Miracle-dz/UniFAR-FTUs huggingface dataset:
stimating the effect of lung collapse and pulmonary shunt on gas exchange uring breath-hold diving: The Scholander and Kooyman legacy

We developed a mathematical model to investigate the effect of lung compression and collapse (pulmonary shunt) on the uptake and removal of O2, CO2 and N2 in blood and tissue of breath-hold diving mammals. We investigated the consequences of pressure (diving depth) and respiratory volume on pulmonary shunt and gas exchange as pressure compressed the alveoli. The model showed good agreement with previous studies of measured arterial O2 tensions (PaO2 ) from freely diving Weddell seals and measured arterial and venous N2 tensions from captive elephant seals compressed in a hyperbaric chamber. Pulmonary compression resulted in a rapid spike in PaO2 and arterial CO2 tension, followed by cyclical variation with a periodicity determined by Q̇tot. The model showed that changes in diving lung volume are an efficient behavioural means to adjust the extent of gas exchange with depth. Differing models of lung compression and collapse depth caused major differences in blood and tissue N2 estimates. Our integrated modelling approach contradicted predictions from simple models, and emphasised the complex nature of physiological interactions between circulation, lung compression and gas exchange. Overall, our
Problem Solving Ability and Working Memory among Children with and without Dyslexia

Dyslexia is a specific learning disability in which children have difficulty in reading, writing, spelling, comprehension including a deficit in working memory. Many researchers have argued that Working Memory(WM) and problem solving ability are related to one another. This study attempted to understand the problem solving ability (PSA) using the Tower of London test and Working Memory (WM) usingImmediate working memory test from Bhatia’s battery among children with and without dyslexia. In this study, a sample of 98 children were taken, out of which, 45 (31 boys,14 girls) children were with dyslexia and 53 (30 boys, 23 girls) children were without dyslexia. Results showed that there was no difference in Direct order memory, but a significant difference in PSA and Reverse order memory was found between the two groups. It was also found that PSA was positively correlated with WM. The post test results showed that the difference in PSA of the two groups was no more significant after the implementation of the strategies.
Word Representations Concentrate and This is Good News!

This article establishes that, unlike the legacy tf*idf representation, recent natural language representations (word embedding vectors) tend to exhibit a so-called concentration of measure phenomenon, in the sense that, as the representation size p and database size n are both large, their behavior is similar to that of large dimensional Gaussian random vectors. This phenomenon may have important consequences as machine learning algorithms for natural language data could be amenable to improvement, thereby providing new theoretical insights into the field of natural language processing.
Impact of Military Expenditure on Economic Growth of Afghanistan

This empirical study estimates and considers the impact of military expenditure on economic growth of Afghanistan for the period of 2004-2018 by applying the VAR model and VAR Granger causality (1980). The model is run by having the dependent (predicted) variable of economic growth and independent variable of military spending and employment. Ultimately, this analysis revealed that arm expenses do not have a significant effect on the economic growth of Afghanistan for the 15 years period from 2004-2018. And after running the VAR Granger causality it revealed that if some policy changes accrue to the GDP or economic then it will cause the military expenditure (unidirectional causality) and policy changes to the military expenditure will not cause GDP. According to the joint test, it is seen that if some policy changes accrue to the employment and military expenditure so it will cause GDP, if some policy changes accrue to the employment and GDP so it will cause military expenditure.
An Empirical Study on API Parameter Rules

Developers build programs based on software libraries to reduce coding effort. If a program inappropriately sets an API parameter, the program may exhibit unexpected runtime behaviors. To help developers correctly use library APIs, researchers built tools to mine API parameter rules. However, it is still unknown (1) what types of parameter rules there are, and (2) how these rules distribute inside documents and source files. In this paper, we conducted an empirical study to investigate the above-mentioned questions. To analyze as many parameter rules as possible, we took a hybrid approach that combines automatic localization of constrained parameters with manual inspection. Our automatic approach–PaRu–locates parameters that have constraints either documented in Javadoc (i.e., document rules) or implied by source code (i.e., code rules). Our manual inspection (1) identifies and categorizes rules for the located parameters, and (2) establishes mapping between document and code rules. By applying PaRu to 9 widely used libraries, we located 5,334 parameters with either document or code rules. Interestingly, there are only 187 parameters that have both types of rules, and 79 pairs of these parameter rules are unmatched. Additionally, PaRu extracted 1,688 rule sentences from Javadoc and code. We manually classified these sentences into six categories, two of which are overlooked by prior approaches. We found that 86.2% of parameters have only code rules; 10.3% of parameters have only document rules; and only 3.5% of parameters have both document and code rules. Our research reveals the challenges for automating parameter rule extraction. Based on our findings, we discuss the potentials of prior approaches and present our insights for future tool design.
The Tragedy of Your Upstairs Neighbors: The Externalities of Home-Sharing Platforms∗

