Alternative Selection Methods Research Articles

Designing Practical Polar Codes Using Simulation-Based Bit Selection

The frozen set selection of polar codes, known as bit selection, determines the error-correcting performance of polar codes. The original bit selection was derived for successive cancellation decoding in a binary erasure channel. Density evolution has been used to evaluate the bit error probability in binary memoryless channel, but the computational complexity is still high and the simplified versions rely on different degrees of approximations. We propose an alternative simulation-based in-order bit selection method that evaluates the error rate of each bit using Monte Carlo decoding simulations and selects the frozen set based on the bit reliability ranking. The simulation-based method does not rely on channel models and it can be applied to any practical channels in the field. The simulation can be accelerated on an FPGA platform to significantly shorten the time required to one day for a 1024-b code design. We use three examples to demonstrate the in-order bit selection method, a (256, 128) code, a (512, 256) code, and a (1024, 512) code. Compared with the codes designed using density evolution for an AWGN channel, our (256, 128) code shows a competitive BER; our (512, 256) code outperforms at low SNR; and our (1024, 512) code outperforms across a wide range of SNR by 0.3 to 0.6 dB. The algorithm and methodology are applicable to any code rate and longer code lengths.

Gradient sperm selection for reproductive techniques in cattle: Is Isolate a suitable replacement for Percoll?

In assisted reproductive techniques, it is essential to perform a sperm selection to obtain spermatozoa with high motility and membrane integrity for in vitro fertilisation (IVF) and high-DNA integrity for intracytoplasmic sperm injection (ICSI). In this study, we evaluated whether Isolate® was a suitable substitute for Percoll® for assisted reproductive techniques. Commercial cryopreserved bovine semen was used after selection in both gradients, and plasma and acrosome membrane integrity, reactive oxygen species (ROS) levels, DNA integrity and mitochondrial membrane potential (ΔΨm) were assessed by flow cytometry. Motility parameters were also evaluated by CASA system. A similar percentage of spermatozoa with intact plasma membrane, acrosome integrity and high ΔΨm was observed in both sperm selection methods, but only Percoll® showed higher percentage of spermatozoa with intact plasma and acrosome membrane compared to the post-thawing group. No differences were observed in the motility, ROS, DNA fragmentation and on the in vitro embryo production in all experimental groups. In conclusion, the selection of bovine spermatozoa with Isolate® generates spermatozoa with similar quality parameters and embryonic development compared to Percoll® providing a suitable alternative sperm selection method for assisted reproductive techniques in this species.

Identifying the nature of lithogeochemical boundaries in drill holes

There is a need for rapid and reliable techniques for extracting geological information from geochemical data derived from exploration drill hole samples because geochemical data bases are becoming too large to interpret manually. Automated boundary detection techniques which use the continuous wavelet transform are a popular method for extracting multi-scale geological boundaries from drill hole signals. However, these boundary detection techniques do not distinguish between sharp and gradational boundaries, which may be an important factor when interpreting the geology of the subsurface or calculating resource estimates. This paper demonstrates how a scale-dependant measure of relative sharpness of the boundary can be extracted from the results of the continuous wavelet transform. The results of the automated method are compared against the interpretations of 15 geologists. The comparison demonstrates the need for alternative boundary selection methods when boundaries are not sharp. The results also demonstrate how the multi-scale nature of the sharpness measure allows for reliable gradient analysis in noisy signals.

Enhancing of accuracy assessment for forest above-ground biomass estimates obtained from remote sensing via hypothesis testing and overfitting evaluation

The evaluation of accuracy is essential for assuring the reliability of ecological models. Usually, the accuracy of above-ground biomass (AGB) predictions obtained from remote sensing is assessed by the mean differences (MD), the root mean squared differences (RMSD), and the coefficient of determination (R2) between observed and predicted values. In this article we propose a more thorough analysis of accuracy, including a hypothesis test to evaluate the agreement between observed and predicted values, and an assessment of the degree of overfitting to the sample employed for model training. Using the estimation of forest AGB from LIDAR and spectral sensors as a case study, we compared alternative prediction and variable selection methods using several statistical measures to evaluate their accuracy. We showed that the hypothesis tests provide an objective method to infer the statistical significance of agreement. We also observed that overfitting can be assessed by comparing the inflation in residual sums of squares experienced when carrying out a cross-validation. Our results suggest that this method may be more effective than analysing the deflation in R2. We proved that overfitting needs to be specifically addressed since, in light of MD, RMSD and R2 alone, predictions may apparently seem reliable even in clearly unrealistic circumstances, for instance when including too many predictor variables. Moreover, Theil’s partial inequality coefficients, which are employed to resolve the proportions of the total errors due to the unexplained variance, the slope and the bias, may become useful to detect averaging effects common in remote sensing predictions of AGB. We concluded that statistical measures of accuracy, precision and agreement are necessary but insufficient for model evaluation. We therefore advocate for incorporating evaluation measures specifically devoted to testing observed-versus-predicted fit, and to assessing the degree of overfitting.

