nils-herrmann/clean_scirepeval huggingface dataset:
Do We Need Word Order Information for Cross lingual Sequence Labeling
Venue: ArXiv
Year: 2020

Most of the recent work in cross lingual adaptation does not consider the word order variances in different languages. We hypothesize that cross lingual models that fit into the source language word order might fail to handle target languages whose word orders are different. To test our conjecture, we build an order agnostic model for cross lingual sequence labeling tasks. Our model does not encode the word order information of the input sequences, and the predictions for each token are based on the attention on the whole sequence. Experimental results on dialogue natural language understanding, part of speech tagging, and named entity recognition tasks show that getting rid of word order information is able to achieve better zero shot crosslingual performance than baseline models.
Tailoring Porous Silicon for Biomedical Applications: From Drug Delivery to Cancer Immunotherapy.
Venue: Advanced materials
Year: 2018

In the past two decades, porous silicon (PSi) has attracted increasing attention for its potential biomedical applications. With its controllable geometry, tunable nanoporous structure, large pore volume/high specific surface area, and versatile surface chemistry, PSi shows significant advantages over conventional drug carriers. Here, an overview of recent progress in the use of PSi in drug delivery and cancer immunotherapy is presented. First, an overview of the fabrication of PSi with various geometric structures is provided, with particular focus on how the unique geometry of PSi facilitates its biomedical applications, especially for drug delivery. Second, surface chemistry and modification of PSi are discussed in relation to the strengthening of its performance in drug delivery and bioimaging. Emerging technologies for engineering PSi based composites are then summarized. Emerging PSi advances in the context of cancer immunotherapy are also highlighted. Overall, very promising research results encourage further exploration of PSi for biomedical applications, particularly in drug delivery and cancer immunotherapy, and future translation of PSi into clinical applications.
The Name and Nature of Translator Studies
Year: 2017

A number of recent research tendencies in Translation Studies focus explicitly on the translator in some way, rather than on translations as texts. These trends might be grouped under the term "Translator Studies" The article argues that this new focus is inadequately represented in Holmes' classic map. Evidence of the recent trends is found especially in translation sociology, but also in translation history and in research into the translator's decision making processes. A broad outline of Translator Studies would cover sociology, culture and cognition, all looking at the translator's agency, in different ways.
An Intelligent Adaptive Pedestrian Navigation Algorithm based on Support Vector Machine
Venue: 2019 Chinese Control And Decision Conference (CCDC)
Year: 2019

Aiming at the problem that the pedestrian navigation algorithm based on quadratic curve fitting zero speed correction technology has low utilization rate of data samples and poor correction performance and instantaneous accuracy which cannot be optimized, an intelligent adaptive pedestrian navigation algorithm based on support vector machine is proposed, which the data of the sensors is obtained by using the optimized wavelet threshold denoising algorithm; the model trained by the SVR(support vector machine regression) is used to fit the three dimensional velocity and the error fitting result is used as the system observations; then the intelligent estimator is formed by the SVM and the Kalman filter to estimate the system errors, thereby improving the system accuracy and reliability. The experimental verification by self developed IMU proves that the method can accurately fit the three dimensional velocity errors, estimate the systematic error optimally, and effectively correct the navigation data. The positioning accuracy is improved by 10.9% in complex environment. The algorithm has theoretical and engineering significance.
Stakeholder Theory: The State of the Art
Year: 2010

For the last 30 years a growing number of scholars and practitioners have been experimenting with concepts and models that facilitate our understanding of the complexities of today's business challenges. Among these, "stakeholder theory" or "stakeholder thinking" has emerged as a new narrative to understand and remedy three interconnected business problems the problem of understanding how value is created and traded, the problem of connecting ethics and capitalism, and the problem of helping managers think about management such that the first two problems are addressed. In this article, we review the major uses and adaptations of stakeholder theory across a broad array of disciplines such as business ethics, corporate strategy, finance, accounting, management, and marketing. We also evaluate and suggest future directions in which research on stakeholder theory can continue to provide useful insights into the practice of sustainable and ethical value creation.
Multi sensor fusion based intelligent sensor relocation for health and safety monitoring in BSNs
Venue: Inf. Fusion
Year: 2020

