scholarly journals Global-Local-Comparison Approach: Understanding Marine Mammal Spatial Behavior by Applying Spatial Statistics and Hypothesis Testing to Passive Acoustic Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Hilary Kates Varghese ◽  
Kim Lowell ◽  
Jennifer Miksis-Olds
Keyword(s):  
Hypothesis Testing ◽  
Marine Mammal ◽  
Large Scale ◽  
Hypothesis Test ◽  
Spatial Behavior ◽  
Moran’S I ◽  
Statistical Hypothesis ◽  
Foraging Activity ◽  
Moran's I ◽  
Local Comparison

Technological innovation in underwater acoustics has progressed research in marine mammal behavior by providing the ability to collect data on various marine mammal biological and behavioral attributes across time and space. But with this comes the need for an approach to distill the large amounts of data collected. Though disparate general statistical and modeling approaches exist, here, a holistic quantitative approach specifically motivated by the need to analyze different aspects of marine mammal behavior within a Before-After Control-Impact framework using spatial observations is introduced: the Global-Local-Comparison (GLC) approach. This approach capitalizes on the use of data sets from large-scale, hydrophone arrays and combines established spatial autocorrelation statistics of (Global) Moran’s I and (Local) Getis-Ord Gi∗ (Gi∗) with (Comparison) statistical hypothesis testing to provide a detailed understanding of array-wide, local, and order-of-magnitude changes in spatial observations. This approach was demonstrated using beaked whale foraging behavior (using foraging-specific clicks as a proxy) during acoustic exposure events as an exemplar. The demonstration revealed that the Moran’s I analysis was effective at showing whether an array-wide change in behavior had occurred, i.e., clustered to random distribution, or vice-versa. The Gi∗ analysis identified where hot or cold spots of foraging activity occurred and how those spots varied spatially from one analysis period to the next. Since neither spatial statistic could be used to directly compare the magnitude of change between analysis periods, a statistical hypothesis test, using the Kruskal-Wallis test, was used to directly compare the number of foraging events among analysis periods. When all three components of the GLC approach were used together, a comprehensive assessment of group level spatial foraging activity was obtained. This spatial approach is demonstrated on marine mammal behavior, but it can be applied to a broad range of spatial observations over a wide variety of species.

Wayfinding during a wildfire evacuation

2019 ◽  
Vol 29 (3) ◽  
pp. 249-265
Author(s):  
Micah L. Brachman ◽  
Richard Church ◽  
Benjamin Adams ◽  
Danielle Bassett
Keyword(s):  
Network Analysis ◽  
Hypothesis Testing ◽  
Emergency Evacuation ◽  
Spatial Behavior ◽  
Statistical Hypothesis ◽  
Data Set ◽  
Content Type ◽  
Evacuation Plans ◽  
Shortest Distance ◽  
Wildfire Evacuation

Purpose Emergency evacuation plans are often developed under the assumption that evacuees will use wayfinding strategies such as taking the shortest distance route to their nearest exit. The purpose of this paper is to analyze empirical data from a wildfire evacuation analyzed to determine whether evacuees took a shortest distance route to their nearest exit and to identify any alternate wayfinding strategies that they may have used. Design/methodology/approach The wildfire evacuation analysis presented in this paper is the outcome of a natural experiment. A post-fire online survey was conducted, which included an interactive map interface that allowed evacuees to identify the route that they took. The survey results were integrated with several additional data sets using a GIS. Network analysis was used to compare the routes selected by evacuees to their shortest distance routes, and statistical hypothesis testing was employed to identify the wayfinding strategies that may have been used. Findings The network analysis revealed that 31 percent of evacuees took a shortest distance route to their nearest exit. Hypothesis testing showed that evacuees selected routes that had significantly longer distances and travel times than the shortest distance routes, and indicated that factors such as the downhill slope percentage of routes and the elevation of exits may have impacted the wayfinding process. Research limitations/implications This research is best regarded as a spatiotemporal snapshot of wayfinding behavior during a single wildfire evacuation, but could inspire additional research to establish more generalizable results. Practical implications This research may help emergency managers develop more effective wildfire evacuation plans. Originality/value This research presents an analysis of an original data set that contributes to the broader body of scientific knowledge on wayfinding and spatial behavior during emergency evacuations.

