nils-herrmann/clean_scirepeval huggingface dataset:
Extreme Uncertainty and Forward Looking Disclosure Properties

This study investigates the effect of extreme uncertainty on disclosure behavior by analyzing the quality and quantity of forward looking disclosures during the global financial crisis and pre crisis periods, controlling for other determinants of disclosure behavior. Prior research has struggled to distinguish between the quality and quantity dimensions of forward looking disclosures. Also, the impact of the recent financial crisis on these forward looking disclosure attributes has not yet been examined systematically. We address this gap by exploiting the unique setting of German publicly traded firms. These firms must provide forward looking information within their audited financial statements, although relevant regulation is sufficiently vague to allow great variation in the quality, scope and quantity of forward looking disclosures actually observed. Using hand collected data from 2005 to 2009, we provide evidence of a significantly negative association between crisis and disclosure quality. This finding is robust to several different disclosure quality proxies and regression specifications. In contrast, we find no negative significant relation between crisis and disclosure quantity; rather, there is evidence that reported volume increases during the crisis. Our results are consistent with extreme uncertainty, as occurring during times of crisis, negatively affecting the quality of voluntary disclosures, while firms maintain or increase disclosure quantity, ultimately diluting the information density of forward looking disclosures.
Does intellectual capital disclosure in analysts' reports vary by firm characteristics?

In this paper, we explore how Australian sell side financial analysts contribute to the supply of intellectual capital (IC) information in the capital market. Toward this end, we examine how types and semantic properties of IC disclosures in analyst reports vary by a number of firm specific characteristics likely to be associated with voluntary corporate disclosure. Consistent with our expectations, we find that the uncertainty associated with forecasting firm's earnings and the IC intensity of the industry in which the firm operates are associated positively with the extent as well as several semantic properties of IC disclosure in analyst reports. Highlighting that IC disclosure in analyst reports is not always a function of the extent of IC disclosed by firm, we find a statistically significant but negative association between firm profitability and the extent and certain semantic properties of IC disclosure in analyst reports. Firm size was significantly and positively associated with only the extent of forward looking IC disclosure. Of the three categories of IC, only relation capital disclosure varied with any of the explanatory variables. Our findings highlight the importance of analyst reports as an IC communication media that could complement corporate communications of IC not only for firms disclosing less IC information voluntarily but also for those firms known to disclose more.
A Unified Multi scale Deep Convolutional Neural Network for Fast Object Detection
Venue: ECCV

A unified deep neural network, denoted the multi scale CNN (MS CNN) is proposed for fast multi scale object detection. The MS CNN consists of a proposal sub network and a detection sub network. In the proposal sub network, detection is performed at multiple output layers, so that receptive fields match objects of different scales. These complementary scale specific detectors are combined to produce a strong multi scale object detector. The unified network is learned end to end, by optimizing a multi task loss. Feature upsampling by deconvolution is also explored, as an alternative to input upsampling, to reduce the memory and computation costs. State of the art object detection performance, at up to 15 fps, is reported on datasets, such as KITTI and Caltech, containing a substantial number of small objects.
Image Style Transfer Using Convolutional Neural Networks
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
Wide Residual Networks
Venue: BMVC

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16 layer deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand layer deep networks, achieving new state of the art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL
Text Enhanced Representation Learning for Knowledge Graph
Venue: IJCAI

Learning the representations of a knowledge graph has attracted significant research interest in the field of intelligent Web. By regarding each relation as one translation from head entity to tail entity, translation based methods including TransE, TransH and TransR are simple, effective and achieving the state of the art performance. However, they still suffer the following issues: (i) low performance when modeling 1 to N, N to 1 and N to N relations. (ii) limited performance due to the structure sparseness of the knowledge graph. In this paper, we propose a novel knowledge graph representation learning method by taking advantage of the rich context information in a text corpus. The rich textual context information is incorporated to expand the semantic structure of the knowledge graph and each relation is enabled to own different representations for different head and tail entities to better handle 1 to N, N to 1 and N to N relations. Experiments on multiple benchmark datasets show that our proposed method successfully addresses the above issues and significantly outperforms the state of the art methods.
The impact of caring for an adult with intellectual disability and psychiatric comorbidity on carer stress and psychological distress.
Venue: Journal of intellectual disability research JIDR

