Showing papers by "University of Waterloo published in 2017"
TL;DR: The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers.
Abstract: Recent progress implies that a crossover between machine learning and quantum information processing benefits both fields. Traditional machine learning has dramatically improved the benchmarking an ...
2,162 citations
Posted Content•
TL;DR: It is discovered that modern neural networks, unlike those from a decade ago, are poorly calibrated, and on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
Abstract: Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
1,883 citations
Proceedings Article•
17 Jul 2017TL;DR: This article found that depth, width, weight decay, and batch normalization are important factors influencing confidence calibration of neural networks, and that temperature scaling is surprisingly effective at calibrating predictions.
Abstract: Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
1,853 citations
TL;DR: The subcommittee reviewed the prevalence, incidence, risk factors, natural history, morbidity and questionnaires reported in epidemiological studies of dry eye disease and confirmed that prevalence increases with age, however signs showed a greater increase per decade than symptoms.
Abstract: The subcommittee reviewed the prevalence, incidence, risk factors, natural history, morbidity and questionnaires reported in epidemiological studies of dry eye disease (DED). A meta-analysis of published prevalence data estimated the impact of age and sex. Global mapping of prevalence was undertaken. The prevalence of DED ranged from 5 to 50%. The prevalence of signs was higher and more variable than symptoms. There were limited prevalence studies in youth and in populations south of the equator. The meta-analysis confirmed that prevalence increases with age, however signs showed a greater increase per decade than symptoms. Women have a higher prevalence of DED than men, although differences become significant only with age. Risk factors were categorized as modifiable/non-modifiable, and as consistent, probable or inconclusive. Asian ethnicity was a mostly consistent risk factor. The economic burden and impact of DED on vision, quality of life, work productivity, psychological and physical impact of pain, are considerable, particularly costs due to reduced work productivity. Questionnaires used to evaluate DED vary in their utility. Future research should establish the prevalence of disease of varying severity, the incidence in different populations and potential risk factors such as youth and digital device usage. Geospatial mapping might elucidate the impact of climate, environment and socioeconomic factors. Given the limited study of the natural history of treated and untreated DED, this remains an important area for future research.
1,322 citations
TL;DR: It is shown that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians.
Abstract: The success of machine learning techniques in handling big data sets proves ideal for classifying condensed-matter phases and phase transitions. The technique is even amenable to detecting non-trivial states lacking in conventional order. Condensed-matter physics is the study of the collective behaviour of infinitely complex assemblies of electrons, nuclei, magnetic moments, atoms or qubits1. This complexity is reflected in the size of the state space, which grows exponentially with the number of particles, reminiscent of the ‘curse of dimensionality’ commonly encountered in machine learning2. Despite this curse, the machine learning community has developed techniques with remarkable abilities to recognize, classify, and characterize complex sets of data. Here, we show that modern machine learning architectures, such as fully connected and convolutional neural networks3, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians. Readily programmable through modern software libraries4,5, neural networks can be trained to detect multiple types of order parameter, as well as highly non-trivial states with no conventional order, directly from raw state configurations sampled with Monte Carlo6,7.
1,161 citations
TL;DR: The role of the Tear Film and Ocular Surface Society (TFOS) Dry Eye Workshop (DEWS) II Diagnostic Methodology Subcommittee was to identify tests used to diagnose and monitor dry eye disease (DED) to identify those most appropriate to fulfil the definition of DED and its sub-classifications.
