12,505 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    UniverSeg: Universal Medical Image Segmentation

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    While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.eduComment: Victor and Jose Javier contributed equally to this work. Project Website: https://universeg.csail.mit.ed

    Antenna Arrangement in UWB Helmet Brain Applicators for Deep Microwave Hyperthermia

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    Deep microwave hyperthermia applicators are typically designed as narrow-band conformal antenna arrays with equally spaced elements, arranged in one or more rings. This solution, while adequate for most body regions, might be sub-optimal for brain treatments. The introduction of ultra-wide-band semi-spherical applicators, with elements arranged around the head and not necessarily aligned, has the potential to enhance the selective thermal dose delivery in this challenging anatomical region. However, the additional degrees of freedom in this design make the problem non-trivial. We address this by treating the antenna arrangement as a global SAR-based optimization process aiming at maximizing target coverage and hot-spot suppression in a given patient. To enable the quick evaluation of a certain arrangement, we propose a novel E-field interpolation technique which calculates the field generated by an antenna at any location around the scalp from a limited number of initial simulations. We evaluate the approximation error against full array simulations. We demonstrate the design technique in the optimization of a helmet applicator for the treatment of a medulloblastoma in a paediatric patient. The optimized applicator achieves 0.3\ua0 (Formula presented.) C higher T90 than a conventional ring applicator with the same number of elements

    Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction

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    Saliency Prediction aims to predict the attention distribution of human eyes given an RGB image. Most of the recent state-of-the-art methods are based on deep image feature representations from traditional CNNs. However, the traditional convolution could not capture the global features of the image well due to its small kernel size. Besides, the high-level factors which closely correlate to human visual perception, e.g., objects, color, light, etc., are not considered. Inspired by these, we propose a Transformer-based method with semantic segmentation as another learning objective. More global cues of the image could be captured by Transformer. In addition, simultaneously learning the object segmentation simulates the human visual perception, which we would verify in our investigation of human gaze control in cognitive science. We build an extra decoder for the subtask and the multiple tasks share the same Transformer encoder, forcing it to learn from multiple feature spaces. We find in practice simply adding the subtask might confuse the main task learning, hence Multi-task Attention Module is proposed to deal with the feature interaction between the multiple learning targets. Our method achieves competitive performance compared to other state-of-the-art methods

    Qluster: An easy-to-implement generic workflow for robust clustering of health data

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    The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors

    MaPLe: Multi-modal Prompt Learning

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    Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text prompts and require careful selection of prompt templates to perform well. Inspired by the Natural Language Processing (NLP) literature, recent CLIP adaptation approaches learn prompts as the textual inputs to fine-tune CLIP for downstream tasks. We note that using prompting to adapt representations in a single branch of CLIP (language or vision) is sub-optimal since it does not allow the flexibility to dynamically adjust both representation spaces on a downstream task. In this work, we propose Multi-modal Prompt Learning (MaPLe) for both vision and language branches to improve alignment between the vision and language representations. Our design promotes strong coupling between the vision-language prompts to ensure mutual synergy and discourages learning independent uni-modal solutions. Further, we learn separate prompts across different early stages to progressively model the stage-wise feature relationships to allow rich context learning. We evaluate the effectiveness of our approach on three representative tasks of generalization to novel classes, new target datasets and unseen domain shifts. Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable performance and achieves an absolute gain of 3.45% on novel classes and 2.72% on overall harmonic-mean, averaged over 11 diverse image recognition datasets. Our code and pre-trained models are available at https://github.com/muzairkhattak/multimodal-prompt-learning.Comment: Accepted at CVPR202

    Computertomographie-basierte Bestimmung von Aortenklappenkalk und seine Assoziation mit Komplikationen nach interventioneller Aortenklappenimplantation (TAVI)

