Miracle-dz/UniFAR-FTUs huggingface dataset:
Decomposing the focus effect: Evidence from reading

Investigations of linguistic focus in reading have found mixed results: Some report a decrease in reading times on focused material (Morris & Folk, 1998; Birch & Rayner, 2010), while others report an increase (Birch & Rayner, 1997; Benatar & Clifton, 2014; Lowder & Gordon, 2015; Sloggett, Rysling, & Staub, 2019). In three studies using the Maze task (Forster, Guerrera, & Elliot, 2009), newness was crossed with focus, and the context preceding a focused target manipulated whether or not alternatives to that target word were mentioned. A slowdown in response times on focused targets compared to non-focused targets was found, as well as a slowdown on new foci compared to given foci, suggesting a focus cost that does not reduce to newness. When alternatives to foci were contextually mentioned, the slowdown on new foci was significantly reduced, suggesting that presenting information about alternatives aids reading of foci, and providing converging evidence for the role of alternatives in focus processing (Braun & Tagliapietra, 2010; Fraundorf et al., 2010, 2013; Gotzner et al., 2016; Husband & Ferreira, 2015). Controlling for newness versus focus and contextual mention of alternatives clarifies earlier reading results: previous reading work only found a focus speed-up after contextual mention of alternatives with no newness contrast between foci and baselines, and only found a slowdown in the absence of alternatives.
Investigating Elements of Student Persistence in an Introductory Computer Science Course (Full Paper)

We explore how different elements of student persistence on computer programming problems may be related to learning outcomes and inform us about which elements may distinguish between productive and unproductive persistence. We collected data from an introductory computer science course at a large midwestern university in the U.S. hosted on an open-source, problem-driven learning system. We defined a set of features quantifying various aspect of persistence during problem solving and used a predictive modeling approach to predict student scores on subsequent and related quiz questions. We focused on careful feature engineering and model interpretation to shed light on the intricacies of both productive and unproductive persistence. Feature importance was analyzed using SHapley Additive exPlanations (SHAP) values. We found that the most impactful features were persisting until solving the problem, rapid guessing, and taking a break, while those with the strongest correlation between their values and their impact on prediction were the number of submissions, total time, and (again) taking a break. This suggests that the former are important features for accurate prediction, while the latter are indicative of the differences between productive persistence and wheel spinning in a computer science context
CASE STUDY Boron adsorption in semiarid Mediterranean soils under the influence of back ground electrolytes

BACKGROUND AND OBJECTIVES: Boron is a micronutrient of high importance, both for plant development and normal growth. The range between boron deficiency and toxicity is very narrow, which makes boron unique among the essential micronutrients. Boron adsorption is one of the most important factors determining the release and fixation of this micronutrient, though its adsorption has not been widely studied in semiarid Tunisian soils. This study aims to improve knowledge of B adsorption process in calcareous salt-affected soils in semiarid areas. It equally focuses on the type of cation (monovalent and divalent) in function of the soil texture and time of shaking. These three latter factors influence boron adsorption, which also influence the availability for plants. METHODS: A study was carried out on boron adsorption at different shaking time intervals (1, 3, 6 and 9 hours) in two soils of different textures in the absence and presence of different background electrolytes solutions (0.02 N CaCl2, 0.02 N MgCl2 , 0.02 N sodium chloride and 0.02 N potassium chloride. FINDINGS: The soil-A (clay loam) adsorbed more boron than soil-B (sandy loam). Boron adsorption was the highest in Soil-A under the presence of potassium chloride, close to the mean values given when using calcium chloride. In Soil-B, it was found with calcium chloride background electrolyte. Minor boron adsorption was observed in both soils when boric acid solution was used without background electrolytes. Adsorbed boron showed significant differences with the shaking time in all treatments used with background electrolytes solutions, except for boron solution treatment without background electrolyte in both soils. As a comparison of divalent and monovalent cations, boron adsorbed content was higher with the solution containing calcium than in sodium chloride solution, due to the fact that calcium carbonate is an important boron adsorbing surface. CONCLUSION: This study reveals that the best conditions for maximum boron adsorption are defined by calcium chloride background electrolyte in this type of soil in a determined shaking time interval of 3 hours. This causes a low rate of boron assimilated by plants, which leads to the decrease of the crop yield and the agricultural production, and subsequently hurt the Tunisian national economy. ABSTRACT
Unknown Family Detection Based on Family-Invariant Representation

