Miracle-dz/UniFAR-FTUs huggingface dataset:
Coronary Heart Disease

Coronary heart disease (CHD) is the most important cause of death and the single biggest cause of premature death in modern industrialized countries (Lorimer, 1997). It accounts for up to 28 per cent of all deaths in the United Kingdom (UK) (Office of Health Economics, 1992). A government action plan for tackling poor health and improving health specifically highlights the need to reduce the death rate from CHD (Department of Health [DOH], 1999). Several modifiable risk factors for CHD are well-recognized and healthcare professionals have an important role to play in its prevention, as well as, the diagnosis and management of the disorder (Lindsay and Gaw, 1997). The National Service Framework for CHD (DOH, 2000) reaffirms the government’s eagerness to tackle the problem. The purpose of the framework is to transform the prevention, diagnosis and treatment of CHD. It is viewed as a means of helping professionals to give better, fairer and faster care everywhere. It is quite important that prevention is seen as a significant measure to be adopted (standards three and four) if heart disease is to be reduced in the population as a whole (standards one and two).
Detecting abandoned object in surveillance videos

The motive of this project was to detect and localize anomalies in surveillance videos. The main idea of this work is based on [1] (Anomaly Localization in Topic-based Analysis of Surveillance Videos). The objective was completed by extending [1], by considering a different foreground extraction mechanism such that it also includes static objects as anomalies. Figure 1:Car stopping after parking spot should treated as anomaly. CS365A Project Report
Effect of Prenatal Citalopram Exposure on Motor and Cognitive Functions of Rats

Aim: Physicians mostly prescribe selective serotonin reuptake inhibitors in the treat-ment of depression in pregnancy. However, there is little information on teratogenic effects of selective serotonin reuptake inhibitors. This study aims to investigate the ef-fects of prenatal exposure to citalopram, one of the most prescribed antidepressants, on developmental characteristics, reflex and motor functions of rat pups. Materials and Methods: 12-14 weeks old, pregnant Sprague-Dawley rats were used in the experiments. Rats were divided into 3 groups and separated into individual cag-es. When treatment groups received 5 and 20 mg/kg/d citalopram by orogastric ga-vage from gestational days 1 to 18, control group received the same amount of saline (2 ml/kg/d). After birth duration of gestation, number of live and dead pups and gross malformation are evaluated. Eye opening, pinna detachment, incisor eruption, the development of fur and weight gain were monitored as developmental parameters. Markers for reflex development were identified as righting reflex, negative geotaxis and grip response. Motor developments and cognitive functions were established with locomotor activity test, T-maze, holeboard, Y-maze and passive avoidance test. Results: Developmental parameters, reflex, motor and cognitive development as-sessments of pups were not significantly different in treatment groups compared to control group. Conclusion: The results of the study show that prenatal exposure to citalopram has no effect on motor and cognitive functions of rat offspring.
Functional load — comparisons over measures and texts

Functional load is a measure of the work that some feature of a language does, usually a phonological feature. It has its origins in the idea of the phonology of a language serving to differentiate the words of the language and avoid homonymy, as a measure of the degree of utilisation of a phonemic opposition or phonological unit. In English for instance c distinguishes cat from bat, chat, fat, hat, mat, gnat, pat, rat, sat, tat, vat, the pronunciations of which are similar except for the initial sound (phoneme). The functional load of the distinction between t and d is the number of wordpairs (minimal pairs) distinguished: tin/din, ton/done, writer/rider, bit/bid, etc. The concept can apply not just to phonemes but to phonological distinctions in general. The functional load of voice could be measured as the number of minimal pairs distinguished by voice: tin/din, pin/bin, tie/dye, thigh/thy, pie/bye, fie/vye, etc. King (1967b) traces the idea back to the early twentieth century, noting similar thinking in the work of Gilléron (1918, p. 14), later developed by Mathesius (1929) and other linguists of the Prague Circle, and particularly by Martinet (1933; 1955). Though the basic idea is clear enough, functional load is hard to define precisely in terms of minimal pairs (see below). Hockett in 1955 suggested conceiving of functional load in a different way, one based on the then-new load: comparis ns over measur s and text
Does a dog desire cake?-Expanding Knowledge Base Assertions Through Deep Relationship Discovery

