Economic impact of COVID-19: Industry offered full support to PM Modi, says Pawan Goenka

Lord Swraj Paul today met PM Narendra Modi and conveyed his appreciation of the recent decision of Indian government to treat NRI investments at par with resident investments.

Swaraj said this while chairing a meeting of India Development Foundation of Overseas Indians, a trust established to supplement development efforts.

As the financial markets scenario is changing, it is important to review and modify (if needed) the existing asset allocation. However, before going ahead, you should consult your financial advisor.

Here is how SMC Global expects commodities to fare today:

The company has already started operations in the Middle East, with the re-opening of 9 standalone showrooms in UAE and 3 showrooms in Qatar on the occasion of Akshaya Tritiya. Kalyan Jewellers will be following the highest level of safety and precautionary measures across operational showrooms, to safeguard the health and safety of both customers and staff alike.

The disruption caused by the coronavirus pandemic could be one such tipping point, provided the states that supply much of the migrant labour have the foresight and political will to reform their industrial environment. It is evident from many accounts that many migrant workers who have fled to their home states will not return.

In a statement issued on Tuesday the company said it will "enforce all the guidelines set by government authorities to ensure health and safety of the employees and all other stakeholders involved in the manufacturing process."

A federal jury in Salt Lake City, Utah, convicted California businessman Lev Aslan Dermen, also known as Levon Termendzhyan, of criminal charges today relating to a $1 billion renewable fuel tax credit fraud scheme, announced Principal Deputy Assistant Attorney General Richard E. Zuckerman of the Justice Department’s Tax Division, U.S. Attorney John W. Huber for the District of Utah, Don Fort Chief of Internal Revenue Service (IRS) - Criminal Investigation, Acting Special Agent-in-Charge Lance Ehrig for the Denver Area Office of Environmental Protection Agency (EPA) - Criminal Investigation Division, and Special Agent-in-Charge Michael Mentavlos for the Denver Area Office of Defense Criminal Investigative Service.

Hill 'n' Dale Farm in Lexington, Ky., announced on Wednesday the purchase of undefeated Grade 1 winner Charlatan's breeding rights. “Horses with Charlatan's immense ability combined with superior pedigree, conformation, depth of female family, and star-quality appeal are a very rare commodity,” said John G. Sikura, president of Hill 'n' Dale. Charlatan's career to date […]

The post ‘A Very Rare Commodity’: Hill ‘N’ Dale Farms Buys Breeding Rights To Charlatan appeared first on Horse Racing News | Paulick Report.

Two bodies recovered from Colorado River in Grand Canyon https://www.nps.gov/grca/learn/news/two-bodies-recovered.htm

Residents and visitors to the South Rim of Grand Canyon National Park over the last week and a half have reduced their water consumption, resulting in the ability of the park to refill water storage tanks to sustainable levels. https://www.nps.gov/grca/learn/news/south-rim-modified-level-3.htm

Grand Canyon National Park is announcing modifications to operations to implement the latest guidance from the White House, Centers for Disease Control & Prevention (CDC), and local and state authorities to promote social distancing. https://www.nps.gov/grca/learn/news/grand-canyon-np-modifying-operations-to-implement-latest-health-guidance.htm

Grand Canyon National Park is announcing modifications to operations to implement the latest guidance from the White House, Centers for Disease Control & Prevention (CDC), and local and state authorities to promote public safety. As of, March 24, 2020, Grand Canyon river rafting trips including administrative, research, private and commercial trips, are suspended until May 21, 2020. https://www.nps.gov/grca/learn/news/grca-modify-river-ops-for-health-guidance-03-20-2020.htm

Grand Canyon National Park, in response to guidance from Center for Disease Control and Prevention, is announcing additional modifications to operations to support federal, state, and local efforts to slow the spread of the novel coronavirus (COVID-19). https://www.nps.gov/grca/learn/news/grca-modify-additional-opps-to-implem-loc-health-guid-03-26-2020.htm

New York's Gov. Andrew Cuomo plans to pursue the treatment following its relative success in treating influenza and Ebola.

The post FDA Okays Historic Blood Treatment for COVID; Clinical Trials to Use Antibodies From Recovered Patients appeared first on Good News Network.

The researchers from Queen Mary University of London say the discovery could lead to ground-breaking treatments with “a reduced chance of side effects.”

The post Antibodies Could Be ‘Radically Life-Changing’ New Treatment for OCD and Other Mental Disorders appeared first on Good News Network.

