Boffins in Germany have devised a technique to subvert neural network frameworks so they misidentify images without any telltale signs of tampering.

Erwin Quiring, David Klein, Daniel Arp, Martin Johns, and Konrad Rieck, computer scientists at TU Braunschweig, describe their attack in a pair of papers, slated for presentation at technical conferences in May and in August this year events that may or may not take place given the COVID-19 global health crisis.

The papers, titled "Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning" [PDF] and "Backdooring and Poisoning Neural Networks with Image-Scaling Attacks [PDF]," explore how the preprocessing phase involved in machine learning presents an opportunity to fiddle with neural network training in a way that isn't easily detected. The idea being: secretly poison the training data so that the software later makes bad decisions and predictions.

This example image, provided by the academics, of a cat has been modified so that when downscaled by an AI framework for training, it turns into a dog, thus muddying the training dataset

There have been numerous research projects that have demonstrated that neural networks can be manipulated to return incorrect results, but the researchers say such interventions can be spotted at training or test time through auditing.

"Our findings show that an adversary can significantly conceal image manipulations of current backdoor attacks and clean-label attacks without an impact on their overall attack success rate," explained Quiring and Rieck in the Backdooring paper. "Moreover, we demonstrate that defenses designed to detect image scaling attacks fail in the poisoning scenario."

Their key insight is that algorithms used by AI frameworks for image scaling a common preprocessing step to resize images in a dataset so they all have the same dimensions do not treat every pixel equally. Instead, these algorithms, in the imaging libraries of Caffe's OpenCV, TensorFlow's tf.image, and PyTorch's Pillow, specifically, consider only a third of the pixels to compute scaling.

"This imbalanced influence of the source pixels provides a perfect ground for image-scaling attacks," the academics explained. "The adversary only needs to modify those pixels with high weights to control the scaling and can leave the rest of the image untouched."

On their explanatory website, the eggheads show how they were able to modify a source image of a cat, without any visible sign of alteration, to make TensorFlow's nearest scaling algorithm output a dog.

This sort of poisoning attack during the training of machine learning systems can result in unexpected output and incorrect classifier labels. Adversarial examples can have a similar effect, the researchers say, but these work against one machine learning model.

Image scaling attacks "are model-independent and do not depend on knowledge of the learning model, features or training data," the researchers explained. "The attacks are effective even if neural networks were robust against adversarial examples, as the downscaling can create a perfect image of the target class."

The attack has implications for facial recognition systems in that it could allow a person to be identified as someone else. It could also be used to meddle with machine learning classifiers such that a neural network in a self-driving car could be made to see an arbitrary object as something else, like a stop sign.

To mitigate the risk of such attacks, the boffins say the area scaling capability implemented in many scaling libraries can help, as can Pillow's scaling algorithms (so long as it's not Pillow's nearest scaling scheme). They also discuss a defense technique that involves image reconstruction.

The researchers plan to publish their code and data set on May 1, 2020. They say their work shows the need for more robust defenses against image-scaling attacks and they observe that other types of data that get scaled like audio and video may be vulnerable to similar manipulation in the context of machine learning.

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FYI: You can trick image-recog AI into, say, mixing up cats and dogs by abusing scaling code to poison training data - The Register

Engineers at the AMRC have researched the use of the cloud to capture data from machine tools with Tier 2 member Amido

Cloud data solutions being trialled at the University of Sheffield Advanced Manufacturing Research Centre (AMRC) could provide a secure and cost-effective way for SME manufacturers to explore how machine learning and Industry 4.0 technologies can boost their productivity.

Jon Stammers, AMRC technical fellow in the process monitoring and control team, says: Data is available on every shopfloor but a lot of time it isnt being captured due to lack of connectivity, and therefore cannot be analysed. If the cloud can capture and analyse that data then the possibilities are massive.

Engineers in the AMRCs Machining Group have researched the use of the cloud to capture data from machine tools with new Tier Two member Amido, an independent technical consultancy specialising in assembling, integrating and building cloud-native solutions.

Mr Stammers adds: Typically we would have a laptop sat next to a machine tool capturing its data; a researcher might do some analysis on that laptop and share the data on our internal file system or on a USB stick. There is a lot of data generated on the shopfloor and it is our job to capture it, but there are plenty of unanswered questions about the analysis process and the cloud has a lot to bring to that.

