A recent study demonstrates, for the first time, a method for tracking nitrogen oxide (NOx) emissions and applies it specifically to soils. The ‘chemical fingerprinting’ method allows soil NOx to be distinguished from other sources of NOx, such as vehicles and power plants. It, therefore, paves the way for a more precise understanding of agriculture’s contribution particularly to air pollution, climate change, ecosystems and environmental damage.

Disease-fighting microbes, insect-eating predators and mating-disrupting pheromones are among the tools listed in a new review of methods that can be used to reduce synthetic pesticide use on grapevines in Europe. Using these alternative methods can reduce the environmental and health risks associated with chemical pesticides, but further development is required to make them attractive to growers.

The chemical 1,4-dioxane, a solvent suspected of causing cancer, is very difficult to clean up once it enters the environment. However, hope is offered by recent scientific developments that use plants, bacteria and fungi to decontaminate water resources. Scientists provided a round-up of these 1,4-dioxane bioremediation techniques in a recent analysis.

A minimum of four wells will be drilled in the initial program year, which starts in 3Q 2020.

The record-breaking synchronized dance of 100 drones is unlike anything you've ever seen.

The style-conscious soap wizards at Method unveil plans for a wind and solar-powered manufacturing facility that will also include rooftop food production.

Researchers stumble upon a method that could replace toxic cyanide with plain old cornstarch.

Attractive but not arresting in appearance, the Paradigm series of green prefabs from Seattle's Method Homes has the potential to achieve recognition from the w

The green cleaning powerhouse reintroduces its home fragrance line with a collection of spray air fresheners powered by pressurized air technology.

At long last, Method is set to release the world's first soap container — or any sort of container, for that matter — made partially from plastic trash reco

A new study suggests there should be official rules on how often doctors clean these essential tools.

Rising senior Evie Sobczak's new method for turning algae into biofuel is cleaner and more efficient than current methods.

The organisation purchased Hughes & King for an undisclosed sum adding £4m in GWP to its business as it presses on with pipeline amid Covid-19.

The Forex market offers tremendous advantages over other types of investments. While no method is perfect, the Forex is the best all worlds for the adventurous investor, and offers returns many times greater than can be found in the stock market.

6 sigma methodology is very effective, yet its ethical aspects are questionable.

Learn more Twitter Research more here. Reference links. Keywords: Private elementary schools richmond hill, Private versus public schools, Montessori toronto, Montessori schools toronto, Private schools in toronto, Advantage of preschool education. Graphics: Share This Visual

The post The Benefits of Different Methods of Education appeared first on RSS News Feed.

Great link Twitter Good references here. Continue your research here. Keywords: Toronto private schools, Elementary schools toronto, Montessori richmond hill, Private elementary schools toronto, Advantage of preschool education, Private school richmond hill. Graphics: Share This

The post More Educational Methods Means Greater Choice for Parents and Their Children appeared first on RSS News Feed.

Contribution provides much needed dental care through the Texas Mission of Mercy dental clinic

Two Sage North America Business Partners unite Midwest with Southwest and Rocky Mountain regions.

Institute of Noetic Sciences Dr. Garret Yount patents an easy-to-use oral rinse to harvest RNA, DNA's essential partner in gene expression and regulation.

Jewelry containing toxic Lead and Cadmium may be in stores today if tested using EPA methods designed to test for soil and not jewelry. Metal Containing Jewelry Law must remove EPA methods and require CPSC methods to insure accurate and safe results.

Jewelry is only safe from toxic Lead and Cadmium if tested accurately. The Safe Jewelry Act SB647 is really the "UNSAFE Jewelry Act" unless EPA soil test methods are removed and proper testing equipment is required.

Going Against Federal Testing Guidelines in California's SB647(Mitchell) Could Cause Unsafe Jewelry to be Sold

Dovanna, an iconic fashion model of the 1980s, is presented by projected images from her work with photographers Robert Maplethorpe, Pierre & Gilles and Andrew Brucker. Opening attire by Marc Jacobs.

Sage construction accounting solution provider connects COVID-19 medical shortage with potential client base inventory

While over 20 technical and scientific institutions are working on developing a vaccine for coronavirus, IITs have already set up "COVID-19 specific research centres", encouraging developments in the innovation ecosystem.

