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

Due its possibilities in security systems and robotics, face recognition is one of the most researched areas within the biometric field. In a common scenario from real life face recognition problem, the dimension in the sample space is larger than the number of training samples per class. This is known as the “small sample size problem”. Discriminative Common Vectors (DCV) technique has been used to face this problem successfully. In this paper, we introduce a new approach based on DCV theory to increase its performance in face verification tasks. This modification uses a specific set of projecting vectors selected by an optimization algorithm based on the classifier's performance, and in the fact that no such thing as common vectors exists when this set contains vectors from the range of the within-class scattering matrix (SW ). Based on these two ideas, we may call this approach Discriminative Multi-Projection Vectors (DMPV) as it projects samples in both range and null space of SW. We tested the system with different databases and results show that DMPV outperforms classic DCV method.

Discriminative Multi-Projection Vectors: Modifying the Discriminative Common Vectors approach for face verification

In work we propose a multi-channel active noise equalizer (ANE) that can deal with multi-frequency noise signals and assigns simultaneously different equalization gains to each frequency component at each monitoring sensor. For this purpose, we state a pseudo-error noise signal for each sensor of the ANE, which has to be cancelled out in order to get the desired equalization profiles. Firstly, the optimal analytic solution for the ANE filters in the case of single-frequency noise is provided, and an adaptive algorithm based on the Least Mean Squared (LMS) is proposed for the same case. We also show that this adaptive strategy reaches the theoretical solution in steady state. Secondly, we state an equivalent approach for the case of multi-frequency noise based on two alternatives: a common pseudo-error signal at each sensor for all frequencies, and a different pseudo-error signal at each sensor for each frequency. The analytic and adaptive solutions for the ANE control filters have been developed for both pseudo-error alternatives. Finally, the ability of the proposed ANE to achieve simultaneously different user-selected noise profiles in different locations has been validated by their transfer functions and simulations.

https://ieeexplore.ieee.org/ielx7/6570655/6633080/09913622.pdf

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

The tick Ixodes uriae is an ectoparasite widely distributed among seabirds throughout circumpolar regions, usually associated with seabird colonies. Nevertheless, potential effects of infestations, especially in chicks, are not well documented. In this article, we studied factors associated with probability and intensity of infestations in Black-browed Albatross Thalassarche melanophrys nestlings in a colony at the Falklands Islands. We compared the body measurements, physiological parameters, distance to other nests and position inside the colony between infested and non-infested 40 Black-browed Albatross nestlings. Ticks were present in 60% of the nestlings with a mean number per nestlings of 1.47. None of the 12 analysed blood parameters but LDH (Lactate dehydrogenase) showed significant differences between infested and not infested nestlings. Nestlings infected showed significant higher level of LDH than those without ticks. The number of ticks found on each nestling was positively and significantly correlated with their LDH blood levels. Tarsus length (as a proxy of age) and distance to the nearest nest showed a significant effect, with younger nestlings with closer nests showing higher probability to be infected. These variables also affect intensity of infestation. No effects of nutritional condition (butyrate or urea levels) or body condition (residuals of cubic root of mass/tarsus length regression) on probability or intensity of infestation were found. Both small anaemias and tick scars would explain the different values of LDH between infected/non-infected chicks. The low level of infestation found in our colony could be the cause of a non-detectable effect of the presence of ticks on nestling body condition and other blood parameters related to metabolism of fat or protein.

Tick infestations correlates at a Falkland Islands Black-browed Albatross colony

Currently, there is a demand for more energy-efficient lighting sources, however, light emitted by different lighting systems differs in primary properties such as intensity, propagation direction, and wavelength spectrum, among others, and these properties may affect insect light attraction. Despite the energetic benefits of light-emitting diodes (LED) as street light systems, their ecological impacts on insects have not yet been tested on a wide range of taxa. Using an experimental approach, we showed that LED street lights lead to a reduction in the total number of insects captured with light traps in a wide range of families. Coleoptera and Lepidoptera orders were the most sensitive groups to ecological light pollution in the study area. We suggest that LED was the least attractive light system for most of the affected groups both because of its very little emitted short-wavelength light and because of its lower light intensity. We expect that the more and more widespread use of LED lights as a measure to reduce economic costs of outdoor lighting should lead to a lower attraction to street lights in most of the affected insect taxa and to diminish the negative impacts of artificial light attraction on the ecosystems.

/pdf/light-emitting-diodes-led-a-promising-street-light-system-to-4toeqpvcns.pdf

Light-Emitting Diodes (LED): A Promising Street Light System to Reduce the Attraction to Light of Insects

Online fraud often involves identity theft. Since most security measures are weak or can be spoofed, we investigate a more nuanced and less explored avenue: behavioral biometrics via handwriting movements. This kind of data can be used to verify whether a user is operating a device or a computer application, so it is important to distinguish between human and machine-generated movements reliably. For this purpose, we study handwritten symbols (isolated characters, digits, gestures, and signatures) produced by humans and machines, and compare and contrast several deep learning models. We find that if symbols are presented as static images, they can fool state-of-the-art classifiers (near 75% accuracy in the best case) but can be distinguished with remarkable accuracy if they are presented as temporal sequences (95% accuracy in the average case). We conclude that an accurate detection of fake movements has more to do with how users write, rather than what they write. Our work has implications for computerized systems that need to authenticate or verify legitimate human users, and provides an additional layer of security to keep attackers at bay.

Miguel Ferrer

Papers

Discriminative Multi-Projection Vectors: Modifying the Discriminative Common Vectors approach for face verification

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Tick infestations correlates at a Falkland Islands Black-browed Albatross colony

Light-Emitting Diodes (LED): A Promising Street Light System to Reduce the Attraction to Light of Insects

Human or Machine? It Is Not What You Write, But How You Write It