Abstract: The role of the Tear Film and Ocular Surface Society (TFOS) Dry Eye Workshop (DEWS) II Diagnostic Methodology Subcommittee was 1) to identify tests used to diagnose and monitor dry eye disease (DED), 2) to identify those most appropriate to fulfil the definition of DED and its sub-classifications, 3) to propose the most appropriate order and technique to conduct these tests in a clinical setting, and 4) to provide a differential diagnosis for DED and distinguish conditions where DED is a comorbidity. Prior to diagnosis, it is important to exclude conditions that can mimic DED with the aid of triaging questions. Symptom screening with the DEQ-5 or OSDI confirms that a patient might have DED and triggers the conduct of diagnostic tests of (ideally non-invasive) breakup time, osmolarity and ocular surface staining with fluorescein and lissamine green (observing the cornea, conjunctiva and eyelid margin). Meibomian gland dysfunction, lipid thickness/dynamics and tear volume assessment and their severity allow sub-classification of DED (as predominantly evaporative or aqueous deficient) which informs the management of DED. Videos of these diagnostic and sub-classification techniques are available on the TFOS website. It is envisaged that the identification of the key tests to diagnose and monitor DED and its sub-classifications will inform future epidemiological studies and management clinical trials, improving comparability, and enabling identification of the sub-classification of DED in which different management strategies are most efficacious.
1,152 citations
TL;DR: The reaction mechanism of electrically rechargeable zinc-air batteries is discussed, different battery configurations are compared, and an in depth discussion is offered of the major issues that affect individual cellular components, along with respective strategies to alleviate these issues to enhance battery performance.
Abstract: Zinc-air batteries have attracted much attention and received revived research efforts recently due to their high energy density, which makes them a promising candidate for emerging mobile and electronic applications. Besides their high energy density, they also demonstrate other desirable characteristics, such as abundant raw materials, environmental friendliness, safety, and low cost. Here, the reaction mechanism of electrically rechargeable zinc-air batteries is discussed, different battery configurations are compared, and an in depth discussion is offered of the major issues that affect individual cellular components, along with respective strategies to alleviate these issues to enhance battery performance. Additionally, a section dedicated to battery-testing techniques and corresponding recommendations for best practices are included. Finally, a general perspective on the current limitations, recent application-targeted developments, and recommended future research directions to prolong the lifespan of electrically rechargeable zinc-air batteries is provided.
1,071 citations
TL;DR: In this paper, the authors present a set of guidelines for stated preference studies that are more comprehensive than those of the original National Oceanic and Atmospheric Administration (NOAA) Blue Ribbon Panel on contingent valuation, and reflect the two decades of research since that time.
Abstract: This article proposes contemporary best-practice recommendations for stated preference (SP) studies used to inform decision making, grounded in the accumulated body of peer-reviewed literature. These recommendations consider the use of SP methods to estimate both use and non-use (passive-use) values, and cover the broad SP domain, including contingent valuation and discrete choice experiments. We focus on applications to public goods in the context of the environment and human health but also consider ways in which the proposed recommendations might apply to other common areas of application. The recommendations recognize that SP results may be used and reused (benefit transfers) by governmental agencies and nongovernmental organizations, and that all such applications must be considered. The intended result is a set of guidelines for SP studies that is more comprehensive than that of the original National Oceanic and Atmospheric Administration (NOAA) Blue Ribbon Panel on contingent valuation, is more germane to contemporary applications, and reflects the two decades of research since that time. We also distinguish between practices for which accumulated research is sufficient to support recommendations and those for which greater uncertainty remains. The goal of this article is to raise the quality of SP studies used to support decision making and promote research that will further enhance the practice of these studies worldwide.
896 citations
TL;DR: This review discusses various soil microorganisms that have the ability to solubilize phosphorus and hence have the potential to be used as bio fertilizers and concludes that this technology is ready for commercial exploitation in various regions worldwide.
Abstract: The use of excess conventional Phosphorus (P) fertilizers to improve agricultural productivity, in order to meet constantly increasing global food demand, potentially causes surface and ground water pollution, waterway eutrophication, soil fertility depletion, and accumulation of toxic elements such as high concentration of selenium (Se), arsenic (As) in the soil. Quite a number of soil microorganisms are capable of solubilizing/mineralizing insoluble soil phosphate to release soluble P and making it available to plants. These microorganisms improve the growth and yield of a wide variety of crops. Thus, inoculating seeds/crops/soil with Phosphate Solubilizing Microorganisms (PSM) is a promising strategy to improve world food production without causing any environmental hazard. Despite their great significance in soil fertility improvement, phosphorus-solubilizing microorganisms have yet to replace conventional chemical fertilizers in commercial agriculture. A better understanding of recent developments in PSM functional diversity, colonizing ability, mode of actions and judicious application should facilitate their use as reliable components of sustainable agricultural systems.In this review, we discussed various soil microorganisms that have the ability to solubilize phosphorus and hence have the potential to be used as bio fertilizers. The mechanisms of inorganic phosphate solubilization by PSM and the mechanisms of organic phosphorus mineralization are highlighted together with some factors that determine the success of this technology. Finally we provide some indications that the use of PSM will promote sustainable agriculture and conclude that this technology is ready for commercial exploitation in various regions worldwide.
