6,567 research outputs found
Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review
Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)
Dynamic Nanophotonic Structures Leveraging Chalcogenide Phase-Change Materials
Chip-scale nanophotonic devices have the potential to enable next-generation imaging, computing, communication, and engineered quantum systems with very stringent performance requirements on size, power, integrability, stability, and bandwidth. The emergence of meta-optic devices with deep subwavelength features has enabled the formation of ultra-thin flat optical structures to replace bulky conventional counterparts in free-space applications. Nevertheless, progress in meta-optics has been slowed due to the passive nature of existing devices and the urgent need for a reliable, fast, low-power, and robust reconfiguration mechanism.
In this research, I devised a new material and device platform to resolve this challenge. Through detailed theoretical design, nanofabrication, and experimental demonstration, I demonstrated the unique features of my proposed platform as an essential building block of truly scalable adaptive flat optics for the active manipulation of optical wavefronts. One of the key attributes of this research is the integration of CMOS-compatible materials for the fabrication of passive devices with phase-change materials that provide the largest known modulation of the index of refraction upon stimulation with an optical or electrical signal. A unique selection of phase-change materials for operation in the near-infrared and visible wavelengths has been made, followed by developing the optimum deposition and fabrication processes for the realization of nanophotonics devices that integrate these functional materials with semiconductor and plasmonic materials. A major breakthrough in this process was the design and realization of integrated electrical stimulation circuitry with far better performance compared to existing solutions.
Using this platform, I experimentally demonstrated the first electrically tunable meta-optic structure for fast optical switching with a high contrast ratio and dynamic wavefront scanning with a large steering angle. This is a major achievement as it essentially allows the engineering of a desired optical wavefront with fast reconfigurability at low power consumption. In an independent work, I demonstrated, for the first time, a nonvolatile meta-optic structure for high-resolution, wide-gamut, and high-contrast microdisplays with added polarization controllability and the possibility of implementation on a flexible substrate. Further features of this metaphotonic display include: 1) full addressability at the microscale pixel via fast electrical pulses; 2) super-resolution pixels with controllable brightness and contrast; and 3) a wide range of colors with high saturation and purity. Lastly, for the first time, I realized a hybrid photonic-plasmonic meta-optic platform with active control over the spatial, spectral, and temporal properties of an optical wavefront. This is a major achievement as it essentially allows the engineering of a desired optical wavefront with fast reconfigurability at low power consumption. These demonstrations are now being pursued in different directions for novel systems for imaging, sensing, computing, and quantum applications, just to name a few.Ph.D
2023-2024 Undergraduate Catalog
2023-2024 undergraduate catalog for Morehead State University
Production Optimization Indexed to the Market Demand Through Neural Networks
Connectivity, mobility and real-time data analytics are the prerequisites for a new model of intelligent
production management that facilitates communication between machines, people and
processes and uses technology as the main driver.
Many works in the literature treat maintenance and production management in separate approaches,
but there is a link between these areas, with maintenance and its actions aimed at ensuring the
smooth operation of equipment to avoid unnecessary downtime in production.
With the advent of technology, companies are rushing to solve their problems by resorting to technologies
in order to fit into the most advanced technological concepts, such as industries 4.0 and
5.0, which are based on the principle of process automation. This approach brings together database
technologies, making it possible to monitor the operation of equipment and have the opportunity
to study patterns of data behavior that can alert us to possible failures.
The present thesis intends to forecast the pulp production indexed to the stock market value.The
forecast will be made by means of the pulp production variables of the presses and the stock exchange
variables supported by artificial intelligence (AI) technologies, aiming to achieve an effective
planning. To support the decision of efficient production management, in this thesis algorithms
were developed and validated with from five pulp presses, as well as data from other sources, such
as steel production and stock exchange, which were relevant to validate the robustness of the model.
