Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Transcutaneous Electrical Nerve Stimulation on Unilateral Neglect Following Acute Stroke: A Case Report. -

A 58 year old female had a right side ganglio-capsular region ischemic stroke with presentation of left sided weakness in the upper, lower extremity and unilateral neglect since 1 week. Physiotherapy for maintenance of range of motion and prevention of respiratory and immobility related complication in the ICU was a part of the multi disciplinary treatment approach. The patient was treated with standard rehabilitation program along with 2 electrodes placed on the neck for delivering transcutaneous electrical nerve stimulation (TENS) of continuous rectangular wave form with a frequency 100 Hz, pulse width 200 µSec for duration of 20 minutes per day. The treatment was continued for 5 days. Results of the above treatment showed an improvement on the Line Bisection Test which decreased from 5.2 cm to 1.1 cm. Severity of the neglect was assessed using Catherine Bergego Scale, which scores reduced from 25 to 18. Functional outcome was measured using Barthel Index which improved after the treatment with TENS along with standard care for 5 days from a score of 25 to 45. These results suggests that TENS is beneficial in the treatment of unilateral neglect following acute stroke and that further more research is required to certain the results.
Studies on antigen presenting cells and T cells in airways and skin

Childhood represents an immunological window of vulnerability in which individuals are at increased risk for both serious infections and development of allergic diseases, particularly in the airways. This relative failure in immune regulation in the airways may be due to functional differences at the level of the local mucosal immune system. However, little is known about how the airway mucosal immune system is organized and functions during early age. Here, we characterized the organization of immune cells in bronchial mucosal specimens obtained post-mortem from nine children aged 2-15 years without signs of respiratory disease who died from non-inflammatory causes. In all nine cases isolated subepithelial lymphoid follicles (ILFs) interpreted as bronchus-associated lymphoid tissue (BALT) were found constituting an average frequency of 60 ILFs per cm mucosa. Outside these ILFs dense networks of CD11c myeloid DCs, CD68 macrophages, and CD3CD45RA memory T cells were found both within and beneath the surface epithelium. Plasmacytoid DCs also occurred in low numbers. Importantly, intraepithelial antigen presenting cells were found to extend cellular projections into the airway lumen. In conclusion, we show that the density and location of antigen presenting cells and T cells are similar to that observed in adults. However, in contrast to adults, BALT appears to be a normal feature of the airway mucosa throughout childhood, suggesting that these structures contribute to regional immunity and homeostasis. Our findings thus indicate that the local immune system in the airways of children have unique features which should be taken into account when studying its role in relation to respiratory immunopathology.
The Effectiveness and Fairness of "Jock Taxes"

This study focused on the effectiveness and fairness of non-resident taxes paid by professional athletes. Specifically, I examined whether states increase their income tax revenues from the practice of taxing non-resident athletes after factoring in the credits they must grant. Pogrowski (2009) analyzed states’ non-resident income tax revenues, and concluded that a few states were able to significantly increase their tax revenues, while the majority only had slight increases. I completed a similar data analysis using 2013 data. This analysis includes the tax credits that a state must grant to its home athletes. I hypothesized that due to these tax credits, states only marginally increase their tax revenues. The results showed that while some states increased their revenues, some lost revenues, and many showed only marginal increases.
Three Papers on Social Interactions and Labor Market Outcomes

Recent research found a positive relationship between male adolescents’ alcohol consumption and their future incomes. One hypothesis is that adolescents gain sociability from drinking activities may help them get wage premiums in the future labor market. In this paper, I exploit the quasi-random variations in high school peer compositions as a treatment to teenage alcohol consumption and test whether the treatment has similar influences on teenage future income. This paper finds that peer variables that can explain teenage binge drinking do not have significant impacts on future incomes. This result suggests that the influences of high school peers cannot be transmitted to future incomes through binge drinking. Thus, the effects of teenage binge drinking on future income might not be causal. This paper also shows that being exposed to peers with higher possibilities of binge drinking does not necessarily increase sociability.
Female patient after correction of tetralogy of Fallot with severe pulmonary regurgitation and significant left-to-right shunt at the ventricular septal level (RCD code: IV-5A2)

