Miracle-dz/UniFAR-FTUs huggingface dataset:
On Benchmarking Cell Nuclei Segmentation Algorithms for Fluorescence Microscopy

: Cell nuclei detection is an essential step in the context of many image analysis tasks related to microscopy images and therefore also plays a role in highly topical ﬁelds of research like the development of personalised immunotherapy approaches against several types of cancer. Motivated by this observation, a whole zoo of advanced methods to accomplish cell nuclei segmentation has been proposed in recent years. This development in turn stresses the need to set up a well-justiﬁed and reproducible standard of comparison for the evaluation of these sophisticated approaches. In this paper, we describe how such a reference framework based on standard seeded watershed segmentation for fully automatic cell nuclei detection can be obtained. In particular, we provide a detailed review of a publicly available dataset, give a detailed account of the methods and evaluation measures we consider to enable the highest possible reproducibility of our results and discuss the suitability of different variants of seeded watershed segmentation for the mentioned purposes.
CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection

The limitations in obtaining sufficient datasets for training deep learning networks is preventing many applications from achieving accurate results, especially when detecting new constructions using time-series satellite imagery, since this requires at least two images of the same scene and it must contain new constructions in it. To tackle this problem, we introduce Chronological Order Reverse Network (CORN)—an architecture for detecting newly built constructions in time-series SAR images that does not require a large quantity of training data. The network uses two U-net adaptations to learn the changes between images from both Time 1–Time 2 and Time 2–Time 1 formats, which allows it to learn double the amount of changes in different perspectives. We trained the network with 2028 pairs of 256 × 256 pixel SAR images from ALOS-PALSAR, totaling 4056 pairs for the network to learn from, since it learns from both Time 1–Time 2 and Time 2–Time 1. As a result, the network can detect new constructions more accurately, especially at the building boundary, compared to the original U-net trained by the same amount of training data. The experiment also shows that the model trained with CORN can be used with images from Sentinel-1. The source code is available at https://github.com/Raveerat-titech/CORN.
Can MERRA-2 Reanalysis Data Reproduce the Three-Dimensional Evolution Characteristics of a Typical Dust Process in East Asia? A Case Study of the Dust Event in May 2017

This study used the MERRA-2 reanalysis dataset and ground-based and satellite observational data to comprehensively analyze a typical dust storm event in east Asia on 2–7 May 2017 which engulfed most of China as well as ocean and Japan, and explore the accuracy and comprehensiveness of the MERRA-2 dataset in the analysis of dust processes. The results of comparison show that the description of the spatiotemporal distribution and evolution of the dust aerosols in the dust event using the MERRA-2 data is consistent with the data of AERONET, National Urban Air Quality Real-time Publishing Platform and Hamawari-8. Gobi Deserts was the most influential source area of this dust event with the highest emissions reaching 1.9 × 106 μg/m3. The vertical motion of the atmosphere can lift dust from the source area above 500 hPa. There were low-pressure troughs at 500 and 850 hPa and the winds behind and in front of the trough led to the high-altitude, long-distance transport of dust. Dust gradually affected the northwest China, north China, northeast China, and even the ocean and Japan on 2–7 May. This study demonstrates that although there is some uncertainty about the source of dust emission in the MERRA-2 model, the data accurately simulated the evolution of the dust event and analyze it comprehensively, while the accuracy of simulating the long-term evolution of dust requires further evaluation.
Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

This article spanned a new, consistent framework for production, archiving, and provision of analysis ready data (ARD) from multi-source and multi-temporal satellite acquisitions and an subsequent image fusion. The core of the image fusion was an orthogonal transform of the reflectance channels from optical sensors on hypercomplex bases delivered in Kennaugh-like elements, which are well-known from polarimetric radar. In this way, SAR and Optics could be fused to one image data set sharing the characteristics of both: the sharpness of Optics and the texture of SAR. The special properties of Kennaugh elements regarding their scaling—linear, logarithmic, normalized—applied likewise to the new elements and guaranteed their robustness towards noise, radiometric sub-sampling, and therewith data compression. This study combined Sentinel-1 and Sentinel-2 on an Octonion basis as well as Sentinel-2 and ALOS-PALSAR-2 on a Sedenion basis. The validation using signatures of typical land cover classes showed that the efficient archiving in 4 bit images still guaranteed an accuracy over 90% in the class assignment. Due to the stability of the resulting class signatures, the fuzziness to be caught by Machine Learning Algorithms was minimized at the same time. Thus, this methodology was predestined to act as new standard for ARD remote sensing data with an subsequent image fusion processed in so-called data cubes.
Artificial Neural Networks for Quantitative Microwave Breast Imaging

