Miracle-dz/UniFAR-FTUs huggingface dataset:
Atmospheric chemical loss processes of isocyanic acid (HNCO): a combined theoretical kinetic and global modelling study

Abstract. Isocyanic acid (HNCO) is a chemical constituent suspected to be harmful to humans if ambient concentrations exceed ∼1 ppbv. HNCO is mainly emitted by combustion processes but is also inadvertently released by NOx mitigation measures in flue gas treatments. With increasing biomass burning and more widespread usage of catalytic converters in car engines, good prediction of HNCO atmospheric levels with global models is desirable. Little is known directly about the chemical loss processes of HNCO, which limits the implementation in global Earth system models. This study aims to close this knowledge gap by combining a theoretical kinetic study on the major oxidants reacting with HNCO with a global modelling study. The potential energy surfaces of the reactions of HNCO with OH and NO3 radicals, Cl atoms, and ozone were studied using high-level CCSD(T)/CBS(DTQ)//M06-2X/aug-cc-pVTZ quantum chemical methodologies, followed by transition state theory (TST) theoretical kinetic predictions of the rate coefficients at temperatures of 200–3000 K. It was found that the reactions are all slow in atmospheric conditions, with k(300K)≤7×10-16 cm3molecule-1s-1, and that product formation occurs predominantly by H abstraction; the predictions are in good agreement with earlier experimental work, where available. The reverse reactions of NCO radicals with H2O, HNO3, and HCl, of importance mostly in combustion, were also examined briefly. The findings are implemented into the atmospheric model EMAC (ECHAM/MESSy Atmospheric Chemistry) to estimate the importance of each chemical loss process on a global scale. The EMAC predictions confirm that the gas-phase chemical loss of HNCO is a negligible process, contributing less than 1 % and leaving heterogeneous losses as the major sinks. The removal of HNCO by clouds and precipitation contributes about 10 % of the total loss, while globally dry deposition is the main sink, accounting for ∼90 %. The global simulation also shows that due to its long chemical lifetime in the free troposphere, HNCO can be efficiently transported into the UTLS by deep convection events. Daily-average mixing ratios of ground-level HNCO are found to regularly exceed 1 ppbv in regions dominated by biomass burning events, but rarely exceed levels above 10 ppt in other areas of the troposphere, though locally instantaneous toxic levels are expected.
Pose evaluation for dance learning application using joint position and angular similarity

In this paper, we propose a dance pose evaluation method for a dance learning application using a smartphone. In the past, methods for classifying and comparing dance gestures through 3-D joint information obtained through a 3-D camera have been proposed, but there is a problem in using them for accurate dance pose evaluation. That is, these methods simply compare the similarity between the dance gestures without evaluation of the exact dance pose. To solve this problem, we propose a new method that can be operated on a smartphone for exact dance pose evaluation that simultaneously performs an affine transformation and an evaluation method to compare the joint position and joint angle information. In addition, we prove that the proposed method is suitable for dance learning applications through comparative experiments on a smartphone with real-world datasets.
Respiratory events screening using consumer smartwatches

Respiratory related events (RE) during nocturnal sleep disturb the natural physiological pattern of sleep. This events may include all types of apnea and hypopnea, respiratory-event-related arousals and snoring. The particular importance of breath analysis is currently associated with the COVID-19 pandemic. The proposed algorithm is a deep learning model with long short-term memory cells for RE detection for each 1 minute epoch during nocturnal sleep. Our approach provides the basis for a smartwatch based respiratory-related sleep pattern analysis (accuracy of epoch-by-epoch classification is greater than 80 %), can be applied for a potential risk of respiratory-related diseases screening (mean absolute error of AHI estimation is about 6.5 events/h on the test set, which includes participants with all types of apnea severity; two class screening accuracy (AHI threshold is 15 events/h) is greater than 90 %).
Collaborative edge-network content replication: a joint user preference and mobility approach

Today's mobile video users have unsatisfactory quality of experience mainly due to the large network distance to the centralized infrastructure. To improve users' quality of experience, content providers are pushing content distribution capacity to the edge-networks. However, existing content replication approaches cannot provide sufficient quality of experience for mobile video delivery. Because they fail to consider the knowledge of user-behavior such as user preference and mobility, which can capture the dynamically changing content popularity. To address the problem, we propose a user-behavior driven collaborative edge-network content replication solution in which user preference and mobility are jointly considered. More specifically, using user-bahavior driven measurement studies of videos and trajectories, we first reveal that both users' intrinsic preferences and mobility patterns play a significant role in edge-network content delivery. Second, based on the measurement insights, it is proposed that a joint user preference- and mobility-based collaborative edge-network content replication solution, namely APRank. It is comprised of preference-based demand prediction to predict the requests of video content, mobility-based collaboration to predict the movement of users across edge access points (APs), and workload-based collaboration to enables collaborative replication across adjacent APs. APRank is able to predict the fine-grained content popularity distribution of each AP, handle the trajectory data sparseness problem, and make dynamic and collaborative content replication for edge APs. Finally, through extensive trace-driven experiments, we demonstrate the effectiveness of our design: APRank achieves 20% less content access latency and 32% less workload against traditional approaches.
Popularity Agnostic Evaluation of Knowledge Graph Embeddings