Grand Canyon National Park implemented Level 2 water conservation measures on Wednesday, October 3, due to a series of breaks in the Transcanyon Waterline. This morning Water Utility staff at the park responded to another pipeline break. Additionally, attempts to pump water beyond Indian Garden yesterday failed. Park staff are currently assessing the situation and attempting to resolve the problem as quickly as possible. https://www.nps.gov/grca/learn/news/grand-canyon-nat-pk-prepares-for-potential-increase-in-water-conservation-methods.htm

Rather than opting for the costly and complex process of using stem cells to cure age-related macular degeneration, scientists used skin cells.

The post Irreversible No Longer: Blind Mice See Again Thanks To New Method of Synthesizing Lost Cells appeared first on Good News Network.

Using my latest techniques to produce and improved tabbed pages with persist

Increasing concern over the management and harvest of special forest products (SFP) from national forest lands has led to the development of new Forest Service policy directives.

The Fall River research site in coastal Washington is an affiliate installation of the North American Long-Term Soil Productivity (LTSP) network, which constitutes one of the world's largest coordinated research programs addressing forest management impacts on sustained productivity. Overall goals of the Fall River study are to assess effects of biomass removals, soil compaction, tillage, and vegetation control on site properties and growth of planted Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Biomass-removal treatments included removal of commercial bole (BO), bole to 5-cm top diameter (BO5), total tree (TT), and total tree plus all legacy woody debris (TT+). Vegetation control (VC) effects were tested in BO, while soil compaction and compaction plus tillage were imposed in BO+VC treatment. All treatments were imposed in 1999. The preharvest stand contained similar amounts of carbon (C) above the mineral soil (292 Mg/ha) as within the mineral soil to 80- cm depth including roots (298 Mg/ha). Carbon stores above the mineral soil ordered by size were live trees (193 Mg/ha), old-growth logs (37 Mg/ha), forest floor (27 Mg/ha), old-growth stumps and snags (17 Mg/ha), coarse woody debris (11 Mg/ha), dead trees/snags (7 Mg/ha), and understory vegetation (0.1 Mg/ha). The mineral soil to 80-cm depth contained 248 Mg C/ha, and roots added 41 Mg/ha. Total nitrogen (N) in mineral soil and roots (13 349 kg/ha) was more than 10 times the N store above the mineral soil (1323 kg/ha). Postharvest C above mineral soil decreased to 129, 120, 63, and 50 Mg/ha in BO, BO5, TT, and TT+, respectively. Total N above the mineral soil decreased to 722, 747, 414, and 353 Mg/ha in BO, BO5, TT, and TT+, respectively. The ratio of total C above the mineral soil to total C within the mineral soil was markedly altered by biomass removal, but proportions of total N stores were reduced only 3 to 6 percent owing to the large soil N reservoir on site.

The marbled murrelet (Brachyramphus marmoratus) ranges from Alaska to California and is listed under the Endangered Species Act as a threatened species in Washington, Oregon, and California. Marbled murrelet recovery depends, in large part, on conservation and restoration of breeding habitat on federally managed lands. A major objective of the Northwest Forest Plan (the Plan) is to conserve and restore nesting habitat that will sustain a viable marbled murrelet population. Under the Plan, monitoring is an essential component and is designed to help managers understand the degree to which the Plan is meeting this objective. This report describes methods used to assess the status and trend of marbled murrelet populations under the Plan.

FRIDAY night, MAY 1st, from 7 to 10P (ET) was the kick-off of #United4Cincy, a virtual community telethon produced by NUVOODOO MEDIA SERVICES, directed by NUVOODOO's RUSS GILBERT, and … more

BEASLEY MEDIA GROUP Classic Rock WCSX/DETROIT's BIG JIM O'BRIEN (host of BIG JIM'S HOUSE in mornings) and former WCSX morning personality KEN CALVERT are hosting a telethon to … more

Method Products (branded as method) is a San Francisco-based corporation which produces nontoxic, biodegradable natural cleaning supplies with a focus onminimalist product design. (source: Wikipedia) Why it’s in the Showcase: Beautiful graphics, an easy to use navigation, and an integrated...

Every time we contribute to an open source project, in any way, we are answering an important question: Why don’t I walk away and start a new fork? I’ve been working in and with and around open source software for the better part of 15 years, and over that time I’ve seen the rise of […]

The post Walking Away and the Ethos of Open Source appeared first on MOR10.

