Reviewerly/exHarmony huggingface dataset:
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search

With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus.Clinicians, researchers, and policymakers need to be able to search these articles effectively.In this work, we present a zeroshot ranking algorithm that adapts to COVIDrelated scientific literature.Our approach filters training data from another collection down to medical-related queries, uses a neural reranking model pre-trained on scientific text (SciBERT), and filters the target document collection.This approach ranks top among zeroshot methods on the TREC COVID Round 1 leaderboard, and exhibits a P@5 of 0.80 and an nDCG@10 of 0.68 when evaluated on both Round 1 and 2 judgments.Despite not relying on TREC-COVID data, our method outperforms models that do.As one of the first search methods to thoroughly evaluate COVID-19 search, we hope that this serves as a strong baseline and helps in the global crisis.
Visualization of Information Retrieval in Smart Library Based on Virtual Reality Technology

Starting from the virtual reality technology, the characteristics of its most suitable combination with the library are explored, so as to lay a foundation in theory and practice to promote the development of virtual reality in the library. In the concentration camp of the latest advanced technology, the relevant technologies used in the various levels of models in the smart library are extracted, and their functional principles and applications are systematically introduced; Chapter 4 builds the level of the smart library information retrieval technology model. The structure diagram, various levels of functions, and related smart service models are discussed. Research is on context construction of extended resources of knowledge service in smart libraries. The elements and content of the resource context are introduced, and the strategy of constructing the resource context for the extension of the knowledge service of the smart library is proposed. Research is on the construction of the context of the extension and interconnection of knowledge services in smart libraries. The relevant elements and contents of the technical context and the spatial context are introduced, and a strategy for constructing a connected context for the extension of the knowledge service of a smart library is proposed. Research is on the context construction of knowledge service extension of smart library. The elements and contents of the service context are introduced, and the strategy of constructing the service context of the knowledge service extension of the smart library is proposed. A visual model of information retrieval is constructed. The model integrates the core steps of the information visualization process and introduces the information space and functional system (navigation, organization, indexing, and retrieval) in the information construction theory into it and through five mapping layers (functional space mapping, visualization space mapping, visualization mapping, view mapping, and interactive mapping) and six spatial layers (role space, information space, functional space, visualized information space, visualized object space, and visualized view space), which describe visualization applications that target the user experience model in information construction build process.
Powering One-Shot Topological NAS with Stabilized Share-Parameter Proxy

One-shot NAS method has attracted much interest from the research community due to its remarkable training efficiency and capacity to discover high performance models. However, the search spaces of previous one-shot based works usually relied on hand-craft design and were short for flexibility on the network topology. In this work, we try to enhance the one-shot NAS by exploring high-performing network architectures in our large-scale Topology Augmented Search Space (i.e, over $$3.4 \times 10^{10}$$ different topological structures). Specifically, the difficulties for architecture searching in such a complex space has been eliminated by the proposed stabilized share-parameter proxy, which employs Stochastic Gradient Langevin Dynamics to enable fast shared parameter sampling, so as to achieve stabilized measurement of architecture performance even in search space with complex topological structures. The proposed method, namely Stablized Topological Neural Architecture Search (ST-NAS), achieves state-of-the-art performance under Multiply-Adds (MAdds) constraint on ImageNet. Our lite model ST-NAS-A achieves $$76.4\%$$ top-1 accuracy with only 326M MAdds. Our moderate model ST-NAS-B achieves $$77.9\%$$ top-1 accuracy just required 503M MAdds. Both of our models offer superior performances in comparison to other concurrent works on one-shot NAS.
Performance analysis of sentiments in Twitter dataset using SVM models

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.
Performance comparison of multi-container deployment schemes for HPC workloads: an empirical study

