Miracle-dz/UniFAR-FTUs huggingface dataset:
DYCUSBoost: Adaboost-Based Imbalanced Learning Using Dynamic Clustering and Undersampling

Ensemble learning is a powerful approach to classifying imbalanced data in machine learning. Adaboost as one of Ensemble learning, which often modified to deal with imbalanced problem. However, due to the variation of sample weights in Adaboost algorithm, the distribution of datasets is not consistent for each weak classifier. As a result, feature space-based resampling fails to reflect the transformation of distribution. Aiming at this problem, this paper proposes DYCUSBoost, an Adaboost-based imbalanced learning approach using dynamic clustering and undersampling. In DYCUSBoost, the clustering process is synchronized with the iteration of Adaboost, where clusters formed in different periods of Adaboost are adjusted, which make DYCUSBoost grasp the transformation of the distribution. The undersampling method assesses the importance of each cluster, and make important ones collect more samples. Through experimental verification, DYCUSBoost demonstrates desirable performance in terms of commonly-accepted evaluating metrics, e.g., AUC, G-Mean, F-Measure, etc. Moreover, the prediction stability of DYCUSBoost outperforms most undersampling methods.
Demonstration of a Nonlinear Model Predictive Control of a Thermal Management System for Electric Vehicles in Real-Time

The range of electric vehicles can dramatically decrease in winter due to high thermal demands of the passenger cabin. Energy-efficient thermal management systems help to decrease energy consumption and range drop in winter by integrating waste heat from powertrain components. The associated rising system complexity can be handled by optimization-based control approaches. In (Fischer et al., 12th Int. Modelica Conf, 2017), we have presented the development of a virtual Nonlinear Model Predictive Control (NMPC) approach of a thermal management system and showed the advantages over a conventional PID-controlled system. In this article, we build upon this work and show such an NMPC setting in real-time with a real-world thermal management system integrated in a vehicle demonstrator. The resulting feedback time between acquisition of measurement data and new NMPC controller outputs is in the order of milliseconds and proves the real-time feasibility of the approach.
Evaluation of Allergic Diseases, Symptom Control, and Relation to Infections in a Group of Italian Elite Mountain Bikers.

OBJECTIVES This study estimates the prevalence of allergic diseases in a group of Italian elite mountain bikers, compares the prevalence of infectious episodes between allergic and nonallergic athletes, and evaluates asthma and rhinitis symptom control in allergic athletes. DESIGN Two hundred twenty-six Italian nonsmoking mountain bikers received by mail the Allergy Questionnaire for Athletes (AQUA) and completed it. The RhinAsthma Patient Perspective (RAPP) questionnaire was sent to the 108 participants with a positive AQUA score and 104 returned the questionnaire. METHODS Athletes with an AQUA score ≥5 or <5 were defined AQUA+ (allergic) or AQUA- (nonallergic), respectively. RhinAsthma Patient Perspective questionnaire total score ≥15 was indicative of a poor control of symptoms. RESULTS Of the 226 athletes, 47.8% were AQUA+, whereas 52.2% were AQUA-. A higher number of AQUA+ athletes reported frequent upper respiratory tract infections (URTIs) and herpes labialis than AQUA- athletes (P < 0.001), and the prevalence of URTI was greater in the subgroup of AQUA+ athletes who trained ≥3 hours per session. According to RAPP questionnaire score, 21.1% of AQUA+ mountain bikers had a poor control of asthma and rhinitis symptoms. CONCLUSIONS Our study shows a high prevalence of allergy among Italian elite mountain bikers whose asthma and rhinitis symptoms are poorly controlled in about a fifth of the sample. Allergic athletes, mainly those training more than 3 hours per session, are at higher risk of URTI and herpes labialis. Screening programs to detect allergic diseases and to evaluate symptom control in athletes should be strongly encouraged.
The “Real” Number of Washington State Adolescents Using Marijuana, and Why: A Misclassification Analysis