The body sensor networks (BSNs) have attracted great attention due to their numerous important features and wide applications in health and safety monitoring, especially in some hostile or potentially dangerous workplaces. In this paper, aiming to provide continuous coverage from the initial Region of Interest (ROI) to a New Region of Interest (NROI) to trace the BSNs for human health and safety monitoring, we formulate and define the novel confident information coverage (CIC) based Intelligent Sensor Relocation for NROI (CIC ISR NROI) problem in mobile wireless sensor networks (MWSNs) based on the novel CIC model. For handing the CIC ISR NROI problem, we develop two energy efficient intelligent sensor relocation algorithms based on multi sensor fusion, the Rapid Relocation Time Low Energy Consumption (RRTLEC) algorithm and optimized RRTLEC algorithm, to relocate redundant mobile sensors from the ROI to cover the NROI in a timely and energy efficient manner while constructing the communication chain to connect the ROI and the NROI. To verify the effectiveness and efficiency of the proposed schemes, we conduct a series of experiments which correspond to the realistic scenarios in health and safety monitoring in the 272 uranium tailing. Experimental results show that the performance of our approaches is better than the other typical peer approaches by the metrics of the total moving energy consumption and the maximum relocation time.
SegNet: A Deep Convolutional Encoder Decoder Architecture for Robust Semantic Pixel Wise Labelling
Venue: CVPR 2015
Year: 2015

We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy, and (iii) it is easy to visualise the effect of feature activation(s) in the pixel label space at any depth. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft max classification layer. The decoders help map low resolution feature maps at the output of the encoder stack to full input image size feature maps. This addresses an important drawback of recent deep learning approaches which have adopted networks designed for object categorization for pixel wise labelling. These methods lack a mechanism to map deep layer feature maps to input dimensions. They resort to ad hoc methods to upsample features, e.g. by replication. This results in noisy predictions and also restricts the number of pooling layers in order to avoid too much upsampling and thus reduces spatial context. SegNet overcomes these problems by learning to map encoder outputs to image pixel labels. We test the performance of SegNet on outdoor RGB scenes from CamVid, KITTI and indoor scenes from the NYU dataset. Our results show that SegNet achieves state of the art performance even without use of additional cues such as depth, video frames or post processing with CRF models.
Radialbewegliches Loslager mit einstellbarer Anfederung
Year: 2008

Die Erfindung betrifft ein radialbewegliches Loslager (1) fur eine Welle (2) eines Getriebes (3) eines Lenksystems, mit einer Buchse oder einem Innenring (4) oder einem Walzlager, der oder das sich in einer Aufnahme (5) eines Gestells (6) oder Gehauses des Getriebes (3) abstutzt, wobei der Innenring (4) oder die Buchse oder das Walzlager durch mindestens ein in einem Spalt (7) zwischen der Aufnahme (5) und dem Innenring (4) eingefugten Stutzelement (8) aus einem Werkstoff mit elastomerem Verhalten gehalten ist, welches als Schwenklager (9) ausgefuhrt ist. Fur eine besonders gerauscharme und kostengunstig zu realisierende Anfederung der gelagerten Welle (2) wird eine Anpressvorrichtung eingesetzt, die als ein einstuckig aus einem elastischen Material (M0) gefertigtes Anpresselement (170) gestaltet ist, das in einem Aufnahmeabschnitt (260) des Gehauses (6) formschlussig eingepasst ist. Das Anpresselement (170) kann zumindest einen Fullraum zum Einbringen von Fullmaterial aufweisen, wobei das Material (M0) des Anpresselements (170) selbst verschieden zu dem Fullmaterial des Fullraums sein kann, wodurch sich das Anfederungsverhalten sehr genau einstellen lasst.
A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes
Venue: Nanomaterials
Year: 2020

Carbon nanotubes (CNTs) have been extensively studied as one of the most interesting nanomaterials for over 25 years because they exhibit excellent mechanical, electrical, thermal, optical, and electrical properties. In the past decade, the number of publications and patents on cellulose and nanocellulose (NC) increased tenfold. Research on NC with excellent mechanical properties, flexibility, and transparency is accelerating due to the growing environmental problems surrounding us such as CO2 emissions, the accumulation of large amounts of plastic, and the depletion of energy resources such as oil. Research on mixed materials of cellulose, NC, and CNTs has been expanding because these materials exhibit various characteristics that can be controlled by varying the combination of cellulose, NC to CNTs while also being biodegradable and recyclable. An understanding of these mixed materials is required because these characteristics are diverse and are expected to solve various environmental problems. Thus far, many review papers on cellulose, NC or CNTs have been published. Although guidance for the suitable application of these mixed materials is necessary, there are few reviews summarizing them. Therefore, this review introduces the application and feature on mixed materials of cellulose, NC and CNTs.
A Pathologist Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study.
Year: 2020

