Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety and tolerability of nintedanib for the treatment of idiopathic pulmonary fibrosis in routine UK clinical practice

Nintedanib, a tyrosine kinase inhibitor approved for the treatment of idiopathic pulmonary fibrosis (IPF), reduces annual forced vital capacity (FVC) decline in these patients by ∼50% with combined analysis of data from clinical trials showing a trend towards reduction in mortality [1–3]. Nintedanib prescription criteria for the treatment of IPF vary between countries and in 2016, the National Institute for Health and Care Excellence approved nintedanib in England and Wales for patients with an FVC between 50% and 80% predicted [4]. Prior to this approval, nintedanib was available through a manufacturer-funded programme with varying prescribing criteria. We investigated the safety and tolerability of nintedanib in UK clinical practice during this period in which FVC prescription criteria differed from those now available in routine practice. In IPF, commencement of antifibrotic therapy in patients with preserved lung volume may increase the duration of therapy http://ow.ly/4HeM30lFS67
Hybrid Supervised Deep Learning for Ethnicity Classification using Face Images

Ethnicity information is an integral part of human identity, and a useful identifier for various applications ranging from video surveillance, targeted advertisement to social media profiling. In recent years, Convolutional Neural Networks (CNNs) have shown state-of-the-art performance in many visual recognition problems. Currently, there are a few CNN-based approaches on ethnicity classification [1], [2]. However, the approaches suffer from the following limitations: (i) most face datasets do not include ethnicity information, and those with ethnicity information are typically small to medium in size, thereby they do not provide sufficient samples for training of CNNs from the scratch, and (ii) the CNN methods often treat ethnicity classification as a multi-class classification where the likelihood of each class label is generated. However, it does not utilize the intermediate activation functions of CNNs which provide rich hierarchical features to assist in ethnicity classification. In view of this, this paper proposes a new hybrid supervised learning method to perform ethnicity classification that uses both the strength of CNN as well as the rich features obtained from the network. The method combines the soft likelihood of CNN classification output with an image ranking engine that leverages on matching of the hierarchical features between the query and dataset images. A supervised Support Vector Machine (SVM) hybrid learning is developed to train the combined feature vectors to perform ethnicity classification. The performance of the proposed method is evaluated using a dataset consisting of Bangladeshi, Chinese and Indian ethnicity groups, and it outperforms the state-of-the-art methods [2], [3] by up to 3% in recognition accuracy.
Evolution of Bacterial Gene Transfer Agents

Bacterial gene transfer agents (GTAs) are small virus-like particles that package DNA fragments and inject them into cells. They are encoded by gene clusters resembling defective prophages, with genes for capsid head and tail components. These gene clusters are usually assumed to be maintained by selection for the benefits of GTA-mediated recombination, but this has never been tested. We rigorously examined the potential benefits of GTA-mediated recombination, considering separately transmission of GTA-encoding genes and recombination of all chromosomal genes. In principle GTA genes could be directly maintained if GTA particles spread them to GTA− cells often enough to compensate for the loss of GTA-producing cells. However careful bookkeeping showed that losses inevitably exceed gains for two reasons. First, cells must lyse to release particles to the environment. Second, GTA genes are not preferentially replicated before DNA is packaged. A simulation model was then used to search for conditions where recombination of chromosomal genes makes GTA+ populations fitter than GTA− populations. Although the model showed that both synergistic epistasis and some modes of regulation could generate fitness benefits large enough to overcome the cost of lysis, these benefits neither allowed GTA+ cells to invade GTA− populations, nor allowed GTA+ populations to resist invasion by GTA− cells. Importantly, the benefits depended on highly improbable assumptions about the efficiencies of GTA production and recombination. Thus, the selective benefits that maintain GTA gene clusters over many millions of years must arise from consequences other than transfer of GTA genes or recombination of chromosomal genes.
Explaining the declined affordability of housing for low-income private renters across Western Europe

