Miracle-dz/UniFAR-FTUs huggingface dataset:
Race, Ethnicity, Neighborhood Characteristics, and In-Hospital Coronavirus Disease-2019 Mortality

Background: Despite many studies reporting disparities in coronavirus disease-2019 (COVID-19) incidence and outcomes in Black and Hispanic/Latino populations, mechanisms are not fully understood to inform mitigation strategies. Objective: The aim was to test whether neighborhood factors beyond individual patient-level factors are associated with in-hospital mortality from COVID-19. We hypothesized that the Area Deprivation Index (ADI), a neighborhood census-block-level composite measure, was associated with COVID-19 mortality independently of race, ethnicity, and other patient factors. Research Design: Multicenter retrospective cohort study examining COVID-19 in-hospital mortality. Subjects: Inclusion required hospitalization with positive SARS-CoV-2 test or COVID-19 diagnosis at three large Midwestern academic centers. Measure(s): The primary study outcome was COVID-19 in-hospital mortality. Patient-level predictors included age, sex, race, insurance, body mass index, comorbidities, and ventilation. Neighborhoods were examined through the national ADI neighborhood deprivation rank comparing in-hospital mortality across ADI quintiles. Analyses used multivariable logistic regression with fixed site effects. Results: Among 5999 COVID-19 patients median age was 61 (interquartile range: 44–73), 48% were male, 30% Black, and 10.8% died. Among patients who died, 32% lived in the most disadvantaged quintile while 11% lived in the least disadvantaged quintile; 52% of Black, 24% of Hispanic/Latino, and 8.5% of White patients lived in the most disadvantaged neighborhoods. Living in the most disadvantaged neighborhood quintile predicted higher mortality (adjusted odds ratio: 1.74; 95% confidence interval: 1.13–2.67) independent of race. Age, male sex, Medicare coverage, and ventilation also predicted mortality. Conclusions: Neighborhood disadvantage independently predicted in-hospital COVID-19 mortality. Findings support calls to consider neighborhood measures for vaccine distribution and policies to mitigate disparities.
Interactive comment on “On the Role of Volcanism in Dansgaard-Oeschger Cycles” by Johannes Lohmann and Anders Svensson

The authors of this interesting discussion paper study the temporal co-occurrence of Dansgaard-Oeschger (DO) event related rapid glacial climate changes inferred from a suite of published Greenland ice cores with synchronous sulfur spikes in bipolar high-latitude ice cores indicative of strong volcanic eruptions. They report detectable volcanic signatures within very few decades prior to the onset of DO events to occur more frequently than expected by chance. This finding may be indicative of strong volcanic activity superimposed to a complex and intrinsically bistable dynamics being able to provide the essential “momentum” for the climate system close to a transition point becoming forced to tip to the other state. This is an interesting observation –
Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.
Recent Advances in Theory and Application of the Optimal Queueing Strategy SRPT - a Survey

Recent investigations of queueing system M/G/1-SRPT have shown that the preemptive strategy SRPT offers for typical mixed traffic conditions and for high traffic loads a significantly smaller mean value T D and variance O'b of delay time T D compared to other strategies like FIFO, LIFO and PS (Processor Sharing). It has been also found that the correlation of subsequent values of delay time T Dj -1, T Dj , . is near zero compared to a strong correlation in case of LIFO and especially of FIFO. Simulation studies have shown that these performance advantages of SRPT can be realized in practical communi cation systems with good approximation by introducing the newly defined combined strategy SRPT/RR and by using the priority mechanisms of standardized LAN -protocols
The effect of neighbourhood diversity on volunteering: Evidence from New Zealand

We make two contributions to the emerging empirical literature that identifies a negative relationship between neighbourhood heterogeneity by factors such as race, ethnicity, income etc., and individuals’ likelihood of contributing to public goods or trusting their neighbors. First, we show that studies that attempt to estimate the effect of a concave neighbourhood characteristic like heterogeneity on outcomes of interest may obtain biased results if they use small or large neighbourhood boundaries alone. Such approaches omit the effect of heterogeneity between small neighbourhoods, and can result in biased estimates of heterogeneity’s effects even when this “between heterogeneity” has no economic effect. Second, with this problem in view, we use two levels of neighbourhood cross section and panel data from the 1996, 2001 and 2006 censuses in New Zealand to test whether heterogeneity by race/ethnicity, birthplace, income or language negatively affect New Zealander’s probability of volunteering. We find that addressing neighbourhood size matters. We then find robust evidence that ethnic/racial neighbourhood heterogeneity is associated with lower volunteering rates. We also find some evidence that language, birthplace and household income heterogeneity lower volunteering rates, but the evidence is less robust, particularly for language and income.
AI for Adaptive Brain-Computer Interfaces: Challenges and Opportunities

Research in brain-computer interfaces (BCIs) has explored a set of methods to capture information about users’ state from brain signals, with the goal of developing adaptive interfaces. The rise of modern artiﬁcial intelligence and machine learning methods has been central to the advancement of the BCI ﬁeld. While these methods show promise for addressing some problems for brain data modeling, there also exist some challenges. We discuss the challenges faced when using deep learning in this ﬁeld, including the tension caused by the small sizes of brain datasets, issues related to the generalizability of the models and the need for deriving interpretable results from these models. Furthermore, we discuss the approaches we are taking to address these challenges.
Transition Metal Complexes with Antipyrine‐Derived Schiff Bases: Synthesis and Antibacterial Activity

The increase of death rate, associated with infectious diseases, is directly linked to the bacteria that have multiple resistance to antibiotics. The lack of efficient medical treat-ment is the main cause of this problem. The synthesis of new antibacterial agents, through various methods, is, for sure, an emergency medical issue. Recent research focuses more and more on the synthesis of complexes of the transitional metals with ligands of Schiff-base type, as a result of the biological properties which they have. This article presents the synthesis of several complexes with base Schiff ligands, derived from 4-aminoantipyrine and in vitro research of their antibacterial activities. The new compounds were tested for their in vitro antibacterial activity against Staphylococcus aureus var. Oxford 6538, Klebsiella pneumoniae ATCC 100131, Escherichia coli ATCC 10536, and Pseudomonas aeruginosa ATCC 9027 strains. Based on the “ in vitro ” studies, we can say that ten of the complexes synthetized can be successfully used instead of streptomycin, where there is resistance to this antibiotic.
A Partition Resilient Overlay Network via Blockchain

—Blockchains use peer-to-peer networks to handle underlying inter-peer communication, and these networks currently do not have any provable guarantees for desirable properties such as small diameter or partition-resilience. This is not just a theoretical problem: Heilman et al. [1] exploited bugs in bitcoind to isolate a subset of peers in the Bitcoin network by poisoning their address tables. Cryptocurrency blockchains are safety critical systems, so we need principled algorithms to maintain their underlying overlay networks. Our key insight is that we can leverage the blockchain itself to share information among the peers, and thus simplify the overlay maintenance process. We provide communication-efﬁcient protocols to maintain a dynamic hypercubic network for proof-of-work blockchains. Given that the peers have restricted computational resources, and ρ < 0 . 5 constant fraction of peers are Byzantine, we prove that the network (formed by the honest peers) remains connected for a polynomial number of rounds with high probability, even with polynomial variation in the size of the network. Moreover, these properties hold despite signiﬁcant churn. A key contribution is a secure mechanism for joining the overlay that relies on the blockchain to help new peers to contact existing members of the overlay. Furthermore, by examining how peers join the system, i.e., the “bootstrapping service”, we give a lower bound showing that (within log factors) our overlay network tolerates the maximum churn rate possible. In fact, we can give a lower bound on churn for any fully distributed service that requires connectivity.
Surrogate Model-Based Explainability Methods for Point Cloud Neural Networks (cid:63)

In the ﬁeld of autonomous driving and robotics, point clouds are showing their excellent real-time performance as raw data from most of the mainstream 3D sensors. Therefore, point cloud neural networks have become a popular research direction in recent years. So far, however, there has been little discussion about the explainability of deep neural networks for point clouds. In this paper, we propose new explainability approaches for point cloud deep neural networks based on local surrogate model-based methods to show which components make the main contribution to the classiﬁcation. Moreover, we propose a quantitative validation method for explainability methods of point clouds which enhances the persuasive power of explainability by dropping the most positive or negative contributing features and monitoring how the classiﬁcation scores of speciﬁc categories change. To enable an intuitive explanation of misclassiﬁed instances, we display features with confounding contributions. Our new explainability approach provides a fairly accurate, more intuitive and widely applicable explanation for point cloud classiﬁcation tasks. Our code is available at https://github.com/Explain3D/Expla inable3D
Re-discovering Robert E. Horton's Lake Evaporation Formulae: New Directions for Evaporation Physics

