Intelligent System for Estimation of the Spatial Position of Apples Based on YOLOv3 and Real Sense Depth Camera D415

Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric coordinate system takes place by means of simple linear transformations. Estimating the position in a symmetric coordinate system allows estimating not only the magnitude of the shift but also the location of the object relative to the camera. The proposed approach makes it possible to obtain position estimates with high accuracy. The approximate root mean square error is 7–12 mm, depending on the range and axis. As for precision and recall metrics, the first is 100% and the second is 90%.

Financing of Immunization Programs by Local Government Units in Poland as an Element of Health Policy

Introduction: The scope and schedule of immunization in Poland is regulated by the Immunization Programme prepared and announced by the State Sanitary Inspector. There are two kinds of vaccines: compulsory vaccines, financed by the state budget at the disposal of the Minister of Health, and vaccines recommended by the central health authorities but financed by local governments within health policy programmes. Compulsory vaccines cover people up to 19 years of age and individuals at higher risk of infections. The public health programmes organized and financed by local governments play an important role in infectious disease control in the country. Objective: The objective of this study is to analyse health policy programmes including immunization programmes, which were developed, implemented and financed by local government units of all levels in Poland between 2016 and 2019. Material and Methods: This analysis covers data compiled by voivodes and submitted to the Minister of Health as annual information on public health tasks carried out by local government units. From the aggregate information, data on all health policy programmes conducted by individual local government units between 2016 and 2019, including immunization, were extracted and analysed. The data were obtained pursuant to the provisions of the act on access to public information. Results: In the analysed period, local government units implemented a total of 1737 health policy programmes that financed the purchase of vaccines, qualification tests for immunization and carrying out immunization by authorized medical entities. Among the vast majority of programmes, promotional activities were also implemented. Conclusions: In Poland, local governments are deeply engaged in the immunization of their citizens by organizing and financing specific health care programmes. These programmes are an essential addition to the state financial resources in infectious disease control. This engagement expresses local government maturity regarding the health needs of the population and public health measures. Communes are the most engaged units among all levels of local governments. It is probably due to close mutual communication between the people and local governments. The growing awareness of the important role of HPV immunization in the prevention of cervical cancer among local government units is reflected in the increase in the number of girls vaccinated against HPV and the increase in financial resources allocated for primary HPV prevention. The decrease in the number of people vaccinated against pneumococci may result from including pneumococcal vaccines in the compulsory immunization schedule.

Market Efficient Portfolios in a Systemic Economy

Diversification vs. Diversity – How is the Efficiency of Markets Affected? Prices aggregate information that is dispersed in the economy; they thereby facilitate the allocation of scarce resources. Inefficiencies may arise from deviations of market prices from their fundamental values. In “Market Efficient Portfolios in a Systemic Economy”, Awiszus, Capponi, and Weber investigate the impact of the trade-off between diversity and diversification on inefficiencies in secondary markets due to asset illiquidity and leverage constraints of financial institutions. The authors identify two key determining factors. These are the systemic significance of the banks and the statistical properties of the fundamental asset shocks. Systemic significance is driven by the banks’ target leverage, their trading strategies, and the illiquidity characteristics of the assets. The paper demonstrates that portfolio diversification is typically not efficient. In fact, efficient portfolio holdings may strongly deviate from this standard paradigm, especially if the banks have similar characteristics.

Independent Country Program Review Suriname 2016-2020

This Independent Country Program Review (ICPR) analyzes the IDB Group's country strategy (CS) and country program with Suriname during the 2016-2020 period. ICPRs assess the relevance of the Bank's CS and provide aggregate information on the program alignment and execution. If the available information allows it, ICPRs also report on progress toward achieving the objectives that the IDB Group established by the CS. This review by the Office of Evaluation and Oversight (OVE) is intended to provide the Boards of Executive Directors of the IDB and IDB Invest with useful information to analyze the country strategies submitted for their consideration.

Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes

For the success of video deblurring, it is essential to utilize information from neighboring frames. Most state-of-the-art video deblurring methods adopt motion compensation between video frames to aggregate information from multiple frames that can help deblur a target frame. However, the motion compensation methods adopted by previous deblurring methods are not blur-invariant, and consequently, their accuracy is limited for blurry frames with different blur amounts. To alleviate this problem, we propose two novel approaches to deblur videos by effectively aggregating information from multiple video frames. First, we present blur-invariant motion estimation learning to improve motion estimation accuracy between blurry frames. Second, for motion compensation, instead of aligning frames by warping with estimated motions, we use a pixel volume that contains candidate sharp pixels to resolve motion estimation errors. We combine these two processes to propose an effective recurrent video deblurring network that fully exploits deblurred previous frames. Experiments show that our method achieves the state-of-the-art performance both quantitatively and qualitatively compared to recent methods that use deep learning.

The Condorcet Jury Theorem with Information Acquisition

We analyze a committee decision in which individuals with common preferences are uncertain which of two alternatives is better for them. Members can acquire costly information. Private signals and information choice are both continuous. As is consistent with Down’s rational ignorance hypothesis, each member acquires less information in a larger committee and tends to acquire zero information when the committee size goes to infinity. However, with more members, a larger committee can gather more aggregate information in equilibrium. The aggregate information is infinite with the size going to infinity if and only if marginal cost at “zero information acquisition” is zero. When the marginal cost at “zero information acquisition” is positive, the probability of making an appropriate decision tends to be less than one.

