nils-herrmann/clean_scirepeval huggingface dataset:
Fundamentals of Biomass Pretreatment by Fractionation

With the rise in global energy demand and environmental concerns about the use of fossil fuels, the need for rapid development of alternative fuels from sustainable, non-food sources is now well acknowledged. The effective utilization of low-cost high-volume agricultural and forest biomass for the production of transportation fuels and bio-based materials will play a vital role in addressing this concern . The processing of lignocellulosic biomass, especially from mixed agricultural and forest sources with varying composition, is currently significantly more challenging than the bioconversion of corn starch or cane sugar to ethanol [1,2]. This is due to the inherent recalcitrance of lignocellulosic biomass to enzymatic and microbial deconstruction, imparted by the partly crystalline nature of cellulose and its close association with hemicellulose and lignin in the plant cell wall [2,3]. Pretreatments that convert raw lignocellulosic biomass to a form amenable to enzymatic degradation are therefore an integral step in the production of bioethanol from this material . Chemical or thermochemical pretreatments act to reduce biomass recalcitrance in various ways. These include hemicellulose removal or degradation, lignin modification and/or delignification, reduction in crystallinity and degree of polymerization of cellulose, and increasing pore volume. Biomass pretreatments are an active focus of industrial and academic research efforts, and various strategies have been developed. Among commonly studied pretreatments, organosolv pretreatment, in which an aqueous organic solvent mixture is used as the pretreatment medium, results in the fractionation of the major biomass components, cellulose, lignin, and hemicellulose into three process streams [5,6]. Cellulose and lignin are recovered as separate solid streams, while hemicelluloses and sugar degradation products such as furfural and hydroxymethylfurfural (HMF) are released as a water-soluble fraction. The combination of ethanol as the solvent and
Full-Capacity Unitary Recurrent Neural Networks

Recurrent neural networks are powerful models for processing sequential data, but they are generally plagued by vanishing and exploding gradient problems. Unitary recurrent neural networks (uRNNs), which use unitary recurrence matrices, have recently been proposed as a means to avoid these issues. However, in previous experiments, the recurrence matrices were restricted to be a product of parameterized unitary matrices, and an open question remains: when does such a parameterization fail to represent all unitary matrices, and how does this restricted representational capacity limit what can be learned? To address this question, we propose full-capacity uRNNs that optimize their recurrence matrix over all unitary matrices, leading to significantly improved performance over uRNNs that use a restricted-capacity recurrence matrix. Our contribution consists of two main components. First, we provide a theoretical argument to determine if a unitary parameterization has restricted capacity. Using this argument, we show that a recently proposed unitary parameterization has restricted capacity for hidden state dimension greater than 7. Second, we show how a complete, fullcapacity unitary recurrence matrix can be optimized over the differentiable manifold of unitary matrices. The resulting multiplicative gradient step is very simple and does not require gradient clipping or learning rate adaptation. We confirm the utility of our claims by empirically evaluating our new full-capacity uRNNs on both synthetic and natural data, achieving superior performance compared to both LSTMs and the original restricted-capacity uRNNs. Advances in Neural Information Processing Systems (NIPS) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Finding event, temporal and causal structure in text: a machine learning approach

Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Humans often describe their experiences through the event, temporal and causal structures they perceive. These structures are often expressed in textual forms, for example in timelines, where text is summarized by aligning events with the times at which they occurred. These same sorts of temporal-causal structures are also useful for a variety of computational tasks, like summarization and question answering. However, to reason over such structures they must first be extracted from their textual representations and organized into a machine readable form. This work demonstrates that various important parts of the event, temporal and causal structure of a text can be extracted automatically using machine learning methods. Events, which serve as the basic anchors of temporal and causal relations, can be extracted with F-measures in the 70s and 80s using a word-chunking approach. Temporal relations between adjacent events in some common syntactic constructions can be identified with almost 90% accuracy using pair-wise classification. Causal relations are much more difficult, but initial work suggests that even this task may become tractable to machine learning methods. Analyses of the various tasks lead to several conclusions about how best to approach the automatic extraction of temporal-causal structure. Tasks with little linguistic motivation had low agreement between humans and low machine learning model performance. Tasks with clear annotation guidelines based on known linguistic constructions had much higher inter-annotator agreement and much better model performance. Thus, future progress will depend on careful task selection guided by linguistic knowledge about how event, temporal and causal relations are expressed in text. Acknowledgements My deepest thanks to: My family, for a variety of emotional support, and for putting up with research terms that all too frequently slipped into otherwise pleasant conversations. Jim Martin, who has offered not just advice, but an opportunity to develop ideas together. I can't even count the times that I walked into his office with only a vague idea in my head, and walked out with a plan and several experiments to run. Matthew Woitaszek, for always being available for a random conversation about research problems, for serving as a great board to bounce ideas off of, and for being the thankless maintainer of the cluster on which many …
Chorusing, synchrony, and the evolutionary functions of rhythm

A central goal of biomusicology is to understand the biological basis of human musicality. One approach to this problem has been to compare core components of human musicality (relative pitch perception, entrainment, etc.) with similar capacities in other animal species. Here we extend and clarify this comparative approach with respect to rhythm. First, whereas most comparisons between human music and animal acoustic behavior have focused on spectral properties (melody and harmony), we argue for the central importance of temporal properties, and propose that this domain is ripe for further comparative research. Second, whereas most rhythm research in non-human animals has examined animal timing in isolation, we consider how chorusing dynamics can shape individual timing, as in human music and dance, arguing that group behavior is key to understanding the adaptive functions of rhythm. To illustrate the interdependence between individual and chorusing dynamics, we present a computational model of chorusing agents relating individual call timing with synchronous group behavior. Third, we distinguish and clarify mechanistic and functional explanations of rhythmic phenomena, often conflated in the literature, arguing that this distinction is key for understanding the evolution of musicality. Fourth, we expand biomusicological discussions beyond the species typically considered, providing an overview of chorusing and rhythmic behavior across a broad range of taxa (orthopterans, fireflies, frogs, birds, and primates). Finally, we propose an "Evolving Signal Timing" hypothesis, suggesting that similarities between timing abilities in biological species will be based on comparable chorusing behaviors. We conclude that the comparative study of chorusing species can provide important insights into the adaptive function(s) of rhythmic behavior in our "proto-musical" primate ancestors, and thus inform our understanding of the biology and evolution of rhythm in human music and language.
The Context-Dependent Additive Recurrent Neural Net

Contextual sequence mapping is one of the fundamental problems in Natural Language Processing. Instead of relying solely on the information presented in a text, the learning agents have access to a strong external signal given to assist the learning process. In this paper, we propose a novel family of Recurrent Neural Network unit: the Context-dependent Additive Recurrent Neural Network (CARNN) that is designed specifically to leverage this external signal. The experimental results on public datasets in the dialog problem (Babi dialog Task 6 and Frame), contextual language model (Switchboard and Penn Discourse Tree Bank) and question answering (TrecQA) show that our novel CARNN-based architectures outperform previous methods.
A prognostics and health management roadmap for information and electronics-rich systems

Prognostics and systems health management (PHM) is an enabling discipline of technologies and methods with the potential of solving reliability problems that have been manifested due to complexities in design, manufacturing, environmental and operational use conditions, and maintenance. Over the past decade, research has been conducted in PHM of information and electronics-rich systems as a means to provide advance warnings of failure, enable forecasted maintenance, improve system qualification, extend system life, and diagnose intermittent failures that can lead to field failure returns exhibiting no-fault-found symptoms. This paper presents an assessment of the state of practice in prognostics and health management of information and electronics-rich systems. While there are two general methods of performing PHM— model-based and data-driven methods—these methods by themselves have some key disadvantages. This paper presents a fusion prognostics approach, which combines or ‘‘fuses together” the model-based and data-driven approaches, to enable markedly better prognosis of remaining useful life. A case study of a printed circuit card assembly is given in order to illustrate the implementation of the fusion approach
Parkinson disease: from pathology to molecular disease mechanisms.

