Showing papers on "Context (language use) published in 2021"

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Abstract: Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasing the receptive field, through either dilated/atrous convolutions or inserting attention modules. However, the encoder-decoder based FCN architecture remains unchanged. In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task. Specifically, we deploy a pure transformer (i.e., without convolution and resolution reduction) to encode an image as a sequence of patches. With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR). Extensive experiments show that SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes. Particularly, we achieve the first position in the highly competitive ADE20K test server leaderboard on the day of submission.

Normalizing Flows: An Introduction and Review of Current Methods

Abstract: Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution learning. We aim to provide context and explanation of the models, review current state-of-the-art literature, and identify open questions and promising future directions.

Entrepreneurial ecosystem elements

Abstract: There is a growing interest in ecosystems as an approach for understanding the context of entrepreneurship at the macro level of an organizational community. It consists of all the interdependent actors and factors that enable and constrain entrepreneurship within a particular territory. Although growing in popularity, the entrepreneurial ecosystem concept remains loosely defined and measured. This paper shows the value of taking a systems view of the context of entrepreneurship: understanding entrepreneurial economies from a systems perspective. We use a systems framework for studying entrepreneurial ecosystems, develop a measurement instrument of its elements, and use this to compose an entrepreneurial ecosystem index to examine the quality of entrepreneurial ecosystems in the Netherlands. We find that the prevalence of high-growth firms in a region is strongly related to the quality of its entrepreneurial ecosystem. Strong interrelationships among the ecosystem elements reveal their interdependence and need for a systems perspective.

The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review

Abstract: Industry 4.0 (I4.0) encompasses a plethora of digital technologies effecting on manufacturing enterprises. Most research on this topic examines the effects in the smart factory domain, focusing on ...

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future.

Abstract: Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNs in vision research beyond basic object recognition.

Stress and Health: A Review of Psychobiological Processes.

Abstract: The cumulative science linking stress to negative health outcomes is vast. Stress can affect health directly, through autonomic and neuroendocrine responses, but also indirectly, through changes in health behaviors. In this review, we present a brief overview of (a) why we should be interested in stress in the context of health; (b) the stress response and allostatic load; (c) some of the key biological mechanisms through which stress impacts health, such as by influencing hypothalamic-pituitary-adrenal axis regulation and cortisol dynamics, the autonomic nervous system, and gene expression; and (d) evidence of the clinical relevance of stress, exemplified through the risk of infectious diseases. The studies reviewed in this article confirm that stress has an impact on multiple biological systems. Future work ought to consider further the importance of early-life adversity and continue to explore how different biological systems interact in the context of stress and health processes.

QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering.

Abstract: The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing. We evaluate QA-GNN on the CommonsenseQA and OpenBookQA datasets, and show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning, e.g., correctly handling negation in questions.

CGNet: A Light-Weight Context Guided Network for Semantic Segmentation

Abstract: The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification network and ignore the inherent characteristic of semantic segmentation. To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight and efficient network for semantic segmentation. We first propose the Context Guided (CG) block, which learns the joint feature of both local feature and surrounding context effectively and efficiently, and further improves the joint feature with the global context. Based on the CG block, we develop CGNet which captures contextual information in all stages of the network. CGNet is specially tailored to exploit the inherent property of semantic segmentation and increase the segmentation accuracy. Moreover, CGNet is elaborately designed to reduce the number of parameters and save memory footprint. Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing light-weight segmentation networks. Extensive experiments on Cityscapes and CamVid datasets verify the effectiveness of the proposed approach. Specifically, without any post-processing and multi-scale testing, the proposed CGNet achieves 64.8% mean IoU on Cityscapes with less than 0.5 M parameters.

Algorithmic Fairness: Choices, Assumptions, and Definitions

Abstract: A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of sta...

The pursuit of quality in grounded theory

Abstract: This article introduces grounded theory and places this method in its historical context when 1960s quantitative researchers wielded harsh criticisms of qualitative research The originators of gro

Making Pre-trained Language Models Better Few-shot Learners

Abstract: The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF--better few-shot fine-tuning of language models--a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.

Toward a Quantitative Survey of Dimension Reduction Techniques

Abstract: Dimensionality reduction methods, also known as projections, are frequently used in multidimensional data exploration in machine learning, data science, and information visualization. Tens of such techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.

Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment

Abstract: As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

Prioritizing the Mental Health and Well-Being of Healthcare Workers: An Urgent Global Public Health Priority.

Abstract: The COVID-19 pandemic has had an unprecedented impact on health systems in most countries, and in particular, on the mental health and well-being of health workers on the frontlines of pandemic response efforts. The purpose of this article is to provide an evidence-based overview of the adverse mental health impacts on healthcare workers during times of crisis and other challenging working conditions and to highlight the importance of prioritizing and protecting the mental health and well-being of the healthcare workforce, particularly in the context of the COVID-19 pandemic. First, we provide a broad overview of the elevated risk of stress, burnout, moral injury, depression, trauma, and other mental health challenges among healthcare workers. Second, we consider how public health emergencies exacerbate these concerns, as reflected in emerging research on the negative mental health impacts of the COVID-19 pandemic on healthcare workers. Further, we consider potential approaches for overcoming these threats to mental health by exploring the value of practicing self-care strategies, and implementing evidence based interventions and organizational measures to help protect and support the mental health and well-being of the healthcare workforce. Lastly, we highlight systemic changes to empower healthcare workers and protect their mental health and well-being in the long run, and propose policy recommendations to guide healthcare leaders and health systems in this endeavor. This paper acknowledges the stressors, burdens, and psychological needs of the healthcare workforce across health systems and disciplines, and calls for renewed efforts to mitigate these challenges among those working on the frontlines during public health emergencies such as the COVID-19 pandemic.

HiggsSignals-2 : probing new physics with precision Higgs measurements in the LHC 13 TeV era

Abstract: The program HiggsSignals confronts the predictions of models with arbitrary Higgs sectors with the available Higgs signal rate and mass measurements, resulting in a likelihood estimate. A new version of the program, HiggsSignals-2, is presented that contains various improvements in its functionality and applicability. In particular, the new features comprise improvements in the theoretical input framework and the handling of possible complexities of beyond-the-SM Higgs sectors, as well as the incorporation of experimental results in the form of simplified template cross section (STXS) measurements. The new functionalities are explained, and a thorough discussion of the possible statistical interpretations of the HiggsSignals results is provided. The performance of HiggsSignals is illustrated for some example analyses. In this context the importance of public information on certain experimental details like efficiencies and uncertainty correlations is pointed out. HiggsSignals is continuously updated to the latest experimental results and can be obtained at https://gitlab.com/higgsbounds/higgssignals.

Toward an entrepreneurial ecosystem research program

Abstract: Entrepreneurial ecosystems have become a prominent concept, yet in its current state, the concept itself represents a paradox. While it draws on a rich intellectual history and provides an opportun...

Deep learning in nano-photonics: inverse design and beyond

Abstract: Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. Many of the recent works on machine-learning inverse design are highly specific, and the drawbacks of the respective approaches are often not immediately clear. In this review we want therefore to provide a critical review on the capabilities of deep learning for inverse design and the progress which has been made so far. We classify the different deep-learning-based inverse design approaches at a higher level as well as by the context of their respective applications and critically discuss their strengths and weaknesses. While a significant part of the community’s attention lies on nano-photonic inverse design, deep learning has evolved as a tool for a large variety of applications. The second part of the review will focus therefore on machine learning research in nano-photonics “beyond inverse design.” This spans from physics-informed neural networks for tremendous acceleration of photonics simulations, over sparse data reconstruction, imaging and “knowledge discovery” to experimental applications.

End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling

Abstract: This article proposes an end-to-end learnt lossy image compression approach, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure with Non-Local Attention optimization and Improved Context modeling (NLAIC). Our NLAIC 1) embeds non-local network operations as non-linear transforms in both main and hyper coders for deriving respective latent features and hyperpriors by exploiting both local and global correlations, 2) applies attention mechanism to generate implicit masks that are used to weigh the features for adaptive bit allocation, and 3) implements the improved conditional entropy modeling of latent features using joint 3D convolutional neural network (CNN)-based autoregressive contexts and hyperpriors. Towards the practical application, additional enhancements are also introduced to speed up the computational processing (e.g., parallel 3D CNN-based context prediction), decrease the memory consumption (e.g., sparse non-local processing) and reduce the implementation complexity (e.g., a unified model for variable rates without re-training). The proposed model outperforms existing learnt and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, on both Kodak and Tecnick datasets with the state-of-the-art compression efficiency, for both PSNR and MS-SSIM quality measurements. We have made all materials publicly accessible at https://njuvision.github.io/NIC for reproducible research.

