Periodicals
Analyzing e-government research: Perspectives, philosophies, theories, methods, and practice
[no abstract]
Architecture & Planning• 2006• Elsevier BV
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in Architecture & Planning
Periodicals
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
[no abstract]
Architecture & Planning• 2019• Elsevier BV
Periodicals
The ITensor Software Library for Tensor Network Calculations
ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagrams, allowing users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interface rules out common programming errors and enables rapid prototyping of algorithms. After discussing the philosophy behind the ITensor approach, we show examples of each part of the interface including Index objects, the ITensor product operator, tensor factorizations, tensor storage types, algorithms for matrix product state (MPS) and matrix product operator (MPO) tensor networks, quantum number conserving block sparse tensors, and the NDTensors library. We also review publications that have used ITensor for quantum many-body physics and for other areas where tensor networks are increasingly applied. To conclude we discuss promising features and optimizations to be added in the future.
Architecture & Planning• 2022
Periodicals
MEASURING IMPLICIT AND EXPLICIT KNOWLEDGE OF A SECOND LANGUAGE: A Psychometric Study
A problem facing investigations of implicit and explicit learning is the lack of valid measures of second language implicit and explicit knowledge. This paper attempts to establish operational definitions of these two constructs and reports a psychometric study of a battery of tests designed to provide relatively independent measures of them. These tests were (a) an oral imitation test involving grammatical and ungrammatical sentences, (b) an oral narration test, (c) a timed grammaticality judgment test (GJT), (d) an untimed GJT with the same content, and (e) a metalinguistic knowledge test. Tests (a), (b), and (c) were designed as measures of implicit knowledge, and tests (d) and (e) were designed as measures of explicit knowledge. All of the tests examined 17 English grammatical structures. A principal component factor analysis produced two clear factors. This analysis showed that the scores from tests (a), (b), and (c) loaded on Factor 1, whereas the scores from ungrammatical sentences in test (d) and total scores from test (e) loaded on Factor 2. These two factors are interpreted as corresponding to implicit and explicit knowledge, respectively. A number of secondary analyses to support this interpretation of the construct validity of the tests are also reported.This research was funded by a Marsden Fund grant awarded by the Royal Society of Arts of New Zealand to Rod Ellis and Cathie Elder. Other researchers who contributed to the research are Shawn Loewen, Rosemary Erlam, Satomi Mizutani, and Shuhei Hidaka.The author wishes to thank Nick Ellis, Jim Lantolf, and two anonymous SSLA reviewers. Their constructive comments have helped me to present the theoretical background of the study more convincingly and to remove errors from the results and refine my interpretations of them.
Architecture & Planning• 2005• Cambridge University Press
Periodicals
Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.
Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
Periodicals
A Survey of Techniques for Approximate Computing
Approximate computing trades off computation quality with effort expended, and as rising performance demands confront plateauing resource budgets, approximate computing has become not merely attractive, but even imperative. In this article, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU, and FPGA), processor components, memory technologies, and so forth, as well as programming frameworks for AC. We classify these techniques based on several key characteristics to emphasize their similarities and differences. The aim of this article is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.
Architecture & Planning• 2016• Association for Computing Machinery
Periodicals
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
Periodicals
Tackling the digitalization challenge: how to benefit from digitalization in practice
Digitalization has been identified as one of the major trends changing society and business. Digitalization causes changes for companies due to the adoption of digital technologies in the organization or in the operation environment. This paper discusses digitalization from the viewpoint of diverse case studies carried out to collect data from several companies, and a literature study to complement the data. This paper describes the first version of the digital transformation model, derived from synthesis of these industrial cases, explaining a starting point for a systematic approach to tackle digital transformation. The model is aimed to help companies systematically handle the changes associated with digitalization. The model consists of four main steps, starting with positioning the company in digitalization and defining goals for the company, and then analyzing the company’s current state with respect to digitalization goals. Next, a roadmap for reaching the goals is defined and implemented in the company. These steps are iterative and can be repeated several times. Although company situations vary, these steps will help to systematically approach digitalization and to take the steps necessary to benefit from it.
Architecture & Planning• 2022• SciKA
Periodicals
The Oracle Problem in Software Testing: A Survey
Testing involves examining the behaviour of a system in order to discover potential faults. Given an input for a system, the challenge of distinguishing the corresponding desired, correct behaviour from potentially incorrect behavior is called the “test oracle problem”. Test oracle automation is important to remove a current bottleneck that inhibits greater overall test automation. Without test oracle automation, the human has to determine whether observed behaviour is correct. The literature on test oracles has introduced techniques for oracle automation, including modelling, specifications, contract-driven development and metamorphic testing. When none of these is completely adequate, the final source of test oracle information remains the human, who may be aware of informal specifications, expectations, norms and domain specific information that provide informal oracle guidance. All forms of test oracles, even the humble human, involve challenges of reducing cost and increasing benefit. This paper provides a comprehensive survey of current approaches to the test oracle problem and an analysis of trends in this important area of software testing research and practice.