847 citations
22 Nov 2017
TL;DR: NewsQA as mentioned in this paper ) is a dataset of over 100,000 human-generated question-answer pairs from CNN news articles, with answers consisting of spans of text in the articles.
Abstract: We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text in the articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. Analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (13.3% F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available online.
833 citations
TL;DR: In this paper, Nazar et al. present a protection method for Li metal by an in situ synthesis of Li-based surface alloy composites, and demonstrate promising battery applications.
Abstract: Li dendrite formation is a major obstacle in the development of Li metal batteries. Nazar and colleagues present a protection method for the Li metal by an in situ synthesis of Li-based surface alloy composites, and demonstrate promising battery applications.
University of Waterloo1, University of Melbourne2, Complutense University of Madrid3, Massachusetts Eye and Ear Infirmary4, University of California, Los Angeles5, University of Düsseldorf6, University of São Paulo7, Yonsei University8, Federal University of São Paulo9, Fudan University Shanghai Medical College10, Aston University11, University of Auckland12
TL;DR: It became clear that many of the treatments available for the management of dry eye disease lack the necessary Level 1 evidence to support their recommendation, often due to a lack of appropriate masking, randomization or controls and in some cases due to issues with selection bias or inadequate sample size.
Abstract: The members of the Management and Therapy Subcommittee undertook an evidence-based review of current dry eye therapies and management options. Management options reviewed in detail included treatments for tear insufficiency and lid abnormalities, as well as anti-inflammatory medications, surgical approaches, dietary modifications, environmental considerations and complementary therapies. Following this extensive review it became clear that many of the treatments available for the management of dry eye disease lack the necessary Level 1 evidence to support their recommendation, often due to a lack of appropriate masking, randomization or controls and in some cases due to issues with selection bias or inadequate sample size. Reflecting on all available evidence, a staged management algorithm was derived that presents a step-wise approach to implementing the various management and therapeutic options according to disease severity. While this exercise indicated that differentiating between aqueous-deficient and evaporative dry eye disease was critical in selecting the most appropriate management strategy, it also highlighted challenges, based on the limited evidence currently available, in predicting relative benefits of specific management options, in managing the two dry eye disease subtypes. Further evidence is required to support the introduction, and continued use, of many of the treatment options currently available to manage dry eye disease, as well as to inform appropriate treatment starting points and understand treatment specificity in relation to dry eye disease subtype.
University of Washington1, University of British Columbia2, Stanford University3, University of Guelph4, University of Saskatchewan5, American Museum of Natural History6, University of Victoria7, University of Wyoming8, University of Waterloo9, International Union for Conservation of Nature and Natural Resources10, Oregon State University11, Memorial University of Newfoundland12, Cornell University13, Colorado State University14, Slippery Rock University of Pennsylvania15, Georgia State University16
TL;DR: In this paper, the authors examine the scope and purpose of eighteen subfields of classic, interdisciplinary and applied conservation social sciences and articulates ten distinct contributions that the social sciences can make to understanding and improving conservation.
TL;DR: The simulation results show that the proposed path-planning approach is effective for many driving scenarios, and the MMPC-based path-tracking controller provides dynamic tracking performance and maintains good maneuverability.
