Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Exposure to and Sharing Knowledge of Drivers of Environmental Change in the St. Lawrence System in Canada

The St. Lawrence is a vast and complex socio-ecological system providing a wealth of services that sustain numerous economic sectors. This ecosystem is subject to significant human pressures that overlap and potentially interact with climate-driven environmental changes. Our objective in this paper was to systematically characterize the distribution and intensity of drivers of environmental change (hereafter, drivers) in the St. Lawrence System. We gathered data-based indicators for 22 coastal, climate, fisheries, and marine traffic drivers through collaborations, existing environmental initiatives and open data portals. We show that few areas of the St. Lawrence are free of cumulative exposure. The Estuary, Anticosti Gyre, and coastal areas are particularly exposed, especially in the vicinity of urban centers. We identified six distinct clusters with similar suites of co-occurring drivers and show that certain driver combinations are inherent to different regions of the St. Lawrence and that coastal areas are exposed to all driver types. Of particular concern are two clusters capturing most exposure hotspots and that show the convergence of contrasting cumulative exposure profiles at the head of the Laurentian Channel. Sharing knowledge of drivers emerged as a priority to facilitate future environmental assessment efforts. We thus launch eDrivers, an open knowledge platform gathering experts committed to structuring, standardizing and sharing knowledge on drivers of environmental change in support of holistic science and management. eDrivers was built on a series of guiding principles upholding existing data management and open science standards. We therefore expect it to evolve through time to address knowledge gaps and refine current driver layers. Ultimately, we believe that eDrivers represents a much needed solution that could radically influence broad scale research and management practices by increasing knowledge accessibility and interoperability.
Effects of incorporating straw, using different cultivation systems, and of burning it, on diseases of winter barley

SUMMARY An experiment at Rothamsted in 1985–89 and another at Whaddon in 1986 studied the effects of incorporating straw on diseases of winter barley. Net blotch (Pyrenophora teres) and leaf blotch (Rhynchosporium secalis) were initially less severe where straw was burnt or incorporated by ploughing than where cultivations only partially buried it. However, by summer both diseases were usually more severe where straw had been burnt than where it had been incorporated. At Whaddon, eyespot (Pseudocercosporella herpotrichoides) tended to be less severe in tine-cultivated plots where straw was incorporated than where it was burnt, but at Rothamsted, where the straw treatments were confounded with cultivations, there was no consistent effect. The disease was usually more severe where straw was incorporated by ploughing than where it was incorporated using other methods. In contrast, the severity of take-all was generally decreased by ploughing. Seedlings usually grew better where straw had been burnt rather than incorporated and grain yields were often larger. However, yields at Rothamsted in 1987 were unusually, and inexplicably, smaller after burning the straw so that the 5-year mean yields showed no significant differences between treatments.
The Role of Port Economic Benefits in the Coordinated Development of Society

ABSTRACT Yin, W. , 2020. The role of port economic benefits in the coordinated development of society. In: Yang, Y. ; Mi, C. ; Zhao, L. , and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 686–690. Coconut Creek (Florida), ISSN 0749-0208. There is a problem of low accuracy in the existing analysis method of the role of port economic benefits in the coordinated development of society. Therefore, the research on the analysis method of the role of port economic benefits in the coordinated development of society is proposed. This paper analyzes the relationship between port economic benefits and social development. Based on the analysis results, it uses the analytic method of AHP and grey correlation degree to construct the influencing factor system of port economic benefits, establishes the contribution model of port economic benefits according to the theory of system dynamics, takes the contribution model of Port Economic benefits as the tool, and realizes the social cooperation of port economic benefits by using DEA model Analysis of the role of regulatory development. The experimental data shows that the analysis method of port economic benefits in the social coordinated development is feasible, and greatly improves the analysis accuracy, which fully shows that the analysis method of port economic benefits in the social coordinated development has better analysis performance.
Field Evaluation of 64 Apple Rootstocks for Orchard Performance and Fire Blight Resistance

In 2002, apple rootstock trials using three scion cultivars were established at Geneva, NY, to evaluate 64 apple (Malus xdomestica Borkh.) rootstocks for horticultural performance and fire blight resistance. Field trials compared several elite Geneva® apple rootstocks, which were bred for tolerance to fire blight and Phytophthora root rot, to both commercial standards and elite rootstock clones from around the world. Three rootstocks performed well with all scion cultivars: 'B.9', 'Geneva® 935', and 'Geneva® 41'. All three rootstocks were similar in size to 'M.9' clones but with elevated yield efficiency and superior resistance to fire blight. 'Geneva® 11' also performed very well with 'Golden Delicious' and 'Honeycrisp' with regard to yield efficiency and disease resistance. Resistant rootstocks greatly enhanced the survival of young trees, particularly with the susceptible scion cultivars 'Gala' and 'Honeycrisp'. Results demonstrate the ability of new rootstock clones to perform better than current commercial standards, reducing financial risk to producers while promoting orchard health with enhanced disease resistance.
Symmetries, Graph Properties, and Quantum Speedups

