Empirical evidence of climate change and urbanization impacts on warming stream temperatures.

Climate change and urbanization threaten streams and the biodiversity that rely upon them worldwide. Emissions of greenhouse gases are causing air and sea surface temperatures to increase, and even small areas of urbanization are degrading stream biodiversity, water quality and hydrology. However, empirical evidence of how increasing air temperatures and urbanization together affect stream temperatures over time and their relative influence on stream temperatures is limited.

Relating instance hardness to classification performance in a dataset: a visual approach.

Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The results obtained are usually summarized through average statistics, either in numeric tables or simple plots. Such approaches fail to reveal interesting subtleties about algorithmic performance, including which observations an algorithm may find easy or hard to classify, and also which observations within a dataset may present unique challenges.

Revisiting Facial Age Estimation With New Insights From Instance Space Analysis.

When demonstrating the effectiveness of a new algorithm, researchers are traditionally encouraged to compare their algorithm's performance against existing algorithms on well-studied benchmark test suites. In the absence of more nuanced methodologies, algorithm performance is typically summarized on average across the test suite examples. This paper highlights the potential bias of conclusions drawn by analyzing "on average" performance, and the opportunities offered by a recent testing methodology known as instance space analysis.

Early classification of spatio-temporal events using partial information.

This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing.

Parameter estimation for a point-source diffusion-decay morphogen model.

In this paper we present a novel method for finding unknown parameters for an unknown morphogen. We postulate the existence of an unknown morphogen in a given three-dimensional domain due to the spontaneous arrangement of a downstream species on the domain boundary for which data is known. Assuming a modified Helmholtz model for the morphogen and that it is produced from a single source in the domain, our method accurately estimates the source location and other model parameters.

Generating New Space-Filling Test Instances for Continuous Black-Box Optimization.

This article presents a method to generate diverse and challenging new test instances for continuous black-box optimization. Each instance is represented as a feature vector of exploratory landscape analysis measures. By projecting the features into a two-dimensional instance space, the location of existing test instances can be visualized, and their similarities and differences revealed.

Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space.

This article presents a method for the objective assessment of an algorithm's strengths and weaknesses. Instead of examining the performance of only one or more algorithms on a benchmark set, or generating custom problems that maximize the performance difference between two algorithms, our method quantifies both the nature of the test instances and the algorithm performance. Our aim is to gather information about possible phase transitions in performance, that is, the points in which a small change in problem structure produces algorithm failure.

Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density.

Background: Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on the experimental data sets, in particular, when the data are sparse and the regulatory network is stochastic.

Results: To address this issue, this work proposed a new algorithm to estimate parameters in stochastic models using simulated likelihood density in the framework of approximate Bayesian computation.

A note on the relationship between turbulent coherent structures and phase correlation.

Various definitions of coherent structures exist in turbulence research, but a common assumption is that coherent structures have correlated spectral phases. As a result, randomization of phases is believed, generally, to remove coherent structures from the measured data. Here, we reexamine these assumptions using atmospheric turbulence measurements.

Mathematical modeling of GATA-switching for regulating the differentiation of hematopoietic stem cell.

Background: Hematopoiesis is a highly orchestrated developmental process that comprises various developmental stages of the hematopoietic stem cells (HSCs). During development, the decision to leave the self-renewing state and selection of a differentiation pathway is regulated by a number of transcription factors. Among them, genes GATA-1 and PU.

Stochastic modelling of biochemical systems of multi-step reactions using a simplified two-variable model.

Background: A fundamental issue in systems biology is how to design simplified mathematical models for describing the dynamics of complex biochemical reaction systems. Among them, a key question is how to use simplified reactions to describe the chemical events of multi-step reactions that are ubiquitous in biochemistry and biophysics. To address this issue, a widely used approach in literature is to use one-step reaction to represent the multi-step chemical events.

Method for optimizing coating properties based on an evolutionary algorithm approach.

In industry as well as many areas of scientific research, data collected often contain a number of responses of interest for a chosen set of exploratory variables. Optimization of such multivariable multiresponse systems is a challenge well suited to genetic algorithms as global optimization tools. One such example is the optimization of coating surfaces with the required absolute and relative sensitivity for detecting analytes using devices such as sensor arrays.

Mathematical modelling of stem cell differentiation: the PU.1-GATA-1 interaction.

The transcription factors PU.1 and GATA-1 are known to be important in the development of blood progenitor cells. Specifically they are thought to regulate the differentiation of progenitor cells into the granulocyte/macrophage lineage and the erythrocyte/megakaryocite lineage.

Face image modeling by multilinear subspace analysis with missing values.

Multilinear subspace analysis (MSA) is a promising methodology for pattern-recognition problems due to its ability in decomposing the data formed from the interaction of multiple factors. The MSA requires a large training set, which is well organized in a single tensor, which consists of data samples with all possible combinations of the contributory factors. However, such a "complete" training set is difficult (or impossible) to obtain in many real applications.

Identifying patterns in primary care consultations: a cluster analysis.

Background: A literature review revealed that little is known about the systems context of general practice consultations and their outcomes.

Objectives: To describe the systems context and resulting underlying patterns of primary care consultations in a local area.

Design: Cross-sectional multi-practice study based on a three-part questionnaire.

Individual stable space: an approach to face recognition under uncontrolled conditions.

There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a "divide and conquer" strategy, this paper attempts to directly address face recognition under uncontrolled conditions.

Automatic age estimation based on facial aging patterns.

While recognition of most facial variations, such as identity, expression and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace).