bluuebunny/crossref_metadata_2025 huggingface dataset:
SOFRIMENTO EMOCIONAL DOS ENFERMEIROS NO CONTEXTO HOSPITALAR FRENTE À PANDEMIA DE COVID-19
Authors: Pereira Mara Dantas , Torres Erivelton Cunha , Pereira Míria Dantas , Antunes Paola Fernanda Santos , Costa Cleberson Franclin Tavares
Published: June 2020

Este trabalho parte da preocupação com as crescentes taxas de problemas emocionais que vem acometendo o profissional enfermeiro, e que estão especialmente exacerbadas durante à pandemia de COVID-19. Neste ínterim, o objetivo do presente artigo é realizar uma reflexão teórica sobre o sofrimento emocional dos enfermeiros no contexto hospitalar frente à pandemia de COVID-19. Trata-se de uma revisão narrativa da literatura desenvolvida por meio da busca por publicações obtidas nas bases de dados: PubMed, BVS, LILACS e SciELO. Foram utilizados os descritores angústia emocional, COVID-19, enfermeiro, pandemias, sofrimento emocional e saúde mental. Ao final, selecionou-se 33 publicações para compor o estudo. Através da análise dos estudos, foi possível evidenciar que os enfermeiros em tempos de COVID-19 vêm desenvolvendo diversos problemas em sua saúde mental, como ansiedade, estresse, e até mesmo depressão. Isso ocorre devido à frustação, exaustão física e mental, sentimento de impotência e insegurança profissional. Conclui-se, portanto, que é primordial maiores cuidados com a saúde psicoemocional dos enfermeiros, a partir de investimentos e ações que comtemplem melhores ambientes e condições de trabalho.
Sobre a eficiência de barreiras sanitárias restritivas para conter o avanço da COVID-19: Uma modelagem matemática simples
Authors: Ferreira Silvio
Published: June 2020

Em maio de 2020, a COVID-19 avançou agressivamente no interior chegando à maior parte dos municípios brasileiros. Uma medida recorrentemente defendida e aplicada por administrações municipais brasileiras é a implantação de barreiras sanitárias restritivas que impedem parcialmente o fluxo de pessoas para evitar que o vírus chegue aos seus municípios por meio de casos importados. Tais barreiras, entretanto, têm se mostrado insuficientes em muitos municípios e novos registros dos primeiros casos acontecem a cada dia. Neste texto de caráter elucidativo usamos exemplos matemáticos simples para explicar o motivo desta ineficiência e apontar situações em que a barreira teria algum efeito expressivo. Todas as barreiras, mesmo as mais rigorosas, possuem alguma permeabilidade em que pessoas a atravessam e têm contato com os munícipes. Essa permeabilidade, associada com o baixo nível de distanciamento social, é uma explicação para o aparecimento precoce de casos de COVID-19 nos municípios queadotam restrições de acesso. Barreiras que geram impactos significativos nas curvas de prevalência epidêmica são muito difíceis de implementar e, portanto, a alternativa segura é reduzir a taxa de contágio comunitário por meio do distanciamento social e da proteção individual.
Social Influence Does Matter: User Action Prediction for In-Feed Advertising
Authors: Wang Hongyang , Meng Qingfei , Fan Ju , Li Yuchen , Cui Laizhong , Zhao Xiaoman , Peng Chong , Chen Gong , Du Xiaoyong
Published: June 2020

Social in-feed advertising delivers ads that seamlessly fit inside a user’s feed, and allows users to engage in social actions (likes or comments) with the ads. Many businesses pay higher attention to “engagement marketing” that maximizes social actions, as social actions can effectively promote brand awareness. This paper studies social action prediction for in-feed advertising. Most existing works overlook the social influence as a user’s action may be affected by her friends’ actions. This paper introduces an end-to-end approach that leverages social influence for action prediction, and focuses on addressing the high sparsity challenge for in-feed ads. We propose to learn influence structure that models who tends to be influenced. We extract a subgraph with the near neighbors a user interacts with, and learn topological features of the subgraph by developing structure-aware graph encoding methods. We also introduce graph attention networks to learn influence dynamics that models how a user is influenced by neighbors’ actions. We conduct extensive experiments on real datasets from the commercial advertising platform of WeChat and a public dataset. The experimental results demonstrate that social influence learned by our approach can significantly boost performance of social action prediction.
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
Authors: Fioretto Ferdinando , Mak Terrence W.K. , Van Hentenryck Pascal
Published: June 2020

