Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simpler Standard Error Formula for Multiple Imputation Based on a Maximum Likelihood Estimate

Maximum likelihood (ML) imputation is a form of multiple imputation (MI) that draws random imputations conditionally on a maximum likelihood estimate. ML imputation is simpler and more efficient than the more popular Bayesian approach to MI which draws imputations from the posterior predictive distribution. A disadvantage of ML imputation has been the difficulty of estimating the standard error of the ML imputation point estimate. This paper derives a simple new standard error formula, then tests it through simulation. The formula performs well if the fraction of missing information is small to moderate, but has a negative bias if the fraction of missing information is large.
An Optimization Transfer Algorithm for Quantile Regression

The q quantiles of an integrable random variable solve a minimization problem involving a certain expectation. This optimality principle suggests an algorithm for nding a sample quantile without sorting. The current paper explores generalizations of the algorithm to nonlinear quantile regression. In particular, we devise a quantile regression algorithm that operates by transferring optimization of the nondi erentiable objective function to a quadratic function. It is conceptually simple and easy to code, and our numerical tests suggest that it is computationally competitive with the interior point algorithm of Koenker and Park (1996).
Prosthetic Arm with Functional Fingers and Wireless Recharge on Walk Function

The efficiency, tactile features, and weight of prosthetic limbs have all improved over time, but the long charging period that comes with these benefits reduces their overall appeal. The purpose of this study is to incorporate a portable power source into the prosthetic arm in order to decrease downtime and increase reliability. The use of special footwear that serves as a regenerative power source for the prosthetic arm’s battery, a piezoelectric generator that generates energy while walking, and a charging station that transfers power directly to the prosthetic arm while on the move. The complete device allows the user to comfortably use the prosthetic arm for longer periods of time.
Transfer learning approaches in deep learning for Indian sign language classification

Speech is the major way of human communication, but when it is limited, humans move to tactile kinaesthetic communication. People with speech-hearing impairments use sign language as an example of such adaptations. The deaf community uses Indian sign language (ISL) throughout India. In India, 250 licensed sign language interpreters are serving a deaf population of 1.8 to 7 million individuals. ISL interpreters are badly needed at institutes and places where persons with hearing impairments communicate. An Indian sign language picture database for English alphabets is established in this project. To prepare it for training, several pre-processing techniques were used. The effectiveness of deep learning neural networks is frequently influenced by the quantity of data available. As a result, data augmentation, a strategy for adding more and diverse samples to train datasets, was used to boost the effectiveness and outcomes of machine learning models. Our model is trained in CNN models utilizing transfer learning methodologies, with an accuracy of 95% for vgg16 and an accuracy of 92% for the inception model. More study on this research, as well as real-time implementation, has the potential to better connect people with hearing loss to society.
Analysis And Evaluation Of Consumers’ Cognitive Responses To Visual And Gustatory Stimuli For Neuromarketing Application

Neuromarketing merges viewpoints of marketing, neuroscience, economics, choice hypothesis that are required to analyze the psychology of consumers’ preference to product development. The traditional methods involve product ratings, conducting questionnaire surveys that stumble upon verbal declarations provided by the vendees. Consumer Neuroscience describes the emotional, cognitive aspects that form the base of human decision making. Our study aims to utilize the neuroscientific information that distinguishes contrasts between healthy subjects’ EEG signals for examining the brain activity during visual and gustatory stimuli of different flavours of a beverage brand. The EEG montage assigned according to brain-region-specific localization draws out the subjects’ true elicited subconscious response regardless of whether the subject attempts to control his/her affective state. The results showed the activation of theta and delta bands of EEG signals during the given stimuli. These elicited signal variations can be used to identify the best favoured item for successful product dispatch and reduction in loss. Another major application is directed towards the customization of liquid food intake of locked-in, comatose, vegetative state patients by observing their brain response to the various fluid intake and determining the best response among them. This aids physicians to put the patients on a path to recovery.
Medication adherence manager and its clinical application.

Adherence to prescribed medication affects global clinical practice especially on chronic disease management resulting in adverse medical and economic burden. Reliable standard and practice in measuring adherence among patient is yet to be established clinically although aspects from user behavioural, clinical needs and technology-assisted devices design have been studied. Lack of real-time monitoring and inefficient method of medication consumption validation has been identified as the challenges in monitoring patient medication consumption’s pattern. The aim of this study is to design and deploy an IoT driven medication adherence manager. The proposed IoT architecture enabled by real time monitoring through a multi-sensor alert option in medicinal compartment to ensure adherence integrity. The reliability of the proposed device further enhanced with the mobile application and cloud storage and usage analytic support application. The medication adherence manager expected to digitize the medication consumption process thus improving the patient health and well-being at the same time reduce pharmaceutical wastage due to nonadherence.
It's Always Darkest Before the Cosmic Dawn: Early Results from Novel Tools and Telescopes for 21 cm Cosmology