In this paper, we show that the distribution of entities and relations in common knowledge graphs is highly skewed, with some entities and relations being much more popular than the rest. We show that while knowledge graph embedding models give state-of-the-art performance in many relational learning tasks such as link prediction, current evaluation metrics like hits @ k and mrr are biased towards popular entities and relations. We propose two new evaluation metrics, strat - hits @ k and strat - mrr , which are unbiased estimators of the true hits @ k and mrr when the items follow a power-law distribution. Our new metrics are generalizations of hits @ k and mrr that take into account the popularity of the entities and relations in the data, with a tuning parameter determining how much emphasis the metric places on popular vs. unpopular items. Using our metrics, we run experiments on benchmark datasets to show that the performance of embedding models degrades as the popularity of the entities and relations decreases, and that current reported results overestimate the performance of these models by magnifying their accuracy on popular items.
Using Deep Learning to Predict Motility and Morphology of Human Sperm

In the Medico Task 2019, the main focus is to predict sperm quality based on videos and other related data. In this paper, we present the approach of team LesCats which is based on deep convolution neural networks, where we experiment with different data preprocessing methods to predict the morphology and motility of human sperm. The achieved results show that deep learning is a promising method for human sperm analysis. Out best method achieves a mean absolute error of 8 . 962 for the motility task and a mean absolute error of 5 . 303 for the morphology task.
The Power of String Solving: Simplicity of Comparison

The increased interest in string solving in the recent years has made it very hard to identify the right tool to address a particular user’s purpose. Firstly, there is a multitude of string solving, each addressing essentially some subset of the general problem. Generally, the addressed fragments are relevant and well motivated, but the lack of comparisons between the existing tools on an equal set of benchmarks, cannot go unnoticed, especially as a common framework to compare solvers seems to be missing. In this paper we gather a set of relevant benchmarks and introduce our new benchmarking framework ZaligVinder to address this purpose.
Region Proposal Network for Lung Nodule Detection and Segmentation

Lung nodule detection and segmentation play a critical role in detecting and determining the stage of lung cancer. This paper proposes a two-stage segmentation method which is capable of improving the accuracy of detecting and segmentation of lung nodules from 2D CT images. The ﬁrst stage of our approach proposes multiple regions, potentially containing the tumour, and the second stage performs the pixel-level segmentation from the resultant regions. Moreover, we propose an adaptive weighting loss to effectively address the issue of class imbalance in lung CT image segmentation. We evaluate our proposed solution on a widely adopted benchmark dataset of LIDC. We have achieved a promising result of 92.78% for average DCS that puts our method among the top lung nodule segmentation methods.
Cotylar Fossa, Its Interpretation and Functionality. The Case from South American Native Ungulates

Abstract. The cotylar fossa is a complex anatomical character in the astragalar medial malleolar facet. It represents a dynamic relationship between the astragalus and the tibia in the upper tarsal joint. The astragalus must accommodate the medial tibial malleolus when the tibia is in an extreme flexion. It is one of the three morphological synapomorphies considered for Afrotheria, although it is also a recurrent trait among different groups of mammals. Here, fossil South American Native Ungulates and extant mammals were surveyed to reevaluate how much this character is spread and how variable it is. Beyond afrotherians, it is observed that it also appears in primates, macropodid marsupials, laurasiatherian archaic ungulates, perissodactyls, pantodonts, and dinoceratans; it is also in some but not all of the extinct endemic ungulates from South America. No function has been suggested before for the presence of a cotylar fossa. The cotylar fossa could be an adaptation to a passive, rest related posture. Extreme flexion of the tibia/fibula over the foot happens when animal sits or lies down. Because of this, the cotylar fossa may not represent a synapomorphy of afrotherians but a homoplastic feature convergently developed several times. The cotylar fossa, as first described, is more characteristic of the order Primates than of any other taxa, including Afrotheria. But, there is more than one kind of cotylar fossa, and this variation could correspond to more than one cause, including phylogenetic constraints.
Low-Intensity Cutting Leads to Similar Forest Structure and Tree Diversity as in Protected Stands

