Data augmentation and semi-supervised learning for deep neural networks-based text classifier

User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance

Torsion Testicular Patient Characteristics

Testicular torsion is an emergency urological condition that is caused by the torsion of the spermatic cord structures, causing disruption of circulation of the affected testicle. This study aimed to describe the characteristics of patients with testicular torsion treated at Dr. Hasan Sadikin General Hospital Bandung from January 2016 to January 2020. This was a retrospective descriptive study on 34 medical records of patients diagnosed and treated for testicular torsion. Nine patients (29.4%) were 21 years old. The onset was mostly between 6 to 24 hours (38.2%), followed by between 2–7 days (23.5%), less than 6 hours (20.6%), between 1–2 weeks (8.8%), and between 2–4 weeks (8.8%). Left testicular torsion were more frequent than the right torsion (61.8% vs. 38.2%). The etiology of the torsion was mostly idiopathic with no identifiable precedent (88%). Orchidectomy was more frequently performed compared to orchiopexy (78.4% vs. 21.6%). All but one patient (97.1%) presented with testicular pain as the main symptom. Patients presented mostly with a high risk TWIST score (64.7%); however, more presented with low risk compared to the intermediate risk TWIST score (26.5% vs. 8.8%). Orchidectomy is the most frequently performed operation on pre-pubertal and adult patients, possibly due to relatively delayed presentation (>24 hours) to the hospital to receive treatment. Patients were mostly younger; predominantly with high TWIST score and affected left testicle

Exploring metacognition as support for learning transfer

The ability to transfer learning to new situations lies at the heart of lifelong learning and the employability of university graduates. Because students are often unaware of the importance of learning transfer and staff do not always explicitly articulate this expectation, this article explores the idea that metacognition (intentional awareness and the use of that awareness) might enhance the development of learning transfer. Our exploratory study includes results from a survey of 74 staff and 118 students from five institutions in Australia, Belgium, UK, and USA. Our data indicate that many staff and a majority of students do not have a clear understanding of what learning transfer entails, and that there are many mismatches between staff and student perceptions, attitudes, and behaviors regarding learning transfer. This helps explain why learning transfer does not occur as often as it could. We found significant positive correlations between thinking about transfer and thinking about learning processes and the likelihood to use awareness of metacognition to guide practice. Our findings suggest a positive relationship between metacognition and learning transfer. Implications for the scholarship of teaching and learning are discussed

BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision

We study the open-domain named entity recognition (NER) problem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yields highly incomplete and noisy distant labels via external knowledge bases. To address this challenge, we propose a new computational framework -- BOND, which leverages the power of pre-trained language models (e.g., BERT and RoBERTa) to improve the prediction performance of NER models. Specifically, we propose a two-stage training algorithm: In the first stage, we adapt the pre-trained language model to the NER tasks using the distant labels, which can significantly improve the recall and precision; In the second stage, we drop the distant labels, and propose a self-training approach to further improve the model performance. Thorough experiments on 5 benchmark datasets demonstrate the superiority of BOND over existing distantly supervised NER methods. The code and distantly labeled data have been released in https://github.com/cliang1453/BOND.Comment: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20

Fatty acid nitroalkenes ameliorate glucose intolerance and pulmonary hypertension in high-fat diet-induced obesity

Aims Obesity is a risk factor for diabetes and cardiovascular diseases, with the incidence of these disorders becoming epidemic. Pathogenic responses to obesity have been ascribed to adipose tissue (AT) dysfunction that promotes bioactive mediator secretion from visceral AT and the initiation of pro-inflammatory events that induce oxidative stress and tissue dysfunction. Current understanding supports that suppressing pro-inflammatory and oxidative events promotes improved metabolic and cardiovascular function. In this regard, electrophilic nitro-fatty acids display pleiotropic anti-inflammatory signalling actions. Methods and results It was hypothesized that high-fat diet (HFD)-induced inflammatory and metabolic responses, manifested by loss of glucose tolerance and vascular dysfunction, would be attenuated by systemic administration of nitrooctadecenoic acid (OA-NO2). Male C57BL/6j mice subjected to a HFD for 20 weeks displayed increased adiposity, fasting glucose, and insulin levels, which led to glucose intolerance and pulmonary hypertension, characterized by increased right ventricular (RV) end-systolic pressure (RVESP) and pulmonary vascular resistance (PVR). This was associated with increased lung xanthine oxidoreductase (XO) activity, macrophage infiltration, and enhanced expression of pro-inflammatory cytokines. Left ventricular (LV) end-diastolic pressure remained unaltered, indicating that the HFD produces pulmonary vascular remodelling, rather than LV dysfunction and pulmonary venous hypertension. Administration of OA-NO2 for the final 6.5 weeks of HFD improved glucose tolerance and significantly attenuated HFD-induced RVESP, PVR, RV hypertrophy, lung XO activity, oxidative stress, and pro-inflammatory pulmonary cytokine levels. Conclusions These observations support that the pleiotropic signalling actions of electrophilic fatty acids represent a therapeutic strategy for limiting the complex pathogenic responses instigated by obesity.Fil: Kelley, Eric E.. University of Pittsburgh; Estados UnidosFil: Baust, Jeff. University of Pittsburgh; Estados UnidosFil: Bonacci, Gustavo Roberto. University of Pittsburgh; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Bioquímica Clínica e Inmunología; ArgentinaFil: Golin Bisello, Franca. University of Pittsburgh; Estados UnidosFil: Devlin, Jason E.. University of Pittsburgh; Estados UnidosFil: Croix, Claudette M. St.. University of Pittsburgh; Estados UnidosFil: Watkins, Simon C.. University of Pittsburgh; Estados UnidosFil: Gor, Sonia. University of Pittsburgh; Estados UnidosFil: Cantu Medellin, Nadiezhda. University of Pittsburgh; Estados UnidosFil: Weidert, Eric R.. University of Pittsburgh; Estados UnidosFil: Frisbee,Jefferson C.. University of Virginia; Estados UnidosFil: Gladwin, Mark T.. University of Pittsburgh; Estados UnidosFil: Champion, Hunter C.. University of Pittsburgh; Estados UnidosFil: Freeman, Bruce A.. University of Pittsburgh; Estados UnidosFil: Khoo, Nicholas K.H.. University of Pittsburgh; Estados Unido

