Reviewerly/exHarmony huggingface dataset:
Review: Elemental Analysis of Individual Fluid Inclusions by Laser Ablation-ICP-MS

Fluid inclusions are the most direct record of the chemical and physical properties of ancient fluids trapped deep in the Earth's crust and provide essential information about the geological formation of hydrothermal ore deposits.Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has become a most powerful technique for the elemental analysis of individual fluid inclusions trapped within crystals of gangue as well as ore minerals.This review aims to summarize recent developments in elemental analysis of individual fluid inclusions by LA-ICP-MS and discusses the solutions for improving the success rate of ablation, accurate quantification, and selection of suitable fluid inclusions.Significant future progress can be achieved by developing a non-transparent ore mineral-hosted fluid inclusions analytical method using the combination of near-infrared light microscopy (NIR) and LA-ICP-MS, and further improving the detection limit by using high sensitive mass spectrometry and a fast extracting LA cell, accurate measurement of the internal standard element concentration, and development of the well-determined calibration materials and/or the fluid inclusion certified reference materials (CRMs).
Immunocytochemistry practices in European cytopathology laboratories—Review of European Federation of Cytology Societies (EFCS) online survey results with best practice recommendations

Background Variability in preanalytical and analytical steps for immunocytochemistry (ICC) on cytology samples is poorly defined. The objective of this study was to evaluate current practices for ICC on cytology samples in European laboratories. Methods A link to an online survey with 19 questions about ICC practices was distributed to cytology laboratories through national representatives in the European Federation of Cytology Societies. Results In total, 245 laboratories responded to the survey by January 30, 2019. Cell blocks, cytospins, liquid‐based cytology (LBC) preparations, and smears alone or in combination with other preparations were used for ICC in 38%, 22%, 21%, and 19% of laboratories, respectively. In general, various combinations of preparations were used for ICC in greater than one‐half of laboratories (147 of 245; 60%), whereas only 1 specific type of cytology preparation was used in the remaining 98 of 245 laboratories (40%) laboratories. The majority of laboratories (217 of 226; 96%) performed ICC on automated platforms using protocols that were the same as those used for formalin‐fixed, paraffin‐embedded samples (238 of 527 laboratories; 45%), either optimized (138 of 527 laboratories; 26%) or optimized and validated (151 of 527 laboratories; 29%) for cytology preparations. Positive control slides, negative control slides, and external quality control were used in 174 of 223 (78%), 112 of 223 (50%), and 111 of 120 (50%) laboratories, respectively. Greater than 1000 ICC tests were performed yearly in 34% of laboratories (65 of 191; average, 1477 tests; median, 500 tests). Conclusions ICC is extensively performed in European laboratories using variously prepared cytology preparations on automated platforms, mostly without quality‐assurance measures.
Trust and corporate R&amp;D investment: Cross-country evidence

This paper examines the relation between societal trust and corporate innovation in the wider world. Using data on firms located in 72 countries and regions from 1992 to 2016, we find strong evidence that firms in countries with a higher level of societal trust have higher research and development (R&D) investment input. This is because that higher trust could mitigate information asymmetries, lower monitoring and transaction costs, and ease corporate financial constraints to motivate innovation. Moreover, we find the impact of societal trust on R&D investment is more pronounced in the countries with poor intellectual property rights (IPRs) protection.
Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.
SEMEDA: Enhancing segmentation precision with semantic edge aware loss

Per-Pixel Cross entropy (PPCE) is a commonly used loss on semantic segmentation tasks. However, it suffers from a number of drawbacks. Firstly, PPCE only depends on the probability of the ground truth class since the latter is usually one-hot encoded. Secondly, PPCE treats all pixels independently and does not take the local structure into account. While perceptual losses (e.g. matching prediction and ground truth in the embedding space of a pre-trained VGG network) would theoretically address these concerns, it does not constitute a practical solution as segmentation masks follow a distribution that differs largely from natural images. In this paper, we introduce a SEMantic EDge-Aware strategy (SEMEDA) to solve these issues. Inspired by perceptual losses, we propose to match the ’probability texture’ of predicted segmentation mask and ground truth through a proxy network trained for semantic edge detection on the ground truth masks. Through thorough experimental validation on several datasets, we show that SEMEDA steadily improves the segmentation accuracy with negligible computational overhead and can be added with any popular segmentation networks in an end-to-end training framework.
Privacy Regulations, Smart Roads, Blockchain, and Liability Insurance: Putting Technologies to Work