Abstract: A path planning and tracking framework is presented to maintain a collision-free path for autonomous vehicles. For path-planning approaches, a 3-D virtual dangerous potential field is constructed as a superposition of trigonometric functions of the road and the exponential function of obstacles, which can generate a desired trajectory for collision avoidance when a vehicle collision with obstacles is likely to happen. Next, to track the planned trajectory for collision avoidance maneuvers, the path-tracking controller formulated the tracking task as a multiconstrained model predictive control (MMPC) problem and calculated the front steering angle to prevent the vehicle from colliding with a moving obstacle vehicle. Simulink and CarSim simulations are conducted in the case where moving obstacles exist. The simulation results show that the proposed path-planning approach is effective for many driving scenarios, and the MMPC-based path-tracking controller provides dynamic tracking performance and maintains good maneuverability.
TL;DR: Level as mentioned in this paper can automatically locate the bound and/or quasibounded levels of any smooth single- or double-minimum potential, and calculate inertial rotation and centrifugal distortion constants and various expectation values for those levels.
Abstract: This paper describes program LEVEL, which can solve the radial or one-dimensional Schrodinger equation and automatically locate either all of, or a selected number of, the bound and/or quasibound levels of any smooth single- or double-minimum potential, and calculate inertial rotation and centrifugal distortion constants and various expectation values for those levels. It can also calculate Franck–Condon factors and other off-diagonal matrix elements, either between levels of a single potential or between levels of two different potentials. The potential energy function may be defined by any one of a number of analytic functions, or by a set of input potential function values which the code will interpolate over and extrapolate beyond to span the desired range.
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TL;DR: In this paper, the authors apply image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier.
Abstract: This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier. Our experiments on ImageNet show that total variance minimization and image quilting are very effective defenses in practice, in particular, when the network is trained on transformed images. The strength of those defenses lies in their non-differentiable nature and their inherent randomness, which makes it difficult for an adversary to circumvent the defenses. Our best defense eliminates 60% of strong gray-box and 90% of strong black-box attacks by a variety of major attack methods
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TL;DR: Each metal ion and the known DNA sequences for its sensing are reviewed and the fundamental aspect of metal binding is emphasized, emphasizing the distinct chemical property of each metal.
Abstract: Metal ions are essential to many chemical, biological, and environmental processes. In the past two decades, many DNA-based metal sensors have emerged. While the main biological role of DNA is to store genetic information, its chemical structure is ideal for metal binding via both the phosphate backbone and nucleobases. DNA is highly stable, cost-effective, easy to modify, and amenable to combinatorial selection. Two main classes of functional DNA were developed for metal sensing: aptamers and DNAzymes. While a few metal binding aptamers are known, it is generally quite difficult to isolate such aptamers. On the other hand, DNAzymes are powerful tools for metal sensing since they are selected based on catalytic activity, thus bypassing the need for metal immobilization. In the last five years, a new surge of development has been made on isolating new metal-sensing DNA sequences. To date, many important metals can be selectively detected by DNA often down to the low parts-per-billion level. Herein, each me...
TL;DR: In this paper, the cosmological constant is interpreted as thermodynamic pressure and treated as a thermodynamic variable in its own right, whereas the mass of the black hole is identified with the chemical enthalpy.
Abstract: We review recent developments on the thermodynamics of black holes in extended phase space, where the cosmological constant is interpreted as thermodynamic pressure and treated as a thermodynamic variable in its own right. In this approach, the mass of the black hole is no longer regarded as internal energy, rather it is identified with the chemical enthalpy. This leads to an extended dictionary for black hole thermodynamic quantities, in particular a notion of thermodynamic volume emerges for a given black hole spacetime. This volume is conjectured to satisfy the reverse isoperimetric inequality—an inequality imposing a bound on the amount of entropy black hole can carry for a fixed thermodynamic volume. New thermodynamic phase transitions naturally emerge from these identifications. Namely, we show that black holes can be understood from the viewpoint of chemistry, in terms of concepts such as Van der Waals fluids, reentrant phase transitions, and triple points. We also review the recent attempts at extending the AdS/CFT dictionary in this setting, discuss the connections with horizon thermodynamics, applications to Lifshitz spacetimes, and outline possible future directions in this field.