Tetralogy of Fallot (ToF) is the most common cyanotic congenital heart disease after 1 year of age. Less than 5% of all patients with uncorrected ToF live beyond the age of 40 years. Prognosis after surgical correction is good with a 32-year survival of 86% of the cases. Predictors of long-term mortality include older age at operation and postoperative right-to-left ventricular peak systolic pressure ratio of 0.5 or higher [1]. Surgery is performed to close ventricular septal defect (VSD) and relieve right ventricular outflow tract obstruction(RVOTO). Adult patients with surgical repair of tetralogy of Fallot in history often develop complications, which include severe pulmonary regurgitation, RVOTO, right ventricular dilation and dysfunction, residual VSD, aortic root dilation with aortic regurgitation, left ventricular dysfunction, endocarditis, atrial and ventricular tachycardia, and sudden cardiac death. We present a case of a female patient with several long-term complications after ToF repair. JRCD 2014; 1 (7): 24–28
Do We Know If Prices Are Forward Looking

This paper reexamines the debate on the relative importance of forward versus backward looking price-setting behavior in a hybrid Phillips curve model. We first discuss the challenges in identifying the separate effects of expected future inflation as opposed to lagged inflation. We then develop a model where short run fluctuations in flexible-price-sector inflation resemble measurement errors relative to the long-term-contract/core component of inflation modelled in standard New Keynesian Phillips curves. The measurement error perspective suggests that one should be careful to lag instruments, in order to avoid correlations between the measurement errors in lagged, current, and lead overall inflation and the measurement errors in the instruments. When we do this, the estimated hybrid Phillips curve tends to become significantly more backwards looking. To offset the decline in instrument strength caused by lagging instruments for additional periods, we find that expanding the instrument set by including alternative inflation measures, such as the producer price index for finished goods, improves identification of model parameters. The resulting estimates still point to significantly more backward-looking behavior, implying that the share of backward-looking firms may be close to one.
Dermoscopy : A Literature Review

Dermoscopy is a surface skin microscopy technique that rapidly grew during the past years enhancing the non invasive dermatological diagnostic techniques effectively. Its main applications include classic dermoscopy, trichoscopy, entomodermoscopy, inflammoscopy, capillaroscopy and dermoscopy for treatment decision and monitoring.
Old-Age Income Support in the 21st Century: The World Bank's Perspective on Pension Systems and Reform *

This report attempts to clarify and update the World Bank’s perspective on pension reform, incorporating the lessons learned from recent experience and research that has advanced the understanding of how best to proceed in the future. It has been developed as a policy note, not as a research paper. As such, it is intended to conceptualize and explain current policy thinking within the Bank, rather than to present new research results or to announce a new policy approach. This articulation of policies and priorities is intended to assist Bank clients and the broader international public to better understand and appreciate the Bank’s pension reform framework and to facilitate its ability to work effectively with it’s clients in meeting the challenges ahead. This report should provide a guide to the criteria and standards that the World Bank will apply in deciding when and where it will provide financial and technical support to pension reforms. The report has a comprehensive introduction cum executive summary and two main parts. Part I presents the conceptual underpinnings for the Bank’s thinking on pension systems and reforms, including the structure of Bank lending in this area. Part II highlights key design and implementation issues where it signals areas of confidence and areas for further research and experience, and includes a section on regional reform experiences.
An Orthogonal-SGD Based Learning Approach for MIMO Detection under Multiple Channel Models

In this paper, an orthogonal stochastic gradient descent (O-SGD) based learning approach is proposed to tackle the wireless channel over-training problem inherent in artificial neural network (ANN)-assisted MIMO signal detection. Our basic idea lies in the discovery and exploitation of the training-sample orthogonality between the current training epoch and past training epochs. Unlike the conventional SGD that updates the neural network simply based upon current training samples, O-SGD discovers the correlation between current training samples and historical training data, and then updates the neural network with those uncorrelated components. The network updating occurs only in those identified null subspaces. By such means, the neural network can understand and memorize uncorrelated components between different wireless channels, and thus is more robust to wireless channel variations. This hypothesis is confirmed through our extensive computer simulations as well as performance comparison with the conventional SGD approach.
Denoising IMU Gyroscopes With Deep Learning for Open-Loop Attitude Estimation

