nils-herrmann/clean_scirepeval huggingface dataset:
A source independent framework for research paper recommendation

As the number of research papers available on the Web has increased enormously over the years, paper recommender systems have been proposed to help researchers on automatically finding works of interest. The main problem with the current approaches is that they assume that recommending algorithms are provided with a rich set of evidence (e.g., document collections, citations, profiles) which is normally not widely available. In this paper we propose a novel source independent framework for research paper recommendation. The framework requires as input only a single research paper and generates several potential queries by using terms in that paper, which are then submitted to existing Web information sources that hold research papers. Once a set of candidate papers for recommendation is generated, the framework applies content-based recommending algorithms to rank the candidates in order to recommend the ones most related to the input paper. This is done by using only publicly available metadata (i.e., title and abstract). We evaluate our proposed framework by performing an extensive experimentation in which we analyzed several strategies for query generation and several ranking strategies for paper recommendation. Our results show that good recommendations can be obtained with simple and low cost strategies.
Biological pattern formation: fmm basic mechanisms ta complex structures

The reliable development of highly complex organisms is an intriguing and fascinating problem. The genetic material is, as a rule, the same in each cell of an organism. How do then cells, under the influence of their common genes, produce spatial patterns ? Simple models are discussed that describe the generation of patterns out of an initially nearly homogeneous state. They are based on nonlinear interactions of at least two chemicals and on their diffusion. The concepts of local autocatalysis and of long range inhibition play a fundamental role. Numerical simulations show that the models account for many basic biological observations such as the regeneration of a pattern after excision of tissue or the production of regular (or nearly regular) arrays of organs during production of regular (or nearly regular) arrays of organs during (or after) completion of growth. Very complex patterns can be generated in a reproducible way by hierarchical coupling of several such elementary reactions. Applications to animal coating and to the generation of polygonally shaped patterns are provided. It is further shown how to generate a strictly periodic pattern of units that themselves exhibit a complex and polar fine structure. This is illustrated by two examples : the assembly of photoreceptor cells in the eye of Drosophila and the positioning of leaves and axillary buds in a growing shoot. In both cases, the substructures have to achieve an internal polarity under the influence of some primary pattern forming system existing in the fly’s eye or in the plant. The fact that similar models can describe essential steps in so distantly related organisms as animals and plants suggests that they reveal some universal mechanisms.
Cooperative Multi-agent Control Using Deep Reinforcement Learning

This work considers the problem of learning cooperative policies in complex, partially observable domains without explicit communication. We extend three classes of single-agent deep reinforcement learning algorithms based on policy gradient, temporal-difference error, and actor-critic methods to cooperative multi-agent systems. To effectively scale these algorithms beyond a trivial number of agents, we combine them with a multi-agent variant of curriculum learning. The algorithms are benchmarked on a suite of cooperative control tasks, including tasks with discrete and continuous actions, as well as tasks with dozens of cooperating agents. We report the performance of the algorithms using different neural architectures, training procedures, and reward structures. We show that policy gradient methods tend to outperform both temporal-difference and actor-critic methods and that curriculum learning is vital to scaling reinforcement learning algorithms in complex multiagent domains.
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder–Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically.
Deep Density-based Image Clustering

Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in real-world tasks. In addition, the initial cluster centers in the learned feature space are generated by k-means. This only works well on spherical clusters and probably leads to unstable clustering results. In this paper, we propose a two-stage deep densitybased image clustering (DDC) framework to address these issues. The first stage is to train a deep convolutional autoencoder (CAE) to extract low-dimensional feature representations from high-dimensional image data, and then apply t-SNE to further reduce the data to a 2-dimensional space favoring density-based clustering algorithms. The second stage is to apply the developed density-based clustering technique on the 2-dimensional embedded data to automatically recognize an appropriate number of clusters with arbitrary shapes. Concretely, a number of local clusters are generated to capture the local structures of clusters, and then are merged via their density relationship to form the final clustering result. Experiments demonstrate that the proposed DDC achieves comparable or even better clustering performance than state-of-the-art deep clustering methods, even though the number of clusters is not given.
Impact-Driven Process Model Repair

