Prediction of mesophotic coral distributions in the Au‘au Channel, Hawaii

The primary objective of this study was to predict the distribution of mesophotic hard corals in the Au‘au Channel in the Main Hawaiian Islands (MHI). Mesophotic hard corals are light-dependent corals adapted to the low light conditions at approximately 30 to 150 m in depth. Several physical factors potentially influence their spatial distribution, including aragonite saturation, alkalinity, pH, currents, water temperature, hard substrate availability and the availability of light at depth. Mesophotic corals and mesophotic coral ecosystems (MCEs) have increasingly been the subject of scientific study because they are being threatened by a growing number of anthropogenic stressors. They are the focus of this spatial modeling effort because the Hawaiian Islands Humpback Whale National Marine Sanctuary (HIHWNMS) is exploring the expansion of its scope—beyond the protection of the North Pacific Humpback Whale (Megaptera novaeangliae)—to include the conservation and management of these ecosystem components. The present study helps to address this need by examining the distribution of mesophotic corals in the Au‘au Channel region. This area is located between the islands of Maui, Lanai, Molokai and Kahoolawe, and includes parts of the Kealaikahiki, Alalākeiki and Kalohi Channels. It is unique, not only in terms of its geology, but also in terms of its physical oceanography and local weather patterns. Several physical conditions make it an ideal place for mesophotic hard corals, including consistently good water quality and clarity because it is flushed by tidal currents semi-diurnally; it has low amounts of rainfall and sediment run-off from the nearby land; and it is largely protected from seasonally strong wind and wave energy. Combined, these oceanographic and weather conditions create patches of comparatively warm, calm, clear waters that remain relatively stable through time. Freely available Maximum Entropy modeling software (MaxEnt 3.3.3e) was used to create four separate maps of predicted habitat suitability for: (1) all mesophotic hard corals combined, (2) Leptoseris, (3) Montipora and (4) Porites genera. MaxEnt works by analyzing the distribution of environmental variables where species are present, so it can find other areas that meet all of the same environmental constraints. Several steps (Figure 0.1) were required to produce and validate four ensemble predictive models (i.e., models with 10 replicates each). Approximately 2,000 georeferenced records containing information about mesophotic coral occurrence and 34 environmental predictors describing the seafloor’s depth, vertical structure, available light, surface temperature, currents and distance from shoreline at three spatial scales were used to train MaxEnt. Fifty percent of the 1,989 records were randomly chosen and set aside to assess each model replicate’s performance using Receiver Operating Characteristic (ROC), Area Under the Curve (AUC) values. An additional 1,646 records were also randomly chosen and set aside to independently assess the predictive accuracy of the four ensemble models. Suitability thresholds for these models (denoting where corals were predicted to be present/absent) were chosen by finding where the maximum number of correctly predicted presence and absence records intersected on each ROC curve. Permutation importance and jackknife analysis were used to quantify the contribution of each environmental variable to the four ensemble models.

Temperature dependence of photoluminescence from single and ensemble InAs/GaAs quantum dots

We investigate the temperature dependence of photoluminescence from single and ensemble InAs/GaAs quantum dots systematically. As temperature increases, the exciton emission peak for single quantum dot shows broadening and redshift. For ensemble quantum dots, however, the exciton emission peak shows narrowing and fast redshift. We use a simple steady-state rate equation model to simulate the experimental data of photoluminescence spectra. It is confirmed that carrier-phonon scattering gives the broadening of the exciton emission peak in single quantum dots while the effects of carrier thermal escape and retrapping play an important role in the narrowing and fast redshift of the exciton emission peak in ensemble quantum dots.

Material transport in self-assembled InAs/GaAs quantum dot ensemble

Introducing the growth interruption between the InAs deposition and subsequent GaAs growth in self-assembled quantum dot (QD) structures, the material transport process in the InAs layers has been investigated by photoluminescence and transmission electron microscopy measurement. InAs material in structures without misfit dislocations transfers from the wetting layer to QDs corresponding to the red-shift of PL peak energy due to interruption. On the other hand, the PL peak shifts to higher energy in the structures with dislocations. In this case, the misfit dislocations would capture the InAs material from the surrounding wetting layer and coherent islands leading to the reduction of the size of these QDs. The variations in the PL intensity and Linewidth are also discussed.

on a class of pseudorandom sequences from elliptic curves over finite fields

Following the idea of Xing et al., we investigate a general method for constructing families of pseudorandom sequences with low correlation and large linear complexity from elliptic curves over finite fields in this correspondence. With the help of the tool of exponential sums on elliptic curves, we study their periods, linear complexities, linear complexity profiles, distributions of r-patterns, periodic correlation, partial period distributions, and aperiodic correlation in detail. The results show that they have nice randomness.

Material transport in self-assembled InAs/GaAs quantum dot ensemble

Introducing the growth interruption between the InAs deposition and subsequent GaAs growth in self-assembled quantum dot (QD) structures, the material transport process in the InAs layers has been investigated by photoluminescence and transmission electron microscopy measurement. InAs material in structures without misfit dislocations transfers from the wetting layer to QDs corresponding to the red-shift of PL peak energy due to interruption. On the other hand, the PL peak shifts to higher energy in the structures with dislocations. In this case, the misfit dislocations would capture the InAs material from the surrounding wetting layer and coherent islands leading to the reduction of the size of these QDs. The variations in the PL intensity and Linewidth are also discussed.

Permutation Tests for Classification

We introduce and explore an approach to estimating statistical significance of classification accuracy, which is particularly useful in scientific applications of machine learning where high dimensionality of the data and the small number of training examples render most standard convergence bounds too loose to yield a meaningful guarantee of the generalization ability of the classifier. Instead, we estimate statistical significance of the observed classification accuracy, or the likelihood of observing such accuracy by chance due to spurious correlations of the high-dimensional data patterns with the class labels in the given training set. We adopt permutation testing, a non-parametric technique previously developed in classical statistics for hypothesis testing in the generative setting (i.e., comparing two probability distributions). We demonstrate the method on real examples from neuroimaging studies and DNA microarray analysis and suggest a theoretical analysis of the procedure that relates the asymptotic behavior of the test to the existing convergence bounds.

A hyper-heuristic ensemble method for static job-shop scheduling.

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyperheuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

Information sets and partial permutation decoding for codes from finite geometries

Mavron, Vassili; McDonough, T.P.; Key, J.D., (2006) 'Information sets and partial permutation decoding for codes from finite geometries', Finite Fields and their applications 12(2) pp.232-247 RAE2008

A survey of fingerprint classification Part II: experimental analysis and ensemble proposal

In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.