Researchers and security vendors have proposed detection systems leveraging machine learning. However, these systems potentially have a vulnerability; they poorly detect malicious data created with newly developed attack tools (unknown families). If features of unknown families signiﬁcantly differ from those of known families, detecting unknown families is extremely difﬁcult. We focus on the tendency that some features are inherent across different families because attackers have to use such features to exploit vulnerabilities or reduce the cost of attacks. Based on this insight, we propose a method, i.e., a neural network, that classiﬁes based on an invariant representation in similar known families for improving in the detection of unknown families. The family-invariant representation is learned with adversarial training: an optimization method used in domain adaptation methods. Speciﬁcally, we calculate the prediction probabilities of families using a conventional neural network and optimize the representation to confuse families whose predicted probabilities are similar. We applied our proposed method to a malicious communication-sequence detection system. Our method outperformed a conventional neural network in terms of the true positive rates of unknown families by at most 19%.
DB121228 3839..3850

Oxidative stress and endothelial dysfunction contribute to vascular complication in diabetes. Extracellular superoxide dismutase (SOD3) is one of the key antioxidant enzymes that obtains copper via copper transporter ATP7A. SOD3 is secreted from vascular smooth muscles cells (VSMCs) and anchors at the endothelial surface. The role of SOD3 and ATP7A in endothelial dysfunction in type 1 diabetes mellitus (T1DM) is entirely unknown. Here we show that the specific activity of SOD3, but not SOD1, is decreased, which is associated with increased O2 c2 production in aortas of streptozotocin-induced and genetically induced Ins2 T1DM mice. Exogenous copper partially rescued SOD3 activity in isolated T1DM vessels. Functionally, acetylcholine-induced, endotheliumdependent relaxation is impaired in T1DM mesenteric arteries, which is rescued by SOD mimetic tempol or gene transfer of SOD3. Mechanistically, ATP7A expression in T1DM vessels is dramatically decreased whereas other copper transport proteins are not altered. T1DM-induced endothelial dysfunction and decrease of SOD3 activity are rescued in transgenic mice overexpressing ATP7A. Furthermore, SOD3-deficient T1DM mice or ATP7A mutant T1DM mice augment endothelial dysfunction and vascular O2 c2
CAMBRIDGE WORKING PAPERS IN ECONOMICS JANEWAY INSTITUTE WORKING PAPERS People-centric Emission Reduction in Buildings: A Data-driven and Network Topology-based Investigation

There is a growing consensus among policymakers that we need a human-centric low-carbon transition. There are few studies on how to do it effectively, especially in the context of emissions reduction in the building sector. It is critical to investigate public sentiment and attitudes towards this aspect of climate action, as the building and construction sector accounts for 40% of global carbon emissions. Our methodology involves a multi-method approach, using a data-driven exploration of public sentiment using 256,717 tweets containing #emission and #building between 2009 2021. Using graph theory-led metrics, a network topology-based investigation of hashtag co-occurrences was used to extract highly influential hashtags. Our results show that public sentiment is reactive to global climate policy events. Between 2009-2012, #greenbuilding, #emissions were highly influential, shaping the public discourse towards climate action. In 2013-2016, #lowcarbon, #construction and #energyefficiency had high centrality scores, which were replaced by hashtags like #climatetec, #netzero, #climateaction, #circulareconomy, and #masstimber, #climatejustice in 2017-2021. Results suggest that the current building emission reduction context emphasises the social and environmental justice dimensions, which is pivotal to an effective people-centric policymaking. Reference Details 2202 Cambridge Working Papers in Economics 2022/01 Janeway Institute Working Paper Series Published 5 January 2022
Using topography to aid cell phone geolocation

Kim, Jaeyoung. M.S.M.E, Purdue University, December 2015. Using Topography to Aid Cell Phone Geolocation. Major Professor: Kartik Ariyur, School of Mechanical Engineering. In daily life, people demand accuracy of the Global Positioning System (GPS) receiver. The current problem of GPS on mobile phones is that it is not available in areas such as urban, natural canyons, forests, and indoor environments. Several methods have been developed to obtain more accurate position estimation over the past years. The received signal strength (RSS) and time difference of arrival (TDOA) are the main approaches to use available mobile signals and errors around 4 ∼ 12 dB and 10 ∼ 60 meters, respectively. Another approach to make a better performance of the sensor is to use radio frequency identification (RFID) with indoor Wi-Fi. A new method from our group shows that using magnetic field intensity maps based on interval analysis can perform better than the RSS, TDOA and RFID and reduce error for geolocation in some areas where GPS is not accessible. In our study, we develop a novel algorithm where sensor measurements on the cell phone are used to construct the topographic maps and aid cell phone geolocation which focuses on the angles of inclination in user’s pathway when GPS is spotty. This can be particularly useful on uneven terrain outdoors. For sensor characterization, we use application in android operating system of smartphone by name of sensor stream IMU+GPS. The sensor stream allows for users to observe, select or record the current values of various measurements such as accelerations, angular rates (gyroscope), magnetic fields, GPS position and received signal strength indication (RSSI) in 3-dimensional coordinate system. We firstly develop algorithms of fast fourier transform and low pass filter to find the accurate vertical acceleration measurements which impact values are corresponded to step occurrences. Before analyzing position estimation, we use
Towards optical diagnostics and control in aerotaxy semiconductor nanowire growth Towards optical diagnostics and control in aerotaxy semiconductor nanowire growth