Using CSS3 to produce an expanding stack of image links, suitable for the iPad etc.

The Pacific Northwest Research Station launched a Biodiversity Initiative to assist natural resource professionals in integrating complex biodiversity concepts into natural resource management processes. We canvassed clients from various affiliations to determine the main challenges they face in biodiversity management, to define their information needs, and to understand how best to deliver biodiversity information within a collaborative framework. The biodiversity management challenges that emerged included (1) the lack of well-defined biodiversity management policies, (2) understanding and quantifying the interaction effects between a number of factors (e.g., disturbance types, management practices) and biodiversity, (3) the lack of applied biodiversity monitoring strategies, (4) difficulty in locating and accessing biodiversity information, and (5) balancing conflicting values relating to biodiversity. We also list the biodiversity information product needs of clients, as well as preferred technology transfer methods, and we discuss the future direction of the Biodiversity Initiative.

Lichens are highly valued ecological indicators known for their sensitivity to a wide variety of environmental stressors like air quality and climate change. This report summarizes baseline results from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Lichen Community Indicator covering the first full cycle of data collection (1998-2001, 2003) for Washington, Oregon, and California. During this period, FIA conducted 972 surveys of epiphytic macrolichen communities for monitoring both spatial and long-term temporal trends in forest health. Major research findings are presented with emphasis on lichen biodiversity as well as bioindication of air quality and climate. Considerable effort is devoted to mapping geographic patterns and defining lichen indicator species suitable for estimating air quality and climate.

This paper synthesizes the existing literature about traditional and local ecological knowledge relating to biodiversity in Pacific Northwest forests in order to assess what is needed to apply this knowledge to forest biodiversity conservation efforts. We address four topics: (1) views and values people have relating to biodiversity, (2) the resource use and management practices of local forest users and their effects on biodiversity, (3) methods and models for integrating traditional and local ecological knowledge into biodiversity conservation on public and private lands, and (4) challenges to applying traditional and local ecological knowledge for biodiversity conservation. We focus on the ecological knowledge of three groups who inhabit the region: American Indians, family forest owners, and commercial nontimber forest product (NTFP) harvesters.

This paper describes a growth model for young plantations of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) growing in the Pacific Northwest. The overall model has three major components. The first is a yield model for diameter and height distributions describing stands prior to pruning or precommercial thinning. The second component is an annual per-acre net increment model adapted from a recent model for Douglas-fir plantations in New Zealand; thinning and pruning are features of the model. The third component is growth equations for cohorts of individual trees; the results from this component are adjusted to match those from the second component. Fitting data are from Stand Management Cooperative experiments, with top heights generally below 75 ft. An intended use of the model is the evaluation of pruning regimes, in conjunction with the ORGANON model for growth at older ages, and TREEVAL model for clear-wood recovery and economic evaluation.

Forest disturbances are increasing in extent and intensity, annually altering the structure and function of affected systems across millions of acres. Land managers need rapid assessment tools that can be used to characterize disturbance events across space and to meet forest planning needs.

The Pacific Northwest Research Station launched a Biodiversity Initiative to assist natural resource professionals in integrating complex biodiversity concepts into natural resource management processes. We canvassed clients from various affiliations to determine the main challenges they face in biodiversity management, to define their information needs, and to understand how best to deliver biodiversity information within a collaborative framework. The biodiversity management challenges that emerged included (1) the lack of well-defined biodiversity management policies, (2) understanding and quantifying the interaction effects between a number of factors (e.g., disturbance types, management practices) and biodiversity, (3) the lack of applied biodiversity monitoring strategies, (4) difficulty in locating and accessing biodiversity information, and (5) balancing conflicting values relating to biodiversity. We also list the biodiversity information product needs of clients, as well as preferred technology transfer methods, and we discuss the future direction of the Biodiversity Initiative.

MADONNA said that she has tested positive for CORONAVIRUS antibodies. She went on her “QUARANTINE DIARY” series of INSTAGRAM videos and announced the positive antibody test … more

Daily Photo – Happy monks in Cambodia Most of the monks I saw in Cambodia were not really doing monk-like things. I expected to see lots of meditating or chanting or something. Most of the time, they were just goofing, playing games, smoking, and just acting like pretty much everyone else! Happy monks in Cambodia […]

The Biodiversity Photography Contest has as main objective to promote the theme of biological natural heritage, namely the natural regions, the ecosystems, the habitats and [...]

The post The Biodiversity Photography Contest appeared first on Photocompete.

