Miracle-dz/UniFAR-FTUs huggingface dataset:
Accounting for Pre-Treatment Exposure in Panel Data: Re-Estimating the Effect of Mass Public Shootings

eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item.
Apply lightweight deep learning on internet of things for low-cost and easy-to-access skin cancer detection

Melanoma is the most dangerous form of skin cancer that often resembles moles. Dermatologists often recommend regular skin examination to identify and eliminate Melanoma in its early stages. To facilitate this process, we propose a hand-held computer (smart-phone, Raspberry Pi) based assistant that classifies with the dermatologist-level accuracy skin lesion images into malignant and benign and works in a standalone mobile device without requiring network connectivity. In this paper, we propose and implement a hybrid approach based on advanced deep learning model and domain-specific knowledge and features that dermatologists use for the inspection purpose to improve the accuracy of classification between benign and malignant skin lesions. Here, domain-specific features include the texture of the lesion boundary, the symmetry of the mole, and the boundary characteristics of the region of interest. We also obtain standard deep features from a pre-trained network optimized for mobile devices called Google's MobileNet. The experiments conducted on ISIC 2017 skin cancer classification challenge demonstrate the effectiveness and complementary nature of these hybrid features over the standard deep features. We performed experiments with the training, testing and validation data splits provided in the competition. Our method achieved area of 0.805 under the receiver operating characteristic curve. Our ultimate goal is to extend the trained model in a commercial hand-held mobile and sensor device such as Raspberry Pi and democratize the access to preventive health care.
Phishing Attacks on Modern Android

Modern versions of Android have introduced a number of features in the name of convenience. This paper shows how two of these features, mobile password managers and Instant Apps, can be abused to make phishing attacks that are significantly more practical than existing ones. We have studied the leading password managers for mobile and we uncovered a number of design issues that leave them open to attacks. For example, we show it is possible to trick password managers into auto-suggesting credentials associated with arbitrary attacker-chosen websites. We then show how an attacker can abuse the recently introduced Instant Apps technology to allow a remote attacker to gain full UI control and, by abusing password managers, to implement an end-to-end phishing attack requiring only few user's clicks. We also found that mobile password managers are vulnerable to "hidden fields" attacks, which makes these attacks even more practical and problematic. We conclude this paper by proposing a new secure-by-design API that avoids common errors and we show that the secure implementation of autofill functionality will require a community-wide effort, which this work hopes to inspire.
FERRITE CORE STRATIFIED HALF-SPACE IWORKPIECEI

The classical work of Dodd and his coworkers at the Oak Ridge National Laboratory deals with the analysis, design and optimization of eddy-current probe coils wound around an air core. Many applications, however, require that the magnetic field produced by the probe coil be "shaped" or confined to certain regions of space, especially at higher frequencies, and this necessitates the use of highly permeable core materials, such as ferrites.
Variations in stratospheric inorganic chlorine between 1991 and 2006

A consistent time series of stratospheric inorganic chlorine Cly from 1991 to present is formed using space‐borne observations together with neural networks. A neural network is first used to account for inter‐instrument biases in HCl observations. A second neural network is used to learn the abundance of Cly as a function of HCl and CH4, and to form a time series using available HCl and CH4 measurements. The estimates of Cly are broadly consistent with calculations based on tracer fractional releases and previous estimates of stratospheric age of air. These new estimates of Cly provide a critical test for global models, which exhibit significant differences in predicted Cly and ozone recovery.
Diagnosing Ocean Stirring: Comparison of Relative Dispersion and Finite-Time Lyapunov Exponents

AbstractThe relationship between two commonly used diagnostics of stirring in ocean and atmospheric flows, the finite-time Lyapunov exponents λ and relative dispersion R2, is examined for a simple uniform strain flow and ocean flow inferred from altimetry. Although both diagnostics are based on the separation of initially close particles, the two diagnostics measure different aspects of the flow and, in general, there is not a one-to-one relationship between the diagnostics. For a two-dimensional flow with time-independent uniform strain, there is a single time-independent λ, but there is a wide range of values of R2 for individual particle pairs. However, it is shown that the upper and lower limits of R2 for individual pairs, the mean value over a large ensemble of pairs, and the probability distribution function (PDF) of R2 have simple relationships with λ. Furthermore, these analytical expressions provide a reasonable approximation for the R2–λ relationship in the surface ocean flow based on geostrophi.
Reactivity of Monolayer Protected Silver Clusters toward Excess Ligand: A Calorimetric Study

