Miracle-dz/UniFAR-FTUs huggingface dataset:
The Aggregate Productivity Effect of Labour and Capital Market Distortions in Canada

How efficiently are workers, investment capital, and production distributed across firms in Canada? And how have they varied over time, across regions, and between sectors? To answer these questions, we present novel measures of the degree of resourcemisallocation over time and space using uniquely detailed firm-level data (T2-LEAP) between 2001 and 2015. We find the dispersion of marginal returns to both capital and labour across firms have increased significantlyduring this period, suggesting allocative efficiency is deteriorating. Using a richbut tractable multi-sector model of heterogeneous firms, we find this rising misallocation accounts for over three-quarters of the increased productivity gap between Canada and the United States over this period. Specifically, our estimates suggest that increasing misallocation of labour and capital may lower aggregate productivity by 8.5 percent – equivalent to a real income decline of $200 billion per year. JEL Classification: D24, E24, O4, O51
Biotic interactions in a changing world: the role of feeding interactions in the response of multitrophic communities to rising temperature and nitrogen deposition

vii Acknowledgements ix Authorship declaration xi Chapter I: Introduction 1 1.1 A perspective of climate change in ecological research 1 1.2 How does climate change affect us? 2 1.3 The multiple axes of real-world complexity 4 1.3. 1 Biodiversity and the importance of interactions among species 4 1.3. 2 Biotic complexity: from species to networks 5 1.3. 3 Synergistic global environmental change drivers 7 1.4 Thesis objectives and outline 8 Chapter II: Plant-mediated and non-additive effects of two global change drivers on an insect herbivore community 12 2.1 Abstract 12 2.2 Introduction 13 2.3 Material and Methods 15 2.3. 1 Study site 15 2.3. 2 Experimental design 16 2.3. 3 Vegetation survey 18 2.3. 4 Data analysis 18 2.4 Results 20 2.4. 1 Plant community response to the environmental change drivers: . 22
DYNAMIC RESPONSE ANALYSIS FOR DEVELOPMENT OF FLEXIBLE LIGHTWEIGHT VEHICLE CHASSIS USING CAE TOOLS

This paper is elaborated the works done in studying dynamic response analysis of flexible lightweight vehicle chassis using CAE tools. The dynamic response analysis is implemented to analyze the lightweight material as pursue the automaker’s goal in making of the lighter vehicle. Light weighting is the concept that being used by an automaker in order to overcome the fuel consumption and performance. This analysis is developed by comparing the conventional structure which is steel and aluminum as an alternative material which is lighter than steel, as one of the ways in reducing the weight of a vehicle. This simulation model is designed with the geometric and conditions similarity for both types of material to develop the accurate analysis of dynamic response analysis of lightweight vehicle chassis. The study on the chassis is developed on the go-kart model. The simulation model of go-kart is developed as a multi-body model which is consisted of four tires linked to the chassis. The tires receive the disturbance force from the road and transmit to the chassis. Chassis is the important structure that carried the entire load attached to the vehicle such as, engine system, braking systems and steering systems. Chassis structure also carried the driver who is affected by vibration. As the go-kart model is designed without suspension systems, thus, the chassis structure can produce high level of vibration from the little source of disturbance. This vibration phenomena would effect on human health if exceeds the limit of endurance. The effect of the vibration of the structure is measured in acceleration, velocity and displacement. To validate the simulation model, the result of the physical model is used. Through this dynamic response analysis, indicating the steel chassis produces high levels of vibration rather than aluminum chassis. This simulation analysis method has described the ability of CAE in order analyzed the anticipated disturbance during the design stage of lightweights vehicle.
Performance Evaluation of Deep Learning Networks on Printed Odia Characters

Deep machine learning includes a series of layers to mimic the working of the human brain for taking a decision. Deep learning networks have shown good results in character recognition in the past. This paper evaluates the performance of different deep learning networks like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) based recurrent neural network and Convolutional LSTM on printed Odia characters. The Odia character database contains more than 24,000 images of printed Odia characters (including simple as well as complex characters) out of which 23,857 nos. of images are chosen for this work. Besides these three, a nested Convolutional neural network model is developed for different categories of printed character image groups which are formed based on their writing style. Here, in this study, the nested model is showing the best results in terms of error rate, accuracy and no. of epochs in comparison to the other three. Different pre-processing steps like binarization, size-normalization, blurring, interpolation, etc. are involved before passing the images to the deep neural networks to increase the recognition accuracy.
WORKING DRAFT: PLEASE DO NOT CITE OR QUOTE