This article proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data, and estimating in real time the orientation (attitude) of a robot in dead reckoning. The obtained algorithm outperforms the state-of-the-art on the (unseen) test sequences. The obtained performances are achieved, thanks to a well-chosen model, a proper loss function for orientation increments, and through the identification of key points when training with high-frequency inertial data. Our approach builds upon a neural network based on dilated convolutions, without requiring any recurrent neural network. We demonstrate how efficient our strategy is for 3D attitude estimation on the EuRoC and TUM-VI datasets. Interestingly, we observe our dead reckoning algorithm manages to beat top-ranked visual-inertial odometry systems in terms of attitude estimation although it does not use vision sensors. We believe this article offers new perspectives for visual-inertial localization and constitutes a step toward more efficient learning methods involving IMUs. Our open-source implementation is available at https://github.com/mbrossar/denoise-imu-gyro.
Magnetic twist profile inside magnetic clouds derived with a superposed epoch analysis

Context. Magnetic clouds (MCs) are large-scale interplanetary transient structures in the heliosphere that travel from the Sun into the interplanetary medium. The internal magnetic field lines inside the MCs are twisted, forming a flux rope (FR). This magnetic field structuring is determined by its initial solar configuration, by the processes involved during its eruption from the Sun, and by the dynamical evolution during its interaction with the ambient solar wind. Aims. One of the most important properties of the magnetic structure inside MCs is the twist of the field lines forming the FR (the number of turns per unit length). The detailed internal distribution of twist is under debate mainly because the magnetic field (B) in MCs is observed only along the spacecraft trajectory, and thus it is necessary to complete observations with theoretical assumptions. Estimating the twist from the study of a single event is difficult because the field fluctuations significantly increase the noise of the observed B time series and thus the bias of the deduced twist. Methods. The superposed epoch applied to MCs has proven to be a powerful technique, permitting the extraction of their common features, and removing the peculiarity of individual cases. We apply a superposed epoch technique to analyse the magnetic components in the local FR frame of a significant sample of moderately asymmetric MCs observed at 1 au. Results. From the superposed profile of B components in the FR frame, we determine the typical twist distribution in MCs. The twist is nearly uniform in the FR core (central half part), and it increases moderately, up to a factor two, towards the MC boundaries. This profile is close to the Lundquist field model limited to the FR core where the axial field component is above about one-third of its central value.
Diversity-Based Generalization for Neural Unsupervised Text Classification under Domain Shift

Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the state-of-the-art domain adaptation approaches for subjective text classification problems are semi-supervised; and use unlabeled target data along with labeled source data. In this paper, we propose a novel method for domain adaptation of single-task text classification problems based on a simple but effective idea of diversity-based generalization that does not require unlabeled target data. Diversity plays the role of promoting the model to better generalize and be indiscriminate towards domain shift by forcing the model not to rely on same features for prediction. We apply this concept on the most explainable component of neural networks, the attention layer. To generate sufficient diversity, we create a multi-head attention model and infuse a diversity constraint between the attention heads such that each head will learn differently. We further expand upon our model by tri-training and designing a procedure with an additional diversity constraint between the attention heads of the tri-trained classifiers. Extensive evaluation using the standard benchmark dataset of Amazon reviews and a newly constructed dataset of Crisis events shows that our fully unsupervised method matches with the competing semi-supervised baselines. Our results demonstrate that machine learning architectures that ensure sufficient diversity can generalize better; encouraging future research to design ubiquitously usable learning models without using unlabeled target data.
On the regularity and conditioning of low rank semidefinite programs

Low rank matrix recovery problems appear widely in statistics, combinatorics, and imaging. One celebrated method for solving these problems is to formulate and solve a semidefinite program (SDP). It is often known that the exact solution to the SDP with perfect data recovers the solution to the original low rank matrix recovery problem. It is more challenging to show that an approximate solution to the SDP formulated with noisy problem data acceptably solves the original problem; arguments are usually ad hoc for each problem setting, and can be complex. In this note, we identify a set of conditions that we call regularity that limit the error due to noisy problem data or incomplete convergence. In this sense, regular SDPs are robust: regular SDPs can be (approximately) solved efficiently at scale; and the resulting approximate solutions, even with noisy data, can be trusted. Moreover, we show that regularity holds generically, and also for many structured low rank matrix recovery problems, including the stochastic block model, $\mathbb{Z}_2$ synchronization, and matrix completion. Formally, we call an SDP regular if it has a surjective constraint map, admits a unique primal and dual solution pair, and satisfies strong duality and strict complementarity. However, regularity is not a panacea: we show the Burer-Monteiro formulation of the SDP may have spurious second-order critical points, even for a regular SDP with a rank 1 solution.
FPConv: Learning Local Flattening for Point Convolution