Smart streets promise widely available traffic information to help improve people's safety. Unfortunately, gathering that data may threaten privacy. We describe an architecture that exploits a blockchain and the Internet of Vehicles and show its compliance with the General Data Protection Regulation.
Discovering the Predictive Value of Clinical Notes: Machine Learning Analysis with Text Representation

Hospital readmission shortly after discharge is threatening to plague the quality of inpatient care. Readmission is a severe episode that leads to increased medical care costs. Federal regulations and early readmission penalties have created an incentive for healthcare facilities to reduce their readmission rates by predicting patients at a high risk of readmission. Scientists have developed prediction models by using rule-based assessment scores and traditional statistical methods, and most have focused on structured patient records. Recently, a few researchers utilized unstructured clinical notes. However, they achieved moderate prediction accuracy by making predictions of a single diagnosis subpopulation via extensive feature engineering. This study proposes the use of machine learning to learn deep representation of patient notes for the identification of high-risk readmission in a hospital-wide population. We describe and train several predictive models (standard machine learning and neural network), to which several setups have not been applied. Results show that complex deep learning models significantly outperform ( P &lt; 0.001) conventionally applied simple models in terms of discrimination ability. We also demonstrate a simple feature evaluation using a standard model, which allows the determination of potential clinical conditions/procedures for targeting. Unlike modeling using structured patient information with considerable variability in structure when different templates or databases are adopted, this study shows that the machine learning approach can be applied to prognosticate readmission with clinical free text in various healthcare settings. Using minimum feature engineering, the trained models perform comparably well or better than other predictive models established in previous literature.
Inter-scanner reproducibility of brain volumetry: influence of automated brain segmentation software

Abstract Background The inter-scanner reproducibility of brain volumetry is important in multi-site neuroimaging studies, where the reliability of automated brain segmentation (ABS) tools plays an important role. This study aimed to evaluate the influence of ABS tools on the consistency and reproducibility of the quantified brain volumetry from different scanners. Methods We included fifteen healthy volunteers who were scanned with 3D isotropic brain T1-weighted sequence on three different 3.0 Tesla MRI scanners (GE, Siemens and Philips). For each individual, the time span between image acquisitions on different scanners was limited to 1 h. All the T1-weighted images were processed with FreeSurfer v6.0, FSL v5.0 and AccuBrain ® with default settings to obtain volumetry of brain tissues (e.g. gray matter) and substructures (e.g. basal ganglia structures) if available. Coefficient of variation (CV) was calculated to test inter-scanner variability in brain volumetry of various structures as quantified by these ABS tools. Results The mean inter-scanner CV values per brain structure among three MRI scanners ranged from 6.946 to 12.29% (mean, 9.577%) for FreeSurfer, 7.245 to 20.98% (mean, 12.60%) for FSL and 1.348 to 8.800% (mean value, 3.546%) for AccuBrain ® . In addition, AccuBrain ® and FreeSurfer achieved the lowest mean values of region-specific CV between GE and Siemens scanners (from 0.818 to 5.958% for AccuBrain ® , and from 0.903 to 7.977% for FreeSurfer), while FSL-FIRST had the lowest mean values of region-specific CV between GE and Philips scanners (from 2.603 to 16.310%). AccuBrain ® also had the lowest mean values of region-specific CV between Siemens and Philips scanners (from 1.138 to 6.615%). Conclusion There is a large discrepancy in the inter-scanner reproducibility of brain volumetry when using different processing software. Image acquisition protocols and selection of ABS tool for brain volumetry quantification have impact on the robustness of results in multi-site studies.
A Graph-based Ant-like Approach to Optimal Path Planning

Motion planning of an autonomous mobile robot is involved in generating safe, optimal, short, and/or reasonable trajectories in its workspace and finally reaching its final target while avoiding collision with obstacles and escaping traps. This paper presents a new hybrid model to optimize trajectory of the global path of a mobile robot using a graph-based search algorithm associated with an ant colony optimization (ACO) method. Once a graph representing the robot workspace populated with obstacles is modelled by MAKLINK graph theory, Dijkstra algorithm is utilized to seek the sub-optimal collision-free robot trajectory. On the basis of the initial global sub-optimal trajectory generated by Dijkstra algorithm, the motion trajectory of the mobile robot is optimized in Cartesian space through the ACO method. Most importantly, a Bspline curve based smoothing scheme is, in a greater degree, applied to generate safer and smother trajectories with reasonable distance from obstacles. Results of simulation and comparison studies in various sorts of environments are addressed in order to demonstrate the superiority of the proposed hybrid graph-based model.
Ultrasound Image Segmentation Method for Thyroid Nodules Using ASPP Fusion Features