Aerosol growth of semiconductor nanowires has the potential to dramatically lower the cost of for solar cell production. The objective of the work was to study the growth of aerosol nanoparticles and nanowires, in order to understand the chemical and physical processes involved. This was accomplished by studying the early parts of growths using optical diagnostic techniques. The work involved developing a platform providing the chemical environment enabling in-situ measurements, as well as developing the necessary optical and spectroscopic techniques. The thesis contains two main parts. The first part concerns optical measurements in a spark discharge for generation of gold nanoparticles that acts as seeds for nanowire growth. A discharge system was designed which produces metal nano-aerosols by condensation of atomic vapor ablated from the gold electrodes. The discharge system was characterized electrically, and ex-situ with respect to the particles generated. A laser triggering scheme was developed, enabling synchronization of the self-pulsing discharge to a pulsed probe laser. The discharge plasma was studied using a combination of broadband emission spectroscopy, pulsed laser-induced fluorescence, and highresolution absorption spectroscopy in a spatially and temporally resolved manner. In order to quantify the particles produced in-situ, the photoluminescence from airborne gold, silver, and copper nanoparticles was studied, and spectrally characterized. The quantum efficiency of photoluminescence from gold nanoparticles was determined, providing an optical diagnostic tool for future quantitative in-situ measurements. The second part concerns the metal-organic vapor providing the metal atom that constitute the trunk of the nanowires. An optically accessible reactor setup was constructed. Indium atoms were produced both by pyrolysis and pulsed laser photolysis and studied quantitatively using high resolution laser-induced fluorescence and absorption spectroscopy, spatially and temporally resolved. Scattering measurements revealed large amounts of indium droplets produced by pulsed photolysis.
WIRELESS TELEMETRY: CHANNEL CHARACTERIZATION AND STATISTICAL IMPUTATION OF MISSING VALUES WIRELESS TELEMETRY: CHANNEL CHARACTERIZATION AND STATISTICAL IMPUTATION OF MISSING VALUES

variations. An implemented communication wireless telemetric system is presented. It has been built to serve as a measuring station at Thermopiles hot springs. The present work analyzes the method applying several models for adjusting and optimizing the functions of the system. The system also includes a unit that receives and stores data in an appropriate form, ready to be used for statistics. Data of critical physicochemical parameters are continuously measured, processed and transmitted over a wireless link. Path loss is investigated in order to ensure reliable signal reception. In the examined case study of low SNR region with the integrated in the open field sensors, missing data can be a common occurrence. The development of a mathematical technique, based on best prediction, for mitigating the missing values problem is of great significance in establishing a reliable system of wireless telemetry. A novel technique for handling missing values is proposed. An algorithm that evaluates missing values for imputation has been developed, based on a new general linear model analysis for prediction. The full detailed code, is explained and applied on the data that have been captured by the measuring station.
Tomato Leaf Disease Detection using Deep Learning Techniques

— Plant diseases cause low agricultural productivity. Plant diseases are challenging to control and identify by the majority of farmers. In order to reduce future losses, early disease diagnosis is necessary. This study looks at how to identify tomato plant leaf disease using machine learning techniques, including the Fuzzy Support Vector Machine (Fuzzy-SVM), Convolution Neural Network (CNN), and Region-based Convolution Neural Network (R-CNN). The findings were confirmed using images of tomato leaves with six diseases and healthy samples. Image scaling, color thresholding, flood filling approaches for segmentation, gradient local ternary pattern, and Zernike moments’ features are used to train the pictures. R-CNN classifiers are used to classify the illness kind. The classification methods of Fuzzy SVM and CNN are analyzed and compared with R-CNN to determine the most accurate model for plant disease prediction. The R-CNN-based Classifier has the most remarkable accuracy of 96.735 percent compared to the other classification approaches.
Combining Binary Classifiers Leads to Nontransitive Paradoxes

—We study collections of binary classifiers trained on different pairs of target classes and observe cases of nontransitive disagreement. Noting a similar phenomenon in voting theory, we seek to understand how this nontransitivity emerges from aggregation and the limits of its behavior. We prove that a previously developed framework, called expert graphs , is equivalent to a framework used in voting theory called linear ordering graphs (LOGs). Finally, we show that the curl condition , which limits the degree of nontransitivity in expert graphs and LOGs, precisely characterizes the maximum possible nontransitivity in two types of planar graphs, namely triangulated cycles and wheel graphs, and we conjecture that it does so for all planar graphs.
Adaptive Least Squares: Recursive LS with Constant Noise-to-Signal Ratio