This paper is focused on the use of artificial neural networks (ANNs) for biomedical microwave imaging of breast tissues in the framework of advanced breast cancer imaging techniques. The proposed scheme processes the scattered field collected at receivers locations of a multiview-multistatic system and aims at providing an estimate of the morphological and dielectric features of the breast tissues, which represents a strongly nonlinear scenario with several challenging aspects. In order to train the network, a simulated data set has been created by implementing the forward problem and an automatic randomly-shaped breast profile generator based on the statistical distribution of complex permittivity of breast biological tissues was developed. Some numerical tests were carried out to evaluate the performance of the proposed method and, in conclusion, we found that the use of ANNs for quantitative biomedical imaging purposes seems to be very promising.
Parkinson's Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions

The study of speech and voice in people diagnosed with a neurodegenerative disorder for the purposes of detection and monitoring has known a very relevant push forward in these last years, but it is far from being completed. One of the main concerns nowadays is that once the deterioration of speech and phonation quality has been informed by machine learning relying upon clinical expertise, there is insufficient evidence to resolve if quality deterioration may come from organic causes, neuromotor degeneration or simply from aging. The present work is part of a more ambitious plan to shed light on this problem by resorting to a theoretical modelling of glottal signals under the main known causes affecting phonation quality, which are closure deficits during the phonation cycle. These deficits may be due to anatomical, organic pathologic or neuromotor reasons. Simulation examples explaining them in the glottal excitation signals are given and contrasted with real examples. Finally, relevant scores from an experimental separation of Parkinson Disease phonation samples from 24 male and 24 female subjects against aging 24 male and 24 female controls on the same age taken from a male-female balanced dataset confronted to a normative subset of 24 male and 24 female speakers are presented to exemplify an analysis study deepening into this problem. Although classification accuracy scores as high as 99.69 and 99.59 were attained in 10-fold cross-validation using an SVM classifier, there is still the impression that co-morbidity and aging effects are not well taken into account, requiring a further semantic study on the features behind the discrimination scores obtained.
Years of life lost due to colorectal cancer in Poland between 2000 and 2014 according to voivodships

Background. colorectal cancers (crcs) are among the most important oncological causes of death in europe. Poland belongs to countries where the mortality rates due to this cause exceed the average values for eu-28. Objectives. comparison of the number of years of life lost (ylls) due to crc between 2000 and 2014 in Poland by voivodships. Material and methods. the study was based on a dataset containing information from the death certificates of Poles who died in 2000 and 2014 (368,028 and 376,467 records, respectively). the data on deaths caused by crc (c18–c21 according to icD-10) was used for the analysis (8,517 deaths in 2000 and 11,411 deaths in 2014). seyll (standard expected years of life lost) was implemented to assess ylls. Results. in 2000, the highest number of ylls per 10,000 men was recorded in zachodniopomorskie (55.7 years), and in 2014 – in opolskie (77.5 years). the increasing tendency of this measure between 2000 and 2014 in the group of men was observed in all voivodships. in the group of women, in 2000 the highest number of ylls per 10,000 was reported in lodzkie (46 years), and in 2014 – in warminskoMazurskie (49.6 years). in women, an seyllp decline over time was noted only in lodzkie and Dolnoslaskie, and in other voivodships, its values increased. Conclusions. between 2000 and 2014, an upward tendency of ylls due to crc was observed in Poland, though with territorial differentiation. it is advisable to search for more effective methods of reducing existing inequalities between individual provinces of Poland.
Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN

Abstract Tunnel geological radar image has been widely used in tunnel engineering quality detection for its advantages of fast, nondestructive, continuous detection, real-time imaging, intuitive data processing and high detection accuracy. However, the traditional defect detection method, which is judged by surveyors visually, consumes energy. In order to detect the quality of tunnel engineering accurately and quickly, an improved method of void defect detection based on Faster RCNN (Regional Convolutional Neural Network) is proposed in depth learning. The image data of the tunnel geological radar is collected for annotation, which fills the blank of the defect data set in the tunnel engineering. Through the method of this paper proposed, the feature extraction is optimized to improve the performance of the detection model, and the detection accuracy of the model is verified by expert knowledge.
EXPERIMENTAL RATIONALE OF THE USE OF CELL THERAPY FOR THE TREATMENT OF GLAUCOMA OPTICAL NEUROPATHY