In the trial, data from two CNC machines in the AMRCs Factory of the Future: a Starrag STC 1250 and a DMG Mori DMU 40 eVo, was transferred to the Microsoft Azure Data Lake cloud service and converted into a parquet format, which allowed Amido to run a series of complex queries over a long period of time.

Steve Jones, engagement director at Amido, explains handling those high volumes of data is exactly what the cloud was designed for: Moving the data from the manufacturing process into the cloud means it can be stored securely and then structured for analysis. The data cant be intercepted in transit and it is immediately encrypted by Microsoft Azure.

Security is one of the huge benefits of cloud technology, Mr Stammers comments. When we ask companies to share their data for a project, it is usually rejected because they dont want their data going offsite. Part of the work were doing with Amido is to demonstrate that we can anonymise data and move it off site securely.

In addition to the security of the cloud, Mr Jones says transferring data into a data lake means large amounts can be stored for faster querying and machine learning.

One of the problems of a traditional database is when you add more data, you impact the ability for the query to return the answers to the questions you put in; by restructuring into a parquet format you limit that reduction in performance. Some of the queries that were taking one of the engineers up to 12 minutes to run on the local database, took us just 12 seconds using Microsoft Azure.

It was always our intention to run machine learning against this data to detect anomalies. A reading in the event data that stands out may help predict maintenance of a machine tool or prevent the failure of a part.

Storing data in the cloud is extremely inexpensive and that is why, according to software engineer in the process monitoring and control team Seun Ojo, cloud technology is a viable option for SMEs working with the AMRC, part of the High Value Manufacturing (HVM) Catapult.

He says: SMEs are typically aware of Industry 4.0 but concerned about the return on investment. Fortunately, cloud infrastructure is hosted externally and provided on a pay-per-use basis. Therefore, businesses may now access data capture, storage and analytics tools at a reduced cost.

Mr Jones adds: Businesses can easily hire a graphics processing unit (GPU) for an hour or a quantum computer for a day to do some really complicated processing and you can do all this on a pay-as-you-go basis.

The bar to entry to doing machine learning has never been lower. Ten years ago, only data scientists had the skills to do this kind of analysis but the tools available from cloud platforms like Microsoft Azure and Google Cloud now put a lot of power into the hands of inexpert users.

Mr Jones says the trials being done with Amido could feed into research being done by the AMRC into non-geometric validation.

He concludes: Rather than measuring the length and breadth of a finished part to validate that it has been machined correctly; I want to see engineers use data to determine the quality of a job.

That could be really powerful and if successful would make the process of manufacturing much quicker. That shows the value of data in manufacturing today.

AMRC http://www.amrc.co.uk

Amido http://www.amido.com

Michael Tyrrell

Digital Coordinator

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Proof in the power of data - PES Media

PHOTO: Charles

Lets say you have a customer who has taken a certain action: downloaded an ebook, filled out an application, added a product to their cart, called into your call center or walked into your branch office, to name a few. What content, offer or message should you deliver to them next? What next step should you recommend? How can you best add value for that individual, while nurturing the person, wherever they are in their relationship with your business?

Based on your history (or even lack of history) with a given individual, you and your company might also have questions such as: Whats the best product to upsell to this particular client? (and should I even try to upsell that person?); Whats the right promotion to show an engaged shopper on my ecommerce site? and Whats the right item to promote to someone logged into my application? The list goes on.

These types of questions are all important to businesses today, who often talk about next best actions. This customer-centric (often 1-to-1) approach and sequencing strategy can take a number of forms. But at a basic level, the concept means what it sounds like: determining the most relevant or appropriate next action (or offer, promotion, content, etc.) to show a person in the moment, based on their current and previous actions or other information youve gathered about them across your online and offline channels. Next best actions can also include triggering messages to call center agents or sales reps to alert them of important activity, or to suggest the next best action they should take with a customer.

Companies put awide variety of thought, time and effort into establishing sequencing paths from none at all (with a one-size-fits-all message, promotion, offer, etc.) to a lot. At a majority of organizations, though, determining the next best action for their customers is very important, involving multiple teams of people across functions and divisions.

