Miracle-dz/UniFAR-FTUs huggingface dataset:
Chapter 4 Visually Situated Language Comprehension in Children and in Adults

Prompted by the desire to test claims about procedural modularity of the language and vision systems (Fodor 1983), psycholinguistic research has examined the interplay of language comprehension with visual perception. This interplay is rapid, incremental, and closely temporally coordinated. It further appears to be bidirectional such that language guides visual attention and attended non-linguistic visual information in turn affects comprehension (henceforth ‘visually situated’ language comprehension). Information from a visual context can crucially inform syntactic disambiguation within a few hundred milliseconds, a finding that suggests core comprehension processes are not procedurally modular (Spivey et al. 2002; Tanenhaus et al. 1995). These and other findings have shaped accounts of visually situated language processing in which language guides (visual) attention and attended non-linguistic information can rapidly influence comprehension processes (e.g., Altmann and Kamide 2007; Knoeferle and Crocker 2006, 2007). However, most eye-tracking evidence on this temporally coordinated interplay comes from studies with healthy university students (approximately 18–31 years of age), thus focusing on a short interval of adult life and on a small population segment. To ensure the generality of language comprehension accounts, we could test them more broadly. This includes tests across the lifespan (e.g., in infancy, childhood, young adulthood, and older age, e.g., Carminati and Knoeferle 2013; Trueswell et al. 1999; Zhang and Knoeferle 2012), on diverse social population segments (e.g., working class people, academics, illiterates, or dialect speakers, e.g.
Exploring the Combined Effect of Shopping Values and Website Atmospherics on Online Shopping Behaviour – The Moderating Role of Perceived Risk

Authors Details: 1) Name: Subarna Nandy Professional Title: Research Scholar Institution: International Management Institute Address of Institution: B-10, Qutub Institutional Area, Tara Crescent, New Delhi110016 Email: subarna.f16@imi.edu 2) Name: Richa Dubey Professional Title: Research Scholar Institution: International Management Institute Address of Institution: B-10, Qutub Institutional Area, Tara Crescent, New Delhi110016 Email: richa.f16@imi.edu 3) Name: Pinaki Dasgupta Professional Title: Professor, Dean Administration & Placements & Corporate Relations Institution: International Management Institute Address of Institution: B-10, Qutub Institutional Area, Tara Crescent, New Delhi110016 Email: pinaki@imi.edu
Opening Up CA – An Interactional Linguist’s View on ICCA-14

Following the much-lauded conferences at Copenhagen in 2002, Helsinki in 2006, and Mannheim in 2010, the 4 installment of the International Conference on Conversation Analysis (ICCA-14) was the first of its kind to not take place in continental Europe. With that, the ICCA-14 moved to the home university of Conversation Analysis (CA): The University of California, Los Angeles (UCLA). It was there, some 50 years ago, in Fall 1964, that a young and aspiring sociology professor named Harvey Sacks gave his first Lectures on Conversation (now published as Sacks 1995a & b). Ten years later, at the very same university,
Experiments on weighted classifier fusion for autism detection using gene tic data

—Research in the health related field involves the use of high dimensional data where microarray gene expression data are used for the classifier based detection of diseases and abnormalities. Many machine learning tools and methods have been presented and proposed to detect diseases from microarray gene expression datasets where the overwhelming majority of work is for the detection of cancer. However, less attention is made to the detection of autism using such data. We experiment with autism detection using five gene expression data sets from five chromosomes. This data includes a low number of samples and a high number of features that reach tens of thousands. The task is difficult due to the large dimension of the data set and the high overlap in the class distributions. Therefore, a feature selection stage is necessary before the classifier and combiner design stages. We experiment with four feature selection methods, five classifier types and two existing combiner methods. Additionally, we propose six variants of a weighted fusion method, where this proposed method influences the classifier decision on a test sample based on its previous performance on the validation set. This is achieved by multiplying its decision by a predetermined weight. Results show that it outperforms or is equal to existing methods. This is achieved when the feature set size is very low reaching 50 or less
A Study on Interpersonal Relations among Employees in Dover Company in Bangalore