A group adaptive elastic-net approach for variable selection in high-dimensional linear regression

In practice, predictors possess grouping structures spontaneously. Incorporation of such useful information can improve statistical modeling and inference. In addition, the high-dimensionality often leads to the collinearity problem. The elastic net is an ideal method which is inclined to reflect a grouping effect. In this paper, we consider the problem of group selection and estimation in the sparse linear regression model in which predictors can be grouped. We investigate a group adaptive elastic-net and derive oracle inequalities and model consistency for the cases where group number is larger than the sample size. Oracle property is addressed for the case of the fixed group number. We revise the locally approximated coordinate descent algorithm to make our computation. Simulation and real data studies indicate that the group adaptive elastic-net is an alternative and competitive method for model selection of high-dimensional problems for the cases of group number being larger than the sample size.

Are We Getting Better at Using Wild Potato Species in Light of New Tools?

Potato (Solanum tuberosum L.), mankind's third most consumed food crop, originated as an interspecific hybrid in the Andean highlands. More than 100 species closely related to potato exist throughout Central and South America. Potato was introduced to Europe, Asia, and North America in the 16th and 17th centuries, but most cultivars were destroyed by late blight epidemics in the mid‐19th century. Late blight resistance genes from the wild relative Solanum demissum Lindl. were subsequently introduced into cultivated potato. Thus, a precedent for the use of wild relatives in potato genetic improvement was established a century ago. Intensive efforts have been made to collect and maintain wild relatives of potato in public germplasm repositories. Genetic diversity in these species is high, and most are sexually compatible with cultivated potato. Perhaps more than any other vegetable crop, potato can benefit from the introgression of genes for valuable traits from wild relatives. However, a century of breeding has realized that potential on only a few notable occasions. Important examples include improved processing quality and resistance to viruses and nematodes. Substantial barriers prevent the facile incorporation of genetic material from wild relatives into potato cultivars. Overcoming these barriers may require an expansion of germplasm collections to include well‐characterized individuals as a complement to population‐based accessions. To be most useful for potato improvement, individual wild species plants will need to be cataloged by allelic composition, haplotype, biochemical properties, and physiological responses to stress. Effectively using the genetic diversity in wild relatives is likely to rely heavily on alternative methods of potato breeding, augmenting reassortment, and selection at the tetraploid level with diploid breeding and genetic modification using biotechnology.

An alternative bandwidth selection method for estimating functional coefficient models

Functional coefficient regression models are very useful for many statistics and economics applications and there exists a large body of literature on kernel estimation of the coefficient functions. Fan and Zhang (1999) point out that the traditional estimation method is not optimal if the coefficient functions possess different degrees of smoothness. They propose a two-step method to attenuate the drawback. To apply the two-step method, one needs to identify which coefficient function is the smoothest one and undersmooth all other functions in the first stage. In this paper we propose an alternative approach which assigns each smooth function a different bandwidth and we choose the bandwidths simultaneously. Simulation results show that our proposed method compare well with existing methods.

Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data

Mapping crop types is of great importance for assessing agricultural production, land-use patterns, and the environmental effects of agriculture. Indeed, both radiometric and spatial resolution of Landsat’s sensors images are optimized for cropland monitoring. However, accurate mapping of crop types requires frequent cloud-free images during the growing season, which are often not available, and this raises the question of whether Landsat data can be combined with data from other satellites. Here, our goal is to evaluate to what degree fusing Landsat with MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data can improve crop-type classification. Choosing either one or two images from all cloud-free Landsat observations available for the Arlington Agricultural Research Station area in Wisconsin from 2010 to 2014, we generated 87 combinations of images, and used each combination as input into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to predict Landsat-like images at the nominal dates of each 8-day MODIS NBAR product. Both the original Landsat and STARFM-predicted images were then classified with a support vector machine (SVM), and we compared the classification errors of three scenarios: 1) classifying the one or two original Landsat images of each combination only, 2) classifying the one or two original Landsat images plus all STARFM-predicted images, and 3) classifying the one or two original Landsat images together with STARFM-predicted images for key dates. Our results indicated that using two Landsat images as the input of STARFM did not significantly improve the STARFM predictions compared to using only one, and predictions using Landsat images between July and August as input were most accurate. Including all STARFM-predicted images together with the Landsat images significantly increased average classification error by 4% points (from 21% to 25%) compared to using only Landsat images. However, incorporating only STARFM-predicted images for key dates decreased average classification error by 2% points (from 21% to 19%) compared to using only Landsat images. In particular, if only a single Landsat image was available, adding STARFM predictions for key dates significantly decreased the average classification error by 4 percentage points from 30% to 26% (p<0.05). We conclude that adding STARFM-predicted images can be effective for improving crop-type classification when only limited Landsat observations are available, but carefully selecting images from a full set of STARFM predictions is crucial. We developed an approach to identify the optimal subsets of all STARFM predictions, which gives an alternative method of feature selection for future research.

Identification of Useful Nanobodies by Phage Display of Immune Single Domain Libraries Derived from Camelid Heavy Chain Antibodies.

The discovery of functional heavy chain-only antibodies devoid of light chains in sera of camelids and sharks in the early nineties provided access to the generation of minimal-sized, single-domain, in vivo affinity-matured, recombinant antigenbinding fragments, also known as Nanobodies. Recombinant DNA technology and adaptation of phage display vectors form the basis to construct large naïve, synthetic or medium sized immune libraries from where multiple Nanobodies have been retrieved. Alternative selection methods (i.e. bacterial display, bacterial two-hybrid, Cis-display and ribosome display) have also been developed to identify Nanobodies. The antigen affinity, stability, expression yields and structural details of the Nanobodies have been determined by standard technology. Nanobodies were subsequently engineered for higher stability and affinity, to have a sequence closer to that of human immunoglobulin domains, or to add designed effector functions. Antigen specific Nanobodies recognizing with high affinity their cognate antigen were retrieved from various libraries. High expression yields are obtained from microorganisms, even when expressed in the cytoplasm. The purified Nanobodies are shown to possess beneficial biochemical and biophysical properties. The crystal structure of Nanobody::antigen complexes reveal the preference of Nanobodies for cavities on the antigen surface. Thanks to the properties described above, Nanobodies became a highly valued and versatile tool for biomolecular research. Moreover, numerous diagnostic and therapeutic Nanobody-based applications have been developed in the past decade.

An ANN-Based Method to Predict Surface Roughness in Turning Operations

In recent years, there has been a growing interest for the prediction of machining characteristics (such as surface roughness and tool wear) during machining. Several machining parameters such as cutting speed and cutting depth are known to affect the surface characteristics. Various methods are used to investigate the relative contribution of these parameters on the surface characteristics. Therefore, selecting a set of parameters according to the relative contributions is important in the prediction of the surface characteristics effectively. In this paper, a new alternative parameter selection method based on artificial neural networks is suggested. Within this scope, forward and stepwise selection methods are proposed. A statistical hypothesis test is used as an elimination criterion. The suggested methods are used to predict the surface roughness in turning operations effectively. Successful results were obtained in the prediction of surface roughness by using these methods.

Selection criteria for a radiography programme in South Africa: Predictors for academic success in the first year of study

Background: Selection criteria used to admit students to a radiography programme at the Central University of Technology (CUT) included academic criteria, as well as the General Scholastic Aptitude Test (GSAT) and Self-directed Search (SDS) Questionnaire.Aims and objectives: The aim of the study was to identify which selection criteria were predictors of academic success in the first year of study. As a four year Bachelor's degree in Radiography (480 credits) was to replace the three year National Diploma (NDip) in Radiography (360 credits), selection criteria would come under review.Design and method: Data from 130 students were gathered in a retrospective quantitative study. Data were edited, categorised and summarised. A statistical analysis was undertaken to identify which selection criteria predicted academic success in the first year of study.Results: Statistics showed that the matriculation Admission Points Score (National Senior Certificate/NCS APS) and core matriculation subject results in Mathematics, Physical Sciences and English were adequate predictors for first-year academic success, and the subjects Life Sciences for the NSC and Biology for the Senior Certificate (SC), showed strong predictive values for first-year academic success. According to the statistical analysis, the GSAT and SDS Questionnaire did not contribute any significant information which could predict academic success.Conclusion: Matriculation marks and NSC APS were adequate predictors for academic success, with a focus on Life Sciences or Biology marks as the strongest predictor. The usefulness of the GSAT and SDS Questionnaire could be questioned, and a recommendation was made to replace these tests with alternative student selection methods.