The private rented sector (PRS) recently enjoyed a revival, in particular in the years before and after the Great Financial Crisis (GFC). At the same time however, affordability concerns have come to the fore. The main aim of this paper is to explain trends in housing affordability for lower-income households in the PRS across Western European countries, from a supply versus demand perspective. To this end we: (1) related trends in housing affordability to wider changes in housing systems, welfare regimes, demographic indicators and housing market financialisation; and (2) decomposed affordability trends in terms of rents and incomes, controlling for compositional shifts. We incorporated the spatial dimension by distinguishing between urban and rural regions. Although we could not explicitly test for the more fine-grained mechanisms relating housing market financialisation to increased ‘unaffordability’ of PRS-housing, our findings nevertheless warrant future research into this topic. In particular in countries with strong financialisation (Ireland, the Netherlands, Spain and Portugal) decreasing affordability arises from the fact that during the period 1995–2007 private rent increases were not compensated for sufficiently by income growth. We furthermore found that across urban regions, between 1995 and 2007, affordability worsened through demand pressure arising from in-migration. Changes after the GFC (up to 2013) were more limited and diverse.
Is the Retinal Vasculature Related to β-Peripapillary Atrophy in Nonpathological High Myopia? An Optical Coherence Tomography Angiography Study in Chinese Adults

Purpose The association between β-peripapillary atrophy and the retinal vasculature in nonpathological high myopia is unclear. The aim of this study is to investigate whether β-peripapillary atrophy contribute to the changes of the retinal vasculature using optical coherence tomography angiography. Methods In a cross-sectional study, one hundred and thirty eyes with nonpathological high myopia were included. β-peripapillary atrophy was analysed using Image J software based on fundus photographs. A 3.0 × 3.0 mm2 grid and a 4.5 × 4.5 mm2 grid were used to scan parafoveal and peripapillary regions using optical coherence tomography angiography, respectively. Vessel density and fractal dimensions of the retina and foveal avascular zone were analysed and quantified using en face projection images. Correlations between the vascular density, foveal avascular zone, and β-peripapillary atrophy were determined. Results Using multivariate analysis, β-peripapillary atrophy was negatively correlated with the vessel density in radial peripapillary capillaries (p=0.002) even after adjusting for other variables. This relationship was also confirmed in the macula (superficial retinal plexus: p < 0.05; deep retinal plexus: p < 0.05). The vessel densities in the nasal and inferior sectors were more strongly correlated with β-peripapillary atrophy. Conclusions There was a negative correlation between β-peripapillary atrophy and the retinal vasculature in highly myopic eyes, especially in radial peripapillary capillaries and deep retinal plexus. β-peripapillary atrophy can be visualized and is a convenient structural feature that can benefit the early diagnosis and detection of chorioretinal atrophy in high myopia.
Architecture Generator for Type-3 Unum Posit Adder/Subtractor

This paper is aimed towards the hardware architecture aspect of a recently proposed posit number system under type-3 unum (universal number system). Here, an algorithmic flow for the posit addition/subtraction arithmetic is developed and its hardware architecture is designed. Compare to floating point, posit provides better dynamic range and accuracy over same word size, along with more accurate and exact arithmetic support. Posit format includes a run-time varying exponent component, provided by a combination of regime-bits (of run-time varying length) and exponent-bits (of size up to ES bits). Thus, the mantissa precision also varies at run-time. This provides a combination of dynamic range and precision under a given word size (N). This possible variation in format along dynamic range and precision may attract various applications with different(accuracy and dynamic range) requirement. However, this run-time variation in posit format also poses a hardware design challenge. So, this paper is aimed towards the construction of an open-source parameterized Verilog HDL (Hardware Description Language) generator for posit adder/subtractor arithmetic, with parameterized N and ES.
A Sub-pico Joules Per Bit Robust Physical Unclonable Function Based on Subthreshold Voltage References