The high-performance computing (HPC) community has recently started to use containerization to obtain fast, customized, portable, flexible, and reproducible deployments of their workloads. Previous work showed that deploying an HPC workload into a single container can keep bare-metal performance. However, there is a lack of research on multi-container deployments that partition the processes belonging to each application into different containers. Partitioning HPC applications has shown to improve their performance on virtual machines by allowing to set affinity to a non-uniform memory access (NUMA) domain for each of them. Consequently, it is essential to understand the performance implications of distinct multi-container deployment schemes for HPC workloads, focusing on the impact of the container granularity and its combination with processor and memory affinity. This paper presents a systematic performance comparison and analysis of multi-container deployment schemes for HPC workloads on a single-node platform, which considers different containerization technologies (including Docker and Singularity), two different platform architectures (UMA and NUMA), and two application subscription modes (exact subscription and over-subscription). Our results indicate that finer-grained multi-container deployments, on the one side, can benefit the performance of some applications with low interprocess communication, especially in over-subscribed scenarios and when combined with affinity, but, on the other side, they can incur some performance degradation for communication-intensive applications when using containerization technologies that deploy isolated network namespaces.
Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology

Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. However, as with any analysis method, the quality of generated results is dependent on a well-designed quality control system for the entire digital pathology workflow. Such system requires clear procedural controls, appropriate user training, and involvement of specialists to oversee key steps of the workflow. The toxicologic pathologist is responsible for reporting data obtained by digital image analysis and therefore needs to ensure that it is correct. To accomplish that, they must understand the main parameters of the quality control system and should play an integral part in its conception and implementation. This manuscript describes the most common digital tissue image analysis end points and potential sources of analysis errors. In addition, it outlines recommended approaches for ensuring quality and correctness of results for both classical and machine-learning based image analysis solutions, as adapted from a recently proposed Food and Drug Administration regulatory framework for modifications to artificial intelligence/machine learning-based software as a medical device. These approaches are beneficial for any type of toxicopathologic study which uses the described end points and can be adjusted based on the intended use of the image analysis solution.
Quantum Computation of Fluid Dynamics

Studies of strongly nonlinear dynamical systems such as turbulent flows call for superior computational prowess. With the advent of quantum computing, a plethora of quantum algorithms have demonstrated, both theoretically and experimentally, more powerful computational possibilities than their classical counterparts. Starting with a brief introduction to quantum computing, we will distill a few key tools and algorithms from the huge spectrum of methods available, and evaluate possible approaches of quantum computing in fluid dynamics.
Scaleable input gradient regularization for adversarial robustness

In this work we revisit gradient regularization for adversarial robustness with some new ingredients. First, we derive new per-image theoretical robustness bounds based on local gradient information. These bounds strongly motivate input gradient regularization. Second, we implement a scaleable version of input gradient regularization which avoids double backpropagation: adversarially robust ImageNet models are trained in 33 h on four consumer grade GPUs. Finally, we show experimentally and through theoretical certification that input gradient regularization is competitive with adversarial training. Moreover we demonstrate that gradient regularization does not lead to gradient obfuscation or gradient masking.
A dataset of labelled objects on raw video sequences

We present an object labelled dataset called SFU-HW-Objects-v1, which contains object labels for a set of raw video sequences. The dataset can be useful for the cases where both object detection accuracy and video coding efficiency need to be evaluated on the same dataset. Object ground-truths for 18 of the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences have been labelled. The object categories used for the labeling are based on the Common Objects in Context (COCO) labels. A total of 21 object classes are found in test sequences, out of the 80 original COCO label classes. Brief descriptions of the labeling process and the structure of the dataset are presented.
Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence

Multidisciplinary computational anatomy (MCA) is a new frontier of science that provides a mathematical analysis basis for the comprehensive and useful understanding of "dynamic living human anatomy." It defines a new mathematical modeling method for the early detection and highly intelligent diagnosis and treatment of incurable or intractable diseases. The MCA is a method of scientific research on innovative areas based on the medical images that are integrated with the information related to: (1) the spatial axis, extending from a cell size to an organ size; (2) the time series axis, extending from an embryo to post mortem body; (3) the functional axis on physiology or metabolism which is reflected in a variety of medical image modalities; and (4) the pathological axis, extending from a healthy physical condition to a diseased condition. It aims to integrate multiple prediction models such as multiscale prediction model, temporal prediction model, anatomy function prediction model, and anatomy-pathology prediction model. Artificial intelligence has been introduced to accelerate the calculation of statistic mathematical analysis. The future perspective is expected to promote the development of human resources as well as a new MCA-based scientific interdisciplinary field composed of mathematical statistics, information sciences, computing data science, robotics, and biomedical engineering and clinical applications. The MCA-based medicine might be one of the solutions to overcome the difficulties in the current medicine.
Impact on teaching in times of COVID-19 pandemic: A qualitative study