Parkinson disease (PD) is a complex neurodegenerative disorder with both motor and nonmotor symptoms owing to a spreading process of neuronal loss in the brain. At present, only symptomatic treatment exists and nothing can be done to halt the degenerative process, as its cause remains unclear. Risk factors such as aging, genetic susceptibility, and environmental factors all play a role in the onset of the pathogenic process but how these interlink to cause neuronal loss is not known. There have been major advances in the understanding of mechanisms that contribute to nigral dopaminergic cell death, including mitochondrial dysfunction, oxidative stress, altered protein handling, and inflammation. However, it is not known if the same processes are responsible for neuronal loss in nondopaminergic brain regions. Many of the known mechanisms of cell death are mirrored in toxin-based models of PD, but neuronal loss is rapid and not progressive and limited to dopaminergic cells, and drugs that protect against toxin-induced cell death have not translated into neuroprotective therapies in humans. Gene mutations identified in rare familial forms of PD encode proteins whose functions overlap widely with the known molecular pathways in sporadic disease and these have again expanded our knowledge of the neurodegenerative process but again have so far failed to yield effective models of sporadic disease when translated into animals. We seem to be missing some key parts of the jigsaw, the trigger event starting many years earlier in the disease process, and what we are looking at now is merely part of a downstream process that is the end stage of neuronal death.
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections

High-level shape understanding and technique evaluation on large repositories of 3D shapes often benefit from additional information known about the shapes. One example of such information is the semantic segmentation of a shape into functional or meaningful parts. Generating accurate segmentations with meaningful segment boundaries is, however, a costly process, typically requiring large amounts of user time to achieve high quality results. In this paper we present an active learning framework for large dataset segmentation, which iteratively provides the user with new predictions by training new models based on already segmented shapes. Our proposed pipeline consists of three novel components. First, we a propose a fast and relatively accurate feature-based deep learning model to provide datasetwide segmentation predictions. Second, we propose an information theory measure to estimate the prediction quality and for ordering subsequent fast and meaningful shape selection. Our experiments show that such suggestive ordering helps reduce users time and effort, produce high quality predictions, and construct a model that generalizes well. Finally, we provide effective segmentation refinement features to help the user quickly correct any incorrect predictions. We show that our framework is more accurate and in general more efficient than state-of-the-art, for massive dataset segmentation with while also providing consistent segment boundaries.
Visualizing Features Learned by Convolutional Neural Networks

A pattern recognition pipeline consists of three stages: data pre-processing, feature extraction, and classification. Traditionally, most research effort is put into extracting appropriate features. With the advent of GPU-accelerated computing and Deep Learning, appropriate features can be discovered as part of the training process. Understanding these discovered features is important: we might be able to learn something new about the domain in which our model operates, or be comforted by the fact that the model extracts “sensible” features. This work discusses and applies methods of visualizing the features learned by Convolutional Neural Networks (CNNs). Our main contribution is an extension of an existing visualization method. The extension makes the method able to visualize the features in intermediate layers of a CNN. Most notably, we show that the features extracted in the deeper layers of a CNN trained to diagnose Diabetic Retinopathy are also the features used by human clinicians. Additionally, we published our visualization method in a software package.
A Survey: Detection of Duplicate Record

The problem of identifying approximately duplicate record in database is an essential step for data cleaning & data integration process. A dynamic web page is displayed to show the results as well as other relevant advertisements that seem relevant to the query. The real world entities have two or more representation in databases. When dealing with large amount of data it is important that there be a well defined and tested mechanism to filter out duplicate result. This keeps the result relevant to the queries. Duplicate record exists in the query result of many web databases especially when the duplicates are defined based on only some of the fields in a record. Using exact matching technique Records that are exactly same can be detected. The system that helps user to integrate and compares the query results returned from multiple web databases matches the different sources records that referred to the same real world entity. In this paper, we analyze the literature on duplicate record detection. We cover similarity metrics which are commonly used to detect similar field entries, and present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database also the techniques for improving the efficiency and scalability of approximate duplicate detection algorithms are covered. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area.
Using Decision Tree for Diagnosing Heart Disease Patients

