7,493 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

    Modular lifelong machine learning

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    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

    Graduate Council Minutes - February 16, 2023

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    Mutational Status of SMAD4 and FBXW7 Affects Clinical Outcome in TP53-Mutated Metastatic Colorectal Cancer

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    Next-generation sequencing (NGS) provides a molecular rationale to inform prognostic stratification and to guide personalized treatment in cancer patients. Here, we determined the prognostic and predictive value of actionable mutated genes in metastatic colorectal cancer (mCRC). Among a total of 294 mCRC tumors examined by targeted NGS, 200 of them derived from patients treated with first-line chemotherapy plus/minus monoclonal antibodies were included in prognostic analyses. Discriminative performance was assessed by time-dependent estimates of the area under the curve (AUC). The most recurrently mutated genes were TP53 (64%), KRAS or NRAS (49%), PIK3CA (15%), SMAD4 (14%), BRAF (13%), and FBXW7 (9.5%). Mutations in FBXW7 correlated with worse OS rates (p = 0.036; HR, 2.24) independently of clinical factors. Concurrent mutations in TP53 and FBXW7 were associated with increased risk of death (p = 0.02; HR, 3.31) as well as double-mutated TP53 and SMAD4 (p = 0.03; HR, 2.91). Analysis of the MSK-IMPACT mCRC cohort (N = 1095 patients) confirmed the same prognostic trend for the previously identified mutated genes. Addition of the mutational status of these genes upon clinical factors resulted in a time-dependent AUC of 87%. Gene set enrichment analysis revealed specific molecular pathways associated with SMAD4 and FBXW7 mutations in TP53-defficient tumors. Conclusively, SMAD4 and FBXW7 mutations in TP53-altered tumors were predictive of a negative prognostic outcome in mCRC patients treated with first-line regimens

    Maternal transfer and toxicity pathways of hexabromocyclododecane in the fathead minnow (Pimephales promelas)

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    Hexabromocyclododecane (HBCD) is a persistent organic pollutant (POP) that undergoes maternal transfer and hinders development and growth of early-life stages of fish. However, there is limited understanding of the maternal transfer kinetics and subsequent molecular mechanisms that drive the embryotoxicity of HBCD. The purpose of this study was to (1) characterize the accumulation of dietary HBCD (11.5, 36.4, 106 mg/kg, ww [wet weight]) in adult fathead minnows (FHM) and the subsequent maternal transfer kinetics to eggs, and (2) link transcriptomics responses to apical and physiological effects in larvae exposed through maternal transfer at seven- and 14-days post-fertilization (dpf), respectively. Maternal transfer kinetics displayed similar egg-to-muscle ratios (EMR) in the low and medium treatment groups (1.65 and 1.27, respectively). However, the high treatment group deviated from other treatments with an EMR of 4.2, potentially due to reaching diffusion and/or lipid saturation limits. A positive correlation was observed between egg HBCD concentration and time of exposure. Larvae sampled at 7dpf revealed dysregulation of pathways involved in membrane integrity (inhibition of calcium channel) and metabolic processes (downregulation of amino acid, glucose, and lipid biosynthesis), while the larvae reared for 14 days exhibited a significant decrease in survival at the highest treatment condition. These results indicate that maternal transfer of HBCD is of concern in fish, which may act through indirect mechanisms involving the inhibition of membrane transport leading to disruption in metabolic processes, collectively resulting in energy depletion and subsequently mortality. This study is part of the EcoToxChip project (www.ecotoxchip.ca). The data derived will be used to inform the development of EcoToxChips, which are qPCR arrays that aim to predict apical endpoints of ecological and regulatory relevance for three model species and three native species for eight model chemicals

    VIMA: General Robot Manipulation with Multimodal Prompts

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    Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task specified by input prompts. Yet task specification in robotics comes in various forms, such as imitating one-shot demonstrations, following language instructions, and reaching visual goals. They are often considered different tasks and tackled by specialized models. We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts, interleaving textual and visual tokens. Accordingly, we develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation learning, and a four-level evaluation protocol for systematic generalization. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively. VIMA features a recipe that achieves strong model scalability and data efficiency. It outperforms alternative designs in the hardest zero-shot generalization setting by up to 2.9×2.9\times task success rate given the same training data. With 10×10\times less training data, VIMA still performs 2.7×2.7\times better than the best competing variant. Code and video demos are available at https://vimalabs.github.io/Comment: ICML 2023 Camera-ready version. Project website: https://vimalabs.github.io

    Design as a catalyst for innovation in science

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    Science, as a broad field of study, is faced with the imperative to innovate, not just invent. However, innovation is often considered intangible or unattainable – a lofty, unrealistic goal. Design has been demonstrated as a valuable approach to innovation, and thus this research seeks to understand the role of design as a catalyst for innovation in science. This study involves a longitudinal case study, where the PhD candidate was positioned within a scientific team over nine months, assuming the role of both embedded designer and case study researcher. This thesis synthesises the findings around the changing perceptions towards design, as well as the opportunities and challenges experienced along the way, to deliver key recommendations for design and science. These recommendations fall under five themes: embracing design as a mindset, drawing parallels and contrasts between design and science, recognising systemic challenges and barriers, adopting a team-centred approach, and empowerment through experiential learning These recommendations are intended to support three audiences – design practitioners working with scientists, scientists interested in adopting design, and researchers working at the intersection of design and science

    University of Windsor Graduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp
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