&lt;p&gt;&lt;span lang="EN-US"&gt;This article makes an analysis of the pedagogical teaching practices used and developed by Chilean physical education teachers during the coronavirus disease 19 (COVID-19) pandemic periods. The virus from China spread around the world, changed our daily lives and as such, at the same time impacted the educational system in a matter of weeks. Confinement led educational systems to eliminate face-to-face and use virtual teaching in order to continue with the teaching-learning processes, including the physical education discipline. From the methodological point of view, the study is installed in the interpretive-qualitative perspective, in such a way that 14 semi-structured interviews were carried by physical education teachers from the Maule-Chile region, via team, Zoom, Skype, because of the pandemic, face-to-face contact was impossible. The result of the study shows the impact on the quality of life of teachers and students because of the confinement. The pedagogical reconfiguration that teachers experienced in a matter of days, led them to use different virtual platforms to continue connected with their students, in order to deploy the learning objectives defined by the Chilean Ministry of Education. However, the foregoing shows the scarce possibility of verifying student learning, due to the difficulties of monitoring and feedback.&lt;/span&gt;&lt;/p&gt;
AI-Guided Reasoning-Based Operator Support System for the Nuclear Power Plant Management

The decision-making process in the Nuclear Power Plant (NPP) control room faces some challenges: operator incomplete knowledge, insufficient time for responding to the highly dynamic events, and a large number of indicators to monitor. Because of the complexity of the NPP system, it is hard to pre-plan all the failures/mitigative actions. An intelligent operator support system is vital to mitigate these shortcomings. In this paper, an AI declarative approach (Answer Set Programming (ASP)) is employed to represent our knowledge of the nuclear power plant in the form of logic rules. This represented knowledge is structured to form a reasoning-based operator support system. When an incident occurs, this ASP-based reasoning support system is demonstrated to be capable of fault identification (diagnosis), informing the operator of different scenarios and consequences, and generating the control options (decision making).
Decay of negativity of randomized multiqubit mixed states

Abstract In this paper, we investigate the decay of entanglement of multiqubit randomized entangled states. We start by introducing separable states of collection of n independent qubits. The randomized entangled mixed states are generated from factorisable initial states by coupling the qubits through randomized entangling operators. The bipartite and multipartite entanglement of randomized states are analyzed by employing respectively the concepts of the negativity and the genuine multiparticle negativity as measures of amount of entanglement. The decreases of the amount of entanglement of randomized entangled mixed states is studied in terms of the noisy parameter p .
Min-max exclusive virtual machine placement in cloud computing for scientific data environment

Abstract In cloud computing, there is a trade-off between SLAV (Service Level Agreement Violation) and system operating cost. Violation rates can be decreased when using more hosts, which increases system operating costs. Therefore, to manage the resources of those hosts efficiently, finding an optimal balancing point between SLAV and system operating cost is critical. In addition, a cost-effective load balancing approach based on the proper migration of VMs (Virtual Machines) in the hosts is needed. For this purpose, some indicators are also necessary to identify the abnormal hosts that violate SLA. One of the primary indicators, CPU usage, is closely related to energy consumption and can be used to reduce SLAV and energy consumption effectively. Our approach focuses on the special environment such as the cloud environment for the scientific data. Here, most of the jobs are data-intensive and a large amount of disk operations is required. Owing to disk operations are likely to affect the performance degradation of the host, disk bandwidth usage as well as CPU usage should be also considered. In this study, we propose the Min-Max Exclusive VM Placement (MMEVMP) strategy to minimize both SLAV and energy consumption. The current working system called KIAF (KISTI Analysis Facility), the CERN ALICE experimental cloud environment for scientific data analysis, is used to analyze the characteristics of data-intensive jobs within it. In this experiment, a lightweight version of the CloudSim simulator was developed and the results were compared to the other methods of different policies. Our evaluation showed that our proposed strategy can reduce SLA violation reasonably as well as the system operating cost-effectively.
Does open data boost journal impact: evidence from Chinese economics

