Average Cosine Research Articles

Placement Optimization for Renewable Energy Sources: Ontology, Tools, and Wake Models

Due to intermittent nature of renewable energy sources (RESs) and their strong dependence on various environmental factors, it is imperative to carefully deploy these sources. A survey of recent works published in the area of optimal deployment of RESs is presented in this article. The existing works are categorized according to the type of energy source, objective function and model of operation. An optimal mix of hybrid-RESs along with energy storage system (ESS) is presented as solution to overcome the randomness and inconstancy of a single RES such as wind or solar power. We outline mathematical formulations for different objective functions, i.e., minimization of cost, maximization of power generation, maximization of the average cosine efficiency and minimization of the distribution losses. We present different wake models and simulation tools being used for the wind form layout optimization. These simulation tools are used to grade particular RESs according to technical and financial feasibility. This review paper depicts multifaceted coverage of the subject to provide the readers with state of the art developments in the area and can serve as a foundation for further research in the area. In addition, it can be used to find optimal mix of RESs for particular geographical area to satisfy the energy demands of that particular area. © 2013 IEEE.

A Study on the Synthetic ECG Generation for User Recognition

심전도 신호는 시간 및 환경 변화에 따라 측정되는 시계열 데이터로 매번 등록 데이터와 동일한 크기의 비교 데이터를 취득해야 하는 문제점이 발생한다. 본 논문에서는 신호 크기 부적합 문제를 해결하기 위해 가상 생체신호 생성을 위한 보조 분류기 기반 적대적 생성 신경망(Auxiliary Classifier Generative Adversarial Networks)의 네트워크 모델을 제안한다. 생성된 가상생체신호의 유사성을 확인하기 위해 코사인 각도와 교차 상관관계를 이용하였다. 실험 결과, 코사인 유사도 측정 결과로 평균 유사도는 0.991의 결과를 나타냈으며, 교차 상관관계를 이용한 유클리디언 거리 기반 유사성 측정 결과는 평균 0.25 유사도 결과를 나타냈다. 이는 등록 데이터와 실험 데이터간의 크기가 일치하지 않더라도 가상 생체신호 생성을 통해 신호 크기 부적합 문제를 해결함을 확인하였다.Because the ECG signals are time-series data acquired as time elapses, it is important to obtain comparative data the same in size as the enrolled data every time. This paper suggests a network model of GAN (Generative Adversarial Networks) based on an auxiliary classifier to generate synthetic ECG signals which may address the different data size issues. The Cosine similarity and Cross-correlation are used to examine the similarity of synthetic ECG signals. The analysis shows that the Average Cosine similarity was 0.991 and the Average Euclidean distance similarity based on cross-correlation was 0.25: such results indicate that data size difference issue can be resolved while the generated synthetic ECG signals, similar to real ECG signals, can create synthetic data even when the registered data are not the same as the comparative data in size.

An Accurate Prediction of Daylight Pipe Harvesting of Interior Space

The performance of a light guide under arbitrary meteorological conditions is difficult to predict numerically because none of the present methods can account for the random configuration of clouds. Although, partly cloudy skies typically occur very frequently in many regions over the globe, the effects of cloud sizes, distributions, or altitudes on the luminous effectiveness of a light guide remain virtually unknown. The physical installation of light guides does not allow for a controlled experiment in which a single parameter can be varied within a defined range, while holding all other parameters constant. Numerical modeling is the only practical way to provide deep insights on the role of a specific property, such as cloud coverage, and its importance relative to all others (e.g., atmospheric turbidity). We have developed a unique solution through linking the UniSky simulator and HOLIGILM (hollow light guide interior illumination method) tool, while providing for the first time a powerful method that can provide accurate predictions and understanding of the fundamental differences of light guide behaviors under homogeneous and inhomogeneous sky states. The use of homogeneous skies in low-accuracy empirical models often results in overestimated/underestimated effectiveness of some light-guide systems. The model we have developed can predict complex optical signatures that are normally impossible to reproduce using up-to date empirical models (e.g., the illuminance patterns due to isolated clouds or clouds traversing across the sky). The optical efficiency, average cosine and working-place illuminance are analyzed and compared in order to demonstrate the model capabilities.

