Miracle-dz/UniFAR-FTUs huggingface dataset:
Discretionary Task Ordering: Queue Management in Radiological Services

Work scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million radiological diagnoses, we find that doctors prioritize similar tasks (batching) and those tasks they expect to complete faster (shortest expected processing time). Moreover, they exercise more discretion as they accumulate experience. Exploiting random assignment of tasks to doctors' queues, instrumental variable models reveal that these deviations erode productivity. This productivity decline lessens as doctors learn from experience. Prioritizing the shortest tasks is particularly detrimental to productivity. Actively grouping similar tasks also reduces productivity, in stark contrast to productivity gains from exogenous grouping, indicating deviation costs outweigh benefits from repetition. By analyzing task completion times, our work highlights the tradeoffs between the time required to exercise discretion and the potential gains from doing so, which has implications for how discretion over scheduling should be delegated.
Zinc Oxide Nanoparticles from Waste Zn-C Battery via Thermal Route: Characterization and Properties

Disposable batteries are becoming the primary sources of powering day-to-day gadgets and consequently contributing to e-waste generation. The emerging e-waste worldwide is creating concern regarding environmental and health issues. Therefore, a sustainable recycling approach of spent batteries has become a critical focus. This study reports the detail characterization and properties of ZnO nanoparticles recovered from spent Zn-C batteries via a facile thermal synthesis route. ZnO nanoparticles are used in many applications including energy storage, gas sensors, optoelectronics, etc. due to the exceptional physical and optical properties. A thermal treatment at 900 °C under an inert atmosphere of argon was applied to synthesize ZnO nanoparticles from a spent Zn-C battery using a horizontal quartz tube furnace. X-ray diffraction (XRD), selected area electron diffraction (SAED) and X-ray photoelectron spectroscopy (XPS) results confirmed the formation of crystalline ZnO nanoparticles. Field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) analysis confirmed that the size of synthesised ZnO particles were less than 50 nm and mainly composed of sphere shaped nanoparticles. Synthesized ZnO exhibited BET surface area of 9.2629 m2/g and showed absorption of light in the UV region. Excitation of ZnO by UV light showed photoluminescence in the visible range. This study will create an opportunity for potential applications of ZnO nanoparticles from spent batteries and will benefit the environment by reducing the volume of e-waste in landfills.
Martial Arts as a Form of Undertaking Physical Activity in Leisure Time Analysis of Factors Determining Participation of Poles

Background: The aim of this paper was to analyze selected sociodemographic and economic factors that determine practicing martial arts (MA) in Poland. Our hypothesis states that MA constitute a niche sport, which is a result of conditions shaping the decision to undertake them: perception through the lens of the media/entertainment business (rather than primary values), insufficient space for practicing, as well as high costs. Methods: The study was based on a survey conducted on a representative sample of Poles (n = 12,183). Results for 470 participants declaring some level of proficiency in MA were presented (including 124 declaring active participation). In order to verify statistically significant differences, a Chi-Square test, single-agent logistic regression analysis, and adds ratio were applied. Results: 3.0% of respondents declared basic skills in MA, while advanced skills were declared by 0.9% participants. Only 1% were active practitioners. The leading motivation was “pleasure” (62.1%), followed by “keeping fit and maintaining healthy body shape” (21.8%) and “health” (7.3%). The probability of participation in MA decreased with age (40 years of age being the turning point). As for young people, there is a 15 times lower chance of married individuals undertaking MA, while possessing advanced skills doubles the probability. School provides a place to practice MA during school years, but there is a problem with continuation at a later age (18.8% declaring classes organized at school vs. 5.4% organized at work). Conclusions: Reversing the trend of decreasing popularity of MA requires promotion on each level of education and creating opportunities to continue participation after graduation. There is a need to increase the availability of cheap sport facilities in the vicinity of the place of residence or work. In case of adults, it is important to build platforms facilitating contact with MA centers.
Consumer engagement with eWOM on social media: the role of social capital

