7,970 research outputs found
Bayesian networks for disease diagnosis: What are they, who has used them and how?
A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem,
used to show dependencies or cause-and-effect relationships between variables.
They are widely applied in diagnostic processes since they allow the
incorporation of medical knowledge to the model while expressing uncertainty in
terms of probability. This systematic review presents the state of the art in
the applications of BNs in medicine in general and in the diagnosis and
prognosis of diseases in particular. Indexed articles from the last 40 years
were included. The studies generally used the typical measures of diagnostic
and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the
area under the ROC curve. Overall, we found that disease diagnosis and
prognosis based on BNs can be successfully used to model complex medical
problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state-of-the-art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher-level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation
Preferentialism and the conditionality of trade agreements. An application of the gravity model
Modern economic growth is driven by international trade, and the preferential trade agreement constitutes the primary fit-for-purpose mechanism of choice for establishing, facilitating, and governing its flows. However, too little attention has been afforded to the differences in content and conditionality associated with different trade agreements. This has led to an under-considered mischaracterisation of the design-flow relationship. Similarly, while the relationship between trade facilitation and trade is clear, the way trade facilitation affects other areas of economic activity, with respect to preferential trade agreements, has received considerably less attention. Particularly, in light of an increasingly globalised and interdependent trading system, the interplay between trade facilitation and foreign direct investment is of particular importance.
Accordingly, this thesis explores the bilateral trade and investment effects of specific conditionality sets, as established within Preferential Trade Agreements (PTAs).
Chapter one utilises recent content condition-indexes for depth, flexibility, and constraints on flexibility, established by Dür et al. (2014) and Baccini et al. (2015), within a gravity framework to estimate the average treatment effect of trade agreement characteristics across bilateral trade relationships in the Association of Southeast Asian Nations (ASEAN) from 1948-2015. This chapter finds that the composition of a given ASEAN trade agreement’s characteristic set has significantly determined the concomitant bilateral trade flows. Conditions determining the classification of a trade agreements depth are positively associated with an increase to bilateral trade; hereby representing the furthered removal of trade barriers and frictions as facilitated by deeper trade agreements. Flexibility conditions, and constraint on flexibility conditions, are also identified as significant determiners for a given trade agreement’s treatment effect of subsequent bilateral trade flows. Given the political nature of their inclusion (i.e., the appropriate address to short term domestic discontent) this influence is negative as regards trade flows. These results highlight the longer implementation and time frame requirements for trade impediments to be removed in a market with higher domestic uncertainty.
Chapter two explores the incorporation of non-trade issue (NTI) conditions in PTAs. Such conditions are increasing both at the intensive and extensive margins. There is a concern from developing nations that this growth of NTI inclusions serves as a way for high-income (HI) nations to dictate the trade agenda, such that developing nations are subject to ‘principled protectionism’. There is evidence that NTI provisions are partly driven by protectionist motives but the effect on trade flows remains largely undiscussed. Utilising the Gravity Model for trade, I test Lechner’s (2016) comprehensive NTI dataset for 202 bilateral country pairs across a 32-year timeframe and find that, on average, NTIs are associated with an increase to bilateral trade. Primarily this boost can be associated with the market access that a PTA utilising NTIs facilitates. In addition, these results are aligned theoretically with the discussions on market harmonisation, shared values, and the erosion of artificial production advantages. Instead of inhibiting trade through burdensome cost, NTIs are acting to support a more stable production and trading environment, motivated by enhanced market access. Employing a novel classification to capture the power supremacy associated with shaping NTIs, this chapter highlights that the positive impact of NTIs is largely driven by the relationship between HI nations and middle-to-low-income (MTLI) counterparts.
