Showing papers on "Quality (business) published in 2020"

FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

Abstract: Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duration prediction (to provide more information as input) and knowledge distillation (to simplify the data distribution in output), which can ease the one-to-many mapping problem (i.e., multiple speech variations correspond to the same text) in TTS. However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated and time-consuming, 2) the duration extracted from the teacher model is not accurate enough, and the target mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality. In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs in training and use predicted values in inference. We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of fully end-to-end inference. Experimental results show that 1) FastSpeech 2 achieves a 3x training speed-up over FastSpeech, and FastSpeech 2s enjoys even faster inference speed; 2) FastSpeech 2 and 2s outperform FastSpeech in voice quality, and FastSpeech 2 can even surpass autoregressive models. Audio samples are available at this https URL.

Perceptual image quality assessment: a survey

Abstract: Perceptual quality assessmentplays a vital role in the visual communication systems owing to theexistence of quality degradations introduced in various stages of visual signalacquisition, compression, transmission and display.Quality assessment for visual signals can be performed subjectively andobjectively, and objective quality assessment is usually preferred owing to itshigh efficiency and easy deployment. A large number of subjective andobjective visual quality assessment studies have been conducted during recent years.In this survey, we give an up-to-date and comprehensivereview of these studies.Specifically, the frequently used subjective image quality assessment databases are firstreviewed, as they serve as the validation set for the objective measures.Second, the objective image quality assessment measures are classified and reviewed according to the applications and the methodologies utilized in the quality measures.Third, the performances of the state-of-the-artquality measures for visual signals are compared with an introduction of theevaluation protocols.This survey provides a general overview of classical algorithms andrecent progresses in the field of perceptual image quality assessment.

Machine learning in additive manufacturing: State-of-the-art and perspectives

Abstract: Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology. However, its broad adoption in industry is still hindered by high entry barriers of design for additive manufacturing (DfAM), limited materials library, various processing defects, and inconsistent product quality. In recent years, machine learning (ML) has gained increasing attention in AM due to its unprecedented performance in data tasks such as classification, regression and clustering. This article provides a comprehensive review on the state-of-the-art of ML applications in a variety of AM domains. In the DfAM, ML can be leveraged to output new high-performance metamaterials and optimized topological designs. In AM processing, contemporary ML algorithms can help to optimize process parameters, and conduct examination of powder spreading and in-process defect monitoring. On the production of AM, ML is able to assist practitioners in pre-manufacturing planning, and product quality assessment and control. Moreover, there has been an increasing concern about data security in AM as data breaches could occur with the aid of ML techniques. Lastly, it concludes with a section summarizing the main findings from the literature and providing perspectives on some selected interesting applications of ML in research and development of AM.

The Number, Quality, and Coverage of Randomized Controlled Trials in Nephrology

Abstract: Randomized controlled trials (RCT) are the optimal study design to answer intervention questions. The authors evaluated the number, quality, and coverage of RCT in nephrology. MEDLINE was searched using the relevant medical subject headings for nephrology and 12 major specialties in internal medicine, limited by ?randomized controlled trial? as a publication type. A random selection of 160 RCT in nephrology (40 for each decade) published since 1966 and an additional 270 RCT from ongoing or published Cochrane systematic reviews in various areas of nephrology, dialysis, and transplantation were evaluated for quality of reporting using standard criteria. The number of RCT published in nephrology from 1966 to 2002 (2779) is fewer than all other specialties of internal medicine (range: 5335 in hematology to 27109 in cardiology) with the proportion of all citations which are RCT being the third lowest (1.15%). There has been an increase in both indices from 1966 to 1996, but not at a greater rate than other specialties, and there has been no increase over the past 5 yr. Some areas of nephrology, in particular glomerulonephritis, are clear outliers with very low numbers of RCT to guide clinical decision-making. Overall the quality of RCT reporting in nephrology is low and has not improved over the past 30 yr with unclear allocation concealment (89%), lack of reported blinding of outcome assessors (92%), and failure to perform ?intention-to-treat analysis? (50%) particularly frequent. The challenges of improving the quality and quantity of trials in nephrology are substantial, but they can be overcome by using standard guidelines and checklists for trial reporting, greater attention to the trial methods and not just the results, involving experts in trial design and reporting, multicenter collaboration, and larger and simpler trials.

Time Series Data Augmentation for Deep Learning: A Survey

Abstract: Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series anomaly detection, classification, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance.