Given that carers of individuals with intellectual disability (ID) and carers of individuals with psychiatric disorders experience elevated levels of stress and psychological distress, carers of individuals with both ID and a comorbid psychiatric disorder are potentially at even greater risk for psychological difficulties. The aim of the present study was to investigate the psychological well being of carers of adults with a dual diagnosis compared with carers of adults with intellectual disability alone.  Four hundred and forty two questionnaires were sent to four community services and seventy five family carers of adults with intellectual disability responded. Psychological well being of carers was assessed using the Questionnaire on Resources and Stress Friedrich edition (QRS F) and the General Health Questionnaire (GHQ) Comorbid psychopathology for their family member with ID was assessed using the Reiss Screen for Maladaptive Behaviour (RSMB)  Twenty four percent of the individuals with ID were reported to have comorbid psychopathology. Between group analyses compared carers of people with ID and comorbid psychopathology to carers of people with ID alone. Regression analyses examined the relationship between psychopathology and other care related variables to carer stress and psychological distress. Carers of people with ID and comorbid psychopathology were found to have significantly higher levels of stress and psychological distress than carers of people with ID alone. Autism was found to be the only significant predictor of both stress and psychological distress among measures of psychopathology.  Additional comorbid psychopathology in individuals with intellectual disability has a significant impact on their carers' psychological well being.
Neutrophil Extracellular Traps in Pulmonary Diseases: Too Much of a Good Thing?
Venue: Front. Immunol.

Neutrophil extracellular traps (NETs) arise from the release of granular and nuclear contents of neutrophils in the extracellular space in response to different classes of microorganisms, soluble factors, and host molecules. NETs are composed by decondensed chromatin fibers coated with antimicrobial granular and cytoplasmic proteins, such as myeloperoxidase, neutrophil elastase (NE) and a defensins. Besides being expressed on NET fibers, NE and MPO also regulate NET formation. Furthermore, histone deimination by peptidylarginine deiminase 4 (PAD4) is a central step to NET formation. NET formation has been widely demonstrated to be an effective mechanism to fight against invading microorganisms, as deficiency in NET release or dismantling NET backbone by bacterial DNases renders the host susceptible to infections. Therefore, the primary role of NETs is to prevent microbial dissemination, avoiding overwhelming infections. However, an excess of NET formation has a dark side. The pathogenic role of NETs has been described for many human diseases, infectious and non infectious. The detrimental effect of excessive NET release is particularly important to lung diseases, because NETs can expand more easily in the pulmonary alveoli, causing lung injury. Moreover, NETs and its associated molecules are able to directly induce epithelial and endothelial cell death. In this regard, massive NET formation has been reported in several pulmonary diseases, including asthma, chronic obstructive pulmonary disease, cystic fibrosis, respiratory syncytial virus bronchiolitis, influenza, bacterial pneumonia, and tuberculosis, among others. Thus, NET formation must be tightly regulated in order to avoid NET mediated tissue damage. Recent development of therapies targeting NETs in pulmonary diseases includes DNA disintegration with recombinant human DNase, neutralization of NET proteins, with anti histone antibodies and protease inhibitors. In this review, we summarize the recent knowledge on the pathophysiological role of NETs in pulmonary diseases as well as some experimental and clinical approaches to modulate their detrimental effects.
"Space, the Final Frontier" How Good are Agent Based Models at Simulating Individuals and Space in Cities?
Venue: Syst.

Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g. social media, mobile phone data, etc. that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
Venue: Acta Neuropathologica

The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH wildtype and glioblastoma, IDH mutant; diffuse midline glioma, H3 K27M mutant; RELA fusion positive ependymoma; medulloblastoma, WNT activated and medulloblastoma, SHH activated; and embryonal tumour with multilayered rosettes, C19MC altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue type grading system for the now combined entity of solitary fibrous tumor hemangiopericytoma a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
Context aware restricted tourism domain question answering system
Venue: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT)

The advancement in the world of internet has made information retrieval an easy task. Search engines provide users the information they need but not in specific form, instead floods the user with lots of documents/web pages that contain the information required by user. User waste their time to scan all documents in order to retrieve the needed information.  Answering System reduces this drawback by automating the whole process. In this paper a tourism domain Question Answering System is proposed that contains the information about the tourist places of five different cities of Rajasthan. The system follows the principles of information retrieval and natural language processing. The proposed system is experimented with the set of 300 questions and it is observed that the system showed 70% precision and 80% accuracy rate. Lastly, proposed QAS (Question Answering System) is compared with other three existing Question Answering systems: AnswerBus [29] START  and NaLURI [28]
Continuous flow photochemistry: A need for chemical engineering