There are teams of marketers and designers, for instance, who create elaborate promotions and offers with different media for different channels. And there are customer experience teams who devote many cycles to thinking about call-center scripts and next best actions.

So when it comes to deploying those next best actions, it can devolve into an inter-departmental war about who gets the prime real estate. For example, when new visitors hit the homepage or when customers log into the app, what gets displayed in the hero area?

Why all the effort and involvement? Its because next best actions are strategically important to engagement and the bottom line. Present the right, relevant offer or action to a customer or prospect, and youre helping elicit interest and drive conversions. Present the wrong (e.g., outdated, irrelevant, mismatched to sales cycle stage, etc.) one, and youre losing customer interest or even turning them off your brand.

Related Article: Good Personalization Hinges on Good Data

For many years, organizations have taken a rule-based approach to determining the right next best action for a particular customer in a particular channel or at a particular stage in their journey. Rules are manually created and structured with if-then logic (e.g., IF a person takes this action or belongs to this group, THEN display this next). They govern the experiences and actions for audience segments which can be broad or get very narrow.

Three types of rules are the most frequently applied to next-best-action decisioning. These can be used on their own or, typically, in concert:

Related Article: Why Personalization Efforts Fail

But one problem with rules is the more targeted and relevant you want to get, the greater the number of rules you need to make. With rules, personalization of the next best action is inversely correlated to simplicity. In other words, to deliver truly relevant and highly specific actions and experiences using rules only, you quickly enter a world of nearly unmanageable complexity.

Theres also the time factor to consider. As you have likely experienced, it takes a lot of hours to create and prescribe sequencing via rules for the multitude of scenarios customers can encounter and the paths they can take. And unraveling a heavily nested set of rules in order to make minor adjustments (and make them correctly) can take many more hours.

Another problem with rules is that they are just a human guessing. Suppose youre wrong in the next best action youve set up for a customer to receive in fact, it may actually be hurting revenues or customer loyalty.

So while rules do play a vital role in determining and displaying next best actions, a rules-only-based approach generally isnt optimal or scalable in the long-term.

Related Article: Refine Your Personalization Efforts by Ditching Tech-First Tendencies

Machine learning, a type of artificial intelligence (AI), can supplement rules and play a powerful role in prioritization and other next-best-action decisions: pulling in everything known about an individual in the channel of engagement and across channels, factoring in data from similar people, and then computing and displaying the optimal, relevant next best action or offer at the 1-to-1 level. Typically, this all occurs in milliseconds faster than you can blink an eye.

Across industries, theres an enormous amount of behavioral data to parse through to uncover trends and indicators of what to do next with any given individual. This can be combined with attribute and transaction data to build a rich profile and predictive intelligence. Machine-learning algorithms automate this process, make surprising discoveries and keep learning based on ever-growing data: from studying both the individual customer and customers with similar attributes and behaviors, and from learning from how customers are reacting to the actions being suggested to them.

In addition, when multiple promotions or next actions are valid, you can apply machine learning to decide on and display the truly optimal one, balancing whats best for the customer with whats best for your business.

Optimized machine-learning-driven next best actions outperform manual ones, even when what they suggest might seem counter-intuitive. For example, a banking institution might promote its most popular cash-back credit card offer to all new site visitors. But for return visitors located in colder climate regions, a continuous learning algorithm might determine that the banks travel rewards card offer performs much better. Only machine learning can pick up on behavioral signals and information at scale (including seemingly unimportant information) in a way that humans simply cannot.

Related Article: 5 Drivers of Personalized Experiences: A Walk Through the AI Food Chain

Determining and displaying next best actions involve integrations and interplay across channels. One system is informing another of an action a customer has taken and what to do next. For example: a customer who joined the loyalty program could be eligible to receive a certain promotion in their email. Or a shopper who browsed purses online can be push-notified a coupon code to use in-store, thanks to beacon technology. An alert might get triggered to a call center agent based on a customers unfinished loan application letting the agent know to provide information on interest rates or help set up an appointment at the customers local branch as that person is calling in.

Given the wide range of activity and vast quantities of data, its important to have a single system that can arbitrate all these actions, apply prioritization and act as the central brain. This helps keep customer information unified and up-to-date, and aids in real-time interaction management and experience delivery.