Organizations will get benefit when workers are having good relationship among themselves. An interpersonal relationship is the nature of interaction that occurs between two or more people. People in an interpersonal relationship may interact overtly, covertly, face-to-face or even anonymously. Interpersonal relationships occur between people who fill each other's explicit or implicit physical or emotional needs in some way. Interpersonal relationships may occur with their colleagues, superiors and subordinators in Dover Company in Bangalore. The main intentions of this paper is explaining the importance, types and how to improve interpersonal relations at work between employees, how the interpersonal relations at work are influenced by employees’ backgrounds. The demographic characteristics of employees that were expected to influence their perceptions of interpersonal relations were: the age, gender, educational level, and hierarchical level for which they work. The interpersonal relations between the coworkers, superiors and subordinators can be understood from this paper.
Inefficiencies due to Skill Choice

Some college degrees, such as engineering, provide multiple job options, which incentivize individuals to invest in them while making skill choices. However, this may lead to over investment in this skill type, which can eventually lead to higher unemployment among engineers, owing to the search frictions that are present in the economy. In this paper, I use a two-sector two-skill search theoretic model to analyze this problem and I find that this may lead to inefficient outcomes. A government intervention which penalizes individuals who invest in these skills and rewards workers who invest in skills which has less job options can help to reduce this inefficiency. 1 JEL classification: D61, E24, J24, J64
Some possibilities of risk measuring for a small and rather homogenous currency portfolio

For financial institutions, foreign exchange (FX) rates commonly constitute the most important part of the market risk. In order to assess the risk of a position several models can be used. However, since real FX returns exhibit higher than normal kurtosis and since the very far tails of the distribution should also be measured, the Monte Carlo simulation of multidimensional Lévy processes seems to be the most efficient approach. In this paper we show how a simplifying multidimensional Lévy model can provide a substantial improvement over a more standard approach of the (geometric) Brownian motion, when VaR (AVaR) is calculated and the backtesting procedure is regarded to be the most important criterion. The improvement is apparent mainly for a rather small and homogenous portfolio with requirement for a high degree of confidence of risk covering.
Classifying Information Sources in Arabic Twitter to Support Online Monitoring of Infectious Diseases

There is vast untapped potential in relation to the use of social media for monitoring the spread of infectious diseases around the world. Much previous research has focussed on English only, but the Arabic twitter universe has been comparatively much less studied. Motivated by important issues related to levels of trust, quality and reliability of the information online, here we consider the variety of information sources. As a first step, we find that numerous accounts disseminate information via Arabic social media, and we group them into five types of sources: academic, media, government, health professional, and public. We perform two experiments. First, native speakers judge whether they can manually classify tweets into these five groups, and then we repeat the experiment using various Machine Learning (ML) classifiers. We find that inter-annotator agreement is 0.84 for this task, and ML classifiers are able to correctly identify the type of source of a tweet with 77.2% accuracy without knowledge of the user and their bio or profile, but with 99.9% accuracy when provided with this information.
Proceedings of the International Workshop on the Scientific approach to the Acheiropoietos Images

Luigi Garlaschelli recently provided an interesting “Shroud-like” image. He used a variant of the well-known Nickell’s rubbing technique on a sheet lying on the body of a volunteer and a bas relief for the face. For the first time a beautiful whole front and back image made by chemical discoloration of the cellulose was obtained. After having explained the experiments, we examine the characteristics of the image at macroscopic level as well as at fabric, threads and fibers level to compare them with those of the Turin Shroud image. We conclude that most of the critical characteristics of the Turin Shroud image are very different from those of Garlaschelli’s image. As a consequence, it is unlikely a forger may have produced the body image on the Turin Shroud by this technique. We conclude the image is still not reproducible.
WHAT WE LEARN FROM CHINA’S RISING SHADOW BANKING: EXPLORING THE NEXUS OF MONETARY TIGHTENING AND BANKS’ ROLE IN ENTRUSTED LENDING