The abundance of event data in today’s information systems makes it possible to “confront” process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations, it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this article assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness—that is, the fraction of behavior in the log not possible according to the model is minimized. To compute fitness, we need to align the model and log, which can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs.
Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation

e recent boom of AI has seen the emergence of many humancomputer conversation systems such as Google Assistant, Microso Cortana, Amazon Echo and Apple Siri. We introduce and formalize the task of predicting questions in conversations, where the goal is to predict the new question that the user will ask, given the past conversational context. is task can be modeled as a “sequence matching” problem, where two sequences are given and the aim is to learn a model that maps any pair of sequences to a matching probability. Neural matching models, which adopt deep neural networks to learn sequence representations and matching scores, have aracted immense research interests of information retrieval and natural language processing communities. In this paper, we rst study neural matching models for the question retrieval task that has been widely explored in the literature, whereas the eectiveness of neural models for this task is relatively unstudied. We further evaluate the neural matching models in the next question prediction task in conversations. We have used the publicly available ora data and Ubuntu chat logs in our experiments. Our evaluations investigate the potential of neural matching models with representation learning for question retrieval and next question prediction in conversations. Experimental results show that neural matching models perform well for both tasks.
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing

Event and relation extraction are central tasks in biomedical text mining. Where relation extraction concerns the detection of semantic connections between pairs of entities, event extraction expands this concept with the addition of trigger words, multiple arguments and nested events, in order to more accurately model the diversity of natural language. In this work we develop a convolutional neural network that can be used for both event and relation extraction. We use a linear representation of the input text, where information is encoded with various vector space embeddings. Most notably, we encode the parse graph into this linear space using dependency path embeddings. We integrate our neural network into the open source Turku Event Extraction System (TEES) framework. Using this system, our machine learning model can be easily applied to a large set of corpora from e.g. the BioNLP, DDI Extraction and BioCreative shared tasks. We evaluate our system on 12 different event, relation and NER corpora, showing good generalizability to many tasks and achieving improved performance on several corpora.
Disentangled Representation Learning for Domain Adaptation and Style Transfer

In order to solve unsupervised domain adaptation problem, recent methods focus on the use of adversarial learning to learn the common representation among domains. Although many designs are proposed, they seem to ignore the negative influence of domain-specific characteristics in transferring process. Besides, they also tend to obliterate these characteristics when extracted, although they are useful for other tasks and somehow help preserve the data. Take into account these issues, in this paper, we want to design a novel domainadaptation architecture which disentangles learned features into multiple parts to answer the questions: what features to transfer across domains and what to preserve within domains for other tasks. Towards this, besides jointly matching domain distributions in both image-level and feature-level, we offer new idea on feature exchange across domains combining with a novel feed-back loss and a semantic consistency loss to not only enhance the transferability of learned common feature but also preserve data and semantic information during exchange process. By performing domain adaptation on two standard digit datasets – MNIST and USPS, we show that our architecture can solve not only the full transfer problem but also partial transfer problem efficiently. The translated image results also demonstrate the potential of our architecture in image style transfer application.
Immune-Related Adverse Events Associated with Immune Checkpoint Blockade.

Immunotherapy enhances a patient’s immune system to fight disease and has recently been a source of promising new cancer treatments. Among the many immunotherapeutic strategies, immune checkpoint blockade has shown remarkable benefit in the treatment of a range of cancer types. Immune checkpoint blockade increases antitumor immunity by blocking intrinsic downregulators of immunity, such as cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1) or its ligand, programmed cell death ligand 1 (PD-L1). Several immune checkpoint–directed antibodies have increased overall survival for patients with various cancers and are approved by the Food and Drug Administration (Table 1). By increasing the activity of the immune system, immune checkpoint blockade can have inflammatory side effects, which are often termed immune-related adverse events. Although any organ system can be affected, immune-related adverse events most commonly involve the gastrointestinal tract, endocrine glands, skin, and liver.1 Less often, the central nervous system and cardiovascular, pulmonary, musculoskeletal, and hematologic systems are involved. The wide range of potential immune-related adverse events requires multidisciplinary, collaborative management by providers across the clinical spectrum . No prospective trials have defined strategies for effectively managing specific immune-related adverse events; thus, clinical practice remains variable. Nevertheless, several professional organizations are working to harmonize expert consensus on managing specific immune-related adverse events. In this review, we focus on 10 essential questions practitioners will encounter while caring for the expanding population of patients with cancer who are being treated with immune checkpoint blockade (Table 2).
Foundations of Statistical Natural Language Processing

(6.24) Briefly noted Bell et al. (1990) and and Bell (1991) introduce a number of smoothing algorithms for the goal of improving text compression. Their “Method is normally referred to as smoothing and has been used for smoothing speech language models. The idea is to model the probability of a previously unseen event by estimating the probability of seeing such a new (previously unseen) event at each point as one proceeds through the training corpus. In particular, this probability is worked out relative to a certain history. So to calculate the probability of seeing a new word after, say, sat in one is calculating from the training data how often one saw a new word after sat in, which is just the count of the number of types seen which begin with sat in. It is thus an instance of generalized linear interpolation: where the probability mass given to new n-grams is given by:
2007 Guideline for Isolation Precautions: Preventing Transmission of Infectious Agents in Health Care Settings.

