56,093 research outputs found
Can Accountable Care Organizations Improve the Value of Health Care by Solving the Cost and Quality Quandaries?
Explores proposed models and configurations of accountable care organizations, which combine provider payment and delivery system reforms, and their potential to slow the rise of healthcare costs. Outlines key issues and program features under debate
Multilayer Aggregation with Statistical Validation: Application to Investor Networks
Multilayer networks are attracting growing attention in many fields,
including finance. In this paper, we develop a new tractable procedure for
multilayer aggregation based on statistical validation, which we apply to
investor networks. Moreover, we propose two other improvements to their
analysis: transaction bootstrapping and investor categorization. The
aggregation procedure can be used to integrate security-wise and time-wise
information about investor trading networks, but it is not limited to finance.
In fact, it can be used for different applications, such as gene,
transportation, and social networks, were they inferred or observable.
Additionally, in the investor network inference, we use transaction
bootstrapping for better statistical validation. Investor categorization allows
for constant size networks and having more observations for each node, which is
important in the inference especially for less liquid securities. Furthermore,
we observe that the window size used for averaging has a substantial effect on
the number of inferred relationships. We apply this procedure by analyzing a
unique data set of Finnish shareholders during the period 2004-2009. We find
that households in the capital have high centrality in investor networks,
which, under the theory of information channels in investor networks suggests
that they are well-informed investors
The Impact Of Federal Legislation On Governmental And Special Libraries
It has become increasingly obvious that libraries today cannot
provide adequate service under conditions of local self-sufficiency.
A variety of programs are required on the national level which cannot
be provided by individual libraries or combinations of libraries. Few
libraries can justify or afford the massive resource development
which has been a responsibility of such institutions as the Library of
Congress, the National Agricultural Library and the National Library
of Medicine. None can finance the very expensive and complex bibliographic
services provided by these national libraries. And practically
none of our non- governmental libraries are in a position to
assume leadership in implementing national bibliographic programs
in the years ahead. These are the general reasons why our governmental
libraries and the attendant legislative authorization are so
critically important to the entire scholarly community.
It is not the purpose of this paper to consider the detailed development
of our governmental libraries or library legislative history.
It would be accurate to say that in the past this development
has taken place under uncoordinated circumstances, frequently in response
to ad hoc situations. There was certainly no grand design or
master plan to shape the future.
This does not imply that librarians were limited in their vision
or lacked the capability for basic planning. It is more a reflection of
the fact that, at the Federal level, there has not been sufficient political
support to allow more than one step being taken at a time.published or submitted for publicatio