Low power, lightweight and robust physical unclonable function (PUF) is a sought-after for IoT device identification/authentication. This paper presents a low power PUF design with high reliability against temperature and supply voltage variations. A response bit is extracted by comparing a pair of identically designed subthreshold voltage references. The voltage difference due to device mismatch is digitized and registered in a bidirectional counter, which can be used to identify and filter out the unstable response bits. The readout circuit works in tandem with the proposed double sampling technique to reduce the bias of components that are not the main entropy source of response bits. The proposed design is evaluated by extensive simulation using standard 65 nm CMOS process. It consumes merely 0.16 pJ/bit. The simulated uniqueness is an almost ideal 50.03%. Due to the intrinsic stability of voltage references, the reliability of its native response is 98.17% for the supply voltage variation from 1 V to 1.4 V and 97.60% for the temperature variation from 0 ° C to 80 °C. The generated response bitstream has passed both the autocorrelation test and NIST randomness test.
nGraph-HE: a graph compiler for deep learning on homomorphically encrypted data

Homomorphic encryption (HE)---the ability to perform computation on encrypted data---is an attractive remedy to increasing concerns about data privacy in deep learning (DL). However, building DL models that operate on ciphertext is currently labor-intensive and requires simultaneous expertise in DL, cryptography, and software engineering. DL frameworks and recent advances in graph compilers have greatly accelerated the training and deployment of DL models to various computing platforms. We introduce nGraph-HE, an extension of nGraph, Intel's DL graph compiler, which enables deployment of trained models with popular frameworks such as TensorFlow while simply treating HE as another hardware target. Our graph-compiler approach enables HE-aware optimizations- implemented at compile-time, such as constant folding and HE-SIMD packing, and at run-time, such as special value plaintext bypass. Furthermore, nGraph-HE integrates with DL frameworks such as TensorFlow, enabling data scientists to benchmark DL models with minimal overhead.
Assessment of nurses knowledge and skills following cardiopulmonary resuscitation training at Mbarara Regional Referral Hospital, Uganda

Introduction Cardiopulmonary resuscitation (CPR) is considered a core emergency skill in which all health care professionals must be proficient. CPR remains a new procedure in developing compared to develop countries. The objective of this study was to assess Nurses Knowledge and Skills following Cardiopulmonary Resuscitation Training at Mbarara Regional Referral Hospital. Methods A prospective pre/post intervention design was adopted. CPR knowledge and skills of 32 nurses from MRRH were assed using two tools. Tool I consisted of 17 item of multiple choice questions that assessed CPR knowledge. Tool II involved an observation checklist of 15-point skills questions. A penalty score of 5 or 10 or 20 was set for each question, based on the guideline. Results The average score prior to instruction was 53.8 for knowledge and posttest 82.5, and for skills was 46 pre-instruction and 81.5 post instruction. There was a statistically significant (p < 0.001) improvement in the CPR knowledge and (p = 0.02) for CPR skills. The percentage change in respondent's knowledge and skills ranged from 16.8% to137.2% with a mean of 59.9% for knowledge and from 19.18% to 2115.6% with a mean of 159.8% for the skills assessment. Conclusion Respondents had inadequate CPR knowledge and skills at pretest. The study revealed statistically significant improvement in both knowledge and skills of CPR for all nurses post training. There was a significant change in nurses' skills than in knowledge post training.
Random Subsampling based Signal Detection for Spatial Correlated Massive MIMO Channels

Massive MIMO systems have become more popular in wireless communications due to their improved spectral efficiency compared to existing small-scale MIMO systems. However, current estimation methodes take too long for larger numbers of antennas. In this paper, a near-optimal iterative linear signal detection for massive MIMO is introduced exploiting the random projection method to approximate the channel matrix in a significantly lower dimensional space. This is then used as a preconditioner in the conjugate gradient least squares algorithm to enhance the convergence rate. For evaluation, different scenarios of spatial correlation in a massive MIMO system are considered. In contrast to other low-complexity signal detectors, our approach achieves excellent results in terms of robustness and determined latency.
Image-Classifier Deep Convolutional Neural Network Training by 9-bit Dedicated Hardware to Realize Validation Accuracy and Energy Efficiency Superior to the Half Precision Floating Point Format

