Miracle-dz/UniFAR-FTUs huggingface dataset:
Multidisciplinary Neonatal Emergencies Simulation Team-Training (NEST) Improves Participant’s Confidence in Dealing With Simulated Neonatal Emergencies

Abstract Background: A traditional medical curriculum has limited emphasis on communication skills, teamworking, and human factors. Simulation-based training emphasizes these skills and positively impacts patient safety. A group of Indian neonatologists in collaboration with their UK colleagues conducted adapted Neonatal Emergencies Simulation Team-training (NEST) courses in India. Aim: To evaluate the effectiveness of NEST courses in India and analyze the participant’s feedback Methods: A total of 16 NEST courses were conducted between November 2014 and February 2016. Structured pre- and postcourse feedback from participants was analyzed. Key domains studied included structured approach, human factors, situation-background-assessment-recommendation tool, and teamwork in an emergency. Each category was rated from 1 to 5, indicating a low to high scale. Pre- and posttraining scores and scores between centers were compared using the Wilcoxon signed-rank test and Kruskal-Wallis test, respectively. Results: A total of 191 feedbacks from 58 nurses and 133 doctors from 4 centers were analyzed. The comparison of self-reported pre- and postcourses showed a significant improvement across all domains. There was no statistical difference between scores from different centers showing consistent course results. Conclusions: We have demonstrated that the NEST multiprofessional course can be collaboratively delivered. The analysis of feedback shows a significant improvement in the candidate’s self-reported ability across all domains related to the application of the course principles to simulated neonatal emergencies.
Use of Trigger Tool for Detection of Adverse Drug Reactions among Hospitalized Patients in a Nigerian University Teaching Hospital

Department of Pharmacy, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria. 2 Department of Pharmacy, Center for Community Medicine and Primary Healthcare, Nnamdi Azikiwe University Teaching Hospital, Neni, Anambra State, Nigeria. 3 Department of Clinical Pharmacy and Pharmacy management, Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. Department of Accident and Emergency, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria. Department of Community Medicine, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria.
Outside the Wall: Hydrodynamics of Type I Supernovae Interacting with a Partially Swept-up Circumstellar Medium

Explaining the observed diversity of supernovae (SNe) and the physics of explosion requires knowledge of their progenitor stars, which can be obtained by constraining the circumstellar medium (CSM). Models of the SN ejecta colliding with the CSM are necessary to infer the structure of the CSM and tie it back to a progenitor model. Recent SNe I revealed CSM concentrated at a distance r ∼ 1016 cm, for which models of SN interaction are extremely limited. In this paper, we assume the concentrated region is a “wall” representing swept-up material, and unswept material lies outside the wall. We simulate one-dimensional hydrodynamics of SNe Ia and Ib impacting 300 unique CSM configurations using RT1D, which captures the Rayleigh–Taylor instability. We find that the density ratio between the wall and ejecta—denoted A0 or “wall height”—is key, and higher walls deviate more from self-similar evolution. Functional fits accounting for A0 are presented for the forward-shock radius evolution. We show that higher walls have more degeneracy between CSM properties in the deceleration parameter, slower shocks, deeper-probing reverse shocks, slower shocked ejecta, less ejecta mass than CSM in the shock, and more mixing of ejecta into the CSM at early times. We analyze observations of SN 2014C (Type Ib) and suggest that it had a moderately high wall (10 ≲ A0 ≲ 200) and wind-like outer CSM. We also postulate an alternate interpretation for the radio data of SN 2014C, that the radio rise occurs in the wind rather than the wall. Finally, we find that hydrodynamic measurements at very late times cannot distinguish the presence of a wall, except perhaps as an anomalously wide shock region.
Trust in Recommender Systems: A Deep Learning Perspective

A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that filter untruthful noises (e.g., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items. We focus on the work based on deep learning techniques, an emerging area in the recommendation research.
Wisdom of the crowds in forecasting COVID-19 spreading severity

In this work we report that the public reacted on social media at an early stage of the COVID-19 pandemic in a surprisingly accurate way, with activity levels reflecting the severity of the contagion figures registered almost a month later. Specifically, the intensity of COVID-related social media activity from different Italian regions at the beginning of the epidemic (21-24/2/2020), predicts well the total number of deaths reached almost a month later (21/3/2020) in each region. It should be noted that at the time of the initial twitter reaction no tabled regional data on the epidemic was readily available. By the 24th February 2020 only two regions reported death cases and only three reported infected subjects.
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-End Speech Enhancement

