nils-herrmann/clean_scirepeval huggingface dataset:
Generative Topic Embedding: a Continuous Representation of Documents
Venue: ACL

Word embedding maps words into a low dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low dimensional topic space, by utilizing the global word collocation patterns in the same document. These two types of patterns are complementary. In this paper, we propose a generative topic embedding model to combine the two types of patterns. In our model, topics are represented by embedding vectors, and are shared across documents. The probability of each word is influenced by both its local context and its topic. A variational inference method yields the topic embeddings as well as the topic mixing proportions for each document. Jointly they represent the document in a low dimensional continuous space. In two document classification tasks, our method performs better than eight existing methods, with fewer features. In addition, we illustrate with an example that our method can generate coherent topics even based on only one document.
Unsupervised Learning for Physical Interaction through Video Prediction
Venue: NIPS

A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment. Many existing methods for learning the dynamics of physical interactions require labeled object information. However, to scale real world interaction learning to a variety of scenes and objects, acquiring labeled data becomes increasingly impractical. To learn about physical object motion without labels, we develop an action conditioned video prediction model that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames. Because our model explicitly predicts motion, it is partially invariant to object appearance, enabling it to generalize to previously unseen objects. To explore video prediction for real world interactive agents, we also introduce a dataset of 59,000 robot interactions involving pushing motions, including a test set with novel objects. In this dataset, accurate prediction of videos conditioned on the robot's future actions amounts to learning a "visual imagination" of different futures based on different courses of action. Our experiments show that our proposed method produces more accurate video predictions both quantitatively and qualitatively, when compared to prior methods.
Non Host Plant Volatiles Disrupt Sex Pheromone Communication in a Specialist Herbivore
Venue: Scientific reports

The ecological effects of plant volatiles on herbivores are manifold. Little is known, however, about the impacts of non host plant volatiles on intersexual pheromonal communication in specialist herbivores. We tested the effects of several prominent constitutive terpenoids released by conifers and Eucalyptus trees on electrophysiological and behavioral responses of an oligophagous species, Plutella xylostella, which feeds on Brassicaceae. The non host plant volatile terpenoids adversely affected the calling behavior (pheromone emission) of adult females, and the orientation responses of adult males to sex pheromone were also significantly inhibited by these terpenoids in a wind tunnel and in the field. We suggest that disruption of both pheromone emission and orientation to sex pheromone may explain, at least in part, an observed reduction in herbivore attack in polyculture compared with monoculture plantings. We also propose that mating disruption of both male and female moths with non host plant volatiles may be a promising alternative pest management strategy.
Patterns of Sculptural Hypertext in Location Based Narratives
Venue: HT

Location based narratives are an emerging form of digital storytelling that use location technologies to trigger content on smart devices according to a user's location. In previous work on the Canyons, Deltas and Plains (CDP) model we argued that they are best considered as a form of sculptural hypertext, but sculptural hypertext is a relatively unexplored medium with few examples, and limited critical theory. This means that there is little guidance for authors on what is possible with the medium, and no common authoring tools, both of which impede adoption and experimentation. In this paper we describe our work to tackle this problem by working with creative writing students to create 40 location based sculptural hypertexts using an approach similar to paper prototyping, and then analysing these for common patterns (structures of nodes, rules, and conditions used for a poetic purpose) We present seven key patterns: Parallel Threads, Gating, Concurrent Nodes, Alternative Nodes, Foldbacks, Phasing, and Unlocking. In doing so we see some overlap with the patterns identified in traditional (calligraphic) hypertext, but in many cases these patterns are particularly suited to sculptural hypertext, and hint at a different poetics for the form. Our findings refine our original CDP model, but also present a starting point for educating writers on how to approach sculptural stories, and form a foundation for future location based authoring tools.
Effectiveness of E Learning in Oral Radiology Education: A Systematic Review.
Venue: Journal of dental education