Impulsive spike enhancement on gamelan audio using harmonic percussive separation

Impulsive spikes often occur in audio recording of gamelan where most existing methods reduce it. This research offers new method to enhance audio impulsive spike in gamelan music that is able to reduce, eliminate and even strengthen spikes. The process separates audio components into harmonics and percussive components. Percussion component is set to rise or lowered, and the results of the process combined with harmonic components again. This study proposes a new method that allows reducing, eliminating and even amplifying the spike. From the similarity test using the Cosine Distance method, it is seen that spike enhancement through Harmonic Percussive Source Separation (HPSS) has an average Cosine Distance value of 0.0004 or similar to its original, while Mean Square Error (MSE) has an average value of 0.0004 that is very small in average error and also very similar. From the Perceptual Evaluation of Audio Quality (PEAQ) testing with Harmonic Percussive Source Separation (HPSS), it has a better quality with an average Objective Difference Grade (ODG) of -0.24 or Imperceptible.

Pulmonary nodule segmentation with CT sample synthesis using adversarial networks.

Segmentation of pulmonary nodules is critical for the analysis of nodules and lung cancer diagnosis. We present a novel framework of segmentation for various types of nodules using convolutional neural networks (CNNs). The proposed framework is composed of two major parts. The first part is to increase the variety of samples and build a more balanced dataset. A conditional generative adversarial network (cGAN) is employed to produce synthetic CT images. Semantic labels are generated to impart spatial contextual knowledge to the network. Nine attribute scoring labels are combined as well to preserve nodule features. To refine the realism of synthesized samples, reconstruction error loss is introduced into cGAN. The second part is to train a nodule segmentation network on the extended dataset. We build a three-dimensional (3D) CNN model that exploits heterogeneous maps including edge maps and local binary pattern maps. The incorporation of these maps informs the model of texture patterns and boundary information of nodules, which assists high-level feature learning for segmentation. Residual unit, which learns to reduce residual error, is adopted to accelerate training and improve accuracy. Validation on LIDC-IDRI dataset demonstrates that the generated samples are realistic. The mean squared error and average cosine similarity between real and synthesized samples are and 0.9534, respectively. The Dice coefficient, positive predicted value, sensitivity, and accuracy are, respectively, 0.8483, 0.8895, 0.8511, and 0.9904 for the segmentation results. The proposed 3D CNN segmentation framework, based on the use of synthesized samples and multiple maps with residual learning, achieves more accurate nodule segmentation compared to existing state-of-the-art methods. The proposed CT image synthesis method can not only output samples close to real images but also allow for stochastic variation in image diversity.

Average cosine coefficient and spectral distribution of the light field under sea ice: Implications for primary production

The Arctic spring phytoplankton bloom has been reported to commence under a melting sea ice cover as transmission of photosynthetically active radiation (PAR; 400–700 nm) suddenly increases with the formation of surface melt ponds. Spatial variability in ice surface characteristics, i.e., snow thickness or melt pond distributions, and subsequent impact on transmitted PAR makes estimating light-limited primary production difficult during this time of year. Added to this difficulty is the interpretation of data from various sensor types, including hyperspectral, multispectral, and PAR-band irradiance sensors, with either cosine-corrected (planar) or spherical (scalar) sensor heads. To quantify the impact of the heterogeneous radiation field under sea ice, spectral irradiance profiles were collected beneath landfast sea ice during the Green Edge ice-camp campaigns in May–June 2015 and June–July 2016. Differences between PAR measurements are described using the downwelling average cosine, μd, a measure of the degree of anisotropy of the downwelling underwater radiation field which, in practice, can be used to convert between downwelling scalar, E0d, and planar, Ed, irradiance. A significantly smallerμd(PAR) was measured prior to snow melt compared to after (0.6 vs. 0.7) when melt ponds covered the ice surface. The impact of the average cosine on primary production estimates, shown in the calculation of depth-integrated daily production, was 16% larger under light-limiting conditions when E0d was used instead of Ed. Under light-saturating conditions, daily production was only 3% larger. Conversion of underwater irradiance data also plays a role in the ratio of total quanta to total energy (EQ/EW, found to be 4.25), which reflects the spectral shape of the under-ice light field. We use these observations to provide factors for converting irradiance measurements between irradiance detector types and units as a function of surface type and depth under sea ice, towards improving primary production estimates.