scholarly journals Noise-Aware and Light-Weight VLSI Design of Bilateral Filter for Robust and Fast Image Denoising in Mobile Systems

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4722
Author(s):  
Sung-Joon Jang ◽  
Youngbae Hwang
Keyword(s):  
Large Scale ◽  
Hypothesis Test ◽  
Vlsi Design ◽  
Bilateral Filter ◽  
Test Method ◽  
Mobile Systems ◽  
Noise Model ◽  
Statistical Hypothesis ◽  
Intensity Difference ◽  
Pixel Intensity

The range kernel of bilateral filter degrades image quality unintentionally in real environments because the pixel intensity varies randomly due to the noise that is generated in image sensors. Furthermore, the range kernel increases the complexity due to the comparisons with neighboring pixels and the multiplications with the corresponding weights. In this paper, we propose a noise-aware range kernel, which estimates noise using an intensity difference-based image noise model and dynamically adjusts weights according to the estimated noise, in order to alleviate the quality degradation of bilateral filters by noise. In addition, to significantly reduce the complexity, an approximation scheme is introduced, which converts the proposed noise-aware range kernel into a binary kernel while using the statistical hypothesis test method. Finally, blue a fully parallelized and pipelined very-large-scale integration (VLSI) architecture of a noise-aware bilateral filter (NABF) that is based on the proposed binary range kernel is presented, which was successfully implemented in field-programmable gate array (FPGA). The experimental results show that the proposed NABF is more robust to noise than the conventional bilateral filter under various noise conditions. Furthermore, the proposed VLSI design of the NABF achieves 10.5 and 95.7 times higher throughput and uses 63.6–97.5% less internal memory than state-of-the-art bilateral filter designs.

scholarly journals EmbNum+: Effective, Efficient, and Robust Semantic Labeling for Numerical Values

2019 ◽  
Vol 37 (4) ◽  
pp. 393-427 ◽  
Author(s):  
Phuc Nguyen ◽  
Khai Nguyen ◽  
Ryutaro Ichise ◽  
Hideaki Takeda
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Hypothesis Test ◽  
Knowledge Bases ◽  
Statistical Hypothesis ◽  
Compact Embedding ◽  
Similarity Metric ◽  
Feature Vectors ◽  
Statistical Hypothesis Test ◽  
Semantic Labeling

Abstract In recent years, there has been an increasing interest in numerical semantic labeling, in which the meaning of an unknown numerical column is assigned by the label of the most relevant columns in predefined knowledge bases. Previous methods used the p value of a statistical hypothesis test to estimate the relevance and thus strongly depend on the distribution and data domain. In other words, they are unstable for general cases, when such knowledge is undefined. Our goal is solving semantic labeling without using such information while guaranteeing high accuracy. We propose EmbNum+, a neural numerical embedding for learning both discriminant representations and a similarity metric from numerical columns. EmbNum+ maps lists of numerical values of columns into feature vectors in an embedding space, and a similarity metric can be calculated directly on these feature vectors. Evaluations on many datasets of various domains confirmed that EmbNum+ consistently outperformed other state-of-the-art approaches in terms of accuracy. The compact embedding representations also made EmbNum+ significantly faster than others and enable large-scale semantic labeling. Furthermore, attribute augmentation can be used to enhance the robustness and unlock the portability of EmbNum+, making it possible to be trained on one domain but applicable to many different domains.

scholarly journals Identifying Common Patterns and Unusual Dependencies in Faults, Failures and Fixes for Large-scale Safety-critical Software

2010 ◽  
Author(s):  
◽  
Margaret Hamill ◽  
Keyword(s):  
Data Mining ◽  
Quality Assurance ◽  
Life Cycle ◽  
Hypothesis Testing ◽  
Software Quality ◽  
Large Scale ◽  
Later Life ◽  
Statistical Hypothesis ◽  
Software Quality Assurance ◽  
Safety Critical