21 cm cosmology, the statistical observation of the high redshift universe using the hyperfine transition of neutral hydrogen, has the potential to revolutionize our understanding of cosmology and the astrophysical processes that underlie the formation of the first stars, galaxies, and black holes during the "Cosmic Dawn. " By making tomographic maps with low frequency radio interferometers, we can study the evolution of the 21 cm signal with time and spatial scale and use it to understand the density, temperature, and ionization evolution of the intergalactic medium over this dramatic period in the history of the universe. For my Ph.D. thesis, I explore a number of advancements toward detecting and characterizing the 21 cm signal from the Cosmic Dawn, especially during its final stage, the epoch of reionization. In seven different previously published papers, I explore new techniques for the statistical analysis of interferometric measurements, apply them to data from current generation telescopes like the Murchison Widefield Array, and look forward to what we might measure with the next generation of 21 cm observatories. I focus in particular on estimating the power spectrum of 21 cm brightness temperature fluctuations in the presence enormous astrophysical foregrounds and how those measurements may constrain the physics of the Cosmic Dawn. Thesis Supervisor: Max Tegmark
Jets in Photoproduction and at Low Q2 at HERA

Recent H1 and ZEUS measurements of two and three jet cross sections in electron‐proton interactions in the photoproduction region or at low photon virtuality, Q 2, are presented and compared to NLO QCD calculations. Phase space regions where data are not well reproduced by the NLO theory are pointed out. A recent ZEUS measurement of color dynamics sensitive to the underlying gauge group in QCD is presented.
Prone Positioning for Patients With COVID-19–Associated Acute Respiratory Distress Syndrome

We have read with interest the experimental, physiologic study by Fossali et al (1), assessing the physiologic effects of pronation by means of CT scan and electrical impedance tomography. This study raises some interesting issues that might contribute to the study and future investigations. As shown by the present study, the authors pointed out that prone positioning can improve oxygenation in COVID-19–associated acute respiratory distress syndrome (C-ARDS) by increasing lung recruitment, decreasing atelectrauma, and improving ventilation-perfusion matching. Ventilationperfusion matching can improve rapidly with the change of body position, which can be explained the dramatically changes of physiologic effects, including oxygenation within a few minutes after prone positioning, as shown by this present study and another study (2), but how long this improvement will be sustained is unknown. Theoretically, with the extension of prone position, pulmonary edema and atelectasis in dependent lung zones will occur in the ventral lung regions and the overall effect will be reversed, just as Fralick et al (3) found that there was no sustained improvement in oxygenation related to prone positioning. In addition, this article has not yet provided the effects of the prone position on mortality and long-term prognosis. Whether prone positioning is an effective therapy to decrease mortality in patients with C-ARDS remains unclear. A systematic review and meta-analysis showed that patients with mild or moderate ARDS who received prone positioning had improved oxygenation, but the improvement did not correlate with a mortality benefit (4). The physiologic differences between ARDS from COVID-19 and other causes appear small (5). A multicenter pragmatic randomized clinical trial showed that prone positioning of COVID-19 patients with moderate hypoxemia did not improve the risk of the composite of death, mechanical ventilation, or worsening respiratory failure (3). COVID-19 patients are considered to have heterogeneity in respiratory mechanics and interindividually variable recruitability (5). How longer duration of time spent prone is associated with beneficial clinical outcomes is still uncertain. Previous studies suggested that a mortality benefit was observed only with longer durations (12–16 hr) of prone positioning for mechanically ventilated ARDS patients with or without COVID-19 (4). Considering that many questions about prone positioning remain unknown, such as suitable patient selection, timing of starting, and stopping prone positioning and its cost-effectiveness, rigorous randomized controlled trials that examine clinical outcomes of prone positioning in patients with COVID-19 are needed. Chunqi Wang, NP1
The influences of depression and loneliness on A1C among middle-aged and older adults with diabetes

ABSTRACT Even before increased social isolation associated with the COVID-19 pandemic, 43% of adults aged 60 and older reported experiencing loneliness. Depression and loneliness often co-exist and are significant issues faced by middle-aged as well as older adults because each condition is likely to worsen health outcomes. This study of middle-aged and older adults examined how depression and loneliness affect diabetes (DM) control (A1C levels). This study is a secondary analysis of data from the Midlife in the United States Refresher (MIDUS-R) survey, a national survey of adults aged 25–74 years. Correlation analyses were conducted, and a hierarchical logistic regression was estimated to predict A1C levels ≤7% (recommended goal) or >7 using 1) demographics and physical health (ethnicity, gender, education, age, and comorbidities), 2) family and friend support, and 3) depression and loneliness. The sample of 92 participants with DM and A1C data from the MIDUS-R had mean age = 57.37, were 51% male, 68% non-Hispanic White; 39.1% had A1C >7. The average level of depression was low (CES-D mean 9.42) and loneliness was moderate (UCLA scale mean 12.43). Loneliness was correlated with A1C (r= .26, p< .05); depressive symptoms (r= .71, p< .001), family and friends support (r= −.36, r= −.38, respectively, both p< .001). Only loneliness significantly predicted higher A1C levels. People with higher levels of loneliness had increased odds of having A1C >7 (OR = 1.18, p < .05) after controlling for depression and all other variables. Loneliness had a greater impact than depression on A1C level among persons with DM. Healthcare providers should assess patients for loneliness as well as depression and reduce adverse health impacts by referring to psychosocial support as needed.
Using Localised Quadratic Functions on an Irregular Grid for Pricing High-Dimensional American Options

