Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomic Nervous System Activity Predicts Increasing Serum Inflammatory Cytokines in Children

Systemic inflammation is associated with increased risk for prevalent and costly diseases, and animal models implicate the autonomic nervous system in the control of inflammatory processes. In humans, research on autonomic-immune connections has been much more limited, and has focused on single branch autonomic measures (i.e., either parasympathetic or sympathetic). The current study utilized cardiac autonomic balance (CAB), derived from dual-branch cardiac autonomic recordings, to test the relation between resting autonomic function and inflammatory reactivity to challenge in children. Methods. Participants included 96 children (51 boys, 45 girls) ages 9 to 11 years (mean age = 9.93 years, SD = .57 years). CAB values were calculated as the standardized relative contribution of parasympathetic to sympathetic activity, derived from resting respiratory sinus arrhythmia and pre-ejection period data, respectively. Children provided two blood samples, one before and one following exposure to an acute social stressor or control condition. Serum was assayed for four cytokines that orchestrate inflammation: interleukin-6 (IL6), interleukin-8 (IL8), interleukin-10 (IL10), and tumor necrosis factor-alpha (TNFa). Results. We discovered large individual differences in inflammatory marker production across children, and no average main effect of stress condition. CAB significantly predicted these individual differences, such that children lower on CAB showed increasing serum cytokines from time 1 to time 2. In contrast, children with greater CAB tended to show declining inflammatory markers across the session. Discussion. Low cardiac autonomic balance may be a useful marker of proinflammatory tendencies in children, suggesting novel paths for early risk detection and intervention.
Analyzing the misperception of exponential growth in graphs

Exponential growth is frequently underestimated, an error that can have a heavy social cost in the context of epidemics. To clarify its origins, we measured the human capacity (N = 521) to extrapolate linear and exponential trends in scatterplots. Four factors were manipulated: the function underlying the data (linear or exponential), the response modality (pointing or venturing a number), the scale on the y axis (linear or logarithmic), and the amount of noise in the data. While linear extrapolation was precise and largely unbiased, we observed a consistent underestimation of noisy exponential growth, present for both pointing and numerical responses. A biased ideal-observer model could explain these data as an occasional misperception of noisy exponential graphs as quadratic curves. Importantly, this underestimation bias was mitigated by participants' math knowledge, by using a logarithmic scale, and by presenting a noiseless exponential curve rather than a noisy data plot, thus suggesting concrete avenues for interventions.
News Media Coverage of Poverty in the United States and the UK: A Systematic Review and Agendas for Future Research

News Media Coverage of Poverty in the United States and the UK: A Systematic Review and Agendas for Future Research Recently, public concerns on poverty and economic disparity have surged all around the world. This may sound far-fetched to those who remember that the validity of “class” as an analytical concept was once questioned in academic fields and was even deemed as a ‘zombie category’ (Beck & Beck-Gernsheim, 2002). However, recurring economic crises in various parts of the world, the subsequent widening economic inequality, and even the current COVID-19 global pandemic have signaled an urgent need to bring our attention back to and address relevant issues, as numerous academics have insisted (for example, Piketty, 2013; Stiglitz, 2015, among others). This urgency to tackle economic inequality and poverty is motivating scholars in various fields to reassess relevant issues. The studies of media and mass communication are no exception. Rather, a more imperative request should be made to the field given the crucial influence that mass media wield on public attitudes toward relevant issues through the delivery of news and information on poverty and depictions of people living in poverty (Iyengar, 1991; Vossen & Schulpen, 2019). In this sense, two recent publications in the field are noteworthy: The Routledge Companion to Media and Poverty (Borden, 2021) and Poverty in the News Media: Continuities, Ruptures, and Change in the Reporting Socioeconomic Inequality (Lugo-Ocando, 2019). The significance of the former is that it is the first attempt to proclaim “poverty and (news) media” as a distinct field of study and invited relevant research in discrete fields. While similar attempts have been observed, such as Class and News (Heider, 2004), Media and Class (Deery & Press, 2017), and The Routledge Companion to Media and Class (Polson, Schofield & Gajjala, 2020), they embraced a comparatively wider range of topics, types of media, and media genres compared to The Routledge Companion to Media and Poverty (Borden, 2021), which focuses only on news media. In addition, a critical difference is that previous publications approached the issues from the class analysis perspective, hence touching upon the issues on middleand upper-class as well, whereas the lat-
How Machine Learning Is Solving the Binary Function Similarity Problem