Abstract. Evaporation from open water is among the most rigorously studied problems in hydrology. Robert E. Horton, unbeknownst to most investigators on the subject, studied it in great detail by conducting experiments and heuristically relating his observations to physical laws. His work furthered known theories of lake evaporation, but it appears that it got dismissed as simply empirical. This is unfortunate, because Horton’s century-old insights on the topic, which we summarize here, seem relevant for contemporary climate change-era problems. In re-discovering his overlooked lake evaporation works, in this paper we: 1) examine his several publications in the period 1915–1944 and identify his theory sources for evaporation physics among scientists of the late 1800s; 2) illustrate his lake evaporation formulae which require several equations, tables, thresholds, and conditions based on physical factors and assumptions; and 3) assess his evaporation results over continental U.S., and analyse the performance of his formula in a subarctic Canadian catchment by comparing it with five other calibrated (aerodynamic and mass transfer) evaporation formulae of varying complexity. We find that Horton’s method, due to its unique variable vapor pressure deficit (VVPD) term, outperforms all other methods by ~ 3–15 % of R2 consistently across timescales (days to months), and an order of magnitude higher at sub-daily scales (we assessed up to 30 mins). Surprisingly, when his method uses input vapor pressure disaggregated from reanalysis data, it still outperforms other methods which use local measurements. This indicates that the vapor pressure deficit (VPD) term currently used in all other evaporation methods is not as good an independent control for lake evaporation as Horton's VVPD. Therefore, Horton's evaporation formula is held to be a major improvement in lake evaporation theory which, in part, may: A) supplant or improve existing evaporation formulae including the aerodynamic part of the combination (Penman) method; B) point to new directions in lake evaporation physics as it leads to a "constant" and a non-dimensional ratio – the former is due to him, John Dalton (1802), and Gustav Schübler (1831), and the latter to him and Josef Stefan (1881); C) offer better insights behind the physics of the evaporation paradox (i.e. globally, decreasing trends in pan evaporation are unanimously observed, while the opposite is expected due to global warming). Curiously, his rare observations of convective vapor plumes from lakes may also help explain the mythical origins of Greek deity Venus and the dancing Nereids.
Efficient Spike Timing Dependent Plasticity rule for Complex-Valued Neurons

Donald Hebb proposed in his 1949 book The Organization of Behavior that cell assemblies organized by temporally-asymmetric excitation form the basis of cognition. This basic idea has inspired a large body of research in neuroscience, and to a lesser extent in artiﬁcial intelligence. The modern manifestation of Hebb’s principle is Spike-Timing Dependent Plasticity (STDP), and though we have a large body of experimental work investigating STDP, there is still little understanding of how networks of spiking neurons organize themselves into complex functional circuits, even though some progress has been made with models such as Liquid State Machines. Networks popular in artiﬁcial intelligence (e.g. MLPs) and in artiﬁcial life (e.g. CTRNNs) tend to eschew Hebb’s insight and use error-backpropagation by gradient descent, in the case of AI, or an a-temporal Hebbian learning rule based on the outer product of neural activities, in the case of AL. Both of these approaches have greater interpretability than Spiking Neural Networks (SNNs), but both lack the mechanism that Hebb claimed was fundamental to cognition. This paper proposes to use complex-valued neurons (CVNs) to address this limitation, simultaneously promoting biological interpretation and computational tractability. The CVNs encode the ﬁring rate and spike-time of a spiking neuron in the magnitude and angle, respectively, of a complex number. We also introduce an unsupervised piecewise-linear STDP learning rule compatible with CVNs, which for brevity we call complex-valued STDP (CVSTDP). We demonstrate both learning through error-backpropagation, and the spontaneous formation and dissolution of cell assemblies via the CVSTDP rule.
Interactive comment on ”Contribution of shipping NOx emissions to the marine nitrogen budget of the western Baltic Sea – A case study” by D. Neumann et al

The western Baltic Sea is impacted by various anthropogenic activities and stressed by high riverine and atmospheric nutrient loads. Atmospheric deposition accounts for up to a third of the nitrogen input into the Baltic Sea and contributes to 5 eutrophication. Amongst other emission sources, the shipping sector is a relevant contributor to atmospheric concentrations of nitrogen oxides (NOX) in marine regions. Thus, it also contributes to atmospheric deposition of bioavailable oxidized nitrogen into the Baltic Sea. In this study, the contribution of shipping emissions to the nitrogen budget in the western Baltic Sea is evaluated with the coupled three-dimensional physical biogeochemical model MOM-ERGOM in order to assess the relevance of shipping emissions for eutrophication. The ::::::::: atmospheric : input of bioavailable nitrogen impacts eutrophication differently de10 pending on time and place of input– e.g. nitrogen is processed and denitrified faster in flat coastal regions. The shipping sector contributes up to 5% to the total nitrogen concentrations in the water. The impact of shipping-related nitrogen is highest in the off-shore regions distant to the coast in early summer but :: its :::::::::: contribution : is considerably reduced during blooms of cyanobacteria in later summer ::: late ::::::: summer ::::::: because ::: the :::::::::::: cyanobacteria :: fix ::::::::: molecular ::::::: nitrogen. Although absolute shipping-related total nitrogen concentrations are high in some coastal regions, the relative contribution of the shipping sector is low in the vicinity 15 to the coast because of high riverine nutrient loads. Copyright statement. TEXT
WNT/β-Catenin Signaling Promotes TGF-β-Mediated Activation of Human Cardiac Fibroblasts by Enhancing IL-11 Production

