Miracle-dz/UniFAR-FTUs huggingface dataset:
Arguments for transformational reform of mental health care for young people

We have good reason to be concerned about the mental health of our young people. Emerging epidemiological evidence indicates that mental ill-health is now the key health issue facing young people world-wide (Bloom et al. 2011), contributing 45% of the burden of disability in those aged between 10 and 25 years (Gore et al. 2011). Furthermore, because the onset of mental illness peaks in late adolescence/early adulthood and subsequently impacts the most productive years of life (Kessler et al. 2005), it has recently been calculated to pose the greatest threat to the GDP of both the developed and developing nations over the next 20 years (Bloom et al. 2011). Fortunately, the enormity of this problem, and the growing recognition of the scale of our neglect of it, has sparked a call to action among researchers, clinicians, service developers and policymakers world-wide, which has led to a growing international momentum to address the mental health needs of young people and their families. The need for transformational reform of mental health care, based on the principles of early intervention and with a priority focus on the developmental period of greatest need and capacity to benefit from investment – emerging adulthood – is increasingly being accepted world-wide, and has already led to the development of new models of care for our young people.
Method for Computing Frequency Response and Radiation Pattern of Magnetized Cylindrical Ferrite Resonator Antenna

This paper presents a theoretical model of a cylindrical ferrite resonator (FR) antenna that predicts resonance frequencies, the frequency tuning range, and radiation patterns. The FR antenna is placed on a PEC ground plane and is excited by a coaxial probe with a dc magnetic bias field applied in the direction transverse to the ground plane. An open-boundary model to predict the resonance frequency is introduced and compared with the measured results. The all-boundary model is in good agreement with the measured results compared with the other models. The discrepancy of the proposed model is less than 3%. The ${\text {HE}}_{11\delta }$ mode-splitting behavior caused by the tensor nature of the permeability of a biased ferrite is investigated. It is shown that the frequency and polarization of the antenna are tunable. The operating frequency can be chosen in the range from 8.54 to 10.115 GHz. In addition, the linear polarization is radiated at no bias, while the circular polarization is radiated at the magnetized case. The results of the theory correspond approximately with the measured and simulated results for radiation patterns.
An Auto-Focusing Approach for Micro Objects at Different Focal Planes

In this paper, we propose an auto focusing method to find the best plane of focus amongst different depth micro objects. By achieving this, the ultimate goal of the project will be autofocusing on motile micro-organisms to check their quality, e.g., semen motility. Firstly, the auto focusing system cycles through the rough focal range of the micro objects, which represent cell culture by moving in steps of 2 μ$m$ towards best focus, passing it, and completing the scanning process. Simultaneously, at each step, an image of the sample is collected and transformed by use of a Prewitt edge detection operator in order to ascertain focal quality. Secondly, the system moves to the best focus position that is calculated via Prewitt edge detection approach. Thirdly, to begin actively monitoring the focal plane, the system makes minute adjustments to keep the target objects in-focus. This accounts for any changes in the culture, such as cells moving in and out of the focal window or up and down within the semen fluid. The Prewitt operator is tested for focusing performance on multi-microobjects at different focal planes and is compared with other edge detection gradient functions to confirm superiority.
Cloud-Based Framework for Scalable and Real-Time Multi-Robot SLAM

In the past decade, multi-robot simultaneous localization and mapping (SLAM) has been widely studied. However, the problem of collaborative SLAM with a large number of robots, such as dozens of robots, is far from being well solved. The challenges stem from not only the computation complexity in large-scale map merging but also the inefficiency to enable the parallel computing in this process, which is indispensable for us to make avail of the frontier of computing technology such as powerful cloud infrastructure. To effectively address these challenges, especially the latter one, we propose a scalable and real-time multi-robot visual SLAM framework based on the cloud robotic paradigm. The prominent feature of our framework is that it can distribute the SLAM process to multiple computing hosts in a cluster, which enables map building in parallel. To eliminate the bottleneck from data sharing between different sub-tasks, we also introduce diversified messaging pattern for various messaging scenarios, as well as the consistency policies for map data. The evaluations on the prototype of our framework, have shown that our method can do support as many as 256 robot entities simultaneously, without any compromising on the precision of poses estimation and map building.
Spectral–spatial classification of hyperspectral image using semisupervised tritraining and extended attribute profiles

