14,913 research outputs found

    Corporate Social Responsibility: the institutionalization of ESG

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    Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective

    Neural Architecture Search: Insights from 1000 Papers

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    In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries

    Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation

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    This paper focuses on the comparison of networks on the basis of statistical inference. For that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that is able to capture complex structural patterns. The graphon itself can be viewed more broadly as density or intensity function on networks, making the model a natural choice for comparison purposes. Extending graphon estimation towards modeling multiple networks simultaneously consequently provides substantial information about the (dis-)similarity between networks. Fitting such a joint model - which can be accomplished by applying an EM-type algorithm - provides a joint graphon estimate plus a corresponding prediction of the node positions for each network. In particular, it entails a generalized network alignment, where nearby nodes play similar structural roles in their respective domains. Given that, we construct a chi-squared test on equivalence of network structures. Simulation studies and real-world examples support the applicability of our network comparison strategy.Comment: 25 pages, 6 figure

    The place where curses are manufactured : four poets of the Vietnam War

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    The Vietnam War was unique among American wars. To pinpoint its uniqueness, it was necessary to look for a non-American voice that would enable me to articulate its distinctiveness and explore the American character as observed by an Asian. Takeshi Kaiko proved to be most helpful. From his novel, Into a Black Sun, I was able to establish a working pair of 'bookends' from which to approach the poetry of Walter McDonald, Bruce Weigl, Basil T. Paquet and Steve Mason. Chapter One is devoted to those seemingly mismatched 'bookends,' Walt Whitman and General William C. Westmoreland, and their respective anthropocentric and technocentric visions of progress and the peculiarly American concept of the "open road" as they manifest themselves in Vietnam. In Chapter, Two, I analyze the war poems of Walter McDonald. As a pilot, writing primarily about flying, his poetry manifests General Westmoreland's technocentric vision of the 'road' as determined by and manifest through technology. Chapter Three focuses on the poems of Bruce Weigl. The poems analyzed portray the literal and metaphorical descent from the technocentric, 'numbed' distance of aerial warfare to the world of ground warfare, and the initiation of a 'fucking new guy,' who discovers the contours of the self's interior through a set of experiences that lead from from aerial insertion into the jungle to the degradation of burning human feces. Chapter Four, devoted to the thirteen poems of Basil T. Paquet, focuses on the continuation of the descent begun in Chapter Two. In his capacity as a medic, Paquet's entire body of poems details his quotidian tasks which entail tending the maimed, the mortally wounded and the dead. The final chapter deals with Steve Mason's JohnnY's Song, and his depiction of the plight of Vietnam veterans back in "The World" who are still trapped inside the interior landscape of their individual "ghettoes" of the soul created by their war-time experiences