OCNet: Object Context for Semantic Segmentation

Abstract: In this paper, we address the semantic segmentation task with a new context aggregation scheme named object context, which focuses on enhancing the role of object information. Motivated by the fact that the category of each pixel is inherited from the object it belongs to, we define the object context for each pixel as the set of pixels that belong to the same category as the given pixel in the image. We use a binary relation matrix to represent the relationship between all pixels, where the value one indicates the two selected pixels belong to the same category and zero otherwise. We propose to use a dense relation matrix to serve as a surrogate for the binary relation matrix. The dense relation matrix is capable to emphasize the contribution of object information as the relation scores tend to be larger on the object pixels than the other pixels. Considering that the dense relation matrix estimation requires quadratic computation overhead and memory consumption w.r.t. the input size, we propose an efficient interlaced sparse self-attention scheme to model the dense relations between any two of all pixels via the combination of two sparse relation matrices. To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling (Zhao et al. 2017) and atrous spatial pyramid pooling (Chen et al. 2018). We empirically show the advantages of our approach with competitive performances on five challenging benchmarks including: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff.

PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery

Abstract: In recent years, deep learning-based algorithms have brought great improvements to rigid object detection. In addition to rigid objects, remote sensing images also contain many complex composite objects, such as sewage treatment plants, golf courses, and airports, which have neither a fixed shape nor a fixed size. In this paper, we validate through experiments that the results of existing methods in detecting composite objects are not satisfying enough. Therefore, we propose a unified part-based convolutional neural network (PBNet), which is specifically designed for composite object detection in remote sensing imagery. PBNet treats a composite object as a group of parts and incorporates part information into context information to improve composite object detection. Correct part information can guide the prediction of a composite object, thus solving the problems caused by various shapes and sizes. To generate accurate part information, we design a part localization module to learn the classification and localization of part points using bounding box annotation only. A context refinement module is designed to generate more discriminative features by aggregating local context information and global context information, which enhances the learning of part information and improve the ability of feature representation. We selected three typical categories of composite objects from a public dataset to conduct experiments to verify the detection performance and generalization ability of our method. Meanwhile, we build a more challenging dataset about a typical kind of complex composite objects, i.e., sewage treatment plants. It refers to the relevant information from authorities and experts. This dataset contains sewage treatment plants in seven cities in the Yangtze valley, covering a wide range of regions. Comprehensive experiments on two datasets show that PBNet surpasses the existing detection algorithms and achieves state-of-the-art accuracy.

The value proposition of food delivery apps from the perspective of theory of consumption value

Abstract: Purpose The theory of consumption values (TCV) has successfully explained much consumer choice behavior, but few studies have investigated the values that drive food-delivery application (FDA) use. This study aims to bridge this gap by extending the TCV to the FDA context to examine food consumption-related values and interpreting and rechristening generic consumption values to adapt the TCV to the FDA context. Design/methodology/approach An explorative mixed-method research approach was taken to conduct focus group discussions with 20 target users to develop the questionnaire and then administer it for a cross-sectional survey (pen and pencil) to FDA users aged 22–65 years; 423 complete responses so received were analyzed using structural equation modeling. Findings The findings show that epistemic value (“visibility”) is the chief driver of purchase intentions toward FDAs, followed by conditional (“affordances”), price (part of functional value) and social value (“prestige”). Food-safety concerns and health consciousness (proposed as part of functional value) did not share any statistically significant association with purchase intentions toward FDAs. Research limitations/implications The findings of this study are insightful for FDA service providers competing for higher shares in the market by helping them understand ways to influence consumer choices and purchase intentions. Originality/value It is the first study that combines FDAs 2014 an online service that it is attracting a lot of investment 2014and TCV which has continued to be one of the most relevant theories of consumer behavior. It extends the TCV by adapting it to the FDA context with food-consumption-related values. Thus, it adds to the relatively scant literature on FDAs on the whole which is essential, as FDAs represent the business model of new economy, i.e. online-to-offline (O2O). Finally, this study formulates a conceptual framework that may serve as the basis of future research.

