Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

This document is an international evidence-based guideline on the diagnosis and management of idiopathic pulmonary fibrosis, and is a collaborative effort of the American Thoracic Society, the European Respiratory Society, the Japanese Respiratory Society, and the Latin American Thoracic Association. It represents the current state of knowledge regarding idiopathic pulmonary fibrosis (IPF), and contains sections on definition and epidemiology, risk factors, diagnosis, natural history, staging and prognosis, treatment, and monitoring disease course. For the diagnosis and treatment sections, pragmatic GRADE evidence-based methodology was applied in a question-based format. For each diagnosis and treatment question, the committee graded the quality of the evidence available (high, moderate, low, or very low), and made a recommendation (yes or no, strong or weak). Recommendations were based on majority vote. It is emphasized that clinicians must spend adequate time with patients to discuss patients' values and preferences and decide on the appropriate course of action.

An Official ATS/ERS/JRS/ALAT Statement: Idiopathic Pulmonary Fibrosis: Evidence-Based Guidelines for Diagnosis and Management

Acute kidney injury (AKI) is a complex disorder for which currently there is no accepted definition. Having a uniform standard for diagnosing and classifying AKI would enhance our ability to manage these patients. Future clinical and translational research in AKI will require collaborative networks of investigators drawn from various disciplines, dissemination of information via multidisciplinary joint conferences and publications, and improved translation of knowledge from pre-clinical research. We describe an initiative to develop uniform standards for defining and classifying AKI and to establish a forum for multidisciplinary interaction to improve care for patients with or at risk for AKI. Members representing key societies in critical care and nephrology along with additional experts in adult and pediatric AKI participated in a two day conference in Amsterdam, The Netherlands, in September 2005 and were assigned to one of three workgroups. Each group's discussions formed the basis for draft recommendations that were later refined and improved during discussion with the larger group. Dissenting opinions were also noted. The final draft recommendations were circulated to all participants and subsequently agreed upon as the consensus recommendations for this report. Participating societies endorsed the recommendations and agreed to help disseminate the results. The term AKI is proposed to represent the entire spectrum of acute renal failure. Diagnostic criteria for AKI are proposed based on acute alterations in serum creatinine or urine output. A staging system for AKI which reflects quantitative changes in serum creatinine and urine output has been developed. We describe the formation of a multidisciplinary collaborative network focused on AKI. We have proposed uniform standards for diagnosing and classifying AKI which will need to be validated in future studies. The Acute Kidney Injury Network offers a mechanism for proceeding with efforts to improve patient outcomes.

Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury

Signature recognition is a relevant area in secure applications referred to as biometric identification. The image of the signature to be recognized (in off-line systems) can be considered as a spatio-temporal signal due to the shapely geometric and sequential character of the pencil drawing. The recognition and classification methods known to us are based on the extraction of geometric parameters and their classification by either a linear or nonlinear classifier. This procedure neglects the temporal information of the signature. In order to alleviate this, this paper proposes to use signature parameters with spatio-temporal information and its classification by a classifier capable of dealing with spatio-temporal problems as hidden Markov models (HMM). The proposed parameters are calculated in two stages; first, the preprocessing stage which includes noise reduction and outline detection through a skeletonization or thinning algorithm; and second, a parameterization stage in which the signature is encoded following the signature line and recording the length and direction of the pencil drawing obtaining a vector that includes the signature spatio-temporal information. The classification of the above parameters is done by a HMM classifier working in the same way as isolated word recognition systems. To design (train and test) the HMM classifier we have built a database of 24 signatures of 60 different writers.

Signature classification by hidden Markov model

Traffic light detection is an important matter in urban environments during the transition to fully autonomous driving. Many literature has been generated in the recent years approaching different pattern recognition strategies. In this paper we present a survey summarizing relevant works in the field of detection of both suspended and supported traffic light. This survey organizes different methods highlighting main reasearch areas in the computer vision field.

/pdf/a-survey-on-traffic-light-detection-3mso7um7z3.pdf

A Survey on Traffic Light Detection

We present a new idea about the possible effects of human-induced mortality on different age cohorts (i.e., breeders vs. juveniles) in long-lived animals. Our hypothesis is based on Curio’s idea on the effect of natural selective processes on cohorts to explain age-related increases in fecundity (selection hypothesis). We believe that negative human pressure may modify such contribution to reproduction of good versus low quality phenotypes, altering the genetic structure of the population. Ecologists and environmental managers in general should be aware of how stochastic events provoked by humans may induce changes in the genetic structure of populations.

/pdf/humans-act-against-the-natural-process-of-breeder-selection-2tm8mrr52z.pdf

Humans act against the natural process of breeder selection: A modern sickness for animal populations?

Summary


1
Using information on the Donana population of Spanish imperial eagles Aquila adalberti from 1959 to 2004, we present strong empirical support to theoretical models on the regulation of population trajectories by the relationships between breeder mortality and floater availability.

2
During the study period, the eagle population showed three distinct phases: (i) a population increase with negative density-dependent fecundity; (ii) a period of stability without any relationship between density and fecundity; and (iii) a population decrease with a positive relationship between density and fecundity.

3
A dramatic increase in annual adult mortality due to an increase in poisoning in hunting areas surrounding the Donana National Park was recorded. The use of poison against generalist predators accounted for more than 54% of the total number of breeding eagles found dead since 1990, increasing annual adult mortality from 6·07 to 12·01%.

4
This high mortality reduced the population annually by 6% during the 1992–2004 period. Also, the population changed from a negative to a positive relationship between density and fecundity (Allee effect). These trends made the population approach extinction due to the double effect of increasing breeder mortality and low availability of floaters.

5
A supplementary feeding programme established in 1990 did not increase fecundity because it was a consequence of high adult mortality rather than low food availability. The high mortality of adults increased the risk of breeding failure and also decreased the availability of floaters, decreasing the likelihood of mate substitution.

6
Synthesis and applications. The main target in species conservation management plans is often a single demographic parameter (typically, fecundity in raptor populations). Our research demonstrates, however, that demographic parameters must not be considered as independent variables when formulating management programmes. The essential relationship between adult mortality and the availability of floaters must be better understood to avoid the risk of implementing inefficient management strategies. Although fecundity was low in this eagle population, it was a result of high adult mortality rates. Consequently, management must aim to correct the unusual mortality and recover the floater population.

/pdf/non-independence-of-demographic-parameters-positive-density-47d8q9lh62.pdf

Non-independence of demographic parameters: positive density-dependent fecundity in eagles

Handwritten signatures are generally considered a powerful biometric traits for personal verification. Recently, handwritten signatures have been also investigated for early diagnosis of neurodegenerative diseases. This paper presents a new approach for early diagnosis of neurodegenerative diseases by the analysis of handwritten dynamic signatures. For the purpose, the sigma-lognormal model was considered and dynamic parameters are extracted for signatures. Based on these parameters, the health condition of the signer is analysed in terms of Alzheimer disease. The approach is cheap and effective, therefore it can be considered as a very promising direction for further research.

/pdf/early-diagnosis-of-neurodegenerative-diseases-by-handwritten-41kmfm306a.pdf

Miguel Ferrer

Papers

Signature classification by hidden Markov model

A Survey on Traffic Light Detection

Humans act against the natural process of breeder selection: A modern sickness for animal populations?

Non-independence of demographic parameters: positive density-dependent fecundity in eagles

Early Diagnosis of Neurodegenerative Diseases by Handwritten Signature Analysis