Architecture & Planning• 2014• IEEE Computer Society
Periodicals
Machine Learning Applications for Precision Agriculture: A Comprehensive Review
Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.
Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
Periodicals
Consumers and Artificial Intelligence: An Experiential Perspective
Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: (1) data capture, (2) classification, (3) delegation, and (4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations’ investments into AI and to lay out an agenda for future research.
Architecture & Planning• 2020• SAGE Publishing
Periodicals
The role of big data in smart city
[no abstract]
Architecture & Planning• 2016• Elsevier BV
Periodicals
Toward Causal Representation Learning
The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.
Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
Periodicals
The Arcade Learning Environment: An Evaluation Platform for General Agents
In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.
Architecture & Planning• 2013• AI Access Foundation
Periodicals
Ecological interface design: theoretical foundations
A theoretical framework for designing interfaces for complex human-machine systems is proposed. The framework, called ecological interface design (EID), is based on the skills, rules, and knowledge taxonomy of cognitive control. The basic goals of EID are not to force processing to a higher level than the demands of the task require, and to support each of the three levels of cognitive control. Thus, an EID interface should not contribute to the difficulty of the task, and at the same time, it should support the entire range of activities that operators will be faced with. Three prescriptive design principles are suggested to achieve this objective, each directed at supporting a particular level of cognitive control. Particular attention is paid to presenting a coherent deductive argument justifying the principles of EID. Support for the EID framework is discussed. Some issues for future research are outlined.>
Architecture & Planning• 1992• Institute of Electrical and Electronics Engineers
Periodicals
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.
Architecture & Planning• 2019• Public Library of Science
Periodicals
An Integrated Brain-Machine Interface Platform With Thousands of Channels
Brain-machine interfaces hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical brain-machine interfaces have not yet been widely adopted, in part, because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth brain-machine interface system. We have built arrays of small and flexible electrode “threads,” with as many as 3072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitization: The package for 3072 channels occupies less than 23×18.5×2 mm3. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 70% in chronically implanted electrodes. Neuralink’s approach to brain-machine interface has unprecedented packaging density and scalability in a clinically relevant package.
Architecture & Planning• 2019• JMIR Publications
Periodicals
Notch signaling pathway: architecture, disease, and therapeutics
The NOTCH gene was identified approximately 110 years ago. Classical studies have revealed that NOTCH signaling is an evolutionarily conserved pathway. NOTCH receptors undergo three cleavages and translocate into the nucleus to regulate the transcription of target genes. NOTCH signaling deeply participates in the development and homeostasis of multiple tissues and organs, the aberration of which results in cancerous and noncancerous diseases. However, recent studies indicate that the outcomes of NOTCH signaling are changeable and highly dependent on context. In terms of cancers, NOTCH signaling can both promote and inhibit tumor development in various types of cancer. The overall performance of NOTCH-targeted therapies in clinical trials has failed to meet expectations. Additionally, NOTCH mutation has been proposed as a predictive biomarker for immune checkpoint blockade therapy in many cancers. Collectively, the NOTCH pathway needs to be integrally assessed with new perspectives to inspire discoveries and applications. In this review, we focus on both classical and the latest findings related to NOTCH signaling to illustrate the history, architecture, regulatory mechanisms, contributions to physiological development, related diseases, and therapeutic applications of the NOTCH pathway. The contributions of NOTCH signaling to the tumor immune microenvironment and cancer immunotherapy are also highlighted. We hope this review will help not only beginners but also experts to systematically and thoroughly understand the NOTCH signaling pathway.