This thesis demonstrated the importance of data processing methods and that they have great relevance
in the model input since they facilitate the process of training and testing the models. The
chosen technologies demonstrated good efficiency and versatility in performing the prediction of
the values of the variables of the equipment, also demonstrating robustness and optimization in
computational processing. The thesis also presents proposals for future developments, namely
in further exploration of these technologies, so that there are market variables that can calibrate
production through forecasts supported on these same variables.Conectividade, mobilidade e análise de dados em tempo real são pré-requisitos para um novo
modelo de gestão inteligente da produção que facilita a comunicação entre máquinas, pessoas e
processos, e usa a tecnologia como motor principal.
Muitos trabalhos na literatura tratam a manutenção e a gestão da produção em abordagens separadas,
mas existe uma correlação entre estas áreas, sendo que a manutenção e as suas polÃticas
têm como premissa garantir o bom funcionamento dos equipamentos de modo a evitar paragens
desnecessárias na linha de produção.
Com o advento da tecnologia há uma corrida das empresas para solucionar os seus problemas
recorrendo às tecnologias, visando a sua inserção nos conceitos tecnológicos, mais avançados,
tais como as indústrias 4.0 e 5.0, as quais têm como princÃpio a automatização dos processos.
Esta abordagem junta as tecnologias de sistema de informação, sendo possÃvel fazer o acompanhamento
do funcionamento dos equipamentos e ter a possibilidade de realizar o estudo de padrões
de comportamento dos dados que nos possam alertar para possÃveis falhas.
A presente tese pretende prever a produção da pasta de papel indexada às bolsas de valores. A
previsão será feita por via das variáveis da produção da pasta de papel das prensas e das variáveis
da bolsa de valores suportadas em tecnologias de artificial intelligence (IA), tendo como objectivo
conseguir um planeamento eficaz. Para suportar a decisão de uma gestão da produção eficiente,
na presente tese foram desenvolvidos algoritmos, validados em dados de cinco prensas de pasta de
papel, bem como dados de outras fontes, tais como, de Produção de Aço e de Bolsas de Valores,
os quais se mostraram relevantes para a validação da robustez dos modelos.
A presente tese demonstrou a importância dos métodos de tratamento de dados e que os mesmos
têm uma grande relevância na entrada do modelo, visto que facilita o processo de treino e testes dos
modelos. As tecnologias escolhidas demonstraram uma boa eficiência e versatilidade na realização
da previsão dos valores das variáveis dos equipamentos, demonstrando ainda robustez e otimização
no processamento computacional.
A tese apresenta ainda propostas para futuros desenvolvimentos, designadamente na exploração
mais aprofundada destas tecnologias, de modo a que haja variáveis de mercado que possam calibrar
a produção através de previsões suportadas nestas mesmas variáveis
An uncertainty prediction approach for active learning - application to earth observation
Mapping land cover and land usage dynamics are crucial in remote sensing since farmers
are encouraged to either intensify or extend crop use due to the ongoing rise in the world’s
population. A major issue in this area is interpreting and classifying a scene captured in
high-resolution satellite imagery. Several methods have been put forth, including neural
networks which generate data-dependent models (i.e. model is biased toward data) and
static rule-based approaches with thresholds which are limited in terms of diversity(i.e.
model lacks diversity in terms of rules). However, the problem of having a machine learning
model that, given a large amount of training data, can classify multiple classes over different
geographic Sentinel-2 imagery that out scales existing approaches remains open.
On the other hand, supervised machine learning has evolved into an essential part of many
areas due to the increasing number of labeled datasets. Examples include creating classifiers
for applications that recognize images and voices, anticipate traffic, propose products, act
as a virtual personal assistant and detect online fraud, among many more. Since these
classifiers are highly dependent from the training datasets, without human interaction or
accurate labels, the performance of these generated classifiers with unseen observations
is uncertain. Thus, researchers attempted to evaluate a number of independent models
using a statistical distance. However, the problem of, given a train-test split and classifiers
modeled over the train set, identifying a prediction error using the relation between train
and test sets remains open.
Moreover, while some training data is essential for supervised machine learning, what
happens if there is insufficient labeled data? After all, assigning labels to unlabeled datasets
is a time-consuming process that may need significant expert human involvement. When
there aren’t enough expert manual labels accessible for the vast amount of openly available
data, active learning becomes crucial. However, given a large amount of training and
unlabeled datasets, having an active learning model that can reduce the training cost of
the classifier and at the same time assist in labeling new data points remains an open
problem.