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. Decision Tree is one of the successful data mining techniques used. However, most research has applied J4.8 Decision Tree, based on Gain Ratio and binary discretization. Gini Index and Information Gain are two other successful types of Decision Trees that are less used in the diagnosis of heart disease. Also other discretization techniques, voting method, and reduced error pruning are known to produce more accurate Decision Trees. This research investigates applying a range of techniques to different types of Decision Trees seeking better performance in heart disease diagnosis. A widely used benchmark data set is used in this research. To evaluate the performance of the alternative Decision Trees the sensitivity, specificity, and accuracy are calculated. The research proposes a model that outperforms J4.8 Decision Tree and Bagging algorithm in the diagnosis of heart disease patients.
Social media use in medical education: a systematic review.

The authors conducted a systematic review of the published literature on social media use in medical education to answer two questions: (1) How have interventions using social media tools affected outcomes of satisfaction, knowledge, attitudes, and skills for physicians and physicians-in-training? and (2) What challenges and opportunities specific to social media have educators encountered in implementing these interventions?



The authors searched the MEDLINE, CINAHL, ERIC, Embase, PsycINFO, ProQuest, Cochrane Library, Web of Science, and Scopus databases (from the start of each through September 12, 2011) using keywords related to social media and medical education. Two authors independently reviewed the search results to select peer-reviewed, English-language articles discussing social media use in educational interventions at any level of physician training. They assessed study quality using the Medical Education Research Study Quality Instrument.



Fourteen studies met inclusion criteria. Interventions using social media tools were associated with improved knowledge (e.g., exam scores), attitudes (e.g., empathy), and skills (e.g., reflective writing). The most commonly reported opportunities related to incorporating social media tools were promoting learner engagement (71% of studies), feedback (57%), and collaboration and professional development (both 36%). The most commonly cited challenges were technical issues (43%), variable learner participation (43%), and privacy/security concerns (29%). Studies were generally of low to moderate quality; there was only one randomized controlled trial.



Social media use in medical education is an emerging field of scholarship that merits further investigation. Educators face challenges in adapting new technologies, but they also have opportunities for innovation.
Inductive Representation Learning on Large Graphs

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.
Neural Network Models for Natural Language Inference Fail to Capture the Semantics of Inference

Neural network models have been very successful for natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmark tasks is the same or similar. We train five state-of-the-art neural network models on different datasets and show that each one of these fail to generalize outside of the respective benchmark. In light of these results we conclude that the current neural network models are not able to generalize in capturing the semantics of natural language inference, but seem to be overfitting to the specific dataset.
Neuromorphic Computing Based on Emerging Memory Technologies

In this paper, we review some of the novel emerging memory technologies and how they can enable energy-efficient implementation of large neuromorphic computing systems. We will highlight some of the key aspects of biological computation that are being mimicked in these novel nanoscale devices, and discuss various strategies employed to implement them efficiently. Though large scale learning systems have not been implemented using these devices yet, we will discuss the ideal specifications and metrics to be satisfied by these devices based on theoretical estimations and simulations. We also outline the emerging trends and challenges in the path towards successful implementations of large learning systems that could be ubiquitously deployed for a wide variety of cognitive computing tasks.
Autonomous navigation for BigDog

BigDog is a four legged robot with exceptional rough-terrain mobility. In this paper, we equip BigDog with a laser scanner, stereo vision system, and perception and navigation algorithms. Using these sensors and algorithms, BigDog performs autonomous navigation to goal positions in unstructured forest environments. The robot perceives obstacles, such as trees, boulders, and ground features, and steers to avoid them on its way to the goal. We describe the hardware and software implementation of the navigation system and summarize performance. During field tests in unstructured wooded terrain, BigDog reached its goal position 23 of 26 runs and traveled over 130 meters at a time without operator involvement.