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.
Multi-Scale Anomaly Detection on Attributed Networks
Authors: Gutiérrez-Gómez Leonardo , Bovet Alexandre , Delvenne Jean-Charles
Published: June 2020

Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes. Finding anomalies in these systems is crucial for detecting abuses such as credit card frauds, web spams or network intrusions. Intuitively, anomalous nodes are defined as nodes whose attributes differ starkly from the attributes of a certain set of nodes of reference, called the context of the anomaly. While some methods have proposed to spot anomalies locally, globally or within a community context, the problem remain challenging due to the multi-scale composition of real networks and the heterogeneity of node metadata. Here, we propose a principled way to uncover outlier nodes simultaneously with the context with respect to which they are anomalous, at all relevant scales of the network. We characterize anomalous nodes in terms of the concentration retained for each node after smoothing specific signals localized on the vertices of the graph. Besides, we introduce a graph signal processing formulation of the Markov stability framework used in community detection, in order to find the context of anomalies. The performance of our method is assessed on synthetic and real-world attributed networks and shows superior results concerning state of the art algorithms. Finally, we show the scalability of our approach in large networks employing Chebychev polynomial approximations.
Pairwise Learning with Differential Privacy Guarantees
Authors: Huai Mengdi , Wang Di , Miao Chenglin , Xu Jinhui , Zhang Aidong
Published: June 2020

Pairwise learning has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. Many machine learning tasks can be categorized as pairwise learning, such as AUC maximization and metric learning. Existing techniques for pairwise learning all fail to take into consideration a critical issue in their design, i.e., the protection of sensitive information in the training set. Models learned by such algorithms can implicitly memorize the details of sensitive information, which offers opportunity for malicious parties to infer it from the learned models. To address this challenging issue, in this paper, we propose several differentially private pairwise learning algorithms for both online and offline settings. Specifically, for the online setting, we first introduce a differentially private algorithm (called OnPairStrC) for strongly convex loss functions. Then, we extend this algorithm to general convex loss functions and give another differentially private algorithm (called OnPairC). For the offline setting, we also present two differentially private algorithms (called OffPairStrC and OffPairC) for strongly and general convex loss functions, respectively. These proposed algorithms can not only learn the model effectively from the data but also provide strong privacy protection guarantee for sensitive information in the training set. Extensive experiments on real-world datasets are conducted to evaluate the proposed algorithms and the experimental results support our theoretical analysis.
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Authors: Huang Kexin , Xiao Cao , Hoang Trong , Glass Lucas , Sun Jimeng
Published: June 2020

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and society. Although several computational models have been proposed for DDI prediction, there are still limitations: (1) specialized design of drug representation for DDI predictions is lacking; (2) predictions are based on limited labelled data and do not generalize well to unseen drugs or DDIs; and (3) models are characterized by a large number of parameters, thus are hard to interpret. In this work, we develop a ChemicAl SubstrucTurE Representation (CASTER) framework that predicts DDIs given chemical structures of drugs. CASTER aims to mitigate these limitations via (1) a sequential pattern mining module rooted in the DDI mechanism to efficiently characterize functional sub-structures of drugs; (2) an auto-encoding module that leverages both labelled and unlabelled chemical structure data to improve predictive accuracy and generalizability; and (3) a dictionary learning module that explains the prediction via a small set of coefficients which measure the relevance of each input sub-structures to the DDI outcome. We evaluated CASTER on two real-world DDI datasets and showed that it performed better than state-of-the-art baselines and provided interpretable predictions.
A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Authors: Ke Ziqi , Vikalo Haris
Published: June 2020