TL;DR: To realize the objective of worldwide sustainable agriculture, it is essential that the many mechanisms employed by PGPB first be thoroughly understood thereby allowing workers to fully harness the potentials of these microbes.
Abstract: The idea of eliminating the use of fertilizers which are sometimes environmentally unsafe is slowly becoming a reality because of the emergence of microorganisms that can serve the same purpose or even do better. Depletion of soil nutrients through leaching into the waterways and causing contamination are some of the negative effects of these chemical fertilizers that prompted the need for suitable alternatives. This brings us to the idea of using microbes that can be developed for use as biological fertilizers (biofertilizers). They are environmentally friendly as they are natural living organisms. They increase crop yield and production and, in addition, in developing countries, they are less expensive compared to chemical fertilizers. These biofertilizers are typically called plant growth-promoting bacteria (PGPB). In addition to PGPB, some fungi have also been demonstrated to promote plant growth. Apart from improving crop yields, some biofertilizers also control various plant pathogens. The objective of worldwide sustainable agriculture is much more likely to be achieved through the widespread use of biofertilizers rather than chemically synthesized fertilizers. However, to realize this objective it is essential that the many mechanisms employed by PGPB first be thoroughly understood thereby allowing workers to fully harness the potentials of these microbes. The present state of our knowledge regarding the fundamental mechanisms employed by PGPB is discussed herein.
TL;DR: The members of the Tear Film Subcommittee reviewed the role of the tear film in dry eye disease (DED), biophysical and biochemical aspects of tears and how these change in DED and recommended areas for future research.
Abstract: The members of the Tear Film Subcommittee reviewed the role of the tear film in dry eye disease (DED). The Subcommittee reviewed biophysical and biochemical aspects of tears and how these change in DED. Clinically, DED is characterized by loss of tear volume, more rapid breakup of the tear film and increased evaporation of tears from the ocular surface. The tear film is composed of many substances including lipids, proteins, mucins and electrolytes. All of these contribute to the integrity of the tear film but exactly how they interact is still an area of active research. Tear film osmolarity increases in DED. Changes to other components such as proteins and mucins can be used as biomarkers for DED. The Subcommittee recommended areas for future research to advance our understanding of the tear film and how this changes with DED. The final report was written after review by all Subcommittee members and the entire TFOS DEWS II membership.
Roswell Park Cancer Institute1, Food and Drug Administration2, United States Department of Health and Human Services3, Westat4, University of Waterloo5, Medical University of South Carolina6, Johns Hopkins University7, University of California, San Diego8, Dartmouth College9, Ontario Institute for Cancer Research10
TL;DR: The cumulative, population-based data generated over time by the PATH Study will contribute to the evidence base to inform FDA's regulatory mission under the Family Smoking Prevention and Tobacco Control Act and efforts to reduce the Nation's burden of tobacco-related death and disease.
Abstract: Background This paper describes the methods and conceptual framework for Wave 1 of the Population Assessment of Tobacco and Health (PATH) Study data collection. The National Institutes of Health, through the National Institute on Drug Abuse, is partnering with the Food and Drug Administration9s (FDA) Center for Tobacco Products to conduct the PATH Study under a contract with Westat. Methods The PATH Study is a nationally representative, longitudinal cohort study of 45 971 adults and youth in the USA, aged 12 years and older. Wave 1 was conducted from 12 September 2013 to 15 December 2014 using Audio Computer-Assisted Self-Interviewing to collect information on tobacco-use patterns, risk perceptions and attitudes towards current and newly emerging tobacco products, tobacco initiation, cessation, relapse behaviours and health outcomes. The PATH Study9s design allows for the longitudinal assessment of patterns of use of a spectrum of tobacco products, including initiation, cessation, relapse and transitions between products, as well as factors associated with use patterns. Additionally, the PATH Study collects biospecimens from consenting adults aged 18 years and older and measures biomarkers of exposure and potential harm related to tobacco use. Conclusions The cumulative, population-based data generated over time by the PATH Study will contribute to the evidence base to inform FDA9s regulatory mission under the Family Smoking Prevention and Tobacco Control Act and efforts to reduce the Nation9s burden of tobacco-related death and disease.