Purpose The nature of digital media channels are important factors in explaining consumers’ behavior over the Web, and specifically on social network sites (SNS). The purpose of this paper is to propose a conceptual framework explaining consumer engagement with electronic word of mouth (eWOM) communication via SNS, based on key attributes of this media channel. Design/methodology/approach Based on the expectancy value theory (EVT), a conceptual framework is proposed to model the effect of eWOM channel attributes on eWOM engagement process. Consumer eWOM engagement is conceptualized as a second-order construct. A structural equation modeling procedure was employed to empirically test the model using data collected from two social media communication channels. Findings First, results suggest that engagement with eWOM can be conceptualized as a second-order construct based on user tendency to receive or share eWOM with other network members. Second, the path analysis model supports the employment of EVT and shows that two key attributes of eWOM channels, social capital and credibility, significantly affect consumer attitude toward eWOM via SNS. Attitude toward eWOM, in turn, affects eWOM engagement. Third, SNS channel type moderates the effect on attitude. Practical implications Marketing communication practitioners should note that the strength of social ties plays a key role in spreading eWOM on SNS effectively. This insight should be employed a part of social media marketing strategy. Originality/value This is first research that models the effect of social media attributes on eWOM engagement and demonstrates the moderating role of channel type. The model is highly valuable in light of the importance of the concept of engagement in internet research.
A Compact Parametric Solution to Depth Sensor Calibration

We present a method for calibration of low-cost depth sensors such as the Microsoft Kinect. We show this method is effective at correcting the structured sensor error using a compact parametric solution, that uses only a small fraction of the number of parameters used in many existing approaches. Additionally we provide this calibration as an open-source implementation, with limited required external dependencies. We demonstrate our approach can optimise directly for a geometric depth and radial distortion calibration function in minutes on modern hardware.
Cost Effectiveness of Chimeric Antigen Receptor T-Cell Therapy in Relapsed or Refractory Pediatric B-Cell Acute Lymphoblastic Leukemia.

Purpose The anti-CD19 chimeric antigen receptor T-cell therapy tisagenlecleucel was recently approved to treat relapsed or refractory pediatric acute lymphoblastic leukemia. With a one-time infusion cost of $475,000, tisagenlecleucel is currently the most expensive oncologic therapy. We aimed to determine whether tisagenlecleucel is cost effective compared with currently available treatments. Methods Markov modeling was used to evaluate tisagenlecleucel in pediatric relapsed or refractory acute lymphoblastic leukemia from a US health payer perspective over a lifetime horizon. The model was informed by recent multicenter, single-arm clinical trials. Tisagenlecleucel (under a range of plausible long-term effectiveness) was compared with blinatumomab, clofarabine combination therapy (clofarabine, etoposide, and cyclophosphamide), and clofarabine monotherapy. Scenario and probabilistic sensitivity analyses were used to explore uncertainty. Main outcomes were life-years, discounted lifetime costs, discounted quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (3% discount rate). Results With an assumption of a 40% 5-year relapse-free survival rate, tisagenlecleucel increased life expectancies by 12.1 years and cost $61,000/QALY gained. However, at a 20% 5-year relapse-free survival rate, life-expectancies were more modest (3.8 years) and expensive ($151,000/QALY gained). At a 0% 5-year relapse-free survival rate and with use as a bridge to transplant, tisagenlecleucel increased life expectancies by 5.7 years and cost $184,000/QALY gained. Reduction of the price of tisagenlecleucel to $200,000 or $350,000 would allow it to meet a $100,000/QALY or $150,000/QALY willingness-to-pay threshold in all scenarios. Conclusion The long-term effectiveness of tisagenlecleucel is a critical but uncertain determinant of its cost effectiveness. At its current price, tisagenlecleucel represents reasonable value if it can keep a substantial fraction of patients in remission without transplantation; however, if all patients ultimately require a transplantation to remain in remission, it will not be cost effective at generally accepted thresholds. Price reductions would favorably influence cost effectiveness even if long-term clinical outcomes are modest.
ARIADNA: machine learning method for ancient DNA variant discovery

Abstract Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and despite showing extreme sensitivity to aDNA quality, these methods have been used in many published studies, sometimes with additions of arbitrary filters or modifications, designed to overcome aDNA degradation and contamination problems. The general lack of best practices for aDNA mutation calling may lead to inaccurate results. To address these problems, we present ARIADNA (ARtificial Intelligence for Ancient DNA), a novel approach based on machine learning techniques, using specific aDNA characteristics as features to yield improved mutation calls. In our comparisons of variant callers across several ancient genomes, ARIADNA consistently detected higher-quality genome variants with fast runtimes, while reducing the false positive rate compared with other approaches.
Implementasi Kompetensi Kepala Sekolah Dalam Meningkatkan Kompetensi Guru