In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images (WSIs) We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor infiltrating lymphocytes (sTILs) in breast cancer.  We digitized 64 glass slides of hematoxylin and eosin stained ductal carcinoma core biopsies prepared at a single clinical site. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI.  The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm.  We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the FDA via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned.
Unsupervised Domain Adaptation with Dual Scheme Fusion Network for Medical Image Segmentation
Venue: IJCAI
Year: 2020

Domain adaptation aims to alleviate the problem of retraining a pre trained model when applying it to a different domain, which requires large amount of additional training data of the target domain. Such an objective is usually achieved by establishing connections between the source domain labels and target domain data. However, this imbalanced source to target one way pass may not eliminate the domain gap, which limits the performance of the pre trained model. In this paper, we propose an innovative Dual Scheme Fusion Network (DSFN) for unsupervised domain adaptation. By building both source to target and target to source connections, this balanced joint information flow helps reduce the domain gap to further improve the network performance. The mechanism is further applied to the inference stage, where both the original input target image and the generated source images are segmented with the proposed joint network. The results are fused to obtain more robust segmentation. Extensive experiments of unsupervised cross modality medical image segmentation are conducted on two tasks brain tumor segmentation and cardiac structures segmentation. The experimental results show that our method achieved significant performance improvement over other state of the art domain adaptation methods.
Adaptation to climate change and economic growth in developing countries
Venue: Environment and Development Economics
Year: 2014

Developing countries are vulnerable to the adverse effects of climate change, yet there is disagreement about what they should do to protect themselves from anticipated damages. In particular, it is unclear what the optimal balance is between investments in traditional productive capital (which increases output but is vulnerable to climate change) and investments in adaptive capital (which is unproductive in the absence of climate change but 'climate proofs' vulnerable capital) We develop a model of investment in adaptive and productive capital stocks, and show that while it is unlikely that the optimal strategy involves no adaptation, the scale and composition of optimal investments depends on empirical context. Application of our model to sub Saharan Africa suggests, however, that in most contingencies it will be optimal to grow the adaptive sector more rapidly than the vulnerable sector over the coming decades, although it never exceeds 1 per cent of the economy. Our sensitivity analysis goes well beyond the existing literature in evaluating the robustness of this finding.
Impacts of Social Media (Facebook) on Human Communication and Relationships: A View on Behavioral Change and Social Unity
Year: 2017

Article history: Received 03 May 2017 Revised 11 November 2017 Accepted 03 December 2017 The impact of social networking is varied from good to bad. Online activities have also been categorized into pros and cons of social networking, either as reported as hiding Internet activities among teenagers or killing loneliness among elderly. In terms of relationships, there have been argument over its closeness and quality of an online relationship in Internet settings. Looking at the contradiction in an innovative interaction between classic community communication and social media, there is an unknown scent of the future struggling and challenging both human communication and relationships in the presence of digital culture. This research uses Diffusion of Innovation to study the wide and continuous spread of digital culture in human communication; and, Media Dependency in learning and structuring the cognitive, affective and behavioral effects of social media on each person uses the media in different ways. This research will be using online survey to gain opinions from a social network site as an update of views and reflection of self awareness to all levels of people. Social media like Facebook (FB) is perceived as a good tool of communication that it is able to bring closeness among the family members. The results show that social media like FB brings positive impact towards family members; it would help to build a better and harmonic society; and, relationships among family members and communication shall be improved and enhanced to the level of a united society.
User or student: constructing the subject in Edtech incubator
Year: 2019

This research focuses on 'sociotechnical imaginaries' about education and learning emanating from the Edtech research and development sector. MindCet, the first Edtech incubator in Israel, aims to bring 'disruptive innovation' to the educational field, mainly by bringing in tech start ups with their problem solving culture and practices. This centre acts as an intermediary between techno business and education. Based on Biesta's notion of 'the new language of learning, my research shows how the learning imagined and constructed in techno business production casts the student as a 'user. I will argue that the construction of learners as 'users, not 'students, is crucial to understanding education technology production and discourse. This user focused understanding of learning has implications for the commodification and depoliticization of education, and is an important factor in neo liberal educational reforms and venture philanthropy interventions, both globally and locally.
Classical System of Martin Lof's Inductive Definitions Is Not Equivalent to Cyclic Proof System
Venue: FoSSaCS
Year: 2017