TL;DR: Interweaving carbon nanotubes between the MXene layers creates a porous, conductive network with high polysulfide adsorptivity, enabling sulfur hosts with excellent performance even at high loading (5.5 mg cm-2 ).
Abstract: The complex surface chemistry that dictates the interaction between MXene and polysulfides - the formation of thiosulfate via consumption of -OH surface groups, followed by Lewis acid-base interaction between the exposed Ti atoms and polysulfides - is unravelled. Interweaving carbon nanotubes between the MXene layers creates a porous, conductive network with high polysulfide adsorptivity, enabling sulfur hosts with excellent performance even at high loading (5.5 mg cm-2 ).
TL;DR: This review surveys entropic uncertainty relations that capture Heisenberg’s idea that the results of incompatible measurements are impossible to predict, covering both finite- and infinite-dimensional measurements.
Abstract: Heisenberg’s uncertainty principle forms a fundamental element of quantum mechanics. Uncertainty relations in terms of entropies were initially proposed to deal with conceptual shortcomings in the original formulation of the uncertainty principle and, hence, play an important role in quantum foundations. More recently, entropic uncertainty relations have emerged as the central ingredient in the security analysis of almost all quantum cryptographic protocols, such as quantum key distribution and two-party quantum cryptography. This review surveys entropic uncertainty relations that capture Heisenberg’s idea that the results of incompatible measurements are impossible to predict, covering both finite- and infinite-dimensional measurements. These ideas are then extended to incorporate quantum correlations between the observed object and its environment, allowing for a variety of recent, more general formulations of the uncertainty principle. Finally, various applications are discussed, ranging from entanglement witnessing to wave-particle duality to quantum cryptography.
TL;DR: This review will aid in the improvement of design of non-invasive, smart hydrogels that can be utilized for tissue engineering and other biomedical applications and a future outlook of the field of biocompatibility within the context of hydrogel-based scaffolds is concluded.
TL;DR: This work establishes a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them, and presents three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test.
Abstract: The great content diversity of real-world digital images poses a grand challenge to image quality assessment (IQA) models, which are traditionally designed and validated on a handful of commonly used IQA databases with very limited content variation. To test the generalization capability and to facilitate the wide usage of IQA techniques in real-world applications, we establish a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them. Instead of collecting the mean opinion score for each image via subjective testing, which is extremely difficult if not impossible, we present three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test (P-test). We compare 20 well-known IQA models using the proposed criteria, which not only provide a stronger test in a more challenging testing environment for existing models, but also demonstrate the additional benefits of using the proposed database. For example, in the P-test, even for the best performing no-reference IQA model, more than 6 million failure cases against the model are “discovered” automatically out of over 1 billion test pairs. Furthermore, we discuss how the new database may be exploited using innovative approaches in the future, to reveal the weaknesses of existing IQA models, to provide insights on how to improve the models, and to shed light on how the next-generation IQA models may be developed. The database and codes are made publicly available at: https://ece.uwaterloo.ca/~k29ma/exploration/ .
University of Auckland1, University of Minnesota2, HealthPartners3, University of Illinois at Chicago4, Spanish National Research Council5, Anglia Ruskin University6, University of Oxford7, Massachusetts Eye and Ear Infirmary8, Baylor College of Medicine9, Federal University of São Paulo10, University of Waterloo11, University of Alabama at Birmingham12, University of California, Davis13, Rafael Advanced Defense Systems14, University of New South Wales15, Aston University16
TL;DR: This article presents an Executive Summary of the conclusions and recommendations of the 10-chapter TFOS DEWS II report.
Abstract: This article presents an Executive Summary of the conclusions and recommendations of the 10-chapter TFOS DEWS II report. The entire TFOS DEWS II report was published in the July 2017 issue of The Ocular Surface. A downloadable version of the document and additional material, including videos of diagnostic and management techniques, are available on the TFOS website: www.TearFilm.org.