We argue that China’s rising shadow banking was inextricably linked to potential balance-sheet risks in the banking system. We substantiate this argument with three didactic findings: (1) commercial banks in general were prone to engage in channeling risky entrusted loans; (2) shadow banking through entrusted lending masked small banks’ exposure to balance-sheet risks; and (3) two well-intended regulations and institutional asymmetry between large and small banks combined to give small banks an incentive to exploit regulatory arbitrage by bringing off-balance-sheet risks into the balance sheet. We reveal these findings by constructing a comprehensive transaction-based loan dataset, providing robust empirical evidence, and developing a theoretical framework to explain the linkages between monetary policy, shadow banking, and traditional banking (the banking system) in
BUSINESS RISK MANAGEMENT AND ORGANISATIONAL PERFORMANCE : EMPIRICAL EVIDENCE FROM SMALL AND MEDIUM ENTERPRISES ( SMEs ) IN NIGERIA

Risks can have adverse consequence of business organizations particularly SMEs. This research therefore looked at the relationship between risk management practices and its effect on organizational performance of SMEs in the metropolis of Lagos. The survey design for this study was descriptive in nature. The research instrument for data gathering was interview schedule. The sample population is consisted of 58 respondents. The findings from the study thus revealed that there was no structured means for managing their risks and that the knowledge of individuals does not really apply in their risk management practice. The study recommends that Insurance companies should endeavour to increase insurance awareness among small and medium business opertaors; and also design packaged or combined policies that will cover a broad spectrum of losses that is suffered by SMEs such as fire, flood, and theft. Also, SMEs operators should endeavor to develop a culture of managing their risks.
Accarino, C. et al. (2019) A 64x64 SPAD array for portable colorimetric sensing, fluorescence and x-ray imaging. IEEE Sensors

We present the design and application of a 64x64 pixel SPAD array to portable colorimetric sensing, and fluorescence and x-ray imaging. The device was fabricated on an unmodified 180 nm CMOS process and is based on a square p+/n active junction SPAD geometry suitable for detecting green fluorescence emission. The stand-alone SPAD shows a photodetection probability greater than 60% at 5 V excess bias, with a dark count rate of less than 4 cps/μm and sub-ns timing jitter performance. It has a global shutter with an in-pixel 8-bit counter; four 5-bit decoders and two 64-to-1 multiplexer blocks allow the data to be read-out. The array of sensors was able to detect fluorescence from a fluorescein isothiocyanate (FITC) solution down to a concentration of 900 pM with a SNR of 9.8 dB. A colorimetric assay was performed on top of the sensor array with a limit of quantification of 3.1 μM. X-rays images, using energies ranging from 10 kVp to 100 kVp, of a lead grating mask were acquired without using a scintillation crystal.
eneralized shape constrained spline fitting for qualitative nalysis of trends

In this work, we present a generalized method for analysis of data series based on shape constraint spline fitting which constitutes the first step toward a statistically optimal method for qualitative analysis of trends. The presented method is based on a branch-and-bound (B&B) algorithm which is applied for globally optimal fitting of a spline function subject to shape constraints. More specifically, the B&B algorithm searches for optimal argument values in which the sign of the fitted function and/or one or more of its derivatives change. We derive upper and lower bounding procedures for the B&B algorithm to efficiently eywords:
Modeling temperature-dependent development and demography of Adalia decempunctata L.(Coleoptera: Coccinellidae) reared on Aphis gossypii (Glover) (Homoptera: Aphididae)

