Miracle-dz/UniFAR-FTUs huggingface dataset:
Women in sport: Challenges and solutions in India

The Women in Sport Movement is becoming a global phenomenon; and rightly so as women comprise half of the global population. Change has been slow, however, and many long-standing traditions remain. However we believe that there is great potential for the status quo to be challenged with disruption, and change at the systemic level. Given the known benefits of physical activity, exercise and sport on individual’s health, and specifically the protective benefits against cardiovascular disease (CVD) [1], such interruption has the potential for profound influence on public health within India. A country where the burden of CVD within the Indian subcontinent itself has been underemphasized [2], and yet may be one of the regions with the highest burden of CVD in the world. As such we would like to present some key themes that emerged at a recent workshop on the topic of engaging women in sport in India with a view to pursue any collaboratively opportunities in this space moving forward.
Lung tissue microbial profile in lung cancer is distinct from emphysema.

OBJECTIVES The composition and structure of site-specific microbiota have been investigated as potential biomarkers for a variety of chronic inflammatory diseases and cancers. While many studies have focused on the changes in the airway microbiota using respiratory specimens from patients with various respiratory diseases, more research is needed to explore the microbial profiles within the distal lung parenchyma in smokers with lung cancer and/or emphysema. MATERIALS AND METHODS To describe and contrast lung tissue-associated microbial signatures in smokers with lung cancer and/or emphysema, we employed culture-independent pyrosequencing of 16S rRNA gene hypervariable V4 region and compositional analysis in non-malignant lung tissue samples obtained from 40 heavy smokers, including 10 emphysema-only, 11 lung cancer-only, and 19 with both lung cancer and emphysema. RESULTS AND CONCLUSION The emphysema-only group presented a lower bacterial community evenness defined by a significantly lower Shannon diversity index compared to the lung cancer patients with or without emphysema (P = 0.006). Furthermore, community compositions of lung cancer patients with or without emphysema were characterized by a significantly lower abundance of Proteobacteria (primary the genera Acinetobacter and Acidovorax) and higher prevalence of Firmicutes (Streptococcus) and Bacteroidetes (Prevotella), compared to emphysema-only patients. In conclusion, the lung microbial composition and communities structures of smokers with lung cancer are distinct from the emphysema-only patients. Although preliminary, our findings suggest that lung microbiome changes could be a biomarker of lung cancer that could eventually be used to help screening for the disease.
Power Allocation for Multi-Way Massive MIMO Relaying

We consider a multi-way decode-and-forward relaying network with very large antenna arrays at the relay station. In this system, each user and the relay operate in half-duplex and time-division duplexing modes. To exchange information among all users, we propose a new transmission protocol which combines massive multiple-input multiple-output technology with linear processing, self-interference cancelation, and successive cancelation decoding. Our proposed transmission protocol reduces the number of time-slots for data exchange among users by approximately 2 times, compared with the conventional data transmission protocol. For this new topology, we derive a very tight approximation of the spectral efficiency in closed-form assuming perfect channel state information (CSI). Then, a CSI acquisition method at the relay and the users is provided and analyzed. We show via numerical simulations, that the performance gap between imperfect and perfect CSI cases is small. The closed-form expression of the spectral efficiency enables us to design two power allocation schemes. In the first power allocation scheme, we choose the transmit powers at the users and the relay to maximize the sum spectral efficiency, subject to a given quality-of-service requirement for each user. In the second power allocation scheme, the objective is the energy efficiency taking into account the hardware power consumption. Both power allocation schemes can be efficiently executed by iteratively solving a sequence of convex problems. Numerical results verify the effectiveness of the proposed transmission protocol and the power allocation schemes compared with the state of the art.
The Loss Surface of Deep Linear Networks Viewed Through the Algebraic Geometry Lens

By using the viewpoint of modern computational algebraic geometry, we explore properties of the optimization landscapes of deep linear neural network models. After providing clarification on the various definitions of “flat” minima, we show that the geometrically flat minima, which are merely artifacts of residual continuous symmetries of the deep linear networks, can be straightforwardly removed by a generalized <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="tang-ieq1-3071289.gif"/></alternatives></inline-formula>-regularization. Then, we establish upper bounds on the number of isolated stationary points of these networks with the help of algebraic geometry. Combining these upper bounds with a method in numerical algebraic geometry, we find <italic>all</italic> stationary points for modest depth and matrix size. We demonstrate that, in the presence of the non-zero regularization, deep linear networks can indeed possess local minima which are not global minima. Finally, we show that even though the number of stationary points increases as the number of neurons (regularization parameters) increases (decreases), higher index saddles are surprisingly rare.
Application of NOMA in Wireless System with Wireless Power Transfer Scheme: Outage and Ergodic Capacity Performance Analysis