Aaronson and Ambainis (2009) and Chailloux (2018) showed that fully symmetric (partial) functions do not admit exponential quantum query speedups. This raises a natural question: how symmetric must a function be before it cannot exhibit a large quantum speedup? In this work, we prove that hypergraph symmetries in the adjacency matrix model allow at most a polynomial separation between randomized and quantum query complexities. We also show that, remarkably, permutation groups constructed out of these symmetries are essentially the only permutation groups that prevent super-polynomial quantum speedups. We prove this by fully characterizing the primitive permutation groups that allow super-polynomial quantum speedups. In contrast, in the adjacency list model for bounded-degree graphs—where graph symmetry is manifested differently—we exhibit a property testing problem that shows an exponential quantum speedup. These results resolve open questions posed by Ambainis, Childs, and Liu (2010) and Montanaro and de Wolf (2013).
Targeting SphK2 Reverses Acquired Resistance of Regorafenib in Hepatocellular Carcinoma

Background: Regorafenib is a second-line therapy drug used for advanced hepatocellular carcinoma (HCC). Unfortunately, the survival benefit of the patients receiving this treatment is modest, which may be attributed to drug resistance. In the present study, sphingosine kinase 2 (SphK2) was targeted to reverse regorafenib resistance in HCC. Methods: The functions of SphK2 and sphingosine-1-phosphate (S1P), the catalytic product of SphK2 in regorafenib resistance of HCC cells, were evaluated by cell counting kit-8 assay, colony formation, cell cycle evaluation, and annexin V–fluorescein isothiocyanate/propidium iodide double-staining assay. The antitumor activity of combined treatment of regorafenib and the SphK2-specific inhibitor ABC294640 was examined in HCC cells in vitro and xenograft model in vivo. The molecular mechanisms of SphK2/S1P-mediating regorafenib resistance were investigated using cell line establishment and Western blot analysis. Results: Well-developed regorafenib-resistant HCC cells indicated high expression levels of SphK2. The sensitivity to regorafenib of regorafenib-resistant HCC cells was restored following SphK2 knockdown or pharmacological inhibition by ABC294640. In addition, ectopic expression of SphK2 and exogenous addition of S1P decreased the sensitivity of HCC cells to regorafenib. Furthermore, the combination treatment with ABC294640 sensitized resistant tumor to regorafenib in xenograft model of HCC. The phosphorylation levels of nuclear factor κB (NF-κB), as well as those of signal transducer and activator of transcription 3 (STAT3), were positively associated with SphK2 and S1P. Conclusions: SphK2/S1P mediates regorafenib resistance of HCC through NF-κB and STAT3 activation. Targeting SphK2 by ABC294640 potently reduces regorafenib resistance of HCC cells both in vitro and in vivo. The combination of ABC294640 and regorafenib could be developed as a novel potential treatment strategy for advanced HCC.
Impact of dust in the decay of blast waves produced by a nuclear explosion

In this paper, we have studied the impact created by the introduction of up to 5% dust particles in enhancing the decay of blast waves produced by a nuclear explosion. A mathematical model is designed and modified using appropriate assumptions, the most important being treating a nuclear explosion as a point source of energy. A system of partial differential equations describing the one-dimensional, adiabatic, unsteady flow of a relaxing gas with dust particles and radiation effects is considered. The symmetric nature of an explosion is captured using the Lie group invariance and self-similar solutions obtained for the gas undergoing strong shocks. The enhancements in decay caused by varying the quantity of dust are studied. The energy released and the damage radius are found to decrease with time with an increase in the dust parameters.
Secondary Energization in Compressing Plasmoids during Magnetic Reconnection

Plasmoids—magnetized quasi-circular structures formed self-consistently in reconnecting current sheets—were previously considered to be the graveyards of energetic particles. In this paper, we demonstrate the important role of plasmoids in shaping the particle energy spectrum in relativistic reconnection (i.e., with upstream magnetization σ up ≫ 1). Using 2D particle-in-cell simulations in pair plasmas with σ up = 10 and 100, we study a secondary particle energization process that takes place inside compressing plasmoids. We demonstrate that plasmoids grow in time, while their interiors compress, amplifying the internal magnetic field. The magnetic field felt by particles injected in an isolated plasmoid increases linearly with time, which leads to particle energization as a result of magnetic moment conservation. For particles injected with a power-law distribution function, this energization process acts in such a way that the shape of the injected power law is conserved, while producing an additional nonthermal tail f(E) ∝ E −3 at higher energies, followed by an exponential cutoff. The cutoff energy, which increases with time as , can greatly exceed σ up m e c 2. We analytically predict the secondary acceleration timescale and the shape of the emerging particle energy spectrum, which can be of major importance in certain astrophysical systems, such as blazar jets.
Foreign Direct Investment, Production Factors Productivity and Income Inequalities in Selected CEE Countries