ABSTRACT Forest structure in Europe has been shaped by forest management which may vary from intensive clearcuttings to single-tree selection cuttings. Most investigations on silver fir forests have been carried out in the mountains. Forests in lowlands have been less studied, although many of them are of natural origin. We investigated and assessed potential stand structural differences between managed and partially protected mixed silver fir stands. We also assessed the impact of silvicultural activities that led to the development of current stand structures of mixed Abies alba stands. The study was conducted in Janów Forests in south-eastern Poland. We randomly established 40 circular nested plots in three managed and three partially protected stands. Although both managed and partially protected stands differed in the intensity of management and the type of selection cuttings, we found little difference in the composition and stand structure. Regardless of management intensity all stands were characterized by diversified natural regeneration, where the main tree species, especially in the phase of seedlings, was silver fir. The basal area of standing dead trees was higher for the protected than managed stands, but the differences were not significant, whereas the mean volume of dead standing trees was significantly higher in the reserve. Both in managed and protected stands, silver fir benefited from the management measures mimicking natural processes. Our results suggest that low intensity forest management for silver fir dominated forests will be crucial for conserving these forests and their unique structure in the region.
Cytokine storm and COVID-19: a chronicle of pro-inflammatory cytokines

Coronavirus disease 2019 (COVID-19) has swept the world, unlike any other pandemic in the last 50 years. Our understanding of the disease has evolved rapidly since the outbreak; disease prognosis is influenced mainly by multi-organ involvement. Acute respiratory distress syndrome, heart failure, renal failure, liver damage, shock and multi-organ failure are strongly associated with morbidity and mortality. The COVID-19 disease pathology is plausibly linked to the hyperinflammatory response of the body characterized by pathological cytokine levels. The term ‘cytokine storm syndrome’ is perhaps one of the critical hallmarks of COVID-19 disease severity. In this review, we highlight prominent cytokine families and their potential role in COVID-19, the type I and II interferons, tumour necrosis factor and members of the Interleukin family. We address various changes in cellular components of the immune response corroborating with changes in cytokine levels while discussing cytokine sources and biological functions. Finally, we discuss in brief potential therapies attempting to modulate the cytokine storm.
DESIGNING CMOS CIRCUITS FOR LOW POWER European Low-Power Initiative for Electronic System Design

An important issue in the design of VLSI Circuits is the choice of the basic circuit approach and topology for implementing various logic and arithmetic functions such as adders and multipliers. In this chapter, several static and dynamic CMOS circuit design styles are evaluated in terms of area, propagation delay and power dissipation. The different design styles are compared by performing detailed transistor-level simulations on a benchmark circuit (ripple carry adder) using HSPICE, and analyzing the results in a statistical way. After the comparison between the different design styles, a number of well known types of adders (ripple carry, carry skip, carry lookahead, carry select etc.) are compared in terms of propagation delay, number of gates and logic transition’s average number. Furthermore, power measurements and comparisons for a number of well-known multipliers are provided. Based on the results of the provided analysis some of the tradeoffs that are possible during the design phase in order to improve the circuit power-delay product are identiﬁed.
HmwkCheck: a homework auto-checking system based on arithmetic operation recognition using smartphones

The homework for low-grade pupils often contains simple arithmetic problems, i.e., four arithmetic operations. To evaluate the learning quality of pupils, teachers and parents often need to check the homework manually, which is time and labor consuming. In this paper, we propose a homework auto-checking system HmwkCheck, which checks the four arithmetic operations automatically. Specifically, HmwkCheck utilizes the embedded camera of a smartphone to capture the homework as an image, and then processes the image in the smartphone to detect, segment and recognize both printed characters and handwritten characters. We implement HmwkCheck in an Android smartphone. The experiment results show that HmwkCheck can check homework efficiently, i.e., the average precision, recall and F1-score of character recognition achieve 94.03%, 93.41% and 93.72%, respectively.
Automated Analysis of Flakiness-mitigating Delays

During testing of parallel systems, which allow asynchronous communication, test flakiness is sometimes avoided by explicitly inserting delays in test code. The choice of delay approach can be a trade-off between short-term gain and long-term robustness. In this work, we present an approach for automatic detection and classification of delay insertions, with the goal of identifying those that could be made more robust. The approach has been implemented using an open-source compiler tooling framework and validated using test code from the telecom industry.
Life Table Analysis and Sustainable Fisheries

In this study, the Life Table Method also known as the Cutler-Ederer life table technique, was introduced and practicability of this technique for determining life period of fish species of which natural mortalities were found by estimation using parameter of length weight relationships has also been searched. The aim of this study is to show that life table method used for human beings, can be also used to predict fish species. The data employed in the present study is that of scaldfish Arnoglossus laterna (Walbaum, 1792) (Pisces: Bothidae) obtained from the Aegean Sea. Life span of five years and its ages were directly taken into account in the table prepared by the data concerned. Consequently, according to the data estimated, the life expectancy of the species in the Aegean Sea is approximately 11 years. Furthermore, the number of individuals estimated to live more than 5 years from the scaldfish population including 1000 individuals was calculated to be some 970.
Responses to Inter-group Encounters of the Hainan Gibbon Nomascus hainanus