Chapter Three employs the gravity model, theoretically augmented for foreign direct investment (FDI), to estimate the effects of trade facilitation conditions utilising indexes established by Neufeld (2014) and the bilateral FDI data curated by UNCTAD (2014). The resultant dataset covers 104 countries, covering a period of 12 years (2001–2012), containing 23,640 observations. The results highlight the bilateral-FDI enhancing effects of trade facilitation conditions in the ASEAN context, aligning itself with the theoretical branch of FDI-PTA literature that has outlined how the ratification of a trade agreement results in increased and positive economic prospect between partners (Medvedev, 2012) resulting from the interrelation between trade and investment as set within an improving regulatory environment. The results align with the expectation that an enhanced trade facilitation landscape (one in which such formalities, procedures, information, and expectations around trade facilitation are conditioned for) is expected to incentivise and attract FDI
Towards A Graphene Chip System For Blood Clotting Disease Diagnostics
Point of care diagnostics (POCD) allows the rapid, accurate measurement of analytes near to a patient. This enables faster clinical decision making and can lead to earlier diagnosis and better patient monitoring and treatment. However, despite many prospective POCD devices being developed for a wide range of diseases this promised technology is yet to be translated to a clinical setting due to the lack of a cost-effective biosensing platform.This thesis focuses on the development of a highly sensitive, low cost and scalable biosensor platform that combines graphene with semiconductor fabrication tech-niques to create graphene field-effect transistors biosensor. The key challenges of designing and fabricating a graphene-based biosensor are addressed. This work fo-cuses on a specific platform for blood clotting disease diagnostics, but the platform has the capability of being applied to any disease with a detectable biomarker.Multiple sensor designs were tested during this work that maximised sensor ef-ficiency and costs for different applications. The multiplex design enabled different graphene channels on the same chip to be functionalised with unique chemistry. The Inverted MOSFET design was created, which allows for back gated measurements to be performed whilst keeping the graphene channel open for functionalisation. The Shared Source and Matrix design maximises the total number of sensing channels per chip, resulting in the most cost-effective fabrication approach for a graphene-based sensor (decreasing cost per channel from £9.72 to £4.11).The challenge of integrating graphene into a semiconductor fabrication process is also addressed through the development of a novel vacuum transfer method-ology that allows photoresist free transfer. The two main fabrication processes; graphene supplied on the wafer “Pre-Transfer” and graphene transferred after met-allisation “Post-Transfer” were compared in terms of graphene channel resistance and graphene end quality (defect density and photoresist). The Post-Transfer pro-cess higher quality (less damage, residue and doping, confirmed by Raman spec-troscopy).Following sensor fabrication, the next stages of creating a sensor platform involve the passivation and packaging of the sensor chip. Different approaches using dielec-tric deposition approaches are compared for passivation. Molecular Vapour Deposi-tion (MVD) deposited Al2O3 was shown to produce graphene channels with lower damage than unprocessed graphene, and also improves graphene doping bringing the Dirac point of the graphene close to 0 V. The packaging integration of microfluidics is investigated comparing traditional soft lithography approaches and the new 3D printed microfluidic approach. Specific microfluidic packaging for blood separation towards a blood sampling point of care sensor is examined to identify the laminar approach for lower blood cell count, as a method of pre-processing the blood sample before sensing.To test the sensitivity of the Post-Transfer MVD passivated graphene sensor de-veloped in this work, real-time IV measurements were performed to identify throm-bin protein binding in real-time on the graphene surface. The sensor was function-alised using a thrombin specific aptamer solution and real-time IV measurements were performed on the functionalised graphene sensor with a range of biologically relevant protein concentrations. The resulting sensitivity of the graphene sensor was in the 1-100 pg/ml concentration range, producing a resistance change of 0.2% per pg/ml. Specificity was confirmed using a non-thrombin specific aptamer as the neg-ative control. These results indicate that the graphene sensor platform developed in this thesis has the potential as a highly sensitive POCD. The processes developed here can be used to develop graphene sensors for multiple biomarkers in the future
Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos
Data charts are widely used in our daily lives, being present in regular media,
such as newspapers, magazines, web pages, books, and many others. A well constructed
data chart leads to an intuitive understanding of its underlying data
and in the same way, when data charts have wrong design choices, a redesign
of these representations might be needed. However, in most cases, these
charts are shown as a static image, which means that the original data are not
usually available. Therefore, automatic methods could be applied to extract the
underlying data from the chart images to allow these changes. The task of
recognizing charts and extracting data from them is complex, largely due to the
variety of chart types and their visual characteristics.