Abstract Background: Although causality is difficult to establish, the regular use of marijuana has been associated with many adverse physiological and sociological consequences. There is considerable concern regarding marijuana use among adolescents, as the likelihood of adverse consequences increases significantly for this age group. The most comprehensive data for identifying risk factors for adolescent marijuana use is typically self-report, which may be over- or under-reported for a variety of reasons, including stigmatization, peer-pressure, or fear of being discovered. Objectives: To identify the prevalence of adolescent marijuana use in Washington State, and the associated risk and protective factors, while controlling for and estimating the extent of misreporting, and its determinants. Method: Data came from the 2014 Washington State Healthy Youth Survey. We accounted for missingness using chained multivariate imputation equations, resulting in 33,320 complete observations. Our model was estimated using a maximum likelihood multiple regression designed to control for systematic misclassification in binary dependent variables. Results: Approximately 12% of Washington adolescents claimed to have used marijuana in the past 30 days. Our estimates indicate this figure is likely closer to 18%. Determinants of use included use of other substances, gender, age, and measures of deviant social influences, personality/attitude, school and family bonds, bullying, and acquisition ease. Determinants of misreporting included use of other substances, gender, parental education, and family bonds. Conclusions: Failing to control for misreporting considerably underestimates the prevalence of marijuana use among adolescents. Our model allows us to better identify at-risk adolescents and inform focused prevention efforts.
Dysregulation of microRNA‐657 influences inflammatory response via targeting interleukin‐37 in gestational diabetes mellitus

A number of studies have implicated that microRNAs (miRNAs) play a critical role in the development of gestational diabetes mellitus (GDM). However, the role of miR‐657 in GDM remains vague up to date. We aim to investigate the modifying effect of miR‐657 on GDM, which will provide new insight into the pathogenesis of GDM and may help to identify new diagnostic or therapeutic targets for GDM. Increased expression of miR‐657 but decreased expression of interleukin‐37 (IL‐37) was observed in patients with GDM. Besides, negative association between miR‐657 and IL‐37 was demonstrated in this study. miR‐657 could targetedly regulate IL‐37 and enhance the proliferation of mononuclear macrophages. Moreover, miR‐657 promoted the generation of inflammatory cytokines (IL‐6 and tumor necrosis factor‐α [TNF‐α]) and activation of nuclear factor κB (NF‐κB) in lipopolysaccharide‐induced mononuclear macrophages, while its effect was significantly inhibited when exogenous recombinant IL‐37 was administrated into cells. Accordingly, dysregulation of miR‐657 contributes to the pathogenesis of GDM via IL‐37/NF‐κB signaling axis.
Wavelet Transform and Support Vector Machine- Based Current-Only Directional Overcurrent Relay for Transmission Line Protection

Conventionally, for power protective relay applications, overcurrent relaying is made directional by use of two inputs, the operating current and a reference or polarizing quantity (either voltage or current) that does not change with fault location. This work presents a pattern model, which is capable of providing the characteristic of directionality to a nondirectional overcurrent relay that is installed on a transmission line by monitoring the current signal only, without the reference signal. In addition, a second model which has the ability to identify the type of fault occurred in the protected line is presented. The electrical test system was implemented in ATP/EMTP program, and simulations were driven by a MATLAB program for several short-circuit fault types at different distances. The simulation results constitute the database. These files were processed and analyzed on MATLAB platform mainly using two mathematical tools: Wavelet Transform and Support Vector Machine. Finally, a graphical user interface was implemented for academic purposes, so the user can easily and didactically appreciate the developed methodology, as well as its effectiveness.
Maxilloturbinal Aids in Nasophonation in Horseshoe Bats (Chiroptera: Rhinolophidae)