Reconstructing components of a genomic mixture from data obtained by means of DNA sequencing is a challenging problem encountered in a variety of applications including single individual haplotyping and studies of viral communities. High-throughput DNA sequencing platforms oversample mixture components to provide massive amounts of reads whose relative positions can be determined by mapping the reads to a known reference genome; assembly of the components, however, requires discovery of the reads' origin – an NP-hard problem that the existing methods struggle to solve with the required level of accuracy. In this paper, we present a learning framework based on a graph auto-encoder designed to exploit structural properties of sequencing data. The algorithm is a neural network which essentially trains to ignore sequencing errors and infers the posterior probabilities of the origin of sequencing reads. Mixture components are then reconstructed by finding consensus of the reads determined to originate from the same genomic component. Results on realistic synthetic as well as experimental data demonstrate that the proposed framework reliably assembles haplotypes and reconstructs viral communities, often significantly outperforming state-of-the-art techniques. Source codes, datasets and supplementary document are available at https://github.com/WuLoli/GAEseq.
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning
Authors: Li Wentian , Feng Xidong , An Haotian , Ng Xiang Yao , Zhang Yu-Jin
Published: June 2020

Compressed sensing magnetic resonance imaging (CS-MRI) is a technique aimed at accelerating the data acquisition of MRI. While down-sampling in k-space proportionally reduces the data acquisition time, it results in images corrupted by aliasing artifacts and blur. To reconstruct images from the down-sampled k-space, recent deep-learning based methods have shown better performance compared with classical optimization-based CS-MRI methods. However, they usually use deep neural networks as a black-box, which directly maps the corrupted images to the target images from fully-sampled k-space data. This lack of transparency may impede practical usage of such methods. In this work, we propose a deep reinforcement learning based method to reconstruct the corrupted images with meaningful pixel-wise operations (e.g. edge enhancing filters), so that the reconstruction process is transparent to users. Specifically, MRI reconstruction is formulated as Markov Decision Process with discrete actions and continuous action parameters. We conduct experiments on MICCAI dataset of brain tissues and fastMRI dataset of knee images. Our proposed method performs favorably against previous approaches. Our trained model learns to select pixel-wise operations that correspond to the anatomical structures in the MR images. This makes the reconstruction process more interpretable, which would be helpful for further medical analysis.
Learning Geo-Contextual Embeddings for Commuting Flow Prediction
Authors: Liu Zhicheng , Miranda Fabio , Xiong Weiting , Yang Junyan , Wang Qiao , Silva Claudio
Published: June 2020

Predicting commuting flows based on infrastructure and land-use information is critical for urban planning and public policy development. However, it is a challenging task given the complex patterns of commuting flows. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios where many factors need to be considered. Meanwhile, most existing machine learning-based methods ignore the spatial correlations and fail to model the influence of nearby regions. To address these issues, we propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction. Specifically, we first construct a geo-adjacency network containing the geographic contextual information. Then, an attention mechanism is proposed based on the framework of graph attention network (GAT) to capture the spatial correlations and encode geographic contextual information to embedding space. Two separate GATs are used to model supply and demand characteristics. To enhance the effectiveness of the embedding representation, a multitask learning framework is used to introduce stronger restrictions, forcing the embeddings to encapsulate effective representation for flow prediction. Finally, a gradient boosting machine is trained based on the learned embeddings to predict commuting flows. We evaluate our model using real-world dataset from New York City and the experimental results demonstrate the effectiveness of our proposed method against the state of the art.
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Authors: Ma Liantao , Zhang Chaohe , Wang Yasha , Ruan Wenjie , Wang Jiangtao , Tang Wen , Ma Xinyu , Gao Xin , Gao Junyi
Published: June 2020

Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between visits. Although those works have shown superior performances in healthcare prediction, they fail to explore the personal characteristics during the clinical visits thoroughly. Moreover, existing works usually assume that the more recent record weights more in the prediction, but this assumption is not suitable for all conditions. In this paper, we propose ConCare to handle the irregular EMR data and extract feature interrelationship to perform individualized healthcare prediction. Our solution can embed the feature sequences separately by modeling the time-aware distribution. ConCare further improves the multi-head self-attention via the cross-head decorrelation, so that the inter-dependencies among dynamic features and static baseline information can be effectively captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. The medical findings extracted by ConCare are also empirically confirmed by human experts and medical literature.
FuzzE: Fuzzy Fairness Evaluation of Offensive Language Classifiers on African-American English
Authors: Rios Anthony
Published: June 2020