Using psychoneuroimmunity against COVID-19.

Abstract: The worldwide outbreak of coronavirus disease 2019 (COVID-19) raises concerns of widespread panic and anxiety in individuals subjected to the real or perceived threat of the virus. Compared to general populations, patients who are institutionalized in a closed unit are also very vulnerable to COVID-19 infection and complications. This crisis touched on difficult issues of not only psychiatric care and ethics, but also psychological impacts to psychiatric care givers. In this Viewpoint, we address both physical and biopsychosocial aspects of this infection, as well as the psychoneuroimmunity of preventive strategies of healthy lifestyle, regular exercise, balanced nutrition, quality sleep and a strong connection with people. Social distancing and wearing masks might help us from pathogen exposure, yet such these measures also prevent us from expressing compassion and friendliness. Therefore, all forms of psychological support should be routinely implemented not only to consider psychological resilience but also to enhance psychoneuroimmunity against COVID-19.

MLQA: Evaluating Cross-lingual Extractive Question Answering

Abstract: Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making building QA systems that work well in other languages challenging. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to measure progress. We present MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area. MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA has over 12K instances in English and 5K in each other language, with each instance parallel between 4 languages on average. We evaluate state-of-the-art cross-lingual models and machine-translation-based baselines on MLQA. In all cases, transfer results are shown to be significantly behind training-language performance.

Bioactive Compounds and Quality of Extra Virgin Olive Oil.

Abstract: (1) Background: Extra virgin olive oil (EVOO) is responsible for a large part of many health benefits associated to Mediterranean diet as it is a fundamental ingredient of this diet. The peculiarities of this golden, highly valued product are in part due to the requirements that must be met to achieve this title, namely, it has to be obtained using exclusively mechanical procedures, its free acidity cannot be greater than 0.8%, it must not show sensory defects, and it has to possess a fruity taste. (2) Methods: All these characteristics are key factors to EVOO quality, thus the chemical composition of these many health-promoting compounds, such as unsaturated fatty acids (which are also the major compounds, especially oleic acid), as well as minor components such as tocopherols or phenolic compounds (which behave as natural antioxidants) must be preserved. (3) Results: Due to the presence of all these compounds, the daily consumption of EVOO entails health benefits such as cardioprotective, antioxidant, anti-inflammatory, anti-tumor properties or acting as regulator of the intestinal microbiota, among others. (4) Conclusions: Taking all together, conserving EVOO chemical composition is essential to preserve its properties, so it is worth to control certain factors during storage like exposure to light, temperature, oxygen presence or the chosen packaging material, to maintain its quality and extend its shelf-life until its consumption.

The digital revolution in the travel and tourism industry

Abstract: The digital revolution is radically changing the world we live in. Sensors in smart homes are able to interconnect devices such as thermostats, washing machines, television sets, laptops, tablets, and other objects to the Internet of Things platforms. New digital technologies have introduced important innovations in factories, hospitals, hotels, cities and territories. Industry 4.0 is signaling the end of well-established patterns and is asking scholars, managers and citizens willing to survive in this ever-changing and increasingly complex environment to observe it through different lenses and new paradigms. The tourism sector, also, is very much involved in digital transformations, increasingly qualifying them with expressions such as Tourism 4.0 or Smart Tourism. What impact does the digital revolution have on tourism? What do tourism 4.0 and smart tourism have in common? What are the main differences? Adopting a conceptual approach and focusing on the travel and tourism industry, our work aims to provide a point of view and some preliminary answers to the above questions. In paragraph 1 we illustrate the main changes brought about by the digital revolution 4.0 in industry, the Web, and tourism. Then, the concepts of tourism 4.0 and smart tourism are compared in Sect. 2. Section 3 illustrates how the consumer experience of digital tourists changes before, during, and after the trip. Paragraphs 4 and 5 illustrate the challenges of tourism destinations and tourism ecosystems in the smart perspective. In the final section of the paper, we highlight that in the near future it will not be possible for tourism ecosystems and territories to only take into account digital innovations, but they will have to include smart tourism perspectives like sustainability, circular economy, quality of life, and social value; they should also aim to enhance tourism experiences and to increase the competitive advantage of smart tourism destinations. Tourism 4.0 technologies need to be geared toward the improvement of the quality of tourism practices, assuming smartness and sustainability as the right paradigm for improving the quality of life and the social value of both guests and local residents.