Horseshoe bats (Family Rhinolophidae) show an impressive array of morphological traits associated with use of high duty cycle echolocation calls that they emit via their nostrils (nasophonation). Delicate maxilloturbinal bones inside the nasal fossa of horseshoe bats have a unique elongated strand‐like shape unknown in other mammals. Maxilloturbinal strands also vary considerably in length and cross‐sectional shape. In other mammals, maxilloturbinals help direct respired air and prevent respiratory heat and water loss. We investigated whether strand‐shaped maxilloturbinals in horseshoe bats perform a similar function to those of other mammals, or whether they were shaped for a role in nasophonation. Using histology, we studied the mucosa of the nasal fossa in Rhinolophus lepidus, which we compared with Hipposideros lankadiva (Hipposideridae) and Megaderma lyra (Megadermatidae). Using micro‐CT scans of 30 horseshoe bat species, we quantified maxilloturbinal surface area and skull shape within a phylogenetic context. Histological results showed horseshoe bat maxilloturbinals are covered in a thin, poorly vascularized, sparsely ciliated mucosa poorly suited for preventing respiratory heat and water loss. Maxilloturbinal surface area was correlated with basicranial width, but exceptionally long and dorsoventrally flat maxilloturbinals did not show enhanced surface area for heat and moisture exchange. Skull shape variation appears to be driven by structures linked to nasophonation, including maxilloturbinals. Resting echolocation call frequency better predicted skull shape than did skull size, and was specifically correlated with dimensions of the rostral inflations, palate, and maxilloturbinals. These traits appear to form a morphological complex, indicating a nasophonatory role for the strand‐shaped rhinolophid maxilloturbinals. Anat Rec, 2018. © 2018 American Association for Anatomy.
Tree-Stack LSTM in Transition Based Dependency Parsing

We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. There are 4 main components of this model: stack’s σ-LSTM, buffer’s β-LSTM, actions’ LSTM and tree-RNN. All LSTMs use continuous dense feature vectors (embeddings) as an input. Tree-RNN updates these embeddings based on transitions. We show that our model improves performance with low resource languages compared with its predecessors. We participate in CoNLL 2018 UD Shared Task as the “KParse” team and ranked 16th in LAS, 15th in BLAS and BLEX metrics, of 27 participants parsing 82 test sets from 57 languages.
HeatCraft: Playing with Ingestible Sensors via Localised Sensations

Ingestible sensors are digital devices that can measure the user's body data after being swallowed and hence have great potential in medical use. Unfortunately, few studies have considered the playful experiences afforded by ingestible sensors. We believe that the use of localised sensations, such as those created by heat, to represent the data measured by ingestible sensors offers opportunities to support experiencing the body as play. To explore this opportunity, we introduce a two-player system called HeatCraft that uses an ingestible sensor to measure the users' body temperature and employs thermal stimuli to provide feedback. Similar to open-ended games, HeatCraft allows players to decide when and what to do in order to know more about their body, facilitating playful experiences of exploration and discovery. With this work, we aim to inspire game designers and HCI researchers to consider localised sensations when designing playful and engaging experiences around ingestible sensors.
IoT Driven Ambient Intelligence Architecture for Indoor Intelligent Mobility

Personal robots are set to assist humans in their daily tasks. Assisted living is one of the major applications of personal assistive robots, where the robots will support health and wellbeing of the humans in need, especially elderly and disabled. Indoor environments are extremely challenging from a robot perception and navigation point of view, because of the ever-changing decorations, internal organizations and clutter. Furthermore, human-robot-interaction in personal assistive robots demands intuitive and human-like intelligence and interactions. Above challenges are aggravated by stringent and often tacit requirements surrounding personal privacy that may be invaded by continuous monitoring through sensors. Towards addressing the above problems, in this paper we present an architecture for "Ambient Intelligence" for indoor intelligent mobility by leveraging IoTs within a framework of Scalable Multi-layered Context Mapping Framework. Our objective is to utilize sensors in home settings in the least invasive manner for the robot to learn about its dynamic surroundings and interact in a human-like manner. The paper takes a semi-survey approach to presenting and illustrating preliminary results from our in-house built fully autonomous electric quadbike.
Breast Region Segmentation being Convolutional Neural Network in Dynamic Contrast Enhanced MRI