To encourage research transparency and replication, more and more journals have been requiring authors to share original datasets and analytic procedures supporting their publications. Does open data boost journal impact? In this article, we report one of the first empirical studies to assess the effects of open data on journal impact. China Industrial Economics (CIE) mandated authors to open their research data in the end of 2016, which is the first to embrace open data among Chinese journals and provides a natural experiment for policy evaluation. We use the data of 37 Chinese economics journals from 2001 to 2019 and apply synthetic control method to causally estimate the effects of open data, and our results show that open data has significantly increased the citations of journal articles. On average, the current- and second-year citations of articles published with CIE have increased by 1 ~ 4 times, and articles published before the open data policy also benefited from the spillover effect. Our findings suggest that journals can leverage compulsory open data to develop reputation and amplify academic impacts.
Aadhaar in a Box? Legitimizing Digital Identity in Times of Crisis

The political drive to develop digital identity systems, in particular in the Global South with financial and technical support provided by international development actors, is accelerating during the COVID-19 pandemic. There are different reasons for this redoubling of attention to digitizing identities. These include an increased emphasis on digital social protection during the pandemic and the emergence of proposals for digital vaccination/immunity certificates to facilitate the reopening of societies and restoration of economies, which it is argued would need to be supported by robust digital identity infrastructures. Without evaluating the merits of claims about the capacity of digital identity systems to address the various challenges posed by COVID-19, in this paper I instead make a theoretical observation before raising a policy concern. First, I draw attention to the rise of India’s Aadhaar as an exemplar of developmental digital identity, which has intensified during the pandemic, and what this might mean for concepts of surveillance. I then conclude with a call to the development community to take more seriously investments in data protection regulatory capacity in countries where they are supporting digital identity projects.
A generalization of Otsu method for linear separation of two unbalanced classes in document image binarization

The classical Otsu method is a common tool in document image binarization. Often, two classes, text and background, are imbalanced, which means that the assumption of the classical Otsu method is not met. In this work, we considered the imbalanced pixel classes of background and text: weights of two classes are different, but variances are the same. We experimentally demonstrated that the employment of a criterion that takes into account the imbalance of the classes' weights, allows attaining higher binarization accuracy. We described the generalization of the criteria for a two-parametric model, for which an algorithm for the optimal linear separation search via fast linear clustering was proposed. We also demonstrated that the two-parametric model with the proposed separation allows increasing the image binarization accuracy for the documents with a complex background or spots.
The COVID-19 pandemic and contact tracing technologies, between upholding the right to health and personal data protection

Countries responded to the COVID-19 pandemic with various levels of restrictions and lockdown in an effort to save lives and prevent the saturation and collapse of national health systems. Unfortunately, the blockades have entailed hefty socioeconomic costs. In order to contrast the spread of the virus, states have used contact tracing technology, in the form of mobile phone applications designed to track close contacts of those infected with COVID-19. Recent research has shown the effectiveness of this solution, particularly when used in conjunction with manual tracking. Nonetheless, the contact tracing app raises concerns due to the potential privacy implications. The authors have delved into the European legislation that protects privacy through the principles of proportionality and minimization, arguing that in order to quickly resolve the pandemic caused by COVID-19, one cannot blindly trust the exclusive help of technology. Instead, we need the involvement of health personnel, scientists, and no less importantly, the citizenry's sense of solidarity and the duty to abide by the rules of social distancing, the use of protective devices and hygiene rules to protect public health.
Image Preprocessing for Artistic Robotic Painting

Artistic robotic painting implies creating a picture on canvas according to a brushstroke map preliminarily computed from a source image. To make the painting look closer to the human artwork, the source image should be preprocessed to render the effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and brushstroke coherence. We propose an algorithm for aerial perspective amplification based on principles of light scattering using a depth map, an algorithm for gamut compression using nonlinear hue transformation and an algorithm for image gradient filtering for obtaining a well-coherent brushstroke map with a reduced number of brushstrokes, required for practical robotic painting. The described algorithms allow interactive image correction and make the final rendering look closer to a manually painted artwork. To illustrate our proposals, we render several test images on a computer and paint a monochromatic image on canvas with a painting robot.