Abstract. Recent studies have shown that spatial information is necessary for hyperspectral image classification, but how to appropriately integrate the distinct spectral and spatial information is still an open issue. Meanwhile, the illposed classification problem due to the limited labeled samples also results in deterioration of recognition performance. An integration of the labeled and unlabeled samples for class discriminant information through semisupervised learning is one of the promising solutions. An integration method of combining different kinds of spectral–spatial features through a semisupervised tritraining learning scheme is proposed. In the method, the dimensionality reduction process is first conducted for a hyperspectral image to reduce the data amount. Subsequently, an extended attribute profile (EAP) is used for texture extraction and spectral–spatial feature stacking. Three different attributions including area, moment of inertia, and diagonal of the box bounding the region are calculated based on the reduced image. Finally, the three kinds of concatenated features are used as inputs of tritraining with three supervised classifiers. These classifiers are trained and refined by the labeled and unlabeled samples in the tritraining process, and an improvement in the final classification accuracy is achieved. In tritraining, to address the problem of the invalid estimation of the classifier error due to the limited labeled samples, a technique based on the posterior probabilities of samples is adopted to estimate the error. Experiments on three benchmark hyperspectral datasets indicate that the proposed method can effectively integrate the information from the spectra, texture, labeled, and unlabeled samples for land cover classification.
Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell

This paper presents an approach for investigating the nature of semantic information captured by word embeddings. We propose a method that extends an existing human-elicited semantic property dataset with gold negative examples using crowd judgments. Our experimental approach tests the ability of supervised classifiers to identify semantic features in word embedding vectors and compares this to a feature-identification method based on full vector cosine similarity. The idea behind this method is that properties identified by classifiers, but not through full vector comparison are captured by embeddings. Properties that cannot be identified by either method are not. Our results provide an initial indication that semantic properties relevant for the way entities interact (e.g. dangerous) are captured, while perceptual information (e.g. colors) is not represented. We conclude that, though preliminary, these results show that our method is suitable for identifying which properties are captured by embeddings.
Comprensión de estudiantes de bachillerato y de profesorado sobre la naturaleza de la ciencia mediante el análisis del caso histórico de "Rosalind Franklin y la estructura del ADN"

Se presenta un estudio cualitativo comparativo del aprendizaje logrado por estudiantes de profesorado de biología y geología (estudiantes MAES) y estudiantes de segundo de bachillerato (estudiantes BAC) sobre naturaleza de la ciencia (NDC), empleando como recurso una narración del caso histórico de “Rosalind Franklin y la estructura molecular del ADN”. La actividad se implementó con un enfoque explícito y reflexivo en ambos casos. Los resultados globales revelan que los dos colectivos de estudiantes alcanzaron una mejoría notable en la comprensión de los aspectos de NDC tratados, lo que pone de manifiesto el potencial didáctico de la actividad para aprender sobre NDC. No obstante, también reflejan algunas diferencias cualitativas entre los estudiantes MAES y los estudiantes BAC.
Variational Bayesian Inference for Jump Markov Linear Systems with Unknown Transition Probabilities

Jump Markov linear systems (JMLSs) switch among simpler models according to a finite Markov chain, whose parameter, namely transition probability matrix (TPM), is rarely known and would cause significant loss in performance of estimator if not sufficient, thus needs to be estimated in practice. This paper considers the general situation where TPM is unknown and random, and presents a variational Bayesian method for recursive joint estimation of system state and unknown TPM. Under the assumption of transition probabilities following Dirichlet distributions, a variational Bayesian approximation is made to the joint posterior distribution of TPM, system and modal state on each time step separately. The resulting recursive method is applicable to various Bayesian multiple model state estimation algorithms for JMLSs and an application to IMM algorithm is demonstrated as an example. The performance of proposed method is illustrated by numerical simulations of maneuvering target tracking.
A Proposal of Super Long Reach Articulated Manipulator with Gravity Compensation using Thrusters

This paper proposes a super long reach articulated manipulator with gravity compensation using thrusters. The proposed manipulator has (1) ground fixed base, (2) tethers for power and information transmission, (3) articulated links connected by joints, and (4) thruster(s) for weight compensation. Because of weight compensation by thruster(s), the proposed manipulator can be super long reach due to free from gravity. After an experiment using 1 D.O. F experimental setup for principle confirmation, and dynamics numerical simulation, an experimental prototype consisted of passive four-bar linkages, active yaw joints and paired counter-rotating propellers, was developed. We successfully demonstrated the proposed concept by three dimensional motions controlled by thrusters and yaw joints.
[Expression of interleukin-9 in colon cancer tissues and its clinical significance].