Breast density and background parenchymal enhancement (BPE) are suggested to be related to the risk of breast cancer. The first step to quantitative analysis of breast density and BPE is segmenting the breast from body. Nowadays, convolutional neural networks (CNNs) are widely used in image segmentation and work well in semantic segmentation, however, CNNs have been rarely used in breast region segmentation. In this paper, the CNN was employed to segment the breast region in transverse fat-suppressed breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Image normalization was initially performed. Subsequently, the dataset was divided into three sets randomly: train set validation set and test set. The 2-D U-Net was trained by train set and the optimum model was chosen by validation set. Finally, segmentation results of test set obtained by U-Net were adjusted in the postprocessing. In this step, two largest volumes were computed to determine whether the smaller volume is the scar after mastectomy. With the limitation of small dataset, 5-fold cross-validation and data augmentation were used in this study. Final results on the test set were evaluated by volume-based and boundary-based metrics with manual segmentation results. By using this method, the mean dice similarity coefficient (DSC), dice difference coefficient (DDC), and root-mean-square distance reached 97.44%, 5.11%, and 1.25 pixels, respectively.
Closed-Loop Data-Driven Attitude Control Design for a Multirotor UAV

Data-driven controller design methods allow a fast tuning of controller parameters directly from data, relying on limited prior knowledge of the plant dynamics. In this paper, the problem of tuning the attitude control system of a multirotor UAV is tackled and a data-driven approach is proposed. With respect to previous work, in which open-loop experiments were carried out on a test bench allowing only a single attitude degree of freedom of the vehicle, in this paper data collected in flight, during closed-loop experiments, is used to tune the controller gains. The results, based on experimental work carried out on a small-scale quadrotor, show that a performance level comparable to that of model-based methods can be achieved.
K-2 "Make It Count" Students' Views of Mathematics.

Reports that Australian Indigenous2 students, on average, perform well below their nonIndigenous peers on traditional measures of achievement are prevalent. “At both Year 4 and Year 8 in TIMSS3 2007, non-Indigenous students scored at a substantially higher level than Indigenous students – 91 score points at Year 4 and 70 score points at Year 8” (Thomson, 2010, p. 79). Examination of the Australian 2009 Programme for International Student Assessment [PISA] results also reveals a substantial difference between the average performance of Indigenous and non-Indigenous students on the mathematical literacy assessment component (Thomson, De Bortoli, Nicholas, Hillman, & Buckley (2011, p. 189). An excerpt from a report published under the auspices of the Queensland government draws on data from the National Assessment Program Literacy and Numeracy [NAPLAN] tests to compare the performance of Indigenous and non-Indigenous students. Although the distribution of Indigenous students varies by state, the Queensland data offer a useful guide to the national situation.
A Thread-Saving Schedule with Graph Analysis for Parallel Deep Learning Applications on Embedded Systems

The training of neural networks in embedded systems has become the direction of development nowadays. Because of the limited resources of embedded systems, it is not friendly to resource-consuming model training. What is more unfortunate is that during the training process, the allocation of thread resources by the operating system is not very appropriate, which can cause mis-allocation, and it will lead to wasting of resources or reducing performance during training. For the above problems, we propose a thread resource allocation method based on neural network graph analysis. Through the analysis of graphs, we dynamically allocate threads to achieve the full use of embedded system resources.
DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback

Exploration has been one of the greatest challenges in reinforcement learning (RL), which is a large obstacle in the application of RL to robotics. Even with state-of-the-art RL algorithms, building a well-learned agent often requires too many trials, mainly due to the difficulty of matching its actions with rewards in the distant future. A remedy for this is to train an agent with real-time feedback from a human observer who immediately gives rewards for some actions. This study tackles a series of challenges for introducing such a human-in-the-loop RL scheme. The first contribution of this work is our experiments with a precisely modeled human observer: binary, delay, stochasticity, unsustainability, and natural reaction. We also propose an RL method called DQN-TAMER, which efficiently uses both human feedback and distant rewards. We find that DQN-TAMER agents outperform their baselines in Maze and Taxi simulated environments. Furthermore, we demonstrate a real-world human-in-the-loop RL application where a camera automatically recognizes a user's facial expressions as feedback to the agent while the agent explores a maze.
Parameter Instability Regimes in Sparse Proximal Denoising Programs