Taking your first steps in programming is like picking up a foreign language. At first, the syntax makes no sense, the vocabulary is unfamiliar, and everything looks and sounds unintelligible. If you’re anything like me when I started, fluency feels impossible. I promise it isn’t. When I began coding, the learning curve hit me — hard. I spent ten months teaching myself the basics while trying to stave off feelings of self-doubt that I now recognize as imposter syndrome.

In this paper, for a family of second-order parabolic equation with rapidly oscillating and time-dependent periodic coefficients, we are interested in an approximate two-sphere one-cylinder inequality for these solutions in parabolic periodic homogenization, which implies an approximate quantitative propagation of smallness. The proof relies on the asymptotic behavior of fundamental solutions and the Lagrange interpolation technique.

Consider that $u_0$ nodes are aware of some piece of data $d_0$. This note derives the expected time required for the data $d_0$ to be disseminated through-out a network of $n$ nodes, when communication between nodes evolves according to a graphical Markov model $overline{ mathcal{G}}_{n,hat{p}}$ with probability parameter $hat{p}$. In this model, an edge between two nodes exists at discrete time $k in mathbb{N}^+$ with probability $hat{p}$ if this edge existed at $k-1$, and with probability $(1-hat{p})$ if this edge did not exist at $k-1$. Each edge is interpreted as a bidirectional communication link over which data between neighbors is shared. The initial communication graph is assumed to be an Erdos-Renyi random graph with parameters $(n,hat{p})$, hence we consider a emph{stationary} Markov model $overline{mathcal{G}}_{n,hat{p}}$. The asymptotic "$u_0$-expected flooding time" of $overline{mathcal{G}}_{n,hat{p}}$ is defined as the expected number of iterations required to transmit the data $d_0$ from $u_0$ nodes to $n$ nodes, in the limit as $n$ approaches infinity. Although most previous results on the asymptotic flooding time in graphical Markov models are either emph{almost sure} or emph{with high probability}, the bounds obtained here are emph{in expectation}. However, our bounds are tighter and can be more complete than previous results.

We observe that the DeTurck Laplacian flow of G2-structures introduced by Bryant and Xu as a gauge fixing of the Laplacian flow can be regarded as a flow of G2-structures (not necessarily closed) which fits in the general framework introduced by Hamilton in [4].

Our goal in this article is to study the global Lorentz estimates for gradient of weak solutions to $p$-Laplace double obstacle problems involving the Schr"odinger term: $-Delta_p u + mathbb{V}|u|^{p-2}u$ with bound constraints $psi_1 le u le psi_2$ in non-smooth domains. This problem has its own interest in mathematics, engineering, physics and other branches of science. Our approach makes a novel connection between the study of Calder'on-Zygmund theory for nonlinear Schr"odinger type equations and variational inequalities for double obstacle problems.

Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment. In this work, we propose the Embodied Scene Description, which exploits the embodiment ability of the agent to find an optimal viewpoint in its environment for scene description tasks. A learning framework with the paradigms of imitation learning and reinforcement learning is established to teach the intelligent agent to generate corresponding sensorimotor activities. The proposed framework is tested on both the AI2Thor dataset and a real world robotic platform demonstrating the effectiveness and extendability of the developed method.

Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting. However, little is known about the differences in the cortical activation between standing and sitting, especially how the brain's intention modulates the pre-movement sensorimotor rhythm as they do for switching movements. In this study, we aim to investigate the decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for standing and sitting. We developed a behavioral task in which participants were instructed to perform both AO and MI/ME in regard to the actions of sit-to-stand and stand-to-sit. Our results demonstrated that the ERD was prominent during AO, whereas ERS was typical during MI at the alpha band across the sensorimotor area. A combination of the filter bank common spatial pattern (FBCSP) and support vector machine (SVM) for classification was used for both offline and pseudo-online analyses. The offline analysis indicated the classification of AO and MI providing the highest mean accuracy at 82.73$pm$2.38\% in stand-to-sit transition. By applying the pseudo-online analysis, we demonstrated the higher performance of decoding neural intentions from the MI paradigm in comparison to the ME paradigm. These observations led us to the promising aspect of using our developed tasks based on the integration of both AO and MI to build future exoskeleton-based rehabilitation systems.