Adaptive Least Squares (ALS), a refinement of the Constant Gain Recursive Least Squares (CG-RLS) algorithm proposed by Ljung (1992) and Sargent (1993, 1999), is a method of estimating time-varying relationships and of proxying agents’ timeevolving expectations. By holding the noise-to-signal ratio constant rather than the Kalman gain as in CG-RLS, it nests the univariate Local Level Model, with its optimally declining but bounded gain, and permits the hyperparameters to be estimated by Maximum Likelihood from the time series itself. The algorithm is easily initialized with an uninformative prior on the regression coefficients. The ALS filter algorithm is illustrated with a univariate time series model of PCE inflation through Nov. 2021, when there had just been a run of high inflation months. With the preferred AR(2) model, the estimated noise-to-signal standard deviation ratio is 28.4 months, implying an asymptotic gain of 1/28.9 mo-1. The sum of the AR coefficients varies over the decades from near-unit root behavior to virtually zero, with often substantial difference between short-run and long-run inflation forecasts. Although the one-month-ahead forecast from Nov. 2021 was 5.68%, the estimated "entrenched" long-run inflation rate then was only 3.10%. An ALS smoother, that uses both past and future data, is developed. Simulations are used to quantify the uncertainty in the model's implied forecasts, which is considerable. The Jarque-Bera statistic rejects i.i. d. normality, suggesting that a particle filter with non-Gaussian errors would be an appropriate extension.
3D-RPP: a novel 3D vision-based Pose Perception Approach for Industrial Robots

Enhanced by artificial intelligence, industrial robots are becoming more powerful, gaining a large variety of applications in intelligent factories. Pose perception, the aim of which is to obtain the joint coordinates of the robot in the camera coordinate system, has been proposed as a promising technology for multi-robot intelligent interaction. A large number of efforts have been made on studying robot pose perception. However, most of existing studies estimate the position of robot joints in 3D space by just using 2D color images, and perform pose perception by estimating the position of joint key points in the image, which could become infeasible in scenarios with various camera views and background environments. To address the issue, considering the information about the robot itself as a prior knowledge, we propose a novel approach for robot pose perception, named 3D-RPP. We adopt a 3D visual point cloud to estimate the rigid transformation of the camera coordinate system with respect to the robot base coordinate system, which effectively improves the accuracy of the obtained robot joint position in the camera coordinate system. We conduct extensive experiments on ROKAE xMate3 robot to investigate the performance of 3D-RPP, and the experimental results show that 3D-RPP could solve the pose perception problem well.
Mitigation of voltage sags in distribution line system using static VAR compensator and static synchronous compensator

Voltage sag is one of the power quality disturbances most frequently by customers because it can cause economic loss for the customers especially industries and commercial customers. The main source of voltage sags are faults in transmission and distribution beside two others namely motor large starting and transformer energizing, both the voltage sags have less effect on the equipment. Voltage sags can cause degradation performance of the equipment, and it depends on the magnitude and the duration. The voltage sags that occur in the power system can be compensated with installed Static VAR compensator (SVC) and Static synchronous compensator (STATCOM) in the distribution system bus. The purpose of the study is to compare both types of the device used to mitigate voltage sags in simulation using Alternative transient program (ATP) software. The voltage sag results from the short circuit faults. The method proposes the installation of the two compensator devices alternatively at one of the IEEE thirteen busses systems. The result shows the STATCOM is better than the SVC to handle mitigation of voltage sags compared with the SVC.
Joint Dilated Convolution and Self-attention for Cross-domain Sentiment Analysis

Cross-domain sentiment analysis aims to use source domain resources or models to serve the sentiment analysis tasks in the target domain, and it can effectively alleviate the problem of insufficient tagged data in the target domain. In this paper, we investigate deep transfer learning for text sentiment analysis, and focus on customer reviews for different domains. Given the shortcomings of the conventional deep neural network, such as convolutional neural network (CNN) cannot obtain the long-term dependence between features, recurrent neural network (RNN) cannot achieve parallel computing, we propose a novel neural network which joint dilated convolution and self-attention (ADC). The ADC takes residual learning as its basic framework, which can capture more distant information by introducing a dilation rate in the convolution. Self-attention is used to make the ADC pay more attention to important sentiment information. First, the ADC is pre-trained with the data of the source domain, and the weight of the pre-training is frozen. Then, the model is fine-tuned by the method of gradual unfreezing, and the weight is updated through the data of the target domain. Finally, the updated model can be utilized for the target domain sentiment analysis. By using customer review datasets, we carry out extensive experiments to demonstrate that this method has superior performance and can better achieve cross-domain sentiment analysis.