Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study

Some real-world domains, such as Agriculture and Healthcare, comprise early-stage disease indications whose recording constitutes a rare event, and yet, whose precise detection at that stage is critical. In this type of highly imbalanced classification problems, which encompass complex features, deep learning (DL) is much needed because of its strong detection capabilities. At the same time, DL is observed in practice to favor majority over minority classes and consequently suffer from inaccurate detection of the targeted early-stage indications. To simulate such scenarios, we artificially generate skewness (99% vs. 1%) for certain plant types out of the PlantVillage dataset as a basis for classification of scarce visual cues through transfer learning. By randomly and unevenly picking healthy and unhealthy samples from certain plant types to form a training set, we consider a base experiment as fine-tuning ResNet34 and VGG19 architectures and then testing the model performance on a balanced dataset of healthy and unhealthy images. We empirically observe that the initial F1 test score jumps from 0.29 to 0.95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19. We demonstrate that utilizing an additional BN layer before the output layer in modern CNN architectures has a considerable impact in terms of minimizing the training time and testing error for minority classes in highly imbalanced data sets. Moreover, when the final BN is employed, minimizing the loss function may not be the best way to assure a high F1 test score for minority classes in such problems. That is, the network might perform better even if it is not confident enough while making a prediction; leading to another discussion about why softmax output is not a good uncertainty measure for DL models.Comment: Accepted for presentation and inclusion in ICPR 2020, the 25th International Conference on Pattern Recognitio

An island based hybrid evolutionary algorithm for optimization

This is a post-print version of the article - Copyright @ 2008 Springer-VerlagEvolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

Contextual determinants of CHILDREN'S health care and policy in Europe

BACKGROUND: The main objective of this study was to explore the contextual determinants of child health policies. METHODS: The Horizon 2020 Models of Child Health Appraised (MOCHA) project has one Country Agent (CA) in all 30 EU and EEA countries. A questionnaire designed by MOCHA researchers as a semi-structured survey instrument asked CAs to identify and report the predominating public and professional discussions related to child health services within the last 5 years in their country and the various factors which may have influenced these. The survey was issued to CAs following validation by an independent Expert Advisory Board. The data were collected between July and December 2016. The data was qualitatively analysed using software Nvivo11 for data coding and categorization and constructing the scheme for identified processes or elements. RESULTS: Contextual determinants of children's health care and policy were grouped into four categories. 1) Socio-cultural determinants: societal activation, awareness, communication, trust, freedom, contextual change, lifestyle, tolerance and religion, and history. 2) Structural determinants which were divided into: a) external determinants related to elements indirectly correlated with health care and b) internal determinants comprising interdependent health care and policy processes. 3) International determinants such as cross-nationality of child health policy issues. 4) The specific situational determinants: events which contributed to intensification of debates which were reflected by behavioural, procedural, institutional and global factors. CONCLUSIONS: The influence of context across European countries, in the process of children's health policy development is clearly evident from our research. A number of key categories of determinants which influence child health policy have been identified and can be used to describe this context. Child health policy is often initiated in reaction to public discontentment. The multiple voices of society resulted, amongst others, in the introduction of new procedures, action plans and guidelines; raising levels of awareness, intensifying public scrutiny, increasing access and availability of services and provoking introduction of structural changes or withdrawing unfavourable changes

How does societal reaction to children's health issues contribute to health policy in Europe? Results of a survey

BACKGROUND: In the European context the awareness of societal responsibility for children's health has increased with greater attention to children's rights and child empowerment processes. Child health issues are considered particularly sensitive; thus, they often provoke strong societal reactions, which, as a consequence, influence national health policies across Europe. Effectiveness of societal influences increases with the involvement of various actors in the context. METHODS: A qualitative approach was used to identify the level of societal involvement in health decision-making. A questionnaire was sent to the Country Agents (CAs) of the Models of Child Health Appraised (MOCHA) project. CAs are contact points in each of the 30 participating in the project countries and were asked to identify strong public and professional discussions related to child health services in their countries. Data collection was undertaken between July and December 2016. RESULTS: Based on 71 case studies, we identified eight thematic patterns, which characterize societal reactions to the currently worrisome child health issues across Europe. We devoted our attention to the three most controversial: child vaccination, child poverty and child abuse. The cases described by the CAs show the broad perspective in the perception of child health problems. Child health issues involve the public and raise nationwide debates. Public concerns were directly or indirectly related to child health and depicted the national overtone. CONCLUSIONS: Concerns in Europe about child health care are twofold: they are devoted to systemic issues (indirect patient orientation) and to child health and well-being (direct patient orientation). The phenomenon of societal responsibility for children's health is important for the support of public acceptance of child health policy

Stand type affects fluxes of volatile organic compounds from the forest floor in hemiboreal and boreal climates

The forest floor is a significant contributor to the stand-scale fluxes of biogenic volatile organic compounds. In this study, the effect of tree species (Scots pine vs. Norway spruce) on forest floor fluxes of volatile organic compounds (VOC) was compared in boreal and hemiboreal climates.Peer reviewe

Application of quantum-inspired generative models to small molecular datasets

Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-term devices. Theoretical contributions point toward generative modeling as a promising direction to realize the first examples of real-world quantum advantages from these technologies. A few empirical studies also demonstrate such potential, especially when considering quantum-inspired models based on tensor networks. In this work, we apply tensor-network-based generative models to the problem of molecular discovery. In our approach, we utilize two small molecular datasets: a subset of $4989$ molecules from the QM9 dataset and a small in-house dataset of $516$ validated antioxidants from TotalEnergies. We compare several tensor network models against a generative adversarial network using different sample-based metrics, which reflect their learning performances on each task, and multiobjective performances using $3$ relevant molecular metrics per task. We also combined the output of the models and demonstrate empirically that such a combination can be beneficial, advocating for the unification of classical and quantum(-inspired) generative learning.Comment: First versio