The ability to accurately compute the similarity between two pieces of binary code plays an important role in a wide range of different problems. Several research communities such as security, programming language analysis, and machine learning, have been working on this topic for more than ﬁve years, with hundreds of papers published on the subject. One would expect that, by now, it would be possible to answer a number of research questions that go beyond very speciﬁc techniques presented in papers, but that generalize to the entire research ﬁeld. Unfortunately, this topic is affected by a number of challenges, ranging from reproducibility issues to opaqueness of research results, which hinders meaningful and effective progress. In this paper, we set out to perform the ﬁrst measurement study on the state of the art of this research area. We begin by systematizing the existing body of research. We then identify a number of relevant approaches, which are representative of a wide range of solutions recently proposed by three different research communities. We re-implemented these approaches and created a new dataset (with binaries compiled with different compilers, optimizations settings, and for three different architectures), which enabled us to perform a fair and meaningful comparison. This effort allowed us to answer a number of research questions that go beyond what could be inferred by reading the individual research papers. By releasing our entire modular framework and our datasets (with associated documentation), we also hope to inspire future work in this interesting research area.
Defending against Adversarial Patches with Robust Self-Attention

We introduce a new defense against adversarial patch attacks based on our proposed Robust Self-Attention (RSA) layer. Robust Self-Attention re-places the outlier-sensitive weighted mean operation used by standard Self-Attention with a robust aggregation mechanism that detects and masks outlier tokens. Vision Transformer (ViT) models using our RSA layer achieve promising robust classiﬁcation accuracy, outperforming patch adversarial training as well as a prior provable defense, all with zero additional parameters or training. Additionally, we provide further evidence for the strength of simple patch adversarial training as a baseline defense.
Why Do Temporary Workers Have Higher Disability Insurance Risks Than Permanent Workers?

Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are encourage discussion. a provisional ABSTRACT Workers with fixed-term contracts typically have worse health than workers with permanent contracts. We show that these differences in health translate into a substantially higher (30%) risk of applying for disability insurance (DI) in the Netherlands. Using unique administrative data on health and labor market outcomes of all employees in the Netherlands, we decompose this differential into: (i) selection of workers types into fixed-term contracts; (ii) the causal impact of temporary work conditions on worker health; (iii) the impact of differential employer incentives to reintegrate ill workers; and (iv) the differential impact of labor market prospects on the decision to apply for DI benefits. We find that selection actually masks part of the DI risk premium, whereas the causal impact of temporary work conditions on worker health is limited. At the same time, the differences in employer commitment during illness and differences in labor market prospects between fixed-term and permanent workers jointly explain more than 80% of the higher DI risk.
Magnusiomyces capitatus : Rising Colonisation, A Concern?

Background: Magnusiomyces capitatus is an emerging yeast that has been increasingly isolated from clinical samples nowadays which warrants the need for surveillance of its isolation, clinical significance as well as drug resistance . Material and Methods: The present observational study was conducted (January 2016 to June 2017) on various clinical samples. All isolates confirmed as Magnusiomyces capitatus by MALDI-TOF MS were included in the study. The antifungal susceptibility testing was done as per the CLSI guidelines. Results: Among 4038 samples, 37 M. capitatus were isolated . All these strains were from pulmonary samples. The male: female ratio was 2.3:1. The age range was 21 -85 years. Risk factors were tuberculosis (35.1%), COPD (16.2%), diabetes mellitus (10.8%), carcinoma (5.4%), immunosuppressive drug therapy (5.4%) and HIV (2.7%). MIC values were very high for caspofungin and fluconazole in all of our isolates. Conclusion: M. capitatus is establishing itself as an emerging pathogen and so, should not be ruled out just as an insignificant finding in mycology laboratories.
Registration uncertainty in deforming organs: a novel approach for ensuring navigational confidence during image-guided procedures

Successful estimation of target registration error (TRE) would provide immense opportunities for controlling risks associated with navigation during image-guided surgery. While developed theories exist for predicting spatial distributions of TRE for rigid point-based registration, similar capabilities in the domain of deformable registration are still needed to develop truly reliable image guidance systems for navigation in soft tissue organs. Recently, breakthrough work derived two analytic uncertainty metrics based on the dissipation of constraint energy over distance to measure the susceptibility of elastic deformable registration to errors originating from unknown effects that occur where registration constraints are missing. In this work, these registration uncertainties are leveraged to classify error thresholds for detecting spatial regions that become vulnerable to inaccuracy in sparse data driven elastic registrations. With a large dataset of over 6000 registrations, receiver operating characteristic (ROC) analysis was performed to assess discriminatory performance of these uncertainty metrics to clinically relevant levels of registration error and to identify optimal binary cutoffs for their prediction. Both uncertainty metrics were capable of detecting regions of the organ where deformable registration accuracy exceeded the average magnitude of rigid registration error with AUC above 0.87. Furthermore, both metrics detected regions of the organ with TRE greater than 10 mm with AUC of approximately 0.8. These new capabilities will enhance clinical confidence in image-guided technologies in deforming organs through enabling immediate quantification and communication of navigational reliability and system accuracy during soft tissue surgery.
Fuzzing with Data Dependency Information