Feasibility of Diagnosing Both Severity and Features of Diabetic Retinopathy in Fundus Photography

Diabetic retinopathy (DR) diagnosis methods in the literature are usually criticized as being limit in diagnosing DR-related features or being lack of interpretability. To deal with these issues, this paper investigates the feasibility of diagnosing both DR severity levels and the presence of DR-related features in a two-step procedure. Specifically, this paper first analyzes the quality of annotations in DR grading by measuring inter-grader variability. Cosine similarity is considered to evaluate the inter-grader variability of the presence of DR-related features, and quadratic weighted Cohen's kappa is employed to assess the inter-grader variability of DR severity levels. Next, different annotation methods as follows are compared to DR severity prediction performance using logistic regression: 1) single annotations by single grader (SASG); 2) single annotations from multiple graders (SAMG); 3) multiple annotations by voting (MAV); and 4) double annotations with adjudication of disagreement (DAAD). Based on the comparison results, the feasibility of diagnosing both DR severity and features is investigated. In the experiments, 1589 fundus images graded by three retinal specialists and four general ophthalmologists are considered. The results demonstrate that retinal specialists are more consistent than general ophthalmologists in grading both the presence of DR-related features and DR severity. The SASG and MAV should be avoided if possible while the DAAD is the good option when prediction performance is the highest priority and the SAMG is especially beneficial when both prediction performance and grading costs are considered. The upper limit performance of DR severity prediction gets accuracy 95.6% and kappa 0.962. When DR-related feature prediction achieves average cosine similarity 0.823, it is potential to get accuracy 91.2% and kappa 0.905 for DR severity prediction in real applications. These results together suggest the potential of diagnosis of both DR severity and the presence of DR-related features in a two-step procedure.

A text-Image feature mapping algorithm based on transfer learning

Abstract The traditional uniform distribution algorithm does not filter the image data when extracting the approximate features of text-image data under the event, so the similarity between the image data and the text is low, which leads to low accuracy of the algorithm. This paper proposes a text-image feature mapping algorithm based on transfer learning. The existing data is filtered by ‘clustering technology’ to obtain similar data with the target data. The significant text features are calculated through the latent Dirichlet allocation (LDA) model and information gain based on Gibbs sampling. Bag of visual word (BOVW) model and Naive Bayesian method are used to model image data. With the help of the text-image co-occurrence data in the same event, the text feature distribution is mapped to the image feature space, and the feature distribution of image data under the same event is approximated. Experimental results show that the proposed algorithm can obtain the feature distribution of image data under different events, and the average cosine similarity is as high as 92%, the average dispersion is as low as 0.06%, and the accuracy of the algorithm is high.

Characterization of the Light Field and Apparent Optical Properties in the Ocean Euphotic Layer Based on Hyperspectral Measurements of Irradiance Quartet

Although the light fields and apparent optical properties (AOPs) within the ocean euphotic layer have been studied for many decades through extensive measurements and theoretical modeling, there is virtually a lack of simultaneous high spectral resolution measurements of plane and scalar downwelling and upwelling irradiances (the so-called irradiance quartet). We describe a unique dataset of hyperspectral irradiance quartet, which was acquired under a broad range of environmental conditions within the water column from the near-surface depths to about 80 m in the Gulf of California. This dataset enabled the characterization of a comprehensive suite of AOPs for realistic non-uniform vertical distributions of seawater inherent optical properties (IOPs) and chlorophyll-a concentration (Chl) in the common presence of inelastic radiative processes within the water column, in particular Raman scattering by water molecules and chlorophyll-a fluorescence. In the blue and green spectral regions, the vertical patterns of AOPs are driven primarily by IOPs of seawater with weak or no discernible effects of inelastic processes. In the red, the light field and AOPs are strongly affected or totally dominated by inelastic processes of Raman scattering by water molecules, and additionally by chlorophyll-a fluorescence within the fluorescence emission band. The strongest effects occur in the chlorophyll-a fluorescence band within the chlorophyll-a maximum layer, where the average cosines of the light field approach the values of uniform light field, irradiance reflectance is exceptionally high approaching 1, and the diffuse attenuation coefficients for various irradiances are exceptionally low, including the negative values for the attenuation of upwelling plane and scalar irradiances. We established the empirical relationships describing the vertical patterns of some AOPs in the red spectral region as well as the relationships between some AOPs which can be useful in common experimental situations when only the downwelling plane irradiance measurements are available. We also demonstrated the applicability of irradiance quartet data in conjunction with Gershun’s equation for estimating the absorption coefficient of seawater in the blue-green spectral region, in which the effects of inelastic processes are weak or negligible.