Due to the simple design pipeline, end-to-end (E2E) neural models for speech enhancement (SE) have attracted great interest. In order to improve the performance of the E2E model, the local and sequential properties of speech should be efficiently taken into account when modelling. However, in most current E2E models for SE, these properties are either not fully considered or are too complex to be realized. In this letter, we propose an efficient E2E SE model, termed WaveCRN. Compared with models based on convolutional neural networks (CNN) or long short-term memory (LSTM), WaveCRN uses a CNN module to capture the speech locality features and a stacked simple recurrent units (SRU) module to model the sequential property of the locality features. Different from conventional recurrent neural networks and LSTM, SRU can be efficiently parallelized in calculation, with even fewer model parameters. In order to more effectively suppress noise components in the noisy speech, we derive a novel restricted feature masking approach, which performs enhancement on the feature maps in the hidden layers; this is different from the approaches that apply the estimated ratio mask to the noisy spectral features, which is commonly used in speech separation methods. Experimental results on speech denoising and compressed speech restoration tasks confirm that with the SRU and the restricted feature map, WaveCRN performs comparably to other state-of-the-art approaches with notably reduced model complexity and inference time.
Constructing clock-transition-based two-qubit gates from dimers of molecular nanomagnets

A good qubit must have a coherence time long enough for gate operations to be performed. Avoided level crossings allow for clock transitions in which coherence is enhanced by the insensitivity of the transition to fluctuations in external fields. Because of this insensitivity, it is not obvious how to effectively couple qubits together while retaining clock-transition behavior. Here we present a scheme for using a heterodimer of two coupled molecular nanomagnets, each with a clock transition at zero magnetic field, in which all of the gate operations needed to implement one- and two-qubit gates can be implemented with pulsed radio-frequency radiation. We show that given realistic coupling strengths between the nanomagnets in the dimer, good gate fidelities ($\sim$99.4\%) can be achieved. We identify the primary sources of error in implementing gates and discuss how these may be mitigated, and investigate the range of coherence times necessary for such a system to be a viable platform for implementing quantum computing protocols.
Guessing What's Plausible But Remembering What's True: Accurate Neural Reasoning for Question-Answering

Neural approaches to natural language processing (NLP) often fail at the logical reasoning needed for deeper language understanding. In particular, neural approaches to reasoning that rely on embedded \emph{generalizations} of a knowledge base (KB) implicitly model which facts that are \emph{plausible}, but may not model which facts are \emph{true}, according to the KB. While generalizing the facts in a KB is useful for KB completion, the inability to distinguish between plausible inferences and logically entailed conclusions can be problematic in settings like as KB question answering (KBQA). We propose here a novel KB embedding scheme that supports generalization, but also allows accurate logical reasoning with a KB. Our approach introduces two new mechanisms for KB reasoning: neural retrieval over a set of embedded triples, and"memorization"of highly specific information with a compact sketch structure. Experimentally, this leads to substantial improvements over the state-of-the-art on two KBQA benchmarks.
Salience Estimation with Multi-Attention Learning for Abstractive Text Summarization

Attention mechanism plays a dominant role in the sequence generation models and has been used to improve the performance of machine translation and abstractive text summarization. Different from neural machine translation, in the task of text summarization, salience estimation for words, phrases or sentences is a critical component, since the output summary is a distillation of the input text. Although the typical attention mechanism can conduct text fragment selection from the input text conditioned on the decoder states, there is still a gap to conduct direct and effective salience detection. To bring back direct salience estimation for summarization with neural networks, we propose a Multi-Attention Learning framework which contains two new attention learning components for salience estimation: supervised attention learning and unsupervised attention learning. We regard the attention weights as the salience information, which means that the semantic units with large attention value will be more important. The context information obtained based on the estimated salience is incorporated with the typical attention mechanism in the decoder to conduct summary generation. Extensive experiments on some benchmark datasets in different languages demonstrate the effectiveness of the proposed framework for the task of abstractive summarization.
DeepStreamCE: A Streaming Approach to Concept Evolution Detection in Deep Neural Networks

