nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on Neural Network-Based Summarization Methods

ive models The core of abstractive summarization techniques is to identify the main ideas in the documents and encode them into feature representations. These encoded features are then passed to natural language generation (NLG) systems, such as the one proposed in [Reiter and Dale, 1997], for summary generation. Most of the early work on abstractive summarization uses semi-manual process of identifying the main ideas of the document(s). Prior knowledge such as scripts and templates are usually used to produce summaries. Thus, the abstractive summary is produced through slot fillings and simple smoothing techniques such as in [DeJong, 1982, Radev and McKeown, 1998]. 3 Neural-Based Summarization Techniques Since the bloom of deep learning, neural-based summarizers have attracted considerable attention for automatic summarization. Compared to the traditional models, neural-based models achieve better performance with less human involvement if the training data is abundant. In this section, five extractive and five abstractive neural-based models are examined in details. Most neural-based summarizers use the following pipeline: 1) words are transformed to continuous vectors, called word embeddings, by a look-up table; 2) sentences/documents are encoded as continuous vectors using the word embeddings; 3) sentence/document representations (sometimes also word embeddings) are then fed to a model for selection (extractive summarization) or generation (abstractive summarization). Neural networks can be used in any of the above three steps. In step 1, we can use neural networks to obtain pre-learned look-up tables (such as Word2Vec, CW vectors, and GloVe). In step 2, neural networks, such as convolutional neural networks (CNNs) or recurrent neural networks(RNNs), can be used as encoders for extracting sentence/document features. In step 3, neural network models can be used as regressors for ranking/selection (extraction) or decoders for generation (abstraction). CNNs and RNNs: CNNs and RNNs are commonly used in neural-based summarizers. Both CNNs and RNNs serve the same purpose: transform a sequence of word embeddings x1, . . . ,xT ∈ R d to a vector (sentence representation) s ∈ R. • CNNs achieve this purpose by using h filters and sliding them over the input sequence. Each filter performs local convolution on the sub-sequences of the input to obtain a set of feature maps (scalars), then a global max-pooling-over-time is performed to obtain a scalar. These scalars from the h filters are then concatenated into the sequence representation vector s ∈ R. The convolution operation used here is basically element-wise matrix multiplication followed by a summation.
Beneficial psychological effects of a probiotic formulation (Lactobacillus helveticus R0052 and Bifidobacterium longum R0175) in healthy human volunteers.

*Correspondence to: Michaël Messaoudi;  mmessaoudi@etap-lab.com In a recent clinical study, we demonstrated in the general population that Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 (PF) taken in combination for 30 days decreased the global scores of hospital anxiety and depression scale (HADs), and the global severity index of the Hopkins symptoms checklist (HSCL90), due to the decrease of the sub-scores of somatization, depression and angerhostility spheres. Therefore, oral intake of PF showed beneficial effects on anxiety and depression related behaviors in human volunteers. From there, it is interesting to focus on the role of this probiotic formulation in the subjects with the lowest urinary free cortisol levels at baseline. This addendum presents a secondary analysis of the effects of PF in a subpopulation of 25 subjects with urinary free cortisol (UFC) levels less than 50 ng/ml at baseline, on psychological distress based on the percentage of change of the perceived stress scale (PSs), the HADs and the HSCL-90 scores between baseline and follow-up. The data show that PF improves the same scores as in the general population (the HADs global score, the global severity index of the HSCL-90 and three of its sub-scores, i.e., somatization, depression and anger-hostility), as well as the PSs score and three other subscores of the HSCL-90, i.e., “obsessive compulsive,” “anxiety” and “paranoidideation.” Moreover, in the HSCL-90, Beneficial psychological effects of a probiotic formulation (Lactobacillus helveticus R0052 and Bifidobacterium longum R0175) in healthy human volunteers
LSTM Fully Convolutional Networks for Time Series Classification

Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our proposed models significantly enhance the performance of fully convolutional networks with a nominal increase in model size and require minimal preprocessing of the data set. The proposed long short term memory fully convolutional network (LSTM-FCN) achieves the state-of-the-art performance compared with others. We also explore the usage of attention mechanism to improve time series classification with the attention long short term memory fully convolutional network (ALSTM-FCN). The attention mechanism allows one to visualize the decision process of the LSTM cell. Furthermore, we propose refinement as a method to enhance the performance of trained models. An overall analysis of the performance of our model is provided and compared with other techniques.
What mechanisms underlie diastolic dysfunction in heart failure?