E learning has been used recently in dental curricula to support traditional learning methods. However, the published literature concerning e learning in oral radiology has shown mixed conclusions. The aim of this systematic review was to provide a synthesis of the effectiveness of e learning in oral radiology education when compared with traditional classroom learning methods. A search of the literature was conducted on the LILACS, PubMed, Science Direct, Scopus, and Web of Science databases. Trials registries were also consulted for ongoing trials, and a partial grey literature search was conducted. Controlled trials about oral radiology education that compared any e learning method with a control group using any traditional classroom instruction method were included. E learning effectiveness was measured using three outcomes from Kirkpatrick's model of evaluation: attitudes about e learning, knowledge gain, and performance on clinical procedures. Data were analyzed descriptively. Qualitative appraisal was performed according to the Cochrane risk of bias tool for randomized trials and MINORS tool for non randomized trials. Eleven studies met the inclusion criteria. Risk of bias was identified related to the selection procedures, blinding, lack of sample size calculation, and incomplete analyses. Ten studies reported that students had positive attitude when using e learning. Results from the knowledge gain outcome were mixed. Only two studies examined performance on clinical procedures, showing contrasting results. The evidence reviewed in this study suggests that e learning in oral radiology is at least as effective as traditional learning methods and that students have positive attitudes about e learning.
Trajectory generation and control of a knee exoskeleton based on dynamic movement primitives for sit to stand assistance
Venue: Adv. Robotics

This paper presents a novel control approach for a knee exoskeleton to assist individuals with lower extremity weakness during sit to stand motion. The proposed method consists of a trajectory generator and an impedance controller. The trajectory generator uses a library of sample trajectories as the training data and the initial joint angles as the input to predict the user's intended sit to stand trajectory. Utilizing the dynamic movement primitives theory, the trajectory generator represents the predicted trajectory in a time normalized and rather a flexible framework. The impedance controller is then employed to provide assistance by guiding the knee joint to move along the predicted trajectory. Moreover, the human exoskeleton interaction force is used as the feedback for on line adaptation of the trajectory speed. The proposed control strategy was tested on a healthy adult who wore the knee exoskeleton on his leg. The subject was asked to perform a number of sit to stand movements from different sitting positions. Next, the measured data and the inverse dynamic model of the human exoskeleton system are used to calculate the knee power and torque profiles. The results reveal that average muscle activity decreases when the subject is assisted by the exoskeleton.
Is overfeat useful for image based surface defect classification tasks?
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

One of the challenges for real world image based surface defect classification task is the lack of labeled training samples to extract useful features to confidently classify defects. In this paper, we present results on our investigation on whether features derived from OverFeat, a variant of Convolution Neural Network, can be used directly for image based surface defect classification task. We show that the classification performance of two real world defect images datasets can be significantly different. For the harder classification task, OverFeat features are useful for some types of surface defects, but performs poorly when the defects demonstrate characteristics beyond texture patterns. We propose a simple heuristic approach called Approximate Surface Roughness (ASR) that provides auxiliary information on the relationship between spatial regions in the defect image to be used together with the OverFeat features. Empirical results show improvement in classification performance for those defect types that do not classify well using only OverFeat features.
ROS homeostasis and metabolism: a dangerous liason in cancer cells
Venue: Cell Death and Disease

Tumor cells harbor genetic alterations that promote a continuous and elevated production of reactive oxygen species. Whereas such oxidative stress conditions would be harmful to normal cells, they facilitate tumor growth in multiple ways by causing DNA damage and genomic instability, and ultimately, by reprogramming cancer cell metabolism. This review outlines the metabolic dependent mechanisms that tumors engage in when faced with oxidative stress conditions that are critical for cancer progression by producing redox cofactors. In particular, we describe how the mitochondria has a key role in regulating the interplay between redox homeostasis and metabolism within tumor cells. Last, we will discuss the potential therapeutic use of agents that directly or indirectly block metabolism.
Dangerous hitchhikers? Evidence for potentially pathogenic Vibrio spp. on microplastic particles.
Venue: Marine environmental research

The taxonomic composition of biofilms on marine microplastics is widely unknown. Recent sequencing results indicate that potentially pathogenic Vibrio spp. might be present on floating microplastics. Hence, these particles might function as vectors for the dispersal of pathogens. Microplastics and water samples collected in the North and Baltic Sea were subjected to selective enrichment for pathogenic Vibrio species. Bacterial colonies were isolated from CHROMagar(tm)Vibrio and assigned to Vibrio spp. on the species level by MALDI TOF MS (Matrix Assisted Laser Desorption/Ionisation Time of Flight Mass Spectrometry) Respective polymers were identified by ATR FT IR (Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy) We discovered potentially pathogenic Vibrio parahaemolyticus on a number of microplastic particles, e.g. polyethylene, polypropylene and polystyrene from North/Baltic Sea. This study confirms the indicated occurrence of potentially pathogenic bacteria on marine microplastics and highlights the urgent need for detailed biogeographical analyses of marine microplastics.
Detection and clustering of mixed type defect patterns in wafer bin maps