Deep neural networks have experimentally demonstrated superior performance over other machine learning approaches in decision-making predictions. However, one major concern is the closed set nature of the classification decision on the trained classes, which can have serious consequences in safety critical systems. When the deep neural network is in a streaming environment, fast interpretation of this classification is required to determine if the classification result is trusted. Un-trusted classifications can occur when the input data to the deep neural network changes over time. One type of change that can occur is concept evolution, where a new class is introduced that the deep neural network was not trained on. In the majority of deep neural network architectures, the only option is to assign this instance to one of the classes it was trained on, which would be incorrect. The aim of this research is to detect the arrival of a new class in the stream. Existing work on interpreting deep neural networks often focuses on neuron activations to provide visual interpretation and feature extraction. Our novel approach, coined DeepStreamCE, uses streaming approaches for real-time concept evolution detection in deep neural networks. DeepStreamCE applies neuron activation reduction using an autoencoder and MCOD stream-based clustering in the offline phase. Both outputs are used in the online phase to analyse the neuron activations in the evolving stream in order to detect concept evolution occurrence in real time. We evaluate DeepStreamCE by training VGG16 convolutional neural networks on combinations of data from the CIFAR-10 dataset, holding out some classes to be used as concept evolution. For comparison, we apply the data and VGG16 networks to an open-set deep network solution - OpenMax. DeepStreamCE outperforms OpenMax when identifying concept evolution for our datasets.
Dense Residual Network for Retinal Vessel Segmentation

Retinal vessel segmentation plays an imaportant role in the field of retinal image analysis because changes in retinal vascular structure can aid in the diagnosis of diseases such as hypertension and diabetes. In recent research, numerous successful segmentation methods for fundus images have been proposed. But for other retinal imaging modalities, more research is needed to explore vascular extraction. In this work, we propose an efficient method to segment blood vessels in Scanning Laser Ophthalmoscopy (SLO) retinal images. Inspired by U-Net, "feature map reuse" and residual learning, we propose a deep dense residual network structure called DRNet. In DRNet, feature maps of previous blocks are adaptively aggregated into subsequent layers as input, which not only facilitates spatial reconstruction, but also learns more efficiently due to more stable gradients. Furthermore, we introduce DropBlock to alleviate the overfitting problem of the network. We train and test this model on the recent SLO public dataset. The results show that our method achieves the state-of-the-art performance even without data augmentation[[fn1]]1* Corresponding author
The loss surfaces of neural networks with general activation functions

The loss surfaces of deep neural networks have been the subject of several studies, theoretical and experimental, over the last few years. One strand of work considers the complexity, in the sense of local optima, of high dimensional random functions with the aim of informing how local optimisation methods may perform in such complicated settings. Prior work of Choromanska et al (2015) established a direct link between the training loss surfaces of deep multi-layer perceptron networks and spherical multi-spin glass models under some very strong assumptions on the network and its data. In this work, we test the validity of this approach by removing the undesirable restriction to ReLU activation functions. In doing so, we chart a new path through the spin glass complexity calculations using supersymmetric methods in random matrix theory which may prove useful in other contexts. Our results shed new light on both the strengths and the weaknesses of spin glass models in this context.
START — Self-Tuning Adaptive Radix Tree

structures like the Adaptive Radix Tree (ART) are a central part of in-memory database systems. However, we found that radix nodes that index a single byte are not optimal for read-heavy workloads. In this work, we introduce START, a self-tuning variant of ART that uses nodes spanning multiple keybytes. To determine where to introduce these new node types, we propose a cost model and an optimizer. These components allow us to fine-tune an existing ART, reducing its overall height, and improving performance. As a result, START performs on average 85 % faster than a regular ART on a wide variety of read-only workloads and 45% faster for read-mostly workloads.
Bias-optimal vol-of-vol estimation: the role of window overlapping

The simplest and most natural vol-of-vol estimator, the pre-estimated spot variance-based realized variance, is typically plagued by a large finite-sample bias. In this paper, we analytically show that allowing for the overlap of consecutive local windows to pre-estimate the spot variance may correct for this bias. In particular, we provide a feasible rule for the bias-optimal selection of the length of local windows when the volatility is a CKLS process. The effectiveness of this rule for practical applications is supported by numerical and empirical analyses.
Non-Hermitian extension of the Nambu–Jona-Lasinio model in 
3+1
 and 
1+1
 dimensions

