9,381 research outputs found

    Procedure-Aware Pretraining for Instructional Video Understanding

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    Our goal is to learn a video representation that is useful for downstream procedure understanding tasks in instructional videos. Due to the small amount of available annotations, a key challenge in procedure understanding is to be able to extract from unlabeled videos the procedural knowledge such as the identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or the potential next steps given partial progress in its execution. Our main insight is that instructional videos depict sequences of steps that repeat between instances of the same or different tasks, and that this structure can be well represented by a Procedural Knowledge Graph (PKG), where nodes are discrete steps and edges connect steps that occur sequentially in the instructional activities. This graph can then be used to generate pseudo labels to train a video representation that encodes the procedural knowledge in a more accessible form to generalize to multiple procedure understanding tasks. We build a PKG by combining information from a text-based procedural knowledge database and an unlabeled instructional video corpus and then use it to generate training pseudo labels with four novel pre-training objectives. We call this PKG-based pre-training procedure and the resulting model Paprika, Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We evaluate Paprika on COIN and CrossTask for procedure understanding tasks such as task recognition, step recognition, and step forecasting. Paprika yields a video representation that improves over the state of the art: up to 11.23% gains in accuracy in 12 evaluation settings. Implementation is available at https://github.com/salesforce/paprika.Comment: CVPR 202

    Annals [...].

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    Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin

    Development of in-vitro in-silico technologies for modelling and analysis of haematological malignancies

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    Worldwide, haematological malignancies are responsible for roughly 6% of all the cancer-related deaths. Leukaemias are one of the most severe types of cancer, as only about 40% of the patients have an overall survival of 10 years or more. Myelodysplastic Syndrome (MDS), a pre-leukaemic condition, is a blood disorder characterized by the presence of dysplastic, irregular, immature cells, or blasts, in the peripheral blood (PB) and in the bone marrow (BM), as well as multi-lineage cytopenias. We have created a detailed, lineage-specific, high-fidelity in-silico erythroid model that incorporates known biological stimuli (cytokines and hormones) and a competing diseased haematopoietic population, correctly capturing crucial biological checkpoints (EPO-dependent CFU-E differentiation) and replicating the in-vivo erythroid differentiation dynamics. In parallel, we have also proposed a long-term, cytokine-free 3D cell culture system for primary MDS cells, which was firstly optimized using easily-accessible healthy controls. This system enabled long-term (24-day) maintenance in culture with high (>75%) cell viability, promoting spontaneous expansion of erythroid phenotypes (CD71+/CD235a+) without the addition of any exogenous cytokines. Lastly, we have proposed a novel in-vitro in-silico framework using GC-MS metabolomics for the metabolic profiling of BM and PB plasma, aiming not only to discretize between haematological conditions but also to sub-classify MDS patients, potentially based on candidate biomarkers. Unsupervised multivariate statistical analysis showed clear intra- and inter-disease separation of samples of 5 distinct haematological malignancies, demonstrating the potential of this approach for disease characterization. The work herein presented paves the way for the development of in-vitro in-silico technologies to better, characterize, diagnose, model and target haematological malignancies such as MDS and AML.Open Acces

    The Adirondack Chronology

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    The Adirondack Chronology is intended to be a useful resource for researchers and others interested in the Adirondacks and Adirondack history.https://digitalworks.union.edu/arlpublications/1000/thumbnail.jp

    Investigating the mechanism of human beta defensin-2-mediated protection of skin barrier in vitro