Perceptual Maps to Aggregate Information from Decision Makers

Understanding different perceptions of human being when using linguistic terms is a crucial issue in human-machine interaction. In this paper, we propose the concept of perceptual maps to model human opinions in a group decision-making context. The proposed approach considers a multi-granular structure using unbalanced hesitant linguistic term sets. An illustrative case is presented in the location decisions made by multinationals enterprises of the energy sector within the European smart city context.

The Unmind Index: development and UK validation of a new digital assessment of mental health and wellbeing (Preprint)

BACKGROUND Unmind is a workplace digital mental health platform, with tools to help users track, maintain, and improve their mental health and wellbeing (MHWB). Psychological measurement plays a key role on this platform, providing users with insights on their current MHWB, the ability to track it over time, and personalised recommendations, while providing employers with aggregate information about the MHWB of their workforce. OBJECTIVE Due to the limitations of existing measures for this purpose, we aimed to develop and validate a novel wellbeing index for digital use, to capture symptoms of common mental health problems and key aspects of positive wellbeing. METHODS In Study 1A, questionnaire items were generated by clinicians and screened for face validity. In Study 1B, these items were presented to a large sample (N = 1,104) of UK adults and exploratory factor analysis was used to reduce the item pool and identify coherent subscales. In Study 2, the final measure was presented to a new nationally representative UK sample (N = 976), along with a battery of existing measures, with 238 participants retaking the Index after one week. The factor structure and measurement invariance of the Unmind Index was evaluated using confirmatory factor analysis, convergent and discriminant validity by estimating correlations with existing measures, and reliability by examining internal consistency and test-retest intraclass correlations. RESULTS Studies 1A and 1B yielded a 26-item measure with 7 subscales: Calmness, Connection, Coping, Happiness, Health, Fulfilment, and Sleep. Study 2 showed that the Unmind Index is fitted well by a second-order factor structure, where the 7 subscales all load onto an overall MHWB factor, and established measurement invariance by age and gender. Subscale and total scores correlate well with existing mental health measures, and generally diverge from personality measures. Reliability was good or excellent across all subscales. CONCLUSIONS The Unmind Index is a robust measure of mental health and wellbeing that can help to identify target areas for intervention in non-clinical users of an MHapp. We argue that there is value in measuring mental ill health and mental wellbeing together, rather than treating them as separate constructs.

KNOWLEDGE GRAPH CONSTRUCTION FOR SUBSURFACE OBJECTS INCLUDING UNCERTAINTY AND TIME VARIATION

In the recent years the concept of knowledge graph has emerged as a way to aggregate information from various sources without imposing too strict data modelling constraints. Several graph models have been proposed during the years, ranging from the “standard” RDF to more expressive ones, such as Neo4J and RDF-star. The adoption of knowledge graph has become established in several domains. It is for instance the case of the 3D geoinformation domain, where the adoption of semantic web technologies has led to several works in data integration and publishing. However, yet there is not a well-defined model or technique to represent 3D geoinformation including uncertainty and time variation in knowledge graphs. In this paper we propose a model to represent parameterized geometries of subsurface objects. The vocabulary of the model has been defined as an OWL ontology and it extends existing ontologies by adding classes and properties to represent the uncertainty and the spatio-temporal behaviour of a geometry, as well as additional attributes, such as the data provenance. The model has been validated on significant use cases showing different types of uncertainties on 3D subsurface objects. A possible implementation is also presented, using RDF-star for the data representation.

CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles

Motivation Ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Blockade of hERG channels may cause prolonged QT intervals that potentially could lead to cardiotoxicity. Various in-silico techniques including deep learning models are widely used to screen out small molecules with potential hERG related toxicity. Most of the published deep learning methods utilize a single type of features which might restrict their performance. Methods based on more than one type of features such as DeepHIT struggle with the aggregation of extracted information. DeepHIT shows better performance when evaluated against one or two accuracy metrics such as negative predictive value (NPV) and sensitivity (SEN) but struggle when evaluated against others such as Matthew correlation coefficient (MCC), accuracy (ACC), positive predictive value (PPV) and specificity (SPE). Therefore, there is a need for a method that can efficiently aggregate information gathered from models based on different chemical representations and boost hERG toxicity prediction over a range of performance metrics. Results In this paper, we propose a deep learning framework based on step-wise training to predict hERG channel blocking activity of small molecules. Our approach utilizes five individual deep learning base models with their respective base features and a separate neural network to combine the outputs of the five base models. By using three external independent test sets with potency activity of IC50 at a threshold of 10 $$\upmu$$ μ m, our method achieves better performance for a combination of classification metrics. We also investigate the effective aggregation of chemical information extracted for robust hERG activity prediction. In summary, CardioTox net can serve as a robust tool for screening small molecules for hERG channel blockade in drug discovery pipelines and performs better than previously reported methods on a range of classification metrics.