    Breast mass segmentation from mammograms with deep transfer learning

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    Abstract. Mammography is an x-ray imaging method used in breast cancer screening, which is a time consuming process. Many different computer assisted diagnosis have been created to hasten the image analysis. Deep learning is the use of multilayered neural networks for solving different tasks. Deep learning methods are becoming more advanced and popular for segmenting images. One deep transfer learning method is to use these neural networks with pretrained weights, which typically improves the neural networks performance. In this thesis deep transfer learning was used to segment cancerous masses from mammography images. The convolutional neural networks used were pretrained and fine-tuned, and they had an an encoder-decoder architecture. The ResNet22 encoder was pretrained with mammography images, while the ResNet34 encoder was pretrained with various color images. These encoders were paired with either a U-Net or a Feature Pyramid Network decoder. Additionally, U-Net model with random initialization was also tested. The five different models were trained and tested on the Oulu Dataset of Screening Mammography (9204 images) and on the Portuguese INbreast dataset (410 images) with two different loss functions, binary cross-entropy loss with soft Jaccard loss and a loss function based on focal Tversky index. The best models were trained on the Oulu Dataset of Screening Mammography with the focal Tversky loss. The best segmentation result achieved was a Dice similarity coefficient of 0.816 on correctly segmented masses and a classification accuracy of 88.7% on the INbreast dataset. On the Oulu Dataset of Screening Mammography, the best results were a Dice score of 0.763 and a classification accuracy of 83.3%. The results between the pretrained models were similar, and the pretrained models had better results than the non-pretrained models. In conclusion, deep transfer learning is very suitable for mammography mass segmentation and the choice of loss function had a large impact on the results.Rinnan massojen segmentointi mammografiakuvista syvä- ja siirto-oppimista hyödyntäen. Tiivistelmä. Mammografia on röntgenkuvantamismenetelmä, jota käytetään rintäsyövän seulontaan. Mammografiakuvien seulonta on aikaa vievää ja niiden analysoimisen avuksi on kehitelty useita tietokoneavusteisia ratkaisuja. Syväoppimisella tarkoitetaan monikerroksisten neuroverkkojen käyttöä eri tehtävien ratkaisemiseen. Syväoppimismenetelmät ovat ajan myötä kehittyneet ja tulleet suosituiksi kuvien segmentoimiseen. Yksi tapa yhdistää syvä- ja siirtooppimista on hyödyntää neuroverkkoja esiopetettujen painojen kanssa, mikä auttaa parantamaan neuroverkkojen suorituskykyä. Tässä diplomityössä tutkittiin syvä- ja siirto-oppimisen käyttöä syöpäisten massojen segmentoimiseen mammografiakuvista. Käytetyt konvoluutioneuroverkot olivat esikoulutettuja ja hienosäädettyjä. Lisäksi niillä oli enkooderi-dekooderi arkkitehtuuri. ResNet22 enkooderi oli esikoulutettu mammografia kuvilla, kun taas ResNet34 enkooderi oli esikoulutettu monenlaisilla värikuvilla. Näihin enkoodereihin yhdistettiin joko U-Net:n tai piirrepyramidiverkon dekooderi. Lisäksi käytettiin U-Net mallia ilman esikoulutusta. Nämä viisi erilaista mallia koulutettiin ja testattiin sekä Oulun Mammografiaseulonta Datasetillä (9204 kuvaa) että portugalilaisella INbreast datasetillä (410 kuvaa) käyttäen kahta eri tavoitefunktiota, jotka olivat binääriristientropia yhdistettynä pehmeällä Jaccard-indeksillä ja fokaaliin Tversky indeksiin perustuva tavoitefunktiolla. Parhaat mallit olivat koulutettu Oulun datasetillä fokaalilla Tversky tavoitefunktiolla. Parhaat tulokset olivat 0,816 Dice kerroin oikeissa positiivisissa segmentaatioissa ja 88,7 % luokittelutarkkuus INbreast datasetissä. Esikoulutetut mallit antoivat parempia tuloksia kuin mallit joita ei esikoulutettu. Oulun datasetillä parhaat tulokset olivat 0,763:n Dice kerroin ja 83,3 % luokittelutarkkuus. Tuloksissa ei ollut suurta eroa esikoulutettujen mallien välillä. Tulosten perusteella syvä- ja siirto-oppiminen soveltuvat hyvin massojen segmentoimiseen mammografiakuvista. Lisäksi tavoitefunktiovalinnalla saatiin suuri vaikutus tuloksiin

    Estudo da remodelagem reversa miocárdica através da análise proteómica do miocárdio e do líquido pericárdico