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Abstract: The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths.,Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities.,This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions.,It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities.,Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities.,This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

Individual differences in behaviour explain variation in survival: a meta-analysis.

Abstract: Research focusing on among-individual differences in behaviour ('animal personality') has been blooming for over a decade. Central theories explaining the maintenance of such behavioural variation posits that individuals expressing greater "risky" behaviours should suffer higher mortality. Here, for the first time, we synthesize the existing empirical evidence for this key prediction. Our results did not support this prediction as there was no directional relationship between riskier behaviour and greater mortality; however there was a significant absolute relationship between behaviour and survival. In total, behaviour explained a significant, but small, portion (5.8%) of the variance in survival. We also found that risky (vs. "shy") behavioural types live significantly longer in the wild, but not in the laboratory. This suggests that individuals expressing risky behaviours might be of overall higher quality but the lack of predation pressure and resource restrictions mask this effect in laboratory environments. Our work demonstrates that individual differences in behaviour explain important differences in survival but not in the direction predicted by theory. Importantly, this suggests that models predicting behaviour to be a mediator of reproduction-survival trade-offs may need revision and/or empiricists may need to reconsider their proxies of risky behaviours when testing such theory.

Methodological Guidance Paper: The Art and Science of Quality Systematic Reviews:

Abstract: The purpose of this article is to overview various challenges that prospective authors of quality systematic reviews should be prepared to address. These challenges pertain to all phases of the rev...

COSMIN Risk of Bias tool to assess the quality of studies on reliability or measurement error of outcome measurement instruments: a Delphi study.

Abstract: Scores on an outcome measurement instrument depend on the type and settings of the instrument used, how instructions are given to patients, how professionals administer and score the instrument, etc. The impact of all these sources of variation on scores can be assessed in studies on reliability and measurement error, if properly designed and analyzed. The aim of this study was to develop standards to assess the quality of studies on reliability and measurement error of clinician-reported outcome measurement instruments, performance-based outcome measurement instrument, and laboratory values. We conducted a 3-round Delphi study involving 52 panelists. Consensus was reached on how a comprehensive research question can be deduced from the design of a reliability study to determine how the results of a study inform us about the quality of the outcome measurement instrument at issue. Consensus was reached on components of outcome measurement instruments, i.e. the potential sources of variation. Next, we reached consensus on standards on design requirements (n = 5), standards on preferred statistical methods for reliability (n = 3) and measurement error (n = 2), and their ratings on a four-point scale. There was one term for a component and one rating of one standard on which no consensus was reached, and therefore required a decision by the steering committee. We developed a tool that enables researchers with and without thorough knowledge on measurement properties to assess the quality of a study on reliability and measurement error of outcome measurement instruments.

Editing in Style: Uncovering the Local Semantics of GANs

Abstract: While the quality of GAN image synthesis has improved tremendously in recent years, our ability to control and condition the output is still limited. Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edits to a target output image. This is accomplished by borrowing elements from a source image, also a GAN output, via a novel manipulation of style vectors. Our method requires neither supervision from an external model, nor involves complex spatial morphing operations. Instead, it relies on the emergent disentanglement of semantic objects that is learned by StyleGAN during its training. Semantic editing is demonstrated on GANs producing human faces, indoor scenes, cats, and cars. We measure the locality and photorealism of the edits produced by our method, and find that it accomplishes both.

Board characteristics and integrated reporting quality: an agency theory perspective

Abstract: Integrated reporting is the latest novelty in the corporate reporting field. It is a tool capable of better representing the capacity of companies to create value over time. In recent years, attention to this new reporting tool has grown in both professional and academic fields. However, despite past research that has analysed many aspects of integrated reporting, the integrated reporting quality and its determinants are still little explored. This study aims to fill this gap by analysing the effect of board characteristics on integrated reporting quality according to an agency theory approach. The findings, based on a sample of 134 international firms, show a positive relationship between the size, independence, diversity, and activity of a board with integrated reporting quality. This study contributes to enriching literature in this area in various ways. First, it broadens the scope of application of agency theory; second, it identifies further internal determinants of integrated reporting quality. This is the first study that analyses the impact of the characteristics of a board as a determining factor of integrated reporting quality.