Hate speech and offensive language are rampant on social media. Machine learning has provided a way to moderate foul language at scale. However, much of the current research focuses on overall performance. Models may perform poorly on text written in a minority dialectal language. For instance, a hate speech classifier may produce more false positives on tweets written in African-American Vernacular English (AAVE). To measure these problems, we need text written in both AAVE and Standard American English (SAE). Unfortunately, it is challenging to curate data for all linguistic styles in a timely manner—especially when we are constrained to specific problems, social media platforms, or by limited resources. In this paper, we answer the question, “How can we evaluate the performance of classifiers across minority dialectal languages when they are not present within a particular dataset?” Specifically, we propose an automated fairness fuzzing tool called FuzzE to quantify the fairness of text classifiers applied to AAVE text using a dataset that only contains text written in SAE. Overall, we find that the fairness estimates returned by our technique moderately correlates with the use of real ground-truth AAVE text. Warning: Offensive language is displayed in this manuscript.
Learning to Generate Maps from Trajectories
Authors: Ruan Sijie , Long Cheng , Bao Jie , Li Chunyang , Yu Zisheng , Li Ruiyuan , Liang Yuxuan , He Tianfu , Zheng Yu
Published: June 2020

Accurate and updated road network data is vital in many urban applications, such as car-sharing, and logistics. The traditional approach to identifying the road network, i.e., field survey, requires a significant amount of time and effort. With the wide usage of GPS embedded devices, a huge amount of trajectory data has been generated by different types of mobile objects, which provides a new opportunity to extract the underlying road network. However, the existing trajectory-based map recovery approaches require many empirical parameters and do not utilize the prior knowledge in existing maps, which over-simplifies or over-complicates the reconstructed road network. To this end, we propose a deep learning-based map generation framework, i.e., DeepMG, which learns the structure of the existing road network to overcome the noisy GPS positions. More specifically, DeepMG extracts features from trajectories in both spatial view and transition view and uses a convolutional deep neural network T2RNet to infer road centerlines. After that, a trajectory-based post-processing algorithm is proposed to refine the topological connectivity of the recovered map. Extensive experiments on two real-world trajectory datasets confirm that DeepMG significantly outperforms the state-of-the-art methods.
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
Authors: Song Chao , Lin Youfang , Guo Shengnan , Wan Huaiyu
Published: June 2020

Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms other baselines.
A Deep Neural Network Model of Particle Thermal Radiation in Packed Bed
Authors: Wu Hao , Hao Shuang
Published: June 2020

Prediction of particle radiative heat transfer flux is an important task in the large discrete granular systems, such as pebble bed in power plants and industrial fluidized beds. For particle motion and packing, discrete element method (DEM) now is widely accepted as the excellent Lagrangian approach. For thermal radiation, traditional methods focus on calculating the obstructed view factor directly by numerical algorithms. The major challenge for the simulation is that the method is proven to be time-consuming and not feasible to be applied in the practical cases. In this work, we propose an analytical model to calculate macroscopic effective conductivity from particle packing structures Then, we develop a deep neural network (DNN) model used as a predictor of the complex view factor function. The DNN model is trained by a large dataset and the computational speed is greatly improved with good accuracy. It is feasible to perform real-time simulation with DNN model for radiative heat transfer in large pebble bed. The trained model also can be coupled with DEM and used to analyze efficiently the directional radiative conductivity, anisotropic factor and wall effect of the particle thermal radiation.
Attention Based Data Hiding with Generative Adversarial Networks
Authors: Yu Chong
Published: June 2020

Recently, the generative adversarial network is the hotspot in research and industrial areas. Its application on data generation is the most common usage. In this paper, we propose the novel end-to-end framework to extend its application to data hiding area. The discriminative model simulates the detection process, which can help us understand the sensitivity of the cover image to semantic changes. The generative model is to generate the target image which is aligned with the original cover image. An attention model is introduced to generate the attention mask. This mask can help to generate a better target image without perturbation of the spotlight. The introduction of cycle discriminative model and inconsistent loss can help to enhance the quality of the generated target image in the iterative training process. The training dataset is mixed with intact images and attacked images. The mix training process can further improve robustness. Through the qualitative, quantitative experiments and analysis, this novel framework shows compelling performance and advantages over the current state-of-the-art methods in data hiding applications.
Towards Hands-Free Visual Dialog Interactive Recommendation
Authors: Yu Tong , Shen Yilin , Jin Hongxia
Published: June 2020