We perform an effectivization of classical results concerning universal coding and prediction for stationary ergodic processes over an arbitrary finite alphabet. That is, we lift the well-known almost sure statements to statements about Martin-L"of random sequences. Most of this work is quite mechanical but, by the way, we complete a result of Ryabko from 2008 by showing that each universal probability measure in the sense of universal coding induces a universal predictor in the prequential sense. Surprisingly, the effectivization of this implication holds true provided the universal measure does not ascribe too low conditional probabilities to individual symbols. As an example, we show that the Prediction by Partial Matching (PPM) measure satisfies this requirement. In the almost sure setting, the requirement is superfluous.

One of the main open problems in the qualitative theory of real planar differential systems is the study of limit cycles. In this article, we present an algorithmic approach for detecting how many limit cycles can bifurcate from the periodic orbits of a given polynomial differential center when it is perturbed inside a class of polynomial differential systems via the averaging method. We propose four symbolic algorithms to implement the averaging method. The first algorithm is based on the change of polar coordinates that allows one to transform a considered differential system to the normal form of averaging. The second algorithm is used to derive the solutions of certain differential systems associated to the unperturbed term of the normal of averaging. The third algorithm exploits the partial Bell polynomials and allows one to compute the integral formula of the averaged functions at any order. The last algorithm is based on the aforementioned algorithms and determines the exact expressions of the averaged functions for the considered differential systems. The implementation of our algorithms is discussed and evaluated using several examples. The experimental results have extended the existing relevant results for certain classes of differential systems.

We utilize recent results on the exact block error probability of Gaussian random codes in additive white Gaussian noise to analyze Gaussian random coding for massive multiple-access at finite message length. Soft iterative interference cancellation is found to closely approach the performance bounds recently found in [1]. The existence of two fundamentally different regimes in the trade-off between power and bandwidth efficiency reported in [2] is related to much older results in [3] on power optimization by linear programming. Furthermore, we tighten the achievability bounds of [1] in the low power regime and show that orthogonal constellations are very close to the theoretical limits for message lengths around 100 and above.

A big part of the video content we consume from video providers consists of genres featuring low-light aesthetics. Low light sequences have special characteristics, such as spatio-temporal varying acquisition noise and light flickering, that make the encoding process challenging. To deal with the spatio-temporal incoherent noise, higher bitrates are used to achieve high objective quality. Additionally, the quality assessment metrics and methods have not been designed, trained or tested for this type of content. This has inspired us to trigger research in that area and propose a Grand Challenge on encoding low-light video sequences. In this paper, we present an overview of the proposed challenge, and test state-of-the-art methods that will be part of the benchmark methods at the stage of the participants' deliverable assessment. From this exploration, our results show that VVC already achieves a high performance compared to simply denoising the video source prior to encoding. Moreover, the quality of the video streams can be further improved by employing a post-processing image enhancement method.

A method for encoding information in DNA sequences is described. The method is based on the precision-resolution framework, and is aimed to work in conjunction with a recently suggested terminator-free template independent DNA synthesis method. The suggested method optimizes the amount of information bits per synthesis time unit, namely, the writing rate. Additionally, the encoding scheme studied here takes into account the existence of multiple copies of the DNA sequence, which are independently distorted. Finally, quantizers for various run-length distributions are designed.

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.

In the future we can expect that artificial intelligent agents, once deployed, will be required to learn continually from their experience during their operational lifetime. Such agents will also need to communicate with humans and other agents regarding the content of their experience, in the context of passing along their learnings, for the purpose of explaining their actions in specific circumstances or simply to relate more naturally to humans concerning experiences the agent acquires that are not necessarily related to their assigned tasks. We argue that to support these goals, an agent would benefit from an episodic memory; that is, a memory that encodes the agent's experience in such a way that the agent can relive the experience, communicate about it and use its past experience, inclusive of the agents own past actions, to learn more effective models and policies. In this short paper, we propose one potential approach to provide an AI agent with such capabilities. We draw upon the ever-growing body of work examining the function and operation of the Medial Temporal Lobe (MTL) in mammals to guide us in adding an episodic memory capability to an AI agent composed of artificial neural networks (ANNs). Based on that, we highlight important aspects to be considered in the memory organization and we propose an architecture combining ANNs and standard Computer Science techniques for supporting storage and retrieval of episodic memories. Despite being initial work, we hope this short paper can spark discussions around the creation of intelligent agents with memory or, at least, provide a different point of view on the subject.