Abnormalities of diastolic function are common to virtually all forms of cardiac failure. However, their underlying mechanisms, precise role in the generation and phenotypic expression of heart failure, and value as specific therapeutic targets remain poorly understood. A growing proportion of heart failure patients, particularly among the elderly, have apparently preserved systolic function, and this is fueling interest for better understanding and treating diastolic abnormalities. Much of the attention in clinical and experimental studies has focused on relaxation and filling abnormalities of the heart, whereas chamber stiffness has been less well studied, particularly in humans. Nonetheless, new insights from basic and clinical research are helping define the regulators of diastolic dysfunction and illuminate novel targets for treatment. This review puts these developments into perspective with the major aim of highlighting current knowledge gaps and controversies.
Decoding of Polar Code by Using Deep Feed-Forward Neural Networks

With the success of image classification problems, deep learning is expanding its application areas. In this paper, we apply deep learning to decode a polar code. As an initial step for memoryless additive Gaussian noise channel, we consider a deep feed-forward neural network and investigate its decoding performances with respect to numerous configurations: the number of hidden layers, the number of nodes for each layer, and activation functions. Generally, the higher complex network yields a better performance. Comparing the performances of regular list decoding, we provide a guideline for the configuration parameters. Although the training of deep learning may require high computational complexity, it should be noted that the field application of trained networks can be accomplished at a low level complexity. Considering the level of performance and complexity, we believe that deep learning is a competitive decoding tool.
Evaluating sentiment in financial news articles

Can the choice of words and tone used by the authors of financial news articles correlate to measurable stock price movements? If so, can the magnitude of price movement be predicted using these same variables? We investigate these questions using the Arizona Financial Text (AZFinText) system, a financial news article prediction system, and pair it with a sentiment analysis tool. Through our analysis, we found that subjective news articles were easier to predict in price direction (59.0% versus 50.0% of chance alone) and using a simple trading engine, subjective articles garnered a 3.30% return. Looking further into the role of author tone in financial news articles, we found that articles with a negative sentiment were easiest to predict in price direction (50.9% versus 50.0% of chance alone) and a 3.04% trading return. Investigating negative sentiment further, we found that our system was able to predict price decreases in articles of a positive sentiment 53.5% of the time, and price increases in articles of a negative
Proposition Knowledge Graphs

Open Information Extraction (Open IE) is a promising approach for unrestricted Information Discovery (ID). While Open IE is a highly scalable approach, allowing unsupervised relation extraction from open domains, it currently has some limitations. First, it lacks the expressiveness needed to properly represent and extract complex assertions that are abundant in text. Second, it does not consolidate the extracted propositions, which causes simple queries above Open IE assertions to return insufficient or redundant information. To address these limitations, we propose in this position paper a novel representation for ID – Propositional Knowledge Graphs (PKG). PKGs extend the Open IE paradigm by representing semantic inter-proposition relations in a traversable graph. We outline an approach for constructing PKGs from single and multiple texts, and highlight a variety of high-level applications that may leverage PKGs as their underlying information discovery and representation framework.
Realizing the potential of synthetic biology

Synthetic biology, despite still being in its infancy, is increasingly providing valuable information for applications in the clinic, the biotechnology industry and in basic molecular research. Both its unique potential and the challenges it presents have brought together the expertise of an eclectic group of scientists, from cell biologists to engineers. In this Viewpoint article, five experts discuss their views on the future of synthetic biology, on its main achievements in basic and applied science, and on the bioethical issues that are associated with the design of new biological systems.
Towards a Serendipitous Research Paper Recommender System Using Bisociative Information Networks (BisoNets)

In recent times the rate at which information is being processed and shared through the internet has tremendously increased. Internet users are in need of systems and tools that will help them manage this information overload. Search engines and recommendation systems have been recently adopted to help solve this problem. The aim of this research is to model a spontaneous research paper recommender system that recommends serendipitous research papers from two large normally mismatched information spaces or domains using BisoNets. Set and graph theory methods were employed to model the problem, whereas text mining methodologies were used to develop nodes and links of the BisoNets. Nodes were constructed from keywords, while links between nodes were established through weighting that was determined from the co-occurrence of corresponding keywords in the same title and domain. Preliminary results from the word clouds indicates that there is no obvious relationship between the two domains. The strongest links in the established information networks can be exploited to display associations that can be discovered between the two matrices. Research paper recommender systems exploit these latent relationships to recommend serendipitous articles when Bisociative Knowledge Discovery techniques and methodologies are utilized appropriately.
Deep Residual Learning for Image Recognition Supplementary Materials

