2,685 research outputs found

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Temperature Reduction Technologies Meet Asphalt Pavement: Green and Sustainability

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    This Special Issue, "Temperature Reduction Technologies Meet Asphalt Pavement: Green and Sustainability", covers various subjects related to advanced temperature reduction technologies in bituminous materials. It can help civil engineers and material scientists better identify underlying views for sustainable pavement constructions

    Biomedical and Pharmacological Applications of Marine Collagen

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    Biomimetic polymers and materials have been widely used in a variety of biomedical and pharmacological applications. Particularly, collagen-based biomaterials have been extensively applied in various biomedical fields, such as scaffolds in tissue engineering. However, there are many challenges associated with the use of mammalian collagen, including the issues of religious constrains, allergic or autoimmune reactions, and the spread of animal diseases. Over the past few decades, marine collagen (MC) has emerged as a promising biomaterial for biomedical and pharmacological applications. Marine organisms are a rich source of structurally novel and biologically active compounds, and to date, many biological components have been isolated from various marine resources. MC offers advantages over mammalian collagen due to its water solubility, low immunogenicity, safety, biocompatibility, antimicrobial activity, functionality, and low production costs. Due to its characteristics and physicobiochemical properties, it has tremendous potential for use as a scaffold biomaterial in tissue engineering and regenerative medicine, in drug delivery systems, and as a therapeutic. In this Special Issue, we encourage submissions related to the recent developments, advancements, trends, challenges, and future perspectives in this new research field. We expect to receive contributions from different areas of multidisciplinary research, including—but not restricted to—extraction, purification, characterization, fabrication, and experimentation of MC, with a particular focus on their biotechnological, biomedical and pharmacological uses

    A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

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    Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network’s representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder–decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods

    A comprehensive review of primary strategies for tar removal in biomass gasification

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    In the current energy scenario, the production of heat, power and biofuels from biomass has become of major interest. Amongst diverse thermochemical routes, gasification has stood out as a key technology for the large-scale application of biomass. However, the development of biomass gasification is subjected to the efficient conversion of the biochar and the mitigation of troublesome by-products, such as tar. Syngas with high tar content can cause pipeline fouling, downstream corrosion, catalyst deactivation, as well as adverse impact on health and environment, which obstruct the commercialization of biomass gasification technologies. Since the reduction of tar formation is a key challenge in biomass gasification, a comprehensive overview is provided on the following aspects, which particularly include the definition and complementary classifications of tar, as well as possible tar formation and transformation mechanisms. Moreover, the adverse effects of tar on downstream applications, human health or environment, and tar analyzing techniques (online and off-line) are discussed. Finally, the primary tar removal strategies are summarized. In this respect, the effect of key operation parameters (temperature, ER and S/B), catalysts utilization (natural and supported metal catalysts) and the improvement of reactor design on tar formation and elimination was thoroughly analyzed.This work was carried out with the financial support from Spanish Ministries of Science, Innovation and Universities (RTI2018-098283-J-I00 (MCIU/AEI/FEDER, UE)) and Science and Innovation (PID2019-107357RB-I00 (MCIU/AEI/FEDER, UE) and TED2021-132056B-I00 (MCI/AEI/FEDER, UE)) and the Basque Government (IT1645-22). Moreover, this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 823745

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Spatial frequency domain imaging towards improved detection of gastrointestinal cancers

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    Early detection and treatment of gastrointestinal cancers has been shown to drastically improve patients survival rates. However, wide population based screening for gastrointestinal cancers is not feasible due to its high cost, risk of potential complications, and time consuming nature. This thesis forms the proposal for the development of a cost-effective, minimally invasive device to return quantitative tissue information for gastrointestinal cancer detection in-vivo using spatial frequency domain imaging (SFDI). SFDI is a non-invasive imaging technique which can return close to real time maps of absorption and reduced scattering coefficients by projecting a 2D sinusoidal pattern onto a sample of interest. First a low-cost, conventional bench top system was constructed to characterise tissue mimicking phantoms. Phantoms were fabricated with specific absorption and reduced scattering coefficients, mimicking the variation in optical properties typically seen in healthy, cancerous, and pre-cancerous oesophageal tissue. The system shows accurate retrieval of absorption and reduced scattering coefficients of 19% and 11% error respectively. However, this bench top system consists of a bulky projector and is therefore not feasible for in-vivo imaging. For SFDI systems to be feasible for in-vivo imaging, they are required to be miniaturised. Many conditions must be considered when doing this such as various illumination conditions, lighting conditions and system geometries. Therefore to aid in the miniaturisation of the bench top system, an SFDI system was simulated in the open-source ray tracing software Blender, where the capability to simulate these conditions is possible. A material of tunable absorption and scattering properties was characterised such that the specific absorption and reduced scattering coefficients of the material were known. The simulated system shows capability in detecting optical properties of typical gastrointestinal conditions in an up-close, planar geometry, as well in a non-planar geometry of a tube simulating a lumen. Optical property imaging in the non-planar, tubular geometry was done with the use of a novel illumination pattern, developed for this work. Finally, using the knowledge gained from the simulation model, the bench top system was miniaturised to a 3 mm diameter prototype. The novel use of a fiber array producing the necessary interfering fringe patterns replaced the bulky projector. The system showed capability to image phantoms simulating typical gastrointestinal conditions at two wavelengths (515 and 660 nm), measuring absorption and reduced scattering coefficients with 15% and 6% accuracy in comparison to the bench top system for the fabricated phantoms. It is proposed that this system may be used for cost-effective, minimally invasive, quantitative imaging of the gastrointestinal tract in-vivo, providing enhanced contrast for difficult to detect cancers

    2017 GREAT Day Program

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    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
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