Improving the Boosted Correlogram

Introduced seven years ago, the correlogram is a simple statistical image descriptor that nevertheless performs strongly on image retrieval tasks. As a result it has found wide use as a component inside larger systems for content-based image and video retrieval. Yet few studies have examined potential variants of the correlogram or compared their performance to the original. This paper presents systematic experiments on the correlogram and several variants under different conditions, showing that the results may vary significantly depending on both the variant chosen and its mode of application. As expected, the experimental setup combining correlogram variants with boosting shows the best results of those tested. Under these prime conditions, a novel variant of the correlogram shows a higher average precision for many image categories than the form commonly used

PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting

Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding

Towards Automated Performance Bug Identification in Python

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs

Cougar genetic variation and gene flow in a heterogeneous landscape

Management of game species requires an understanding not just of population abundance, but also the structure of and connections between populations. Like other large-bodied carnivores, the cougar (Puma concolor) exhibits density -dependent dispersal and is capable of long-distance movement; in the absence of barriers to movement, these traits should lead to high connectivity between individuals and a lack of genetic differentiation across areas of continuous habitat. Previous research has suggested that cougar movement may be influenced by landscape variables such as forest cover, elevation, human population density, and highways. I assessed the population structure of cougars (Puma concolor) in Washington and southern British Columbia by examining patterns of genetic variation in 17 microsatellite loci, and the contribution of landscape variables to this genetic variation. I evaluated population structure using genetic clustering algorithms and spatial principal components analysis. I quantified the effect of distance on genetic variation by calculating the correlation between the genetic distance and geographic distance between every pair of individuals, as well as the spatial autocorrelation of genetic distances. To compare the observed pattern of genetic differentiation with that which would arise solely from isolation by distance, I simulated allele frequencies across the study area where the cost to movement between individuals was proportional to the distance between them. I also evaluated the support for evidence of male-biased dispersal in allele frequencies. Bayesian clustering analyses identified four populations in the study area, corresponding to the Olympic Peninsula, Cascade Mountains, northeastern Washington and Blue Mountains; these clusters were supported by patterns of genetic differentiation revealed with spatial PCA. Although I found a significant relationship between the geographic and genetic distance between individuals, simulated allele frequencies displayed no meaningful spatial pattern of differentiation, suggesting that male dispersal would be adequate within the scale of the study area to prevent genetic isolation from occurring if the only factor to affect dispersal was geographic distance. While cougars are capable of long-distance dispersal movements, dispersal in heterogeneous landscapes may be mediated by the resistance of the landscape to movement. I derived resistance surfaces for forest canopy cover, elevation, human population density and highways based on GIS data and estimated the landscape resistance between pairs of individuals using circuit theory. I quantified the effect of the resistance to movement due to each landscape factor on genetic distance using multiple regression on distance matrices and boosted regression tree analysis. Both models indicated that only forest canopy cover and the geographic distance between individuals had an effect on genetic distance, with forest cover exhibiting the greatest relative influence. The boundaries between the genetic clusters I found largely corresponded with breaks in forest cover, showing agreement between population structure and landscape variable selection. The greater relative influence of forest cover may also explain why a significant relationship was found between geographic and genetic distance, yet geographic distance alone could not explain the observed pattern of allele frequencies. While cougars inhabit unforested areas in other parts of their range, forested corridors appear to be important for maintaining population connectivity in the northwest

Impacts of trade openness on Myanmar's economic growth (1962~2019)

Thesis(Master) -- KDI School: Master of Public Policy, 2021This paper examines Myanmar''s economic growth rate by inflow of Trade Openness and the Vector Error Correction Model was applied. Purpose of this study is to see trade openness has long-term positive or negative effects on GDP growth rates. According to this study, in the long run, GDP growth rate is positively associated with Trade Openness. However, the relationship is not statistically significant. GDP growth rate and inflation are also negative long-term relationship. The results proved to be negative for inflation, and people were saving money in the banks because Myanmar''s interest rates were so high compared to other countries. Therefore, this study suggest that Myanmar’s government should change monetary policy like decreasing interest rate. Furthermore, government should adopt suitable tactical trade policies and implement important changes to ensure Myanmar''s long-term economic prosperity. In addition, the findings of this research can be utilized to inform future research in order to develop sound trade liberalization policies that will help Myanmar prosper economically.1. INTRODUCTION 2. LITERATURE REVIEW 3. DATA AND METHODOLOGY 4. DESCRIPTIVE ANALYSIS AND RESULTS & DISCUSSIONS 5. POLICY RECOMMENDATIONS AND CONCLUSIONmasterpublishedKhin Thid

Effects of Hearing Aid Amplification on Robust Neural Coding of Speech

Hearing aids are able to restore some hearing abilities for people with auditory impairments, but background noise remains a significant problem. Unfortunately, we know very little about how speech is encoded in the auditory system, particularly in impaired systems with prosthetic amplifiers. There is growing evidence that relative timing in the neural signals (known as spatiotemporal coding) is important for speech perception, but there is little research that relates spatiotemporal coding and hearing aid amplification. This research uses a combination of computational modeling and physiological experiments to characterize how hearing aids affect vowel coding in noise at the level of the auditory nerve. The results indicate that sensorineural hearing impairment degrades the temporal cues transmitted from the ear to the brain. Two hearing aid strategies (linear gain and wide dynamic-range compression) were used to amplify the acoustic signal. Although appropriate gain was shown to improve temporal coding for individual auditory nerve fibers, neither strategy improved spatiotemporal cues. Previous work has attempted to correct the relative timing by adding frequency-dependent delays to the acoustic signal (e.g., within a hearing aid). We show that, although this strategy can affect the timing of auditory nerve responses, it is unlikely to improve the relative timing as intended. We have shown that existing hearing aid technologies do not improve some of the neural cues that we think are important for perception, but it is important to understand these limitations. Our hope is that this knowledge can be used to develop new technologies to improve auditory perception in difficult acoustic environments