Descriptive analysis of COVID-19 patients in the context of India

Abstract: COVID-19 is now becoming a global issue and declared as pandemic by world health organization. This virus spread out from China to entire world. This paper performed a descriptive analysis of COVID...

Prospective impact of COVID-19 on mental health functioning in adolescents with and without ADHD: protective role of emotion regulation abilities.

Abstract: Background The impact of chronic stressors like the COVID-19 pandemic is likely to be magnified in adolescents with pre-existing mental health risk, such as attention-deficit/hyperactivity disorder (ADHD). This study examined changes in and predictors of adolescent mental health from before to during the COVID-19 pandemic in the Southeastern and Midwestern United States. Methods Participants include 238 adolescents (132 males; ages 15-17; 118 with ADHD). Parents and adolescents provided ratings of mental health symptoms shortly before the COVID-19 pandemic and in spring and summer 2020. Results Adolescents on average experienced an increase in depression, anxiety, sluggish cognitive tempo, inattentive, and oppositional/defiant symptoms from pre-COVID-19 to spring 2020; however, with the exception of inattention, these symptoms decreased from spring to summer 2020. Adolescents with ADHD were more likely than adolescents without ADHD to experience an increase in inattentive, hyperactive/impulsive, and oppositional/defiant symptoms. Adolescents with poorer pre-COVID-19 emotion regulation abilities were at-risk for experiencing increases in all mental health symptoms relative to adolescents with better pre-COVID-19 emotion regulation abilities. Interactive risk based on ADHD status and pre-COVID-19 emotion regulation abilities was found for inattention and hyperactivity/impulsivity, such that adolescents with ADHD and poor pre-COVID-19 emotion regulation displayed the highest symptomatology across timepoints. Lower family income related to increases in inattention but higher family income related to increases in oppositional/defiant symptoms. Conclusions The early observed increases in adolescent mental health symptoms during the COVID-19 pandemic do not on average appear to be sustained following the lift of stay-at-home orders, though studies evaluating mental health across longer periods of time are needed. Emotion dysregulation and ADHD increase risk for sustained negative mental health functioning and highlight the need for interventions for these populations during chronic stressors. Results and clinical implications should be considered within the context of our predominately White, middle class sample.

Measurement and Conceptualization of Gaming Disorder According to the World Health Organization Framework: the Development of the Gaming Disorder Test

Abstract: Previous research on gaming disorder (GD) has highlighted key methodological and conceptual hindrances stemming from the heterogeneity of nomenclature and the use of non-standardized psychometric tools to assess this phenomenon. The recent recognition of GD as an official mental health disorder and behavioral addiction by the World Health Organization (WHO) in the 11th Revision of the International Classification of Diseases (ICD-11) opens up new possibilities to investigate further the psychosocial and mental health implications due to excessive and disordered gaming. However, before further research on GD can be conducted in a reliable way and within a robust cross-cultural context, a valid and reliable standardized psychometric tool to assess the construct as defined by the WHO should be developed. The aim of this study was to develop The Gaming Disorder Test (GDT), a brief four-item measure to assess GD and to further explore its psychometric properties. A sample of 236 Chinese (47% male, mean age 19.22 years, SD = 1.57) and 324 British (49.4% male, mean age 26.74 years, SD = 7.88) gamers was recruited online. Construct validity of the GDT was examined via factorial validity, nomological validity, alongside convergent and discriminant validity. Concurrent validity was also examined using the Internet Gaming Disorder Scale-Short-Form (IGDS9-SF). Finally, reliability indicators involving the Cronbach’s alpha and composite reliability coefficients were estimated. Overall, the results indicated that GDT is best conceptualized within a single-factor structure. Additionally, the four items of the GDT are valid, reliable, and proved to be highly suitable for measuring GD within a cross-cultural context.

The role of block chain technology in circular economy practices to improve organisational performance

Abstract: This paper aims to examine the role of blockchain technology for the circular economy to enhance organisational performance in the context of China–Pakistan-Economic-Corridor (CPEC). A close-ended ...

Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence.

Abstract: Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7's increased intrinsic transmissibility. We further explored how B.1.1.7 spread was shaped by nonpharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.