Ultrasound imaging technology plays an important role to assist doctors in diagnosing thyroid nodules. The tissue structure around the thyroid is very complex, which makes it difficult to segment and extract the ultrasound image of thyroid nodules accurately. For address this problem, this paper proposes a model algorithm for thyroid nodule ultrasound image segmentation using ASPP fusion features. First, spatial pyramid pooling and depthwise separable convolution are combined in order to solve the problem that the size of the mapping feature will change in the process of better capturing the context information. Besides, Atrous Spatial Pyramid Pooling (ASPP) is proposed to achieve the purpose of processing input image channel and spatial information separately. In order to appropriately reduce the dimension and size of feature images, a $1\times 1$ convolution operation is performed before each convolution calculation, and the model size is optimized. In the decoding stage, decoder module appropriately adjusts the feature map with a relatively low resolution previously from decoder module, and sets the output channel number of two convolutions to the same value. All features have the same dimension by adjustment, and features can be fused by element-wise summation. Finally, Dice Similarity Coefficient (DSC), Prevent Match (PM) and Correspondence Patio (CR) are used as evaluation criteria to compare with other model algorithms. The experimental results show that the proposed model can significantly improve the segmentation effect of ultrasound images for thyroid nodules compared with traditional models.
(Re)considering the Concept of Literature Review Reproducibility

Literature reviews play a key role in academic research by describing, understanding, explaining, and testing the constructs and theories within a particular topic area.In recent years, various commentaries, debates, and editorials in the information systems (IS) field's top journals have highlighted the importance of a trustworthy literature review process, including detailed discussions on systematicity and transparency.Although the reproducibility of a literature review has also been noted as important, it remains less recognized because of several terminology-related issues.This ambiguity could result in misunderstandings regarding the degree of trust that should be placed in a literature review's process.In this research essay, we seek to clarify what makes a literature review reproducible, how it is distinct from related concepts, and when achieving it is desirable and feasible.We propose a series of clarifications and remedies to assist scholars within and outside the IS field in the preparation of stand-alone reviews.
Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting

Online services depend primarily on customer feedback and communications. When this kind of input is lacking, the overall approach of the service provider can shift in unintended ways. These services rely on feedback to maintain consumer satisfaction. Online social networks are a rich source of consumer data related to services and products. Well developed methods like sentiment analysis can offer insightful analyses and aid service providers in predicting outcomes based on their reviews-which, in turn, enables decision-makers to develop effective strategic plans. However, gathering this data is more challenging on Arabic online social networks, due to the complexity of the Arabic language and its dialects. In this study, we propose an approach to sentiment analysis that combines a neutrality detector model with eXtreme Gradient Boosting and a genetic algorithm to effectively predict and analyze customers' opinions of an e-Payment service through an Arabic social network. The proposed approach yields excellent results compared to other approaches. Feature analysis is also conducted on consumer reviews to identify influencing keywords.
Robust visual tracking via spatio-temporal adaptive and channel selective correlation filters

In recent years, Discriminative Correlation Filter (DCF) based tracking methods have achieved impressive performance in visual tracking. However, their excellent performance usually comes at the cost of sacrificing the computational speed. Furthermore, training correlation filters using high dimensional raw features may introduce the risk of severe over-fitting. To address the above issues, we propose Spatio-Temporal adaptive and Channel selective Correlation Filters (STCCF) for robust tracking. Specifically, we first select a set of target-specific features from high dimensional features via an effective channel selective scheme based on the Taylor expansion. Then, we reformulate the filter learning problem from ridge regression to elastic net regression to adaptively select the discriminative features inside the target bounding box at the spatial level. Moreover, we constrain the filters to be adaptive across temporal frames by learning a transformation matrix from the initial filters to the previous filters. In particular, with a specific spatio-temporal-channel constraint, STCCF can not only alleviate the over-fitting problem and reduce the computational cost, but also enhance the discriminability and interpretability of the learned filters. The proposed STCCF can be optimized by using a few iterations of Alternating Direction Method of Multipliers (ADMM). Experiments on six challenging datasets show that STCCF can achieve promising performance with fast running speed.
De)Randomized Smoothing for Certifiable Defense against Patch Attacks