With the recent advances of multimodal interactive recommendations, the users are able to express their preference by natural language feedback to the item images, to find the desired items. However, the existing systems either retrieve only one item or require the user to specify (e.g., by click or touch) the commented items from a list of recommendations in each user interaction. As a result, the users are not hands-free and the recommendations may be impractical. We propose a hands-free visual dialog recommender system to interactively recommend a list of items. At each time, the system shows a list of items with visual appearance. The user can comment on the list in natural language, to describe the desired features they further want. With these multimodal data, the system chooses another list of items to recommend. To understand the user preference from these multimodal data, we develop neural network models which identify the described items among the list and further predict the desired attributes. To achieve efficient interactive recommendations, we leverage the inferred user preference and further develop a novel bandit algorithm. Specifically, to avoid the system exploring more than needed, the desired attributes are utilized to reduce the exploration space. More importantly, to achieve sample efficient learning in this hands-free setting, we derive additional samples from the user's relative preference expressed in natural language and design a pairwise logistic loss in bandit learning. Our bandit model is jointly updated by the pairwise logistic loss on the additional samples derived from natural language feedback and the traditional logistic loss. The empirical results show that the probability of finding the desired items by our system is about 3 times as high as that by the traditional interactive recommenders, after a few user interactions.
Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection
Authors: Yu Zeping , Cao Rui , Tang Qiyi , Nie Sen , Huang Junzhou , Wu Shi
Published: June 2020

Binary code similarity detection, whose goal is to detect similar binary functions without having access to the source code, is an essential task in computer security. Traditional methods usually use graph matching algorithms, which are slow and inaccurate. Recently, neural network-based approaches have made great achievements. A binary function is first represented as an control-flow graph (CFG) with manually selected block features, and then graph neural network (GNN) is adopted to compute the graph embedding. While these methods are effective and efficient, they could not capture enough semantic information of the binary code. In this paper we propose semantic-aware neural networks to extract the semantic information of the binary code. Specially, we use BERT to pre-train the binary code on one token-level task, one block-level task, and two graph-level tasks. Moreover, we find that the order of the CFG's nodes is important for graph similarity detection, so we adopt convolutional neural network (CNN) on adjacency matrices to extract the order information. We conduct experiments on two tasks with four datasets. The results demonstrate that our method outperforms the state-of-art models.
Shoreline: Data-Driven Threshold Estimation of Online Reserves of Cryptocurrency Trading Platforms
Authors: Zhang Xitong , Zhu He , Zhou Jiayu
Published: June 2020

With the proliferation of blockchain projects and applications, cryptocurrency exchanges, which provides exchange services among different types of cryptocurrencies, become pivotal platforms that allow customers to trade digital assets on different blockchains. Because of the anonymity and trustlessness nature of cryptocurrency, one major challenge of crypto-exchanges is asset safety, and all-time amount hacked from crypto-exchanges until 2018 is over $1.5 billion even with carefully maintained secure trading systems. The most critical vulnerability of crypto-exchanges is from the so-called hot wallet, which is used to store a certain portion of the total asset of an exchange and programmatically sign transactions when a withdraw happens. Whenever hackers managed to gain control over the computing infrastructure of the exchange, they usually immediately obtain all the assets in the hot wallet. It is important to develop network security mechanisms. However, the fact is that there is no guarantee that the system can defend all attacks. Thus, accurately controlling the available assets in the hot wallets becomes the key to minimize the risk of running an exchange. However, determining such optimal threshold remains a challenging task because of the complicated dynamics inside exchanges. In this paper, we propose Shoreline, a deep learning-based threshold estimation framework that estimates the optimal threshold of hot wallets from historical wallet activities and dynamic trading networks. We conduct extensive empirical studies on the real trading data from a trading platform and demonstrate the effectiveness of the proposed approach.
Dynamic Malware Analysis with Feature Engineering and Feature Learning
Authors: Zhang Zhaoqi , Qi Panpan , Wang Wei
Published: June 2020

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.
Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction
Authors: Zhong Haoxi , Wang Yuzhong , Tu Cunchao , Zhang Tianyang , Liu Zhiyuan , Sun Maosong
Published: June 2020

