nils-herrmann/clean_scirepeval huggingface dataset:
Qualitative research methodologies: ethnography.

Ethnography is the study of social interactions, behaviours, and perceptions that occur within groups, teams, organisations, and communities. Its roots canbe traced back to anthropological studies of small, rural (andoften remote) societies thatwereundertaken in the early 1900s, when researchers such as Bronislaw Malinowski and Alfred Radcliffe-Brown participated in these societies over long periods and documented their social arrangements and belief systems. This approach was later adopted by members of the Chicago School of Sociology (for example, Everett Hughes, Robert Park, Louis Wirth) and applied to a variety of urban settings in their studies of social life. The central aim of ethnography is to provide rich, holistic insights into people’s views and actions, as well as the nature (that is, sights, sounds) of the location they inhabit, through the collection of detailed observations and interviews. As Hammersley states, “The task [of ethnographers] is to document the culture, the perspectives and practices, of the people in these settings.The aim is to ‘get inside’ theway each groupof people sees theworld.” Box 1 outlines the key features of ethnographic research. Examples of ethnographic researchwithin thehealth services literature include Strauss’s study of achieving and maintaining order between managers, clinicians, and patients within psychiatric hospital settings; Taxis and Barber’s exploration of intravenous medication errors in acute care hospitals; Costello’s examination of death and dying in elderly care wards; and Østerlund’s work on doctors’ and nurses’ use of traditional and digital information systems in their clinical communications. Becker and colleagues’ Boys in White, an ethnographic study of medical education in the late 1950s, remains a classic in this field. Newer developments in ethnographic inquiry include auto-ethnography, in which researchers’ own thoughts andperspectives fromtheir social interactions form the central element of a study; meta-ethnography, in which qualitative research texts are analysed and synthesised to empirically create new insights and knowledge; and online (or virtual) ethnography, which extends traditional notions of ethnographic study from situated observation and face to face researcher-participant interaction to technologically mediated interactions in online networks and communities.
HUMAN PROBLEM SOLVING : THE STATE OF THE THEORY IN 1970 1

WHEN the magician pulls the rabbit from the hat, the spectator can respond either with mystification or with curiosity. He can enjoy the surprise and the wonder of the unexplained (and perhaps inexplicable), or he can search for an explanation. Suppose curiosity is his main response—that he adopts a scientist's attitude toward the mystery. What questions should a scientific theory of magic answer? First, it should predict the performance of a magician handling specified tasks—producing a rabbit from a hat, say. It should explain how the production takes place, what processes are used, and what mechanisms perform those processes. It should predict the incidental phenomena that accompany the magic—the magician's patter and his pretty assistant—and the relation of these to the mystification process. It should show how changes in the attendant conditions—both changes "inside" the members of the audience and changes in the feat of magic—alter the magician's behavior. It should explain how specific and general magician's skills are learned, and what the magician "has" when he has learned them.
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation

In this paper we propose a slanted plane model for jointly recovering an image segmentation, a dense depth estimate as well as boundary labels (such as occlusion boundaries) from a static scene given two frames of a stereo pair captured from a moving vehicle. Towards this goal we propose a new optimization algorithm for our SLIC-like objective which preserves connecteness of image segments and exploits shape regularization in the form of boundary length. We demonstrate the performance of our approach in the challenging stereo and flow KITTI benchmarks and show superior results to the state-of-the-art. Importantly, these results can be achieved an order of magnitude faster than competing approaches.
Outlier Detection for Temporal Data: A Survey

In the statistics community, outlier detection for time series data has been studied for decades. Recently, with advances in hardware and software technology, there has been a large body of work on temporal outlier detection from a computational perspective within the computer science community. In particular, advances in hardware technology have enabled the availability of various forms of temporal data collection mechanisms, and advances in software technology have enabled a variety of data management mechanisms. This has fueled the growth of different kinds of data sets such as data streams, spatio-temporal data, distributed streams, temporal networks, and time series data, generated by a multitude of applications. There arises a need for an organized and detailed study of the work done in the area of outlier detection with respect to such temporal datasets. In this survey, we provide a comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel techniques, and application scenarios in which specific definitions and techniques have been widely used.
Multi-modal Auto-Encoders as Joint Estimators for Robotics Scene Understanding