Reactivity of monolayer protected atomically precise clusters of noble metals is of significant research interest. To date very few experimental data are available on the reaction thermodynamics of such clusters. Here we report a calorimetric study of the reaction of glutathione (GSH) protected silver clusters in the presence of excess ligand, GSH, using isothermal titration calorimetry (ITC). We have studied Ag11(SG)7 and Ag32(SG)19 clusters and compared their reactivity with GSH protected silver nanoparticles (AgNPs) and silver ions. Clusters show intermediate reactivity toward excess ligand compared to nanoparticles and silver ions. Several control experiments were performed to understand the degradation mechanism of these silver clusters and nanoparticles. The effect of dissolved oxygen in the degradation process was studied in detail, and it was found that it did not have a significant role, although alternate pathways of degradation with the involvement of oxygen cannot be ruled out. Direct confirma.
Supporting crime analysis through visual design

We describe and discuss a visual analysis prototype to support volume crime analysis, a form of exploratory data analysis that aims to identify and describe patterns of criminality using historical and recent crime reports. Analysis requirements are relatively familiar: analysts wish to identify, define and compare sets of crime reports across multiple attributes (space, time and description). A challenge particular to the domain, identified through workshops with Police analysts in Belgium and the UK, is in developing exploratory data analysis software that offers some sophistication in data selection, aggregation and comparison, but with interaction techniques and representations that can be easily understood, navigated and communicated. In light of ongoing discussion with Police analysts, we propose four visual design and interaction maxims that relate to this challenge and discuss an early visual analysis prototype that we hope conforms to these maxims.
Inequality Generating Processes and Measurement of the Matthew Effect

The first aim of this paper is to clarify the differences and relationships between cumulative advantage/disadvantage and the Matthew effect. Its second aim, which is also its main contribution, is not only to present a new measure of the Matthew effect, but also to show how to estimate this effect from data and how to make statistical inference. We argue that one should utilize the positivity of the natural logarithm of the largest generalized eigenvalue for a non-linear dynamic process as evidence when claiming that the Matthew effect is present in the dynamic process that generates individuals’ socio-economic life-courses. Thus, our measure of the Matthew effect focuses on the dynamic process that generates socio-economic inequality and not on the outcome of this process.
Effects of Hypoxia Stress and Different Level of Mn2+ on Antioxidant Enzyme of Tomato Seedlings

The changes of antioxidant enzyme activities and related genes expression of tomato seedlings were evaluated under hypoxia stress with different levels of Mn2+. Activities of superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxide (APX), glutathione reductase (GR), catalase (CAT), the contents of H2O2, ascorbic (AsA) and malondialdehyde (MDA) were studied to investigate how active oxygen damaged the membrane lipid under hypoxia stress. With 10-200 μmol? L-1 Mn2+, the activities of SOD, POD, APX, GR and the contents of H2O2, AsA, MDA of leaves and roots increased significantly, which indicated that low Mn2+ could eliminate the active oxygen and protect the membrane lipid from hurt. But the activities of catalase (CAT) decreased evidently in the root. When the concentration of Mn2+ reached 400-600 μmol? L-1 under hypoxia stress, the activities of SOD, POD, APX, GR and ASA content decreased remarkably. However, the contents of H2O2 and MDA increased contrarily. A series of resistance genes level achieved peak value with 10 µmol? L-1 Mn2+. The expression level of SOD, CAT, APX, POD, GR were 6.28, 2.19, 5.66, 5.21 and 6.79 times compared to control respectively. These results illustrated appropriate amount of Mn2+ could reduce the damage of active oxygen under hypoxia stress, but reversely, high level of Mn2+ just aggravated the already serious damage to the tomato seedlings.
Does International Financial Integration Spur Economic Growth? Evidence from Pakistan

The degree of international financial integration has seen a drastic change recently, especially during the last two decades. This study attempts to explore the relationship between international financial integration and economic growth in the context of Pakistan through a sample of data from 1975 to 2013 using time series analysis. International financial integration is used as an index that comprises foreign direct investment, remittances, and external debts. The results indicate that international financial integration has a significant and negative effect on economic growth. Cointegration results have found a significant and long run relationship, whereas variance decomposition method shows bidirectional causal relationship. The sensitivity analysis proves that the initial result is robust through fully modified ordinary least square method, while Cusum and Cusum of square proves stability of coefficients over a long period.
Multiband-structured Kalman filter