Abstract The issue of global economic inequality has inspired researchers to explore the potential connection between income inequalities and foreign direct investment (FDI), as it is one of the driving forces of globalization. Although there is a large body of theoretical as well as empirical studies linking these variables, the empirical literature on the relationship between FDI, production factors productivity and income inequalities is not conclusive because most scientists treat FDI as uniform. Therefore there is a lack of reliable empirical evidence on the distributional effects of FDI, especially in emerging countries, such as in Central and Eastern Europe (CEE). The research presented in the article fills this gap. The aim of the study is to analyze the impact of the inflow of foreign direct investment on the productivity of production factors (labor, capital and total factor productivity) and income inequality of households in four Central and Eastern European countries (Poland, the Czech Republic, Slovakia and Hungary) in the period 1990–2016. The four countries were selected for analysis as a classic example of European countries transforming their economic structures and similar in terms of the level of economic development. In turn, the choice of the analysis period was related to the availability of necessary statistical data. According to the theory of economics, the inflow of foreign direct investment should have a positive impact on production factors productivity as well as on income inequalities of households in investment receiving countries. In the study, a research method based on the study of economic literature in macroeconomics and international finance and econometric methods (vector autoregression models—VAR) was used. Results of the research suggest a significant and positive impact of greenfield investment inflow on labor productivity and total factor productivity, as well as a positive impact of brownfield investment inflow (mergers and acquisitions) on capital productivity in countries receiving investments. Moreover, the results also revealed the lack of a statistically significant impact of greenfield and brownfield investment on income inequalities in all of the examined countries. The statistical data used in the study came from the statistical databases of the Organization for Economic Cooperation and Development (OECD), the World Bank (World Development Indicators), World Income Inequality Database (United Nations University World Institute for Development Economics Research) and Total Economy Database (The Conference Board of Canada).
The Corticolous and Lignicolous Sorediate, Blastidiate and Isidiate Species of the Genus Rinodina in Southern Europe

Abstract The morphology, anatomy, chemistry, habitat and distribution of eight southern-Huropean sorediate, blastidiate or isidiate species of the genus Rinodina are treated. One species, R. nimisii, from Corsica and Sardinia, is new to science. Rinodina erysiphaea, R. sorediata and R. sipmanii are synonyms of R. colobinoides. A key is provided. Other known sorediate, blastidiate or isidiate species arc briefly discussed.
Pruned Neural Networks are Surprisingly Modular

The learned weights of a neural network are often considered devoid of scrutable internal structure. To discern structure in these weights, we introduce a measurable notion of modularity for multi-layer perceptrons (MLPs), and investigate the modular structure of MLPs trained on datasets of small images. Our notion of modularity comes from the graph clustering literature: a "module" is a set of neurons with strong internal connectivity but weak external connectivity. We find that training and weight pruning produces MLPs that are more modular than randomly initialized ones, and often significantly more modular than random MLPs with the same (sparse) distribution of weights. Interestingly, they are much more modular when trained with dropout. We also present exploratory analyses of the importance of different modules for performance and how modules depend on each other. Understanding the modular structure of neural networks, when such structure exists, will hopefully render their inner workings more interpretable to engineers.
Genetic Characterisation of African Swine Fever Virus in Outbreaks in Ha Nam Province, Red River Delta Region of Vietnam, and Activity of Antimicrobial Products Against Virus Infection in Contaminated Feed

Abstract Introduction African swine fever (ASF) was officially reported in Vietnam in February 2019 and spread across the whole country, affecting all 63 provinces and cities. Material and Methods In this study, ASF virus (ASFV) VN/Pig/HaNam/2019 (VN/Pig/HN/19) strain was isolated in primary porcine alveolar macrophage (PAM) cells from a sample originating from an outbreak farm in Vietnam’s Red River Delta region. The isolate was characterised using the haemadsorption (HAD) test, real-time PCR, and sequencing. The activity of antimicrobial feed products was evaluated via a contaminated ASFV feed assay. Results Phylogenetic analysis of the viral p72 and EP402R genes placed VN/Pig/HN/19 in genotype II and serogroup 8 and related it closely to Eastern European and Chinese strains. Infectious titres of the virus propagated in primary PAMs were 106 HAD50/ml. Our study reports the activity against ASFV VN/Pig/HN/19 strain of antimicrobial Sal CURB RM E Liquid, F2 Dry and K2 Liquid. Our feed assay findings suggest that the antimicrobial RM E Liquid has a strong effect against ASFV replication. These results suggest that among the Sal CURB products, the antimicrobial RM E Liquid may have the most potential as a mitigant feed additive for ASFV infection. Therefore, further studies on the use of antimicrobial Sal CURB RM E Liquid in vivo are required. Conclusions Our study demonstrates the threat of ASFV and emphasises the need to control and eradicate it in Vietnam by multiple measures.
The Relationship between Lunar Phases and the Number of Pest Microlepidoptera Specimens Caught by Pheromone Traps