Computer Vision techniques for image classification and object detection are
widely used for the problem of recognizing charts, but only in images without
any disturbance. Other features in real-world images that can make this task
difficult are not present in most literature works, like photo distortions, noise,
alignment, etc. Two computer vision techniques that can assist this task and
have been little explored in this context are perspective detection and
correction. These methods transform a distorted and noisy chart in a clear
chart, with its type ready for data extraction or other uses. The task of
reconstructing data is straightforward, as long the data is available the
visualization can be reconstructed, but the scenario of reconstructing it on the
same context is complex.
Using a Visualization Grammar for this scenario is a key component, as these
grammars usually have extensions for interaction, chart layers, and multiple
views without requiring extra development effort.
This work presents a model for automated support for custom recognition, and
reconstruction of charts in images. The model automatically performs the
process steps, such as reverse engineering, turning a static chart back into its
data table for later reconstruction, while allowing the user to make modifications
in case of uncertainties. This work also features a model-based architecture
along with prototypes for various use cases. Validation is performed step by
step, with methods inspired by the literature. This work features three use
cases providing proof of concept and validation of the model.
The first use case features usage of chart recognition methods focused on
documents in the real-world, the second use case focus on vocalization of
charts, using a visualization grammar to reconstruct a chart in audio format,
and the third use case presents an Augmented Reality application that
recognizes and reconstructs charts in the same context (a piece of paper)
overlaying the new chart and interaction widgets. The results showed that with
slight changes, chart recognition and reconstruction methods are now ready for
real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando
presentes nos meios de comunicação regulares, tais como jornais, revistas,
páginas web, livros, e muitos outros. Um gráfico bem construído leva a uma
compreensão intuitiva dos seus dados inerentes e da mesma forma, quando
os gráficos de dados têm escolhas de conceção erradas, poderá ser
necessário um redesenho destas representações. Contudo, na maioria dos
casos, estes gráficos são mostrados como uma imagem estática, o que
significa que os dados originais não estão normalmente disponíveis. Portanto,
poderiam ser aplicados métodos automáticos para extrair os dados inerentes
das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de
reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande
parte devido à variedade de tipos de gráficos e às suas características visuais.
As técnicas de Visão Computacional para classificação de imagens e deteção
de objetos são amplamente utilizadas para o problema de reconhecimento de
gráficos, mas apenas em imagens sem qualquer ruído. Outras características
das imagens do mundo real que podem dificultar esta tarefa não estão
presentes na maioria das obras literárias, como distorções fotográficas, ruído,
alinhamento, etc. Duas técnicas de visão computacional que podem ajudar
nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e
correção da perspetiva. Estes métodos transformam um gráfico distorcido e
ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados
ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que
os dados estejam disponíveis a visualização pode ser reconstruída, mas o
cenário de reconstrução no mesmo contexto é complexo.
A utilização de uma Gramática de Visualização para este cenário é um
componente chave, uma vez que estas gramáticas têm normalmente
extensões para interação, camadas de gráficos, e visões múltiplas sem exigir
um esforço extra de desenvolvimento.
Este trabalho apresenta um modelo de suporte automatizado para o
reconhecimento personalizado, e reconstrução de gráficos em imagens
estáticas. O modelo executa automaticamente as etapas do processo, tais
como engenharia inversa, transformando um gráfico estático novamente na
sua tabela de dados para posterior reconstrução, ao mesmo tempo que
permite ao utilizador fazer modificações em caso de incertezas. Este trabalho
também apresenta uma arquitetura baseada em modelos, juntamente com
protótipos para vários casos de utilização. A validação é efetuada passo a
passo, com métodos inspirados na literatura. Este trabalho apresenta três
casos de uso, fornecendo prova de conceito e validação do modelo.
O primeiro caso de uso apresenta a utilização de métodos de reconhecimento
de gráficos focando em documentos no mundo real, o segundo caso de uso
centra-se na vocalização de gráficos, utilizando uma gramática de visualização
para reconstruir um gráfico em formato áudio, e o terceiro caso de uso
apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói
gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos
gráficos e widgets de interação. Os resultados mostraram que com pequenas
alterações, os métodos de reconhecimento e reconstrução dos gráficos estão
agora prontos para os gráficos do mundo real, tendo em consideração o
tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic
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After Creation: Intergovernmental Organizations and Member State Governments as Co-Participants in an Authority Relationship
This is a re-amalgamation of what started as one manuscript and became two when the length proved to be more than any publisher wanted to consider. The splitting consisted of removing what are now Parts 3, 4, and 5 so that the manuscript focused on the outcome-related shared beliefs holding an authority relationship together. Those parts were last worked on in 2018. The rest were last worked on in late 2021 but also remain incomplete.