Dissecting Characteristics Nonparametrically

We propose a nonparametric method to study which characteristics provide incremental information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large number of characteristics, allows for a flexible functional form, and our implementation is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns, and nonlinearities are important. We study the properties of our method in an extensive simulation study and out-of-sample prediction exercise and find large improvements both in model selection and prediction compared to alternative selection methods. Our proposed method has higher out-of-sample Sharpe ratios and explanatory power compared to linear panel regressions.

Uso de marcadores y selección de gametos para el mejoramiento simultáneo de caracteres múltiples de frijol (Phaseolus vulgaris L.) para Mesoamérica y el Caribe.

The objective is to describe the basic requirements and alternative selection methods for simultaneous improvement of maximum number of agronomic characters in the shortest time possible. This strategy includes: (1) deterrnination of the importance and priorities of bean production problems in the region, (2) definition of objectives and priorities of breeding, (3) identification and use of reliable parental sources considered to be donors of necessary genes, (4) development of multipleparent crosses with large number of pollinations to produce sufficient seed of each cross, (5) evaluation and selection for currently available markers (e.g., for zabrotes, bean common mosaic, bean golden mosaic, rust, anthracnose, apion, common bacterial blight, etc.) from hybridization, (6) alternative selection methods such as gamete selection and single seed descendants (GS-SSD), gamete and pedigree selection using markers (GS-PUM), gamete selection and agronomic evaluation and selection of families in early generations (GS-AEF), or a combination and integration of the GS-PUM and GS-AEF methods; and (7) seeking genuine and direct collaboration and integration in all activities of researchers from different disciplines, institutions, countries and the entire region at key sites foro reliable evaluation of bean nurseries in order to carry out simultaneous selection for a maximum number of agronomic traits.

Selection criteria for a radiography programme in South Africa: Predictors for academic success in the first year of study

BackgroundSelection criteria used to admit students to a radiography programme at the Central University of Technology (CUT) included academic criteria, as well as the General Scholastic Aptitude Test (GSAT) and Self-directed Search (SDS) Questionnaire. Aims and objectivesThe aim of the study was to identify which selection criteria were predictors of academic success in the first year of study. As a four year Bachelor's degree in Radiography (480 credits) was to replace the three year National Diploma (NDip) in Radiography (360 credits), selection criteria would come under review. Design and methodData from 130 students were gathered in a retrospective quantitative study. Data were edited, categorised and summarised. A statistical analysis was undertaken to identify which selection criteria predicted academic success in the first year of study. ResultsStatistics showed that the matriculation Admission Points Score (National Senior Certificate/NCS APS) and core matriculation subject results in Mathematics, Physical Sciences and English were adequate predictors for first-year academic success, and the subjects Life Sciences for the NSC and Biology for the Senior Certificate (SC), showed strong predictive values for first-year academic success. According to the statistical analysis, the GSAT and SDS Questionnaire did not contribute any significant information which could predict academic success. ConclusionMatriculation marks and NSC APS were adequate predictors for academic success, with a focus on Life Sciences or Biology marks as the strongest predictor. The usefulness of the GSAT and SDS Questionnaire could be questioned, and a recommendation was made to replace these tests with alternative student selection methods.

Crowd-Squared: Amplifying the Predictive Power of Search Trend Data

Big data generated by crowds provides a myriad of opportunities for monitoring and modeling people's intentions, preferences, and opinions. A crucial step in analyzing such big data is selecting the relevant part of the data that should be provided as input to the modeling process. In this paper, we offer a novel, structured, crowd-based method to address the data selection problem in a widely used and challenging context: selecting search trend data. We label the method crowd-squared, as it leverages crowds to identify the most relevant terms in search volume data that were generated by a larger crowd. We empirically test this method in two domains and find that our method yields predictions that are equivalent or superior to those obtained in previous studies (using alternative data selection methods) and to predictions obtained using various benchmark data selection methods. These results emphasize the importance of a structured data selection method in the prediction process, and demonstrate the utility of the crowd-squared approach for addressing this problem in the context of prediction using search trend data.