We propose a method for pricing high-dimensional American options on an irregular grid; the method involves using quadratic functions to approximate the local effect of the Black-Scholes operator.Once such an approximation is known, one can solve the pricing problem by time stepping in an explicit or implicit manner.We study stability of the method in two dimensions, and find that the grid structure is important in providing a stable approximation to the operator.
Laterality in functional and metabolic state of the bulbectomised rat brain detected by ASL and 1H MRS: A pilot study

Abstract Objectives Pilot study validating the animal model of depression – the bilateral olfactory bulbectomy in rats – by two nuclear magnetic resonance methods, indirectly detecting the metabolic state of the brain. Furthermore, the study focussed on potential differences in brain laterality. Methods Arterial spin labelling assessed cerebral brain flow in prefrontal, sensorimotor, and piriform cortices, nucleus accumbens, hippocampus, thalamus, circle of Willis, and whole brain. Proton magnetic resonance spectroscopy provided information about relative metabolite concentrations in the cortex and hippocampus. Results Arterial spin labelling found no differences in cerebral perfusion in the group comparison but revealed lateralisation in the thalamus of the control group and the sensorimotor cortex of the bulbectomized rats. Lower Cho/tCr and Cho/NAA levels were found in the right hippocampus in bulbectomized rats. The differences in lateralisation were shown in the hippocampus: mI/tCr in the control group, Cho/NAA, NAA/tCr, Tau/tCr in the model group, and in the cortex: NAA/tCr, mI/tCr in the control group. Conclusion Olfactory bulbectomy affects the neuronal and biochemical profile of the rat brain laterally and, as a model of depression, was validated by two nuclear magnetic resonance methods.
Computational prediction of Lee retention indices of polycyclic aromatic hydrocarbons by using machine learning

Given the difficult of experimental determination, quantitative structure–property relationship (QSPR) and deep learning (DL) provide an important tool to predict physicochemical property of chemical compounds. In this paper, partial least squares (PLS), genetic function approximation (GFA), and deep neural network (DNN) were used to predict the Lee retention index (Lee‐RI) of PAHs in SE‐52 and DB‐5 stationary phases. Four molecular descriptors, molecular weight (MW), quantitative estimate of drug‐likeness (QED), atomic charge weighted negative surface area (Jurs_PNSA_3), and relative negative charge (Jurs_RNCG) were selected to construct regression models based on genetic algorithm. For SE‐52, PLS model showed best prediction power, followed by DNN and GFA. The relative error (RE), root mean square error (RMSE), and regression coefficient (R2) of best PLS regression model are 1.228%, 5.407, and 0.980. For DB‐5, DNN model showed best prediction power, followed by GFA and PLS. The RE, RMSE and R2 of best DNN regression model for DB‐5‐1 and DB‐5‐2 are 1.058%, 4.325%, 0.976%, 0.821%, 3.795%, and 0.970%, respectively. The three regression models not only show good predictive ability, but also highlight the stability and ductility of the models.
Efficient Deobfuscation of Linear Mixed Boolean-Arithmetic Expressions

Mixed Boolean-Arithmetic (MBA) expressions are frequently used for obfuscation. As they combine arithmetic as well as Boolean operations, neither arithmetic laws nor transformation rules for logical formulas can be applied to suitably complex expressions, making MBAs hard to simplify and solve. In 2019, Liu et al. demystified linear MBAs, leveraging a transformation between the set B={0, 1} of bit values and the set Bn of words of length n∊N for linear MBAs, originally introduced by Zhou et al. in 2007. With their MBA-Blast and MBA-Solver algorithms, they outperform existing tools noticably in terms of performance as well as ability to simplify of such MBAs. We propose a surprisingly simple algorithm called SiMBA that improves upon MBA-Blast and MBA-Solver in that it can deobfuscate all linear MBAs, does not miss particularly simple solutions and takes only a fraction of their runtime.
PlaStIL: Plastic and Stable Memory-Free Class-Incremental Learning