From the experimental approaches and findings, the main research contributions, which
concentrate on the issue of optical satellite image scene classification include: building
labeled Sentinel-2 datasets with surface reflectance values; proposal of machine learning
models for pixel-based image scene classification; proposal of a statistical distance based
Evidence Function Model (EFM) to detect ML models misclassification; and proposal of
a generalised sampling approach for active learning that, together with the EFM enables
a way of determining the most informative examples.
Firstly, using a manually annotated Sentinel-2 dataset, Machine Learning (ML) models
for scene classification were developed and their performance was compared to Sen2Cor the reference package from the European Space Agency – a micro-F1 value of 84%
was attained by the ML model, which is a significant improvement over the corresponding
Sen2Cor performance of 59%. Secondly, to quantify the misclassification of the ML models,
the Mahalanobis distance-based EFM was devised. This model achieved, for the labeled
Sentinel-2 dataset, a micro-F1 of 67.89% for misclassification detection. Lastly, EFM was
engineered as a sampling strategy for active learning leading to an approach that attains
the same level of accuracy with only 0.02% of the total training samples when compared
to a classifier trained with the full training set.
With the help of the above-mentioned research contributions, we were able to provide
an open-source Sentinel-2 image scene classification package which consists of ready-touse
Python scripts and a ML model that classifies Sentinel-2 L1C images generating a
20m-resolution RGB image with the six studied classes (Cloud, Cirrus, Shadow, Snow,
Water, and Other) giving academics a straightforward method for rapidly and effectively
classifying Sentinel-2 scene images. Additionally, an active learning approach that uses, as
sampling strategy, the observed prediction uncertainty given by EFM, will allow labeling
only the most informative points to be used as input to build classifiers; Sumário:
Uma Abordagem de Previsão de Incerteza para
Aprendizagem Ativa – Aplicação à Observação da Terra
O mapeamento da cobertura do solo e a dinâmica da utilização do solo são cruciais na
deteção remota uma vez que os agricultores são incentivados a intensificar ou estender as
culturas devido ao aumento contÃnuo da população mundial. Uma questão importante
nesta área é interpretar e classificar cenas capturadas em imagens de satélite de alta resolução.
Várias aproximações têm sido propostas incluindo a utilização de redes neuronais
que produzem modelos dependentes dos dados (ou seja, o modelo é tendencioso em relação
aos dados) e aproximações baseadas em regras que apresentam restrições de diversidade
(ou seja, o modelo carece de diversidade em termos de regras). No entanto, a criação de
um modelo de aprendizagem automática que, dada uma uma grande quantidade de dados
de treino, é capaz de classificar, com desempenho superior, as imagens do Sentinel-2 em
diferentes áreas geográficas permanece um problema em aberto.
Por outro lado, têm sido utilizadas técnicas de aprendizagem supervisionada na resolução
de problemas nas mais diversas áreas de devido à proliferação de conjuntos de dados etiquetados.
Exemplos disto incluem classificadores para aplicações que reconhecem imagem
e voz, antecipam tráfego, propõem produtos, atuam como assistentes pessoais virtuais e
detetam fraudes online, entre muitos outros. Uma vez que estes classificadores são fortemente
dependente do conjunto de dados de treino, sem interação humana ou etiquetas
precisas, o seu desempenho sobre novos dados é incerta. Neste sentido existem propostas
para avaliar modelos independentes usando uma distância estatÃstica. No entanto, o problema
de, dada uma divisão de treino-teste e um classificador, identificar o erro de previsão
usando a relação entre aqueles conjuntos, permanece aberto.
Mais ainda, embora alguns dados de treino sejam essenciais para a aprendizagem supervisionada,
o que acontece quando a quantidade de dados etiquetados é insuficiente? Afinal,
atribuir etiquetas é um processo demorado e que exige perÃcia, o que se traduz num envolvimento
humano significativo. Quando a quantidade de dados etiquetados manualmente por
peritos é insuficiente a aprendizagem ativa torna-se crucial. No entanto, dada uma grande
quantidade dados de treino não etiquetados, ter um modelo de aprendizagem ativa que
reduz o custo de treino do classificador e, ao mesmo tempo, auxilia a etiquetagem de novas
observações permanece um problema em aberto.