Legal Judgment Prediction (LJP) aims to predict judgment results according to the facts of cases. In recent years, LJP has drawn increasing attention rapidly from both academia and the legal industry, as it can provide references for legal practitioners and is expected to promote judicial justice. However, the research to date usually suffers from the lack of interpretability, which may lead to ethical issues like inconsistent judgments or gender bias. In this paper, we present QAjudge, a model based on reinforcement learning to visualize the prediction process and give interpretable judgments. QAjudge follows two essential principles in legal systems across the world: Presumption of Innocence and Elemental Trial. During inference, a Question Net will select questions from the given set and an Answer Net will answer the question according to the fact description. Finally, a Predict Net will produce judgment results based on the answers. Reward functions are designed to minimize the number of questions asked. We conduct extensive experiments on several real-world datasets. Experimental results show that QAjudge can provide interpretable judgments while maintaining comparable performance with other state-of-the-art LJP models. The codes can be found from https://github.com/thunlp/QAjudge.
Deep Reservoir Computing Meets 5G MIMO-OFDM Systems in Symbol Detection
Authors: Zhou Zhou , Liu Lingjia , Chandrasekhar Vikram , Zhang Jianzhong , Yi Yang
Published: June 2020

Conventional reservoir computing (RC) is a shallow recurrent neural network (RNN) with fixed high dimensional hidden dynamics and one trainable output layer. It has the nice feature of requiring limited training which is critical for certain applications where training data is extremely limited and costly to obtain. In this paper, we consider two ways to extend the shallow architecture to deep RC to improve the performance without sacrificing the underlying benefit: (1) Extend the output layer to a three layer structure which promotes a joint time-frequency processing to neuron states; (2) Sequentially stack RCs to form a deep neural network. Using the new structure of the deep RC we redesign the physical layer receiver for multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) signals since MIMO-OFDM is a key enabling technology in the 5th generation (5G) cellular network. The combination of RNN dynamics and the time-frequency structure of MIMO-OFDM signals allows deep RC to handle miscellaneous interference in nonlinear MIMO-OFDM channels to achieve improved performance compared to existing techniques. Meanwhile, rather than deep feedforward neural networks which rely on a massive amount of training, our introduced deep RC framework can provide a decent generalization performance using the same amount of pilots as conventional model-based methods in 5G systems. Numerical experiments show that the deep RC based receiver can offer a faster learning convergence and effectively mitigate unknown non-linear radio frequency (RF) distortion yielding twenty percent gain in terms of bit error rate (BER) over the shallow RC structure.
MixedAD: A Scalable Algorithm for Detecting Mixed Anomalies in Attributed Graphs
Authors: Zhu Mengxiao , Zhu Haogang
Published: June 2020

Attributed graphs, where nodes are associated with a rich set of attributes, have been widely used in various domains. Among all the nodes, those with patterns that deviate significantly from others are of particular interest. There are mainly two challenges for anomaly detection. For one thing, we often encounter large graphs with lots of nodes and attributes in the real-life scenario, which requires a scalable algorithm. For another, there are anomalies w.r.t. both the structure and attribute in a mixed manner. The algorithm should identify all of them simultaneously. State-of-art algorithms often fail in some respects. In this paper, we propose the scalable algorithm called MixedAD. Theoretical analysis is provided to prove its superiority. Extensive experiments are also conducted on both synthetic and real-life datasets. Specifically, the results show that MixedAD often achieves the F1 scores greater than those of others by at least 25% and runs at least 10 times faster than the others.
Re-forming Initial Teacher Education in Wales: A Personal Review of the Literature
Authors: Furlong John
Published: June 2020

The focus of this special issue is the changes to Initial Teacher Education (ITE) that have been instituted in Wales over the last two years. At the heart of the new approach is the insistence that in the future all programmes of ITE should be planned, led and delivered not by universities
alone, but by universities working in close collaboration with a number of partner schools. But what is the justification for these radical changes? Why is a collaborative approach between universities and schools needed? This paper, which takes the form of a personal literature review, sets
out the research evidence on which I drew in contributing the reform process. It considers evidence on three issues: the role of schools; the role of universities; and the ways in which they can effectively work together.
Accountability and Initial Teacher Education Reform: A Perspective from Abroad
Authors: Cochran-Smith Marilyn
Published: June 2020