Patch adversarial attacks on images, in which the attacker can distort pixels within a region of bounded size, are an important threat model since they provide a quantitative model for physical adversarial attacks. In this paper, we introduce a certifiable defense against patch attacks that guarantees for a given image and patch attack size, no patch adversarial examples exist. Our method is related to the broad class of randomized smoothing robustness schemes which provide high-confidence probabilistic robustness certificates. By exploiting the fact that patch attacks are more constrained than general sparse attacks, we derive meaningfully large robustness certificates against them. Additionally, in contrast to smoothing-based defenses against L_p and sparse attacks, our defense method against patch attacks is de-randomized, yielding improved, deterministic certificates. Compared to the existing patch certification method proposed by Chiang et al. (2020), which relies on interval bound propagation, our method can be trained significantly faster, achieves high clean and certified robust accuracy on CIFAR-10, and provides certificates at ImageNet scale. For example, for a 5-by-5 patch attack on CIFAR-10, our method achieves up to around 57.6% certified accuracy (with a classifier with around 83.8% clean accuracy), compared to at most 30.3% certified accuracy for the existing method (with a classifier with around 47.8% clean accuracy). Our results effectively establish a new state-of-the-art of certifiable defense against patch attacks on CIFAR-10 and ImageNet. Code is available at this https URL.
Bridging the Gap between Sample-based and One-shot Neural Architecture  Search with BONAS

Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the search space and evaluating the most promising architectures. However, it is computationally very costly. As a remedy, the one-shot approach has emerged as a popular technique for accelerating NAS using weight-sharing. However, due to the weight-sharing of vastly different networks, the one-shot approach is less reliable than the sample-based approach. In this work, we propose BONAS (Bayesian Optimized Neural Architecture Search), a sample-based NAS framework which is accelerated using weight-sharing to evaluate multiple related architectures simultaneously. Specifically, we apply Graph Convolutional Network predictor as a surrogate model for Bayesian Optimization to select multiple related candidate models in each iteration. We then apply weight-sharing to train multiple candidate models simultaneously. This approach not only accelerates the traditional sample-based approach significantly, but also keeps its reliability. This is because weight-sharing among related architectures are more reliable than those in the one-shot approach. Extensive experiments are conducted to verify the effectiveness of our method over many competing algorithms.
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification

Label inventories for fine-grained entity typing have grown in size and complexity.Nonetheless, they exhibit a hierarchical structure.Hyperbolic spaces offer a mathematically appealing approach for learning hierarchical representations of symbolic data.However, it is not clear how to integrate hyperbolic components into downstream tasks.This is the first work that proposes a fully hyperbolic model for multi-class multi-label classification, which performs all operations in hyperbolic space.We evaluate the proposed model on two challenging datasets and compare to different baselines that operate under Euclidean assumptions.Our hyperbolic model infers the latent hierarchy from the class distribution, captures implicit hyponymic relations in the inventory, and shows performance on par with state-ofthe-art methods on fine-grained classification with remarkable reduction of the parameter size.A thorough analysis sheds light on the impact of each component in the final prediction and showcases its ease of integration with Euclidean layers. 1
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset

Due to the lack of labeled data, previous research on text-to-SQL parsing mainly focuses on English.Representative English datasets include ATIS, WikiSQL, Spider, etc.This paper presents DuSQL, a larges-scale and pragmatic Chinese dataset for the cross-domain text-to-SQL task, containing 200 databases, 813 tables, and 23,797 question/SQL pairs.Our new dataset has three major characteristics.First, by manually analyzing questions from several representative applications, we try to figure out the true distribution of SQL queries in real-life needs.Second, DuSQL contains a considerable proportion of SQL queries involving row or column calculations, motivated by our analysis on the SQL query distributions.Finally, we adopt an effective data construction framework via human-computer collaboration.The basic idea is automatically generating SQL queries based on the SQL grammar and constrained by the given database.This paper describes in detail the construction process and data statistics of DuSQL.Moreover, we present and compare performance of several open-source textto-SQL parsers with minor modification to accommodate Chinese, including a simple yet effective extension to IRNet for handling calculation SQL queries.
An Embedded-Processes Approach to Working Memory

In the embedded-processes model, working memory is represented as a two-phase process with the activated portion of long-term memory holding features temporarily, including newly learned information and, within it, the focus of attention holding up to several integrated items or chunks. This chapter focuses on the motivation to use the structure of modelling in which processes are embedded, and it considers how the model helps in the organization of extant data and guidance of research. It will consider how the model has changed over the years and will clarify some often-misunderstood points about the model.