A common critique of home-sharing platforms is that they enable hosts to impose costs on their neighbors, creating a market failure. To explore potential public policy responses, we develop a model of the markets for home-sharing and long-term rentals, and predict market equilibrium outcomes under different policy regimes. With respect to efficiency, we find that when the home-sharing decision is left to individuals, there is too much home-sharing, whereas if the decision is left to a city that maximizes resident surplus alone, there is too little home-sharing. However, when building owners decide on the home-sharing policy of their buildings, externalities are internalized, and the level of home-sharing activity is socially optimal. Our model predicts that, in equilibrium, building owners will be indifferent between allowing and banning homesharing in their buildings. To assess this “no policy arbitrage” prediction empirically, we construct a dataset of NYC rental apartments listings, and find that, consistent with our prediction, costless policy choices analogous to home-sharing have no detectable effect on long-term rental rates. ∗Thanks to Yannis Bakos, Peter Coles, Bo Cowgill, Michael Egesdal, Andrey Fradkin, David Holtz, Michael Luca, Igor Popov, Justin Rao, Arun Sundararajan, and Richard Zeckhauser for their helpful comments. Financial support from the Center for Global Economy and Business is gratefully acknowledged. Author contact information and code are currently or will be available at http://www.john-josephhorton.com/.
Comparison of some physical parameters of professional and regional amateur league footballers according to leagues and positions

The aim of this study is to compare some physical parameters of professional and regional amateur footballers according to their positions. Totally 100 volunteer football players playing in the professional and regional amateur league participated in the study. Anthropometric measurements (height, body weight, body fat ratio) were performed first in evaluating the physical parameters of football players. Second, anaerobic capacity (flexibility, vertical jumping, speed) and third one is aerobic capacity (yo-yo IR1) measurements were completed. Kolmogorov Smirnow test was used for normality testing of the data used in the study. Thus, it is understood that the data show normal distribution, one-way analysis of variance (one way ANOVA), which is one of the parametric tests, was also used in cases where more than two groups were compared. The statistical analysis used in the study was performed at 0.005 error levels in 95% confidence interval. SPSS 22.0 package program is applied for statistical analysis of the data. As a result, it is observed that the football players participating in the study have statistically difference between the flexibility, vertical jumping and 30 m speed values according to the leagues, and height, body weight and body fat percentage values according to the positions (p < 0.05). Having the flexibility, vertical jumping and 30 m speed works to improve the parameters of the players with meaningful differences can be made to the players who play in all leagues and positions can contribute to the increase in performance.
Active Learning Strategies to Reduce Anomaly Detection False Alarm Rates

A signiﬁcant focus within the commercial aviation community is the discovery of unknown safety events in ﬂight operations data. The rule-based methods which are currently used in aviation are only able to look for known issues. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events; however, they have a higher false-alarm rate—the statistical anomalies that they identify are often not operationally signiﬁcant (i.e., safety concern). Subject Matter Experts (SMEs) must spend signiﬁcant time reviewing these statistical anomalies individually to identify a few operationally signiﬁcant ones. Our previous work (Sharma et al., 2016; Das et al., 2017) developed an active learning algorithm that builds a classiﬁer to distinguish uninteresting anomalies from operationally significant anomalies by obtaining labels from SMEs and their rationales for choosing the labels. In this paper, we apply our past work to multi-Unmanned Aerial Vehicle (UAV) missions. However, the active learning strategy previously used is inadequate for our chosen setting. Therefore, we ex-periment with different active learning strategies. We describe how we went about developing our ﬁnal chosen strategy and show its superiority in experimental results over two baseline strategies including the cited previous work.
Multi-Dimensional Analysis to Predict Students' Grades in Higher Education

This work enhances the analysis of the student performance in the high education level. This model categorizes the features according to their relativeness to the teaching style and to the student activities on an Electronic Learning system. Several new features are proposed and calculated in each of these two categories/dimensions. This approach applies an extra level of machine learning that analyses the data based on a set of dimensions, and each dimensions includes a set of features. The prediction analysis is applied on each dimension separately based on a different classifiers. The best fitting classifier to each dimension ensures the enhancement of the local analysis accuracy and though enhances overall global accuracy. The accuracy of prediction of the student is enhanced to 87%. This study allows the detection of the correlation the features in different dimension. Furthermore, a study is applied on the scanned text documents for extracting and utilizing the features that represent the student uploads.
Detecting Insecure Code Patterns in Industrial Robot Programs