Repeatability of electromyography recordings and muscle synergies during gait among children with cerebral palsy

BackgroundClinical gait analysis is commonly used in the evaluation and treatment of children with cerebral palsy (CP). While the repeatability of kinematic and kinetic measures of gait has previously been evaluated, the repeatability of electromyography (EMG) recordings or measures calculated from EMG data, such as muscle synergies, remains unclear for this population. Research questionAre EMG recordings and muscle synergies from clinical gait analysis repeatable between visits for children with CP? MethodsWe recruited 20 children with bilateral CP who had been referred for clinical gait analysis. The children completed two visits less than six weeks apart with EMG data collected bilaterally from five muscles (rectus femoris, medial hamstrings, vastus lateralis, anterior tibialis, and medial gastrocnemius). Variance ratio and cosine similarity were used to evaluate repeatability of EMG waveforms between visits. Nonnegative matrix factorization was used to calculate synergies from EMG data at each visit to compare synergy weights and activations. Results & significanceThe inter-visit variance ratios of EMG data for children with CP were similar to previously reported results for typically-developing children and unimpaired adults (range: 0.39 for vastus lateralis to 0.66 for rectus femoris). The average cosine similarity of the EMG waveforms between visits was greater than 0.9 for all muscles, while synergy weights and activations also had high similarity – greater than 0.8 and 0.9 between visits, respectively. These results demonstrate that EMG repeatability between visits during clinical gait analysis for children with CP is similar to unimpaired individuals. These results provide a baseline for evaluating whether observed changes in EMG recordings between visits reflect real changes in muscle activity or are within the range of inter-visit variability.

Technique and Results of Measurements of Turbulent Helicity in a Stratified Surface Layer

In August 2014, measurements of the turbulent velocity rotor, turbulent temperature gradient, turbulent helicity, and turbulent potential vortex were performed at the Obukhov Institute of Atmospheric Physics testing ground in Tsimlyansk under different stratification conditions. The measurements were carried out using the technique first used in the Tsimlyansk expedition in 2012 [1]. The measuring facility consisted of four three-component acoustic Gill Windmaster anemometers–thermometers placed at the vertices of a rectangular tetrahedron with a base scale of 0.7 m (in contrast to the experiment in 2012, when the base scale was 5 m). The measuring facility was placed on top of a mast with an adjustable height of 3.5, 5, 13.5, and 25 m and was equipped with a rotator. The temperature profile in the 10–600 m layer was continuously recorded by the Kadygrov microwave profiler [2]. The series of density of instantaneous helicity He = ui'ω'i = u1'ω1' + u2'ω'2 + u3'ω'3 and average values of the total and its summands were calculated for 12 daytime and 10 daytime 2-hour intervals. The helicity value averaged over 12 day realizations is about 0.2 m/s2, and the average cosine is close to 0.08 ± 0.03. At night, the helicity is estimated as 0.07 ± 0.03 m/s2, and the cosine is close to 0.025 ± 0.03. For the abovementioned 12 daytime and 10 daytime 2-hour intervals, the covariance and correlation matrices of temperature components, velocity rotor, velocity, and temperature gradient are calculated. The off-diagonal terms of the covariance matrix exceed by absolute values the diagonal terms several times. Similar characteristics of a potential vortex were estimated in the incompressibility approximation. The systematic error due to spatial averaging of the measured quantities is discussed.