In the end, everything organizations do when communicating and relating to their customers could be viewed as next best actions. In fact, personalization and next best actions are closely intertwined, as two sides of the same coin. Its hard to separate a next best action from the personalization decisioning driving it, which is why the two areas should be (and sometimes are) tied together from a strategy and systems perspective.

By effectively determining and triggering personalized next steps, you can tell a cohesive and consistent cross-channel story that bolsters brand perception, improves the buyer journey and turns next best actions into must-take ones.

Karl Wirth is the CEO and co-founder of Evergage, a Salesforce Company and a leading real-time personalization and interaction management platform provider. Karl is also the author of the award-winning book One-to-One Personalization in the Age of Machine Learning.

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The Power of AI in 'Next Best Actions' - CMSWire

Deep learning chips are customized Silicon chips that integrate AI technology and machine learning. Deep learning and machine learning, which are the sub-sets of Artificial Intelligence (AI) sub-sets, are used in carrying out AI related tasks.

New York, March 20, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Deep Learning Chipset Market By type By Technology By End User By Region, Industry Analysis and Forecast, 2019 - 2025" - https://www.reportlinker.com/p05876895/?utm_source=GNW Deep learning technology has entered many industries around the world and is accomplished through applications like computer vision, speech synthesis, voice recognition, machine translation, drug discovery, game play, and robotics.

The widespread adoption of artificial intelligence (AI) for practical business applications has brought in a range of complexities and risk factors in virtually every industry, but one thing is certain: in todays AI industry, hardware is the key to solving many of the main problems facing the sector, and chipsets are at the heart of that hardware solution. Considering AIs widespread applicability, its almost certain that every chip will have some kind of AI system embedded in future. The engine could make a wide range of forms, from a basic AI library running on a CPU to more complex, custom hardware. The potential for AI is better fulfilled when the chipsets are designed to provide the adequate amount of computing capacity for different AI applications at the right power budget. This is a trend that leads to increased specialization and diversifying of AI-optimized chipsets.

The factors influencing the development of the deep learning chipset market are increased acceptance of cloud-based technology and profound use of learning in big data analytics. A single-chip processor generates lighting effects and transforms objects each time a 3D scene is redrawn, or a graphic processing unit turns out to be very meaningful and efficient when applied to computation styles needed for neural nets. This in turn fuels the growth of the market for deep learning chipsets.

Based on type, the market is segmented into GPU, ASIC, CPU, FPGA and Others. Based on Technology, the market is segmented into System-on-chip (SoC), System-in-package (SIP) and Multi-chip module & Others. Based on End User, the market is segmented into Consumer Electronics, Industrial, Aerospace & Defense, Healthcare, Automotive and Others. Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa.

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix, Google, Inc., Microsoft Corporation, Samsung Electronics Co., Ltd., Intel Corporation, Amazon.com, Inc., and IBM Corporation are some of the forerunners in the Deep Learning Chipset Market. Companies such as Advanced Micro Devices, Inc., Qualcomm, Inc., Nvidia Corporation, and Xilinx, Inc. are some of the key innovators in Deep Learning Chipset Market. The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Samsung Electronics Co., Ltd. (Samsung Group), Microsoft Corporation, Intel Corporation, Nvidia Corporation, IBM Corporation, Google, Inc., Amazon.com, Inc. (Amazon Web Services), Qualcomm, Inc., Advanced Micro Devices, Inc., and Xilinx, Inc.

Recent strategies deployed in Deep Learning Chipset Market

Partnerships, Collaborations, and Agreements:

Jan-2020: Xilinx collaborated with Telechips, a leading Automotive System on Chip (SoC) supplier. The collaboration would provide a comprehensive solution for addressing the integration of in-cabin monitoring systems (ICMS) and IVI systems.

Dec-2019: Samsung Electronics teamed up with Baidu, a leading Chinese-language Internet search provider. Under the collaboration, the companies announced that the development of Baidu KUNLUN, its first cloud-to-edge AI accelerator has been completed. KUNLUN chip provides 512 gigabytes per second (Gbps) memory bandwidth and offers up to 260 Tera operations per second (TOPS) at 150 watts.