The relational approach adopted in this study treats intergovernmental organizations and the governments of member states as co-participants in an authority relationship with the governments of their member states. Authority relationships link two types of actor, defined by their authority-holder or addressee role in the relationship, through a set of shared beliefs about why the relationship exists and how the participants should fulfill their respective roles. The IGO as authority holder has a role that includes a right to instruct other actors about what they should or should not do; the governments of member states as addressees are expected to comply with the instructions. Three sets of shared beliefs provide the conceptual “glue” holding the relationship together. The first defines the goal of the collective effort, providing both the rationale for having the authority relationship and providing a lode star for assessments of the collective effort’s success or lack of success. The second set defines the shared understanding about allocation of roles and the process of interaction by establishing shared expectations about a) the selection process by which particular actors acquire authority holder roles, b) the definitions identifying one or more categories of addressees expected to follow instructions, and c) the procedures through which the authority holder issues instructions. The third set focus on the outcomes of cooperation through the relationship by defining a) the substantive areas in which the authority holder may issue instructions, b) the bases for assessing the relevance actions mandated in instructions for reaching the goal, and c) the relative efficacy of action paths chosen for reaching the goal as compared to other possible action paths.
Using an authority relationship framework for analyzing cooperation through IGOs highlights the inherently bi-directional nature of IGO-member government activity by viewing their interaction as involving a three-step process in which the IGO as authority holder decides when to issue what instruction, the member state governments as followers react to the instruction with anything from prompt and full compliance through various forms of pushback to outright rejection, and the IGO as authority holder responds to how the followers react with efforts to increase individual compliance with instructions and reinforce continuing acceptance of the authority relationship. Foregrounding the dynamics produced by the interaction of these two streams of perception and action reveals more clearly how far intergovernmental organizations acquire capacity to operate as independent actors, the dynamic ways they maintain that capacity, and how much they influence member governments’ beliefs and actions at different times. The approach fosters better understanding of why, when, and for how long governments choose cooperation through an IGO even in periods of rising unilateralism
Path integral based convolution and pooling for graph neural networks
Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose a path integral based graph neural networks (PAN) for classification and regression tasks on graphs. Specifically, we consider a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It generalizes the graph Laplacian to a new transition matrix we call maximal entropy transition (MET) matrix derived from a path integral formalism. Importantly, the diagonal entries of the MET matrix are directly related to the subgraph centrality, thus lead to a natural and adaptive pooling mechanism. PAN provides a versatile framework that can be tailored for different graph data with varying sizes and structures. We can view most existing GNN architectures as special cases of PAN. Experimental results show that PAN achieves state-of-the-art performance on various graph classification/regression tasks, including a new benchmark dataset from statistical mechanics we propose to boost applications of GNN in physical sciences
Algorithms to estimate Shapley value feature attributions
Feature attributions based on the Shapley value are popular for explaining
machine learning models; however, their estimation is complex from both a
theoretical and computational standpoint. We disentangle this complexity into
two factors: (1)~the approach to removing feature information, and (2)~the
tractable estimation strategy. These two factors provide a natural lens through
which we can better understand and compare 24 distinct algorithms. Based on the
various feature removal approaches, we describe the multiple types of Shapley
value feature attributions and methods to calculate each one. Then, based on
the tractable estimation strategies, we characterize two distinct families of
approaches: model-agnostic and model-specific approximations. For the
model-agnostic approximations, we benchmark a wide class of estimation
approaches and tie them to alternative yet equivalent characterizations of the
Shapley value. For the model-specific approximations, we clarify the
assumptions crucial to each method's tractability for linear, tree, and deep
models. Finally, we identify gaps in the literature and promising future
research directions
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