We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis. Unlike previous methods, FPConv doesn't require transforming to intermediate representation like 3D grid or graph and directly works on surface geometry of point cloud. To be more specific, for each point, FPConv performs a local flattening by automatically learning a weight map to softly project surrounding points onto a 2D grid. Regular 2D convolution can thus be applied for efficient feature learning. FPConv can be easily integrated into various network architectures for tasks like 3D object classification and 3D scene segmentation, and achieve comparable performance with existing volumetric-type convolutions. More importantly, our experiments also show that FPConv can be a complementary of volumetric convolutions and jointly training them can further boost overall performance into state-of-the-art results.
Statistical Adaptive Stochastic Gradient Methods

We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods. SALSA first uses a smoothed stochastic line-search procedure to gradually increase the learning rate, then automatically switches to a statistical method to decrease the learning rate. The line search procedure ``warms up'' the optimization process, reducing the need for expensive trial and error in setting an initial learning rate. The method for decreasing the learning rate is based on a new statistical test for detecting stationarity when using a constant step size. Unlike in prior work, our test applies to a broad class of stochastic gradient algorithms without modification. The combined method is highly robust and autonomous, and it matches the performance of the best hand-tuned learning rate schedules in our experiments on several deep learning tasks.
The Effects Of Technology Driven Information Categories On Performance In Electronic Trading Markets

Electronic trading markets have evolved rapidly with continued adoption of new technologies and growing in-formation acquisition and processing capabilities. Traditional perspectives on trading performance adopted a mono-lithic view of information. Past research and practitioner heuristics posit that adopting new technologies and incorpo-rating more information should increase price efficiency and trading performance uniformity. However, along with technological change, information dynamics have evolved significantly resulting in immense growth in data volumes, and increased complexity of information categories. The present research explores behavioral trading performance under varying information category conditions and argues that unfettered technological developments and information consumption will not necessarily lead to consistent improvement in uniformity of trading performance. In this study, we employ an artificial stock market based economic experiment to examine the role of technol-ogy driven information categories in influencing trading decisions in electronic markets. Financial electronic markets are used as an information-rich mature markets representation to analyze information category driven trading perfor-mance. The results show that a variation of information categories can influence trading performance. The findings provide a basis to better understand behavioral phenomena in electronic markets and can be used to explain anomalies as well as to manage trading performance in electronic markets.
Small-scale Structure Traced by Neutral Hydrogen Absorption in the Direction of Multiple-component Radio Continuum Sources

We have studied the small-scale distribution of atomic hydrogen (H i) using 21 cm absorption spectra against multiple-component background radio continuum sources from the 21-SPONGE survey and the Millennium Arecibo Absorption-Line Survey. We have found >5σ optical depth variations at a level of ∼0.03–0.5 between 13 out of 14 adjacent sightlines separated by a few arcseconds to a few arcminutes, suggesting the presence of neutral structures on spatial scales from a few to thousands of au (which we refer to as tiny-scale atomic structure, TSAS). The optical depth variations are strongest in directions where the H i column density and the fraction of H i in the cold neutral medium (CNM) are highest, which tend to be at low Galactic latitudes. By measuring changes in the properties of Gaussian components fitted to the absorption spectra, we find that changes in both the peak optical depth and the linewidth of TSAS absorption features contribute to the observed optical depth variations, while changes in the central velocity do not appear to strongly impact the observed variations. Both thermal and turbulent motions contribute appreciably to the linewidths, but the turbulence does not appear strong enough to confine overpressured TSAS. In a majority of cases, the TSAS column densities are sufficiently high that these structures can radiatively cool fast enough to maintain thermal equilibrium with their surroundings, even if they are overpressured. We also find that a majority of TSAS is associated with the CNM. For TSAS in the direction of the Taurus molecular cloud and the local Leo cold cloud, we estimate densities over an order of magnitude higher than typical CNM densities.
A more abstractive summarization model

Pointer-generator network is an extremely popular method of text summarization. More recent works in this domain still build on top of the baseline pointer generator by augmenting a content selection phase, or by decomposing the decoder into a contextual network and a language model. However, all such models that are based on the pointer-generator base architecture cannot generate novel words in the summary and mostly copy words from the source text. In our work, we first thoroughly investigate why the pointer-generator network is unable to generate novel words, and then address that by adding an Out-of-vocabulary (OOV) penalty. This enables us to improve the amount of novelty/abstraction significantly. We use normalized n-gram novelty scores as a metric for determining the level of abstraction. Moreover, we also report rouge scores of our model since most summarization models are evaluated with R-1, R-2, R-L scores.