We explore the capabilities of Auto-Encoders to fuse the information available from cameras and depth sensors, and to reconstruct missing data, for scene understanding tasks. In particular we consider three input modalities: RGB images; depth images; and semantic label information. We seek to generate complete scene segmentations and depth maps, given images and partial and/or noisy depth and semantic data. We formulate this objective of reconstructing one or more types of scene data using a Multi-modal stacked Auto-Encoder. We show that suitably designed Multi-modal Auto-Encoders can solve the depth estimation and the semantic segmentation problems simultaneously, in the partial or even complete absence of some of the input modalities. We demonstrate our method using the outdoor dataset KITTI that includes LIDAR and stereo cameras. Our results show that as a means to estimate depth from a single image, our method is comparable to the state-of-the-art, and can run in real time (i.e., less than 40ms per frame). But we also show that our method has a significant advantage over other methods in that it can seamlessly use additional data that may be available, such as a sparse point-cloud and/or incomplete coarse semantic labels.
Vehicular Communications: A Network Layer Perspective

Vehicular communications, referring to information exchange among vehicles, infrastructures, etc., have attracted a lot of attention recently due to great potential to support intelligent transportation, various safety applications, and on-road infotainment. In this paper, we provide a comprehensive overview of a recent research on enabling efficient and reliable vehicular communications from the network layer perspective. First, we introduce general applications and unique characteristics of vehicular communication networks and the corresponding classifications. Based on different driving patterns, we categorize vehicular networks into manual driving vehicular networks and automated driving vehicular networks, and then discuss the available communication techniques, network structures, routing protocols, and handoff strategies applied in these vehicular networks. Finally, we identify the challenges confronted by the current vehicular networks and present the corresponding research opportunities.
Event templates in the lexical representations of verbs

Four experiments support the hypothesis that syntactically relevant information about verbs is encoded in the lexicon in semantic event templates. A verb's event template represents the participants in an event described by the verb and the relations among the participants. The experiments show that lexical decision times are longer for verbs with more complex templates than verbs with less complex templates and that, for both transitive and intransitive sentences, sentences containing verbs with more complex templates take longer to process. In contrast, sentence processing times did not depend on the probabilities with which the verbs appear in transitive versus intransitive constructions in a large corpus of naturally produced sentences.
TESOL Methods : Changing Tracks , Challenging Trends

This article traces the major trends in TESOL methods in the past 15 years. It focuses on the TESOL profession’s evolving perspectives on language teaching methods in terms of three perceptible shifts: (a) from communicative language teaching to task-based language teaching, (b) from method-based pedagogy to postmethod pedagogy, and (c) from systemic discovery to critical discourse. It is evident that during this transitional period, the profession has witnessed a heightened awareness about communicative and task-based language teaching, about the limitations of the concept of method, about possible postmethod pedagogies that seek to address some of the limitations of method, about the complexity of teacher beliefs that inform the practice of everyday teaching, and about the vitality of the macrostructures—social, cultural, political, and historical—that shape the microstructures of the language classroom. This article deals briefly with the changes and challenges the trend-setting transition seems to be bringing about in the profession’s collective thought and action.
Corporate bond default risk : A 150-year perspective

We study corporate bond default rates using an extensive new data set spanning the 1866–2008 period. We find that the corporate bond market has repeatedly suffered clustered default events much worse than those experienced during the Great Depression. For example, during the railroad crisis of 1873–1875, total defaults amounted to 36% of the par value of the entire corporate bond market. Using a regime-switching model, we examine the extent to which default rates can be forecast by financial and macroeconomic variables. We find that stock returns, stock return volatility, and changes in GDP are strong predictors of default rates. Surprisingly, however, credit spreads are not. Over the long term, credit spreads are roughly twice as large as default losses, resulting in an average credit risk premium of about 80 basis points. We also find that credit spreads do not adjust in response to realized default rates. & 2011 Elsevier B.V. All rights reserved.
Effect of the Phosphor Permanent Substrate on the Angular CCT for White Thin-Film Flip-Chip Light-Emitting Diodes