Mumps Update [October 2017]: The Healthcare Infection Control Practices Advisory Committee (HICPAC) voted to change the recommendation of isolation for persons with mumps from 9 days to 5 days based on a 2008 MMWR report. . Ebola Virus Disease Update [August 2014]: The recommendations in this guideline for Ebola has been superseded by these CDC documents: • Infection Prevention and Control Recommendations for Hospitalized Patients with Known or Suspected Ebola Virus Disease in U.S. Hospitals (https://www.cdc.gov/vhf/ebola/clinicians/evd/infection-control.html accessed September 2018) • Interim Guidance for Environmental Infection Control in Hospitals for Ebola Virus (https://www.cdc.gov/vhf/ebola/clinicians/cleaning/hospitals.html accessed September 2018) See CDC’s Ebola Virus Disease website (https://www.cdc.gov/vhf/ebola/ accessed September 2018) for current information on how Ebola virus is transmitted.
A Hierarchical LSTM Model for Joint Tasks

Previous work has shown that joint modeling of two Natural Language Processing (NLP) tasks are effective for achieving better performances for both tasks. Lots of task-specific joint models are proposed. This paper proposes a Hierarchical Long Short-Term Memory (HLSTM) model and some its variants for modeling two tasks jointly. The models are flexible for modeling different types of combinations of tasks. It avoids task-specific feature engineering. Besides the enabling of correlation information between tasks, our models take the hierarchical relations between two tasks into consideration, which is not discussed in previous work. Experimental results show that our models outperform strong baselines in three different types of task combination. While both correlation information and hierarchical relations between two tasks are helpful to improve performances for both tasks, the models especially boost performance of tasks on the top of the hierarchical structures.
Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space. Thus, we propose a specific Euler-RegionProposal Network (E-RPN) to estimate the pose of the object by adding an imaginary and a real fraction to the regression network. This ends up in a closed complex space and avoids singularities, which occur by single angle estimations. The E-RPN supports to generalize well during training. Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency. We achieve state of the art results for cars, pedestrians and cyclists by being more than five times faster than the fastest competitor. Further, our model is capable of estimating all eight KITTIclasses, including Vans, Trucks or sitting pedestrians simultaneously with high accuracy.
Parsing-based sarcasm sentiment recognition in Twitter data

Sentiment Analysis is a technique to identify people's opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations, services etc. Sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say (especially in order to insult or wit someone, to show irritation, or to be funny). People often expressed it verbally through the use of heavy tonal stress and certain gestural clues like rolling of the eyes. These tonal and gestural clues are obviously not available for expressing sarcasm in text, making its detection reliant upon other factors. In this paper, two approaches to detect sarcasm in the text of Twitter data were proposed. The first is a parsing-based lexicon generation algorithm (PBLGA) and the second was to detect sarcasm based on the occurrence of the interjection word. The combination of two approaches is also shown and compared with the existing state-of-the-art approach to detect sarcasm. First approach attains a 0.89, 0.81 and 0.84 precision, recall and f -- score respectively. Second approach attains 0.85, 0.96 and 0.90 precision, recall and f -- score respectively in tweets with sarcastic hashtag.
U-Net: Convolutional Networks for Biomedical Image Segmentation

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.
Content-Boosted Collaborative Filtering for Improved Recommendations

Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.
Passive in the world ’ s languages

In this chapter we shall examine the characteristic properties of a construction widespread in the world’s languages, the passive. In section 1 below we discuss defining characteristics of passives, contrasting them with other foregrounding and backgrounding constructions. In section 2 we present the common syntactic and semantic properties of the most widespread types of passives, and in section 3 we consider passives which differ in one or more ways from these. In section 4, we survey a variety of constructions that resemble passive constructions in one way or another. In section 5, we briefly consider differences between languages with regard to the roles passives play in their grammars. Specifically, we show that passives are a more essential part of the grammars of some languages than of others.
Management of Acral Lentiginous Melanoma

Cutaneous malignant melanoma is the most common cause of mortality from skin cancers in Caucasian populations. The incidence rates of malignant melanoma show considerable varia‐ tion worldwide. Annual incidence rates per 100,000 people vary between about 40 in Australia and New Zealand to about 20 in the United States . In contrast, a significantly lower inci‐ dence rate has been reported in Asian populations with rates of 0.65 to 1/100,000 [3-5]. In addi‐ tion, the most common sites of melanoma occurrence in Asians are the extremities at a rate of about 50% of all cases [6,7], compared to only 2-3% in Caucasian populations .