Tumor Mutation Burden(TMB) is a quantifiable clinical indicator that can be used to predict the responses to immunotherapy of a range of tumors. However, the current DNA sequencing-based TMB measurement method represe...
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Workload co-location has become the de-facto approach for hosting applications in Cloud environments, leading, however, to interference and fragmentation in shared resources of the system. To this end, hardware disagg...
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Workload co-location has become the de-facto approach for hosting applications in Cloud environments, leading, however, to interference and fragmentation in shared resources of the system. To this end, hardware disaggregation is introduced as a novel paradigm, that allows fine-grained tailoring of cloud resources to the characteristics of the deployed applications. Towards the realization of hardware disaggregated clouds, novel orchestration frameworks must provide additional knobs to manage the increased scheduling *** present Adrias, a memory orchestration framework for disaggregated cloud systems. Adrias exploits information from low-level performance events and applies deep learning techniques to effectively predict the system state and performance of arriving workloads on memory disaggregated systems, thus, driving cognitive scheduling between local and remote memory allocation modes. We evaluate Adrias on a state-of-art disaggregated testbed and show that it achieves 0.99 and 0.942 R 2 score for system state and application’s performance prediction on average respectively. Moreover, Adrias manages to effectively utilize disaggregated memory, by offloading almost 1/3 of deployed applications with less than 15% performance overhead compared to a conventional local memory scheduling, while clearly outperforms naive scheduling approaches (random and round-robin), by providing up to ×2 better performance.
We present a new typology for classifying signals from robots when they communicate with humans. For inspiration, we use ethology, the study of animal behaviour and previous efforts from literature as guides in defini...
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Data mining is a technique of extracting information that has not been known before in a collection of data in the database. Data mining has been applied in various fields that require extracting information, some of ...
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Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem. Existing methods require reference images to feed style information into semantic layouts, which in...
Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem. Existing methods require reference images to feed style information into semantic layouts, which indicates that the style is constrained by the given image. In this paper, we propose a model named RUCGAN for user controllable semantic image synthesis, which utilizes a singular color to represent the style of a specific semantic region. The proposed network achieves reference-free semantic image synthesis by injecting color as user-desired styles into each semantic layout, and is able to synthesize semantic images with unusual colors. Extensive experimental results on various challenging datasets show that the proposed method outperforms existing methods, and we further provide an interactive UI to demonstrate the advantage of our approach for style controllability. The codes and UI are available at: https://***/BenjaminJonghyun/RUCGAN
In this paper, we consider a class of structured fractional programs, where the numerator part is the sum of a block-separable (possibly nonsmooth nonconvex) function and a locally Lipschitz differentiable (possibly n...
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Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine *** main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization s...
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Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine *** main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster *** Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of ***,ABC has some weaknesses,such as balancing exploration and *** improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is ***,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of *** experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.
In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and...
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ISBN:
(纸本)9798350345995
In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach.
Many organizations still face challenges leveraging data science in production and need strategic planning for organization-wide data science efforts and assets. Data Science Roadmapping (DSR) customizes the widely us...
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This study aims to use data from 57 patients at Rantauprapat Hospital to train a Neural Network using a quantization learning vector method for the categorization of ear, nose, and throat disorders. The input factors ...
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