Non-orthogonal multiple access (NOMA) and energy harvesting (EH) are combined to introduce a dual-hop wireless sensor system. In particular, this paper considers a novel EH protocol based on time power switching-based relaying (TPSR) architecture for amplify-and-forward (AF) mode. We introduce a novel system model presenting wireless network with impacts of energy harvesting fractions and derive analytical expressions for outage probability and ergodic rate for the information transmission link. It confirmed that the right selection of power allocation for NOMA users can be performed to obtain optimal outage and ergodic capacity performance. Theoretical results show that, in comparison with the conventional solutions, the proposed model can achieve acceptable outage performance for sufficiently small threshold signal to noise ratio (SNR) with condition of controlling time switching fractions and power splitting fractions appropriately in considered TPSR protocol. We also examine the impacts of transmitting power at source, transmission rate, the other key parameters of TPSR to outage, and ergodic performance. Simulation results are presented to corroborate the proposed system.
Distributed Transactive Energy Trading Framework in Distribution Networks

In this paper, we propose a novel transactive energy trading (TET) framework to deal with the economic issues in energy trading and the technical issues in distribution system operation in a holistic manner. In particular, we innovatively integrate a bilateral energy trading mechanism with the optimal power flow (OPF) technique to increase economic benefits to individual participants, and meanwhile ensure the reliability and security of the system operation. In order to resolve the inherent conflict of interests, Nash bargaining theory is used to model the TET problem, which is further decomposed into a multiperiod OPF problem and a payment bargaining problem. Moreover, we develop an efficient distributed algorithm for solving the TET problem base on alternating direction method of multipliers (ADMM). Instead of directly solving optimization subproblems like most ADMM-based distributed algorithms, we derive closed-form solutions to all subproblems to significantly improve the computational efficiency. Finally, numerical tests on the IEEE 37-bus and 123-bus distribution systems demonstrate the effectiveness of our proposed framework and the efficiency of our distributed algorithm.
Performance of OFDM With Signal Space Diversity Using Subcarrier Coordinate Interleaving

Coordinate interleaving (CI) is applied to an orthogonal frequency division multiplexing system to exploit signal space diversity with binary phase-shift keying modulation. Two different CI strategies are introduced taking the correlation coefficients among the subcarrier channel gains into account. Accordingly, a number of different scenarios are investigated. Assuming Rayleigh fading, an exact closed-form expression on the bit error probability is derived. It is shown that the error performance of the system can be increased to a great extent with a feedback cost of a few bits and only a negligible increase in complexity. The proposed system is also shown to enhance the communication privacy under limited feedback.
Media Depictions of “Unacceptable” Workplace Violence Toward Nurses

Violence and aggression toward nurses are global concerns. Despite repeated research on causal factors and widespread zero tolerance campaigns, rates of violence and aggression have not declined. Violence and aggression toward nurses can negatively affect their health and ultimately patient care. Media reporting of violence and aggression toward nurses might shape people’s perceptions of the profession, perhaps impeding nurse recruitment and retention efforts in the face of global nursing shortages. The purpose of this study was to determine how print media in Scotland depicted reports of violence and aggression toward nurses. We used qualitative thematic analysis of newspaper articles and online news reporting of incidents of violence and aggression toward nurses between June 1, 2006 and May 31, 2016. Searches of Nexis and British Broadcasting Corporation (BBC) News Online databases returned 92 relevant newspaper articles. Standards for reporting qualitative research informed presentation of results. Key themes included blame (of perpetrator or senior management), helplessness (of nurses specifically or victimization), culture (social or organizational), and prevention and reduction measures. We concluded that media coverage of violence and aggression was overwhelmingly negative and reductionist. Normalization of violence and aggression was an accepted and acceptable part of the nursing role. We conclude with recommendations for policy and call for nurse leaders to challenge this culture of acceptability, especially to support recruitment and retention of nursing staff.
Deep Reinforcement Learning Based Finite-Horizon Optimal Control for a Discrete-Time Affine Nonlinear System

Approximate dynamic programming (ADP) aims to obtain an approximate numerical solution to the discrete-time Hamilton-Jacobi-Bellman (HJB) equation. Heuristic dynamic programming (HDP) is a two-stage iterative scheme of ADP by separating the HJB equation into two equations, one for the value function and another for the policy function, which are referred to as the critic and the actor, respectively. Previous ADP implementations have been limited by the choice of function approximator, which requires significant prior domain knowledge or a large number of parameters to be fitted. However, recent advances in deep learning brought by the computer science community enable the use of deep neural networks (DNN) to approximate high-dimensional nonlinear functions without prior domain knowledge. Motivated by this, we examine the potential of DNNs as function approximators of the critic and the actor. In contrast to the infinite-horizon optimal control problem, the critic and the actor of the finite horizon optimal control (FHOC) problem are time-varying functions and have to satisfy a boundary condition. DNN structure and training algorithm suitable for FHOC are presented. Illustrative examples are provided to demonstrate the validity of the proposed method.
Symptom Trajectories of Adolescents During Hematopoietic Stem Cell Recovery.

