Diversity and Adaptation in Large Population Games

We consider a version of large population games whose players compete for resources using strategies with adaptable preferences. The system efficiency is measured by the variance of the decisions. In the regime where the system can be plagued by the maladaptive behavior of the players, we find that diversity among the players improves the system efficiency, though it slows the convergence to the steady state. Diversity causes a mild spread of resources at the transient state, but reduces the uneven distribution of resources in the steady state.Comment: 8 pages, 3 figure

Models of Financial Markets with Extensive Participation Incentives

We consider models of financial markets in which all parties involved find incentives to participate. Strategies are evaluated directly by their virtual wealths. By tuning the price sensitivity and market impact, a phase diagram with several attractor behaviors resembling those of real markets emerge, reflecting the roles played by the arbitrageurs and trendsetters, and including a phase with irregular price trends and positive sums. The positive-sumness of the players' wealths provides participation incentives for them. Evolution and the bid-ask spread provide mechanisms for the gain in wealth of both the players and market-makers. New players survive in the market if the evolutionary rate is sufficiently slow. We test the applicability of the model on real Hang Seng Index data over 20 years. Comparisons with other models show that our model has a superior average performance when applied to real financial data.Comment: 17 pages, 16 figure

Inference and Optimization of Real Edges on Sparse Graphs - A Statistical Physics Perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge-variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory.Comment: 21 pages, 10 figures, major changes: Sections IV to VII updated, Figs. 1 to 3 replace

Hyperglycemia and Insulin Management in Critically Ill Patients

Hyperglycemia is a leading cause of increased morbidity and mortality in critically ill diabetic and nondiabetic patients in the ICU. Stricter control should be implemented in this setting in order to reduce mortality as well as other complications caused by hyperglycemia. Because hypoglycemia is associated with an increased risk of adverse effects, the optimal intensity of glucose control has been extensively investigated. Hyperglycemia is better controlled through continuous glucose infusions than with intermittent injections or IV infusions because it is easier to titrate the concentration of insulin to achieve a target glucose range. Pharmacists in acute-care and ambulatory-care settings are able to adjust insulin therapy and educate patients about hypoglycemia or hyperglycemia in order to optimize patient outcomes

PalProtect: A Collaborative Security Approach to Comment Spam

Collaborative security is a promising solution to many types of security problems. Organizations and individuals often have a limited amount of resources to detect and respond to the threat of automated attacks. Enabling them to take advantage of the resources of their peers by sharing information related to such threats is a major step towards automating defense systems. In particular, comment spam posted on blogs as a way for attackers to do Search Engine Optimization (SEO) is a major annoyance. Many measures have been proposed to thwart such spam, but all such measures are currently enacted and operate within one administrative domain. We propose and implement a system for cross-domain information sharing to improve the quality and speed of defense against such spam

Dynamical and Stationary Properties of On-line Learning from Finite Training Sets

The dynamical and stationary properties of on-line learning from finite training sets are analysed using the cavity method. For large input dimensions, we derive equations for the macroscopic parameters, namely, the student-teacher correlation, the student-student autocorrelation and the learning force uctuation. This enables us to provide analytical solutions to Adaline learning as a benchmark. Theoretical predictions of training errors in transient and stationary states are obtained by a Monte Carlo sampling procedure. Generalization and training errors are found to agree with simulations. The physical origin of the critical learning rate is presented. Comparison with batch learning is discussed throughout the paper.Comment: 30 pages, 4 figure

A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks

Understanding how the dynamics of a neural network is shaped by the network structure, and consequently how the network structure facilitates the functions implemented by the neural system, is at the core of using mathematical models to elucidate brain functions. This study investigates the tracking dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of neuronal recurrent interactions, CANNs can hold a continuous family of stationary states. They form a continuous manifold in which the neural system is neutrally stable. We systematically explore how this property facilitates the tracking performance of a CANN, which is believed to have clear correspondence with brain functions. By using the wave functions of the quantum harmonic oscillator as the basis, we demonstrate how the dynamics of a CANN is decomposed into different motion modes, corresponding to distortions in the amplitude, position, width or skewness of the network state. We then develop a perturbative approach that utilizes the dominating movement of the network's stationary states in the state space. This method allows us to approximate the network dynamics up to an arbitrary accuracy depending on the order of perturbation used. We quantify the distortions of a Gaussian bump during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable and the reaction time for the network to catch up with an abrupt change in the stimulus.Comment: 43 pages, 10 figure

Aryl Phosphoramidates of 5-Phospho Erythronohydroxamic Acid, A New Class of Potent Trypanocidal Compounds

RNAi and enzymatic studies have shown the importance of 6-phosphogluconate dehydrogenase (6-PGDH) in Trypanosoma brucei for the parasite survival and make it an attractive drug target for the development of new treatments against human African trypanosomiasis. 2,3-O-Isopropylidene-4-erythrono hydroxamate is a potent inhibitor of parasite Trypanosoma brucei 6-phosphogluconate dehydrogenase (6-PGDH), the third enzyme of the pentose phosphate pathway. However, this compound does not have trypanocidal activity due to its poor membrane permeability. Consequently, we have previously reported a prodrug approach to improve the antiparasitic activity of this inhibitor by converting the phosphate group into a less charged phosphate prodrug. The activity of prodrugs appeared to be dependent on their stability in phosphate buffer. Here we have successfully further extended the development of the aryl phosphoramidate prodrugs of 2,3-O-isopropylidene-4-erythrono hydroxamate by synthesizing a small library of phosphoramidates and evaluating their biological activity and stability in a variety of assays. Some of the compounds showed high trypanocidal activity and good correlation of activity with their stability in fresh mouse blood

Strategic sensemaking by social entrepreneurs:Creating strategies for social innovation

Purpose: This study explores how a small minority of social entrepreneurs break free from third sector constraints to conceive, create and grow non-profit organisations that generate social value at scale in new and innovative ways. Design/methodology/approach: Six narrative case histories of innovative social enterprises were developed based on documents and semi-structured interviews with founders and long serving executives. Data were coded “chrono-processually”, which involves locating thoughts, events and actions in distinct time periods (temporal bracketing) and identifying the processes at work in establishing new social ventures. Findings: This study presents two core findings. First, the paper demonstrates how successful social entrepreneurs draw on their lived experiences, private and professional, in driving the development and implementation of social innovations, which are realised through application of their capabilities as analysts, strategists and resources mobilisers. These capabilities are bolstered by personal legitimacy and by their abilities as storytellers and rhetoricians. Second, the study unravels the complex processes of social entrepreneurship by revealing how sensemaking, theorising, strategizing and sensegiving underpin the core processes of problem specification, the formulation of theories of change, development of new business models and the implementation of social innovations. Originality/value: The study demonstrates how social entrepreneurs use sensemaking and sensegiving strategies to understand and address complex social problems, revealing how successful social entrepreneurs devise and disseminate social innovations that substantially add value to society and bring about beneficial social change. A novel process-outcome model of social innovation is presented illustrating the interconnections between entrepreneurial cognition and strategic action.</p