Relationship discovery between two entities is a problem that has to be addressed when constructing a Knowledge Base (KB). A solution to this problem is important because the KB built from the discovered relations can play a key role in downstream tasks, such as analogical reasoning. An example of this kind of reasoning is whether a dog desires cake: a dog is an animal, cake is food, animals desire food therefore a dog desires cake. We constructed a system that that is trained on a commonsense KB and whose inputs are pairs of concepts and its outputs are the strength of commonsense assertions between the concepts. Our approach is unique because it can handle out of vocabulary entities and can generalize commonsense to out of knowledge concepts. We utilize the system to be able to infer the answer for out of knowledge assertions such as the aforementioned whether a dog desires cake.
Number of laminae on the mechanical behavior of glued laminated timber (glulam) of Toona ciliata produced with vegetable polyurethane adhesive

ABSTRACT The glued laminated timber (glulam) is a composite material obtained from the bonding of wood laminae arranged with parallel fibers put together. The purpose of this paper was to evaluate the influence of the number of laminae on the mechanical behavior of glulam of Toona ciliata produced with vegetable-based polyurethane adhesive. The glulam beams were produced with lamellae in the dimensions of 6.0 x 1.5 x 120.0 cm, with 3 beams being made per treatment, which were structurally composed by 5, 7 and 9 laminae. The polyurethane adhesive based on castor oil was used, applying a grammage of 350 g m-². For the technological characterization of the wood the moisture in the dry base, bulk density, chemical characterization, compressive strength parallel to the fibers, shear and modulus of elasticity (MOE) and rupture (MOR) in the static bending test. While the properties of modulus of elasticity (MOE), modulus of rupture (MOR), shear strength, delamination and visualization of the glue line through photomicrographs of the laminated wood samples were determined for glulam beams. The results showed that the beams with 5 laminae presented values similar to solid wood. The increase in the number of laminae provided lower values for stiffness and strength of the beams. The photomicrographs showed the quality of the bonding and the satisfactory values for shear strength and delamination, indicating the viability of the species under study for situations with high moisture content.
Explaining Income Inequality and Social Mobility: The Role of Fertility and Family Transfers∗

How much of social mobility and income inequality is due to initial opportunities relative to adult income risk? Previous studies have yielded very wide estimates due to data limitations. To provide a more precise answer this article builds on a standard heterogeneous agent life cycle model with idiosyncratic income shocks. We propose that fertility differentials between rich and poor households can lead to substantial differences in the resources available for children, which can be important for their adult outcomes. Accounting for this is essential for the proper evaluation of initial opportunities, so we extend the model to introduce the role of families through endogenous fertility, family transfers and education. We find that initial conditions as of age 13 account for more of adult income inequality than do labor income shocks. Moreover, fertility differentials and family transfers are found to account for over 50% of the social mobility in the data. JEL Classifications: D91, J13, J24, J62.
Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment

The increase in the internal temperature of high voltage electrical instruments is due to a variety of factors, particularly, contact problems; environmental factors; unbalanced loads; and cracks in the high voltage current transformers, voltage transformers, insulators, or terminal junctions. This increase in the internal temperature can cause unusual disturbances and damage to high voltage electrical equipment. Therefore, early prevention measures of thermal anomalies in equipment are necessary to prevent high voltage equipment failure that might shut down the whole grid system. In this article, we propose a novel non-destructive approach to defect analysis in high voltage equipment by taking advantage of the infrared thermography and the deep learning (DL) approach from the machine learning paradigm. The infrared images of the components were captured using the FLIR T630 without disturbing the operations of the power grid. In the first stage, rich features maps from the convolutional layers of the AlexNet pretrained model were extracted. After feature extraction, the random forest (RF) and support vector machines (SVM) were trained for learning of the defective and non-defective high voltage electrical equipment. In an experimental analysis, the RF optimally learned the separation between defective and non-defective equipment with greater than 96% accuracy, outperforming all the other comparative approaches for deep and nondeep features. The proposed approach based on the RF is reliable and shows its efficacy for fault detection in high voltage electrical equipment.
Studying school leadership practice: A methodological conversation

This paper seeks to engage the field of educational leadership in a methodological conversation. It outlines a methodological position taken by the author (a focus on leadership practice using Bourdieu) in a currently funded project and seeks discussion with the wider field. The paper is structured to provide the reader with background to the theoretical position of the author and the implications this has for the study of school leadership practice. The discussion highlights the need for multi-layered descriptions of leadership and the dual purpose of leadership research. This provides for a greater understanding of the phenomenon of leadership, and the subsequent implications for leadership practice and, potentially, preparation. The intent of this paper is not merely to critique existing works in the field, nor to create a modernistic binary between the positions of others and those presented here. Rather, the value of this work is its potential to intervene in the scholarship of the field and disrupt the status quo, highlighting the benefits of embracing the complexity, ambiguity and moment-by-moment contestation that defines the social practice that is school leadership.
Modeling Electrical Switching Behavior of Carbon Resistive Memory