We propose a 9-bit floating point format for training image-classifier deep convolutional neural networks. The proposed floating point format has a 5-bit exponent, a 3-bit mantissa with the hidden most significant bit (MSB), and a sign bit. The 9-bit floating point format reduces not only the transistor count of the multiplier in the multipy-accumulate (MAC) unit, but also the data traffic for the forward and backward propagations and the weight update. Both of the reductions realize a power efficient training. To maintain the validation accuracy, the accumulator is implemented with an internal longer-bit-length floating point format while the multiplier accepts the 9-bit format. We examined this format in the training of the AlexNet and the ResNet-50 with the ILSVRC 2012 data set. The trained 9-bit AlexNet and ResNet-50 exhibited the validation accuracy superior to the 16-bit floating point format training by 1.2 % and 0.5 %, respectively. The transistor count in the 9-bit MAC unit is estimated to be reduced by 84% as compared to the 32-bit counterpart.
Joint adaptive step-size and zero-attractor parameters for l0-NLMS algorithm

For the sparse system estimation problem, the l0 norm constraint normalized least mean square (l0-NLMS) can offer improved convergence performance than the standard normalized least mean square (NLMS) algorithm. However, in the l0-NLMS algorithm, both choices of the step-size and zero-attractor parameters involve the conflicting requirement of fast convergence rate and low steady-state error. In this paper, we propose the joint adaptive step-size and zero-attractor l0-NLMS (JASZ-lo-NLMS) algorithm to address this issue. The proposed algorithm can simultaneously estimate the optimal step-size and zero-attractor derived by minimizing the mean-square deviation (MSD) at each iteration. Simulations are conducted to demonstrate the efficiency of the proposed algorithm in different signal-to-noise ratio (SNR) and sparse channel environments.
Public Awareness regarding the Differences between Ophthalmologists and Optometrists among Saudi Adults Living in Riyadh: A Quantitative Study

Awareness of the patients to the differences between ophthalmologists and optometrists has been recognized as an important factor influencing patient utilization of available eye care services. This study aimed at assessing the public's knowledge of the differences between ophthalmologists and optometrists among the residents of Riyadh, Saudi Arabia. An online questionnaire was administered to adults in Riyadh, Saudi Arabia, from January to February in 2018. The number of the respondents was 1579. Results show that 50% (n=789) of the participants had fair knowledge of the differences between ophthalmologists and optometrists, while 32% (n=505) had poor knowledge. After multivariate logistic regression analysis, there was a significant association between satisfactory knowledge and visiting an optometrist (odds ratio (OR): 0.75, 95% CI: 0.57–0.98), age older than 26 (OR: 1.73, 95% CI: 1.17–1.19), higher level of education (OR: 1.92, 95% CI: 1.54–2.4), and wearing spectacles (OR: 0.57, 95% CI: 0.45–0.73). Given the low level of public knowledge regarding the differences in the duties between ophthalmologist and optometrists among a Saudi population, there is great potential for general population education through awareness campaign and social media regarding eye care provider's duties and practices.
A Low Power Fully Intergrated RF Transceiver for Medical Implant Communication

This paper presents a fully integrated RF transceiver, capable of small form factor and ultra-low-power performance. The transceiver is achieved by employing LC-oscillator which has RF antenna as inductor components. The proposed receiver converts oscillation start-up time responding to input OOK signal to digital outputs, sensitively. The low complexity transmitter allows to improved implementation size and power efficiency with class-C operation. The transceiver was manufactured in 0.18 μm CMOS. The transceiver achieves a transmitter efficiency of 32.2% at −28.5 dBm EIRP while dissipating 2.2 mW and receiver sensitivity of −85 dBm in 25kbps OOK input. In experiments of implant condition replicated as human body, transceiver communicates to outside base station at 1m wireless and 8 cm implant depth.
Photonic Spiking Neural Networks - From Devices to Systems