Tujuan penelitian adalah untuk menganalisis dan mendeskripsikan tentang: 1) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi kepribadian dalam meningkatkan kompetensi guru; 2) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi manajerial dalam meningkatkan kompetensi guru; 3) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi kewirausahaan dalam meningkatkan kompetensi guru; 4) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi supervisi dalam meningkatkan kompetensi guru; 5) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi sosial dalam meningkatkan kompetensi guru. Metode penelitian yang digunakan dalam penyusunan tesis ini adalah deskriptif dengan pendekatan kualitatif. Teknik pengumpulan data dilakukan melalui wawancara, observasi, dan studi dokumentasi. Teknik analisis data dilakukan melalui reduksi data, sajian data, dan penarikan simpulan/verifikasi. Adapun informan pada penelitian ini meliputi kepala sekolah, pengawas, dan guru. Hasil penelitian menunjukkan bahwa: 1) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi kepribadian telah diimplementasikan dengan baik sehingga dapat meningkatkan kompetensi guru; 2) Implementasi kompetensi kepala sekolah ditinjau dari kompetensi manajerial telah diimplementasikan dengan baik sehingga dapat meningkatkan kompetensi guru; 3) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi kewirausahaan telah diimplementasikan dengan baik sehingga dapat meningkatkan kompetensi guru; 4) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi supervisi telah diimplementasikan dengan baik sehingga dapat meningkatkan kompetensi guru; 5) Implementasi kompetensi kepala sekolah ditinjau dari aspek kompetensi sosial telah diimplementasikan dengan baik sehingga dapat meningkatkan kompetensi guru.
Statistical Inference with PLSc Using Bootstrap Confidence Intervals

Partial least squares (PLS) is one of the most popular statistical techniques in use in the Information Systems field. When applied to data originating from a common factor model, as is often the case in the discipline, PLS will produce biased estimates. A recent development, consistent PLS (PLSc), has been introduced to correct for this bias. In addition, the common practice in PLS of comparing the ratio of an estimate to its standard error to a t distribution for the purposes of statistical inference has also been challenged. We contribute to the practice of research in the IS discipline by providing evidence of the value of employing bootstrap confidence intervals in conjunction with PLSc, which is a more appropriate alternative than PLS for many of the research scenarios that are of interest to the field. Such evidence is direly needed before a complete approach to the estimation of SEM that relies on both PLSc and bootstrap CIs can be widely adopted. We also provide recommendations for researchers on the use of confidence intervals with PLSc.
Can Trustworthiness in a Supply Chain Be Signaled?

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Anomaly Detection using a Convolutional Winner-Take-All Autoencoder

We propose a method for video anomaly detection using a winner-take-all convolutional autoencoder that has recently been shown to give competitive results in learning for classification task. The method builds on state of the art approaches to anomaly detection using a convolutional autoencoder and a one-class SVM to build a model of normality. The key novelties are (1) using the motion-feature encoding extracted from a convolutional autoencoder as input to a one-class SVM rather than exploiting reconstruction error of the convolutional autoencoder, and (2) introducing a spatial winner-take-all step after the final encoding layer during training to introduce a high degree of sparsity. We demonstrate an improvement in performance over the state of the art on UCSD and Avenue (CUHK) datasets.
Evidence of a Causal Effect of Estradiol on Fracture Risk in Men

Context Observational studies indicate that serum estradiol (E2) is more strongly associated with bone mineral density (BMD) than serum testosterone (T) is, whereas both E2 and T associate with fracture risk in men. Objective To evaluate the possible causal effect of serum E2 and T on fracture risk in men. Design, Setting, and Participants A Mendelian randomization (MR) approach was undertaken using individual-level data on genotypes, BMD as estimated by quantitative ultrasound of the heel (eBMD), fractures (n = 17,650), and relevant covariates of 175,583 unrelated men of European origin from the UK Biobank. The genetic instruments for serum E2 and T were taken from the most recent large-scale genome-wide association study meta-analyses on these hormones in men. Results MR analyses demonstrated a causal effect of serum E2 on eBMD and fracture risk. A 1 SD (or 9.6 pg/mL) genetically instrumented decrease in serum E2 levels was associated with a 0.38 SD decrease in eBMD (P value: 9.7 × 10-74) and an increased risk of any fracture (OR: 1.35; 95% CI: 1.18, 1.55), nonvertebral major osteoporotic fractures (OR: 1.75; 95% CI: 1.35, 2.27), and wrist fractures (OR: 2.27; 95% CI: 1.62, 3.16). These causal effects of serum E2 levels on fracture risk were robust in sensitivity analyses and remained unchanged in stratified analyses for age, body mass index, eBMD, smoking status, and physical activity. MR analyses revealed no evidence of a causal effect of T levels on fracture risk. Conclusion Our findings provide evidence of a robust causal effect of serum E2, but not T, on fracture risk in men.
Real-time, simultaneous myoelectric control using a convolutional neural network