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    The human skin barrier is a biological imperative. Chronic inflammatory skin diseases, such as Atopic Dermatitis (AD), are characterised by a reduction in skin barrier function and an increased number of secondary infections. Staphyloccocus aureus (S. aureus) has an increased presence on AD lesional skin and contributes significantly to AD pathology. It was previously demonstrated that the damage induced by a virulence factor of S. aureus, V8 protease, which causes further breakdown in skin barrier function, can be reduced by induction of human β- defensin (HBD)2 (by IL-1β) or exogenous HBD2 application. Induction of this defensin is impaired in AD skin. This thesis examines the mechanism of HBD2-mediated barrier protection in vitro; demonstrating that in this system, HBD2 was not providing protection through direct protease inhibition, nor was it altering keratinocyte proliferation or migration, or exhibiting specific localisation within the monolayer. Proteomics data demonstrated that HBD2 did not induce expression of known antiproteases but suggested that HBD2 stimulation may function by modulating expression of extracellular matrix proteins, specifically collagen- IVα2 and Laminin-β-1. Alternative pathways of protection initiated by IL-1β and TNFα stimulation were also investigated, as well as their influence over generalised wound healing. Finally, novel 3D human skin epidermal models were used to better recapitulate the structure of human epidermis and examine alterations to skin barrier function in a more physiological system. These data validate the barrier-protective properties of HBD2 and extended our knowledge of the consequences of exposure to this peptide in this context

    Network Geometry

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    Networks are finite metric spaces, with distances defined by the shortest paths between nodes. However, this is not the only form of network geometry: two others are the geometry of latent spaces underlying many networks and the effective geometry induced by dynamical processes in networks. These three approaches to network geometry are intimately related, and all three of them have been found to be exceptionally efficient in discovering fractality, scale invariance, self-similarity and other forms of fundamental symmetries in networks. Network geometry is also of great use in a variety of practical applications, from understanding how the brain works to routing in the Internet. We review the most important theoretical and practical developments dealing with these approaches to network geometry and offer perspectives on future research directions and challenges in this frontier in the study of complexity

    Flexographic printed nanogranular LBZA derived ZnO gas sensors: Synthesis, printing and processing

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    Within this document, investigations of the processes towards the production of a flexographic printed ZnO gas sensor for breath H2 analysis are presented. Initially, a hexamethylenetetramine (HMTA) based, microwave assisted, synthesis method of layered basic zinc acetate (LBZA) nanomaterials was investigated. Using the synthesised LBZA, a dropcast nanogranular ZnO gas sensor was produced. The testing of the sensor showed high sensitivity towards hydrogen with response (Resistanceair/ Resistancegas) to 200 ppm H2 at 328 °C of 7.27. The sensor is highly competitive with non-catalyst surface decorated sensors and sensitive enough to measure current H2 guideline thresholds for carbohydrate malabsorption (Positive test threshold: 20 ppm H2, Predicted response: 1.34). Secondly, a novel LBZA synthesis method was developed, replacing the HMTA by NaOH. This resulted in a large yield improvement, from a [OH-] conversion of 4.08 at% to 71.2 at%. The effects of [OH-]/[Zn2+] ratio, microwave exposure and transport to nucleation rate ratio on purity, length, aspect ratio and polydispersity were investigated in detail. Using classical nucleation theory, analysis of the basal layer charge symmetries, and oriented attachment theory, a dipole-oriented attachment reaction mechanism is presented. The mechanism is the first theory in literature capable of describing all observed morphological features along length scales. The importance of transport to nucleation rate ratio as the defining property that controls purity and polydispersity is then shown. Using the NaOH derived LBZA, a flexographic printing ink was developed, and proof-of-concept sensors printed. Gas sensing results showed a high response to 200 ppm H2 at 300 °C of 60.2. Through IV measurements and SEM analysis this was shown to be a result of transfer of silver between the electrode and the sensing layer during the printing process. Finally, Investigations into the intense pulsed light treatment of LBZA were conducted. The results show that dehydration at 150 °C prior to exposure is a requirement for successful calcination, producing ZnO quantum dots (QDs) in the process. SEM measurements show mean radii of 1.77-2.02 nm. The QDs show size confinement effects with the exciton blue shifting by 0.105 eV, and exceptionally low defect emission in photoluminescence spectra, indicative of high crystalline quality, and high conductivity. Due to the high crystalline quality and amenity to printing, the IPL ZnO QDs have numerous potential uses ranging from sensing to opto-electronic devices
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