The amorphous carbon-based resistive memory has recently attained vast attention due to its non-volatility, fast switching speed, long data retention, and multilevel recording. However, the memory switching mechanism of amorphous carbon media remains mysterious and thus severely restricted its application prospect. To resolve this issue, a comprehensive three-dimensional model by simultaneously solving the current continuity equation, heat transfer equation, and mass concentration equation, is developed to model the physical conversions between sp2 and sp3 clusters. According to simulations, electric field was considered as the sole critical factor that determines the formation of conductive sp2 filament during the ‘SET’ process, whereas the ‘RESET’ process is mainly attributed to the induced high temperature that accelerates the growth of sp3 cluster and causes the rupture of the sp2 filament. It was additionally found that the sp2 filament was preferably formed inside the region having larger sp2 concentrations, and its rupture usually initiates from the filament center. The threshold voltages of carbon resistive memory for different thickness and different sp2 fractions were also calculated and exhibited good agreement with experimental measurements.
Randomized ULV Decomposition for Approximating Low-Rank Matrices

In this paper, we present a low-rank matrix approximation algorithm called Randomized Rank-k ULV (RR-ULV) decomposition. Fundamental in our work is the exploitation of the randomized sampling paradigm, which provides an efficient strategy in order to construct an approximation of a large input matrix. Our proposed RR-ULV is computationally efficient, robust, highly accurate, and can also harness modern computational platforms. We apply RR-ULV to randomly generated data and real world data: we consider reconstructing a low-rank image, and further solving the robust principal component analysis task to validate its efficacy and efficiency. Our experimental results demonstrate that RR-ULV outperforms the existing methods.
Awassi sheep as a genetic resource and efforts for its genetic improvement

Awassi is the most widespread sheep breed of non-European origin. The breed adapts to a wide range of environmental conditions from the steppe to the highly intensive system. Performance of the breed varies according to production environment and strain, the Israeli Improved Awassi being the heaviest and producing the highest amount of milk. Efforts to genetically improve milk production yielded positive results. In Israel the phenotypic average of lactation milk production increased from 297 kg in the 1940’s to over 500 kg in the 1990’s, while in Syria a selection program succeeded to increase it by 13% in eight years. In Turkey, the mean milked yield of ewes increased from 67 to 152 kg in a selection/outcrossing program that lasted for seven years. Although Awassi is best known for its high milk production, it is often used as a triple purpose sheep in most of the countries of its origin in the Middle East. Heritability estimates in different traits are within those for other populations of sheep but recent estimates for milk yield in the Improved Awassi indicated lower heritability and higher contribution of nonadditive genetic effects.
FrictionHaptics : Encountered-Type Haptic Device forTangential Friction Emulation

We present FrictionHaptics which is an encountered-type haptic device that emulates friction when a user touches a virtual object by using a rotating sphere as an end effector to a 3DOF robot arm. Our proposed device has two advantages. Firstly, the device creates a tangential friction sensation of a virtual object, even the size of the real object is limited. Secondly, compared to wearable-type or grip-type, our device does not limit the sensing part of a human. We conducted a perceptual experiment to determine how a human perceives friction generated from our proposed device. As a result, we found that our device renders a reliable illusion of tangential friction even the human perceive it using different sensing part.
Predicting Asthma Attacks: Effects of Indoor PM Concentrations on Peak Expiratory Flow Rates of Asthmatic Children

Despite ample research on the association between indoor air pollution and allergic disease prevalence, public health and environmental policies still lack predictive evidence for developing a preventive guideline for patients or vulnerable populations mostly due to limitation of real-time big data and model predictability. Recent popularity of IoT and machine learning techniques could provide enabling technologies for collecting real-time big data and analyzing them for more accurate prediction of allergic disease risks for evidence-based intervention, but the effort is still in its infancy. This pilot study explored and evaluated the feasibility of a deep learning algorithm for predicting asthma risk. It is based on peak expiratory flow rates (PEFR) of 14 pediatric asthma patients visiting the Korea University Medical Center and indoor particulate matter PM10 and PM2.5 concentration data collected at their residence every 10 minutes using a PM monitoring device with a low-cost sensor between September 1, 2017 and August 31, 2018. We interpolated the PEFR results collected twice a day for each patient throughout the day so that it can be matched to the PM and other weather data. The PEFR results were classified into three categories such as ‘Green’ (normal), ‘Yellow’ (mild to moderate exacerbation) and ‘Red’ (severe exacerbation) with reference to their best peak flow value. Long Short-Term Memory (LSTM) model was trained using the first 10 months of the linked data and predicted asthma risk categories for the next 2 months during the study period. LSTM model is found to predict the asthma risk categories better than multinomial logistic (MNL) regression as it incorporates the cumulative effects of PM concentrations over time. Upon successful modifications of the algorithm based on a larger sample, this approach could potentially play a groundbreaking role for the scientific data-driven medical decision making.
A Hybrid Generative Model for Online User Behavior Prediction