Architecture & Planning• 2022• Springer Nature
Periodicals
Best practices for single-cell analysis across modalities
[no abstract]
Architecture & Planning• 2023• Nature Portfolio
Periodicals
First M87 Event Horizon Telescope Results. II. Array and Instrumentation
Abstract The Event Horizon Telescope (EHT) is a very long baseline interferometry (VLBI) array that comprises millimeter- and submillimeter-wavelength telescopes separated by distances comparable to the diameter of the Earth. At a nominal operating wavelength of ∼1.3 mm, EHT angular resolution ( λ / D ) is ∼25 μ as, which is sufficient to resolve nearby supermassive black hole candidates on spatial and temporal scales that correspond to their event horizons. With this capability, the EHT scientific goals are to probe general relativistic effects in the strong-field regime and to study accretion and relativistic jet formation near the black hole boundary. In this Letter we describe the system design of the EHT, detail the technology and instrumentation that enable observations, and provide measures of its performance. Meeting the EHT science objectives has required several key developments that have facilitated the robust extension of the VLBI technique to EHT observing wavelengths and the production of instrumentation that can be deployed on a heterogeneous array of existing telescopes and facilities. To meet sensitivity requirements, high-bandwidth digital systems were developed that process data at rates of 64 gigabit s −1 , exceeding those of currently operating cm-wavelength VLBI arrays by more than an order of magnitude. Associated improvements include the development of phasing systems at array facilities, new receiver installation at several sites, and the deployment of hydrogen maser frequency standards to ensure coherent data capture across the array. These efforts led to the coordination and execution of the first Global EHT observations in 2017 April, and to event-horizon-scale imaging of the supermassive black hole candidate in M87.
Architecture & Planning• 2019• IOP Publishing
Periodicals
A global perspective on domestic energy deprivation: Overcoming the energy poverty–fuel poverty binary
This paper offers an integrated conceptual framework for the research and amelioration of energy deprivation in the home. It starts from the premise that all forms of energy and fuel poverty – in developed and developing countries alike – are underpinned by a common condition: the inability to attain a socially and materially necessitated level of domestic energy services. We consider the functionings provided by energy demand in the residential domain in order to advance two claims: first, that domestic energy deprivation in its different guises and forms is fundamentally tied to the ineffective operation of the socio-technical pathways that allow for the fulfilment of household energy needs, and as such is best analyzed by understanding the constitution of different energy services (heating, lighting, etc.) in the home. Second, we emphasize the ability of vulnerability thinking to encapsulate the driving forces of domestic energy deprivation via a comprehensive analytical matrix. The paper identifies the main components and implications of energy service and vulnerability approaches as they relate to domestic energy deprivation across the world.
Architecture & Planning• 2015• Elsevier BV
Periodicals
Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and the presence of restricted interests and repetitive behaviors. There have been recent concerns about increased prevalence, and this article seeks to elaborate on factors that may influence prevalence rates, including recent changes to the diagnostic criteria. The authors review evidence that ASD is a neurobiological disorder influenced by both genetic and environmental factors affecting the developing brain, and enumerate factors that correlate with ASD risk. Finally, the article describes how clinical evaluation begins with developmental screening, followed by referral for a definitive diagnosis, and provides guidance on screening for comorbid conditions.
Architecture & Planning• 2020• AME Publishing Company
Periodicals
Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply
The greatest sustainability challenge facing humanity today is the greenhouse gas emissions and the global climate change with fossil fuels led by coal, natural gas and oil contributing 61.3% of global electricity generation in the year 2020. The cumulative effect of the Stockholm, Rio, and Johannesburg conferences identified sustainable energy development (SED) as a very important factor in the sustainable global development. This study reviews energy transition strategies and proposes a roadmap for sustainable energy transition for sustainable electricity generation and supply in line with commitments of the Paris Agreement aimed at reducing greenhouse gas emissions and limiting the rise in global average temperature to 1.5°C above the preindustrial level. The sustainable transition strategies typically consist of three major technological changes namely, energy savings on the demand side, generation efficiency at production level and fossil fuel substitution by various renewable energy sources and low carbon nuclear. For the transition remain technically and economically feasible and beneficial, policy initiatives are necessary to steer the global electricity transition towards a sustainable energy and electricity system. Large-scale renewable energy adoption should include measures to improve efficiency of existing nonrenewable sources which still have an important cost reduction and stabilization role. A resilient grid with advanced energy storage for storage and absorption of variable renewables should also be part of the transition strategies. From this study, it was noted that whereas sustainable development has social, economic, and environmental pillars, energy sustainability is best analysed by five-dimensional approach consisting of environmental, economic, social, technical, and institutional/political sustainability to determine resource sustainability. The energy transition requires new technology for maximum use of the abundant but intermittent renewable sources a sustainable mix with limited nonrenewable sources optimized to minimize cost and environmental impact but maintained quality, stability, and flexibility of an electricity supply system. Technologies needed for the transition are those that use conventional mitigation, negative emissions technologies which capture and sequester carbon emissions and finally technologies which alter the global atmospheric radiative energy budget to stabilize and reduce global average temperature. A sustainable electricity system needs facilitating technology, policy, strategies and infrastructure like smart grids, and models with an appropriate mix of both renewable and low carbon energy sources.
Architecture & Planning• 2022• Frontiers Media
Periodicals
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method
[no abstract]
Architecture & Planning• 2021• Elsevier BV