Improving the shelf-life and quality of fresh and minimally-processed fruits and vegetables for a modern food industry: A comprehensive critical review from the traditional technologies into the most promising advancements

Abstract: The market size of fresh and minimally-processed fruits and vegetables (MPFVs) have grown rapidly in the last years as a result of consumer attitudes change due to their increasing use in prepared mixed salad for fresh, healthy and convenient food. Handling and mechanical operations of cutting and peeling induce injures and release of on-site cellular contents which promote the growth of harmful microbes. Chlorine has been widely adopted in fresh and MPFVs disinfection in washing due to its low cost and high efficacy against a broad spectrum of microorganisms; but, continuous replenishment of chlorine into high organic wash water can promote the formation of suspected carcinogenic compounds. There is a real need to find new alternatives to chlorine to preserve MPFVs quality for longer time. Although several methods and chemicals can be used to achieve similar reduction of microorganism counts without the production of harmful compounds, nor compromising the quality of MPFVs produce, fewer amount of them have gained widespread acceptance by the food industry. The challenge of this work was to give an upgraded level of understanding for producers and retailers to underpin future research directions for a modern food industry in order to resolve existing issues that limit fresh-cut quality and shelf-life. This paper covers a comprehensive review to improve shelf-life and quality of MPFVs, from the traditional technologies toward the most promising advancements.

Do perceived service value, quality, price fairness and service recovery shape customer satisfaction and delight? A practical study in the service telecommunication context

Abstract: Article history: Received November 29, 2019 Received in revised format January 31, 2020 Accepted February 2

COVID-19 y educación a distancia digital: preconfinamiento, confinamiento y posconfinamiento

Abstract: The COVID-19 pandemic has brought changes and disruptions in wide areas of human activity. Education has been one of the most affected due to the administrative imposition of the total closure of educational centres in most of the countries of the world. The modality of distance education, fundamentally in digital support, was able to offer emergency solutions to this crisis. In this paper, a reflection is carried out on the prior perceptions to COVID-19, related to this educational modality. Subsequently, ideas are provided on the way in which the educational response to confinement was mainly undertaken, through remote emergency education that considerably deviated from what was appropriate in designs and developments of quality distance education. Many mistakes were made and, therefore, there were too many negative perceptions from many students, families and teachers, quite a few of the latter, previously reluctant to these latest formats. Finally, the article addresses the most recent problem, related to post-confinement times, in which it is not foreseen that every student can go to physical classrooms in face-to-face centres in the same space and time. Suggestions are offered on how to address this problem, through hybridization solutions, mixed teaching and learning, combined or, better, integrated and flexible.

Evaluation of e-learning for medical education in low- and middle-income countries: A systematic review

Abstract: In low- and middle-income countries (LMICs), e-learning for medical education may alleviate the burden of severe health worker shortages and deliver affordable access to high quality medical education. However, diverse challenges in infrastructure and adoption are encountered when implementing e-learning within medical education in particular. Understanding what constitutes successful e-learning is an important first step for determining its effectiveness. The objective of this study was to systematically review e-learning interventions for medical education in LMICs, focusing on their evaluation and assessment methods. Nine databases were searched for publications from January 2007 to June 2017. We included 52 studies with a total of 12,294 participants. Most e-learning interventions were pilot studies (73%), which mainly employed summative assessments of study participants (83%) and evaluated the e-learning intervention with questionnaires (45%). Study designs, evaluation and assessment methods showed considerable variation, as did the study quality, evaluation periods, outcome and effectiveness measures. Included studies mainly utilized subjective measures and custom-built evaluation frameworks, which resulted in both low comparability and poor validity. The majority of studies self-concluded that they had had an effective e-learning intervention, thus indicating potential benefits of e-learning for LMICs. However, MERSQI and NOS ratings revealed the low quality of the studies' evidence for comparability, evaluation instrument validity, study outcomes and participant blinding. Many e-learning interventions were small-scale and conducted as short-termed pilots. More rigorous evaluation methods for e-learning implementations in LMICs are needed to understand the strengths and shortcomings of e-learning for medical education in low-resource contexts. Valid and reliable evaluations are the foundation to guide and improve e-learning interventions, increase their sustainability, alleviate shortages in health care workers and improve the quality of medical care in LMICs.