University of Alberta1, Montreal Heart Institute2, University Health Network3, McGill University4, McMaster University5, University of Waterloo6, University of Calgary7, Université de Sherbrooke8, University of Western Ontario9, St. Michael's Hospital10, Halifax11, Royal Jubilee Hospital12, University of British Columbia13, St. Boniface General Hospital14, Sunnybrook Health Sciences Centre15, University of Saskatchewan16, Durham University17, Laval University18, University of Colorado Boulder19, Université de Montréal20, St. John's University21
TL;DR: The 2017 HF guidelines provide updated guidance on the diagnosis and management that should aid in day-to-day decisions for caring for patients with HF, with attention to strategies and treatments to prevent HF, to the organization of HF care, comorbidity management, as well as practical issues around the timing of referral and follow-up care.
TL;DR: This article first introduces promising smart city applications and architecture, then discusses several security and privacy challenges in these applications, and introduces some open issues for future research.
Abstract: With the flourishing and advancement of the IoT, the smart city has become an emerging paradigm, consisting of ubiquitous sensing, heterogeneous network infrastructure, and intelligent information processing and control systems. A smart city can monitor the physical world in real time, and provide intelligent services to both local residents and travelers in terms of transportation, healthcare, environment, entertainment, and energy. However, security and privacy concerns arise, since smart city applications not only collect a wide range of privacy-sensitive information from people and their social circles, but also control city facilities and influence people’s lives. In this article, we investigate security and privacy in smart city applications. Specifically, we first introduce promising smart city applications and architecture. Then we discuss several security and privacy challenges in these applications. Some research efforts are subsequently presented to address these security and privacy challenges for intelligent healthcare, transportation, and smart energy. Finally, we point out some open issues for future research.
TL;DR: Molecular imprinting furthers the functional enzyme mimicking aspect of nanozymes, and such hybrid materials will find applications in biosensor development, separation, environmental remediation, and drug delivery.
Abstract: Enzyme-mimicking nanomaterials (nanozymes) are more cost-effective and robust than protein enzymes, but they lack specificity. Herein, molecularly imprinted polymers were grown on Fe3O4 nanozymes with peroxidase-like activity to create substrate binding pockets. Electron microscopy confirmed a shell of nanogel. By imprinting with an adsorbed substrate, moderate specificity was achieved with neutral monomers. Further introducing charged monomers led to nearly 100-fold specificity for the imprinted substrate over the nonimprinted compared to that of bare Fe3O4. Selective substrate binding was further confirmed by isothermal titration calorimetry. The same method was also successfully applied for imprinting on gold nanoparticles (peroxidase mimics) and nanoceria (oxidase mimics). Molecular imprinting furthers the functional enzyme mimicking aspect of nanozymes, and such hybrid materials will find applications in biosensor development, separation, environmental remediation, and drug delivery.
TL;DR: In this paper, a superconducting artificial atom coupled to a 1D waveguide has been shown to reach the nonperturbative regime of ultrastrong coupling, where spontaneous emission rate of the atom exceeds its transition frequency.
Abstract: A superconducting artificial atom coupled to a 1D waveguide tests the limits of light–matter interaction in an unexplored coupling regime, which may enable new perspectives for quantum technologies. The study of light–matter interaction has led to important advances in quantum optics and enabled numerous technologies. Over recent decades, progress has been made in increasing the strength of this interaction at the single-photon level. More recently, a major achievement has been the demonstration of the so-called strong coupling regime1,2, a key advancement enabling progress in quantum information science. Here, we demonstrate light–matter interaction over an order of magnitude stronger than previously reported, reaching the nonperturbative regime of ultrastrong coupling (USC). We achieve this using a superconducting artificial atom tunably coupled to the electromagnetic continuum of a one-dimensional waveguide. For the largest coupling, the spontaneous emission rate of the atom exceeds its transition frequency. In this USC regime, the description of atom and light as distinct entities breaks down, and a new description in terms of hybrid states is required3,4. Beyond light–matter interaction itself, the tunability of our system makes it a promising tool to study a number of important physical systems, such as the well-known spin-boson5 and Kondo models6.