The evolution of deep learning techniques has been transformative as they have allowed complex mappings to be trained between control inputs and outputs without the need for feature engineering. In this work, a myoelectric control system based on convolutional neural networks (CNN) is proposed as a possible alternative to traditional approaches that rely on specifically designed features. This CNN-based system is validated using a real-time Fitts’ law style target acquisition test requiring single and combined wrist motions. The performance of the proposed system is then compared to that of a standard support vector machine (SVM) based myoelectric system using a set of time-domain features. Despite the prevalence and demonstrated performance of these well-known features, no significant difference (p>0.05) was found between the two methods for any of the computed control metrics. This demonstrates the potential for automated learning approaches to extract complex and rich information from stochastic biological signals. This first evaluation of the usability of a CNN in a real-time myoelectric control environment provides a basis for further exploration.
Fractional flow reserve at the crossroad between revascularization and medical therapy.

The current understanding of the pathophysiology of coronary artery disease relies on the potential adverse effect of myocardial ischemia (1). Even though the ischemia hypothesis has been placed at the core of the evaluation of patients with stable coronary artery disease, no clear evidence supports the benefit of revascularization in terms of hard clinical endpoints namely myocardial infarction and death (2).
Peripapillary and macular choroidal area in patients with normal-tension glaucoma

Purpose To evaluate normal and normal-tension glaucoma (NTG) eyes for differences in peripapillary and macular choroidal area measurements. Methods This cross-sectional comparative study enrolled 52 normal subjects and 35 NTG patients. Peripapillary and macular choroidal images were recorded by enhanced depth imaging optical coherence tomography (EDI-OCT), with conversion of the luminal and interstitial areas to binary images performed using the Niblack method. Results While there was a significant difference between normal subjects and NTG patients for the peripapillary choroidal area (1,853,672 ± 626,501 μm2 vs. 1,606,448 ± 418,214 μm2, P = 0.047), there were no significant differences between the normal subjects and NTG patients observed for the macular choroidal area (345,365 ± 119,248 μm2 vs. 316,442 ± 85,732 μm2, P = 0.23). In the NTG patients, multivariate regression analysis demonstrated a correlation between the macular choroidal area and the axial length (β = -0.345, P = 0.04). Furthermore, there was also a significant correlation between the peripapillary choroidal area and the age of the NTG patients (β = -0.469, P = 0.004). In addition, there was no relationship between the glaucoma severity and the peripapillary and macular choroidal areas in the NTG patients. Conclusions There was no correlation between the peripapillary choroidal area and glaucoma severity in NTG patients, even though the area was smaller in these patients.
Jump to better conclusions: SCAN both left and right

Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for. To mitigate this we propose a complementary dataset, which requires mapping actions back to the original commands, called NACS. We show that models that do well on SCAN do not necessarily do well on NACS, and that NACS exhibits properties more closely aligned with realistic use-cases for sequence-to-sequence models.
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generate large amounts of data streams that can be continuously monitored for intrusion events. Unsupervised machine learning techniques can be used to model the system behaviour and classify deviant behaviours as possible attacks. In this work, we proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex networked CPSs. We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS. Instead of treating each sensor's and actuator's time series independently, we model the time series of multiple sensors and actuators in the CPS concurrently to take into account of potential latent interactions between them. To exploit both the generator and the discriminator of our GAN, we deployed the GAN-trained discriminator together with the residuals between generator-reconstructed data and the actual samples to detect possible anomalies in the complex CPS. We used our GAN-AD to distinguish abnormal attacked situations from normal working conditions for a complex six-stage Secure Water Treatment (SWaT) system. Experimental results showed that the proposed strategy is effective in identifying anomalies caused by various attacks with high detection rate and low false positive rate as compared to existing methods.
Variational-Bayesian Single-Image Devignetting

SUMMARY Vignetting is a common type of image degradation that makes peripheral parts of an image darker than the central part. Single-image devignetting aims to remove undesirable vignetting from an image without resorting to calibration, thereby providing high-quality images required for a wide range of applications. Previous studies into single-image devignetting have focused on the estimation of vignetting functions under the assumption that degradation other than vignetting is negligible. However, noise in real-world observations remains unremoved after inversion of vignetting, and prevents stable estimation of vignetting functions, thereby resulting in low quality of restored images. In this paper, we introduce a methodology of image restoration based on variational Bayes (VB) to de-vignetting, aiming at high-quality devignetting in the presence of noise. Through VB inference, we jointly estimate a vignetting function and a latent image free from both vignetting and noise, using a general image prior for noise removal. Compared with state-of-the-art methods, the proposed VB approach to single-image devignetting maintains e ﬀ ectiveness in the presence of noise, as we demonstrate experimentally.