Predicting Mission Alignment and Preventing Mission Drift: Do Revenue Sources Matter?

Activities of nonprofit organizations do not always align with their missions, a managerial problem termed as “mission drift.” Mission drift is difficult to operationalize and quantify; thus, as a critical issue, only a few conceptual pieces or empirical case studies have explored this topic. This paper develops innovative measures to operationalize “mission alignment” using data science methodology, and examines the impact of revenue sources on mission alignment. By using the cosine similarity of text between a mission statement and program description, four measures of mission alignment are devised: the sum cosine similarity, average cosine similarity, weighted sum cosine similarity, and weighted average cosine similarity. Text analysis indicates that a majority of the programs evidence educational purposes, and for-profit business plays an important role in foundations’ projects and funding. The regression analysis shows that personal donation and service revenue can increase mission alignment, while organizational donation and membership dues decrease mission alignment.

Satellite Radiation Products for Ocean Biology and Biogeochemistry: Needs, State-of-the-Art, Gaps, Development Priorities, and Opportunities

Knowing the spatial and temporal distribution of the underwater light field, i.e., the spectral and angular structure of the radiant intensity at any point in the water column, is essential to understanding the biogeochemical processes that control the composition and evolution of aquatic ecosystems and their impact on climate and reaction to climate change. At present, only a few properties are reliably retrieved from space, either directly or via water-leaving radiance. Existing satellite products are limited to planar photosynthetically available radiation (PAR) and ultraviolet (UV) irradiance above the surface and diffuse attenuation coefficient. Examples of operational products are provided, and their advantages and drawbacks are examined. The usefulness and convenience of these products notwithstanding, there is a need, as expressed by the user community, for other products, i.e., sub-surface planar and scalar fluxes, average cosine, spectral fluxes (UV to visible), diurnal fluxes, absorbed fraction of PAR by live algae (APAR), surface albedo, vertical attenuation, and heating rate, and for associating uncertainties to any product on a pixel-by-pixel basis. Methodologies to obtain the new products are qualitatively discussed in view of most recent scientific knowledge and current and future satellite missions, and specific algorithms are presented for some new products, namely sub-surface fluxes and average cosine. A strategy and roadmap (short, medium, and long term) for usage and development priorities is provided, taking into account needs and readiness level. Combining observations from satellites overpassing at different times and geostationary satellites should be pursued to improve the quality of daily-integrated radiation fields, and products should be generated without gaps to provide boundary conditions for general circulation and biogeochemical models. Examples of new products, i.e., daily scalar PAR below the surface, daily average cosine for PAR, and sub-surface spectral scalar fluxes are presented. A procedure to estimate algorithm uncertainties in the total uncertainty budget for above-surface daily PAR, based on radiative simulations for expected situations, is described. In the future, space-borne lidars with ocean profiling capability offer the best hope for improving our knowledge of sub-surface fields.

A Novel Bio-Inspired Algorithm Based on Social Spiders for Improving Performance and Efficiency of Data Clustering

Abstract Since the last decade, the collective intelligent behavior of groups of animals, birds or insects have attracted the attention of researchers. Swarm intelligence is the branch of artificial intelligence that deals with the implementation of intelligent systems by taking inspiration from the collective behavior of social insects and other societies of animals. Many meta-heuristic algorithms based on aggregative conduct of swarms through complex interactions with no supervision have been used to solve complex optimization problems. Data clustering organizes data into groups called clusters, such that each cluster has similar data. It also produces clusters that could be disjoint. Accuracy and efficiency are the important measures in data clustering. Several recent studies describe bio-inspired systems as information processing systems capable of some cognitive ability. However, existing popular bio-inspired algorithms for data clustering ignored good balance between exploration and exploitation for producing better clustering results. In this article, we propose a bio-inspired algorithm, namely social spider optimization (SSO), for clustering that maintains a good balance between exploration and exploitation using female and male spiders, respectively. We compare results of the proposed algorithm SSO with K means and other nature-inspired algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO) and improved bee colony optimization (IBCO). We find it to be more robust as it produces better clustering results. Although SSO solves the problem of getting stuck in the local optimum, it needs to be modified for locating the best solution in the proximity of the generated global solution. Hence, we hybridize SSO with K means, which produces good results in local searches. We compare proposed hybrid algorithms SSO+K means (SSOKC), integrated SSOKC (ISSOKC), and interleaved SSOKC (ILSSOKC) with K means+PSO (KPSO), K means+genetic algorithm (KGA), K means+artificial bee colony (KABC) and interleaved K means+IBCO (IKIBCO) and find better clustering results. We use sum of intra-cluster distances (SICD), average cosine similarity, accuracy and inter-cluster distance to measure and validate the performance and efficiency of the proposed clustering techniques.