Paper prepared for presentation at the annual meeting of the Population Association of America, Detroit, April 2009. Support for this project has been provided by the Spencer Foundation’s Major Grants Program and by the University of Minnesota’s Undergraduate Research Opportunities Program. However, errors and omissions are the responsibility of the authors. Please direct correspondence to Peter Wruck, Department of Sociology, University of Minnesota, 909 Social Sciences, 267 ~ 19 Ave. South, Minneapolis, MN 55455 or email wruc0007@umn.edu. Middle School Operating Levies and 8th Grade Mathematics and Reading
EIGHT CURVED BLADED KINETIC WATER TURBINE PERFORMANCE

The purpose of this research is to optimize the design of a new technology in the form of turbine that only relies on water flow rate to generate electricity to meet the needs of remote areas. This turbine is simple and has existed in some areas but has very low efficiency. A kinetic turbine is tested its prototype under a laboratory scale to get the turbine efficiency as the turbine performance. The results of this laboratory test will be verified with the turbine modeling implementing the CFD modeling software. In this modeling the observation focus is on the pressure distribution within the blade space which will produce the observation thrust. From the test results of this prototype, it is found that the highest kinetic turbine efficiency is 19% that is on a water flow rate of 45 m 3 /hour and 80 rpm turbine rotation. From the modeling observations of every 5 runner movement, it appears that there is only one turbine blade that gets the greatest boost or momentum, although at certain runner angle positions there are two turbine blades that get a boost. This condition is suspected as one cause of the low turbine efficiency. From the modeling of this kinetic turbine the highest water pressure in the blade chamber is about 9.19e + 008 Pa, which occurs at 20 runner position, while the lowest pressure is 5.93e + 008 Pa which occurs at 45 runner position.
Thyme Essential Oil and Disodium Fumarate on Ruminal Fermentation Characteristics , Microbial Population and

Intermediates in the succinate-propionate pathway such as organic acids can reduce the production of methane by acting as hydrogen acceptors (Martin, 1998; Lopez et al. 1999a; Newbold et al. 2005). Fumarate is a hydrogen sink and acting as C3 precursor in the rumen. Fumarate (salts and free acid form) and malate supplementation have been examined for manipulation and enhance the efficiency of the ruminal anaerobic fermentation process (GarciaMartinez et al. 2005; Lin et al. 2013; Riede et al. 2013). Previous studies have reported that inclusion of fumarate in ruminant diets, decrease methane production, C2:C3 ratio and ammonia nitrogen concentration, and increase the number of cellulolytic bacteria and organic matter (OM) disappearance (Lopez et al. 1999b). Newbold et al. (2004) demonstrated that sodium fumarate had efficient effects on fiber digestion, dry matter degradation and hydrogen acceptation in batch culture and rumen simulation technique (RUSITEC) compared with free acids and sodium acrylate. The aim of the present study was to investigate the effects of di-sodium fumarate (DSF) and thyme essential oil (TEO) solely and simultaneously on ruminal fermentation properties and microbial abundance. A dualflow continuous culture system (DFCC) with eight 1400-mL fermenters was used in a period of 12 d that divided to 9 d for adaptation and 3 d for sampling. Fermenters were fed 100 g d [dry matter (DM) basis] of a 50:50 alfalfa hay:concentrate ration. Treatments were no additive (control), 10 mM of DSF, 125 μL/L of TEO and simultaneous use of 10 mM of DSF, and 125 μL/L of TEO (SIMTF). Treatments had no effect on organic matter (OM) and neutral detergent fiber (NDF) disappearance, total and cellulolytic bacteria and protozoa abundance, large peptides and N-NH3 concentration of the effluent, N-NH3 and dietary N flow and molar proportions of acetate (C2), butyrate and isovalerate. DSF significantly increased crude protein (CP) degradation, the molar proportion of propionate (C3) and reduced C2:C3 ratio (P<0.05). TEO decreased (P<0.05) DM disappearance (-14.4%) and total volatile fatty acid (-19.4%) concentration, relative to the DSF. Relative to the control, small peptide plus amino acid N concentration was higher (P<0.05) in TEO treatment and DSF. SIMTF increased (P<0.05) the acid detergent fiber (ADF) disappearance and decreased the N-NH3 concentration from zero to 4 h after feeding. Total N, non-ammonia N and bacterial N flow and efficiency of microbial CP synthesis increased with SIMTF. Findings demonstrated that feed efficiency and ruminal fermentation were improved by the simultaneous use of DSF and TEO.
Who are admitted to a sport school ?