In this section we introduce our detection method based on the baseline Faster R-CNN [6] system. The models are initialized by the ImageNet classification models, and then fine-tuned on the object detection data. We have experimented with ResNet-50/101 at the time of the ILSVRC & COCO 2015 detection competitions. Unlike VGG-16 used in , our ResNet has no hidden fc layers. We adopt the idea of “Networks on Conv feature maps” (NoC)  to address this issue. We compute the full-image shared conv feature maps using those layers whose strides on the image are no greater than 16 pixels (i.e., conv1, conv2 x, conv3 x, and conv4 x, totally 91 conv layers in ResNet-101). We consider these layers as analogous to the 13 conv layers in VGG-16, and by doing so, both ResNet and VGG-16 have conv feature maps of the same total stride (16 pixels). These layers are shared by a region proposal network (RPN, generating 300 proposals)  and a Fast R-CNN detection network . RoI pooling  is performed before conv5 1. On this RoI-pooled feature, all layers of conv5 x and up are adopted for each region, playing the roles of VGG-16’s fc layers. The final classification layer is replaced by two sibling layers (classification and box regression ). For the usage of BN layers, after pre-training, we compute the BN statistics (means and variances) for each layer on the ImageNet training set. Then the BN layers are fixed during fine-tuning for object detection. As such, the BN layers become linear activations with constant offsets and scales, and BN statistics are not updated by fine-tuning. We fix the BN layers mainly for reducing memory consumption in Faster R-CNN training.
Advanced Good Cell Culture Practice for human primary, stem cell-derived and organoid models as well as microphysiological systems.

A major reason for the current reproducibility crisis in the life sciences is the poor implementation of quality control measures and reporting standards. Improvement is needed, especially regarding increasingly complex in vitro methods. Good Cell Culture Practice (GCCP) was an effort from 1996 to 2005 to develop such minimum quality standards also applicable in academia. This paper summarizes recent key developments in in vitro cell culture and addresses the issues resulting for GCCP, e.g. the development of induced pluripotent stem cells (iPSCs) and gene-edited cells. It further deals with human stem-cell-derived models and bioengineering of organo-typic cell cultures, including organoids, organ-on-chip and human-on-chip approaches. Commercial vendors and cell banks have made human primary cells more widely available over the last decade, increasing their use, but also requiring specific guidance as to GCCP. The characterization of cell culture systems including high-content imaging and high-throughput measurement technologies increasingly combined with more complex cell and tissue cultures represent a further challenge for GCCP. The increasing use of gene editing techniques to generate and modify in vitro culture models also requires discussion of its impact on GCCP. International (often varying) legislations and market forces originating from the commercialization of cell and tissue products and technologies are further impacting on the need for the use of GCCP. This report summarizes the recommendations of the second of two workshops, held in Germany in December 2015, aiming map the challenge and organize the process or developing a revised GCCP 2.0.
Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning

Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as “the curse of dimensionality”. This paper presents a deep learning-based approach that can handle general high-dimensional parabolic PDEs. To this end, the PDEs are reformulated as a control theory problem and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function. Numerical results on examples including the nonlinear Black-Scholes equation, the Hamilton-Jacobi-Bellman equation, and the Allen-Cahn equation suggest that the proposed algorithm is quite effective in high dimensions, in terms of both accuracy and speed. This opens up new possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their inter-relationships.
PyramidBox: A Context-Assisted Single Shot Face Detector

Face detection has been well studied for many years and one of remaining challenges is to detect small, blurred and partially occluded faces in uncontrolled environment. This paper proposes a novel contextassisted single shot face detector, named PyramidBox to handle the hard face detection problem. Observing the importance of the context, we improve the utilization of contextual information in the following three aspects. First, we design a novel context anchor to supervise high-level contextual feature learning by a semi-supervised method, which we call it PyramidAnchors. Second, we propose the Low-level Feature Pyramid Network to combine adequate high-level context semantic feature and Low-level facial feature together, which also allows the PyramidBox to predict faces of all scales in a single shot. Third, we introduce a contextsensitive structure to increase the capacity of prediction network to improve the final accuracy of output. In addition, we use the method of Data-anchor-sampling to augment the training samples across different scales, which increases the diversity of training data for smaller faces. By exploiting the value of context, PyramidBox achieves superior performance among the state-of-the-art over the two common face detection benchmarks, FDDB and WIDER FACE. Our code is available in PaddlePaddle: https://github.com/PaddlePaddle/models/tree/develop/ fluid/face_detection.
Analysis of Financial News with NewsStream