Industrial robots are complex and customizable machines that can be programmed with proprietary domain-specific languages. These languages provide not only movement instructions, but also access to low-level system resources such as the network or the file system. Although useful, these features can lead to taint-style vulnerabilities and can be misused to implement malware---on par with general-purpose programming languages. In this paper, we analyze the languages of $8$ leading industrial robot vendors, systematize their technical features, and discuss cases of vulnerable and malicious uses. We then describe a static source-code analyzer that we created to analyze robotic programs and discover insecure or potentially malicious code paths. We focused our proof-of-concept implementation on two popular languages, namely ABB's RAPID and KUKA's KRL. By evaluating our tool on a set of publicly available programs, we show that insecure patterns are found in real-world code; therefore, static source-code analysis is an effective security screening mechanism, for example to prevent commissioning insecure or malicious industrial task programs. Finally, we discuss remediation steps that developers and vendors can adopt to mitigate such issues.
An assessment of baseline hand washing practice in Malawi and the relevance of proxy indicators

The Government of Malawi has launched a National Hand Washing Campaign in order to coordinate and intensify efforts on hand washing promotion. However, much of the reported data on actual hand washing practice is unreliable and scanty as it often relies on responses to questionnaires which may not reflect actual practice. This study collected qualitative information on hand washing practice in a few selected villages in rural Malawi to help qualify estimates of hand washing practice from quantitative information collected in national surveys. The findings of the study revealed that hand washing occurs regularly in 50% of households with hand washing facilities. The presence of a hand washing facility alone did not predict hand washing practice unless it is filled with water. Hence a better proxy indicator for assessing hand washing practice in Malawi is observing for households with water in their hand washing facility.
Implementation of an Intelligent Model Based on Machine Learning in the Application of Macro-Ergonomic Methods in a Human Resources Process Based on ISO 12207

The objective of this chapter is to implement an intelligent model based on machine learning in the application of macro-ergonomic methods in human resources processes based on the ISO 12207 standard. To achieve the objective, a method of constructing a Java language algorithm is applied to select the best prospect for a given position. Machine learning is done through decision trees and algorithm j48. Among the findings, it is shown that the model is useful in identifying the best profiles for a given position, optimizing the time in the selection process and human resources as well as the reduction of work stress.
Aviva Stadium: A Parametric Success

The Aviva Stadium, Dublin, is the first stadium to be designed from start to finish using commercially available parametric modelling software. A single model in Bentley's Generative Components was shared between architects and engineers, which allowed the optimised design of form, structure and façade. The parametric software was extended where necessary to integrate with structural analysis and to automate fabrication. By reducing the overhead associated with design iterations, this approach allowed detailed exploration of options and early identification and resolution of potential problems. In this paper, the authors add to the body of scientific knowledge by describing in detail the methods which led to the construction of the Aviva Stadium. This paper is written in light of the completed building and provides information on the generation and control of the envelope geometry, development and analysis of structure and documentation for construction. Whilst these components have been discussed independently previously [1–4], here these aspects are drawn together for the first time and are presented alongside thoughts on the manufacturing and construction processes from the project architect.
Abundance of endangered shortnose sturgeon (Acipenser brevirostrum) in the Altamaha River in Georgia

Present address for contact author: School of Marine and Atmospheric Sciences Stony Brook University Stony Brook, New York 11790 Abstract—Anthropogenic perturbations during the 19th and 20th centuries resulted in major declines in abundance of populations of shortnose sturgeon (Acipenser brevirostrum). Despite the designation of this species as endangered, most populations are still not recovering. Abundance monitoring is needed to identify fluctuations in recruitment and survival; however, riverspecific assessments are deficient throughout the range of this species. During the summer of 2011, we used anchored nets to fish a closed population of shortnose sturgeon in the Altamaha River in Georgia. Markrecapture tagging resulted in the capture of 288 shortnose sturgeon (272 fish with unique marks and 16 fish recaptured) over 11 weekly sampling occasions. Estimates of the abundance of shortnose sturgeon were derived by using Huggins closedcapture models. The preferred model incorporated effects of temporal variation on weekly capture probability and estimated an abundance of 2218 individuals (95% confidence interval [CI]: 1424–3350), including 725 (95% CI: 455–1192) juveniles and 1493 (95% CI: 954–2409) adults. Point estimates of group abundance indicate that the population is dominated by adults, although dynamic shifts in size structure following periods of high recruitment have been reported to occur. This study continued previous markrecapture efforts in this river system, and its results offer further evidence that the Altamaha River supports the largest population of shortnose sturgeon south of Chesapeake Bay.
Query-augmented Active Learning for Large-scale Clustering