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    Valve replacement remains as the standard therapeutic option for aortic stenosis patients, aiming at abolishing pressure overload and triggering myocardial reverse remodeling. However, despite the instant hemodynamic benefit, not all patients show complete regression of myocardial hypertrophy, being at higher risk for adverse outcomes, such as heart failure. The current comprehension of the biological mechanisms underlying an incomplete reverse remodeling is far from complete. Furthermore, definitive prognostic tools and ancillary therapies to improve the outcome of the patients undergoing valve replacement are missing. To help abridge these gaps, a combined myocardial (phospho)proteomics and pericardial fluid proteomics approach was followed, taking advantage of human biopsies and pericardial fluid collected during surgery and whose origin anticipated a wealth of molecular information contained therein. From over 1800 and 750 proteins identified, respectively, in the myocardium and in the pericardial fluid of aortic stenosis patients, a total of 90 dysregulated proteins were detected. Gene annotation and pathway enrichment analyses, together with discriminant analysis, are compatible with a scenario of increased pro-hypertrophic gene expression and protein synthesis, defective ubiquitinproteasome system activity, proclivity to cell death (potentially fed by complement activity and other extrinsic factors, such as death receptor activators), acute-phase response, immune system activation and fibrosis. Specific validation of some targets through immunoblot techniques and correlation with clinical data pointed to complement C3 β chain, Muscle Ring Finger protein 1 (MuRF1) and the dual-specificity Tyr-phosphorylation regulated kinase 1A (DYRK1A) as potential markers of an incomplete response. In addition, kinase prediction from phosphoproteome data suggests that the modulation of casein kinase 2, the family of IκB kinases, glycogen synthase kinase 3 and DYRK1A may help improve the outcome of patients undergoing valve replacement. Particularly, functional studies with DYRK1A+/- cardiomyocytes show that this kinase may be an important target to treat cardiac dysfunction, provided that mutant cells presented a different response to stretch and reduced ability to develop force (active tension). This study opens many avenues in post-aortic valve replacement reverse remodeling research. In the future, gain-of-function and/or loss-of-function studies with isolated cardiomyocytes or with animal models of aortic bandingdebanding will help disclose the efficacy of targeting the surrogate therapeutic targets. Besides, clinical studies in larger cohorts will bring definitive proof of complement C3, MuRF1 and DYRK1A prognostic value.A substituição da válvula aórtica continua a ser a opção terapêutica de referência para doentes com estenose aórtica e visa a eliminação da sobrecarga de pressão, desencadeando a remodelagem reversa miocárdica. Contudo, apesar do benefício hemodinâmico imediato, nem todos os pacientes apresentam regressão completa da hipertrofia do miocárdio, ficando com maior risco de eventos adversos, como a insuficiência cardíaca. Atualmente, os mecanismos biológicos subjacentes a uma remodelagem reversa incompleta ainda não são claros. Além disso, não dispomos de ferramentas de prognóstico definitivos nem de terapias auxiliares para melhorar a condição dos pacientes indicados para substituição da válvula. Para ajudar a resolver estas lacunas, uma abordagem combinada de (fosfo)proteómica e proteómica para a caracterização, respetivamente, do miocárdio e do líquido pericárdico foi seguida, tomando partido de biópsias e líquidos pericárdicos recolhidos em ambiente cirúrgico. Das mais de 1800 e 750 proteínas identificadas, respetivamente, no miocárdio e no líquido pericárdico dos pacientes com estenose aórtica, um total de 90 proteínas desreguladas foram detetadas. As análises de anotação de genes, de enriquecimento de vias celulares e discriminativa corroboram um cenário de aumento da expressão de genes pro-hipertróficos e de síntese proteica, um sistema ubiquitina-proteassoma ineficiente, uma tendência para morte celular (potencialmente acelerada pela atividade do complemento e por outros fatores extrínsecos que ativam death receptors), com ativação da resposta de fase aguda e do sistema imune, assim como da fibrose. A validação de alguns alvos específicos através de immunoblot e correlação com dados clínicos apontou para a cadeia β do complemento C3, a Muscle Ring Finger protein 1 (MuRF1) e a dual-specificity Tyr-phosphoylation regulated kinase 1A (DYRK1A) como potenciais marcadores de uma resposta incompleta. Por outro lado, a predição de cinases a partir do fosfoproteoma, sugere que a modulação da caseína cinase 2, a família de cinases do IκB, a glicogénio sintase cinase 3 e da DYRK1A pode ajudar a melhorar a condição dos pacientes indicados para intervenção. Em particular, a avaliação funcional de cardiomiócitos DYRK1A+/- mostraram que esta cinase pode ser um alvo importante para tratar a disfunção cardíaca, uma vez que os miócitos mutantes responderam de forma diferente ao estiramento e mostraram uma menor capacidade para desenvolver força (tensão ativa). Este estudo levanta várias hipóteses na investigação da remodelagem reversa. No futuro, estudos de ganho e/ou perda de função realizados em cardiomiócitos isolados ou em modelos animais de banding-debanding da aorta ajudarão a testar a eficácia de modular os potenciais alvos terapêuticos encontrados. Além disso, estudos clínicos em coortes de maior dimensão trarão conclusões definitivas quanto ao valor de prognóstico do complemento C3, MuRF1 e DYRK1A.Programa Doutoral em Biomedicin

