The Analogical Peacock Hypothesis: The Sexual Selection of Mind-Reading and Relational Cognition in Human Communication

This integrative review presents a novel hypothesis as a basis for integrating two evolutionary viewpoints on the origins of human cognition and communication, the sexual selection of human mental capacities, and the social brain hypothesis. This new account suggests that mind-reading social skills increased reproductive success and consequently became targets for sexual selection. The hypothesis proposes that human communication has three purposes: displaying mind-reading abilities, aligning and maintaining representational parity between individuals to enable displays, and the exchange of propositional information. Intelligence, creativity, language, and humor are mental fitness indicators that signal an individual’s quality to potential mates, rivals, and allies. Five features central to the proposed display mechanism unify these indicators, the relational combination of concepts, large conceptual knowledge networks, processing speed, contextualization, and receiver knowledge. Sufficient between-mind alignment of conceptual networks allows displays based upon within-mind conceptual mappings. Creative displays communicate previously unnoticed relational connections and novel conceptual combinations demonstrating an ability to read a receiver’s mind. Displays are costly signals of mate quality with costs incurred in the developmental production of the neural apparatus required to engage in complex displays and opportunity costs incurred through time spent acquiring cultural knowledge. Displays that are fast, novel, spontaneous, contextual, topical, and relevant are hard-to-fake for lower quality individuals. Successful displays result in elevated social status and increased mating options. The review addresses literatures on costly signaling, sexual selection, mental fitness indicators, and the social brain hypothesis; drawing implications for nonverbal and verbal communication.

“There’s a Catch-22”. The complexities of pain management for people with advanced dementia nearing the end of life: a qualitative exploration of physicians’ perspectives

Background: Pain management is a cornerstone of palliative care. The clinical issues encountered by physicians when managing pain in patients dying with advanced dementia, and how these may impact on prescribing and treatment, are unknown. Aim: To explore physicians’ experiences of pain management for patients nearing the end of life, the impact of these on prescribing and treatment approaches, and the methods employed to overcome these challenges. Design: Qualitative, semi-structured interview study exploring: barriers to and facilitators of pain management, prescribing and treatment decisions, and training needs. Thematic analysis was used to elicit key themes. Settings/Participants: Twenty-three physicians, responsible for treating patients with advanced dementia approaching the end of life, were recruited from primary care (n=9), psychiatry (n=7) and hospice care (n=7). Results: Six themes emerged: diagnosing pain, complex prescribing and treatment approaches, side-effects and adverse events, route of administration, importance of sharing knowledge and training needs. Knowledge exchange was often practised through liaison with physicians from other specialties. Cross-specialty mentoring, and the creation of knowledge networks were believed to improve pain management in this patient population. Conclusions: Pain management in end-stage dementia is complex, requiring cross-population of knowledge between palliative care specialists and non-specialists, in addition to collateral information provided by other health professionals and patients’ families. Regular, cost- and time-effective mentoring and ongoing professional development are perceived to be essential in empowering physicians to meet clinical challenges in this area.

Can we cook away veterinary drug residues in food?

When most people think of food safety they think of food poisoning and bacteria. They also, one hopes, generally follow the well-understood public advice on bacterial risks and store their food properly and cook it thoroughly. But what about chemical risks in food? Do many consumers ask the question “if drug residues are in my food, does cooking make it safe?” Or do they assume that following the good advice on bacterial risks also affords some protection against the health risks of chemical contaminants? In this short report we highlight some difficulties in assessing the stability of veterinary drug residues during cooking and summarise our cooking studies on anthelmintics, nitroimidazoles and nitrofuran residues in various foods. safefood Knowledge Networks http://safefood.ning.com/

System oriented neural networks - Problem formulation, methodology and application

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

Structural influence of gene networks on their inference: analysis of C3NET

Background: The availability of large-scale high-throughput data possesses considerable challenges toward their functional analysis. For this reason gene network inference methods gained considerable interest. However, our current knowledge, especially about the influence of the structure of a gene network on its inference, is limited.

Predicting community responses to perturbations in the face of imperfect knowledge and network complexity

How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (similar to 25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities.

EXPERT-SYSTEM SUPPORT USING BAYESIAN BELIEF NETWORKS IN THE DIAGNOSIS OF FINE-NEEDLE ASPIRATION BIOPSY SPECIMENS OF THE BREAST

Aim-To develop an expert system model for the diagnosis of fine needle aspiration cytology (FNAC) of the breast.

