6,309 research outputs found
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges
Measuring and evaluating source code similarity is a fundamental software
engineering activity that embraces a broad range of applications, including but
not limited to code recommendation, duplicate code, plagiarism, malware, and
smell detection. This paper proposes a systematic literature review and
meta-analysis on code similarity measurement and evaluation techniques to shed
light on the existing approaches and their characteristics in different
applications. We initially found over 10000 articles by querying four digital
libraries and ended up with 136 primary studies in the field. The studies were
classified according to their methodology, programming languages, datasets,
tools, and applications. A deep investigation reveals 80 software tools,
working with eight different techniques on five application domains. Nearly 49%
of the tools work on Java programs and 37% support C and C++, while there is no
support for many programming languages. A noteworthy point was the existence of
12 datasets related to source code similarity measurement and duplicate codes,
of which only eight datasets were publicly accessible. The lack of reliable
datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm
languages are the main challenges in the field. Emerging applications of code
similarity measurement concentrate on the development phase in addition to the
maintenance.Comment: 49 pages, 10 figures, 6 table
Modular lifelong machine learning
Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge.
Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand.
This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems.
First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures.
Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations.
Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods.
Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer
Graph Neural Networks For Mapping Variables Between Programs -- Extended Version
Automated program analysis is a pivotal research domain in many areas of
Computer Science -- Formal Methods and Artificial Intelligence, in particular.
Due to the undecidability of the problem of program equivalence, comparing two
programs is highly challenging. Typically, in order to compare two programs, a
relation between both programs' sets of variables is required. Thus, mapping
variables between two programs is useful for a panoply of tasks such as program
equivalence, program analysis, program repair, and clone detection. In this
work, we propose using graph neural networks (GNNs) to map the set of variables
between two programs based on both programs' abstract syntax trees (ASTs). To
demonstrate the strength of variable mappings, we present three use-cases of
these mappings on the task of program repair to fix well-studied and recurrent
bugs among novice programmers in introductory programming assignments (IPAs).
Experimental results on a dataset of 4166 pairs of incorrect/correct programs
show that our approach correctly maps 83% of the evaluation dataset. Moreover,
our experiments show that the current state-of-the-art on program repair,
greatly dependent on the programs' structure, can only repair about 72% of the
incorrect programs. In contrast, our approach, which is solely based on
variable mappings, can repair around 88.5%.Comment: Extended version of "Graph Neural Networks For Mapping Variables
Between Programs", paper accepted at ECAI 2023. Github:
https://github.com/pmorvalho/ecai23-GNNs-for-mapping-variables-between-programs.
11 pages, 5 figures, 4 tables and 3 listing
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Comparative Study of Generative Models for Text-to-Image Generation
The development of deep learning algorithms has tremendously helped computer vision applications, image processing methods, Artificial Intelligence, and Natural Language Processing. One such application is image synthesis, which is the creation of new images from text. Recent techniques for text-to-image synthesis offer an intriguing yet straight forward conversion capability from text to image and have become a popular research topic. Synthesis of images from text descriptors has practical and creative applications in computer-aided design, multimodal learning, digital art creation, etc. Non-Fungible Tokens (NFTs) are a form of digital art that is being used as tokens for trading across the globe. Text-to-image generators let anyone with enough creativity can develop digital art, which can be used as NFTs. They can also be beneficial for the development of synthetic datasets. Generative Adversarial Networks (GANs) is a generative model that can generate new data using a training set. Diffusion Models are another type of generative model which can create desired data samples from the noise by adding random noise to the data and then learning to reverse the diffusion process. This thesis compares both models to determine which is better