In this paper, we propose an active metric learning method for clustering large-scale data. The key idea of the new active clustering is to choose the informative instance pairs actively in terms of estimating the underlying metrics by incorporating unlabeled instance pairs, which leads to a more accurate and eﬃcient clustering process. Speciﬁcally, we formulate estimation and sampling processes based on a uniﬁed framework to select a metric favoring the clustering task to incorporate both within-cluster instance constraints and the implicit structure-level constraints. In addition, the proposed method is designed to increase the robustness on uncertainty from human-machine interaction at each step of active learning algorithm. Therefore we iteratively update a metric to gradually reﬁne a continuous cluster structure in the latent space. Numerical studies and theoretical properties all indicate that the proposed method is especially advantageous when the signal-to-noise ratio between relative features and irrelevant features is low, and the dimension of total features is high.
Suspended Metasurface Loaded Dual Band Dual Polarized Dipole Antenna Array with Polarization Flexibility for RF Energy Harvesting

The paper presents a dual band dual polarized antenna working at frequencies of 2.18 GHz and 2.9 GHz. The antenna has a realized gain of 8.2 dB and 8.78 dB at 2.18 GHz and 2.9 GHz respectively. This is integrated with a energy harvesting circuit through a network consisting of a rat race coupler and a dual frequency wilkinson power divider. The harvesting circuit provides a power conversion efficiency of 68.8 % at 5 dBm input power.
A Collective Approach to Scholar Name Disambiguation

Scholar name disambiguation remains a hard and unsolved problem, which brings various troubles for bibliography data analytics. Most existing methods handle name disambiguation separately that tackles one name at a time, and neglect the fact that disambiguation of one name affects the others. Further, it is typically common that only limited information is available for bibliography data, e.g., only basic paper and citation information is available in DBLP. In this study, we propose a collective approach to name disambiguation, which takes the connection of different ambiguous names into consideration. We reformulate bibliography data as a heterogeneous multipartite network, which initially treats each author reference as a unique author entity, and disambiguation results of one name propagate to the others of the network. To further deal with the sparsity problem caused by limited available information, we also introduce word-word and venue-venue similarities, and we finally measure author similarities by assembling similarities from four perspectives. Using real-life data, we experimentally demonstrate that our approach is both effective and efficient.
Antifungal activity and fire resistance properties of nano-chitosan treated wood

Fungal decay and fire resistance properties of wood treated with nano-chitosan-TPP particles were investigated. Quaternized and non-quaternized nano-chitosan particles crosslinked with a commercial fire-retardant, tripolyphosphate, were prepared from low molecular weight chitosan (with a molecular weight of 50 to 190 kDa). Different treatments were performed on southern yellow pine wood samples via a vacuum impregnation process for both quaternized and non-quaternized nano-chitosan-TPP particles with a concentration ratio of 12% to 4.8% (nano-chitosan to TPP). Both the leached and unleached treated wood samples were exposed to brown rot (Gloeophyllum trabeum) and white rot (Trametes versicolor) fungi according to AWPA standard E10-16. The flammability test was performed with a cone calorimeter according to ASTM standard E1354-15. The heat release rate and the mass loss rate were measured. The results of the fungal tests indicated that the quaternization of the nano-chitosan particles resulted in a reduced mass loss in the pine samples when exposed to Trametes versicolor under leached conditions. Additionally, without the quaternization of the nano-chitosan particles, the mass loss in the pine samples was reduced when exposed to Gloeophyllum trabeum under unleached conditions. The production of nano-chitosan-TPP particles had a significant effect on the fire-retardant activity of the treated wood samples.
APPLICATION OF EMPIRICAL MODE DECOMPOSITION TECHNIQUE TO ULTRASONIC SIGNAL DEVENDRA

In ultrasonic nondestructive testing (NDT), ultrasonic pulse echo-sign of reflection pulse is often disturbed via Electronic noise, structure noise and so forth. with the intention to enhance signal detection and evaluation the ultrasonic defect echo signal should be denoised. In this paper, a new noise cancellation of empirical mode decomposition is proposed for enhancing signal-to-noise of the ultrasonic echo-signal. The ultrasonic echo-signal was gathered through experimental machine system of ultrasonic data acquisition. The sign-to-noise ratio became more enhanced approximately up to 6 decibel (dB) adopting this method and thus it is easy to identify the ultrasonic defected signal. KeywordsUltrasonic Signal; Denoising; Empirical Mode Decomposition, NDT.
Understanding the Amazon from Space Deep Learning for Satellite Image Classiﬁcation

Motivated by the burgeoning commercial and research interest in satellite images of Earth, we developed various models able to efﬁciently and accurately classify the content of such images. In particular, we trained deep convolutional neural networks (CNNs) to learn image features and used multiple classiﬁcation frameworks including long short-term memory (LSTM) label captioning and binary cross entropy to predict multi-class, multi-label images. By ﬁne-tuning an architecture consisting of pre-trained Inception-v3 parameters trained on ImageNet data together with the LSTM decoder, we were able to achieve 88.9% F2 test accuracy – well within ﬁve percent of the state-of-the-art ensemble models used by industry leaders.