(Invited) High-Throughput DFT Thermochemistry Applied to the Design of Cathode Coatings Forlithium-Ion Batteries

We present a high-throughput (HT) density functional theory (DFT) based framework to identify new functional coatings that can suppress cathode degradation in Li-ion batteries. This HT framework is based on reaction models that describe the thermodynamic and electrochemical stabilities and acid-scavenging capabilities of oxide materials. Using the framework, we search for coatings with different functionalities including physical barriers, hydrofluoric acid (HF) barriers and HF scavengers, by screening more than 130,000 candidate oxide materials. Employing two alternative material selection methods, namely, weighted-sum and rank aggregation multi-objective optimization, we predict new promising physical and HF-barrier coating materials such as WO3, LiAl5O8, ZrP2O7, Hf2P2O9, TaPO5, CaSn4(PO4)6, and new or under-explored HF-scavengers such as Sc2O3, Li2CaGeO4, Li3NbO4, TaBO4, LiBO2, Mg3(BO3)2, Ca5(BO3)3F, Ca2Ta2O7 and Li2MgSiO4. Using principal component analysis, we show that the the likelihood of discovery of effective HF-scavenger coatings is highest in silicates and borates among all oxides. We further present a new HT design strategy where thermodynamically optimal HF-scavenger coatings for a given cathode material can be found by (i) incorporating the cathode material itself in the chemical space, (ii) evaluating the cathode-coating reactivity, and (iii) monitoring cathode reactivity in electrochemical (charge and discharge) and HF-attack reactions. Examples of such optimal coatings we found include Li2SrSiO4, Li2CaSiO4 and CaIn2O4 coatings for the layered LiCoO2 and Li2GeO3, Li2TiSiO5, Li4NiTeO6, Ca2Mn3O8, and Li2MnO3 for the spinel LiMn2O4 cathodes. These candidate coating materials have the potential to prolong the cycle-life of lithium-ion batteries and surpass the performance of common coating technologies based on conventional materials such as Al2O3, ZnO, MgO or ZrO2. This work was performed in collaboration with Muratahan Aykol, Soo Kim, Vinay I. Hegde, David Snydacker, Zhi Lu, Shiqiang Hao, Scott Kirklin, and Dane Morgan.

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. The method formulates band selection into a famous problem of archetypal analysis and selects the representative bands by finding the archetypes in the minimal convex hull containing the HSI band points (i.e., one band corresponds to a band point in the high-dimensional feature space). Without any other parameter tuning work except the size of band subset, the SSR optimizes the band selection program using the block-coordinate descent scheme. Four state-of-the-art methods are utilized to make comparisons with the SSR on the Indian Pines and PaviaU HSI datasets. Experimental results illustrate that SSR outperforms all four methods in classification accuracies (i.e., Average Classification Accuracy (ACA) and Overall Classification Accuracy (OCA)) and three quantitative evaluation results (i.e., Average Information Entropy (AIE), Average Correlation Coefficient (ACC) and Average Relative Entropy (ARE)), whereas it takes the second shortest computational time. Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications.

A framework to choose alternative analysis and selection methods

This paper presents a framework to investigate and choose alternative analysis and selection methods based upon a problem and its context. The research began as an investigation into the alternative analysis and selection methods used in national security planning. Existing methods were inconsistent and most violated the decision science body of knowledge. Additionally, there were no tools for planners to match an appropriate method to their problem and its context. These challenges led the researchers to develop a framework that extends beyond the technical dimension considered by legacy decision-making method selection tools by incorporating the human dimension of decision making. The developed framework also considers the sociotechnical interdependencies that occur between methods, decision analysts, and managers. A scalable, collaborative, online tool operationalises the framework for practitioners and decision makers.

A framework to choose alternative analysis and selection methods

A framework to choose alternative analysis and selection methods

Crowd-Squared: Amplifying the Predictive Power of Large-Scale Crowd-Based Data

Big Data generated by crowds provides a myriad of opportunities for monitoring and modeling people's intentions, preferences, and opinions. A crucial step in analyzing such “big data” is selecting the relevant part of the data that should be provided as input to the modeling process. In this paper, we offer a novel, structured, crowd-based method to address the data selection problem in a widely used and challenging context: selecting search trend data. We label the method “crowd-squared,” as it leverages crowds to identify the most relevant terms in search volume data that were generated by a larger crowd. We empirically test this method in two domains and find that our method yields predictions that are equivalent or superior to those obtained in previous studies (using alternative data selection methods) and to predictions obtained using various benchmark data selection methods. These results emphasize the importance of a structured data selection method in the prediction process, and demonstrate the utility of the crowd-squared approach for addressing this problem in the context of prediction using search trend data.