What Drives Customer Satisfaction, Loyalty, and Happiness in Fast-Food Restaurants in China? Perceived Price, Service Quality, Food Quality, Physical Environment Quality, and the Moderating Role of Gender

Abstract: The fast-food service industry has been growing rapidly across China over the last few decades. In accordance with the rising consumption level in the country, Chinese customers care increasingly about their food choices. The purpose of this study is to investigate the factors that can influence customer satisfaction, loyalty, and happiness, with a particular focus on the moderating role of gender. Data were collected through an online survey completed by customers who visited Western fast-food restaurants (KFC, McDonalds, etc.) in China. The structural equation model was applied to test 12 hypotheses. Results showed that perceived price, food, service, and physical environment quality positively affected customer satisfaction. Perceived price can significantly influence customers’ judgement of the quality dimensions of a restaurant. Moreover, customer satisfaction and happiness can lead to a sense of loyalty. Happiness functions as a mediator between satisfaction and loyalty. Nonetheless, our findings indicated that customers’ perceptions of food quality based on price and satisfaction levels based on service quality differ significantly between the genders, which demonstrated that gender moderation exists in food consumption. This study will contribute to a better understanding of managerial and theoretical perspectives, which will be beneficial for subsequent research.

Principles and recent advances in electronic nose for quality inspection of agricultural and food products

Abstract: Background The process of quality evaluation is a challenging concern in food industries to ensure the maximum level of food safety in relation to consumer preference. Growing development has taken place in the detection of agricultural and food product quality based on volatile organic compounds that involve distinct and unique characteristics. Scope and approach Quality evaluation is a challenging concern to ensure the maximum level of food safety since it is one of the important key attributes in determining consumer preference. However, until today, suppliers and retailers are still relying on the traditional methods which are labour skill intensive, subjective, and mostly destructive. In this review, the potential and ability of an electronic nose (e-nose) application as a monitoring device in quality evaluation of agricultural and food products are investigated. Key findings and conclusions Attention to the e-nose device has greatly increased due to the promising alternative in the quality inspection of agricultural and food products. The characteristics of aroma and flavour in relation to volatile compounds are also discussed. Further, this paper provides critical insight into the challenges and future trends in the development of an e-nose as a reliable non-destructive evaluation in the food supply chain of different agricultural and food products.

Recent advance in edible coating and its effect on fresh/fresh-cut fruits quality

Abstract: Background Numerous studies have been conducted to investigate the capability of edible coatings for preserving the quality and prolonging the shelf life of fresh/fresh-cut fruits. Evidence has shown that an edible coating could function as a barrier on the fruit surface, modify the internal gas atmosphere, decrease water losses and delay fruit ripening. Efforts have been exerted to introduce new natural coating materials to sustain the safety and quality of fresh/fresh-cut fruits. Scope and approach This review attempts to provide a summary of the recent studies on the application of edible coatings on different fresh/fresh-cut fruit categories, namely pome fruits (apple and pear), citrus, stone fruits, tropical and exotic fruits, berries, melon, and tomatoes. A fundamental theory behind the edible coating treatment and the effect on the physiological, physicochemical, sensory, and antimicrobial properties of fresh/fresh-cut fruits is discussed. The future perspective of this preservation method is also highlighted. Key findings and conclusions Edible coating can be used as an alternative strategy to prolong the shelf life of fresh/fresh-cut fruits. The materials selection for edible coating play a key role in determining its effectiveness and consumers acceptability. The ability of the selected materials in extending the shelf life of fresh/fresh-cut fruits without reducing the sensory and nutritional characteristics are the main challenges in the edible coating techniques, which demands attention for further research.

BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models

Abstract: Neural architecture search (NAS) has shown promising results discovering models that are both accurate and fast. For NAS, training a one-shot model has become a popular strategy to rank the relative quality of different architectures (child models) using a single set of shared weights. However, while one-shot model weights can effectively rank different network architectures, the absolute accuracies from these shared weights are typically far below those obtained from stand-alone training. To compensate, existing methods assume that the weights must be retrained, finetuned, or otherwise post-processed after the search is completed. These steps significantly increase the compute requirements and complexity of the architecture search and model deployment. In this work, we propose BigNAS, an approach that challenges the conventional wisdom that post-processing of the weights is necessary to get good prediction accuracies. Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs. Our discovered model family, BigNASModels, achieve top-1 accuracies ranging from 76.5% to 80.9%, surpassing state-of-the-art models in this range including EfficientNets and Once-for-All networks without extra retraining or post-processing. We present ablative study and analysis to further understand the proposed BigNASModels.