeostat", and shows the striking properties of such networks. This system stabilises only at fixed points even if it is subjected to small perturbations or intentional breakdowns such as a reversal of power supply or disconnection of one or several motors. Real and simulated experiences are compared and theoretical results are demonstrated.     

Biological Diversity of Prokaryotic Type IV Secretion Systems

Type IV secretion systems (T4SS) translocate DNA and protein substrates across prokaryotic cell envelopes generally by a mechanism requiring direct contact with a target cell. Three types of T4SS have been described: (i) conjugation systems, operationally defined as machines that translocate DNA substrates intercellularly by a contact-dependent process; (ii) effector translocator systems, functioning to deliver proteins or other macromolecules to eukaryotic target cells; and (iii) DNA release/uptake systems, which translocate DNA to or from the extracellular milieu. Studies of a few paradigmatic systems, notably the conjugation systems of plasmids F, R388, RP4, and pKM101 and the Agrobacterium tumefaciens VirB/VirD4 system, have supplied important insights into the structure, function, and mechanism of action of type IV secretion machines. Information on these systems is updated, with emphasis on recent exciting structural advances. An underappreciated feature of T4SS, most notably of the conjugation subfamily, is that they are widely distributed among many species of gram-negative and -positive bacteria, wall-less bacteria, and the Archaea. Conjugation-mediated lateral gene transfer has shaped the genomes of most if not all prokaryotes over evolutionary time and also contributed in the short term to the dissemination of antibiotic resistance and other virulence traits among medically important pathogens. How have these machines adapted to function across envelopes of distantly related microorganisms? A survey of T4SS functioning in phylogenetically diverse species highlights the biological complexity of these translocation systems and identifies common mechanistic themes as well as novel adaptations for specialized purposes relating to the modulation of the donor-target cell interaction.     

Functional morphology of the feeding apparatus of the nymph of Farrodes sp. (Ephemeroptera: Leptophlebiidae)

Farrodes nymphs are specialized periphyton/biofilm scrapers. Their maxillae are the most specialized mouthparts, but other elements, with their systems of variously modified setae, are designed to obtain and transport food particles to the pharynx with a minimum of loss. The morphology and adaptations of these mouthparts and related head areas, as well as associated musculature, are described.     

Turing on Super-Turing and adaptivity

Biological processes are often compared to computation and modeled on the Universal Turing Machine. While many systems or aspects of systems can be well described in this manner, Turing computation can only compute what it has been programmed for. It has no ability to learn or adapt to new situations. Yet, adaptation, choice and learning are all hallmarks of living organisms. This suggests that there must be a different form of computation capable of this sort of calculation. It also suggests that there are current computational models of biological systems that may be fundamentally incorrect. We argue that the Super-Turing model is both capable of modeling adaptive computation, and furthermore, a possible answer to the computational model searched for by Turing himself.     

脑机融合系统综述

脑机接口是生物脑与电脑或机器之间的一种直接连接通路。脑机融合计算系统是一种基于脑机接口技术,综合利用生物智能和机器智能的新型智能融合系统,其在医疗康复、生活娱乐和军事侦查等方面具有巨大的应用开发潜力。首先从信息传输的角度简述由脑机接口到脑机融合的发展趋势;其次重点阐述脑机融合系统的概念及其研究所面临的挑战,并分三个层次介绍当前若干典型相关工作;最后介绍吴朝晖课题组在脑机融合计算系统方面的相关工作。