A Field Guide to Genetic Programming
xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction --
Representation, initialisation and operators in Tree-based GP --
Getting ready to run genetic programming --
Example genetic programming run --
Alternative initialisations and operators in Tree-based GP --
Modular, grammatical and developmental Tree-based GP --
Linear and graph genetic programming --
Probalistic genetic programming --
Multi-objective genetic programming --
Fast and distributed genetic programming --
GP theory and its applications --
Applications --
Troubleshooting GP --
Conclusions.Contents
xi
1 Introduction
1.1 Genetic Programming in a Nutshell
1.2 Getting Started
1.3 Prerequisites
1.4 Overview of this Field Guide I
Basics
2 Representation, Initialisation and GP
2.1 Representation
2.2 Initialising the Population
2.3 Selection
2.4 Recombination and Mutation Operators in Tree-based
3 Getting Ready to Run Genetic Programming 19
3.1 Step 1: Terminal Set 19
3.2 Step 2: Function Set 20
3.2.1 Closure 21
3.2.2 Sufficiency 23
3.2.3 Evolving Structures other than Programs 23
3.3 Step 3: Fitness Function 24
3.4 Step 4: GP Parameters 26
3.5 Step 5: Termination and solution designation 27
4 Example Genetic Programming Run
4.1 Preparatory Steps 29
4.2 Step-by-Step Sample Run 31
4.2.1 Initialisation 31
4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming
5 Alternative Initialisations and Operators in
5.1 Constructing the Initial Population
5.1.1 Uniform Initialisation
5.1.2 Initialisation may Affect Bloat
5.1.3 Seeding
5.2 GP Mutation
5.2.1 Is Mutation Necessary?
5.2.2 Mutation Cookbook
5.3 GP Crossover
5.4 Other Techniques 32
5.5 Tree-based GP 39
6 Modular, Grammatical and Developmental Tree-based GP 47
6.1 Evolving Modular and Hierarchical Structures 47
6.1.1 Automatically Defined Functions 48
6.1.2 Program Architecture and Architecture-Altering 50
6.2 Constraining Structures 51
6.2.1 Enforcing Particular Structures 52
6.2.2 Strongly Typed GP 52
6.2.3 Grammar-based Constraints 53
6.2.4 Constraints and Bias 55
6.3 Developmental Genetic Programming 57
6.4 Strongly Typed Autoconstructive GP with PushGP 59
7 Linear and Graph Genetic Programming 61
7.1 Linear Genetic Programming 61
7.1.1 Motivations 61
7.1.2 Linear GP Representations 62
7.1.3 Linear GP Operators 64
7.2 Graph-Based Genetic Programming 65
7.2.1 Parallel Distributed GP (PDGP) 65
7.2.2 PADO 67
7.2.3 Cartesian GP 67
7.2.4 Evolving Parallel Programs using Indirect Encodings 68
8 Probabilistic Genetic Programming
8.1 Estimation of Distribution Algorithms 69
8.2 Pure EDA GP 71
8.3 Mixing Grammars and Probabilities 74
9 Multi-objective Genetic Programming 75
9.1 Combining Multiple Objectives into a Scalar Fitness Function 75
9.2 Keeping the Objectives Separate 76
9.2.1 Multi-objective Bloat and Complexity Control 77
9.2.2 Other Objectives 78
9.2.3 Non-Pareto Criteria 80
9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80
9.4 Multi-objective Optimisation via Operator Bias 81
10 Fast and Distributed Genetic Programming 83
10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83
10.2 Reducing Cost of Fitness with Caches 86
10.3 Parallel and Distributed GP are Not Equivalent 88
10.4 Running GP on Parallel Hardware 89
10.4.1 Master–slave GP 89
10.4.2 GP Running on GPUs 90
10.4.3 GP on FPGAs 92
10.4.4 Sub-machine-code GP 93
10.5 Geographically Distributed GP 93
11 GP Theory and its Applications 97
11.1 Mathematical Models 98
11.2 Search Spaces 99
11.3 Bloat 101
11.3.1 Bloat in Theory 101
11.3.2 Bloat Control in Practice 104
III
Practical Genetic Programming
12 Applications
12.1 Where GP has Done Well
12.2 Curve Fitting, Data Modelling and Symbolic Regression
12.3 Human Competitive Results – the Humies
12.4 Image and Signal Processing
12.5 Financial Trading, Time Series, and Economic Modelling
12.6 Industrial Process Control
12.7 Medicine, Biology and Bioinformatics
12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii
12.9 Entertainment and Computer Games 127
12.10The Arts 127
12.11Compression 128
13 Troubleshooting GP
13.1 Is there a Bug in the Code?
13.2 Can you Trust your Results?
13.3 There are No Silver Bullets
13.4 Small Changes can have Big Effects
13.5 Big Changes can have No Effect
13.6 Study your Populations
13.7 Encourage Diversity
13.8 Embrace Approximation
13.9 Control Bloat
13.10 Checkpoint Results
13.11 Report Well
13.12 Convince your Customers
14 Conclusions
Tricks of the Trade
A Resources
A.1 Key Books
A.2 Key Journals
A.3 Key International Meetings
A.4 GP Implementations
A.5 On-Line Resources 145
B TinyGP 151
B.1 Overview of TinyGP 151
B.2 Input Data Files for TinyGP 153
B.3 Source Code 154
B.4 Compiling and Running TinyGP 162
Bibliography 167
Inde
Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure
Large-scale transitions in societies are associated with both individual
behavioural change and restructuring of the social network. These two factors
have often been considered independently, yet recent advances in social network
research challenge this view. Here we show that common features of societal
marginalization and clustering emerge naturally during transitions in a
co-evolutionary adaptive network model. This is achieved by explicitly
considering the interplay between individual interaction and a dynamic network
structure in behavioural selection. We exemplify this mechanism by simulating
how smoking behaviour and the network structure get reconfigured by changing
social norms. Our results are consistent with empirical findings: The
prevalence of smoking was reduced, remaining smokers were preferentially
connected among each other and formed increasingly marginalised clusters. We
propose that self-amplifying feedbacks between individual behaviour and dynamic
restructuring of the network are main drivers of the transition. This
generative mechanism for co-evolution of individual behaviour and social
network structure may apply to a wide range of examples beyond smoking.Comment: 16 pages, 5 figure
Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007--2008 credit crisis
We study the cluster dynamics of multichannel (multivariate) time series by
representing their correlations as time-dependent networks and investigating
the evolution of network communities. We employ a node-centric approach that
allows us to track the effects of the community evolution on the functional
roles of individual nodes without having to track entire communities. As an
example, we consider a foreign exchange market network in which each node
represents an exchange rate and each edge represents a time-dependent
correlation between the rates. We study the period 2005-2008, which includes
the recent credit and liquidity crisis. Using dynamical community detection, we
find that exchange rates that are strongly attached to their community are
persistently grouped with the same set of rates, whereas exchange rates that
are important for the transfer of information tend to be positioned on the
edges of communities. Our analysis successfully uncovers major trading changes
that occurred in the market during the credit crisis.Comment: 8 pages, 6 figures, accepted for publication in Chao
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
We introduce the \texttt{pyunicorn} (Pythonic unified complex network and
recurrence analysis toolbox) open source software package for applying and
combining modern methods of data analysis and modeling from complex network
theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully
object-oriented and easily parallelizable package written in the language
Python. It allows for the construction of functional networks such as climate
networks in climatology or functional brain networks in neuroscience
representing the structure of statistical interrelationships in large data sets
of time series and, subsequently, investigating this structure using advanced
methods of complex network theory such as measures and models for spatial
networks, networks of interacting networks, node-weighted statistics or network
surrogates. Additionally, \texttt{pyunicorn} provides insights into the
nonlinear dynamics of complex systems as recorded in uni- and multivariate time
series from a non-traditional perspective by means of recurrence quantification
analysis (RQA), recurrence networks, visibility graphs and construction of
surrogate time series. The range of possible applications of the library is
outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure
Ethical Implications of Predictive Risk Intelligence
open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society
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