    Building body identities - exploring the world of female bodybuilders

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    This thesis explores how female bodybuilders seek to develop and maintain a viable sense of self despite being stigmatized by the gendered foundations of what Erving Goffman (1983) refers to as the 'interaction order'; the unavoidable presentational context in which identities are forged during the course of social life. Placed in the context of an overview of the historical treatment of women's bodies, and a concern with the development of bodybuilding as a specific form of body modification, the research draws upon a unique two year ethnographic study based in the South of England, complemented by interviews with twenty-six female bodybuilders, all of whom live in the U.K. By mapping these extraordinary women's lives, the research illuminates the pivotal spaces and essential lived experiences that make up the female bodybuilder. Whilst the women appear to be embarking on an 'empowering' radical body project for themselves, the consequences of their activity remains culturally ambivalent. This research exposes the 'Janus-faced' nature of female bodybuilding, exploring the ways in which the women negotiate, accommodate and resist pressures to engage in more orthodox and feminine activities and appearances

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Towards a more just refuge regime: quotas, markets and a fair share

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    The international refugee regime is beset by two problems: Responsibility for refuge falls disproportionately on a few states and many owed refuge do not get it. In this work, I explore remedies to these problems. One is a quota distribution wherein states are distributed responsibilities via allotment. Another is a marketized quota system wherein states are free to buy and sell their allotments with others. I explore these in three parts. In Part 1, I develop the prime principles upon which a just regime is built and with which alternatives can be adjudicated. The first and most important principle – ‘Justice for Refugees’ – stipulates that a just regime provides refuge for all who have a basic interest in it. The second principle – ‘Justice for States’ – stipulates that a just distribution of refuge responsibilities among states is one that is capacity considerate. In Part 2, I take up several vexing questions regarding the distribution of refuge responsibilities among states in a collective effort. First, what is a state’s ‘fair share’? The answer requires the determination of some logic – some metric – with which a distribution is determined. I argue that one popular method in the political theory literature – a GDP-based distribution – is normatively unsatisfactory. In its place, I posit several alternative metrics that are more attuned with the principles of justice but absent in the political theory literature: GDP adjusted for Purchasing Power Parity and the Human Development Index. I offer an exploration of both these. Second, are states required to ‘take up the slack’ left by defaulting peers? Here, I argue that duties of help remain intact in cases of partial compliance among states in the refuge regime, but that political concerns may require that such duties be applied with caution. I submit that a market instrument offers one practical solution to this problem, as well as other advantages. In Part 3, I take aim at marketization and grapple with its many pitfalls: That marketization is commodifying, that it is corrupting, and that it offers little advantage in providing quality protection for refugees. In addition to these, I apply a framework of moral markets developed by Debra Satz. I argue that a refuge market may satisfy Justice Among States, but that it is violative of the refugees’ welfare interest in remaining free of degrading and discriminatory treatment

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)
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