A cyclic proof system, called \mathtt{CLKID}\omega gives us another way of representing inductive definitions and efficient proof search. The 2011 paper by Brotherston and Simpson showed that the provability of \mathtt{CLKID}\omega includes the provability of Martin Lof's system of inductive definitions, called \mathtt{LKID} and conjectured the equivalence. Since then, the equivalence has been left an open question. This paper shows that \mathtt{CLKID}\omega and \mathtt{LKID} are indeed not equivalent. This paper considers a statement called 2 Hydra in these two systems with the first order language formed by 0, the successor, the natural number predicate, and a binary predicate symbol used to express 2 Hydra. This paper shows that the 2 Hydra statement is provable in \mathtt{CLKID}\omega but the statement is not provable in \mathtt{LKID} by constructing some Henkin model where the statement is false.
Testing a correlation coefficient s significance Using H 0 0 is preferable to H 0 0
Year: 2007

This paper proposes an alternative approach in correlation analysis to significance testing. It argues that testing the null hypotheses H0: 0 versus the H1: 0 is not an optimal strategy. This is because rejecting the null hypothesis, as traditionally reported in social science papers i.e. a significant correlation coefficient (regardless of how many asterisks it is adorned with) very often has no practical meaning. Confirming a population's correlation coefficient as being merely unequal to zero does not entail much gain of content information, unless this correlation coefficient is sufficiently large enough; and this in turn means that the correlation explains a relevant amount of variance. The alternative approach, however, tests the composite null hypothesis H0: 0 for any 0 1 instead of the simple null hypothesis H0: 0. At best, the value of is chosen as the square root of the expected relative proportion of variance which is explained by a linear regression, the latter being the so called coefficient of determination, The respective test statistic is only asymptotically normally distributed: in this paper a simulation study is used to prove that the factual risk is hardly larger than the nominal risk with regard to the type I risk. An SPSS syntax is given for this test, in order to fill the respective gap in the existing SPSS menu. Furthermore it is demonstrated, how to calculate the sample size according to certain demands for precision. By applying the approach given here, no "use of asterisks" would lead a researcher astray that is to say, would cause him/her to misinterpret the type I risk or, even worse, to overestimate the importance of a study because he/she has misjudged the content relevance of a given correlation coefficient 0.
Multichannel in a complex world
Year: 2012

The proliferation of devices and channels has brought new challenges to just about every organisation in delivering consistently good customer experiences and effectively joining up service provision with marketing activity, data and content. A good multichannel strategy and execution is increasingly becoming essential to marketers and customer experience professionals from every sector. This report seeks to identify the key issues, challenges and opportunities that surround multichannel and provide some best practice insight and principles on the elements that are key to multichannel success. As part of the research for this report, we spoke to six experienced customer experience and marketing practitioners from large organisations across different sectors.  Metrics and Methods for On and Offline Success, Akin Arikan (2008) said: 'Because customers are multichannel beings and demand relevant, consistent experiences across all channels, businesses need to adopt a multichannel mind set when listening to their customers. It was clear from the companies interviewed for this report that it remains challenging for many organisations to maintain consistency across so many customer touchpoints. Not only that, but the ability to balance consistency with the capability to fully exploit the unique attributes of each channel remains an aspiration for many. The proliferation of devices and digital channels has added complexity to customer journeys, making issues around the joining up of customer experience and the attribution of value of key importance to many. Whilst senior leaders within the organisations spoken to seem to be bought in to multichannel, this buy in was not always replicated across the rest of the organisation and did not always translate into a cohesive multichannel strategy. A number of companies were undertaking work around customer journey mapping and customer segmentation, using a variety of passive and actively collected data in order to identify specific areas of poor customer experience and create action plans for improvement. Others were undertaking projects using sophisticated tracking and tagging technologies to develop an understanding of the value and role of specific channels and to provide better intelligence to the business on attribution that might be used to inform future investment decisions. A consistent barrier to improving customer experience is the ability to join up many different legacy systems and data in order to provide a single customer view and form the basis for delivery of a more consistent and cohesive multichannel approach. Whilst there remain significant challenges around multichannel, there are some useful technologies allowing businesses to develop better insight into customer motivation and activity. Nonetheless, delivery of seamless multichannel experience remains a work inprogress for many.