The present paper aims to illustrate that chemical engineering enables to address some of the current challenges and issues in continuous flow photochemistry. For that, some common limitations encountered in industrial photochemistry are firstly highlighted and a general overview on flow photochemistry equipment is presented. The main challenges linked to photochemical (micro)reactor engineering are subsequently stated. By considering only the case of a purely direct photochemical reactions A >B in homogenous medium, the key factors to consider when implementing such photochemical reactions in microstructured technologies are outlined. Their influence on the outputs (conversion, productivity, photonic efficiency) of this simple type of photochemical reaction is then discussed. The significance of chemical engineering frameworks is finally demonstrated using several examples concerning the understanding of the coupling between the different phenomena involved, the predictions of the performances obtained, the acquisition of kinetics data and the elaboration of strategies for photochemical process intensification and smart scale up. In the future, the challenge will be to integrate the complexity of photochemistry (e.g. heterogeneous phase reactions) into the present modelling tools so as to enlarge the spectrum of strategies devoted to photochemical process intensification.
Mathematical Methods In Chemical Engineering

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A review on pyrolysis of plastic wastes

The global plastic production increased over years due to the vast applications of plastics in many sectors. The continuous demand of plastics caused the plastic wastes accumulation in the landfill consumed a lot of spaces that contributed to the environmental problem. The rising in plastics demand led to the depletion of petroleum as part of non renewable fossil fuel since plastics were the petroleum based material. Some alternatives that have been developed to manage plastic wastes were recycling and energy recovery method. However, there were some drawbacks of the recycling method as it required high labor cost for the separation process and caused water contamination that reduced the process sustainability. Due to these drawbacks, the researchers have diverted their attentions to the energy recovery method to compensate the high energy demand. Through extensive research and technology development, the plastic waste conversion to energy was developed. As petroleum was the main source of plastic manufacturing, the recovery of plastic to liquid oil through pyrolysis process had a great potential since the oil produced had high calorific value comparable with the commercial fuel. This paper reviewed the pyrolysis process for each type of plastics and the main process parameters that influenced the final end product such as oil, gaseous and char. The key parameters that were reviewed in this paper included temperatures, type of reactors, residence time, pressure, catalysts, type of fluidizing gas and its flow rate. In addition, several viewpoints to optimize the liquid oil production for each plastic were also discussed in this paper.
OpenMili: a 60 GHz software radio platform with a reconfigurable phased array antenna
Venue: MobiCom

The 60 GHz wireless technology holds great potential for multi Gbps communications and high precision radio sensing. But the lack of an accessible experimental platform has been impeding its progress. In this paper, we overcome the barrier with OpenMili, a reconfigurable 60 GHz radio architecture. OpenMili builds from off the shelf FPGA processor, data converters and 60 GHz RF front end. It employs customized clocking, channelization and interfacing modules, to achieve Gsps sampling bandwidth, Gbps wireless bit rate, and Gsps sample streaming from/to a PC host. It also incorporates the first programmable, electronically steerable 60 GHz phased array antenna. OpenMili adopts programming models that ease development, through automatic parallelization inside signal processing blocks, and modular, rate insensitive interfaces across blocks. It provides common reference designs to bootstrap the development of new network protocols and sensing applications. We verify the effectiveness of OpenMili through benchmark communication/sensing experiments, and showcase its usage by prototyping a pairwise phased array localization scheme, and a learning assisted real time beam adaptation protocol.
Virtual Antenna Array Methodology for Outdoor Millimeter Wave Channel Measurements
Venue: 2016 IEEE Globecom Workshops (GC Wkshps)

The development of new outdoor millimeter wave channel models requires processing of the experimental data which can be used to estimate multipath channel parameters with very high time and angular resolution. The virtual antenna arrays processing technique allows joint narrow beam forming and beam steering. The specific of outdoor experiment does not allow precise positioning of the antennas, like it possible under lab conditions. So some special methodology should be developed to overcome this problem. In this paper we propose the virtual antenna array signal processing which allows for given experi mental data to perform environment scanning with equivalent 1&#x000B0; antenna beamwidth and 30dB sidelobes rejection. As a result, the measurement environment was accurately reconstructed on the base of measurement data. High angular resolution of the proposed virtual antenna array signal processing allows estimat ing the wall reflected rays (channel clusters) power angular spectrum parameters for a Laplacian model.
OpenMili: a 60 GHz software radio with a programmable phased array antenna: demo
Venue: MobiCom

The 60 GHz wireless technology holds great potential for multi Gbps communications and high precision radio sensing. But the lack of an accessible experimental platform has been impeding its progress. We propose to overcome the barrier with OpenMili, a reconfigurable 60 GHz radio architecture. OpenMili builds from off the shelf FPGA processor, data converters and 60 GHz RF front end. It employs customized clocking, channelization and interfacing modules, to achieve Gsps sampling bandwidth, Gbps wireless bit rate, and Gsps sample streaming from/to a PC host. It also incorporates the first programmable, electronically steerable 60 GHz phased array antenna. OpenMili adopts programming models that ease development, through automatic parallelization inside signal processing blocks, and modular, rate insensitive interfaces across blocks. In this demo, we will showcase OpenMili's hardware modules, and demonstrate example communication and sensing applications based on it.