This paper developed a phosphor layer applied for thin-film flip-chip light-emitting diodes (TFFC-LEDs) to produce uniform phosphor-converted TFFC white LEDs (TFFC-WLEDs) by combination of laser liftoff, secondary transferring, and surface roughening process. The spin-coating method was used for phosphor layer fabrication onto a substrate to form the phosphor permanent substrate. The TFFC-LEDs were then bonded onto the permanent substrate. From the results, the blue TFFC GaN-based LED with roughened u-GaN surface on a glass substrate (TFRG-LED) demonstrated a 54.2% (at 350 mA) enhancement in light output power, compared with a blue flip-chip GaN-on-sapphire based LED. As the TFFC GaN/phosphor-/glass-based white LED with roughened u-GaN surface (TFRG-WLED) was operated at a forward-bias current of 350 mA, the enhancement of luminous flux was increased by 75.5%, compared with a TFFC GaN/phosphor template-/glass-based white LED. The angular correlated color temperature (CCT) deviation of a TFRG-WLED can be reduced to 1279 K in the range from -75° to +75° at 5000-6000 K application. The TFRG-WLED was fabricated on the glass substrate with the roughened u-GaN surface and the structure of phosphor layer closed to the u-GaN. These kinds of FRGB-WLED structure contribute to a better light extraction characteristic and a higher CCT stability.
Effect of Self-myofascial Release on Reduction of Physical Stress: A Pilot
Study

This study aims to examined the effect of the self-myofascial release induced with a foam roller on the reduction of stress by measuring the serum concentration of cortisol.  The subjects of this study were healthy females in their 20s. They were divided into the experimental and control groups. Both groups, each consisting of 12 subjects, were directed to walk for 30 minutes on a treadmill. The control group rested for 30 minutes of rest by lying down, whereas the experimental group was performed a 30 minutes of self-myofascial release program.  Statistically significant levels of cortisol concentration reduction were observed in both the experimental group, which used the foam roller, and the control group. There was no statistically significant difference between the two groups.  The Self-myofascial release induced with a foam roller did not affect the reduction of stress.
The Split Brain Revisited Contents

About 35 years ago in Scientific American, I wrote about dramatic new studies of the brain. Three patients who were seeking relief from epilepsy had undergone surgery that severed the corpus callosum--the superhighway of neurons connecting the halv es of the brain. By working with these patients, my colleagues Roger W. Sperry, Joseph E. Bogen, P.J. Vogel and I witnessed what happened when the left and the right hemispheres were unable to communicate with each other.
Rhizosphere microbiome assemblage is affected by plant development

There is a concerted understanding of the ability of root exudates to influence the structure of rhizosphere microbial communities. However, our knowledge of the connection between plant development, root exudation and microbiome assemblage is limited. Here, we analyzed the structure of the rhizospheric bacterial community associated with Arabidopsis at four time points corresponding to distinct stages of plant development: seedling, vegetative, bolting and flowering. Overall, there were no significant differences in bacterial community structure, but we observed that the microbial community at the seedling stage was distinct from the other developmental time points. At a closer level, phylum such as Acidobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria and specific genera within those phyla followed distinct patterns associated with plant development and root exudation. These results suggested that the plant can select a subset of microbes at different stages of development, presumably for specific functions. Accordingly, metatranscriptomics analysis of the rhizosphere microbiome revealed that 81 unique transcripts were significantly (P<0.05) expressed at different stages of plant development. For instance, genes involved in streptomycin synthesis were significantly induced at bolting and flowering stages, presumably for disease suppression. We surmise that plants secrete blends of compounds and specific phytochemicals in the root exudates that are differentially produced at distinct stages of development to help orchestrate rhizosphere microbiome assemblage.
Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) . A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at
Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment

Prepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and semantic features of the word. One promising solution is to use word vectors induced from large amounts of raw text. However, state-of-the-art systems that employ such representations yield modest gains in PP attachment accuracy. In this paper, we show that word vector representations can yield significant PP attachment performance gains. This is achieved via a non-linear architecture that is discriminatively trained to maximize PP attachment accuracy. The architecture is initialized with word vectors trained from unlabeled data, and relearns those to maximize attachment accuracy. We obtain additional performance gains with alternative representations such as dependency-based word vectors. When tested on both English and Arabic datasets, our method outperforms both a strong SVM classifier and state-of-the-art parsers. For instance, we achieve 82.6% PP attachment accuracy on Arabic, while the Turbo and Charniak self-trained parsers obtain 76.7% and 80.8% respectively.
Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations

The Internet of Things promises ubiquitous connectivity of everything everywhere, which represents the biggest technology trend in the years to come. It is expected that by 2020 over 25 billion devices will be connected to cellular networks; far beyond the number of devices in current wireless networks. Machine-to-machine communications aims to provide the communication infrastructure for enabling IoT by facilitating the billions of multi-role devices to communicate with each other and with the underlying data transport infrastructure without, or with little, human intervention. Providing this infrastructure will require a dramatic shift from the current protocols mostly designed for human-to-human applications. This article reviews recent 3GPP solutions for enabling massive cellular IoT and investigates the random access strategies for M2M communications, which shows that cellular networks must evolve to handle the new ways in which devices will connect and communicate with the system. A massive non-orthogonal multiple access technique is then presented as a promising solution to support a massive number of IoT devices in cellular networks, where we also identify its practical challenges and future research directions.
Neural Survival Recommender

The ability to predict future user activity is invaluable when it comes to content recommendation and personalization. For instance, knowing when users will return to an online music service and what they will listen to increases user satisfaction and therefore user retention.
 We present a model based on Long-Short Term Memory to estimate when a user will return to a site and what their future listening behavior will be. In doing so, we aim to solve the problem of Just-In-Time recommendation, that is, to recommend the right items at the right time. We use tools from survival analysis for return time prediction and exponential families for future activity analysis. We show that the resulting multitask problem can be solved accurately, when applied to two real-world datasets.
Object-oriented Neural Programming (OONP) for Document Understanding

Overview An OONP parser consists of a Reader equipped with read/write heads, Inline Memory that represents the document, and Carry-on Memory that summarizes the current understanding of the document at each time step. For each document to parse, OONP first preprocesses it and puts it into the Inline Memory, and then Reader controls the read-heads to sequentially go through the Inline Memory and at the same time update the Carry-on Memory. Object-oriented Neural Programming (OONP) for Document Understanding Zhengdong Lu 1, Xianggen Liu 1,2,
Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis

Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multidimensional variables. It projects both sets of variables onto a lower-dimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction in which the two sets of variables are derived from the data and the class labels, respectively. It is well-known that CCA can be formulated as a least-squares problem in the binary class case. However, the extension to the more general setting remains unclear. In this paper, we show that under a mild condition which tends to hold for high-dimensional data, CCA in the multilabel case can be formulated as a least-squares problem. Based on this equivalence relationship, efficient algorithms for solving least-squares problems can be applied to scale CCA to very large data sets. In addition, we propose several CCA extensions, including the sparse CCA formulation based on the 1-norm regularization. We further extend the least-squares formulation to partial least squares. In addition, we show that the CCA projection for one set of variables is independent of the regularization on the other set of multidimensional variables, providing new insights on the effect of regularization on CCA. We have conducted experiments using benchmark data sets. Experiments on multilabel data sets confirm the established equivalence relationships. Results also demonstrate the effectiveness and efficiency of the proposed CCA extensions.
Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media-and not in the visible or manifest public sphere (official campaign platform, mass media)-where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election campaign, the latter potentially forming an enormous online support army of Abe's agenda.
Dancing-to-Music Character Animation

1. T. Shiratori, A. Nakazawa, K. Ikeuchi, “Dancing-to-Music Character Animation”, In Computer Graphics  Sep. 2006  2. T. Shiratori, A. Nakazawa, K. Ikeuchi, “Synthesizing Dance Performance Using Musical and Motion Features”, In Proc. of IEEE International Conference on Robotics and Automation (ICRA 2006), May 2006 A Dancing-to-Music ability for CG characters & humanoids