1,671 research outputs found
Speed of Publication of Statutes and Regulations in the United Kingdom, Canada, and the United States
No full textExamines reasons for typical delays in publication of statutes & regulations in UK,US, & Canada. Finds US federal laws published more slowly because of time taken to add marginal notes after enactmen
Legal Citation Form: Theory and Practice
No full textSets forth 13 principles as a basis for a system of legal citation forms, and critically reviews various rules in the 13th edition of A Uniform System of Citation
Normative reconstruction and social character of freedom in Axel Honneth
No full textO presente objeto de pesquisa busca proceder ao estudo e identificação dos traços essenciais envolvidos na abordagem teórica das relações sociais e políticas trazidas na obra O Direito da Liberdade do filósofo alemão Axel Honneth. Faz-se uma análise da influência hegeliana sobre o conceito de liberdade, assim como dos fatores relacionados com o suprimento das carências subjetivas, mediadas pelas diferentes “esferas” sociais. Honneth, assim, procura trazer à tona a compreensão de um novo modelo de liberdade advindo da Filosofia do Direito de Hegel, o qual se distingue substancialmente dos modelos tradicionais. O autor busca evidenciar a limitação das teorias da justiça de tradição liberal, invocando a necessidade de uma visão integrada das relações sociais experimentadas nas esferas referidas por Hegel, concebendo-se uma experiência concreta de liberdade social. Nesse sentido, evidencia-se o caráter interdisciplinar e emancipatório do método de reconstrução normativa como base teórica para a justificação pública nas sociedades modernas.This research object aims to study and identify the essential traits involved in the theoretical approach of social and political relations brought in the work Freedom’s Right by the german philosopher Axel Honneth. It analyses the hegelian influence on the concept of freedom, as well as the factors related to the supply of subjective needs, mediated by the different social "spheres". Honneth thus seeks to bring to light the understanding of a new model of freedom stemming from Hegel’s Philosophy of Law, which differs substantially from traditional models. The author seeks to highlight the limitation of liberal theories of justice, invoking the need for an integrated view of the social relations experienced in the spheres referred to by Hegel, conceiving a concrete experience of social freedom. In this sense, the interdisciplinary and emancipatory character of the normative reconstruction method is evidenced, as a theorical basis for public justification in modern societies
sj-docx-1-uro-10.1177_20514158221088451 – Supplemental material for Robot-assisted surgery in horseshoe kidneys: A safety and feasibility multi-centre case series
No full textSupplemental material, sj-docx-1-uro-10.1177_20514158221088451 for Robot-assisted surgery in horseshoe kidneys: A safety and feasibility multi-centre case series by Alexander Ng, Arjun Nathan, Nicholas Campain, Mariella Fortune-Ely, Siddhant Patki, Yuigi Yuminaga, Faiz Mumtaz, Aziz Gulamhusein, Maxine Tran, Senthil Nathan, Ravi Barod, Axel Bex and Prasad Patki in Journal of Clinical Urology</p
Reply to E. Jason Abel Letter to the Editor re: Lorenzo Marconi, Saeed Dabestani, Thomas B. Lam, et al. Systematic Review and Meta-analysis of Diagnostic Accuracy of Percutaneous Renal Tumour Biopsy. Eur Urol 2016;69:660-73
No full textsj-docx-1-uro-10.1177_20514158211068310 – Supplemental material for Interactive virtual 3D image reconstruction to assist renal surgery in patients with fusion anomalies of the kidney
No full textSupplemental material, sj-docx-1-uro-10.1177_20514158211068310 for Interactive virtual 3D image reconstruction to assist renal surgery in patients with fusion anomalies of the kidney by Naomi Morka, Lorenz Berger, Eoin Hyde, Faiz Mumtaz, Ravi Barod, Prasad Patki, Niels Graafland, Kees Hendricksen, Maxine Tran and Axel Bex in Journal of Clinical Urology</p
Management of Metastatic Nonclear-Cell Renal Cell Carcinoma: What are the Options and Challenges?