Oct-2019: Microsoft announced technology collaboration with Nvidia, a technology company. The collaboration was focused on intelligent edge computing, which is designed for helping the industries in gaining and managing the insights from the data created by warehouses, retail stores, manufacturing facilities, urban infrastructure, connected buildings, and other environments.

Oct-2019: Microsoft launched Lakefield, a dual-screen device powered by Intels unique processor. This device combines a hybrid CPU with Intels Foveros 3D packaging technology. This provides more flexibility to device makers for innovating designs, experience, and form factor.

Jun-2019: AMD came into partnership with Samsung following which, the former company is licensing its graphics technology to Samsung for use in future mobile chips. Under this partnership, Samsung paid AMD for getting access to its RDNA graphics architecture.

Jun-2019: Nvidia collaborated with Volvo for developing artificial intelligence that is used in self-driving trucks.

May-2019: Samsung Electronics came into partnership with Efinix, an innovator in programmable product platforms and technologies. Under this partnership, the companies were aimed at developing Quantum eFPGAs on Samsungs 10nm silicon process.

Dec-2018: IBM extended its partnership with Samsung for developing 7-nanometer (nm) microprocessors for IBM Power Systems, LinuxONE, and IBM Z. The expansion was aimed at driving the performance of the unmatched system including encryption and compression speed, acceleration, memory, and I/O bandwidth, as well as system scaling.

Jun-2018: AWS announced its collaboration with Cadence Design Systems. The collaboration was aimed at delivering a Cadence Cloud portfolio to electronic systems and semiconductor design.

Mar-2018: Nvidia came into partnership with Arm for bringing deep learning interface to billions of consumer electronics, mobile, and Internet of Things devices.

Acquisition and Mergers:

Aug-2019: Xilinx took over Solarflare, a provider of high-performance, low latency networking solutions. The acquisition helped in generating more revenues and enabled new marketing and R&D funds for the future.

Apr-2019: Intel completed the acquisition of Omnitek, a provider of video and vision field-programmable gate array (FPGA). Through the acquisition, the FPGA processor business of the company has been doubled.

Jul-2018: Intel took over eASIC, a fabless semiconductor company. The acquisition bolstered the companys business in providing chips.

Apr-2017: AMD acquired Nitero, a company engaged in providing technology to connect VR headsets wirelessly to PCs. The acquisition helped the company in getting control over VR experiences.

Product Launches and Product Expansions:

Dec-2019: Nvidia launched Drive AGX Orin, a new Orin AI processor or system-on-chip (SoC). This processor improves power efficiency and performance. This processor is used in evolving the automotive business.

Dec-2019: AWS unveiled Graviton2, the next-generation of its ARM processors. It is a custom chip that is designed with 7nm architecture and based on 64-bit ARM Neoverse cores.

Nov-2019: AMD launched two new Threadripper 3 CPUs with 24 and 32 cores. Both these CPUs will be integrated into AMDs new TRX40 platform using the new sTRX4 socket.

Nov-2019: Intel unveiled Ponte Vecchio GPUs, a graphics processing unit (GPU) architecture. This chip was designed for handling the artificial intelligence loads and heavy data in the data center.

Nov-2019: Intel launched Stratix 10 GX 10M, a new FPGA. This consists of two large FPGA dies and four transceiver tiles and has a total of 10.2 million logic elements and 2304 user I/O pins.

Oct-2018: Google launched TensorFlow, the popular open-source artificial intelligence framework. This framework runs deep learning, machine learning, and other predictive and statistical analytics workloads. This simplifies training models, the process of acquiring data, refining future results, and serving predictions.

Sep-2019: AWS released Amazon EC2 G4 GPU-powered Amazon Elastic Compute Cloud (Amazon EC2) instances. It delivers up to 1.8 TB of local NVMe storage and up to 100 Gbps of networking throughput to AWS custom Intel Cascade Lake CPUs and NVIDIA T4 GPUs.

Aug-2019: Xilinx released Virtex UltraScale+ VU19P, a 16nm device with 35 billion transistors. It has four chips on an interposer. It is the worlds largest field-programmable gate array (FPGA) and has 9 million logic cells.