The Hainan gibbon ( Nomascus hainanus ) is one of the most endangered primates and the least studied gibbon species in the fieldwork. In the past decades only a little information was known about its population size, population structure, habitat and food kinds. Through four years field work, we observed their inter-group encounters for four times. We found that their encounter behaviors were much different from other gibbon species. Only vocal and chasing behavior happened between the adult females, male sub-adults and male juveniles of the Hainan gibbon encounters. The time of inter-group encounters was much shorter than other gibbon species, just 24-51 minutes. Not like the Hylobates lar encounters, no fighting was observed among the Hainan gibbon encounters; there was also no such behaviors as playing and grooming, even the extra-group-copulations. The adult females of two groups didn’t take par in the chasing and they only rested and watched 20-30m away. We speculated that the behavior of the adult females may be interpreted as protecting their territory and by the way of chasing the sub-adults can learn how to protect their territory in the future.
High genetic variation in a small population of Cherry leaf roll virus in Betula sp. of montane origin in Corsica

Summary Cherry leaf roll virus (CLRV) infection is common in Betula pendula and B. pubescens in Middle and North Europe, easily observable by chlorotic leaf vein banding, mottling and leaf roll, partially adherent with progressive loss of vitality or death of twigs and branches. In Fennoscandia, a severe viral epidemic in various birch species in forest stands, public greens and roadsides is associated with CLRV. In Corsica, CLRV-typical symptoms were observed on birch trees (Betula sp.) in a montane stand (1470 m) at Col de Vergio. CLRV was detected by RT-nested PCR in all leaf samples from 11 randomly selected birch trees exhibiting characteristic symptoms. Along with the fact that this is the first report of CLRV in Betula sp. of both montane and Mediterranean origins, remarkably high genetic variation and a new distinct phylogenetic cluster are comprised by a small randomly sampled CLRV population that has evolved in one of the few scattered birch stands in Corsica.
An Introduction to Proximal Causal Learning

A standard assumption for causal inference from observational data is that one has measured a sufficiently rich set of covariates to ensure that within covariate strata, subjects are exchangeable across observed treatment values. Skepticism about the exchangeability assumption in observational studies is often warranted because it hinges on investigators' ability to accurately measure covariates capturing all potential sources of confounding. Realistically, confounding mechanisms can rarely if ever, be learned with certainty from measured covariates. One can therefore only ever hope that covariate measurements are at best proxies of true underlying confounding mechanisms operating in an observational study, thus invalidating causal claims made on basis of standard exchangeability conditions. Causal learning from proxies is a challenging inverse problem which has to date remained unresolved. In this paper, we introduce a formal potential outcome framework for proximal causal learning, which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails. Sufficient conditions for nonparametric identification are given, leading to the proximal g-formula and corresponding proximal g-computation algorithm for estimation. These may be viewed as generalizations of Robins' foundational g-formula and g-computation algorithm, which account explicitly for bias due to unmeasured confounding. Both point treatment and time-varying treatment settings are considered, and an application of proximal g-computation of causal effects is given for illustration.
A Sentiment Classification on Indian Government Schemes Using PySpark

: With the rapid growth of social media, millions of users share their opinions everyday about different issues such as services, products, persons, politics, events etc. By analysing these social user’s opinions the most valuable data can be obtained which is used for decision making in various domains. Sentiment Analysis in social media plays a vital role in monitoring of public opinion behind certain topics. Actually 92% of marketing professionals thought that social media has intense effect on their business. By doing an accurate sentiment analysis, winning might not be an impossible thing. Success does not depend only in the number of likes, comments or followers. It also depends in how much positive discussions have been held among the users of social media. The objective of this paper is to build a model that performs multi-class classification of Big data with improved accuracy. The proposed model uses a hybrid of Lexicon-based approach and Machine Learning based approach. In this paper, we have performed the sentiment analysis on the three Central Government Schemes namely Digital India, Swachh Bharat and Make-in India. The Apache Spark’s Machine Learning library, MLlib has been used in order to achieve better results in sentiment analysis for the Big Data. The AFINN dictionary was used to label the tweets and bigrams feature set was used. The Hashing TF-IDF method was used to extract the feature vectors from the raw feature set. These vectors were classified by Random Forest classifier to determine positive, negative and neutral sentiments of tweets. The result from this model was tested by using the various testing metrics like accuracy, precision, recall and f-score. The higher accuracy of 89.27% was obtained.