The emergence of division of labour through decentralized social sanctioning.

Human ecological success relies on our characteristic ability to flexibly self-organize into cooperative social groups, the most successful of which employ substantial specialization and division of labour. Unlike most other animals, humans learn by trial and error during their lives what role to take on. However, when some critical roles are more attractive than others, and individuals are self-interested, then there is a social dilemma: each individual would prefer others take on the critical but unremunerative roles so they may remain free to take one that pays better. But disaster occurs if all act thus and a critical role goes unfilled. In such situations learning an optimum role distribution may not be possible. Consequently, a fundamental question is: how can division of labour emerge in groups of self-interested lifetime-learning individuals? Here, we show that by introducing a model of social norms, which we regard as emergent patterns of decentralized social sanctioning, it becomes possible for groups of self-interested individuals to learn a productive division of labour involving all critical roles. Such social norms work by redistributing rewards within the population to disincentivize antisocial roles while incentivizing prosocial roles that do not intrinsically pay as well as others.

Meta-control of social learning strategies.

Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others' learning as an external knowledge base.

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions.

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.

Evolutionary Approach to Constructing a Deep Feedforward Neural Network for Prediction of Electronic Coupling Elements in Molecular Materials.

We present a general framework for the construction of a deep feedforward neural network (FFNN) to predict distance and orientation dependent electronic coupling elements in disordered molecular materials. An evolutionary algorithm automatizes the selection of an optimal architecture of the artificial neural network within a predefined search space. Systematic guidance, beyond minimizing the model error with stochastic gradient descent based backpropagation, is provided by simultaneous maximization of a model fitness that takes into account additional physical properties, such as the field-dependent carrier mobility. As a prototypical system, we consider hole transport in amorphous tris(8-hydroxyquinolinato)aluminum. Reference data for training and validation is obtained from multiscale ab initio simulations, in which coupling elements are evaluated using density-functional theory, for a system containing 4096 molecules. The Coulomb matrix representation is chosen to encode the explicit molecular pair coordinates into a rotation and translation invariant feature set for the FFNN. The final optimized deep feedforward neural network is tested for transport models without and with energetic disorder. It predicts electronic coupling elements and mobilities in excellent agreement with the reference data. Such a FFNN is readily applicable to much larger systems at negligible computational cost, providing a powerful surrogate model to overcome the size limitations of the ab initio approach.

The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.

OBJECTIVE: The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics. METHODS: Sixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials. A set of 1691 type 2 diabetes trials was identified from ClinicalTrials.gov, and their population representativeness was calculated using the published Generalizability Index of Study Traits 2.0 metric. The relationships between population representativeness and number of traits and between trial duration and trial metadata were statistically analyzed. A focused analysis with only phase 2 and 3 interventional trials was also conducted. RESULTS: A total of 869 of 1691 trials (51.4%) and 412 of 776 phase 2 and 3 interventional trials (53.1%) had a population representativeness of <5%. The overall representativeness was significantly correlated with the representativeness of the Hba1c criterion. The greater the number of criteria or the shorter the trial, the less the representativeness. Among the trial metadata, phase, recruitment status, and start year were found to have a statistically significant effect on population representativeness. For phase 2 and 3 interventional trials, only start year was significantly associated with representativeness. CONCLUSIONS: Our study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials.

How Have Cancer Clinical Trial Eligibility Criteria Evolved Over Time?

Knowledge reuse of cancer trial designs may benefit from a temporal understanding of the evolution of the target populations of cancer studies over time. Therefore, we conducted a retrospective analysis of the trends of cancer trial eligibility criteria between 1999 and 2014. The yearly distributions of eligibility concepts for chemicals and drugs, procedures, observations, and medical conditions extracted from free-text eligibility criteria of 32,000 clinical trials for 89 cancer types were analyzed. We identified the concepts that trend upwards or downwards in all or selected cancer types, and the concepts that show anomalous trends for some cancers. Later, concept trends were studied in a disease-specific manner and illustrated for breast cancer. Criteria trends observed in this study are also validated and interpreted using evidence from the existing medical literature. This study contributes a method for concept trend analysis and original knowledge of the trends in cancer clinical trial eligibility criteria.

Similarity-Based Recommendation of New Concepts to a Terminology.

Terminologies can suffer from poor concept coverage due to delays in addition of new concepts. This study tests a similarity-based approach to recommending concepts from a text corpus to a terminology. Our approach involves extraction of candidate concepts from a given text corpus, which are represented using a set of features. The model learns the important features to characterize a concept and recommends new concepts to a terminology. Further, we propose a cost-effective evaluation methodology to estimate the effectiveness of terminology enrichment methods. To test our methodology, we use the clinical trial eligibility criteria free-text as an example text corpus to recommend concepts for SNOMED CT. We computed precision at various rank intervals to measure the performance of the methods. Results indicate that our automated algorithm is an effective method for concept recommendation.

Trend and Network Analysis of Common Eligibility Features for Cancer Trials in ClinicalTrials.gov.

ClinicalTrials.gov has been archiving clinical trials since 1999, with > 165,000 trials at present. It is a valuable but relatively untapped resource for understanding trial design patterns and acquiring reusable trial design knowledge. We extracted common eligibility features using an unsupervised tag-mining method and mined their temporal usage patterns in clinical trials on various cancers. We then employed trend and network analysis to investigate two questions: (1) what eligibility features are frequently used to select patients for clinical trials within one cancer or across multiple cancers; and (2) what are the trends in eligibility feature adoption or discontinuation across cancer research domains? Our results showed that each cancer domain reuses a small set of eligibility features frequently for selecting cancer trial patients and some features are shared across different cancers, with value range adjustments for numerical measures. We discuss the implications for facilitating community-based clinical research knowledge sharing and reuse.