May-2019: Nvidia introduced NVIDIA EGX, an accelerated computing platform. This platform was aimed at allowing the companies in performing low-latency AI at the edge for perceiving, understanding, and acting in real-time on continuous streaming data between warehouses, factories, 5G base stations, and retail stores.

Nov-2018: AWS introduced Inferentia and Elastic Inference, two chips and 13 machine learning capabilities and services. Through these launches, the company aimed towards attracting more developers.

Sep-2018: Qualcomm unveiled Snapdragon Wear 3100 chipset. This chipset is used in smartwatches and has extended battery life.

Aug-2018: AMD introduced B450 chipset for Ryzen processors. The chip runs about 2 watts lower in power than B350 chipset.

Jul-2018: Google introduced Tensor Processing Units or TPUs, the specialized chips. This chip lives in data centers of the company and simplifies the AI tasks. These chips are used in enterprise jobs.

Apr-2018: Qualcomm launched QCS605 and QCS603 SoCs, two new system-on-chips. These chips combine image signal processor, CPU, AI, GPU technology for accommodating several camera applications, smart displays, and robotics.

Scope of the Study

Market Segmentation:

By Compute Capacity

High

Low

By Type

GPU

ASIC

CPU

FPGA

Others

By Technology

System-on-chip (SoC)

System-in-package (SIP)

Multi-chip module & Others

By End User

Consumer Electronics

Industrial

Aerospace & Defense

Healthcare

Automotive

Others

By Geography

North America

o US

o Canada

o Mexico

o Rest of North America

Europe

o Germany

o UK

o France

o Russia

o Spain

o Italy

o Rest of Europe

Asia Pacific

o China

o Japan

o India

o South Korea

o Singapore

o Malaysia

o Rest of Asia Pacific

LAMEA

o Brazil

o Argentina

o UAE

o Saudi Arabia

o South Africa

o Nigeria

o Rest of LAMEA

Companies Profiled

Samsung Electronics Co., Ltd. (Samsung Group)

Microsoft Corporation

Intel Corporation

Nvidia Corporation

IBM Corporation

Google, Inc.

Amazon.com, Inc. (Amazon Web Services)

Qualcomm, Inc.

Advanced Micro Devices, Inc.

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The Global Deep Learning Chipset Market size is expected to reach $24.5 billion by 2025, rising at a market growth of 37% CAGR during the forecast...

Updated: Mar 19, 2020, 06:00 IST

Chief Justice Vikram Nath took the decision considering the seriousness of the pandemic of Coronavirus (Covid-19) and no Cause List will be prepared from Thursday onwards till the month. No cause list shall be prepared and notified including for bail matters and quashing matters, the circular issued by Registrar General states.

It said, Any advocate who is desirous of obtaining an urgent order shall file a note stating the urgency and the same shall be considered by the court. If the court finds that there is genuine urgency, the matter shall be either listed on the same day if no caveat is there or on the next day.

Keeping the threats emanating from the novel corona virus (ncovid-19), the administration of the Sabarmati Ashram has decided to close down the ashram till March 29.

Ashram Director Atul Pandya said that the decision was taken keeping in mind the safety of the citizens. The ashram will be closed for visitors from March 19 to 29.

Mar 19, 2020

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HC to hear only urgent matters - Ahmedabad Mirror

Bhumi Pednekar hosted some special and cute guests on the sets of her upcoming film, 'Durgavati'. Children from the orphanage Abhyudaya Ashram that she has been supporting for a while now, were invited by Bhumi, making their day incredibly memorable! The actress has been supporting Abhyudaya Ashram, a home and school for abandoned children, including girls rescued from prostitution for nearly 3 years now. The Morena-based school was established in 1992 to fight the prostitution of girls in the valley. The school looks to open up job opportunities for the children thus empowering them and also providing a better future to them. Bhumi involved herself by building toilets and a hostel hereafter the blockbuster success and nationwide impact of her film, Toilet: Ek Prem Katha. While shooting for Son Chiraiya in the Chambal Valley, Bhumi interacted with the children here and they also celebrated her birthday to express their love and gratitude for Bhumi. Bhumi has been constantly supporting the Ashram with funds and gifts to ensure that the kids have the best possible education and facilities. A source from the sets reveals, Bhumi had carefully made this plan with the Ashram authorities. She wanted to surprise the kids and ensured they didnt know where they were being taken to. The only thing they knew was its going to be a day out for them. When they landed on the sets of the film and saw Bhumi there waiting for them, the kids freaked out with excitement! They were so happy to see their didi Bhumi and all of them ran towards her and hugged her! The source adds, Bhumi was also so happy to see the girls after so long and she wanted to make their day super memorable by giving them an experience on the sets of her film. The kids saw Bhumi shoot, they hung out with her full day and also had food with her. Bhumi had some super cute gifts for them and they loved chilling with their favourite actor and friend! It was really kind of the production team to support Bhumis wish of having these kids on the set.