BACKGROUND Adolescents undergoing allogeneic hematopoietic stem cell transplantation (HSCT) experience multiple symptoms during and after the transplant. These symptoms can increase the need for medical care and reduce the quality of their life. However, little is known about symptom experiences specific to adolescents undergoing HSCT. OBJECTIVE The primary aim was to describe symptom incidence, severity, and distress trajectories among adolescents from pre-HSCT through 90 days post-HSCT. A secondary aim was to examine the relationship between symptom trajectories and demographic and treatment factors. METHODS A repeated-measures design was used for this prospective study. Demographic and treatment information was collected from the medical record. Symptoms were assessed with the Memorial Symptom Assessment Scale 10-18. Symptom trajectories were identified by latent class growth analysis with growth mixture modeling; logistic regression evaluated relationships of demographic and treatment characteristics on the latent classes of symptom trajectories. RESULTS Two distinct latent class trajectories were identified for symptom incidence, severity, and distress. Symptom incidence declined, but symptom severity and distress remained stable. No significant relationships were noted among any demographic or treatment characteristics to any of the symptom trajectories. CONCLUSIONS Symptoms persist and remain severe and distressing throughout the first 90 days after HSCT, with pain and lack of energy among the highest in incidence, severity, and distress. IMPLICATIONS FOR PRACTICE Awareness of symptom trajectories empowers nurses to assess for symptoms throughout the HSCT process and conduct meaningful symptom discussions with their patients.
Noninvasive measurement of pulmonary gas exchange: comparison with data from arterial blood gases.

A new noninvasive method was used to measure the impairment of pulmonary gas exchange in 34 patients with lung disease, and the results were compared with the traditional ideal alveolar-arterial Po2 difference (AaDO2) calculated from arterial blood gases. The end-tidal Po2 was measured from the expired gas during steady-state breathing, the arterial Po2 was derived from a pulse oximeter if the SpO2 was 95% or less, which was the case for 23 patients. The difference between the end-tidal and the calculated Po2 was defined as the oxygen deficit. Oxygen deficit was 42.7 mmHg (SE 4.0) in this group of patients, much higher than the means previously found in 20 young normal subjects measured under hypoxic conditions (2.0 mmHg, SE 0.8) and 11 older normal subjects (7.5 mmHg, SE 1.6) and emphasizes the sensitivity of the new method for detecting the presence of abnormal gas exchange. The oxygen deficit was correlated with AaDO2 ( R2 0.72). The arterial Po2 that was calculated from the noninvasive technique was correlated with the results from the arterial blood gases ( R2 0.76) and with a mean bias of +2.7 mmHg. The Pco2 was correlated with the results from the arterial blood gases (R2 0.67) with a mean bias of -3.6 mmHg. We conclude that the oxygen deficit as obtained from the noninvasive method is a very sensitive indicator of impaired pulmonary gas exchange. It has the advantage that it can be obtained within a few minutes by having the patient simply breathe through a tube.
Platelet releasate promotes skeletal myogenesis by increasing muscle stem cell commitment to differentiation and accelerates muscle regeneration following acute injury

The use of platelets as biomaterials has gained intense research interest. However, the mechanisms regarding platelet‐mediated skeletal myogenesis remain to be established. The aim of this study was to determine the role of platelet releasate in skeletal myogenesis and muscle stem cell fate in vitro and ex vivo respectively.
Low-Energy Implementation of Feed-Forward Neural Network With Back-Propagation Algorithm Using a Spin-Orbit Torque Driven Skyrmionic Device

In this paper, we have proposed a spin-orbit torque driven skyrmionic device that acts as a synaptic element in a hardware artificial neural network (ANN) and compared its performance with similar domain wall-based device in the presence of defects in the material that can pin the domain wall or the skyrmion. Next, we have simulated a feedforward ANN with either a domain wall device or a skyrmionic device as the synaptic element and solved a standard digit recognition problem with it using the back-propagation algorithm to show that a skyrmionic synapse-based ANN consumes two orders of magnitude lower energy than a domain wall synapse-based ANN.
HotStuff: BFT Consensus in the Lens of Blockchain