The broadband Kalman filter (BKF) and general Kalman filter (GKF) have been proposed for the application of acoustic system identification. Here, the authors present a multiband-structured Kalman filter (MSKF) to speed up the convergence rate of BKF and GKF for highly correlated signal. A simplified version of MSKF (SMSKF) is also provided at the aim of reducing the complexity. It is shown that the BKF and GKF are the special cases of the proposed MSKF, and the SMSKF can be treated as the improved multiband-structured subband adaptive filter algorithm with a variable regularisation matrix. The low-complexity implementation of SMSKF, both in the fast filtering and matrix inversion operation, is discussed. Computer simulations confirm the performance advantage of the proposed algorithm.
Computer Vision on Web Pages: A Study of Man-Made Images

This thesis is focused on the development of computer vision techniques for parsing web pages using an image of the rendered page as evidence, and on understanding this underexplored class of images from the perspective of computer vision. This project is divided into two tracks—applied and theoretical—which complement each other. Our practical motivation is the application of improved web page parsing to assistive technology, such as screenreaders for visually impaired users or the ability to declutter the presentation of a web page for those with cognitive deficit. From a more theoretical standpoint, images of rendered web pages have interesting properties from a computer vision perspective; in particular, low-level assumptions can be made in this domain, but the most important cues are often subtle and can be highly non-local. The parsing system developed in this thesis is a principled Bayesian segmentation-classification pipeline, using innovative techniques to produce valuable results in this challenging domain. The thesis includes both implementation and evaluation solutions. Segmentation of a web page is the problem of dividing it into semantically significant, visually coherent regions. We use a hierarchical segmentation method based on the detection of semantically significant lines (possibly broken lines) which divide regions. The Bayesian design allows sophisticated probability models to be applied to the segmentation process, and our method produces segmentation trees that achieve good performance on a variety of measures. Classification, for our purposes, is identifying the semantic role of regions in the segmentation tree of a page. We achieve promising results with a Bayesian classification algorithm based on the novel use of a hidden Markov tree model, in which the structure of the model is adapted to reflect the structure of the segmentation tree. This allows the algorithm to make effective use of the context in which regions appear as well as the features of each individual region. The methods used to evaluate our page parsing system include qualitative and quantitative evaluation of algorithm performance (using manually-prepared ground truth data) as well as a user study of an assistive interface based on our page segmentation algorithm. We also performed a separate user study to investigate users’ perceptions of web page organization and to generate ground truth segmentations, leading to important insights about consistency. Taken as a whole, this thesis presents innovative work in computer vision which contributes both to addressing the problem of web accessibility and to the understanding of semantic cues in images.
ET-Lasso: Efficient Tuning of Lasso for High-Dimensional Data

The L1 regularization (Lasso) has proven to be a versatile tool to select relevant features and estimate the model coefficients simultaneously. Despite its popularity, it is very challenging to guarantee the feature selection consistency of Lasso. One way to improve the feature selection consistency is to select an ideal tuning parameter. Traditional tuning criteria mainly focus on minimizing the estimated prediction error or maximizing the posterior model probability, such as cross-validation and BIC, which may either be time-consuming or fail to control the false discovery rate (FDR) when the number of features is extremely large. The other way is to introduce pseudo-features to learn the importance of the original ones. Recently, the Knockoff filter is proposed to control the FDR when performing feature selection. However, its performance is sensitive to the choice of the expected FDR threshold. Motivated by these ideas, we propose a new method using pseudo-features to obtain an ideal tuning parameter. In particular, we present the Efficient Tuning of Lasso (ET-Lasso) to separate active and inactive features by adding permuted features as pseudo-features in linear models. The pseudo-features are constructed to be inactive by nature, which can be used to obtain a cutoff to select the tuning parameter that separates active and inactive features. Experimental studies on both simulations and real-world data applications are provided to show that ET-Lasso can effectively and efficiently select active features under a wide range of different scenarios.
Random ReLU Features: Universality, Approximation, and Composition

We propose random ReLU features models in this work. Its motivation is rooted in both kernel methods and neural networks. We prove the universality and generalization performance of random ReLU features. Parallel to Barron's theorem, we consider the ReLU feature class, extended from the reproducing kernel Hilbert space of random ReLU features, and prove a strong quantitative approximation theorem, where both inner weights and outer weights of the the neural network with ReLU nodes as an approximator are bounded by constants. We also prove a similar approximation theorem for composition of functions in ReLU feature class by multi-layer ReLU networks. Separation theorem between ReLU feature class and their composition is proved as a consequence of separation between shallow and deep networks. These results reveal nice properties of ReLU nodes from the view of approximation theory, providing support for regularization on weights of ReLU networks and for the use of random ReLU features in practice. Our experiments confirm that the performance of random ReLU features is comparable with random Fourier features.
Exploring the Remote Ties between Helitron Transposases and Other Rolling-Circle Replication Proteins