Predicting Graphical Passwords

Over the last decade, the popularity of graphical passwords has increased tremendously. They can now be found on various devices and systems, including platforms such as the Windows 8 and Android operating systems. In this paper, we focus on the PassPoints graphical-password scheme and investigate the extent to which these passwords might be predicted based on knowledge of the individual (e.g., their age, gender, education, learning style). We are particularly interested in understanding whether graphical passwords may suffer the same weaknesses as textual passwords, which are often strongly correlated with an individual using memorable information (such as the individuals spouses, pets, preferred sports teams, children, and so on). This paper also introduces a novel metric for graphical-password strength to provide feedback to an individual without the requirement of knowing the image or having password statistics a priori

Toward open sharing of task-based fMRI data: the OpenfMRI project

The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function

Mechanical design and development of TES bolometer detector arrays for the Advanced ACTPol experiment

The next generation Advanced ACTPol (AdvACT) experiment is currently underway and will consist of four Transition Edge Sensor (TES) bolometer arrays, with three operating together, totaling ~5800 detectors on the sky. Building on experience gained with the ACTPol detector arrays, AdvACT will utilize various new technologies, including 150mm detector wafers equipped with multichroic pixels, allowing for a more densely packed focal plane. Each set of detectors includes a feedhorn array of stacked silicon wafers which form a spline profile leading to each pixel. This is then followed by a waveguide interface plate, detector wafer, back short cavity plate, and backshort cap. Each array is housed in a custom designed structure manufactured from high purity copper and then gold plated. In addition to the detector array assembly, the array package also encloses cryogenic readout electronics. We present the full mechanical design of the AdvACT high frequency (HF) detector array package along with a detailed look at the detector array stack assemblies. This experiment will also make use of extensive hardware and software previously developed for ACT, which will be modified to incorporate the new AdvACT instruments. Therefore, we discuss the integration of all AdvACT arrays with pre-existing ACTPol infrastructure.Comment: 9 pages, 5 figures, SPIE Astronomical Telescopes and Instrumentation conference proceeding

Instrumental performance and results from testing of the BLAST-TNG receiver, submillimeter optics, and MKID arrays

Polarized thermal emission from interstellar dust grains can be used to map magnetic fields in star forming molecular clouds and the diffuse interstellar medium (ISM). The Balloon-borne Large Aperture Submillimeter Telescope for Polarimetry (BLASTPol) flew from Antarctica in 2010 and 2012 and produced degree-scale polarization maps of several nearby molecular clouds with arcminute resolution. The success of BLASTPol has motivated a next-generation instrument, BLAST-TNG, which will use more than 3000 linear polarization sensitive microwave kinetic inductance detectors (MKIDs) combined with a 2.5m diameter carbon fiber primary mirror to make diffraction-limited observations at 250, 350, and 500 $\mu$m. With 16 times the mapping speed of BLASTPol, sub-arcminute resolution, and a longer flight time, BLAST-TNG will be able to examine nearby molecular clouds and the diffuse galactic dust polarization spectrum in unprecedented detail. The 250 $\mu$m detector array has been integrated into the new cryogenic receiver, and is undergoing testing to establish the optical and polarization characteristics of the instrument. BLAST-TNG will demonstrate the effectiveness of kilo-pixel MKID arrays for applications in submillimeter astronomy. BLAST-TNG is scheduled to fly from Antarctica in December 2017 for 28 days and will be the first balloon-borne telescope to offer a quarter of the flight for "shared risk" observing by the community.Comment: Presented at SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VIII, June 29th, 201