A single-source photon source model of a linear accelerator for Monte Carlo dose calculation.

PurposeTo introduce a new method of deriving a virtual source model (VSM) of a linear accelerator photon beam from a phase space file (PSF) for Monte Carlo (MC) dose calculation.Materials and methodsA PSF of a 6 MV photon beam was generated by simulating the interactions of primary electrons with the relevant geometries of a Synergy linear accelerator (Elekta AB, Stockholm, Sweden) and recording the particles that reach a plane 16 cm downstream the electron source. Probability distribution functions (PDFs) for particle positions and energies were derived from the analysis of the PSF. These PDFs were implemented in the VSM using inverse transform sampling. To model particle directions, the phase space plane was divided into a regular square grid. Each element of the grid corresponds to an area of 1 mm2 in the phase space plane. The average direction cosines, Pearson correlation coefficient (PCC) between photon energies and their direction cosines, as well as the PCC between the direction cosines were calculated for each grid element. Weighted polynomial surfaces were then fitted to these 2D data. The weights are used to correct for heteroscedasticity across the phase space bins. The directions of the particles created by the VSM were calculated from these fitted functions. The VSM was validated against the PSF by comparing the doses calculated by the two methods for different square field sizes. The comparisons were performed with profile and gamma analyses.ResultsThe doses calculated with the PSF and VSM agree to within 3% /1 mm (>95% pixel pass rate) for the evaluated fields.ConclusionA new method of deriving a virtual photon source model of a linear accelerator from a PSF file for MC dose calculation was developed. Validation results show that the doses calculated with the VSM and the PSF agree to within 3% /1 mm.

Underwater optical environment in the coastal waters of British Columbia, Canada

We describe the underwater light field of the Strait of Georgia in spring and summer, using apparent optical properties (reflectance, attenuation coefficient of downwelling irradiance, the average cosine of downwelling irradiance, and the attenuation of scalar irradiance). Both the attenuation and reflectance of photosynthetically available radiation (PAR; 400–700 nm) are highest in the turbid waters of the Fraser River plume, due to scattering by mainly inorganic particles and absorption by coloured dissolved organic matter, phytoplankton, and other organic particles. Light is most diffuse in the surface waters of the plume and least diffuse at depth and away from the plume. Throughout the Strait, blue and red wavelengths are attenuated most rapidly resulting in a green peak of reflectance, the portion of the electromagnetic spectrum that penetrates the most deeply. PAR is attenuated to 1% of its surface intensity within 6–22 m in the spring and 4–23 m in the summer. For red and blue light, the depth of 1% penetration is never deeper than 9 m. All of the visible radiation, with the exception of some green light, is absorbed within the outflowing layer (15–30 m) that is exported from the Strait with the estuarine circulation. The rapid extinction of light helps to explain the very shallow distribution of phytoplankton.

Scattering and absorption effects on asymptotic light fields in seawater.