Unstructured data, such as news and blogs, can provide valuable insights into the financial world. We present the NewsStream portal, an intuitive and easy-to-use tool for news analytics, which supports interactive querying and visualizations of the documents at different levels of detail. It relies on a scalable architecture for real-time processing of a continuous stream of textual data, which incorporates data acquisition, cleaning, natural-language preprocessing and semantic annotation components. It has been running for over two years and collected over 18 million news articles and blog posts. The NewsStream portal can be used to answer the questions when, how often, in what context, and with what sentiment was a financial entity or term mentioned in a continuous stream of news and blogs, and therefore providing a complement to news aggregators. We illustrate some features of our system in four use cases: relations between the rating agencies and the PIIGS countries, reflection of financial news on credit default swap (CDS) prices, the emergence of the Bitcoin digital currency, and visualizing how the world is connected through news.
Regularizing and Optimizing LSTM Language Models

Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering. In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTMbased models. We propose the weight-dropped LSTM which uses DropConnect on hidden-tohidden weights as a form of recurrent regularization. Further, we introduce NT-ASGD, a variant of the averaged stochastic gradient method, wherein the averaging trigger is determined using a non-monotonic condition as opposed to being tuned by the user. Using these and other regularization strategies, we achieve state-of-the-art word level perplexities on two data sets: 57.3 on Penn Treebank and 65.8 on WikiText-2. In exploring the effectiveness of a neural cache in conjunction with our proposed model, we achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and 52.0 on WikiText-2.
Comparative Analysis of Software Effort Estimation Techniques

Project Failure is the major problem undergoing nowadays as seen by software project managers. Imprecision of the estimation is the reason for this problem. As software grew in size and importance it also grew in complexity, making it very difficult to accurately predict the cost of software development. This was the dilemma in past years. The greatest pitfall of software industry was the fast changing nature of software development which has made it difficult to develop parametric models that yield high accuracy for software development in all domains. Development of useful models that accurately predict the cost of developing a software product. It is a very important objective of software industry. In this paper, several existing methods for software cost estimation are illustrated and their aspects will be discussed. This paper summarizes several classes of software cost estimation models and techniques. To achieve all these goals we implement the simulators. No single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates.
A Boosting Algorithm for Item Recommendation with Implicit Feedback

Many recommendation tasks are formulated as top-N item recommendation problems based on users’ implicit feedback instead of explicit feedback. Here explicit feedback refers to users’ ratings to items while implicit feedback is derived from users’ interactions with items, e.g., number of times a user plays a song. In this paper, we propose a boosting algorithm named AdaBPR (Adaptive Boosting Personalized Ranking) for top-N item recommendation using users’ implicit feedback. In the proposed framework, multiple homogeneous component recommenders are linearly combined to create an ensemble model, for better recommendation accuracy. The component recommenders are constructed based on a fixed collaborative filtering algorithm by using a re-weighting strategy, which assigns a dynamic weight distribution on the observed user-item interactions. AdaBPR demonstrates its effectiveness on three datasets compared with strong baseline algorithms.
The Therapeutic Potential of Psychedelic Drugs: Past, Present, and Future

Plant-based psychedelics, such as psilocybin, have an ancient history of medicinal use. After the first English language report on LSD in 1950, psychedelics enjoyed a short-lived relationship with psychology and psychiatry. Used most notably as aids to psychotherapy for the treatment of mood disorders and alcohol dependence, drugs such as LSD showed initial therapeutic promise before prohibitive legislature in the mid-1960s effectively ended all major psychedelic research programs. Since the early 1990s, there has been a steady revival of human psychedelic research: last year saw reports on the first modern brain imaging study with LSD and three separate clinical trials of psilocybin for depressive symptoms. In this circumspective piece, RLC-H and GMG share their opinions on the promises and pitfalls of renewed psychedelic research, with a focus on the development of psilocybin as a treatment for depression.
Turkish Natural Language Processing

We present a short survey and exposition of some of the important aspects of Turkish that have proved to be interesting and challenging for natural language and speech processing. Most of the challenges stem from the complex morphology of Turkish and how morphology interacts with syntax. Finally we provide a short overview of the major tools and resources developed for Turkish over the last two decades. (Parts of this chapter were previously published as Oflazer (Lang Resour Eval 48(4):639–653, 2014).