With the increase of rich datasets from various online platforms, predicting user behavior has been one of the most active research topics. The user behavior on these online platforms includes listening to music, watching videos, purchasing products, checking-in to places, and joining online sub-communities. Predicting online user behavior is an important challenge for various applications. Personalization, recommendation systems, target advertisements are based on user behavior prediction, where user’s next purchases or actions need to be predicted. In this paper, we propose a hybrid generative model that can predict user behavior considering multiple factors. While previous work has been focused on two aspects individually: predicting repeat behavior or predicting new behavior, our model considers both aspects simultaneously during the learning process. The user-specific preference component is used to capture repeat behavior patterns, while the latent group preference component is used to discover new behavior. Besides these two components, we also consider the exogenous effect, which is not captured in the former two. Our experimental results on real-world datasets show how our proposed model outperforms the state-of-the-art model.
Breakdown and Prebreakdown Conduction in Plain Vacuum Gaps under Variable Frequency Alternating Excitations

Results of experimental investigations leading towards a better understanding of breakdown and prebreakdown behavior of vacuum gaps under variable frequency a.c excitations is presented in this paper. The frequency variation is in the range of 30 to 300 Hz in steps of 10 Hz. The results indicate that the pre-breakdown currents show an inverse relation with the breakdown voltage in general though erratic behavior was observed over a certain range of frequencies. A peaking of breakdown voltage was observed at 130 Hz. This was pronounced when the pair of electrodes was of stainless steel and less pronounced when copper and aluminum electrodes were used. The experimental findings are explained based on F-N emission, I-F emission and also thermal interaction due to quasi-continuous shower of anode micro-particles.
How can Machine Learning Support the Practice of Modeling and Simulation? —A Review and Directions for Future Research

The use of Machine Learning (ML) has achieved a significant momentum across a very wide range of domains. This paper aims to provide a meeting point for discussing the integration of Modeling and Simulation (M&S) with ML. The discussion presents arguments in favour of why and how the M&S practice can avail of ML in different modalities. In this context, the paper reviews key studies published over the past 6 years in main venues including Winter Simulation Conference, SIGSIM PADS, and DS-RT. Further aspects are discussed, which could help reinforce the utilisation of ML in the M&S arena. In general, the study is conceived to foster the presentation of potential ideas and speculative directions towards availing of data-driven knowledge provided by ML.
The Power to Persuade: a study of Social Power in Human-Robot Interaction

Recent advances on Social Robotics raise the question whether a social robot can be used as a persuasive agent. To date, a body of literature has been performed using various approaches to answer this research question, ranging from the use of non-verbal behavior to the exploration of different embodiment characteristics. In this paper, we investigate the role of social power for making social robots more persuasive. Social power is defined as one’s ability to influence another to do something which s/he would not do without the presence of such power. Different theories classify alternative ways to achieve social power, such as providing a reward, using coercion, or acting as an expert. In this work, we explored two types of persuasive strategies that are based on social power (specifically Reward and Expertise) and created two social robots that would employ such strategies. To examine the effectiveness of these strategies we performed a user study with 51 participants using two social robots in an adversarial setting in which both robots try to persuade the user on a concrete choice. The results show that even though each of the strategies caused the robots to be perceived differently in terms of their competence and warmth, both were similarly persuasive.
Towards situational awareness from robotic group motion

The control of multiple robots in the context of tele-exploration tasks is often attentionally taxing, resulting in a loss of situational awareness for operators. Unmanned aerial vehicle swarms require significantly more multitasking than controlling a plane, thus making it necessary to devise intuitive feedback sources and control methods for these robots. The purpose of this article is to examine a swarm's nonverbal behaviour as a possible way to increase situational awareness and reduce the operators cognitive load by soliciting intuitions about the swarm's behaviour. To progress on the definition of a database of nonverbal expressions for robot swarms, we first define categories of communicative intents based on spontaneous descriptions of common swarm behaviours. The obtained typology confirms that the first two levels (as defined by Endsley: elements of environment and comprehension of the situation) can be shared through swarms motion-based communication. We then investigate group motion parameters potentially connected to these communicative intents. Results are that synchronized movement and tendency to form figures help convey meaningful information to the operator. We then discuss how this can be applied to realistic scenarios for the intuitive command of remote robotic teams.