Methods-Knowledge and uncertainty were represented in the form of a Bayesian belief network which permitted the combination of diagnostic evidence in a cumulative manner and provided a final probability for the possible diagnostic outcomes. The network comprised 10 cytological features (evidence nodes), each independently linked to the diagnosis (decision node) by a conditional probability matrix. The system was designed to be interactive in that the cytopathologist entered evidence into the network in the form of likelihood ratios for the outcomes at each evidence node.

Results-The efficiency of the network was tested on a series of 40 breast FNAC specimens. The highest diagnostic probability provided by the network agreed with the cytopathologists' diagnosis in 100% of cases for the assessment of discrete, benign, and malignant aspirates. A typical probably benign cases were given probabilities in favour of a benign diagnosis. Suspicious cases tended to have similar probabilities for both diagnostic outcomes and so, correctly, could not be assigned as benign or malignant. A closer examination of cumulative belief graphs for the diagnostic sequence of each case provided insight into the diagnostic process, and quantitative data which improved the identification of suspicious cases.

Conclusion-The further development of such a system will have three important roles in breast cytodiagnosis: (1) to aid the cytologist in making a more consistent and objective diagnosis; (2) to provide a teaching tool on breast cytological diagnosis for the non-expert; and (3) it is the first stage in the development of a system capable of automated diagnosis through the use of expert system machine vision.

Development of small and medium-sized enterprise horizontal innovation networks: UK agri-food sector study

Within the management literature, there is an emergent discourse on horizontal collaboration among small and medium-sized enterprises (SMEs), whereby individual rivalries are overcome by the need for more resources and innovation, leading to increased competitiveness through joint product development. In particular, a number of these horizontal collaborations between SMEs have occurred within the agri-food sector. As a consequence, this article aims to explore the longitudinal development of horizontal innovation networks within an artisan bakers’ network as part of the UK SME agri-food sector. An interpretivist research approach was used, whereby the development and evolution of an artisan bakers’ horizontal network was studied over a 27-month period. The findings, as summarised in conceptual models which draw upon knowledge-based open innovation and social network constructs, illustrate that a complex three-stage life cycle development occurred within the bakers’ horizontal network.

Dense subgraphs on dynamic networks

In distributed networks, it is often useful for the nodes to be aware of dense subgraphs, e.g., such a dense subgraph could reveal dense substructures in otherwise sparse graphs (e.g. the World Wide Web or social networks); these might reveal community clusters or dense regions for possibly maintaining good communication infrastructure. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs that the member nodes are aware of. The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that (2 + e)-approximate the densest subgraph and (3 + e)-approximate the at-least-k-densest subgraph (for a given parameter k). Our algorithms work for a wide range of parameter values and run in O(D log n) time. Further, a special case of our results also gives the first fully decentralized approximation algorithms for densest and at-least-k-densest subgraph problems for static distributed graphs. © 2012 Springer-Verlag.

Brief announcement: Maintaining large dense subgraphs on dynamic networks

In distributed networks, some groups of nodes may have more inter-connections, perhaps due to their larger bandwidth availability or communication requirements. In many scenarios, it may be useful for the nodes to know if they form part of a dense subgraph, e.g., such a dense subgraph could form a high bandwidth backbone for the network. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs (subgraphs that the member nodes are aware of). The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model in the sense that only messages of O(log n) size are permitted, where n is the number of nodes in the network. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that approximate both the densest subgraph, i.e., the subgraph of the highest density in the network, and the at-least-k-densest subgraph (for a given parameter k), i.e., the densest subgraph of size at least k. We give a (2 + e)-approximation algorithm for the densest subgraph problem. The at-least-k-densest subgraph is known to be NP-hard for the general case in the centralized setting and the best known algorithm gives a 2-approximation. We present an algorithm that maintains a (3+e)-approximation in our distributed, dynamic setting. Our algorithms run in O(Dlog n) time. © 2012 Authors.

Complexity of Inferences in Polytree-shaped Semi-Qualitative Probabilistic Networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the Bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that inferences can be performed in linear time if there is a single observed node, which is a relevant practical case. Because our proof is constructive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynomial-time algorithm for SQPNs. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.