Development of new effective treatments for glaucomatous optic neuropathy is one of the most acute aspects of modern ophthalmology. The aim of the work is to investigate the effectiveness of cell therapy with postnatal multipotent neural crest stem cells (NCSCs) using different cell delivery methods in a model of adrenaline-induced glaucoma. Materials and methods. Glaucoma was induced in Wistar rats by intraperitoneal injections of 10 μg to 15 μg/100 g body weight of 0.18 % adrenaline hydrotartrate. NCSCs were delivered intravenously (5 million cells), retrobulbarly (0.5 million cells) or parabulbarly (0.5 million cells). Histomorphometric analysis of the retina was performed on stained haematoxylin-eosin sections with a thickness of 5 μm one month after the delivery of NCSCs. Results. NCSCs transplantation by all modes of delivery caused positive morphological changes to varying degrees. Intravenous administration induced a decrease in edema in all retinal layers and a slight restoration of the cytoarchitectonics of the retinal layers. The parabulbar administration of NCSCs led to a decrease in edema and the restoration of the cytoarchitectonics of the layers, most pronouncedly the ganglion cell layer and the inner retinal layer. After the retrobulbar administration of NCSCs, the reduction in edema and restoration of the cytoarchitectonics of the layers were the most pronounced. Conclusions. According to the results of the study, the positive effect of NCSCs transplantation in an experimental model of glaucoma was the most pronounced following the retrobulbar injection of cells. Further investigations of the mechanisms of the effect of transplanted NCSCs on retinal structure restoration are needed.
Metaheuristic Tuning Generalisation by Cross-Validated Racing

Many tuning methods are based on concepts of sensitivity analysis combined with heuristics that tend to reduce the search space by eliminating less promising conﬁgurations. Neverthe-less, tuning parameters is a task that requires speciﬁc and time-consuming experiments, especially when involving large problem instances. This is particularly due to existing methods were not designed to efﬁciently generalise a tuning of parameters to other instances that did not participate of the training process. In this paper, the recently proposed tuning method named Cross-Validated Racing (CVR) is revised in order to clarify theoretical fundamentals of tuning problem and expand the experiments to make possible evaluating its generalisation capacity against the reduction in the size of the training set. For validation, the Bi-ased Random-Key Evolutionary Clustering Search (BRKeCS) is applied to solve scalable instance groups of Permutation Flow Shop Scheduling Problem. The computation results have demonstrated that CVR is robust in ﬁnding an effective parameter setting, requiring training process in only a half of total instance set.
The effect of the trading activities of banks on systemic risk: does banking industry concentration matter?

In a context where the authorities of the European Banking Union are showing a keen interest in mergers among banks with a view to consolidating the European banking industry, I explore the specific and joint impact of the trading activities of European deposits-taking banks and banking industry concentration on banks' exposure to systemic risk . I find that trading activities increased banks' exposure to systemic risk only in a concentrated banking industry. This paper therefore does not support the encouragement by the European Banking Union authorities for mergers between banks. I thank the associate editor Armin Schwienbacher and an anonymous referee for their very helpful remarks. I would also like to thank Daniel Goyeau for his very helpful comments on previous versions of this article. However, any remaining errors or omissions are mine. I would also like to thank Research Center for Economic and Financial Integration (C.R.I.E.F) and the Nouvelle Aquitaine region in France for their financial support. This research was conducted within a framework of the "Banking Governance, Financial Stability and Macroeconomic Performance" program supported by the Nouvelle-Aquitaine region. Citation: Eric Fina Kamani, (2020) ''The effect of the trading activities of banks on systemic risk: does banking industry concentration matter? '', Economics Bulletin, Volume 40, Issue 1, pages 542-555 Contact: Eric Fina Kamani eric.fina. kamani01@univ-Poitiers.fr. Submitted: September 03, 2019. Published: February 15, 2020. Submission Number: EB-19-00798 The effect of the trading activities of banks on systemic risk: does banking industry concentration matter? Eric Fina kamani Centre de recherche sur l'intégration économique et financière (CRIEF)
ASSESSMENT OF ENVIRONMENTAL IMPACTS OF THE POST-DRILL CLEANING PROCESS FOR PCB USING LCA

In this study, assessment of quantitative environmental impacts was performed for the cleaning process for PCB by using a LCA method, which is regarded as a useful tool for analyzing diverse environmental impacts at a local, regional, and global aspect. According to three geological aspects, environmental impacts were categorized into ten types. For these impact categories, characterization was carried out for evaluating environmental burdens of the cleaning process under consideration. Then, normalization was done in order to obtain a better understanding of the relative size among impact categories.
The Evolutionary Significance of Learning: what the ‘Baldwin Effect’ Dialogue Failed to Appreciate

The idea that evolution is partly directed by learning has been debated under the assumption that what is meant by this is learned behavior becoming hereditary (instinctive) behavior over time, a phenomenon called the ‘Baldwin effect’. I argue that there is a very simple and compelling reason to grant learning an essential role in shaping evolutionary history. However, despite the preoccupation with the Baldwin effect, this reason has nothing to do, per se, with what becomes of the learned behavior itself. The position I propose is not unprecedented. Not only is it implicit in some of the Baldwin effect literature itself, it has also been well developed in a very different context to discussions about learning: in Ernst Mayr’s views on evolutionary novelty. But while this significance of learning is evident enough from a Modern Synthesis starting point, it also touches on the more radical developmentalist views of Gottlieb (2002) and West-Eberhard (2003).
Emittance growth from luminosity burn-off in future hadron colliders