In semiconductor manufacturing, a wafer bin map (WBM) is a map that consists of assigned bin values for dies based on wafer test results (e.g. value 1 for good dies and value 0 for defective dies) The bin values of adjacent dies are often spatially correlated, forming some systematic defect patterns. These non random defect patterns occur due to assignable causes; therefore, it is important to identify these systematic defect patterns in order to know the root causes of failure and to take actions for quality management and yield enhancement. In particular, as wafer fabrication processes have become more complicated, mixed type defect patterns (two or more different types of defect patterns occur simultaneously in a single wafer) occur more frequently than in the past. For more effective classification of wafers based on their defect patterns, mixed type defect patterns need to be detected and separated into several clusters of different patterns; subsequently, each cluster of a single pattern can be matched to a well known defect type (e.g. scratch, ring) or it may indicate the emergence of a new defect pattern. There are several challenges to be overcome in the detection and clustering of mixed type defect patterns. These include (i) the separation of random defects from systematic defect patterns; (ii) determining the number of clusters; and (iii) the clustering of defect patterns of complex shapes. To address these challenges, in this article, we propose a new framework for detecting and clustering mixed type defect patterns. First, we propose a new filtering method, called the connected path filtering method, to denoise WBMs. Subsequently, we adopt the infinite warped mixture model for the clustering of mixed type defect patterns; this model is flexible in its ability to deal with complex shapes of defect patterns; furthermore, the number of clusters does not need to be specified in advance but is automatically determined simultaneously during the clustering procedure. We validate the proposed method using real data from a semiconductor company. The experimental results demonstrate the effectiveness of the proposed method in estimating the number of underlying clusters as well as in the clustering of mixed type defect patterns.
Experimental methods in entrepreneurship research: the status quo

The purpose of this paper is to evaluate the emerging field of experimental research on entrepreneurship to better understand its development and potential.  A systematic, evidence based literature review was applied, resulting in a sample of 47 articles having used experiments in entrepreneurship research so far. The papers are analyzed according to their topic, methods, and research design, revealing insight into their limitations and prospective contributions.  The paper discusses the potential and disadvantages of experimental methods while arguing for experiments as the method of choice for answering causality questions. This study finds a persistent increase in experimental entrepreneurship research since its introduction in 1990.  The study provides research from the field of entrepreneurship with future directions, with potential research areas and an orientation for those interested in conducting experiments.  Experiments are employed in a variety of research areas and have become more and more popular in the field of entrepreneurship. No study has analyzed the experimental studies in entrepreneurship. This paper contributes by providing an overview of the field, reflecting and discussing the outcomes while characterizing the methods employed.
Cascades of green: A review of ecosystem based adaptation in urban areas

Climate change impacts increase pressure on challenges to sustainability and the developmental needs of cities. Conventional, "hard" adaptation measures are often associated with high costs, inflexibility and conflicting interests related to the dense urban fabric, and ecosystem based adaptation (EbA) has emerged as a potentially cost efficient, comprehensive, and multifunctional approach. This paper reviews and systematises research on urban EbA. We propose an analytical framework that draws on theory from ecosystem services, climate change adaptation and sustainability science. It conceptualises EbA in terms of five linked components: ecological structures, ecological functions, adaptation benefits, valuation, and ecosystem management practices. Our review identified 110 articles, reporting on 112 cities, and analysed them using both quantitative statistical and qualitative content analysis. We found that EbA research in an urban context is fragmented due to different disciplinary approaches and concepts. Most articles focus on heat or flooding, and the most studied ecological structures for reducing the risk of such hazards are green space, wetlands, trees and parks. EbA is usually evaluated in bio geophysical terms and the use of economic or social valuations are rare. While most articles do not mention specific practices for managing ecological structures, those that do imply that urban EbA strategies are increasingly being integrated into institutional structures. Few articles considered issues of equity or stakeholder participation in EbA. We identified the following challenges for future EbA research. First, while the large amount of data generated by isolated case studies contributes to systems knowledge, there is a lack of systems perspectives that position EbA in relation to the wider socio economic and bio geophysical context. Second, normative and ethical aspects of EbA require more thought, such as who are the winners and losers, especially in relation to processes that put people at risk from climate related hazards. Third, there is room for more forward looking EbA research, including consideration of future scenarios, experimentation in the creation of new ecological structures and the role of EbA in transformative adaptation.
Management of children with type 1 diabetes during illness: a national survey
Venue: Postgraduate Medical Journal