at producing images that match the given description. We have implemented the Vector-Quantized GAN (VQGAN) - Connecting Text and Images (CLIP) model. It combines the VQGAN and CLIP machine learning techniques to create images from text input. The diffusion model that we have implemented is Guided Language to Image Diffusion for Generation and Editing (GLIDE). For both models, we use text input from the MS-COCO data set. This thesis is an attempt to assess and compare the images generated using text for both models using metrics like Inception Score (IS) and Fréchet Inception Distance (FID). The semantic object accuracy score (SOA) is another metric that considers the caption used during the image generation process. We compute and compare the results for each label in the MS COCO data set. We highlight the potential causes of why the models may not be able to generate images through analysis of the results obtained. Our experimental results indicate that the GLIDE model outperforms the VQGAN - CLIP for our task of generating images from text
âWild Democracyâ â The figurative conceptualization of the Parliament in Hungarian editorial cartoons (1989 â 2019) [vĂ©dĂ©s elĆtt]
The expression of the Parliament is often associated with abstract concepts such as politics, democracy, or nationhood (KapitĂĄny & KapitĂĄny, 2002; SzabĂł & Oross, 2018) when instead of the literal meaning of the âbuildingâ, we refer to its figurative meanings. It has already been confirmed that political cartoons are rich in figurative devices (e.g., conceptual metaphor) (i.a. El Refaie, 2009) and they serve as a suitable corpus for the investigation of the figurative meaning of the Parliament. In the case of a conceptual metaphor, for instance, the Parliament (considered as a target domain) is understood via the source domain conceptually different from the target (e.g., COLOSSEUM). In that way, certain characteristic features of the source domain are mapped onto the target domain, and we are able to interpret politics, specifically the Parliament itself as the site of real, dangerous, life-or-death physical battles. All these figurative meanings can influence how we think about politics, its processes, and actors, how we argue in the case of a political problem and how we would try to solve it.
The current research aims to examine how the Hungarian Parliament is visually represented in editorial cartoons and how these visual representations â through figurative conceptual devices such as conceptual metaphors and conceptual metonymies â construct the concept of the parliament. Furthermore, the thesis discusses how these cognitive devices cooperate with ironies and cultural references (such as idioms, allusions, and national symbols) which are determinant in evaluation procedures and the creation of emotional bonds between the viewer and the cartoon. In doing so, the dissertation studies the caricaturistic representations of the Parliament in three various periods (KörösĂ©nyi, 2015); thus, the investigation is longitudinal (describing thirty years since 1989) and comparative.
What are the novelties of the research?
First, it examines Hungarian editorial cartoons in a cognitive linguistic framework, unlike this, so far Hungarian political cartoons have been discussed by historians (e.g., Tamås, 2014). Second, although the Parliament is an important concept (Kapitåny & Kapitåny, 2002), its figurative meaning has not been studied so widely yet. Third, it is a multimodal investigation of conceptual processes that fits into the trend of cognitive linguistic research that focuses on the cooperation of different processes. Fourth, this research examines a large data set in context where the contextual factors are limited to three types, namely idioms, allusions, and national symbols (context types are usually not defined in such concrete ways, e.g., Charteris-Black, 2011). Fifth, the dissertation applies Extended Conceptual Metaphor Theory (ECMT) (Kövecses, 2020) in practice in a larger corpus. Sixth, it is a diachronic investigation which is rare in the field of cartoon research (e.g., Frantzich, 2013) also in cognitive research, especially in multimodal research.
The main results show that
1) the representation of the Parliament is strongly linked to such conceptual procedures as conceptual metonymy and conceptual metaphor. These cognitive devices are likely to cooperate with ironies and cultural references.
2) a limited number of cognitive devices (e.g., the conceptual metonymy THE PARLIAMENT STANDS FOR THE GOVERNMENT, or the conceptual metaphor THE PARLIAMENT IS A PLACE FOR PHYSICAL CONFLICT) are recurring in the corpus during the period between 1989 and 2019. However, regarding the perspectivization, content and function of these cognitive devices, it is said that the compared periods of democracy (Körösényi, 2015) show significant differences based on the diverse preferences and distribution of the cognitive devices with specific cultural references in each era.