As software evolves, becoming a more integral part of complex systems, modern society becomes more reliant on the proper functioning of such systems. However, the field of software quality assurance lacks detailed empirical studies from which best practices can be determined. The fundamental factors that contribute to software quality are faults, failures and fixes, and although some studies have considered specific aspects of each, comprehensive studies have been quite rare. Thus, the fact that we establish the cause-effect relationship between the fault(s) that caused individual failures, as well as the link to the fixes made to prevent the failures from (re)occurring appears to be a unique characteristic of our work. In particular, we analyze fault types, verification activities, severity levels, investigation effort, artifacts fixed, components fixed, and the effort required to implement fixes for a large industrial case study. The analysis includes descriptive statistics, statistical inference through formal hypothesis testing, and data mining. Some of the most interesting empirical results include (1) Contrary to popular belief, later life-cycle faults dominate as causes of failures. Furthermore, over 50% of high priority failures (e.g., post-release failures and safety-critical failures) were caused by coding faults. (2) 15% of failures led to fixes spread across multiple components and the spread was largely affected by the software architecture. (3) The amount of effort spent fixing faults associated with each failure was not uniformly distributed across failures; fixes with a greater spread across components and artifacts, required more effort. Overall, the work indicates that fault prevention and elimination efforts focused on later life cycle faults is essential as coding faults were the dominating cause of safety-critical failures and post-release failures. Further, statistical correlation and/or traditional data mining techniques show potential for assessment and prediction of the locations of fixes and the associated effort. By providing quantitative results and including statistical hypothesis testing, which is not yet a standard practice in software engineering, our work enriches the empirical knowledge needed to improve the state-of-the-art and practice in software quality assurance.

scholarly journals Large-Scale Simultaneous Inference with Hypothesis Testing: Multiple Testing Procedures in Practice

2019 ◽  
Vol 1 (2) ◽  
pp. 653-683 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Real World ◽  
Multiple Testing ◽  
Large Scale ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Background Information ◽  
Testing Procedures ◽  
Multiple Testing Procedures ◽  
Wide Range

A statistical hypothesis test is one of the most eminent methods in statistics. Its pivotal role comes from the wide range of practical problems it can be applied to and the sparsity of data requirements. Being an unsupervised method makes it very flexible in adapting to real-world situations. The availability of high-dimensional data makes it necessary to apply such statistical hypothesis tests simultaneously to the test statistics of the underlying covariates. However, if applied without correction this leads to an inevitable increase in Type 1 errors. To counteract this effect, multiple testing procedures have been introduced to control various types of errors, most notably the Type 1 error. In this paper, we review modern multiple testing procedures for controlling either the family-wise error (FWER) or the false-discovery rate (FDR). We emphasize their principal approach allowing categorization of them as (1) single-step vs. stepwise approaches, (2) adaptive vs. non-adaptive approaches, and (3) marginal vs. joint multiple testing procedures. We place a particular focus on procedures that can deal with data with a (strong) correlation structure because real-world data are rarely uncorrelated. Furthermore, we also provide background information making the often technically intricate methods accessible for interdisciplinary data scientists.

scholarly journals ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Interactions

2020 ◽  
Author(s):  
Herty Liany ◽  
Anand Jeyasekharan ◽  
Vaibhav Rajan
Keyword(s):  
Hypothesis Testing ◽  
Large Scale ◽  
Multiple Hypothesis Testing ◽  
Statistical Hypothesis ◽  
Breast Cancers ◽  
Statistical Hypothesis Testing ◽  
Synthetic Lethal ◽  
Synthetic Lethal Interactions ◽  
A New Technique ◽  
Disease Free

AbstractA Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically actionable SL interactions remains challenging. Leveraging large-scale unified gene expression data of both disease-free and cancerous data, we design a new technique, based on statistical hypothesis testing, called ASTER (Analysis of Synthetic lethality by comparison with Tissue-specific disease-free gEnomic and tRanscriptomic data) to identify SL pairs. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our extensive experiments demonstrate the efficacy of ASTER in accurately identifying SL pairs that are therapeutically actionable in stomach and breast cancers.