Adequate sick day management at home can reduce the risk of progression to diabetic ketoacidosis and admission to hospital. The aim of this project was to review the management of diabetes during illness.  The Association of Children's Diabetes Clinicians (ACDC) carried out a questionnaire survey of all paediatric diabetes units. In addition, parents of children with type 1 diabetes completed an online questionnaire.  The survey of 127 units had a 73% response rate. Sick day management guidelines were in place in 93% All guidelines advised giving extra insulin during illness. In 67% the extra dose was based on a fraction of total daily dose. 22% used units per kg body weight (U/kg) 21% used locally derived formulae to calculate extra dose of insulin. 3% of units advised only blood ketone monitoring. Although all units had an out of hours access policy for the families, 45% received advice from the general paediatric registrar. Only in 15% the advice was directly from a member of the paediatric diabetes team. 680 parents completed the questionnaire. 86% reported receiving training on managing sick days. The majority (52.2% receiving an informal session at diagnosis. 40% did not know what to do in the presence of raised blood glucose and high blood ketones.  There was a wide variation in the practice of monitoring and advice given during illness. Both surveys highlight need for national guidance as well and to improve quality of sick day rule education programmes for parents of children with type 1 diabetes.
Big Batch SGD: Automated Inference using Adaptive Batch Sizes
Venue: ArXiv

Classical stochastic gradient methods for optimization rely on noisy gradient approximations that become progressively less accurate as iterates approach a solution. The large noise and small signal in the resulting gradients makes it difficult to use them for adaptive stepsize selection and automatic stopping. We propose alternative "big batch" SGD schemes that adaptively grow the batch size over time to maintain a nearly constant signal to noise ratio in the gradient approximation. The resulting methods have similar convergence rates to classical SGD, and do not require convexity of the objective. The high fidelity gradients enable automated learning rate selection and do not require stepsize decay. Big batch methods are thus easily automated and can run with little or no oversight.
Reevaluating Patent Damages in the Knowledge Economy: The Intellectual Value Chain and the Royalty Base for Standard Essential Patents

Patent valuation is a complex and imprecise process exemplified by the long, contentious history regarding the determination of patent damages in U.S. jurisprudence. This process is made even more difficult as products increasingly undergo technology convergence and market actors deploy divergent business strategies to extract value from their intellectual property.1 This has led to an increase in patent litigation and a greater demand for courts to calculate patent value more  U.S. courts have deployed several principles, including the use of the smallest saleable patent practicing unit (SSPPU) and the entire market value rule (EMVR) as a means to reduce the complexity when attempting to apportion patent value in multicomponent products.3 However, as these principles affect the determination of the royalty base, which can differ by orders of magnitude, the applicability of their use in different market contexts and norms requires investigation, because
Co Definition of Product Structure and Production Network for Frugal Innovation Perspectives: Towards a Modular based Approach

Frugal innovation theory is proposed to help companies rethinking their current product design and production strategies facing competitive challenges. Co evolution of product and production systems is required to reach frugality goals. The success of the co evolution strategy should be based on robust models ensuring the global consistency of the whole development process. Modular based models are a good solution for such problematics since they provide a common semantic for the representation of the physical product structure as well as the organizational structure of the production system through the definition of interfaces between elements and the hierarchical decomposition of a system into different elements. This paper proposes a conceptual modular based approach dealing with the selection of product modules influencing the selection of suppliers and the allocation of orders in a global production network. The indirect linking of the customer to production is also discussed in terms of the timewise restrictions in selecting product modules offering the customer a maximum degree of flexibility in product specification.
Prediction of SCP and COP for adsorption heat pumps and chillers by combining the large temperature jump method and dynamic modeling

Adsorption heat pumps and chillers provide sustainable heating and cooling by transforming waste or solar heat. To increase the efficiency of adsorption heat pumps and chillers, new adsorber designs are constantly proposed. However, evaluating the efficiency of new adsorber designs is a time and money consuming task. To reduce time and costs, we combine two methods in this work: First, the kinetic performance is evaluated experimentally using the Large Temperature Jump method for a representative adsorber part. Second, a dynamic model for the adsorber is developed and validated. The validated adsorber model is used in a dynamic model of a complete adsorption heat pump cycle. Our approach allows for a sound analysis of novel adsorber designs by determination of the efficiency indicators specific cooling power (SCP) and coefficient of performance (COP) based on a representative adsorber part. We studied a representative adsorber part made from an extruded aluminum tube heat exchanger filled with silica gel 123. We determined SCP and COP as function of adsorption and desorption times and found a maximum SCP 268 W kg 1 with a corresponding COP of 0.51 for a desorption time of 125 s and an adsorption time of 200 s.