This article discusses the recruitment of pupils into a secondary school with a physical activity profile in Sweden. Two cohorts of girls and boys were studied longitudinally between the age of 13 – 15 (n=156). They were accepted into education in fierce competition (the number of the applicant was 437). The selection criteria were sport merits, not school marks. Thus the aim of running the school, besides the general curriculum, is sport talent identification and talent development. One of the assignments of the longitudinal and multidisciplinary research project Malmö Youth Sport Study (MYSS) is to establish who the chosen ones are, and to relate the result out of different factors – gender related, physiological, psychological and sociological. The article conclude that a number of social and socio-economic factors clearly influence the talent identification: the socioeconomic status and amount of sport capital of the parents, the pupils’ marks, and their birth month.
DNA methylation signatures of C-reactive protein associations with structural neuroimaging measures and major depressive disorder

Background: Inflammatory processes are implicated in the aetiology of Major Depressive Disorder (MDD); however, the relationship between peripheral inflammation, brain structure and depression remains unclear. This study investigates associations between depression, structural neuroimaging measures and two measures of inflammation: serum-based C-reactive protein (CRP) and a methylation-based score for CRP (DNAm CRP) in a large community-based sample. Methods: Serum CRP and DNAm CRP were assessed for participants in Generation Scotland (nMDD cases= 240, nControls= 558) alongside structural brain scans (T1 and diffusion MRI). Linear regression was used to investigate associations between (i) both CRP measures and depressive symptoms, (ii) both CRP measures and structural imaging variables and (iii) inflammation x MDD interaction effects (for both CRP measures) with imaging measures. Results: Increased serum CRP was significantly associated with overall MDD symptom severity and specifically with somatic symptoms- general interest ({beta}= 0.145, pFDR = 6x10-4) and energy levels ({beta}= 0.101, pFDR = 0.027) and also reduced entorhinal cortex thickness ({beta}= -0.095, pFDR = 0.037). DNAm CRP was significantly associated with reduced global grey matter/cortical volume and reduced integrity of 16 white matter tracts and showed larger effect sizes ({beta}average= -0.15) compared to serum CRP across all measures ({beta}average = 0.01). Conclusions: Acute measures of CRP were related to current depression symptoms, specifically somatic symptoms, whereas methylation signatures of inflammation demonstrated greater differences in global and regional brain structure. This study highlights the utility of combining serological and methylation markers to study chronic inflammation effects on the brain in psychiatric disorders.
Towards Faithful and Meaningful Interpretable Representations

Interpretable representations are the backbone of many black-box explainers. They translate the low-level data representation necessary for good predictive performance into high-level human-intelligible concepts used to convey the explanation. Notably, the explanation type and its cognitive complexity are directly controlled by the interpretable representation, allowing to target a particular audience and use case. However, many explainers that rely on interpretable representations overlook their merit and fall back on default solutions, which may introduce implicit assumptions, thereby degrading the explanatory power of such techniques. To address this problem, we study properties of interpretable representations that encode presence and absence of human-comprehensible concepts. We show how they are operationalised for tabular, image and text data, discussing their strengths and weaknesses. Finally, we analyse their explanatory properties in the context of tabular data, where a linear model is used to quantify the importance of interpretable concepts.
StoRIR: Stochastic Room Impulse Response Generation for Audio Data Augmentation

In this paper we introduce StoRIR - a stochastic room impulse response generation method dedicated to audio data augmentation in machine learning applications. This technique, in contrary to geometrical methods like image-source or ray tracing, does not require prior definition of room geometry, absorption coefficients or microphone and source placement and is dependent solely on the acoustic parameters of the room. The method is intuitive, easy to implement and allows to generate RIRs of very complicated enclosures. We show that StoRIR, when used for audio data augmentation in a speech enhancement task, allows deep learning models to achieve better results on a wide range of metrics than when using the conventional image-source method, effectively improving many of them by more than 5 %. We publish a Python implementation of StoRIR online
Do face masks introduce bias in speech technologies? The case of automated scoring of speaking proficiency

The COVID-19 pandemic has led to a dramatic increase in the use of face masks worldwide. Face coverings can affect both acoustic properties of the signal as well as speech patterns and have unintended effects if the person wearing the mask attempts to use speech processing technologies. In this paper we explore the impact of wearing face masks on the automated assessment of English language proficiency. We use a dataset from a large-scale speaking test for which test-takers were required to wear face masks during the test administration, and we compare it to a matched control sample of test-takers who took the same test before the mask requirements were put in place. We find that the two samples differ across a range of acoustic measures and also show a small but significant difference in speech patterns. However, these differences do not lead to differences in human or automated scores of English language proficiency. Several measures of bias showed no differences in scores between the two groups.
Stochastic Optimization Forests

We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e.g., product characteristics) to improve decision making with uncertain variables (e.g., demand). We show how to train forest decision policies for this problem by growing trees that choose splits to directly optimize the downstream decision quality rather than split to improve prediction accuracy as in the standard random forest algorithm. We realize this seemingly computationally intractable problem by developing approximate splitting criteria that use optimization perturbation analysis to eschew burdensome reoptimization for every candidate split, so that our method scales to large-scale problems. We prove that our splitting criteria consistently approximate the true risk and that our method achieves asymptotic optimality. We extensively validate our method empirically, demonstrating the value of optimization-aware construction of forests and the success of our efficient approximations. We show that our approximate splitting criteria can reduce running time hundredfold while achieving performance close to forest algorithms that exactly reoptimize for every candidate split. This paper was accepted by Hamid Nazerzadeh, data science.
Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance

Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes generalize variably between institutions, but ignored other levels of healthcare organization. We measured SciBERT diagnosis sentiment classifier generalizability between medical specialties using EHR sentences from MIMIC-III. Models trained on one specialty performed better on internal test sets than mixed or external test sets (mean AUCs 0.92, 0.87, and 0.83, respectively; p = 0.016). When models are trained on more specialties, they have better test performances (p < 1e-4). Model performance on new corpora is directly correlated to the similarity between train and test sentence content (p < 1e-4). Future studies should assess additional axes of generalization to ensure deep learning models fulfil their intended purpose across institutions, specialties, and practices.
Making Distributed Mobile Applications SAFE: Enforcing User Privacy Policies on Untrusted Applications with Secure Application Flow Enforcement

Today's mobile devices sense, collect, and store huge amounts of personal information, which users share with family and friends through a wide range of applications. Once users give applications access to their data, they must implicitly trust that the apps correctly maintain data privacy. As we know from both experience and all-too-frequent press articles, that trust is often misplaced. While users do not trust applications, they do trust their mobile devices and operating systems. Unfortunately, sharing applications are not limited to mobile clients but must also run on cloud services to share data between users. In this paper, we leverage the trust that users have in their mobile OSes to vet cloud services. To do so, we define a new Secure Application Flow Enforcement (SAFE) framework, which requires cloud services to attest to a system stack that will enforce policies provided by the mobile OS for user data. We implement a mobile OS that enforces SAFE policies on unmodified mobile apps and two systems for enforcing policies on untrusted cloud services. Using these prototypes, we demonstrate that it is possible to enforce existing user privacy policies on unmodified applications.
Effects of the treatment of the mass quadrupole moment on ray-tracing applications for rapidly rotating neutron stars

The Neutron Star Interior Composition Explorer (NICER) mission has provided a unique opportunity to constrain the equation of state of neutron stars by using the technique of pulse-profile modelling. This technique requires accurate and efficient ray-tracing, that in turn requires a robust representation of the space–time around a neutron star. Several exact and approximate metrics have been proposed, and used, to perform ray-tracing around neutron stars, with both moderate and fast rotation. In this paper, we perform a comparison between several of these metrics, when used for ray-tracing. We calculate the shape of the neutron star as seen by a distant observer using two different surface formulae, the thermal spectrum and pulse profiles from circular and crescent-shaped hotspots, for four configurations of pulsars with rotation rates ranging from 622 to 1000 Hz, and using both a moderate and a stiff equation of state to include realistic and extreme cases. We find small differences between the metrics for rotation frequencies starting at ${\sim}700\, \mathrm{Hz}$ that could theoretically be used for constraining the quadrupole moment or the space–time models. We also determine the practicality of use of each metric in larger-scale applications such as pulse-profile modelling.
Impact of a course transformation on students’ reasoning about measurement uncertainty

Physics lab courses are integral parts of an undergraduate physics education, and offer a variety of opportunities for learning. Many of these opportunities center around a common learning goal in introductory physics lab courses: measurement uncertainty. Accordingly, when the stand-alone introductory lab course at the University of Colorado Boulder (CU) was recently transformed, measurement uncertainty was the focus of a learning goal of that transformation. The Physics Measurement Questionnaire (PMQ), a research-based assessment of student understanding around statistical measurement uncertainty, was used to measure the effectiveness of that transformation. Here, we analyze student responses to the PMQ at the beginning and end of the CU course. We also compare such responses from two semesters: one before and one after the transformation. We present evidence that students in both semesters shifted their reasoning in ways aligned with the measurement uncertainty learning goal. Furthermore, we show that more students in the transformed semester shifted in ways aligned with the learning goal, and that those students tended to communicate their reasoning with greater sophistication than students in the original course. These findings provide evidence that even a traditional lab course can support valuable learning, and that transforming such a course to align with well-defined learning goals can result in even more effective learning experiences.
The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line

We present a holistic data-driven approach to the problem of productivity increase on the example of a metallurgical pickling line. The proposed approach combines mathematical modeling as a base algorithm and a cooperative Multi-Agent Reinforcement Learning (MARL) system implemented such as to enhance the performance by multiple criteria while also meeting safety and reliability requirements and taking into account the unexpected volatility of certain technological processes. We demonstrate how Deep Q-Learning can be applied to a real-life task in a heavy industry, resulting in significant improvement of previously existing automation systems.The problem of input data scarcity is solved by a two-step combination of LSTM and CGAN, which helps to embrace both the tabular representation of the data and its sequential properties. Offline RL training, a necessity in this setting, has become possible through the sophisticated probabilistic kinematic environment.