Between 1982–1988 and 1993–2007 pheromone traps were operating in Borsod-Abauj-Zemplen County (Hungary). These traps attracted 7 Microlepidoptera species as the follows: Phyllonorycter blancardella Fabr. , Anarsia lineatella Z. , Lobesia botrana Den. et Schiff. , Grapholita molesta Busck, Grapholita funebrana Tr. , Cydia pomonella L. and Eupoecilia ambiguella Hbn. We examined the trapping data of these species depending on the moon phases. In case of the European Vine Moth (Lobesia botrana Den.), the Oriental Fruit Moth (Grapholita molesta Busck) and the Codling Moth (Cydia pomonella L.) a catch maximum is detectable in the vicinity of a new Moon. In case of the other 4 species, the catch is higher in the Last Quarter or in the waxing part of the lunation between a Full Moon and a New Moon, than in the waning one (i.e. between a new Moon and a full Moon). In the case of pheromone traps insects do not fly to a light source. The moonlight so may modify it neither the catching distance, neither the flying activity. The recognition that lunar influence is not identical with the influence of moonlight is an important new result of our work.
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models

We present adversarial attacks and defenses for the perceptual adversarial threat model: the set of all perturbations to natural images which can mislead a classifier but are imperceptible to human eyes. The perceptual threat model is broad and encompasses $L_2$, $L_\infty$, spatial, and many other existing adversarial threat models. However, it is difficult to determine if an arbitrary perturbation is imperceptible without humans in the loop. To solve this issue, we propose to use a {\it neural perceptual distance}, an approximation of the true perceptual distance between images using internal activations of neural networks. In particular, we use the Learned Perceptual Image Patch Similarity (LPIPS) distance. We then propose the {\it neural perceptual threat model} that includes adversarial examples with a bounded neural perceptual distance to natural images. Under the neural perceptual threat model, we develop two novel perceptual adversarial attacks to find any imperceptible perturbations to images which can fool a classifier. Through an extensive perceptual study, we show that the LPIPS distance correlates well with human judgements of perceptibility of adversarial examples, validating our threat model. Because the LPIPS threat model is very broad, we find that Perceptual Adversarial Training (PAT) against a perceptual attack gives robustness against many other types of adversarial attacks. We test PAT on CIFAR-10 and ImageNet-100 against 12 types of adversarial attacks and find that, for each attack, PAT achieves close to the accuracy of adversarial training against just that perturbation type. That is, PAT generalizes well to unforeseen perturbation types. This is vital in sensitive applications where a particular threat model cannot be assumed, and to the best of our knowledge, PAT is the first adversarial defense with this property.
Distributive Justice in the Lab: Testing the Binding Role of Agreement

Abstract Lorenzo Sacconi and his coauthors have put forward the hypothesis that impartial agreements on distributive rules may generate a conditional preference for conformity. The observable effect of this preference would be compliance with fair distributive rules chosen behind a veil of ignorance, even in the absence of external coercion. This paper uses a Dictator Game with production and taking option to compare two ways in which the device of the veil of ignorance may be thought to generate a motivation for, and compliance with a fair distributive rule: individually-as a thought experiment that should work as a moral cue- and collectively-as an actual process of agreement among subjects. The main result is that actual agreement proves to be necessary for agents to be led towards a fair distributive principle and to generate a significant amount of compliance in absence of external authority. This conclusion vindicates the role of actual agreements in generating motivational power in correspondence with fair distributive rules.
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm

Convolutional Neural Networks (CNN) have gained great success in many artificial intelligence tasks. However, finding a good set of hyperparameters for a CNN remains a challenging task. It usually takes an expert with deep knowledge, and trials and errors. Genetic algorithms have been used in hyperparameter optimizations. However, traditional genetic algorithms with fixed-length chromosomes may not be a good fit for optimizing deep learning hyperparameters, because deep learning models have variable number of hyperparameters depending on the model depth. As the depth increases, the number of hyperparameters grows exponentially, and searching becomes exponentially harder. It is important to have an efficient algorithm that can find a good model in reasonable time. In this article, we propose to use a variable length genetic algorithm (GA) to systematically and automatically tune the hyperparameters of a CNN to improve its performance. Experimental results show that our algorithm can find good CNN hyperparameters efficiently. It is clear from our experiments that if more time is spent on optimizing the hyperparameters, better results could be achieved. Theoretically, if we had unlimited time and CPU power, we could find the optimized hyperparameters and achieve the best results in the future.