OBJECTIVE To investigate the expression of interleukin-9 (IL-9) in colon cancer tissues and its clinical significance. METHODS Immunohistochenmistry and qRT-PCR were used to detect the expressions of IL-9 protein and mRNA in 92 colon cancer tissues and paired adjacent normal tissues. The correlation of IL-9 expressions with the clinicopathological features and prognosis of the patients was analyzed. RESULTS IL-9 protein and mRNA expressions were significantly higher in adjacent normal tissues than in the colon cancer tissues (P < 0.001). In colon cancer patients, IL-9 expression was significantly correlated with TNM stage (P=0.013), Ducks stage (P=0.025) and lymph node metastasis (P=0.004) but not with gender, age, tumor size, differentiation or hepatic metastasis (P > 0.05). The survival time of colon cancer patients with positive IL-9 expression was significantly longer than that of patients negative for IL-9 expression (P=0.015). CONCLUSIONS IL-9 expression is lowered in colon cancer tissues compoved with in the adjacent normal tissues. IL-9 expression is negatively correlated with TNM staging, Ducks staging and lymph node metastasis but positively with good prognosis, suggesting its important role in the tumor microenvironment of colon cancer.
Geo-Referencing of a Multi-Sensor System Based on Set-Membership Kalman Filter

In this paper, a novel set-membership Kalman filter is applied on a data set which is obtained from a real world experiment. In this experiment, taken from the scope of georeferencing of terrestrial laser scanner, a multi-sensor system has captured the trajectory of two GNSS antennas. The dynamical system contains the random uncertainty and set-membership uncertainty simultaneously. Both estimated results from classic extended Kalman filter and novel set-membership Kalman filter are shown and compared. Detailed analysis of the set-membership Kalman filter is given in the end.
Progesterone and plasma metabolites in women with and in those without premenstrual dysphoric disorder

The molecular mechanisms underpinning the progesterone‐triggering mood symptoms in women with premenstrual dysphoric disorder (PMDD) are unknown. Cell metabolism is a potential source of variability. Very little is known about the effect of progesterone sensitivity on the metabolome. In this study, we aimed to characterize the effects of progesterone on the global metabolic profile and explore the differences between women with PMDD and controls.
Real-Time and Non-digital Feedback E-Learning Tool

This paper provides a new method for enhancing feedback to a lecturer while he/she is teaching in real-time. It is well accepted that interactive teaching enhances learning and critical thinking. However, in a practical situation the lecturer cannot continually ask if the students have any questions and students are normally hesitant to ask questions, especially with a large cohort. Various eLearning tools that provide feedback are available such the Poll Everywhere, Sli.do, Glisser, Turning Technologies ARS, OMBEA, Socrative, iClicker, ARS Nova, Top Hat, Shakespeak, Sendsteps and Via Response. They are well received by students, but these tools are digital in the sense that they are limited to multiple choice. With this new method, the student can highlight on their devices the part(s) that he/she does not understand, and the system provides a real-time analysis and feedback in the form of display on the lecturer's screen. This device has been built and tested on a small number of students and the feedback from staff and students is very encouraging.
Robust Distributed Clustering Algorithm Over Multitask Networks

We propose a new adaptive clustering algorithm that is robust to various multitask environments. Positional relationships among optimal vectors and a reference signal are determined by using the mean-square deviation relation derived from a one-step least-mean-square update. Clustering is performed by combining determinations on the positional relationships at several iterations. From this geometrical basis, unlike the conventional clustering algorithms using simple thresholding method, the proposed algorithm can perform clustering accurately in various multitask environments. Simulation results show that the proposed algorithm has more accurate estimation accuracy than the conventional algorithms and is insensitive to parameter selection.
Tracking Multiple Maneuvering Targets Using Integer Programming and Spline Interpolation

In this paper, we propose an integer programming based model for tracking multiple maneuverable targets in a planar region. The objective function of this model uses both pairs and triplets of observations, which offer more accurate representation for constant velocity targets. Triplet scores in this model are calculated using a novel approach based on cubic spline interpolation, while the data association problem is solved using a specialized multi-dimensional assignment formulation. We show that the spline interpolation based scoring model provides more accurate reconstruction of trajectories, when compared to a naïve model based on linear interpolation, on various randomly generated trajectories, at the expense of modest increase in computation time. The proposed multi-dimensional assignment formulation has nice structural properties and tight linear programming relaxation bound, which results in small computation times.
Does implicit motor learning lead to greater automatization of motor skills compared to explicit motor learning? A systematic review