No full textThis case presents a 68-yr-old female patient with primary metastatic nonclear renal cell carcinoma (RCC) with multiple bone lesions. The patient underwent a single resection of skull bone lesion (diagnostic for poorly differentiated carcinoma of unknown origin) and cytoreductive nephrectomy. The pathology of the kidney specimen demonstrated an oncocytic papillary RCC. Within 3 mo, she developed skeletal progressive disease and was started on systemic therapy (sunitinib). After initial stabilization, bone metastasis progressed during the third cycle of sunitinib and required second-line therapy (cabozantinib). One of the major unmet needs in non-clear cell RCC is the lack of specific systemic therapy. Data on immunotherapy are still limited. Inclusion of these patients in clinical trials is strongly recommended. PATIENT SUMMARY: Patients with metastatic kidney cancer who present with the less common histological subtype (non-clear cell) have poor survival. In this case, the patient responded to second-line therapy. Very few therapies provide response to treatment. Patients should be offered participation in clinical trials testing combinations with immunotherapy
Reply to Jae Heon Kim's Letter to the Editor re: Lorenzo Marconi, Saeed Dabestani, Thomas B. Lam, et al. Systematic Review and Meta-analysis of Diagnostic Accuracy of Percutaneous Renal Tumour Biopsy. Eur Urol 2016;69:660-73
No full textTrustworthy Artificial Intelligence Methods for Image Analysis and Benchmarking of Neural Network Interpretability
No full textThe focus of this thesis is on Artificial Intelligence (AI) and on how AI can be presented
to people in a way that is more explainable, intuitive and trustworthy.
The field of Artificial intelligence is vast and encompasses many subdomains concerned
with how machines and software think and act and how to build intelligent
entities [3]. Broadly speaking, AI deals with a machine’s ability to learn and acquire
knowledge, reason, solve problems and apply and adapt what is learned to new situations.
Several definitions of AI have been used. Some try to define AI in terms of
how humanlike it is vs how rational it is on the one hand, and on the other in how
it acts vs how it thinks. For example, for a machine to pass the Turing test, it needs
to convincingly act humanly. The cognitive modelling approach tries to get machines
to think humanly and deals more with cognitive science and psychology. Another
approach is to make the machine think rationally, by enforcing logic and inference
rules. This is different still from a machine that acts rational, where the machine
needs to do the right or optimal thing. This latter approach is the most common one
as it is more easily captured in mathematical formulation, for example optimizing
a utility or loss function. Throughout the years, the field of AI has continued to
grow, and the number of subfields under its umbrella is numerous. Throughout the
history of modern AI many different methods have been used and proposed, from
artificial neurons [4], Hebbian learning [5], and reasoning as search and heuristics [6],
and later expanded to include logic programming such as Prolog [7], genetic programs
[8], and expert systems (of which Dendral [9] is often considered the first one). In
the 80’s, hidden Markov models [10] became more popular, and Bayesian networks
[11, 12] followed suit, leading to machine learning, Big Data, deep learning, and now
large language models [3]. Presently, AI permeates our daily lives in various forms,from video games and selfie filters to personalised video recommendations and medical
devices, even extending to autonomous vehicles. Large language models, such as
the ones used in chatbots and smart assistants, are used in content generation for
news articles, product descriptions, and academic theses. Such models are currently
garnering significant attention and have become the next major breakthrough in AI.
In this thesis, the focus will mainly be on Deep Neural Networks (DNN) and
their applications. The smallest building block of a neural network is the model of
a neuron, which was first introduced by McCulloch and Pitts [4] and was inspired
by the function of biological neurons. Consider a very high-level abstraction of a
neuron: through dendrites and receptors, the neuron receives stimuli, and when the
stimuli reach a threshold, the neuron fires an electrical signal through the axon.
The mathematical equivalent of such a neuron is a non-linear element with inputs
xi multiplied with weights wi. After adding a bias term b, this is passed through a
non-linear function f, also called the activation function. Originally, a neuron was a
binary classifier, and the non-linearity used was the Heaviside step function or sign
function. However, a variety of other functions have been used, such as the sigmoid
and tanh. In current neural networks, the rectified linear unit (ReLU) is the most
widely used. A single neuron is only able to learn linearly separable concepts. By
combining several neurons in a layer and stacking several layers so that the units in
one layer are fully connected to all the units in the previous network (so that the
output h of layer l is hl = f l (Wlhl−1)), we can build a Multilayer perceptron (MLP)
that can distinguish non-linearly separable data. This type of feed-forward network is
a basic neural network, and can already achieve remarkable results. In Hornik et al.
[13] showed that an MLP with as few as one hidden layer is a universal approximator,
meaning they can approximate any measurable funtion to any degree of accuracy,
given enough hidden units. The networks are trained to optimize a loss function that
quantifies the performance and serves as a proxy for the real objective. The gradients
of this loss function w.r.t the model’s weights can be efficiently calculated using the
back-propagation algorithm [14]. Gradients are propagated layer by layer using the
chain rule. The weights are then updated based on these gradients using the gradient
descent optimisation or variants thereof. Neural networks are discussed further in the
next section, section 1.1.