This article focuses on accountability as a tool for teacher education reform. The article is based on my experience as a teacher education scholar and practitioner over the last 40 years and especially on analyses of teacher preparation accountability in the United States, recently
conducted by Project TEER (Teacher Education and Education Reform), a group of teacher education practitioners, researchers, and scholars at Boston College. The members of the group were united by a growing concern about the direction education reform was taking and the impact it was having
on teacher education in the US and by a commitment to equity for all the students served in the nation's schools. For five years, we tracked US teacher education reform, concentrating on the major accountability initiatives that were shaping the field. This work culminated in the book,
Reclaiming Accountability in Teacher Education (Cochran-Smith et al., 2018). Drawing on this work and on my experience in the national and international teacher education communities, this article has three purposes: to present a framework for unpacking accountability policies
related to initial teacher education; to use that framework to describe briefly the dominant accountability paradigm in the US as well as an alternative to the dominant paradigm –democratic accountability in teacher education; and finally, to use ideas from the framework and from our
US analyses to comment on the current reform of initial teacher education in Wales.
Changing the Lens: Mapping the Development of Research Dispositions in Programmes of Initial Teacher Education (ITE)
Authors: Waters Jane , Sharpling Elaine
Published: June 2020

The vision for ITE in Wales requires that NQTs understand how to conduct 'close-to-practice' research and are able to articulate evidence-informed approaches to pedagogy (Furlong, 2016; Welsh Government 2018). This paper describes the processes by which one HEI-school partnership (the
APLP) developed programmes of ITE to respond to this vision, specifically focussing on the journey of the student-teacher through the development of four research dispositions. The programmes seek to ensure that ITE students develop an 'inquiry stance', where this reflects the understandings
of Cochran-Smith (2011) who uses the term inquiry to refer to teachers' questioning, and the metaphor of stance to allude orientation and position. In order to support the research skills needed to adopt an inquiry stance, the work of Orchard and Winch (2015) has been adapted
and distilled into four dispositions for the student?teacher and progression steps have been identified for differing levels of study. The research dispositions and associated knowledge, skills, understandings and behaviours have been mapped through the content of the modules in each ITE programme
on offer. We include a consideration of the tensions apparent in the development of detailed module content and conjecture that these may be an inevitable result of the professional habit of performativity that results from education systems historically driven by structures of accountability.
Reclamation of ultra-fine coal with scenedesmus microalgae and comprehensive combustion property of the Coalgae® composite
Authors: Ejesieme Vitus O. , Vorster Nicole , Riaza Juan , Dugmore Gary , Zeelie Ben
Published: June 2020

Combustion of South African discard ultra-fine coal (i.e. coal dust), charcoal, microalgae biomass, and composites of the three under air were studied. The study involves to find out the effect of Scenedesmus microalgae biomass on the comprehensive combustion characteristics of the ultra-fines. Coal dust is considered as waste material, but it could be modified and combusted for energy. The composites were designed with Design Expert, and unlike blending with the dry microalgae biomass, fresh slurry was blended with the ultra-fine coal and charcoal. Non-isothermal combustion was carried out at heating rate of 15 C/min from 40 – 900 ºC and at flow rate of 20 ml/min, O2/CO2 air. Combustion properties of composites were deduced from TG-DTGA and analysed using multiple regression. On combustion, the interaction of coal-charcoal-microalgae was antagonistic (b = - 1069.49), while coal-microalgae (b = 39.17), and coal-charcoal (b = 80.37), was synergistic (p = 0.0061). The coal-microalgae (Coalgae®) indicated first order reaction mechanism unlike, coal, and the charcoal. Comprehensive combustion characteristics index of Coalgae®, (S-value = 4.52E8) was superior relative to ultra-fine (S-value = 3.16E8), which indicated high quality fuel. This approach to combusting ultra-fine coal with microalgae biomass is partly renewable, and it would advance the production of heat and electricity.