scholarly journals Spatial Unevenness of Formation, Remediation and Persistence of Post-Agricultural Brownfields

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Tomáš Krejčí ◽  
Josef Navrátil ◽  
Stanislav Martinát ◽  
Ryan J. Frazier ◽  
Petr Klusáček ◽  
...  
Keyword(s):  
Large Scale ◽  
Agricultural Land ◽  
Moran’S I ◽  
Urban Centers ◽  
Moran's I ◽  
The Czech Republic ◽  
South Moravia ◽  
Global Moran’S I

The fall of the Iron Curtain created a vacuum upon which large-scale collectivized agriculture was largely abandoned. Post-agricultural brownfields emerge in multiple manners across national, regional and local levels. While these sites remain rarely explored, we aimed to better understand the spatial consequences of the formation, persistence and reuse of these sites. The regions of South Bohemia and South Moravia in the Czech Republic are used to show the location of post-agricultural brownfields identified in 2004 through 2018. Using Global Moran’s I test we have found that post-agricultural brownfields existing in 2004, long-term brownfields in 2018 and brownfields established between 2004 and 2018 are spatially clustered, but remediated brownfields between 2004 and 2018 are not. Next, the Anselin’s Local Moran’s I test identified where the spatial clusters exist. The clusters identified were examined for differences in their social, economic and environmental development by the means of logistic regression. The results show that the brownfields initially identified in 2004 are concentrated in regions with lower quality agricultural land while simultaneously located in the hinterlands of regional urban centers. In contrast, peripheral regions most often contained long-term brownfields. Brownfield sites identified after 2004 occurred in regions with higher agricultural quality of land and where corn usually grows.

scholarly journals SPATIAL ASSOCIATION TO CHARACTERIZE THE CLIMATE TELECONNECTION PATTERNS IN ECUADOR BASED ON SATELLITE PRECIPITATION ESTIMATES

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 101-106
Author(s):  
D. Ballari ◽  
L. Campozano ◽  
E. Samaniego ◽  
D. Orellana
Keyword(s):  
Spatial Patterns ◽  
Spatial Data ◽  
Large Scale ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Climate Indices ◽  
Moran's I ◽  
Local Climate ◽  
Teleconnection Patterns

Abstract. Climate teleconnections show remote and large-scale relationships between distant points on Earth. Their relations to precipitation are important to monitor and anticipate the anomalies that they can produce in the local climate, such as flood and drought events impacting agriculture, health, and hydropower generation. Climate teleconnections in relation to precipitation have been widely studied. Nevertheless, the spatial association of the teleconnection patterns (i.e. the spatial delineation of regions with teleconnections) has been unattended. Such spatial association allows to characterize how stable (heterogeneity/dependent and statistically significant) is the underlying spatial phenomena for a given pattern. Thus our objective was to characterize the spatial association of climate teleconnection patterns related to precipitation using an exploratory spatial data analysis approach. Global and local indicators of spatial association (Moran’s I and LISA) were used to detect spatial patterns of teleconnections based on TRMM satellite images and climate indices. Moran’s I depicted high positive spatial association for different climate indices, and LISA depicted two types of teleconnections patterns. The homogenous patterns were localized in the Coast and Amazonian regions, meanwhile the disperse patterns had a major presence in the Highlands. The results also showed some areas that, although with moderate to high teleconnection influences, had a random spatial patterns (i.e. non-significant spatial association). Other areas showed both teleconnections and significant spatial association, but with dispersed patterns. This pointed out the need to explore the local underlying features (topography, orientation, wind and micro-climates) that restrict (non-significant spatial association) or reaffirm (disperse patterns) the teleconnection patterns.

Parameter Estimation for Industrial Servomotor by Using Statistical Hypothesis Test

2019 ◽  
Vol 19 (2) ◽  
pp. 134-140
Author(s):  
Baek-Ju Sung ◽  
Sung-kyu Lee ◽  
Mu-Seong Chang ◽  
Do-Sik Kim
Keyword(s):  
Parameter Estimation ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test
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