Plasticity and stability are needed in class-incremental learning in order to learn from new data while preserving past knowledge. Due to catastrophic forgetting, ﬁnding a compromise between these two properties is particularly challenging when no memory buffer is available. Main-stream methods need to store two deep models since they integrate new classes using ﬁne tuning with knowledge distillation from the previous incremental state. We propose a method which has similar number of parameters but distributes them differently in order to ﬁnd a better balance between plasticity and stability. Following an approach already deployed by transfer-based incremental methods, we freeze the feature extractor after the initial state. Classes in the oldest incremental states are trained with this frozen extractor to ensure stability. Recent classes are predicted using partially ﬁne-tuned models in order to introduce plasticity. Our proposed plasticity layer can be incorporated to any transfer-based method designed for memory-free incremental learning, and we apply it to two such methods. Evaluation is done with three large-scale datasets. Results show that performance gains are obtained in all tested conﬁgura-tions compared to existing methods.
Diagnosis of mid-second trimester fetal growth restriction and associated outcomes

Abstract Objective To evaluate maternal and neonatal outcomes of low-risk singleton pregnancies, without underlying maternal medical conditions or genetic and fetal anomalies associated with fetal growth restriction, that were diagnosed with fetal growth restriction (FGR) (EFW < 10th %) in the mid-second trimester (between 17 and 22 weeks and 6 days’ gestation). Methods A retrospective cohort study of all women who underwent a routine fetal anatomy ultrasound between 17 and 22 weeks and 6 days’ gestation at a community-based academic hospital was performed to identify subjects with an EFW <10th%. Pregnancies with inadequate dating, multiple gestations, preexisting maternal vascular disease (chronic hypertension and pregestational diabetes), lethal fetal anomalies, and abnormal prenatal genetic screening were excluded. Descriptive statistics were computed to describe the study population. Subjects were stratified into two groups, estimated fetal weight (EFW) <5th% and EFW 5th–9th%. The primary outcome was a small for gestational age neonate (SGA) at delivery. Secondary outcomes included a composite adverse neonatal outcome, perinatal death, hypertensive disorders of pregnancy, medically indicated delivery, and mode of delivery. A comparison of the two groups, EFW <5th% and EFW 5th–9th %, was performed. Continuous variables were compared utilizing Wilcoxon Rank Sum tests and categorical variables were compared using Fisher’s exact test or Chi-squared tests, and a dichotomous composite variable for adverse neonatal outcomes was also calculated. Results In total, 3,868 unique patient records were screened. Thirty-two patient records (0.8% of the total screened records) were eligible for inclusion. The primary outcome, SGA at delivery, occurred in 13/32 (41%) of the subjects. The secondary outcomes of the composite neonatal morbidity occurred in 9/32 (28%), hypertensive disorders of pregnancy in 10/32 (31%), and medically indicated delivery at <28 weeks gestation in 7/32 (22%) of the subjects. When comparing EFW <5th% and EFW 5th–9th%, EFW <5th% had a larger percentage of SGA newborns (66% vs. 25%, p = .02, OR = 8.0 95% CI 1.5–42.5). EFW <5th% was also significantly associated with a greater composite adverse neonatal outcome when compared to EFW 5th–9th% (54% vs. 10%, p = .015). The subgroup with an EFW <5th% also had higher rates of adverse outcomes including preeclampsia (42% vs. 10%, p = .073), abnormal umbilical artery Doppler studies (50% vs. 15%, p = .049), and medically indicated delivery <28 weeks (42% vs. 10%, p = .07). Conclusions Early onset FGR is a associated with high rates of SGA at delivery, as well as several adverse maternal and neonatal outcomes which include hypertensive disorders of pregnancy, a greater composite neonatal morbidity, perinatal death, and medically indicated preterm delivery. EFW <5th% was associated with worse outcomes when compared to those with an EFW 5th%–9th%.
Snake contours in three-dimensions from colour stereo image pairs

Snakes (active contour models) are extended to segment regions of interest on the surface of 3D objects. Stereo images taken with calibrated cameras are used as input. For this method the depth map for the whole image does not need to be computed. Instead, 3D external forces are designed to keep the contour on the surface of the object while moving it toward the desired boundaries. Color information is used to improve the ability of snakes in detecting the boundaries, in contrast to the majority of previous methods which are based on intensity information alone. The proposed method produces 3D contours on the surface of the object with coordinates in physical units, e.g. millimeters. These contours can be used to view the structure and dimensions of any distinguishable region on the surface of an object. Examples include oral lesions and skin diseases. The whole process requires minimal human interaction; however, user input can be used to improve segmentation. To Shirin, Mohammand, Aaar and Sara for their love, support, tolerance and patience "Do not hover always on the surface of things, nor take up suddenly, with mere appearances; but penetrate into the depth of matters, as far as your time and circumstances allow, especially in those things which relate to your profession. " Isaac Watts