Recent advances in fuzz testing have introduced several forms of feedback mechanisms, motivated by the fact that for a large range of programs and libraries, edgecoverage alone is insufficient to reveal complicated bugs. Inspired by this line of research, we examined existing program representations looking for a match between expressiveness of the structure and adaptability to the context of fuzz testing. In particular, we believe that data dependency graphs (DDGs) represent a good candidate for this task, as the set of information embedded by this data structure is potentially useful to find vulnerable constructs by stressing combinations of def-use pairs that would be difficult for a traditional fuzzer to trigger. Since some portions of the dependency graph overlap with the control flow of the program, it is possible to reduce the additional instrumentation to cover only “interesting” data-flow dependencies, those that help the fuzzer to visit the code in a distinct way compared to standard methodologies. To test these observations, in this paper we propose DDFuzz, a new approach that rewards the fuzzer not only with code coverage information, but also when new edges in the data dependency graph are hit. Our results show that the adoption of data dependency instrumentation in coverage-guided fuzzing is a promising solution that can help to discover bugs that would otherwise remain unexplored by standard coverage approaches. This is demonstrated by the 72 different vulnerabilities that our data-dependency driven approach can identify when executed on 38 target programs from three different datasets.
High-Dimensional Random Forests High-Dimensional Random Forests High-Dimensionl Random Forests with Ridge Regression Based Variable Screening Curriculum Vitae

The signiﬁcant advances in technology have enabled easy collection and management of high-dimensional data in many ﬁelds, however, the process of modeling these data imposes a huge problem in the ﬁeld of data science. Dealing with high-dimensional data is one of the signiﬁcant challenges that degenerate the performance and precision of most classiﬁcation and regression algorithms, e.g., random forests. Random Forest (RF) is among the few methods that can be extended to model high-dimensional data; nevertheless, its performance and precision, like others, are highly aﬀected by high dimensions, especially when the dataset contains a huge number of noise or noninformative features. It is known in literature that data dominated with high number of uninformative features have small number of expected informative variables that could lead to the challenge of obtaining an accurate or robust random forest model. In this study, we present a new algorithm that incorporates ridge regression as a variable screening tool to discern informative features in the setting of high dimensions and apply the classical random forest to a top portion of selected important features. Simulation studies on high dimensions are carried out to test how our proposed method addresses the above problem and improves the performance of random forest models. To illustrate our method, we applied it to a real life dataset (Communities and Crime Dataset), which was sourced from the UCI database. The results show how variable screening using ridge regression could be a very useful tool for building high-dimensional random forests.
Cardinality Estimation using Label Probability Propagation for Subgraph Matching in Property Graph Databases

Estimating query result cardinality is a central task of cost-based database query optimizers, enabling them to identify and avoid excessively large intermediate results. While cardinality estimation has been studied extensively in relational databases, research in the setting of graph databases has been more limited. In this paper, we address the problem of cardinality estimation for subgraph matching on property graph databases. Our novel cardinality estimation technique starts from a small amount of statistical information about node labels and relationship types, which is propagated along the graph query pattern in terms of label probabilities. Additionally, estimation quality can be improved by providing information about labels or properties to our technique, if available. In our experimental evaluation, we compare our approach to state-of-the-art cardinality estimation techniques for subgraph matching for property graph, RDF, and relational databases, and we demonstrate that our technique offers the best trade-off between accuracy and efficiency.
On Gosper’s Pq and Lambert series identities

In an interesting article entitled ‘‘Experiments and discoveries in q-trigonometry’’, R. W. Gosper conjectured few beautiful Pq and Lambert series identities. Many people have attempted confirming some of those identities in the Gosper’s list, mainly by using Gosper’s q-trigonometric identities. In this paper we either prove or disprove all the Pq and Lambert series identities in the Gosper’s list by mainly using S. Ramanujan’s theta function identities and W. N. Bailey’s summation formula. In the process, we obtain three new Gosper kind of identities.
EU Trade Policy Reform: Towards Reciprocal Concessions with Developing Countries