Key words: coal-dust, combustion, s-value, Coalgae®, renewable.
Small signal stability analysis of a four-machine system with placement of multi-terminal high voltage direct current link
Authors: Oni Oluwafemi E. , Swanson Andrew G. , Carpanen Rudiren Pillay
Published: June 2020

Inter-area oscillation caused by weak interconnected lines or low generator inertia is a critical problem facing power systems. This study investigated the performance analysis of a multi-terminal high voltage direct current (MTDC) on the damping of inter-area oscillations of a modified two-area four-machine network. Two case studies were considered, utilising scenario 1: a double alternating current (AC) circuit in linking Bus_10 and Bus_11; and scenario 2: a three-terminal line commutated converter high voltage direct current system in linking Bus_6 and Bus_11 into Bus_9. It was found that scenario 2 utilising MTDC link with a robust controller provided quick support in minimising the network oscillations following a fault on the system. The MTDC converter controllers’ setup offered sufficient support for the inertia of the AC system, thus providing efficient damping of the inter-area oscillation of the system.
Glycine-HCl Buffer v1
Authors: Junior Neilier
Published: June 2020

A buffer solution has the function of resisting changes in pH even when adding powerful acids or bases. However, in the physiological environment the buffered system also provides cofactors for enzymatic reactions, critical salts and even essential nutrients for cells and tissues. Therefore, when trying to reproduce biological conditions in vitro, we must make the appropriate choice of the buffer. After all, it will provide the appropriate medium in which reactions will occur.
Hydrochloric Acid-Potassium Chloride Buffer (HCl-KCl) v1
Authors: Junior Neilier
Published: June 2020

A buffer solution has the function of resisting changes in pH even when adding powerful acids or bases. However, in the physiological environment the buffered system also provides cofactors for enzymatic reactions, critical salts and even essential nutrients for cells and tissues. Therefore, when trying to reproduce biological conditions in vitro, we must make the appropriate choice of the buffer. After all, it will provide the appropriate medium in which reactions will occur.
Phosphate Buffer v1
Authors: Junior Neilier
Published: June 2020

A buffer solution has the function of resisting changes in pH even when adding powerful acids or bases. However, in the physiological environment the buffered system also provides cofactors for enzymatic reactions, critical salts and even essential nutrients for cells and tissues. Therefore, when trying to reproduce biological conditions in vitro, we must make the appropriate choice of the buffer. After all, it will provide the appropriate medium in which reactions will occur.
Tris-HCl Buffer v1
Authors: Junior Neilier
Published: June 2020

A buffer solution has the function of resisting changes in pH even when adding powerful acids or bases. However, in the physiological environment the buffered system also provides cofactors for enzymatic reactions, critical salts and even essential nutrients for cells and tissues. Therefore, when trying to reproduce biological conditions in vitro, we must make the appropriate choice of the buffer. After all, it will provide the appropriate medium in which reactions will occur.
Carbonate-Bicarbonate Buffer v1
Authors: Junior Neilier
Published: June 2020

A buffer solution has the function of resisting changes in pH even when adding powerful acids or bases. However, in the physiological environment the buffered system also provides cofactors for enzymatic reactions, critical salts and even essential nutrients for cells and tissues. Therefore, when trying to reproduce biological conditions in vitro, we must make the appropriate choice of the buffer. After all, it will provide the appropriate medium in which reactions will occur.
Digoxigenin Labeled In Vitro Transcription and Ribogreen Quantification v1
Authors: Institute for Brain Science Allen
Published: June 2020

This protocol is used for in vitro transcription (IVT) reaction generating DIG-labeled anti-sense (AS) RNA from polymerase chain reaction (PCR) product for in situ hybridization (ISH), as well as fluorescent quantification of products using Ribogreen immediately following. This protocol is written for 96 reactions but can also be used for single reactions or in multiples of 96 by adjusting the amount of materials and equipment appropriately. Note: Research reported in this publication was supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Coverslipping: Use of the Leica CV5030 Coverslipper v1
Authors: Institute for Brain Science Allen
Published: June 2020

This protocol describes the coverslipping of glass slides with the Leica CV5030. Note: Research reported in this publication was supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Fragment Analyzer Operation for PCR Products v1
Authors: Institute for Brain Science Allen
Published: June 2020

This protocol describes fragment analyzer operation for polymerase chain reaction (PCR) products. The Fragment Analyzer by Advanced Analytical is used as a quality control step for genotyping PCR products. Peak size, shape, and sample concentration are recorded and used to evaluate samples on a pass/fail level. Note: Research reported in this publication was supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.