8,937 research outputs found
On information captured by neural networks: connections with memorization and generalization
Despite the popularity and success of deep learning, there is limited
understanding of when, how, and why neural networks generalize to unseen
examples. Since learning can be seen as extracting information from data, we
formally study information captured by neural networks during training.
Specifically, we start with viewing learning in presence of noisy labels from
an information-theoretic perspective and derive a learning algorithm that
limits label noise information in weights. We then define a notion of unique
information that an individual sample provides to the training of a deep
network, shedding some light on the behavior of neural networks on examples
that are atypical, ambiguous, or belong to underrepresented subpopulations. We
relate example informativeness to generalization by deriving nonvacuous
generalization gap bounds. Finally, by studying knowledge distillation, we
highlight the important role of data and label complexity in generalization.
Overall, our findings contribute to a deeper understanding of the mechanisms
underlying neural network generalization.Comment: PhD thesi
The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures
Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation.
Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents.
In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI
TeamSTEPPS and Organizational Culture
Patient safety issues remain despite several strategies developed for their deterrence. While many safety initiatives bring about improvement, they are repeatedly unsustainable and short-lived. The index hospital’s goal was to build an organizational culture within a groundwork that improves teamwork and continuing healthcare team engagement. Teamwork influences the efficiency of patient care, patient safety, and clinical outcomes, as it has been identified as an approach for enhancing collaboration, decreasing medical errors, and building a culture of safety in healthcare. The facility implemented Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS), an evidence-based framework which was used for team training to produce valuable and needed changes, facilitating modification of organizational culture, increasing patient safety compliance, or solving particular issues. This study aimed to identify the correlation between TeamSTEPPS enactment and improved organizational culture in the ambulatory care nursing department of a New York City public hospital
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The impact of employees' working relations in creating and retaining trust: the case of the Bahrain Olympic Committee
Introduction: This thesis investigates the impact of employees’ working relations in creating, maintaining and retaining trust in the Bahrain Olympic Committee (BOC).
Aim: The main aim of this thesis is to determine how the three groups of Organisational Trust variables, namely Social System Elements (SSE), Factors of Trustworthiness (FoT) and Third-Party Gossip (TPG), affect employees’ Organisational Trust (OTR) in the BOC and promote Organisational Citizenship Behaviour (OCB). To answer this main aim, a conceptual framework was created that focused on exploring the following research aims: (1) the interrelationship between SSE and FoT, (2) the effect of SSE on OTR, (3) the impact of TPG on OTR and (4) the effect of OTR on overall OCB.
Methodology: The study uses a mixed-method case study research style that included in-depth semi-structured interviews with 17 managers, an online questionnaire survey with 320 employees of the BOC and an analysis of the BOC’s Annual Reports from 2015 to 2018.
Results: The qualitative and quantitative findings indicate, firstly, that there is a significant interrelationship between SSE and FoT, establishing that SSE’s perception of organisational justice (OJ), including that FoTs benevolence and integrity as the most important factors in yielding employees’ trust in the BOC. Secondly, it has been established that SSEs have significant direct and indirect effects on OTR. Thirdly, negative and positive TPG concurrently occurred in the BOC and the prevalence of negative TPG poses more impact on OTR. Finally, this study’s findings demonstrated OTR’s effect in generating OCB, including that Civic Virtue was rated as the most preferred of the five OCB themes; this indicates the managers’ and the employees’ strong emotional attachment and support of the activities taking place at the BOC.
Contributions: Overall, this thesis substantially contributes to OTR literature, particularly in the context of the Middle East. It also proposes several insightful recommendations for future research and practical implications for practitioners in the field of Organisational Trust
Antecedents of customer loyalty in the manufacturing industry
This thesis concerns the study of customer loyalty and its antecedents in the UK
manufacturing sector. It adopts a critical realist perspective to the study of customer loyalty,
locating the concept in the relationship marketing and social psychology literatures. The
findings generated by the literature review and the results of an exploratory qualitative study
leads to the development of a conceptual framework in which functional, social and
emotional relationship value, customer satisfaction, and moderator variable, relationship age,
are believed to influence the level of customer loyalty in the manufacturing industry.
The conceptual framework is tested empirically using a quantitative survey design in the
context of the UK manufacturing industry. Data is analysed through application of the partial
least squares (PLS) structural equation modelling technique.
From a theoretical perspective, the study makes a number of valuable contributions to the
relationship marketing literature. The study confirms the importance of social and emotional
relationship value aspects on customer satisfaction and loyalty outcomes in the
manufacturing industry. The findings offer a new theoretical perspective of the role social and
emotional value play in creating loyal customers and the role emotional value performs in
buyer’s feelings of satisfaction in the B2B domain. The findings also suggest that customer
satisfaction acts as a partial mediator in the relationship between customer value and
customer loyalty. Moreover, a new theoretical concept of emotional value featuring frustration
and human touch in addition to interpersonal relationships is also evidenced from the
research results. Furthermore, the study also shows that the theory of consumption values
can be applied to the B2B manufacturing domain.
