8,140 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    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

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    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

    Data-efficient neural network training with dataset condensation

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    The state of the art in many data driven fields including computer vision and natural language processing typically relies on training larger models on bigger data. It is reported by OpenAI that the computational cost to achieve the state of the art doubles every 3.4 months in the deep learning era. In contrast, the GPU computation power doubles every 21.4 months, which is significantly slower. Thus, advancing deep learning performance by consuming more hardware resources is not sustainable. How to reduce the training cost while preserving the generalization performance is a long standing goal in machine learning. This thesis investigates a largely under-explored while promising solution - dataset condensation which aims to condense a large training set into a small set of informative synthetic samples for training deep models and achieve close performance to models trained on the original dataset. In this thesis, we investigate how to condense image datasets for classification tasks. We propose three methods for image dataset condensation. Our methods can be applied to condense other kinds of datasets for different learning tasks, such as text data, graph data and medical images, and we discuss it in Section 6.1. First, we propose a principled method that formulates the goal of learning a small synthetic set as a gradient matching problem with respect to the gradients of deep neural network weights that are trained on the original and synthetic data. A new gradient/weight matching loss is designed for robust matching of different neural architectures. We evaluate its performance in several image classification benchmarks and explore the usage of our method in continual learning and neural architecture search. In the second work, we propose to further improve the data-efficiency of training neural networks with synthetic data by enabling effective data augmentation. Specifically, we propose Differentiable Siamese Augmentation and learn better synthetic data that can be used more effectively with data augmentation and thus achieve better performance when training networks with data augmentation. Experiments verify that the proposed method obtains substantial gains over the state of the art. While training deep models on the small set of condensed images can be extremely fast, their synthesis remains computationally expensive due to the complex bi-level optimization. Finally, we propose a simple yet effective method that synthesizes condensed images by matching feature distributions of the synthetic and original training images when being embedded by randomly sampled deep networks. Thanks to its efficiency, we apply our method to more realistic and larger datasets with sophisticated neural architectures and obtain a significant performance boost. In summary, this manuscript presents several important contributions that improve data efficiency of training deep neural networks by condensing large datasets into significantly smaller synthetic ones. The innovations focus on principled methods based on gradient matching, higher data-efficiency with differentiable Siamese augmentation, and extremely simple and fast distribution matching without bilevel optimization. The proposed methods are evaluated on popular image classification datasets, namely MNIST, FashionMNIST, SVHN, CIFAR10/100 and TinyImageNet. The code is available at https://github.com/VICO-UoE/DatasetCondensation

    Medical Image Analysis using Deep Relational Learning

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    In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it has not been fully studied. In this thesis, we propose two novel solutions to this problem based on deep relational learning. First, we propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. The network achieves the state-of-the-art segmentation results on the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and our hierarchical homography estimation network outperforms the other state-of-the-art mosaicing methods while generating robust and meaningful mosaicing result on unseen frames.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0778

    Chinese Knitwear Brands: The need for creative design to result in global business success

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    Chinese cashmere knitwear companies have become suppliers of international fashion brands because of their technological excellence, advantages of raw materials and competitive prices. However, their in-house brands are steadily declining. In the past 15 years, Chinese cashmere brands have progressively lost their market share to Chinese and Western fashion brands, with a few notable exceptions. Their brands lack differentiation from other Chinese competitors, causing low price competition, which contributes to sustainability issues such as unsold stock and material/manpower waste. The decline is likely to continue as the brands serve only an ageing market, rather than attracting younger generations to their products. Chinese cashmere companies invest little in design, which is a significant limitation for improving the brands’ opportunity to become successful and sustainable businesses. This study looks for solutions from the design perspective. The research aimed to investigate what design can do to help deal with the current problems of the Chinese knitwear brands to improve their prospects for future business success. The objectives of the study were to enquire into the challenges and opportunities facing the Chinese knitwear sector, to evaluate current design practice in knitwear brands, to understand how design and brand management can be integrated to generate a sustainable brand. Research questions were developed to explore the brand and design problems, the role of design and organisational structure, what the barriers and enablers for a thriving design culture were alongside possible solutions for design improvement. A pragmatic philosophy underpinned research design, guiding the adoption of methods in response to research questions. Interviews with stakeholders from both the knitwear industry and design education were undertaken. In addition, a case study using design action research with immersive field research was developed for investigating the knitwear brand issues; furthermore, a knitwear collection was created using western design approaches to demonstrate an exemplar design process for the sector and to illustrate the differences to current Chinese design methods. The study argues the obstacles to design culture enrichment in Chinese knitwear brands was caused by their design context, lack of brand positioning, limited understanding of their consumers and business models that are not fit for purpose. An absence of experienced leadership creates unclear design direction, instead of collections centred around a theme; Chinese brands sell unconnected designs. Brands lack the distinct brand characteristics that distinguish them from their competitors. The contribution to knowledge made by this study includes the identification of the reasons for the decline in Chinese cashmere brands, an understanding of their barriers to design culture to developing good designs and it also highlights the lack of awareness of sustainability issues in the sector. The study sheds new light on the rarely acknowledged issue of how to upgrade these brands as modern business for younger consumers, and how to enrich the design culture for brand business growth within sustainable contexts. The thesis analyses in depth the causes for the decline in these brands and makes recommendations for how design can make a contribution to reversing the brands’ decline and increasing their sustainability

    Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey

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    Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance. In recent years, a lot of work about multi-task learning recommender system has been yielded, but there is no previous literature to summarize these works. To bridge this gap, we provide a systematic literature survey about multi-task recommender systems, aiming to help researchers and practitioners quickly understand the current progress in this direction. In this survey, we first introduce the background and the motivation of the multi-task learning-based recommender systems. Then we provide a taxonomy of multi-task learning-based recommendation methods according to the different stages of multi-task learning techniques, which including task relationship discovery, model architecture and optimization strategy. Finally, we raise discussions on the application and promising future directions in this area

    Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

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    Delivering customer services through video communications has brought new opportunities to analyze customer satisfaction for quality management. However, due to the lack of reliable self-reported responses, service providers are troubled by the inadequate estimation of customer services and the tedious investigation into multimodal video recordings. We introduce Anchorage, a visual analytics system to evaluate customer satisfaction by summarizing multimodal behavioral features in customer service videos and revealing abnormal operations in the service process. We leverage the semantically meaningful operations to introduce structured event understanding into videos which help service providers quickly navigate to events of their interest. Anchorage supports a comprehensive evaluation of customer satisfaction from the service and operation levels and efficient analysis of customer behavioral dynamics via multifaceted visualization views. We extensively evaluate Anchorage through a case study and a carefully-designed user study. The results demonstrate its effectiveness and usability in assessing customer satisfaction using customer service videos. We found that introducing event contexts in assessing customer satisfaction can enhance its performance without compromising annotation precision. Our approach can be adapted in situations where unlabelled and unstructured videos are collected along with sequential records.Comment: 13 pages. A preprint version of a publication at IEEE Transactions on Visualization and Computer Graphics (TVCG), 202
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