Rolling-circle replication (RCR) elements constitute a diverse group that includes viruses, plasmids, and transposons, present in hosts from all domains of life. Eukaryotic RCR transposons, also known as Helitrons, are found in species from all eukaryotic kingdoms, sometimes representing a large portion of their genomes. Despite the impact of Helitrons on their hosts, knowledge about their relationship with other RCR elements is still elusive. Here, we compared the endonuclease domain sequence of Helitron transposases with the corresponding region from RCR proteins found in a wide variety of mobile genetic elements. To do that, we used a stepwise alignment approach followed by phylogenetic and multidimensional scaling analyses. Although it has been suggested that Helitrons might have originated from prokaryotic transposons or eukaryotic viruses, our results indicate that Helitron transposases share more similarities with proteins from prokaryotic viruses and plasmids instead. We also provide evidence for the division of RCR endonucleases into three groups (Y1, Y2, and Yx), covering the whole diversity of this protein family. Together, these results point to prokaryotic elements as the likely closest ancestors of eukaryotic RCR transposons, and further demonstrate the fluidity that characterizes the boundaries separating viruses, plasmids, and transposons.
Social Media Visual Analytic Toolkits for Disaster Management: A Review of the Literature

The past decade has seen a significant increase in the use of social media for disaster management. This is due especially to the widespread usage of mobile devices and also to the different data types and data formats that social media supports. In recent years, research in the area of social media visual analytics has also gained interest in the scientific community. Research in this area however, lacks a comprehensive overview on social media visual analytics for disaster management. Hence, this paper presents a synthesis of extant research on social media visual analytic and visualization toolkits for disaster management. We survey available literature on these tools with the goal to outline the major characteristics and features, and to examine the extent to which they cover the full cycle of disaster management. Our main purpose is to provide a foundation based on the current literature that can help to shape future research directions to enhance social media visual analytic tools for full cycle disaster management.
LION LBD: a literature-based discovery system for cancer biology

Abstract Motivation The overwhelming size and rapid growth of the biomedical literature make it impossible for scientists to read all studies related to their work, potentially leading to missed connections and wasted time and resources. Literature-based discovery (LBD) aims to alleviate these issues by identifying implicit links between disjoint parts of the literature. While LBD has been studied in depth since its introduction three decades ago, there has been limited work making use of recent advances in biomedical text processing methods in LBD. Results We present LION LBD, a literature-based discovery system that enables researchers to navigate published information and supports hypothesis generation and testing. The system is built with a particular focus on the molecular biology of cancer using state-of-the-art machine learning and natural language processing methods, including named entity recognition and grounding to domain ontologies covering a wide range of entity types and a novel approach to detecting references to the hallmarks of cancer in text. LION LBD implements a broad selection of co-occurrence based metrics for analyzing the strength of entity associations, and its design allows real-time search to discover indirect associations between entities in a database of tens of millions of publications while preserving the ability of users to explore each mention in its original context in the literature. Evaluations of the system demonstrate its ability to identify undiscovered links and rank relevant concepts highly among potential connections. Availability and implementation The LION LBD system is available via a web-based user interface and a programmable API, and all components of the system are made available under open licenses from the project home page http://lbd.lionproject. net. Supplementary information Supplementary data are available at Bioinformatics online.
Investigating Written Text Readability for passive BCI based Neuroadaptive Speed Reading Applications

: Rapid serial visual presentation (RSVP) can prove useful as a reading technique when text is presented on small screens. Optimal text presentation speed for text reading depends on the reader himself, context and features of the text. Readability is a measure which estimates the ease with which a reader can understand a written meaningful text. The presented study investigated whether a passive Brain-Computer Interface (pBCI) can be used to distinguish between texts of distinct levels of readability presented at different presentation speeds. A predictive model was trained on EEG data derived from a cognitive load paradigm. The model was then applied to data collected while participants read easy and difficult texts at a self-adjusted speed and at an increased speed level. Results suggest that predictions made by the predictive model could be used as an estimate for categorization and adaptation of longer text passages, though its robustness and potential for the use in neuroadaptive reading applications should be further investigated.