The results propose that behavioural loyalty can be expressed through customer satisfaction,
and functional and emotional elements of relationship value. Whereas, attitudinal loyalty can
be conveyed by customer satisfaction, and functional and social components of relationship
value. These relationships are in turn also partially mediated through customer satisfaction.
The results also indicate that all three dimensions of functional, social and emotional value
influence customer satisfaction outcomes.
Overall, the study provides recommendations on how to maximise customer loyalty through
strategic combinations of relationship value. It also provides guidance on how to improve
customer satisfaction through different elements of relationship value in the manufacturing
industry. From a practical viewpoint, the research study findings offer suppliers important
guidelines and a toolkit for establishing, developing, and maintaining successful relationships
with their customers in the manufacturing industry
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks
Connected automated driving has the potential to significantly improve urban
traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative
behavior planning can be employed to jointly optimize the motion of multiple
vehicles. Most existing approaches to automatic intersection management,
however, only consider fully automated traffic. In practice, mixed traffic,
i.e., the simultaneous road usage by automated and human-driven vehicles, will
be prevalent. The present work proposes to leverage reinforcement learning and
a graph-based scene representation for cooperative multi-agent planning. We
build upon our previous works that showed the applicability of such machine
learning methods to fully automated traffic. The scene representation is
extended for mixed traffic and considers uncertainty in the human drivers'
intentions. In the simulation-based evaluation, we model measurement
uncertainties through noise processes that are tuned using real-world data. The
paper evaluates the proposed method against an enhanced first in - first out
scheme, our baseline for mixed traffic management. With increasing share of
automated vehicles, the learned planner significantly increases the vehicle
throughput and reduces the delay due to interaction. Non-automated vehicles
benefit virtually alike.Comment: 8 pages, 7 figures, 34th IEEE Intelligent Vehicles Symposium (IV),
updated to accepted versio
Swarm Reinforcement Learning For Adaptive Mesh Refinement
The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow
for a favorable trade-off between computational speed and simulation accuracy.
Classical methods for AMR depend on task-specific heuristics or expensive error
estimators, hindering their use for complex simulations. Recent learned AMR
methods tackle these problems, but so far scale only to simple toy examples. We
formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh
is modeled as a system of simple collaborating agents that may split into
multiple new agents. This framework allows for a spatial reward formulation
that simplifies the credit assignment problem, which we combine with Message
Passing Networks to propagate information between neighboring mesh elements. We
experimentally validate the effectiveness of our approach, Adaptive Swarm Mesh
Refinement (ASMR), showing that it learns reliable, scalable, and efficient
refinement strategies on a set of challenging problems. Our approach
significantly speeds up computation, achieving up to 30-fold improvement
compared to uniform refinements in complex simulations. Additionally, we
outperform learned baselines and achieve a refinement quality that is on par
with a traditional error-based AMR strategy without expensive oracle
information about the error signal.Comment: Version 1 of this paper is a preliminary workshop version that was
accepted as a workshop paper in the ICLR 2023 Workshop on Physics for Machine
Learnin
Using machine learning to predict pathogenicity of genomic variants throughout the human genome
Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität.
Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores.
Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt.
Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity.
Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants.
The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency.
In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org
An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains
This research aimed to develop an empirical understanding of the relationships between integration,
dynamic capabilities and performance in the supply chain domain, based on which, two conceptual
frameworks were constructed to advance the field. The core motivation for the research was that, at
the stage of writing the thesis, the combined relationship between the three concepts had not yet
been examined, although their interrelationships have been studied individually.
To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative
study, which was undertaken via multiple case studies to investigate lines of enquiry that would
address the research questions formulated. This is consistent with the author’s philosophical
adoption of the ontology of relativism and the epistemology of constructionism, which was considered
appropriate to address the research questions. Empirical data and evidence were collected, and
various triangulation techniques were employed to ensure their credibility. Some key features of
grounded theory coding techniques were drawn upon for data coding and analysis, generating two
levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in
improving performance, the performance also informed the former. This reflects a cyclical and
iterative approach rather than one purely based on linearity. Adopting a holistic approach towards
the relationship was key in producing complementary strategies that can deliver sustainable supply
chain performance.
The research makes theoretical, methodological and practical contributions to the field of supply
chain management. The theoretical contribution includes the development of two emerging
conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it
allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed
insight into their correlations. The latter gives a holistic view of their relationships and how they are
connected, reflecting a middle-range theory that bridges theory and practice. The methodological
contribution lies in presenting models that address gaps associated with the inconsistent use of
terminologies in philosophical assumptions, and lack of rigor in deploying case study research
methods. In terms of its practical contribution, this research offers insights that practitioners could
adopt to enhance their performance. They can do so without necessarily having to forgo certain
desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities
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