Gradient Decomposition Methods for Training Neural Networks With Non-ideal Synaptic Devices

While promising for high-capacity machine learning accelerators, memristor devices have non-idealities that prevent software-equivalent accuracies when used for online training. This work uses a combination of Mini-Batch Gradient Descent (MBGD) to average gradients, stochastic rounding to avoid vanishing weight updates, and decomposition methods to keep the memory overhead low during mini-batch training. Since the weight update has to be transferred to the memristor matrices efficiently, we also investigate the impact of reconstructing the gradient matrixes both internally (rank-seq) and externally (rank-sum) to the memristor array. Our results show that streaming batch principal component analysis (streaming batch PCA) and non-negative matrix factorization (NMF) decomposition algorithms can achieve near MBGD accuracy in a memristor-based multi-layer perceptron trained on the MNIST (Modified National Institute of Standards and Technology) database with only 3 to 10 ranks at significant memory savings. Moreover, NMF rank-seq outperforms streaming batch PCA rank-seq at low-ranks making it more suitable for hardware implementation in future memristor-based accelerators.

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The Impact of Shariah Governance and Corporate Governance on the Risk Management Practices: Evidence from Local and Foreign Islamic Banks in Malaysia

The Journal of Muamalat and Islamic Finance Research ◽

10.33102/jmifr.v15i2.174 ◽

2018 ◽

Vol 15 (2) ◽

pp. 1-20

Author(s):

Sabri Embi ◽

Zurina Shafii

Keyword(s):

Risk Management ◽

Corporate Governance ◽

Management Practices ◽

Principal Component ◽

T Test ◽

The Body ◽

Primary Data ◽

Islamic Banks ◽

Shariah Governance ◽

The Impact

The purpose of this study is to examine the impact of Shariah governance and corporate governance (CG) on the risk management practices (RMPs) of local Islamic banks and foreign Islamic banks operating in Malaysia. The Shariah governance comprises the Shariah review (SR) and Shariah audit (SA) variables. The study also evaluates the level of RMPs, CG, SR, and SA between these two type of banks. With the aid of SPSS version 20, the items for RMPs, CG, SR, and SA were subjected to principal component analysis (PCA). From the PCA, one component or factor was extracted each for the CG, SR, and RMPs while another two factors were extracted for the SA. Primary data was collected using a self-administered survey questionnaire. The questionnaire covers four aspects ; CG, SR, SA, and RMPs. The data received from the 300 usable questionnaires were subjected to correlation and regression analyses as well as an independent t-test. The result of correlation analysis shows that all the four variables have large positive correlations with each other indicating a strong and significant relationship between them. From the regression analysis undertaken, CG, SR, and SA together explained 52.3 percent of the RMPs and CG emerged as the most influential variable that impacts the RMPs. The independent t-test carried out shows that there were significant differences in the CG and SA between the local and foreign Islamic banks. However, there were no significant differences between the two types of the bank in relation to SR and RMPs. The study has contributed to the body of knowledge and is beneficial to academicians, industry players, regulators, and other stakeholders.

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Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.727.447 ◽

2017 ◽

Vol 727 ◽

pp. 447-449 ◽

Cited By ~ 1

Author(s):

Jun Dai ◽

Hua Yan ◽

Jian Jian Yang ◽

Jun Jun Guo

Keyword(s):

Principal Component Analysis ◽

Impact Strength ◽

High Density Polyethylene ◽

Tensile Modulus ◽

Principal Component ◽

Component Analysis ◽

High Density ◽

Aging Behavior ◽

Data Set ◽

The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

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Inheritable testicular metabolic memory of high-fat diet causes transgenerational sperm defects in mice

Scientific Reports ◽

10.1038/s41598-021-88981-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Luís Crisóstomo ◽

Ivana Jarak ◽

Luís P. Rato ◽

João F. Raposo ◽

Rachel L. Batterham ◽

...

Keyword(s):

Sperm Quality ◽

Feeding Habits ◽

Reproductive Function ◽

Principal Component ◽

Testicular Tissue ◽

Sperm Parameters ◽

Metabolic Memory ◽

High Fat ◽

And Function ◽

The Impact

AbstractThe consumption of energy-dense diets has contributed to an increase in the prevalence of obesity and its comorbidities worldwide. The adoption of unhealthy feeding habits often occurs at early age, prompting the early onset of metabolic disease with unknown consequences for reproductive function later in life. Recently, evidence has emerged regarding the intergenerational and transgenerational effects of high-fat diets (HFD) on sperm parameters and testicular metabolism. Hereby, we study the impact of high-fat feeding male mice (F0) on the testicular metabolome and function of their sons (F1) and grandsons (F2). Testicular content of metabolites related to insulin resistance, cell membrane remodeling, nutritional support and antioxidative stress (leucine, acetate, glycine, glutamine, inosine) were altered in sons and grandsons of mice fed with HFD, comparing to descendants of chow-fed mice. Sperm counts were lower in the grandsons of mice fed with HFD, even if transient. Sperm quality was correlated to testicular metabolite content in all generations. Principal Component Analysis of sperm parameters and testicular metabolites revealed an HFD-related phenotype, especially in the diet-challenged generation and their grandsons. Ancestral HFD, even if transient, causes transgenerational “inherited metabolic memory” in the testicular tissue, characterized by changes in testicular metabolome and function.