The term deep in Deep Learning refers to the utilisation of a larger number of
consecutive layers in these networks. By adding more layers on top of each other,
each layer is able to learn increasingly complex and meaningful features, enabling the
model to more easily grasp complex interactions in the data and simplify the modelling
of complex functions. In this way, Deep Learning does a form of automated featureengineering by learning relevant features directly from the raw data, potentially capturing
more intricate patterns and relationships that may not be easily identifiable
or feasible with handcrafted features, especially for computer vision. However, fully
connected layers have the disadvantage that they contain a huge amount of learnable
weights, making these models computationally expensive and prone to overfitting.
Therefore, in computer vision applications, convolutional layers are used. The neurons
in these layers are locally connected to a window of the input as opposed to a
fully connected layer. The window will then slide over the whole input to process it.
This reduces the number of weights in the layer and introduces a useful inductive bias:
pixels that are spatially close are processed together, leveraging the spatial structure
of images. Together with convolutional layers, convolutional neural networks (CNN)
use pooling layers to reduce the spatial resolution of features further, further reducing
the number of weights needed. Still, these models can contain millions of parameters,
making it an impossible task to comprehend the function of every single one. They
are considered black-box models, as we often do not know which features exactly have
been learned or how the decisions are being made.
CNNs had been successfully applied in real-world applications before, but it took
until 2011 for them to really take off, as more computation power became more
readily available due to efficient GPU implementations. In 2011, the model by Cire¸san
et al. [15] started winning image competitions, and in 2012, the AlexNet architecture
[16] won the ImageNet Large Scale Visual Recognition Challenge. CNNs have since
delivered state-of-the-art performance on many computer vision tasks. Additional
background is given in section 1.2.
While AI applications have become ubiquitous over the past few years and will
undoubtfully become an even more prevalent part of our lives in the years to come, the
black-box nature of AI models causes friction in AI uptake, especially where transparency,
accountability and interpretability is critical. For example, in healthcare,
where life-and-death decisions are being made. Early detection of a disease at an
early phase is critical to prevent disease progression and massively improve patient
outcomes. A wrong diagnosis can lead to harmful or fatal consequences. In autonomous
vehicles, a wrong detection or a missed traffic sign can cause a fatal crash.
In finance, explanations are needed to assess risks, facilitate decision making and is
needed for regulatory compliance. Moreover, a “right to explanation” is mandated
by the GDPR [17, Articles 13-15, 22]. No matter the field, no model is perfect, and
mistakes will happen. Without a reasonable explanation of the decisions made, it is
difficult for people to trust the AI and justify its use.
Explanations are not just necessary to justify the decisions and predictions beingmade. They are also essential to debug the model in several ways. There are various
ways in which biases can end up in the model, such as biased or skewed training
data or algorithmic bias, which needs to be snuffed out. By having the model explain
predictions, it becomes possible to debug the biases and take action. Explanations
help investigate the errors made by the model, allowing developers to understand the
underlying causes or to detect known failure modes. It also makes it easier to monitor
the performance over time, as a perfectly working system may start to misbehave over
time due to distribution shifts. Furthermore, it allows us to uncover new relations
in the data previously unknown to domain experts, allowing them to formulate new
hypotheses and create new knowledge. Because of these reasons, Explainable AI is a
rapidly evolving field, and new papers are published at a rapid pace. In section 1.3, we
will expand more on several commonly used XAI methods. Part II discusses feature
attribution methods, a group of XAI methods specifically used for image classification
models. These chapters cover a basic introduction of simple and common XAI methods,
and the specific feature attribution methods used in the papers presented. We
focus on methods that are local and model-centric i.e. they explain a specific sample
for a specific model. This is of course only a subset of possible XAI methods. A taxonomy
for the different can be found in [18]. For an overview of the state-of-the-art
methods, we refer to Minh et al. [19] and Linardatos et al. [20]
sj-pdf-1-hsr-10.1177_13558196221082585 – Supplemental Material for Loss associated with subtractive health service change: The case of specialist cancer centralization in England
No full textSupplemental Material, sj-pdf-1-hsr-10.1177_13558196221082585 for Loss associated with subtractive health service change: The case of specialist cancer centralization in England by Georgia Black, Victoria Wood, Angus Ramsay, Cecilia Vindrola-Padros, Catherine Perry, Caroline Clarke, Claire Levermore, Kathy Pritchard-Jones, Axel Bex, Maxine Tran, David Shackley, John Hines, Muntzer Mughal and Naomi J Fulop in Journal of Health Services Research & Policy</p
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