Development and demography of Adalia decempunctata L. were studied under laboratory conditions at seven constant temperatures (12, 16, 20, 24, 28, 32 and 36°C). First instar larvae failed to develop to second instar at 12°С and no development occurred at 36°C. The total developmental time varied from 47.92 days at 16°C to 15.94 days at 28°C and increased at 32°C. The lower temperature thresholds of 11.05 and 9.90°C, and thermal constants of 290.84 day-degree and 326.34 day-degree were estimated by traditional and Ikemoto-Takai linear models, respectively. The lower temperature threshold ( T min ) values estimated by Analytis, Briere-1, Briere-2 and Lactin-2 for total immature stages were 11.99, 12.24, 10.30 and 10.8°C, respectively. The estimated fastest developmental temperatures ( T fast ) by the Analytis, Briere-1, Briere-2 and Lactin-2 for overall immature stages development of A. decempunctata were 31.5, 31.1, 30.7 and 31.7°C, respectively. Analytis, Briere-1, Briere-2 and Lactin-2 measured the upper temperature threshold ( T max ) at 33.14, 36.65, 32.75 and 32.61°C. The age-stage specific survival rate ( s xj ) curves clearly depicted the highest and lowest survival rates at 16 and 32°C for males and females. The age-specific fecundity ( m x ) curves revealed higher fecundity rate when fed A. gossypii at 24 and 28°C. The highest and lowest values of intrinsic rate of increase ( r ) were observed at 28 and 16°C (0.1945 d –1 and 0.0592 d –1 , respectively). Also, the trend of changes in the finite rate of increase ( λ ) was analogous with intrinsic rate of increase. The longest and shortest mean generation time ( T ) was observed at 16 and 28°C, respectively and the highest net reproductive rates ( R 0 ) was estimated at 24 and 28°C. According to the results, the most suitable temperature seems to be 28°C due to the shortest developmental time, highest survival rate, and highest intrinsic rate of increase.
PHYSICALLY CONSISTENT DATA-DRIVEN SOFT-SENSOR DEVELOPMENT

Recent advances in artificial intelligence and data analytics have demonstrated that complex inputoutput relation can be learned by a deep neural network architecture when a large amount of training data is available. In development of data-driven soft-sensor for process control, there is always the concern whether sufficient physics were captured to allow for successful extrapolation and inference of other properties. To make up for the lack of important information, a dynamic model using past sensors readings and operating parameters was commonly used. However, simple input-output models in the form backpropagation neural work were shown to be unable to predict correct differential change of output (i.e. process gain), if the amount of data is not sufficient. To overcome this problem, one approach is to train a preliminary model using simulation data and use transfer learning to match plant data. It was found that gain consistency of the preliminary model can be preserved, while the prediction accuracy of the model can be substantially improved with only a limited amount of actual data. Alternatively, a semisupervised training approach can be used. A nonlinear state-space model can be formulated in an observer-predictor form using a sequence-to-sequence recurrent network encoder. The observer can be trained using the large amount of unlabeled data provided by flow, pressure and temperature sensors. This observer-encoder outputs a limited number of hidden states using unlabeled dynamic data in the past operating window. These hidden states and the current values of manipulated variables were then used as input for a predictor which can be developed with a limited number of labeled data. The gain sign is mostly consistent, indicating that sufficient physics was captured by the model.
Originalbeiträge Total dissolution and digestion methods for engineered metal nanoparticles

The application of engineered nanoparticles (ENPs) may lead to an increased occurrence in the environment. To assess the risk of metal NPs and to understand their behavior in the environment in different matrices ecotoxicological tests are required. For clear-cut conclusions it is often inevitable to quantify the total amount of added ENPs to the test systems. To achieve this task we developed different approaches for total dissolution and digestion of an assortment of metal NPs in various matrices. In this contribution the methods are described and performance characteristics are presented.
Detecting the Landscapes and Hotspots of Scientometrics : A Full-Text Citation Analysis based on Semantic Technology