Future hadron colliders aim at producing larger integrated luminosities by burning-off an increased fraction of the bunch particles. This burn-off removes particles unevenly in the bunch distribution generating an emittance growth. Analytical equations are derived describing the bunch distribution suffering luminosity burn-off and cooling from synchrotron radiation damping, far from the equilibrium emittance, and possibly including transverse collision offsets. This effect is evaluated for HL-LHC [G. Apollinari et al., Report No. CERN-2017-007-M, 2017], HE-LHC [A. Abada et al., Eur. Phys. J. Spec. Top. 228, 1109 (2019)], and FCC-hh [A. Abada et al., Eur. Phys. J. Spec. Top. 228, 755 (2019). ] and included into a step-by-step simulation of the physics fill, together with intrabeam scattering, to evaluate impact on performance.
The Wage Premium and Market Structure

In this paper, we seek to understand why the “wage premium”, the percentage by which wages earned by skilled workers exceed those of unskilled workers, varies among industries featuring different market competitiveness. We construct a theoretical model with CES utility function and constant return to scale production function that allows us to examine the effects of different imperfect market structures (monopolistic competition and oligopoly) on the wage premium. We find that the wage premium is higher under oligopoly than under monopolistic competition. Our findings are supported by empirical evidence from Chilean manufacturers.
EMPLOYEE AGE AND MEDITATION & ITS IMPACT ON COMPANY PERFORMANCE

Yoga – meditation has been integral part of Indians since ages. Even gods are not an exception in practicing yoga. Because of yoga they were able to concentrate and take very calculative decisions. past two / three decades, thanks to the hectic schedules in the Global MNC’s, especially software employees are becoming pray such hectic schedule . if they are asked to practice meditatio time? These kinds of stress are leading to health hazards, frustration, hyper tension and many more such issues. This in tu affecting upon the overall performance of employees relax and spend time for them. Western countries have identified the solution as Meditation to curb such stress related problems, which can be practiced anytime anywhere. In imparted to all the age group people on the similar parlance. The requirement varies as the age progresses. The objective of is to identify the effect of meditation on the select IT professionals hailing from different levels and across different age groups and its impact on their performance at professional level. The age group of the selected sample varies from 22 years to 55 years from level to management level. They have been practicing meditation with a diverse group from beginners to seasoned ones ranging from 3 months old to 25 years. Some of the problems in day to day were addressed for them viz sleep deprivation, reduction of anx becoming fearlessness, controlled emotions (emotional maturity), improved focus, enthusiasm at work etc. The leaders at work suggest their teams to practice meditation as the long term benefits are enormous in improving the quality of the work. believes that meditation and yoga are the continuous methods for attainting pinnacle of self whether it is in personal life or work life. Especially in work life it provides immense benefits both to the employer and em achieving the organizational goals in set standards. Ripples of yoga and meditation across age groups of employees significantly vary in the process of practice. This study explores the relationship between employee age and meditation across various yog meditation practicing managers and its effect on company performance. To explore these dimensions the study will administer a structured questionnaire with standard scales used in research. The study intended to use multivariate statistical tools lik analysis and discriminate analysis, in addition to univariate statistical tools.
PREVENTING CHRONIC DISEASE

Introduction Studying mental and physical health problems in refugees facilitates providing suitable health care, thus improving their quality of life. We studied depression tendency in Syrian refugees in Jordan in the light of chronic diseases and medication availability. Also, depression prevalence and depression comorbidity with chronic diseases were identified. Methods In this multicenter cross-sectional survey, data from Syrian refugees attending Caritas centers in 6 Jordanian cities from November 2013 through June 2014 were analyzed. Participants’ demographics, depression, previously diagnosed chronic diseases, and newly diagnosed chronic diseases and the availability of medications were studied. Logistic regression was used to examine predictors for depression. Results Of 765 refugees who participated, about one-third demonstrated significant depression as measured by the Beck Depression Inventory. Descriptive analyses showed that depression was comorbid in 35% of participants with previously diagnosed chronic diseases and in 40% of participants with newly diagnosed chronic diseases. Newly diagnosed chronic diseases and lack of medications significantly contributed to depression, but the regression model as a whole explained less than 5% of the variance. Conclusion Because the regression model showed low effect size, we concluded that newly diagnosed chronic diseases and medication shortages could not predict depression in Syrian refugees residing in Jordan. Therefore, further studies of additional factors are recommended. Prompt measures have to be taken to prevent the spread of chronic diseases and improve mental health in this fragile population.