This paper presents a non-Hermitian PT-symmetric extension of the Nambu--Jona-Lasinio (NJL) model of quantum chromodynamics in 3+1 and 1+1 dimensions. In 3+1 dimensions, the SU(2)-symmetric NJL Hamiltonian $H_{\textrm{NJL}} = \bar\psi (-i \gamma^k \partial_k + m_0) \psi - G [ (\bar\psi \psi)^2 + (\bar\psi i \gamma_5 \vec{\tau} \psi)^2 ]$ is extended by the non-Hermitian, PT- and chiral-symmetric bilinear term $ig\bar\psi \gamma_5 B_{\mu} \gamma^{\mu} \psi$; in 1+1 dimensions, where $H_{\textrm{NJL}}$ is a form of the Gross-Neveu model, it is extended by the non-Hermitian PT-symmetric but chiral symmetry breaking term $g \bar\psi \gamma_5 \psi$. In each case, the gap equation is derived and the effects of the non-Hermitian terms on the generated mass are studied. We have several findings: in previous calculations for the free Dirac equation modified to include non-Hermitian bilinear terms, contrary to expectation, no real mass spectrum can be obtained in the chiral limit; in these cases a nonzero bare fermion mass is essential for the realization of PT symmetry in the unbroken regime. Here, in the NJL model, in which four-point interactions are present, we {\it do} find real values for the mass spectrum also in the limit of vanishing bare masses in both 3+1 and 1+1 dimensions, at least for certain specific values of the non-Hermitian couplings $g$. Thus, the four-point interaction overrides the effects leading to PT symmetry-breaking for these parameter values. Further, we find that in both cases, in 3+1 and in 1+1 dimensions, the inclusion of a non-Hermitian bilinear term can contribute to the generated mass. In both models, this contribution can be tuned to be small; we thus fix the fermion mass to its value when $m_0=0$ in the absence of the non-Hermitian term, and then determine the value of the coupling required so as to generate a bare fermion mass.
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition

Many studies have applied reinforcement learning to train a dialog policy and show great promise these years. One common approach is to employ a user simulator to obtain a large number of simulated user experiences for reinforcement learning algorithms. However, modeling a realistic user simulator is challenging. A rule-based simulator requires heavy domain expertise for complex tasks, and a data-driven simulator requires considerable data and it is even unclear how to evaluate a simulator. To avoid explicitly building a user simulator beforehand, we propose Multi-Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents. Two agents interact with each other and are jointly learned simultaneously. The method uses the actor-critic framework to facilitate pretraining and improve scalability. We also propose Hybrid Value Network for the role-aware reward decomposition to integrate role-specific domain knowledge of each agent in the task-oriented dialog. Results show that our method can successfully build a system policy and a user policy simultaneously, and two agents can achieve a high task success rate through conversational interaction.
CALM: Continuous Adaptive Learning for Language Modeling

Training large language representation models has become a standard in the natural language processing community. This allows for fine tuning on any number of specific tasks, however, these large high capacity models can continue to train on domain specific unlabeled data to make initialization even more robust for supervised tasks. We demonstrate that in practice these pre-trained models present performance deterioration in the form of catastrophic forgetting when evaluated on tasks from a general domain such as GLUE. In this work we propose CALM, Continuous Adaptive Learning for Language Modeling: techniques to render models which retain knowledge across multiple domains. With these methods, we are able to reduce the performance gap across supervised tasks introduced by task specific models which we demonstrate using a continual learning setting in biomedical and clinical domains.
KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation

The research of knowledge-driven conversational systems is largely limited due to the lack of dialog data which consists of multi-turn conversations on multiple topics and with knowledge annotations. In this paper, we propose a Chinese multi-domain knowledge-driven conversation dataset, KdConv, which grounds the topics in multi-turn conversations to knowledge graphs. Our corpus contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics. To facilitate the following research on this corpus, we provide several benchmark models. Comparative results show that the models can be enhanced by introducing background knowledge, yet there is still a large space for leveraging knowledge to model multi-turn conversations for further research. Results also show that there are obvious performance differences between different domains, indicating that it is worth further explore transfer learning and domain adaptation. The corpus and benchmark models are publicly available.
6G Communications: Envisioning the Key Issues and Challenges

In 2030, we are going to evidence the 6G mobile communication technology, which will enable the Internet of Everything. Yet 5G has to be experienced by people and B5G has to be developed; the researchers have already started planning, visioning, and gathering requirements of the 6G. 6G promises to take everyone to a different era of technology. It promises connecting every smart device to the Internet from smartphone to intelligent vehicles. 6G will provide sophisticated and high QoS such as holographic communication, augmented reality/virtual reality and many more. Also, it will focus on Quality of Experience (QoE) to provide rich experiences from 6G technology. Notably, it is very important to vision the issues and challenges of 6G technology, otherwise, promises may not be delivered on time. The requirements of 6G poses new challenges to the research community. To achieve desired parameters of 6G, researchers are exploring various alternatives. Hence, there are diverse research challenges to envision, from devices to softwarization. Therefore, in this article, we discuss the future issues and challenges to be faced by the 6G technology. We have discussed issues and challenges from all aspects from hardware to the enabling technologies which will be utilized by 6G.