With the rapid development of information technology, the era of Big Data has come. Big Data technology has brought great opportunities for the research of technology mining, while the "data dizzy" and "data redundancy" effects brought by it cannot be ignored. As one of the basic methods of technology mining, the research of scientometrics also faces the same opportunities and challenges. In order to meet the challenges, an in-depth analysis of scientometrics was conducted. By using the papers of Scientometrics in SpringLinker Database from 1978 to 2017, a Full-Text citation analysis based on semantic technology is used to quantitatively assess the basic status, landscapes, hotspots and future development trends of the “Scientometrics” research area. Besides traditional methods such as co-word analysis, main path analysis and sleeping beauty paper recognization, novel methods such as dynamic topic model and word vectors models are used, furthermore a three-dimensional visualization technology was proposed. It shows that these methods can provide a dynamic view of the evolution of scientometrics research landscapes, hotspots and trends from various perspectives which may serve as a potential guide for future research.
Placekeeping Ability as a Component of Fluid Intelligence: Not Just Working Memory Capacity

The question of what cognitive processes contribute to fluid intelligence (Gf)—the ability to solve novel problems—continues to be central in intelligence research. Here, we considered the contribution of placekeeping, which is the ability to perform a sequence of steps in a prescribed order without omissions or repetitions. Placekeeping plays a role in problem solving but also rests on the ability to remember past performance, so it may simply reduce to working memory capacity (WMC). To investigate this possibility, we evaluated whether placekeeping accounts for individual differences in Gf above and beyond WMC. Hierarchical regression analyses revealed that placekeeping ability accounted for 12% of the variance in Gf above and beyond WMC. By contrast, WMC accounted for only 2% of the variance in Gf above and beyond placekeeping ability. Structural equation modeling revealed that placekeeping ability and WMC are distinct at the latent variable level, and together they accounted for 77% of the variance in Gf. However, whereas placekeeping ability significantly predicted Gf in the structural equation model, WMC did not. In general, the results suggest that placekeeping ability is distinct from WMC and contributes substantially to individual differences in Gf.
Phase-field simulation on process-microstructure-property relation in power bed fusion additive manufactured metals

There exists large potential for the mechanical and functional property of the additively manufactured part to be optimized with respect to the process and material parameters. Compared with the expensive trial-and-error principle, physical models and numerical simulations are much more efficient in terms of both cost and time. They allow massive parameter studies which can provide large database for statistical analysis such as machine learning to extract the process-microstructure-property relation. One needs hence a reliable physical model, which can recapture the processing and the microstructure development. In this work we present a thermodynamically consistent phase-field model and the finite element simulations of microstructure evolution (pore and grain structure) during powder bed fusion additive manufacturing of metallic materials. Preliminary work has been done on the particle level [1-2] and the mesoscopic level [3]. Furthermore, we study both the selective laser sintering (SLS) [1] and selective laser melting (SLM) (Fig. 1). The model includes simultaneously complex underlying physics, such as extreme heat diffusion, melting, solidification and grain coalescence. The models are parameterized using measured thermodynamic and kinetic data. For the process simulation, we overcome the numerical challenges on powder bed deposition, power injection and strategies to reduce the computation cost by a novel algorithm analogy to minimum coloring problem and grain tracking approach. Simulation results will be shown for e.g. stainless steel 316L and Fe-alloys. We reveal the influences of process parameters e.g. the laser power and scan speed on the microstructure features, such as porosity, surface morphology, geometric variation of grains and densification. The simulation results will be discussed in comparison with the experimental data available in the literature. Moreover, through micromechanics tools, the effective mechanical properties of the simulated microstructure will be evaluated. Fig. 1 Preliminary FE phase-field simulation of selective laser melting. REFERENCES [1] Y. Yang, O. Ragnvaldsen, Y. Bai, M. Yi, B. -X. Xu, “Three-dimensional non-isothermal phase-field modeling of microstructure evolution during selective laser sintering”, arXiv preprint arXiv:1902.04519, (2019). [2] Y. Yang, M. Yi, B. -X. Xu, L. -Q. Chen, “Phase-field modeling of non-isothermal grain coalescence in the unconventional sintering techniques”, arXiv preprint arXiv:1806.02799, (2018) [3] M. Yi, B. -X. Xu, O. Gutfleisch, “Computational study on microstructure evolution and magnetic property of laser additively manufactured magnetic materials”, Computational Mechanics. https://doi.org/10.1007/s00466-019-01687-2 (2019)