Fact-based dialogue generation is a task of generating a human-like response based on both dialogue context and factual texts. Various methods were proposed to focus on generating informative words that contain facts effectively. However, previous works implicitly assume a topic to be kept on a dialogue and usually converse passively, therefore the systems have a difficulty to generate diverse responses that provide meaningful information proactively. This paper proposes an end-to-end Fact-based dialogue system augmented with the ability of convergent and divergent thinking over both context and facts, which can converse about the current topic or introduce a new topic. Specifically, our model incorporates a novel convergent and divergent decoding that can generate informative and diverse responses considering not only given inputs (context and facts) but also inputs-related topics. Both automatic and human evaluation results on DSTC7 dataset show that our model significantly outperforms state-of-the-art baselines, indicating that our model can generate more appropriate, informative, and diverse responses.

In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in $SE(3)$. The robots have no explicit communication network among them, and they do no know the mass or friction properties of the object, or where they are attached to the object. However, we assume they share data from a common IMU placed arbitrarily on the object. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller's behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller's performance using numerical simulations of a manipulation task in 3D, and with hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodelled effects, such as discretization errors and complex frictional interactions.

React Hooks, introduced in React 16.8, present us with a fundamentally new approach to coding. Some may think of them as a replacement for lifecycles or classes, however, that would be wrong. Like trying to translate a word from another language, sometimes you’re facing a completely new entity, which seems identical on the surface but is very different semantically and can’t be treated as equivalent. 

React not only changed the approach from OOP to Functional. The method of rendering has changed in principle. React is now fully built on functions instead of classes. And this has to be understood on a conceptual level. 

A method for producing fatty acid alkyl esters from biolipids through transesterification and/or esterification reactions uses a flow-through cavitation device for generating cavitation bubbles in a fluidic reaction medium. The fluidic medium is passed through sequential compartments in the cavitation device having varying diameters and inner surface features to create localized reductions in fluid pressure thus vaporizing volatile alcohols and creating an increased surface area and optimized conditions for the reaction to occur at the gas-liquid interface around the bubbles.

The invention relates to the use of a conjugated seed oil modified with an activated vinyl monomer via a Deils-Alder reaction to generate a modified reactive diluent used to enhance the physical properties of coating systems. In one embodiment, a reactive diluent containing tung oil modified by the addition of an acrylate monomer is mixed with an auto-oxidative alkyd, a metal drier package, and a wetting agent to achieve cross-linked films exhibiting improved tensile strength, tensile modulus, cross-link density, and glass transition temperature, among other physical parameters.

This invention relates to an improved process for the preparation of green fatty acid methyl esters (FAME; commonly called as biodiesel) from different triglyceride oils using mixed metal oxides derived from layered double hydroxides (referred here as LDHs) as reusable solid heterogeneous base catalysts. This process uses very low alcohohoil molar ratio and catalyst and/or products are easily separable after the reaction through simple physical processes. The properties of thus obtained biodiesel meet the standard biodiesel values and can directly be used as transport fuel.

Formulations comprising an isosorbide-modified unsaturated polyester comprising maleic acid, fumaric acid, itaconic acid or maleic anhydride and isosorbide and one or more low profile additives. The formulations exhibit better shrink control in molding compound formulations with standard low profile additives than their non-isosorbide-modified analogues. These isosorbide-modified unsaturated polyester resins may be applied in molding compounds like sheet molding compounds or bulk molding compounds and may be components of fiber reinforced composites or other composite materials.

A method of making light olefins in a combined XTO (organics to olefins) and OCP (olefins cracking) process, from an oxygen-containing, halogenide-containing, or sulphur-containing organic feedstock contacted with a catalyst in a first reactor to convert the feedstock into a reactor effluent comprising light olefins and a heavy hydrocarbon fraction; separating the light olefins from the heavy hydrocarbon fraction, then contacting the heavy hydrocarbon fraction in a second reactor with a catalyst to convert a portion of the heavy hydrocarbons into light olefins; wherein the catalyst is a zeolite selected among a H+ or NH4+—form of MFI, MEL, FER, MOR, or clinoptilolite; modifying the zeolite by adding from 0.05 to 7 wt % of phosphorous to the zeolite, and an optional washing and/or drying in either order, then calcination. In an embodiment, the initial zeolite Si:Al atomic ratio of at least one catalyst is 100 or less.

The present invention relates to epoxy group-terminated polymers of the formula (I). Said epoxy group-terminated polymers are suited extremely well as impact resistance modifiers, particularly in epoxy resin compositions. They are particularly suited for use in heat-curing epoxy resin adhesives. It has been found that such epoxy resin compositions not only have excellent mechanical properties and high glass transition temperatures, but also above all improved impact resistance properties, both at room temperature and at low temperatures.