Background Implicit motor learning is considered to be particularly effective for learning sports-related motor skills. It should foster movement automaticity and thereby facilitate performance in multitasking and high-pressure environments. To scrutinize this hypothesis, we systematically reviewed all studies that compared the degree of automatization achieved (as indicated by dual-task performance) after implicit compared to explicit interventions for sports-related motor tasks. Methods For this systematic review (CRD42016038249) conventional (MEDLINE, CENTRAL, Embase, PsycINFO, SportDiscus, Web of Science) and grey literature were searched. Two reviewers independently screened reports, extracted data, and performed risk of bias assessment. Implicit interventions of interest were analogy-, errorless-, dual-task-, and external focus learning. Data analysis involved descriptive synthesis of group comparisons on absolute motor dual-task (DT) performance, and motor DT performance relative to single-task motor performance (motor DTCs). Results Of the 4125 reports identified, we included 25 controlled trials that described 39 implicit-explicit group comparisons. Risk of bias was unclear across trials. Most comparisons did not show group differences. Some comparisons showed superior absolute motor DT performance (N = 2), superior motor DTCs (N = 4), or both (N = 3) for the implicit compared to the explicit group. The explicit group showed superior absolute motor DT performance in two comparisons. Conclusions Most comparisons did not show group differences in automaticity. The remaining comparisons leaned more toward a greater degree of movement automaticity after implicit learning than explicit learning. However, due to an overall unclear risk of bias the strength of the evidence is level 3. Motor learning-specific guidelines for design and especially reporting are warranted to further strengthen the evidence and facilitate low-risk-of-bias trials.
A Co-training Based Hardware Trojan Detection Technique by Exploiting Unlabeled ICs and Inaccurate Simulation Models

Integrated circuits (ICs) are becoming vulnerable to hardware Trojans. Most of existing works require golden chips to provide references for hardware Trojan detection. However, a golden chip is extremely difficult to obtain. In previous work, we have proposed a classification-based golden chips-free hardware Trojan detection technique. However, the algorithm in the previous work are trained by simulated ICs without considering that there may be a shift which occurs between the simulation and the silicon fabrication. It is necessary to learn from actual silicon fabrication in order to obtain an accurate and effective classification model. We propose a co-training based hardware Trojan detection technique exploiting unlabeled fabricated ICs and inaccurate simulation models, to provide reliable detection capability when facing fabricated ICs, while eliminating the need of fabricated golden chips. First, we train two classification algorithms using simulated ICs. During test-time, the two algorithms can identify different patterns in the unlabeled ICs, and thus be able to label some of these ICs for the further training of the another algorithm. Moreover, we use a statistical examination to choose ICs labeling for the another algorithm in order to help prevent a degradation in performance due to the increased noise in the labeled ICs. We also use a statistical technique for combining the hypotheses from the two classification algorithms to obtain the final decision. The theoretical basis of why the co-training method can work is also described. Experiment results on benchmark circuits show that the proposed technique can detect unknown Trojans with high accuracy (92%~97%) and recall (88%~95%).
Sleep Strengthens Predictive Sequence Coding

Predictive-coding theories assume that perception and action are based on internal models derived from previous experience. Such internal models require selection and consolidation to be stored over time. Sleep is known to support memory consolidation. We hypothesized that sleep supports both consolidation and abstraction of an internal task model that is subsequently used to predict upcoming stimuli. Human subjects (of either sex) were trained on deterministic visual sequences and tested with interleaved deviant stimuli after retention intervals of sleep or wakefulness. Adopting a predictive-coding approach, we found increased prediction strength after sleep, as expressed by increased error rates to deviant stimuli, but fewer errors for the immediately following standard stimuli. Sleep likewise enhanced the formation of an abstract sequence model, independent of the temporal context during training. Moreover, sleep increased confidence for sequence knowledge, reflecting enhanced metacognitive access to the model. Our results suggest that sleep supports the formation of internal models which can be used to predict upcoming events in different contexts. SIGNIFICANCE STATEMENT To efficiently interact with the ever-changing world, we predict upcoming events based on similar previous experiences. Sleep is known to benefit memory consolidation. However, it is not clear whether sleep specifically supports the transformation of past experience into predictions of future events. Here, we find that, when human subjects sleep after learning a sequence of predictable visual events, they make better predictions about upcoming events compared with subjects who stayed awake for an equivalent period of time. In addition, sleep supports the transfer of such knowledge between different temporal contexts (i.e., when sequences unfold at different speeds). Thus, sleep supports perception and action by enhancing the predictive utility of previous experiences.
Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms

In the era of the Internet of Things (IoT), efficient localization is essential for emerging mass-market services and applications. IoT devices are heterogeneous in signaling, sensing, and mobility, and their resources for computation and communication are typically limited. Therefore, to enable location awareness in large-scale IoT networks, there is a need for efficient, scalable, and distributed multisensor fusion algorithms. This article presents a framework for designing network localization and navigation (NLN) for the IoT. Multisensor localization and operation algorithms developed within NLN can exploit spatiotemporal cooperation, are suitable for arbitrary, largenetwork sizes, and only rely on an information exchange among neighboring devices. The advantages of NLN are evaluated in a large-scale IoT network with 500 agents. In particular, because of multisensor fusion and cooperation, the presented network localization and operation algorithms can provide attractive localization performance and reduce communication overhead and energy consumption.