Search results for: fine-tune

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fine-tune</title> <meta name="description" content="Search results for: fine-tune"> <meta name="keywords" content="fine-tune"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="fine-tune"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: fine-tune</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Applications of AFM in 4D to Optimize the Design of Genetic Nanoparticles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hosam%20Abdelhady">Hosam Abdelhady</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Filming the behaviors of individual DNA molecules in their environment when they interact with individual medicinal nano-polymers in a molecular scale has opened the door to understand the effect of the molecular shape, size, and incubation time with nanocarriers on optimizing the design of robust genetic Nano molecules able to resist the enzymatic degradation, enter the cell, reach to the nucleus and kill individual cancer cells in their environment. To this end, we will show how we applied the 4D AFM as a guide to finetune the design of genetic nanoparticles and to film the effects of these nanoparticles on the nanomechanical and morphological profiles of individual cancer cells. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AFM" title="AFM">AFM</a>, <a href="https://publications.waset.org/abstracts/search?q=dendrimers" title=" dendrimers"> dendrimers</a>, <a href="https://publications.waset.org/abstracts/search?q=nanoparticles" title=" nanoparticles"> nanoparticles</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA" title=" DNA"> DNA</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20therapy" title=" gene therapy"> gene therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=imaging" title=" imaging"> imaging</a> </p> <a href="https://publications.waset.org/abstracts/157876/applications-of-afm-in-4d-to-optimize-the-design-of-genetic-nanoparticles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157876.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Self-Supervised Pretraining on Sequences of Functional Magnetic Resonance Imaging Data for Transfer Learning to Brain Decoding Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sean%20Paulsen">Sean Paulsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Casey"> Michael Casey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised" title=" self-supervised"> self-supervised</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20decoding" title=" brain decoding"> brain decoding</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=multitask%20training" title=" multitask training"> multitask training</a> </p> <a href="https://publications.waset.org/abstracts/165380/self-supervised-pretraining-on-sequences-of-functional-magnetic-resonance-imaging-data-for-transfer-learning-to-brain-decoding-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165380.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Investigating the Viability of Small-Scale Rapid Alloy Prototyping of Interstitial Free Steels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Talal%20S.%20Abdullah">Talal S. Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahin%20Mehraban"> Shahin Mehraban</a>, <a href="https://publications.waset.org/abstracts/search?q=Geraint%20Lodwig"> Geraint Lodwig</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicholas%20P.%20Lavery"> Nicholas P. Lavery</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The defining property of Interstitial Free (IF) steels is formability, comprehensively measured using the Lankford coefficient (r-value) on uniaxial tensile test data. The contributing factors supporting this feature are grain size, orientation, and elemental additions. The processes that effectively modulate these factors are the casting procedure, hot rolling, and heat treatment. An existing methodology is well-practised in the steel Industry; however, large-scale production and experimentation consume significant proportions of time, money, and material. Introducing small-scale rapid alloy prototyping (RAP) as an alternative process would considerably reduce the drawbacks relative to standard practices. The aim is to finetune the existing fundamental procedures implemented in the industrial plant to adapt to the RAP route. IF material is remelted in the 80-gram coil induction melting (CIM) glovebox. To birth small grains, maximum deformation must be induced onto the cast material during the hot rolling process. The rolled strip must then satisfy the polycrystalline behaviour of the bulk material by displaying a resemblance in microstructure, hardness, and formability to that of the literature and actual plant steel. A successful outcome of this work is that small-scale RAP can achieve target compositions with similar microstructures and statistically consistent mechanical properties which complements and accelerates the development of novel steel grades. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rapid%20alloy%20prototyping" title="rapid alloy prototyping">rapid alloy prototyping</a>, <a href="https://publications.waset.org/abstracts/search?q=plastic%20anisotropy" title=" plastic anisotropy"> plastic anisotropy</a>, <a href="https://publications.waset.org/abstracts/search?q=interstitial%20free" title=" interstitial free"> interstitial free</a>, <a href="https://publications.waset.org/abstracts/search?q=miniaturised%20tensile%20testing" title=" miniaturised tensile testing"> miniaturised tensile testing</a>, <a href="https://publications.waset.org/abstracts/search?q=formability" title=" formability"> formability</a> </p> <a href="https://publications.waset.org/abstracts/158343/investigating-the-viability-of-small-scale-rapid-alloy-prototyping-of-interstitial-free-steels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158343.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Efficient Chiller Plant Control Using Modern Reinforcement Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jingwei%20Du">Jingwei Du</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need of optimizing air conditioning systems for existing buildings calls for control methods designed with energy-efficiency as a primary goal. The majority of current control methods boil down to two categories: empirical and model-based. To be effective, the former heavily relies on engineering expertise and the latter requires extensive historical data. Reinforcement Learning (RL), on the other hand, is a model-free approach that explores the environment to obtain an optimal control strategy often referred to as “policy”. This research adopts Proximal Policy Optimization (PPO) to improve chiller plant control, and enable the RL agent to collaborate with experienced engineers. It exploits the fact that while the industry lacks historical data, abundant operational data is available and allows the agent to learn and evolve safely under human supervision. Thanks to the development of language models, renewed interest in RL has led to modern, online, policy-based RL algorithms such as the PPO. This research took inspiration from “alignment”, a process that utilizes human feedback to finetune the pretrained model in case of unsafe content. The methodology can be summarized into three steps. First, an initial policy model is generated based on minimal prior knowledge. Next, the prepared PPO agent is deployed so feedback from both critic model and human experts can be collected for future finetuning. Finally, the agent learns and adapts itself to the specific chiller plant, updates the policy model and is ready for the next iteration. Besides the proposed approach, this study also used traditional RL methods to optimize the same simulated chiller plants for comparison, and it turns out that the proposed method is safe and effective at the same time and needs less to no historical data to start up. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chiller%20plant" title="chiller plant">chiller plant</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20methods" title=" control methods"> control methods</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title=" energy efficiency"> energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=proximal%20policy%20optimization" title=" proximal policy optimization"> proximal policy optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a> </p> <a href="https://publications.waset.org/abstracts/191163/efficient-chiller-plant-control-using-modern-reinforcement-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191163.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">28</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div 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Search results for: fine-tune