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    Background: Severe aortic valve calcification (AVC) has generally been recognized as a key factor in the occurrence of adverse events after transcatheter aortic valve implantation (TAVI). To date, however, a consensus on a standardized calcium detection threshold for aortic valve calcium quantification in contrast-enhanced computed tomography angiography (CTA) is still lacking. The present thesis aimed at comparing two different approaches for quantifying AVC in CTA scans based on their predictive power for adverse events and survival after a TAVI procedure.   Methods: The extensive dataset of this study included 198 characteristics for each of the 965 prospectively included patients who had undergone TAVI between November 2012 and December 2019 at the German Heart Center Berlin (DHZB). AVC quantification in CTA scans was performed at a fixed Hounsfield Unit (HU) threshold of 850 HU (HU 850 approach) and at a patient-specific threshold, where the HU threshold was set by multiplying the mean luminal attenuation of the ascending aorta by 2 (+100 % HUAorta approach). The primary endpoint of this study consisted of a combination of post-TAVI outcomes (paravalvular leak ≥ mild, implant-related conduction disturbances, 30-day mortality, post-procedural stroke, annulus rupture, and device migration). The Akaike information criterion was used to select variables for the multivariable regression model. Multivariable analysis was carried out to determine the predictive power of the investigated approaches.   Results: Multivariable analyses showed that a fixed threshold of 850 HU (calcium volume cut-off 146 mm3) was unable to predict the composite clinical endpoint post-TAVI (OR=1.13, 95 % CI 0.87 to 1.48, p=0.35). In contrast, the +100 % HUAorta approach (calcium volume cut-off 1421 mm3) enabled independent prediction of the composite clinical endpoint post-TAVI (OR=2, 95 % CI 1.52 to 2.64, p=9.2x10-7). No significant difference in the Kaplan-Meier survival analysis was observed for either of the approaches.   Conclusions: The patient-specific calcium detection threshold +100 % HUAorta is more predictive of post-TAVI adverse events included in the combined clinical endpoint than the fixed HU 850 approach. For the +100 % HUAorta approach, a calcium volume cut-off of 1421 mm3 of the aortic valve had the highest predictive value.Hintergrund: Ein wichtiger Auslöser von Komplikationen nach einer Transkatheter-Aortenklappen-Implantation (TAVI) sind ausgeprägte Kalkablagerung an der Aortenklappe. Dennoch erfolgte bisher keine Einigung auf ein standardisiertes Messverfahren zur Quantifizierung der Kalklast der Aortenklappe in einer kontrastverstärkten dynamischen computertomographischen Angiographie (CTA). Die vorliegende Dissertation untersucht, inwieweit die Wahl des Analyseverfahrens zur Quantifizierung von Kalkablagerungen in der Aortenklappe die Prognose von Komplikationen und der Überlebensdauer nach einer TAVI beeinflusst.   Methodik: Der Untersuchung liegt ein umfangreicher Datensatz von 965 Patienten mit 198 Merkmalen pro Patienten zugrunde, welche sich zwischen 2012 und 2019 am Deutschen Herzzentrum Berlin einer TAVI unterzogen haben. Die Quantifizierung der Kalkablagerung an der Aortenklappe mittels CTA wurde einerseits mit einem starren Grenzwert von 850 Hounsfield Einheiten (HU) (HU 850 Verfahren) und andererseits anhand eines individuellen Grenzwertes bemessen. Letzterer ergibt sich aus der HU-Dämpfung in dem Lumen der Aorta ascendens multipliziert mit 2 (+100 % HUAorta Verfahren). Der primäre klinische Endpunkt dieser Dissertation besteht aus einem aus sechs Variablen zusammengesetzten klinischen Endpunkt, welcher ungewünschte Ereignisse nach einer TAVI abbildet (paravalvuläre Leckage ≥mild, Herzrhythmusstörungen nach einer TAVI, Tod innerhalb von 30 Tagen, post-TAVI Schlaganfall, Ruptur des Annulus und Prothesendislokation). Mögliche Störfaktoren, die auf das Eintreten der Komplikationen nach TAVI Einfluss haben, wurden durch den Einsatz des Akaike Informationskriterium ermittelt. Um die Vorhersagekraft von Komplikationen nach einer TAVI durch beide Verfahren zu ermitteln, wurde eine multivariate Regressionsanalyse durchgeführt.   Ergebnisse: Die multivariaten logistischen Regressionen zeigen, dass die Messung der Kalkablagerungen anhand der HU 850 Messung (Kalklast Grenzwert von 146 mm3) die Komplikationen und die Überlebensdauer nicht vorhersagen konnten (OR=1.13, 95 % CI 0.87 bis 1.48, p=0.35). Die nach dem +100 % HUAorta Verfahren (Kalklast Grenzwert von 1421 mm3) individualisierte Kalkmessung erwies sich hingegen als sehr aussagekräftig, da hiermit Komplikationen nach einer TAVI signifikant vorhergesagt werden konnten (OR=2, 95 % CI 1.52 bis 2.64, p=9.2x10-7). In Hinblick auf die postoperative Kaplan-Meier Überlebenszeitanalyse kann auch mit dem +100 % HUAorta Verfahren keine Vorhersage getroffen werden.   Fazit: Aus der Dissertation ergibt sich die Empfehlung, die Messung von Kalkablagerungen nach dem +100 % HUAorta Verfahren vorzunehmen, da Komplikationen wesentlich besser und zuverlässiger als nach der gängigen HU 850 Messmethode vorhergesagt werden können. Für das +100 % HUAorta Verfahren lag der optimale Kalklast Grenzwert bei 1421 mm3

    Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flow

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    In this paper, we introduce the novel state-of-the-art Dual-attention Transformer and Discriminative Flow (DADF) framework for visual anomaly detection. Based on only normal knowledge, visual anomaly detection has wide applications in industrial scenarios and has attracted significant attention. However, most existing methods fail to meet the requirements. In contrast, the proposed DTDF presents a new paradigm: it firstly leverages a pre-trained network to acquire multi-scale prior embeddings, followed by the development of a vision Transformer with dual attention mechanisms, namely self-attention and memorial-attention, to achieve two-level reconstruction for prior embeddings with the sequential and normality association. Additionally, we propose using normalizing flow to establish discriminative likelihood for the joint distribution of prior and reconstructions at each scale. The DADF achieves 98.3/98.4 of image/pixel AUROC on Mvtec AD; 83.7 of image AUROC and 67.4 of pixel sPRO on Mvtec LOCO AD benchmarks, demonstrating the effectiveness of our proposed approach.Comment: Submission to IEEE Transactions On Industrial Informatic

    Single Image Depth Prediction Made Better: A Multivariate Gaussian Take

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    Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is ill-posed, the fundamental goal is to come up with an approach that can reliably model the scene depth from a set of training examples. In the pursuit of perfect depth estimation, most existing state-of-the-art learning techniques predict a single scalar depth value per-pixel. Yet, it is well-known that the trained model has accuracy limits and can predict imprecise depth. Therefore, an SIDP approach must be mindful of the expected depth variations in the model's prediction at test time. Accordingly, we introduce an approach that performs continuous modeling of per-pixel depth, where we can predict and reason about the per-pixel depth and its distribution. To this end, we model per-pixel scene depth using a multivariate Gaussian distribution. Moreover, contrary to the existing uncertainty modeling methods -- in the same spirit, where per-pixel depth is assumed to be independent, we introduce per-pixel covariance modeling that encodes its depth dependency w.r.t all the scene points. Unfortunately, per-pixel depth covariance modeling leads to a computationally expensive continuous loss function, which we solve efficiently using the learned low-rank approximation of the overall covariance matrix. Notably, when tested on benchmark datasets such as KITTI, NYU, and SUN-RGB-D, the SIDP model obtained by optimizing our loss function shows state-of-the-art results. Our method's accuracy (named MG) is among the top on the KITTI depth-prediction benchmark leaderboard.Comment: Accepted to IEEE/CVF CVPR 2023. Draft info: 17 pages, 13 Figures, 9 Table
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