A partir das abordagens e estudos experimentais, as principais contribuições deste trabalho,
que se concentra na classificação de cenas de imagens de satélite óptico incluem:
criação de conjuntos de dados Sentinel-2 etiquetados, com valores de refletância de superfÃcie;
proposta de modelos de aprendizagem automática baseados em pixels para classificação de cenas de imagens de satétite; proposta de um Modelo de Função de Evidência (EFM)
baseado numa distância estatÃstica para detetar erros de classificação de modelos de aprendizagem;
e proposta de uma abordagem de amostragem generalizada para aprendizagem
ativa que, em conjunto com o EFM, possibilita uma forma de determinar os exemplos mais
informativos.
Em primeiro lugar, usando um conjunto de dados Sentinel-2 etiquetado manualmente,
foram desenvolvidos modelos de Aprendizagem Automática (AA) para classificação de cenas
e seu desempenho foi comparado com o do Sen2Cor – o produto de referência da
Agência Espacial Europeia – tendo sido alcançado um valor de micro-F1 de 84% pelo classificador,
o que representa uma melhoria significativa em relação ao desempenho Sen2Cor
correspondente, de 59%. Em segundo lugar, para quantificar o erro de classificação dos
modelos de AA, foi concebido o Modelo de Função de Evidência baseado na distância de
Mahalanobis. Este modelo conseguiu, para o conjunto de dados etiquetado do Sentinel-2
um micro-F1 de 67,89% na deteção de classificação incorreta. Por fim, o EFM foi utilizado
como uma estratégia de amostragem para a aprendizagem ativa, uma abordagem
que permitiu atingir o mesmo nÃvel de desempenho com apenas 0,02% do total de exemplos
de treino quando comparado com um classificador treinado com o conjunto de treino
completo.
Com a ajuda das contribuições acima mencionadas, foi possÃvel desenvolver um pacote
de código aberto para classificação de cenas de imagens Sentinel-2 que, utilizando num
conjunto de scripts Python, um modelo de classificação, e uma imagem Sentinel-2 L1C,
gera a imagem RGB correspondente (com resolução de 20m) com as seis classes estudadas
(Cloud, Cirrus, Shadow, Snow, Water e Other), disponibilizando à academia um método
direto para a classificação de cenas de imagens do Sentinel-2 rápida e eficaz. Além disso, a
abordagem de aprendizagem ativa que usa, como estratégia de amostragem, a deteção de
classificacão incorreta dada pelo EFM, permite etiquetar apenas os pontos mais informativos
a serem usados como entrada na construção de classificadores
Modular Collaborative Program Analysis
With our world increasingly relying on computers, it is important to ensure the quality, correctness, security, and performance of software systems. Static analysis that computes properties of computer programs without executing them has been an important method to achieve this for decades. However, static analysis faces major chal-
lenges in increasingly complex programming languages and software systems and increasing and sometimes conflicting demands for soundness, precision, and scalability. In order to cope with these challenges, it is necessary to build static analyses for complex problems from small, independent, yet collaborating modules that can be developed in isolation and combined in a plug-and-play manner.
So far, no generic architecture to implement and combine a broad range of dissimilar static analyses exists. The goal of this thesis is thus to design such an architecture and implement it as a generic framework for developing modular, collaborative static analyses. We use several, diverse case-study analyses from which we systematically derive requirements to guide the design of the framework. Based on this, we propose the use of a blackboard-architecture style collaboration of analyses that we implement in the OPAL framework. We also develop a formal model of our architectures core concepts and show how it enables freely composing analyses while retaining their soundness guarantees.
