4,417 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Machine learning applications in search algorithms for gravitational waves from compact binary mergers
Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe.
However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing.
In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software.
Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals
Guided rewriting and constraint satisfaction for parallel GPU code generation
Graphics Processing Units (GPUs) are notoriously hard to optimise for manually due to their scheduling and memory hierarchies. What is needed are good automatic code generators and optimisers for such parallel hardware. Functional approaches such as Accelerate, Futhark and LIFT leverage a high-level algorithmic Intermediate Representation (IR) to expose parallelism and abstract the implementation details away from the user. However, producing efficient code for a given accelerator remains challenging. Existing code generators depend on the user input to choose a subset of hard-coded optimizations or automated exploration of implementation search space. The former suffers from the lack of extensibility, while the latter is too costly due to the size of the search space. A hybrid approach is needed, where a space of valid implementations is built automatically and explored with the aid of human expertise.
This thesis presents a solution combining user-guided rewriting and automatically generated constraints to produce high-performance code. The first contribution is an automatic tuning technique to find a balance between performance and memory consumption. Leveraging its functional patterns, the LIFT compiler is empowered to infer tuning constraints and limit the search to valid tuning combinations only.
Next, the thesis reframes parallelisation as a constraint satisfaction problem. Parallelisation constraints are extracted automatically from the input expression, and a solver is used to identify valid rewriting. The constraints truncate the search space to valid parallel mappings only by capturing the scheduling restrictions of the GPU in the context of a given program. A synchronisation barrier insertion technique is proposed to prevent data races and improve the efficiency of the generated parallel mappings.
The final contribution of this thesis is the guided rewriting method, where the user encodes a design space of structural transformations using high-level IR nodes called rewrite points. These strongly typed pragmas express macro rewrites and expose design choices as explorable parameters. The thesis proposes a small set of reusable rewrite points to achieve tiling, cache locality, data reuse and memory optimisation.
A comparison with the vendor-provided handwritten kernel ARM Compute Library and the TVM code generator demonstrates the effectiveness of this thesis' contributions. With convolution as a use case, LIFT-generated direct and GEMM-based convolution implementations are shown to perform on par with the state-of-the-art solutions on a mobile GPU. Overall, this thesis demonstrates that a functional IR yields well to user-guided and automatic rewriting for high-performance code generation
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
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
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
Machine Learning Approaches for Semantic Segmentation on Partly-Annotated Medical Images
Semantic segmentation of medical images plays a crucial role in assisting medical practitioners in providing accurate and swift diagnoses; nevertheless, deep neural networks require extensive labelled data to learn and generalise appropriately. This is a major issue in medical imagery because most of the datasets are not fully annotated. Training models with partly-annotated datasets generate plenty of predictions that belong to correct unannotated areas that are categorised as false positives; as a result, standard segmentation metrics and objective functions do not work correctly, affecting the overall performance of the models. In this thesis, the semantic segmentation of partly-annotated medical datasets is extensively and thoroughly studied. The general objective is to improve the segmentation results of medical images via innovative supervised and semi-supervised approaches. The main contributions of this work are the following. Firstly, a new metric, specifically designed for this kind of dataset, can provide a reliable score to partly-annotated datasets with positive expert feedback in their generated predictions by exploiting all the confusion matrix values except the false positives. Secondly, an innovative approach to generating better pseudo-labels when applying co-training with the disagreement selection strategy. This method expands the pixels in disagreement utilising the combined predictions as a guide. Thirdly, original attention mechanisms based on disagreement are designed for two cases: intra-model and inter-model. These attention modules leverage the disagreement between layers (from the same or different model instances) to enhance the overall learning process and generalisation of the models. Lastly, innovative deep supervision methods improve the segmentation results by training neural networks one subnetwork at a time following the order of the supervision branches. The methods are thoroughly evaluated on several histopathological datasets showing significant improvements
Modern meat: the next generation of meat from cells
Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community.
The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World.
The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia
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