Compressed sensing theory explains why LASSO programs recover structured high-dimensional signals with minimax order-optimal error. Yet, the optimal choice of the program’s governing parameter is often unknown in practice. It is still unclear how variation of the governing parameter impacts recovery error in compressed sensing, which is otherwise provably stable and robust. We establish a novel notion of instability in LASSO programs when the measurement matrix is identity. This is the proximal denoising setup. We prove asymptotic cusp-like behaviour of the risk as a function of the parameter choice, and illustrate the theory with numerical simulations. For example, a 0.1% underestimate of a LASSO parameter can increase the error significantly; and a 50% underestimate can cause the error to increase by a factor of 109. We hope that revealing parameter instability regimes of LASSO programs helps to inform a practitioner’s choice.
Osteopontin protects against pneumococcal infection in a murine model of allergic airway inflammation

In atopic asthma, chronic Th2‐biased inflammation is associated with an increased risk of pneumococcal infection. The anionic phosphoglycoprotein osteopontin (OPN) is highly expressed in asthma and has been ascribed several roles during inflammation. This study aimed to investigate whether OPN affects inflammation and vulnerability to pneumococcal infection in atopic asthma.
Present Status of Infrared FEL Facility at Kyoto University

An infrared Free Electron Laser (FEL) facility has been developed for energy-related researches at Institute of Advanced Energy, Kyoto University. The facility has two accelerator based infrared light sources. One is the mid-infrared FEL (MIR-FEL) which consists of an RF gun, a 3-m accelerator tube, a 1.8-m undulator and a 5-m optical resonator. The tunable range of the MIR-FEL is from 3.5 to $23 \ \mu \mathrm{m}$. The RF gun can be operated with thermionic emission and photoelectron emission. The other one is the THz Coherent Undulator Radiation (THz-CUR) source which consists of a photocathode RF gun, a bunch compression chicane and a 70-cm undulator with the period length of 7 cm, The present statuses of those accelerator-based light sources are reported.
Alpinia oxyphylla Miq extract ameliorates cardiac fibrosis associated with D‐galactose induced aging in rats

Cardiac fibrosis is a common pathophysiological process observed during chronic and stress‐induced acceleration of cardiac aging. Fibrosis is a necessary process during wound healing and tissue repair. However, its deposition in organs would proceed to scarring and organ damage. Here Alpinate Oxyphyllae Fructus (AOF), a Chinese medicine extract was used to protect aging heart from collagen accumulation. About 8 weeks old, male SD rats were randomly divided into (i) Control, (ii) D‐galactose induced aging (IA), (iii) IA + AOF 50 (AOF low, AL), (iv) IA + AOF 100 (AOF medium, AM), (v) IA + AOF 150 (AOF high, AH) mg/kg/day, AOF was administered orally. After 8 weeks rats were sacrificed and hearts were collected. Results showed collagen deposition and up‐regulation of matrix metalloproteinases‐MMP‐2 and ‐9 in D‐galactose‐induced aging rats. Furthermore, western blotting and immunostaining were also confirmed the upregulation of TGF‐β1 mediated fibrosis in aging induced rats. However, collagen deposition and fibrosis were significantly decreased by AOF treatments (AM and AH). AOF treatments salvaged the cardiac fibrosis. Hence, AOF might be a potential therapeutic agent in the prevention of cardiac fibrosis associated with aging. The protective effects of AOF might have promising results in anti‐aging treatments.
Numerical Simulation and Analysis of Transistor Channel Length and Doping Mismatching in GC SOI nMOSFETs Analog Figures of Merit

This paper presents a two-dimensional numerical simulation study of mismatching on the analog characteristics of fully-depleted graded-channel (GC) SOI MOSFET. The study aims at identifying the mismatching sources that affect the analog performance of GC SOI transistors. The simulations were performed imposing length and doping concentration variations and analyzing its impact on important electrical parameters such as threshold voltage and subthreshold slope, as well as analog parameters, namely transconductance, output conductance, Early voltage and intrinsic voltage gain.