We showcase and evaluate our architecture using the case-study analyses, each of which shows how important and complex problems of static analysis can be addressed using a modular, collaborative implementation style. In particular, we show how a modular architecture for the construction of call graphs ensures consistent soundness of different algorithms. We show how modular analyses for different aspects of immutability mutually benefit each other. Finally, we show how the analysis of method purity can benefit from the use of other complex analyses in a collaborative manner and from exchanging different analysis implementations that exhibit different characteristics. Each of these case studies improves over the respective state of the art in terms of soundness, precision, and/or scalability and shows how our architecture enables experimenting with and fine-tuning trade-offs between these qualities
Development and application of methodologies and infrastructures for cancer genome analysis within Personalized Medicine
[eng] Next-generation sequencing (NGS) has revolutionized biomedical sciences, especially in the area of cancer. It has nourished genomic research with extensive collections of sequenced genomes that are investigated to untangle the molecular bases of disease, as well as to identify potential targets for the design of new treatments. To exploit all this information, several initiatives have emerged worldwide, among which the Pan-Cancer project of the ICGC (International Cancer Genome Consortium) stands out. This project has jointly analyzed thousands of tumor genomes of different cancer types in order to elucidate the molecular bases of the origin and progression of cancer. To accomplish this task, new emerging technologies, including virtualization systems such as virtual machines or software containers, were used and had to be adapted to various computing centers. The portability of this system to the supercomputing infrastructure of the BSC (Barcelona Supercomputing Center) has been carried out during the first phase of the thesis. In parallel, other projects promote the application of genomics discoveries into the clinics. This is the case of MedPerCan, a national initiative to design a pilot project for the implementation of personalized medicine in oncology in Catalonia. In this context, we have centered our efforts on the methodological side, focusing on the detection and characterization of somatic variants in tumors. This step is a challenging action, due to the heterogeneity of the different methods, and an essential part, as it lays at the basis of all downstream analyses.
On top of the methodological section of the thesis, we got into the biological interpretation of the results to study the evolution of chronic lymphocytic leukemia (CLL) in a close collaboration with the group of Dr. ElÃas Campo from the Hospital ClÃnic/IDIBAPS. In the first study, we have focused on the Richter transformation (RT), a transformation of CLL into a high-grade lymphoma that
leads to a very poor prognosis and with unmet clinical needs. We found that RT has greater genomic, epigenomic and transcriptomic complexity than CLL. Its genome may reflect the imprint of therapies that the patients received prior to RT, indicating the presence of cells exposed to these mutagenic treatments which later expand giving rise to the clinical manifestation of the disease. Multiple NGS- based techniques, including whole-genome sequencing and single-cell DNA and RNA sequencing, among others, confirmed the pre-existence of cells with the RT characteristics years before their manifestation, up to the time of CLL diagnosis. The transcriptomic profile of RT is remarkably different from that of CLL. Of particular importance is the overexpression of the OXPHOS pathway, which could be used as a therapeutic vulnerability. Finally, in a second study, the analysis of a case of CLL in a young adult, based on whole genome and single-cell sequencing at different times of the disease, revealed that the founder clone of CLL did not present any somatic driver mutations and was characterized by germline variants in ATM, suggesting its role in the origin of the disease, and highlighting the possible contribution of germline variants or other non-genetic mechanisms in the initiation of CLL
Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding
Artificial intelligence’s (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AI’s conceptual and promissory settings tell about the current rebranding of AI?
The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AI’s contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AI’s shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise
Brain Computations and Connectivity [2nd edition]
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.
Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed.
The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes.
Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.
This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press.
Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
Lessons from Formally Verified Deployed Software Systems (Extended version)
The technology of formal software verification has made spectacular advances,
but how much does it actually benefit the development of practical software?
Considerable disagreement remains about the practicality of building systems
with mechanically-checked proofs of correctness. Is this prospect confined to a
few expensive, life-critical projects, or can the idea be applied to a wide
segment of the software industry?
To help answer this question, the present survey examines a range of
projects, in various application areas, that have produced formally verified
systems and deployed them for actual use. It considers the technologies used,
the form of verification applied, the results obtained, and the lessons that
can be drawn for the software industry at large and its ability to benefit from
formal verification techniques and tools.
Note: a short version of this paper is also available, covering in detail
only a subset of the considered systems. The present version is intended for
full reference.Comment: arXiv admin note: text overlap with arXiv:1211.6186 by other author
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