3) the increase of more aggressive scenes emerges from the metaphoric domain of PHYSICAL CONFLICT, which goes hand in hand with a change in the use of national symbols referring to the perceived extreme nationalist content, and political slogans which are dominated by the direct elements (literal citations, showing violence overtly).
An unexpected result is the detection of a shift in communication acting in the opposite direction, according to which in linguistic changes indirect processes took place (e.g., increasing use of causal type ironies), in visual processes direct changes became predominant, so for instance, violence appeared literally.
In sum, the Parliament seems a permanent phenomenon throughout the years, however, this research points to its different meanings and nuances of meaning variants. So even the stability of the meaning of such a strong national symbol can be questioned
Discriminative Multimodal Learning via Conditional Priors in Generative Models
Deep generative models with latent variables have been used lately to learn
joint representations and generative processes from multi-modal data. These two
learning mechanisms can, however, conflict with each other and representations
can fail to embed information on the data modalities. This research studies the
realistic scenario in which all modalities and class labels are available for
model training, but where some modalities and labels required for downstream
tasks are missing. We show, in this scenario, that the variational lower bound
limits mutual information between joint representations and missing modalities.
We, to counteract these problems, introduce a novel conditional multi-modal
discriminative model that uses an informative prior distribution and optimizes
a likelihood-free objective function that maximizes mutual information between
joint representations and missing modalities. Extensive experimentation
demonstrates the benefits of our proposed model, empirical results show that
our model achieves state-of-the-art results in representative problems such as
downstream classification, acoustic inversion, and image and annotation
generation
Vitalism and Its Legacy in Twentieth Century Life Sciences and Philosophy
This Open Access book combines philosophical and historical analysis of various forms of alternatives to mechanism and mechanistic explanation, focusing on the 19th century to the present. It addresses vitalism, organicism and responses to materialism and its relevance to current biological science. In doing so, it promotes dialogue and discussion about the historical and philosophical importance of vitalism and other non-mechanistic conceptions of life. It points towards the integration of genomic science into the broader history of biology. It details a broad engagement with a variety of nineteenth, twentieth and twenty-first century vitalisms and conceptions of life. In addition, it discusses important threads in the history of concepts in the United States and Europe, including charting new reception histories in eastern and south-eastern Europe. While vitalism, organicism and similar epistemologies are often the concern of specialists in the history and philosophy of biology and of historians of ideas, the range of the contributions as well as the geographical and temporal scope of the volume allows for it to appeal to the historian of science and the historian of biology generally
Loss of a sense of aliveness, bodily unhomeliness and radical estrangement: A phenomenological inquiry into service usersâ experiences of psychiatric medication use in the treatment of early psychosis
Quantitative research drawing on the disease-centred model of psychiatric drug action dominates research on psychiatric medication, while little is known about service usersâ subjective, embodied experiences of taking psychiatric medication. This research explored service usersâ felt, embodied and relational experiences of psychiatric medication use in the
treatment of early psychosis using a multimodal, longitudinal research design. A more in-depth understanding of what it is like and what it means to take psychiatric medication from
service usersâ idiographic perspectives is needed to improve the clinical care and support service users receive and better understand the treatment choices they make. Ten participants between the age of 18 and 30 years were recruited from London-based NHS Early Intervention in Psychosis services and participated in in-depth idiographic interviews. Eight participants took part in a follow-up interview between six and nine months later. Visual methods were used to explore the verbal as well as the pre-reflective, embodied aspects of participantsâ medication experiences. The data was analysed using a combination of interpretative phenomenological analysis and framework analysis. While taking psychiatric medication, participants reported the loss of a sense of aliveness, feelings of radical estrangement from themselves, the world and other people and a sense of being suspended in a liminal, time-locked dimension in which they felt unable to transition from past
experiences of psychosis to future recovery. The findings of this study highlight the highly distressing and adverse iatrogenic effects of psychiatric medication use, including medication-induced coporealisation, disembodiment, estrangement and a loss of belonging. More holistic, human rights-based, recovery-oriented and body-centred ways of treating psychosis are needed
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