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The Effect of Fat Content and Fatty Acids Composition on Color and Textural Properties of Butter

Molecules ◽

10.3390/molecules26154565 ◽

2021 ◽

Vol 26 (15) ◽

pp. 4565

Author(s):

Sergiu Pădureţ

Keyword(s):

Fatty Acids ◽

Principal Component ◽

Textural Properties ◽

Fat Content ◽

Fatty Acids Composition ◽

Color Parameter ◽

Butter Fat ◽

Composition Variation ◽

Texture Parameters ◽

The Impact

The textural properties of butter are influenced by its fat content and implicitly by the fatty acids composition. The impact of butter’s chemical composition variation was studied in accordance with texture and color properties. From 37 fatty acids examined, only 18 were quantified in the analyzed butter fat samples, and approximately 69.120% were saturated, 25.482% were monounsaturated, and 5.301% were polyunsaturated. The butter samples’ viscosity ranged between 0.24 and 2.12 N, while the adhesiveness ranged between 0.286 to 18.19 N·mm. The principal component analysis (PCA) separated the butter samples based on texture parameters, fatty acids concentration, and fat content, which were in contrast with water content. Of the measured color parameters, the yellowness b* color parameter is a relevant indicator that differentiated the analyzed sample into seven statistical groups; the ANOVA statistics highlighted this difference at a level of p < 0.001.

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Exploring Residents’ Perceptions of the Socio-Cultural Benefits of Tourism Development in the Mountain Area

Societies ◽

10.3390/soc11030083 ◽

2021 ◽

Vol 11 (3) ◽

pp. 83

Author(s):

Iulia C. Muresan ◽

Rezhen Harun ◽

Felix H. Arion ◽

Ava Omar Fatah ◽

Diana E. Dumitras

Keyword(s):

Positive Attitude ◽

Statistical Tests ◽

Tourism Development ◽

Principal Component ◽

Local Communities ◽

Three Dimensions ◽

Development Economic ◽

Mountain Area ◽

Sustainable Tourism Development ◽

The Impact

Development of tourism affected the socio-cultural environment of many destinations. Previous studies have focused more on analyzing the impact of tourism on all three dimensions of sustainable development (economic, environment and socio-cultural); therefore, the present paper examines tourism development’s impact with regard to the socio-cultural benefits that enhance sustainable tourism development. A survey based on a questionnaire was employed in June 2018 in a mountain village in Cluj County, Romania. The collected data were analyzed using principal component analysis, and several statistical tests were conducted. The results indicated that the respondents have a positive attitude towards tourism development and socio-cultural perceived benefits. Older people and those running a business tend to perceive more positively the benefits of tourism development. The findings of the research could contribute to future development strategies, as it is well known that supporting local communities influences the success of tourism destination.

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Grill Workers Exposure to Polycyclic Aromatic Hydrocarbons: Levels and Excretion Profiles of the Urinary Biomarkers

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010230 ◽

2020 ◽

Vol 18 (1) ◽

pp. 230

Author(s):

Marta Oliveira ◽

Sílvia Capelas ◽

Cristina Delerue-Matos ◽

Simone Morais

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

High Performance ◽

Principal Component ◽

Fold Increase ◽

Urinary Biomarkers ◽

Analysis Model ◽

Exposed Workers ◽

Polycyclic Aromatic ◽

The Impact

Grilling activities release large amounts of hazardous pollutants, but information on restaurant grill workers’ exposure to polycyclic aromatic hydrocarbons (PAHs) is almost inexistent. This study assessed the impact of grilling emissions on total workers’ exposure to PAHs by evaluating the concentrations of six urinary biomarkers of exposure (OHPAHs): naphthalene, acenaphthene, fluorene, phenanthrene, pyrene, and benzo(a)pyrene. Individual levels and excretion profiles of urinary OHPAHs were determined during working and nonworking periods. Urinary OHPAHs were quantified by high-performance liquid-chromatography with fluorescence detection. Levels of total OHPAHs (∑OHPAHs) were significantly increased (about nine times; p ≤ 0.001) during working comparatively with nonworking days. Urinary 1-hydroxynaphthalene + 1-hydroxyacenapthene and 2-hydroxyfluorene presented the highest increments (ca. 23- and 6-fold increase, respectively), followed by 1-hydroxyphenanthrene (ca. 2.3 times) and 1-hydroxypyrene (ca. 1.8 times). Additionally, 1-hydroxypyrene levels were higher than the benchmark, 0.5 µmol/mol creatinine, in 5% of exposed workers. Moreover, 3-hydroxybenzo(a)pyrene, biomarker of exposure to carcinogenic PAHs, was detected in 13% of exposed workers. Individual excretion profiles showed a cumulative increase in ∑OHPAHs during consecutive working days. A principal component analysis model partially discriminated workers’ exposure during working and nonworking periods showing the impact of grilling activities. Urinary OHPAHs were increased in grill workers during working days.