The European Union (EU) supports developing countries with a unilateral trade preference scheme. The scheme underwent a major reform in 2014, in which many countries lost access to reduced tariff rates. We analyse how this radical step that removed preferences from 103 countries by 2018 fits into the EU’s strategy to promote bilateral agreements and how it affected imports from the removed beneficiaries. Using the gravity model of trade with high-dimensional fixed effects, we show that the removal results in a significant decline in exports of affected developing countries. Some countries formed a bilateral free trade agreement with the EU, in which case the negative effect of removal of unilateral trade preferences is compensated but we do not find significant and consistent additional benefits. Thus, the threat of removal can be seen as a lever for beneficiaries that are about to become ineligible to negotiate a bilateral agreement with the EU.
Parametric Analysis on the Progression of Mechanical Properties on FSW of Aluminum-Copper Plates

The contemporary work manifests that friction stir welding (FSW) is a viable avenue for joining AA1100 aluminium to C12200 copper plates. In this present study, the response of distinctive welding parameters (viz. tool geometries, tool rotational speed, tool travel speed, and tool plunging depth) on weld quality has been investigated. The present work focused on both microstructural investigation and mechanical properties examination. It has been observed that the process parameters have signifi cant eff ects on weld quality. The design of the experiments has been executed considering four welding input parameters in two variables and selected L-16 orthogonal array to limit the experimental replications. It has been observed that good quality of welds produced by keeping the tool pin off set around 4 mm towards the aluminium side and 2 mm towards the copper side. It has also been noticed that right-hand threaded tool pins are giving good weld quality compared to left-handed thread. The joint effi ciencies for the welds E2, E14 which were welded by RHT tools were 75.3% and 74.61% and the strength (UTS) of the welds for the same tools exhibit’s greater than the LHT tools i.e., 98 and 95 MPa. Moderate hardness values are observed for the same welds E1 and E14 with the parameters 1100 rpm, 98 welding speed, and 1.6 mm tool plunge depth. It also noticed that the weld quality can be signifi cantly enhanced by using proper tool plunge and tool pin geometries compared to the other process parameters.
The application of correlation analysis to acoustic emission pulses generated by partial discharges

This paper is concerned with the improvement of the acoustic emission method used for testing the degree of threat caused by the generation of partial discharges in insulation systems of power appliances. The paper will comparatively present the autocovariance and the mutual covariance functions calculated for the acoustic emission pulses generated in spark gaps modeling basic partial discharge forms for the positive and negative supplying voltage polarizations. The research involved the following partial discharge types: point plane; multipoint plane; multipoint plane with a layer of pressboard; surface; that generated in gas bubbles; and on the indeterminate potential particles moving in oil. Within the graphic presentation of the correlation results obtained in the frequency domain, there will be presented the runs of power density spectra calculated for the acoustic emission pulses generated in the positive and negative voltage half times and the runs of the coherence function K(f), which were drawn in the form of a column graph and using a linear interpolation, for all partial discharge forms under study. During the analysis of the results obtained, the range of possibilities of using the autocorrelation and coherence functions for testing the time and frequency dependencies of the measured acoustic emission pulses generated by partial discharges in oil insulation systems will be determined.
Applicability of Hyperdimensional Computing to Seizure Detection

Hyperdimensional (HD) computing is a form of brain-inspired computing which can be applied to numerous classification problems. In past research, it has been shown that seizures can be detected from electroencephalograms (EEG) with high accuracy using local binary pattern (LBP) encoding. This paper explores applicability of binary HD computing to seizure detection from intra-cranial EEG (iEEG) data from the Kaggle seizure detection contest based on using both LBP and power spectral density (PSD) features. In the PSD method, three novel approaches to HD classification are presented for both selected features and all features. These are referred as single classifier long hypervector, multiple classifiers, and single classifier short hypervector. To visualize the quality of classification of test data, a hypervector distance plot is introduced that plots the Hamming distance of the query hpervectors from one class hypervector vs. that from the other. Simulation results show that: 1). LBP method offers an average 80.9% test accuracy, 71.9% sensitivity, 81.4% specificity and 76.6% test AUC whereas the PSD method can achieve an average of 91.0% test accuracy, 81.8% sensitivity, 92.0% specificity and 86.9% test AUC. 2). The average seizure detection latency is 2.5s for LBP method and is 4.5s for the PSD methods. This average latency, less than 5s, is a relevant parameter for fast drug delivery, indicating that both LBP and PSD methods are able to detect the seizures in a timely manner. The performance using selected PSD features is better than that using all features. 3). It is shown that the dimensionality of the hypervector can be reduced to 1, 000 bits for LBP and PSD methods from 10, 000. Futhermore, for some approaches of selected features, the dimensionality of the hypervector can be reduced to 100 bits.