Spiking Neural Networks (SNNs) offer an event-driven and more biologically realistic alternative to standard Artificial Neural Networks based on analog information processing. This can potentially enable energy-efficient hardware implementations of neuromorphic systems which emulate the functional units of the brain, namely, neurons and synapses. Recent demonstrations of ultra-fast photonic computing devices based on phase-change materials (PCMs) show promise of addressing limitations of electrically driven neuromorphic systems. However, scaling these standalone computing devices to a parallel in-memory computing primitive is a challenge. In this work, we utilize the optical properties of the PCM, Ge\textsubscript{2}Sb\textsubscript{2}Te\textsubscript{5} (GST), to propose a Photonic Spiking Neural Network computing primitive, comprising of a non-volatile synaptic array integrated seamlessly with previously explored `integrate-and-fire' neurons. The proposed design realizes an `in-memory' computing platform that leverages the inherent parallelism of wavelength-division-multiplexing (WDM). We show that the proposed computing platform can be used to emulate a SNN inferencing engine for image classification tasks. The proposed design not only bridges the gap between isolated computing devices and parallel large-scale implementation, but also paves the way for ultra-fast computing and localized on-chip learning.
Instance Segmentation and Object Detection with Bounding Shape Masks

Many recent object detection algorithms use the bounding box regressor to predict the position coordinates of an object (i.e., to predict four continuous variables of an object's bounding box information). To improve object detection accuracy, we propose four types of object boundary segmentation masks that provide position information in a different manner than that done by object detection algorithms, Additionally, we investigated the effect of the proposed object bounding shape masks on instance segmentation. To evaluate the proposed masks, our method adds a proposed bounding shape (or box) mask to extend the Faster R-CNN framework; we call this Bounding Shape (or Box) Mask R-CNN. We experimentally verified its performance with two benchmark datasets, MS COCO and Cityscapes. The results indicate that our proposed models generally outperform Faster R-CNN and Mask R-CNN.
High-Secure Low-Latency Ring-LWE Cryptography Scheme for Biomedical Images Storing and Transmitting

In this paper, a low-latency scheme for biomedical images storing and transmitting using ring-learning with errors (ring-LWE) cryptography is presented. The proposed scheme significantly reduces the encryption time and decryption time for biomedical images compared to existing works. Particularly, the encryption time and decryption time of proposed ring-LWE scheme for biomedical images can be reduced up to 70.1% and 52.7% compared to previous studies. In addition, by storing biomedical images under encrypted form at the central server, biomedical data are completely protected. The analysis in similarity and entropy of encrypted image proves the outperformance of the proposed scheme in terms of security level compared to conventional schemes.
Dyadic Dynamics: The Impact of Emotional Responses to Facial Expressions on the Perception of Power

Emotion expressions play a central role in social communication, which, by definition is a dynamic process. Social communication involves the exchange of signals with temporal dynamic properties between two or more individuals. Nonetheless, emotion perception research has strongly focused on the study of single, static, unidirectional images. The goal of this research is to illustrate the dynamic nature of emotion communication by showing how the back and forth of a dyadic emotional interaction affects its perception by uninvolved observers. To that aim, we conducted three studies that investigated how observer’s inferences of social power are influenced by an exchange of emotions between members of a dyad. In Study 1, participants saw one person showing either anger or sadness to which the second member of the dyad reacted by showing either anger, fear or neutrality. In Study 1, only still photos were shown in sequence. In Studies 2 and 3, more dynamic stimuli and other emotions were included. Even though an angry expresser was always perceived as more powerful than a sad expresser, the emotional reactions of the interaction partner modulated perceived power. Across all three studies and different levels of dynamic stimuli, fear reactions always increased perceived power. Happiness, contempt and neutrality affected perceived power more selectively. This effect was mediated by the extent to which participants felt that the reaction of the second interaction partner suggested that the second interaction partner agreed with regard to the power differential between the two. Taken together, these experiments show that the social signal value of emotion expressions changes meaningfully as a function of the emotional response of the expressions’ target. Thus, the social signal value of emotions does not stand alone but has to be understood in the fuller context of the interaction.