Asymptotic theory is based on the principle that the shape of the light field with depth gradually transforms from being dependent on the incident surface light field to being constant azimuthally and dependent only on the absorption and scattering properties of the water column. Properties such as the average cosine of the oceanic light field in the asymptotic regime, μ¯∞, are thus strictly inherent optical properties (IOPs). Because of the close link between asymptotic light fields and IOPs, radiative transfer approximations (RTAs) for the asymptotic regime have been adapted for use in algorithms describing surface remote sensing reflectance RRS ( = Lu/Ed) in terms of the IOPs. For such algorithms to have utility, the asymptotic average cosine needs to be parameterized in terms of IOPs useful for ocean color remote sensing. With this motivation, μ¯∞is approximated as a function of the ratio of total backscattering to total absorption, bb/a. An additional variable in assessments is the fractional water content of pure seawater in total backscattering, ηbb. A full range of representative phase functions for natural particle fields is included in the analysis using the Fournier-Forand analytical approximation. Analytical expressions for multi-order polynomial fits are provided for μ¯∞ as a function of bb/a for each ηbb assessed, for ηbb ≤ 0.49, and for the entire data set. The full range of phase function shapes were included in each fit. Percent absolute errors were a modest 3.4% for the fit for the entire data set. Additionally, a key assumption by Zaneveld that the attenuation coefficient for upwelling nadir radiance KLu in surface waters should be approximately equivalent to the attenuation coefficient in the asymptotic regime K∞ was evaluated. Results provide justification and relationships for the targeted application of asymptotic parameters in ocean color RTAs for the surface ocean.

An ensemble predictive modeling framework for breast cancer classification.

Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size. In this study, a variant of the recently proposed ensemble classification approach is used for predicting good and poor-prognosis breast cancer samples from their molecular expression profiles. In contrast to traditional single and ensemble classifiers, the proposed approach uses multiple base classifiers with varying feature sets obtained from two-dimensional projection of the samples in conjunction with a majority voting strategy for predicting the class labels. In contrast to our earlier implementation, base classifiers in the ensembles are chosen based on maximal sensitivity and minimal redundancy by choosing only those with low average cosine distance. The resulting ensemble sets are subsequently modeled as undirected graphs. Performance of four different classification algorithms is shown to be better within the proposed ensemble framework in contrast to using them as traditional single classifier systems. Significance of a subset of genes with high-degree centrality in the network abstractions across the poor-prognosis samples is also discussed.

The Method of Random Skewers

Ecological and evolutionary studies are often concerned with the properties of covariance matrices. The method of random skewers (RS method) has been used compare a matrix to an a priori vector or to compare two matrices. The method involves multiplying a matrix by many random vectors drawn from a uniform distribution over all possible vector directions. The comparisons are usually made using the average angle (or cosine) of the response vectors to an a priori vector or to the response vectors corresponding from another matrix. Angles are usually constrained to the interval 0°–90° because the distribution of response vectors is bipolar bimodal. The size of the average angle or cosine depends strongly on the relative sizes of the eigenvalues (especially the first). The distribution of angles between pairs of response vectors from two covariance matrices is more complicated because it depends on the differences in orientation of the eigenvectors and the relative sizes of the eigenvalues of the both matrices. The average absolute value of the angles between these pairs of response vectors depends on the relative sizes of the eigenvalues of the matrices making it difficult to interpret its meaning without knowledge of the eigenvalues and eigenvectors of the two matrices. Thus, it is simpler to just directly compare matrices in terms of these quantities.

Predicting Mission Alignment and Preventing Mission Drift: How Revenue Sources Matter?

Activities of nonprofit organizations do not always align with their missions, a managerial problem termed as “mission drift.” Mission drift is difficult to operationalize and quantify thus, as a critical issue, only a few conceptual pieces or empirical case studies explored this topic. This paper develops innovative measures to operationalize “mission alignment” using data science methodology and examines how different revenue sources influence the mission alignment of Chinese foundations. Specifically, four measures of mission alignment are devised based on the cosine similarity of text between mission statement and program description (i.e., the sum cosine similarity, average cosine similarity, weighted sum cosine similarity, and weighted average cosine similarity). The text analysis of programs indicates that the majority of the foundations’ projects have educational purposes and for-profit businesses play a significant role in foundations’ projects and funding. The regression analysis shows that personal donation and service revenue can increase mission alignment while organizational donation and membership dues decrease mission alignment. The results suggest validity of the mission alignment measures.