Cardiac fibrosis is a pathological process associated with the development of heart failure. TGF-β and WNT signaling have been implicated in pathogenesis of cardiac fibrosis, however, little is known about molecular cross-talk between these two pathways. The aim of this study was to examine the effect of exogenous canonical WNT3a and non-canonical WNT5a in TGF-β-activated human cardiac fibroblasts. We found that WNT3a and TGF-β induced a β-catenin-dependent response, whereas WNT5a prompted AP-1 activity. TGF-β triggered profibrotic signatures in cardiac fibroblasts, and co-stimulation with WNT3a or co-activation of the β-catenin pathway with the GSK3β inhibitor CHIR99021 enhanced collagen I and fibronectin production and development of active contractile stress fibers. In the absence of TGF-β, neither WNT3a nor CHIR99021 exerted profibrotic responses. On a molecular level, in TGF-β-activated fibroblasts, WNT3a enhanced phosphorylation of TAK1 and production and secretion of IL-11 but showed no effect on the Smad pathway. Neutralization of IL-11 activity with the blocking anti-IL-11 antibody effectively reduced the profibrotic response of cardiac fibroblasts activated with TGF-β and WNT3a. In contrast to canonical WNT3a, co-activation with non-canonical WNT5a suppressed TGF-β-induced production of collagen I. In conclusion, WNT/β-catenin signaling promotes TGF-β-mediated fibroblast-to-myofibroblast transition by enhancing IL-11 production. Thus, the uncovered mechanism broadens our knowledge on a molecular basis of cardiac fibrogenesis and defines novel therapeutic targets for fibrotic heart diseases.
Towards an Ecology of Interconnected Data Devices

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
The Creativity Contest

Running a contest can help managers elicit creative ideas from employees by providing employees with incentives for developing and sharing ideas that will help the firm. Little is known, however, about how contest design affects the outcomes of subjectively evaluated creativity-based contests. We conducted an experiment to investigate the impact of two contest design features, the number of prizes, and the identity of the contest evaluator, on employee participation and the quality of the shared ideas. Consistent with our predictions, we find that participation and idea quality are significantly impacted by the number of prizes, even when holding the total amount of reward money constant. Similarly, the identity of the evaluators (employees’ peers versus managers) has a significant impact on outcomes. Our results suggest that managers can use contests to elicit creative ideas from their employees, and they highlight the important trade-offs that managers should consider when designing their own creativity contests.
My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition

The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases.
Discovering "Semantics" in Super-Resolution Networks

Abstract Super-resolution (SR) is a fundamental and representative task of low-level vision area. It is generally thought that the features extracted from the SR network have no speciﬁc semantic information, and the network simply learns complex non-linear mappings from input to output. Can we ﬁnd any “semantics” in SR networks? In this paper, we give af-ﬁrmative answers to this question. By analyzing the feature representations with dimensionality reduction and visualization, we successfully discover the deep semantic representations in SR networks, i.e., deep degradation representations (DDR), which relate to the image degradation types and degrees. We also reveal the differences in representation semantics between classiﬁcation and SR networks. Through extensive experiments and analysis, we draw a series of observations and conclusions, which are of great sig-niﬁcance for future work, such as interpreting the intrinsic mechanisms of low-level CNN networks and developing new evaluation approaches for blind SR.
Minimization over the l1-ball using an active-set non-monotone projected gradient

The l1-ball is a nicely structured feasible set that is widely used in many fields (e.g., machine learning, statistics and signal analysis) to enforce some sparsity in the model solutions. In this paper, we devise an active-set strategy for efficiently dealing with minimization problems over the l1-ball and embed it into a tailored algorithmic scheme that makes use of a non-monotone first-order approach to explore the given subspace at each iteration. We prove global convergence to stationary points. Finally, we report numerical experiments, on two different classes of instances, showing the effectiveness of the algorithm.