We present HotStuff, a leader-based Byzantine fault-tolerant replication protocol for the partially synchronous model. Once network communication becomes synchronous, HotStuff enables a correct leader to drive the protocol to consensus at the pace of actual (vs. maximum) network delay--a property called responsiveness--and with communication complexity that is linear in the number of replicas. To our knowledge, HotStuff is the first partially synchronous BFT replication protocol exhibiting these combined properties. HotStuff is built around a novel framework that forms a bridge between classical BFT foundations and blockchains. It allows the expression of other known protocols (DLS, PBFT, Tendermint, Casper), and ours, in a common framework. Our deployment of HotStuff over a network with over 100 replicas achieves throughput and latency comparable to that of BFT-SMaRt, while enjoying linear communication footprint during leader failover (vs. quadratic with BFT-SMaRt).
Hydle: A Deployment Framework for Efficient IoT Surveillance Systems

To build efficient IoT surveillance systems, two fundamental conditions are needed: (1) accurate analysis to reduce false alarms and (2) short latency to have real time emergency notifications. Nevertheless, finding the compromise between data processing accuracy and system performance is generally hard to obtain especially with image processing. In this paper, we propose Hydle, a surveillance processing model and a deployment framework for efficient IoT surveillance systems. Hydle is based on two building blocks: a hybrid deployment of the system and a layered deep learning-based processing. The hybrid deployment mixes different kinds of data sources (WSN and MWSN) and hosting targets(Fogs and Clouds). Hydle dynamically calculates the optimal deployment plan that minimizes data dissemination delays. The layered deep-learning processing brings an incremental precision of image analysis and optimizes the processing delay. Hydle evaluation and usage in a real surveillance system show the advantage of the adopted model compared to classical ones.
Heterogeneous FPGA-Based Cost-Optimal Design for Timing-Constrained CNNs

Field programmable gate array (FPGA) has been one of the most popular platforms to implement convolutional neural networks (CNNs) due to its high performance and cost efficiency; however, limited by the on-chip resources, the existing single-FPGA architectures cannot fully exploit the parallelism in CNNs. In this paper, we explore heterogeneous FPGA-based designs to effectively leverage both task and data parallelism, such that the resultant system can achieve the minimum cost while satisfying timing constraints. In order to maximize the task parallelism, we investigate two critical problems: 1) buffer placement, where to place buffers to partition CNNs into pipeline stages and 2) task assignment, what type of FPGA to implement different CNN layers. We first formulate the system-level optimization problem with a mixed integer linear programming model. Then, we propose an efficient dynamic programming algorithm to obtain the optimal solutions. On top of that, we devise an efficient algorithm that exploits data parallelism within CNN layers to further improve cost efficiency. Evaluations on well-known CNNs demonstrate that the proposed techniques can obtain an average of 30.82% reduction in system cost under the same timing constraint, and an average of 1.5 times speedup in performance under the same cost budget, compared with the state-of-the-art techniques.
Outcomes from stereotactic surgery for essential tremor

There are several different surgical procedures that are used to treat essential tremor (ET), including deep brain stimulation (DBS) and thalamotomy procedures with radiofrequency (RF), radiosurgery (RS) and most recently, focused ultrasound (FUS). Choosing a surgical treatment requires a careful presentation and discussion of the benefits and drawbacks of each. We conducted a literature review to compare the attributes and make an appraisal of these various procedures. DBS was the most commonly reported treatment for ET. One-year tremor reductions ranged from 53% to 63% with unilateral Vim DBS. Similar improvements were demonstrated with RF (range, 74%–90%), RS (range, 48%–63%) and FUS thalamotomy (range, 35%–75%). Overall, bilateral Vim DBS demonstrated more improvement in tremor reduction since both upper extremities were treated (range, 66%–78%). Several studies show continued beneficial effects from DBS up to five years. Long-term follow-up data also support RF and gamma knife radiosurgical thalamotomy treatments. Quality of life measures were similarly improved among patients who received all treatments. Paraesthesias, dysarthria and ataxia were commonly reported adverse effects in all treatment modalities and were more common with bilateral DBS surgery. Many of the neurological complications were transient and resolved after surgery. DBS surgery had the added benefit of programming adjustments to minimise stimulation-related complications. Permanent neurological complications were most commonly reported for RF thalamotomy. Thalamic DBS is an effective, safe treatment with a long history. For patients who are medically unfit or reluctant to undergo DBS, several thalamic lesioning methods have parallel benefits to unilateral DBS surgery. Each of these surgical modalities has its own nuance for treatment and patient selection. These factors should be carefully considered by both neurosurgeons and patients when selecting an appropriate treatment for ET.