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Farmers’ Perceptions of Commercial Insect-Based Feed for Sustainable Livestock Production in Kenya

Sustainability ◽

10.3390/su13105359 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5359

Author(s):

Afrika Onguko Okello ◽

Jonathan Makau Nzuma ◽

David Jakinda Otieno ◽

Michael Kidoido ◽

Chrysantus Mbi Tanga

Keyword(s):

Food Systems ◽

Large Scale ◽

Principal Component ◽

Farm Income ◽

Purchase Decisions ◽

Wealth Status ◽

Feed Formulation ◽

Alternative Protein ◽

Livestock Products ◽

The Impact

The utilization of insect-based feeds (IBF) as an alternative protein source is increasingly gaining momentum worldwide owing to recent concerns over the impact of food systems on the environment. However, its large-scale adoption will depend on farmers’ acceptance of its key qualities. This study evaluates farmer’s perceptions of commercial IBF products and assesses the factors that would influence its adoption. It employs principal component analysis (PCA) to develop perception indices that are subsequently used in multiple regression analysis of survey data collected from a sample of 310 farmers. Over 90% of the farmers were ready and willing to use IBF. The PCA identified feed performance, social acceptability of the use of insects in feed formulation, feed versatility and marketability of livestock products reared on IBF as the key attributes that would inform farmers’ purchase decisions. Awareness of IBF attributes, group membership, off-farm income, wealth status and education significantly influenced farmers’ perceptions of IBF. Interventions such as experimental demonstrations that increase farmers’ technical knowledge on the productivity of livestock fed on IBF are crucial to reducing farmers’ uncertainties towards acceptability of IBF. Public partnerships with resource-endowed farmers and farmer groups are recommended to improve knowledge sharing on IBF.

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A Conceptual Framework Integrating “Building Back Better” and Post-Earthquake Needs for Recovery and Reconstruction

Sustainability ◽

10.3390/su13105608 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5608

Author(s):

Manjiang Shi ◽

Qi Cao ◽

Baisong Ran ◽

Lanyan Wei

Keyword(s):

Disaster Recovery ◽

Social Process ◽

Principal Component ◽

Assessment Method ◽

International Consensus ◽

Long Term Stability ◽

The People ◽

Policy Guidance ◽

The Impact ◽

Post Disaster

Global disasters due to earthquakes have become more frequent and intense. Consequently, post-disaster recovery and reconstruction has become the new normal in the social process. Through post-disaster reconstruction, risks can be effectively reduced, resilience can be improved, and long-term stability can be achieved. However, there is a gap between the impact of post-earthquake reconstruction and the needs of the people in the disaster area. Based on the international consensus of “building back better” (BBB) and a post-disaster needs assessment method, this paper proposes a new (N-BBB) conceptual model to empirically analyze recovery after the Changning Ms 6.0 earthquake in Sichuan Province, China. The reliability of the model was verified through factor analysis. The main observations were as follows. People’s needs focus on short-term life and production recovery during post-earthquake recovery and reconstruction. Because of disparities in families, occupations, and communities, differences are observed in the reconstruction time sequence and communities. Through principal component analysis, we found that the N-BBB model constructed in this study could provide strong policy guidance in post-disaster recovery and reconstruction after the Changning Ms 6.0 earthquake, effectively coordinate the “top-down” and “bottom-up” models, and meet the diversified needs of such recovery and reconstruction.

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Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-021-02071-4 ◽

2021 ◽

Author(s):

Christian Acal ◽

Ana M. Aguilera ◽

Annalina Sarra ◽

Adelia Evangelista ◽

Tonio Di Battista ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Functional Data ◽

Central Italy ◽

Principal Component ◽

Functional Principal Component Analysis ◽

Area Of Interest ◽

Pm 10 ◽

The Impact ◽

Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

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Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

Applied Sciences ◽

10.3390/app11135895 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5895

Author(s):

Kristina Serec ◽

Sanja Dolanski Babić

Keyword(s):

Thin Films ◽

Learning Algorithm ◽

Principal Component Regression ◽

Principal Component ◽

Conformational Transitions ◽

Cancer Diagnostics ◽

Dna Conformation ◽

Support Vector ◽

Multivariate Statistical ◽

The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

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