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Forthcoming CMOS technology nodes are in principle sufficient for achieving both the quantum information density and the speed that are critical for error-free logical qubits. Using data from the roadmap for semiconductor devices from ITRS and IEDM, we applied the standard CMOS design rules to a universal set of quantum logic gates to control silicon qubits. We consequently obtain a scaling law for...
Architectural contributions to energy dissipation in von Neumann and non-von Neumann processors is explored at a fundamental thermodynamic level. Technology agnostic lower bounds on dissipation resulting from irreversible information loss, obtained from a processor thermodynamics methodology, are analyzed and compared for a programmed general-purpose von Neumann processor and a special-purpose cellular...
Analyzing spatio-temporal data like video is a challenging task that requires processing visual and temporal information effectively. Convolutional Neural Networks have shown promise as baseline fixed feature extractors through transfer learning, a technique that helps minimize the training cost on visual information. Temporal information is often handled using hand-crafted features or Recurrent Neural...
In recent times, mobile broadband networks are focused on bringing different capabilities to the edge of the mobile network. Mobile Edge Computing (MEC) addresses this issue by placing the compute and storage resources closer to the Radio Access Network (RAN), with an aim to reduce end-to-end latency, ensure better service delivery, and offer improved user experience. In this work, we propose SDEC...
A fundamental issue in sensor fusion is to detect and remove outliers as sensors often produce inconsistent measurements that are difficult to predict and model. The detection and removal of spurious data is paramount to the quality of sensor fusion by avoiding their inclusion in the fusion pool. In this paper, a general framework of data fusion is presented for distributed sensor networks of arbitrary...
To interact with humans in Human Social Environments (HSEs), robots are expected to possess the ability of situational context perception and behave appropriately. In this paper, we propose two deep learning models, as situational context perception of robot, to learn from observations of human-robot interaction. Based on these models, we endow robot the capability of perceiving human's mentation...
"Girls who..." is an education system belonging to the French national program "Accompanying in Science and Technology in the Primary School" (ASTEP). "Girls who..." is a girl network that develops and maintains an facility called the factory, addressing a double goal: setting an example of science performed by women and foster science and technology in elementary schools...
Context: We investigate the different perceptions of quality provided by leading operational quality models when used to evaluate software systems from an industry perspective. Goal: To compare and evaluate the quality assessments of two competing quality models and to develop an extensible solution to meet the quality assurance measurement needs of an industry stakeholder -The Construction Engineering...
Rebooting computing using in-memory architectures relies on the ability of emerging devices to execute a legacy software stack. In this paper, we present our approach of executing compute kernels written in a subset of the C programming language using flow-based computing on nanoscale memristor crossbars. Our approach also tests the correctness of the design using the parallel Xyces electronic simulation...
Hi-C technique is an important tool for the study of 3D genome organization. In the past few years, we have seen an explosion of Hi-C data in a variety of cell/tissue types. While these publicly available data presents an unprecedented opportunity to interrogate chromosomal architecture, how to quantitatively compare Hi-C data from different tissues and identify tissue-specific chromatin interactions...
In eukaryotes, protein ubiquitylation is an important type of post-translation modification, in which the ubiquitin conjugates to a substrate protein. To have a better insight of the mechanisms underlying ubiquitylation, a key step is to identify protein ubiquitylation sites. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep...
Many scientific experiments in Bioinformatics are executed as computational workflows. Frequently, it is necessary to re-run an experiment under the original circumstances in which it was run to recognize and validate it. Data provenance concerns the origin of data. Knowing the data source facilitates the understanding and analysis of the results, by detailing and documenting the history and the paths...
In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully...
Dense prediction is concerned with predicting a label for each of the input units, such as pixels of an image. Accurate dense prediction for time-varying inputs finds applications in a variety of domains, such as video analysis and medical imaging. Such tasks need to preserve both spatial and temporal structures that are consistent with the inputs. Despite the success of deep learning methods in a...
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture...
In the age of Internet of Things (IoT), sensors form a foundational component of IoT services, yet they are rigid with little capability for programmable configuration or reusability as they are application-specific, manufacturer-specific. Emerging IoT applications often deploy a vast number of sensors which may serve multiple applications. Programmability is thus essential but not found in legacy...
Visible spectrum video based fire detection using non-stationary cameras has been an overlooked research problem. While many authors have successfully developed algorithms to identify and measure the proportions of uncontrolled fire using thermal or stationary surveillance cameras, the development of non-stationary systems provides a much larger application scope. We present a deep learning based...
Since road markings are one of the main landmarks used for traffic guidance, perceiving them may be a crucial task for autonomous vehicles. In visual approaches, road marking detection consists in detecting pixels of an image that corresponds to a road marking. Recently, most approaches have aimed on detecting lane markings only, and few of them proposed methods to detect other types of road markings...
The recent advancement of industry automation is underpinned by the continuous development of the industrial network. This exponential growth is driving the fourth generation industry revolution (FGIR). To meet the requirement of FGIR, there is an ongoing evolution of industrial network from Fieldbus to Ethernet that has emerged a new opportunity to integrate Software Defined Networking (SDN) technique...
As the data traffic demand increases continuously, LTE Heterogeneous Networks (HetNets) with macro-cells and small-cells seems to be the best solution to enhance the quality of user experience by increasing the coverage and capacity of cellular networks. One of the important challenges in such networks is the user association problem. Several approaches have been proposed for this purpose: based for...
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