An inverse spectral problem for the Sturm-Liouville operator with a singular potential from the class $W_2^{-1}$ is solved by the method of spectral mappings. We prove the uniqueness theorem, develop a constructive algorithm for solution, and obtain necessary and sufficient conditions of solvability for the inverse problem in the self-adjoint and the non-self-adjoint cases

We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems. Both for primal and dual oracles the proposed methods are optimal in terms of the number of communication steps. However, for all classes of the objective, the optimality in terms of the number of oracle calls per node in the class of methods with optimal number of communication steps takes place only up to a logarithmic factor and the notion of smoothness. By using mini-batching technique we show that all proposed methods with stochastic oracle can be additionally parallelized at each node.

In this work, we propose a simple modification of the forward-backward splitting method for finding a zero in the sum of two monotone operators. Our method converges under the same assumptions as Tseng's forward-backward-forward method, namely, it does not require cocoercivity of the single-valued operator. Moreover, each iteration only requires one forward evaluation rather than two as is the case for Tseng's method. Variants of the method incorporating a linesearch, relaxation and inertia, or a structured three operator inclusion are also discussed.

Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a Convolutional Neural Network (CNN) pipeline that takes in Region of Interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed from the predicted silhouettes. We show that our method achieves better overall performance on the YCB-Video dataset than two state-of-the art networks for 6D pose estimation from monocular image input.

We present a novel numerical scheme to approximate the solution map $smapsto u(s) := mathcal{L}^{-s}f$ to partial differential equations involving fractional elliptic operators. Reinterpreting $mathcal{L}^{-s}$ as interpolation operator allows us to derive an integral representation of $u(s)$ which includes solutions to parametrized reaction-diffusion problems. We propose a reduced basis strategy on top of a finite element method to approximate its integrand. Unlike prior works, we deduce the choice of snapshots for the reduced basis procedure analytically. Avoiding further discretization, the integral is interpreted in a spectral setting to evaluate the surrogate directly. Its computation boils down to a matrix approximation $L$ of the operator whose inverse is projected to a low-dimensional space, where explicit diagonalization is feasible. The universal character of the underlying $s$-independent reduced space allows the approximation of $(u(s))_{sin(0,1)}$ in its entirety. We prove exponential convergence rates and confirm the analysis with a variety of numerical examples.

Further improvements are proposed in the second part of this investigation to avoid inversion of $L$. Instead, we directly project the matrix to the reduced space, where its negative fractional power is evaluated. A numerical comparison with the predecessor highlights its competitive performance.

Graph convolution network (GCN) have achieved state-of-the-art performance in the task of node prediction in the graph structure. However, with the gradual various of graph attack methods, there are lack of research on the robustness of GCN. At this paper, we will design a robust GCN method for node prediction tasks. Considering the graph structure contains two types of information: node information and connection information, and attackers usually modify the connection information to complete the interference with the prediction results of the node, we first proposed a method to hide the connection information in the generator, named Anonymized GCN (AN-GCN). By hiding the connection information in the graph structure in the generator through adversarial training, the accurate node prediction can be completed only by the node number rather than its specific position in the graph. Specifically, we first demonstrated the key to determine the embedding of a specific node: the row corresponding to the node of the eigenmatrix of the Laplace matrix, by target it as the output of the generator, we designed a method to hide the node number in the noise. Take the corresponding noise as input, we will obtain the connection structure of the node instead of directly obtaining. Then the encoder and decoder are spliced both in discriminator, so that after adversarial training, the generator and discriminator can cooperate to complete the encoding and decoding of the graph, then complete the node prediction. Finally, All node positions can generated by noise at the same time, that is to say, the generator will hides all the connection information of the graph structure. The evaluation shows that we only need to obtain the initial features and node numbers of the nodes to complete the node prediction, and the accuracy did not decrease, but increased by 0.0293.

In this paper, we analyze and implement the Dirichlet spectral-Galerkin method for approximating simply supported vibrating plate eigenvalues with variable coefficients. This is a Galerkin approximation that uses the approximation space that is the span of finitely many Dirichlet eigenfunctions for the Laplacian. Convergence and error analysis for this method is presented for two and three dimensions. Here we will assume that the domain has either a smooth or Lipschitz boundary with no reentrant corners. An important component of the error analysis is Weyl's law for the Dirichlet eigenvalues. Numerical examples for computing the simply supported vibrating plate eigenvalues for the unit disk and square are presented. In order to test the accuracy of the approximation, we compare the spectral-Galerkin method to the separation of variables for the unit disk. Whereas for the unit square we will numerically test the convergence rate for a variable coefficient problem.

Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows.

On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. Next, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products. Finally, we discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems.

This paper investigates the convergence of the randomized Kaczmarz algorithm for the problem of phase retrieval of complex-valued objects. While this algorithm has been studied for the real-valued case}, its generalization to the complex-valued case is nontrivial and has been left as a conjecture. This paper establishes the connection between the convergence of the algorithm and the convexity of an objective function. Based on the connection, it demonstrates that when the sensing vectors are sampled uniformly from a unit sphere and the number of sensing vectors $m$ satisfies $m>O(nlog n)$ as $n, m ightarrowinfty$, then this algorithm with a good initialization achieves linear convergence to the solution with high probability.

High-order entropy-stable discontinuous Galerkin (DG) methods for nonlinear conservation laws reproduce a discrete entropy inequality by combining entropy conservative finite volume fluxes with summation-by-parts (SBP) discretization matrices. In the DG context, on tensor product (quadrilateral and hexahedral) elements, SBP matrices are typically constructed by collocating at Lobatto quadrature points. Recent work has extended the construction of entropy-stable DG schemes to collocation at more accurate Gauss quadrature points.

In this work, we extend entropy-stable Gauss collocation schemes to non-conforming meshes. Entropy-stable DG schemes require computing entropy conservative numerical fluxes between volume and surface quadrature nodes. On conforming tensor product meshes where volume and surface nodes are aligned, flux evaluations are required only between "lines" of nodes. However, on non-conforming meshes, volume and surface nodes are no longer aligned, resulting in a larger number of flux evaluations. We reduce this expense by introducing an entropy-stable mortar-based treatment of non-conforming interfaces via a face-local correction term, and provide necessary conditions for high-order accuracy. Numerical experiments in both two and three dimensions confirm the stability and accuracy of this approach.

Several nonlinear model reduction techniques are compared for the three cases of the non-parallel version of the Kuramoto-Sivashinsky equation, the transient regime of flow past a cylinder at $Re=100$ and fully developed flow past a cylinder at the same Reynolds number. The linear terms of the governing equations are reduced by Galerkin projection onto a POD basis of the flow state, while the reduced nonlinear convection terms are obtained either by a Galerkin projection onto the same state basis, by a Galerkin projection onto a POD basis representing the nonlinearities or by applying the Discrete Empirical Interpolation Method (DEIM) to a POD basis of the nonlinearities. The quality of the reduced order models is assessed as to their stability, accuracy and robustness, and appropriate quantitative measures are introduced and compared. In particular, the properties of the reduced linear terms are compared to those of the full-scale terms, and the structure of the nonlinear quadratic terms is analyzed as to the conservation of kinetic energy. It is shown that all three reduction techniques provide excellent and similar results for the cases of the Kuramoto-Sivashinsky equation and the limit-cycle cylinder flow. For the case of the transient regime of flow past a cylinder, only the pure Galerkin techniques are successful, while the DEIM technique produces reduced-order models that diverge in finite time.

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

We propose some new mixed finite element methods for the time dependent stochastic Stokes equations with multiplicative noise, which use the Helmholtz decomposition of the driving multiplicative noise. It is known [16] that the pressure solution has a low regularity, which manifests in sub-optimal convergence rates for well-known inf-sup stable mixed finite element methods in numerical simulations, see [10]. We show that eliminating this gradient part from the noise in the numerical scheme leads to optimally convergent mixed finite element methods, and that this conceptual idea may be used to retool numerical methods that are well-known in the deterministic setting, including pressure stabilization methods, so that their optimal convergence properties can still be maintained in the stochastic setting. Computational experiments are also provided to validate the theoretical results and to illustrate the conceptional usefulness of the proposed numerical approach.

An approach is given for solving large linear systems that combines Krylov methods with use of two different grid levels. Eigenvectors are computed on the coarse grid and used to deflate eigenvalues on the fine grid. GMRES-type methods are first used on both the coarse and fine grids. Then another approach is given that has a restarted BiCGStab (or IDR) method on the fine grid. While BiCGStab is generally considered to be a non-restarted method, it works well in this context with deflating and restarting. Tests show this new approach can be very efficient for difficult linear equations problems.