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'Durgavati': Bhumi Pednekar takes orphanage kids on a tour of the sets - Times of India

Some children ask Amma whether God dislikes the wicked and likes the virtuous. In reality, God has no partialities. God sees everyone equally. The sun shines equally on all beings, sentient and insentient. Saying God doesnt love me is like closing the doors and windows of the room and complaining that the sun refuses to give me light. The river gives equal water to both the sandalwood tree and the Indian coral tree growing on its bank. The river is not to blame that the sandal tree is fragrant while the coral tree is thorny. Similarly, God showers grace on everyone equally, but we are able to absorb that grace only according to the nature of our mind.

Most people pray to God because they want a need fulfilled. While the coffin-maker prays, O God! Make someone die today so that I can sell at least one coffin, a sick mans wife and child pray for their husband and father to get well soon. Which of these prayers should God accept? What befalls them is based on the results of their own actions. There is no use blaming God for that. God is the dispenser of the results of ones karma; He is never partial.

As our actions, so the fruit. If we perform good deeds, we will be able to enjoy happiness. However, if our actions are bad, we will have to experience sorrow. This rule is the same for everyone. However, some people perform actions without the attitude, I am the doer. They surrender all their actions to God and perform their karma. Selfishness and ego will be relatively less in them. Such people will be able to receive more of Gods grace.

The sun reflects well in clear water. But it reflects indistinctly in water full of moss. Similarly, a mind that is covered with arrogance, selfishness and other dirt will find it difficult to feel the grace of God. For that, ones heart should be pure; one should have compassion towards the suffering. Such people do not have to do anything for Gods grace to flow towards them.

Amma remembers an incident. Many people came to a particular ashram to see and obtain the blessings of the mahatma who lived there. One day, when he was meeting visitors, a small child suddenly vomited on the floor. The stench was unbearable and some people covered their noses, while others walked around the mess. Some others commented on how unhygienic the ashram was and left the place. Some others told the Guru, Guru, a child has vomited there. It smells really bad there. You should tell someone to clean the floor. Hearing all this, the mahatma got up to clean the floor himself. But when he reached there, he saw a young boy clearing away the vomit. Although the place was filled with people, only the young boy had thought of doing this. All that the others did was to complain. The young boys selfless attitude of joyfully doing something good for others attracted the mahatma. He thought, If there were more people in this world with this boys attitude, this world would become a heaven.

Everyone was equal in the eyes of the mahatma. Nevertheless, he felt a special compassion towards this boy. The boys attitude made him a fitting vessel to receive the Gurus grace. Gods grace is also like this. God showers His grace on everyone all the time. If we dig a hole on the riverbank, water will flow into it. Similarly, Gods grace will flow into a heart that has the qualities of selflessness, compassion and virtue. God is impartial. He is beyond all differences, has equal vision and is unattached. We should purify our actions and attitude, and have firm faith in Gods will. If we have this, we will certainly receive Gods grace. We will be able to maintain peace and contentment in happiness as well as sorrow, in gain as well as loss, in success as well as failure.

The writer is a world-renowned spiritual leader and humanitarian

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God is beyond all differences - The New Indian Express

The culling of birds from the bird flu-infected zones continued on day two here on Wednesday.

The operation was carried out by 13 Rapid Response Teams (RRTs) from the Department of Animal Husbandry at Metagalli, B.M. Sri Nagar, Ambedkar Gnana Loka, Kumbarakoppal and Hebbal.

According to the office of the Deputy Director of Animal Husbandry, 989 birds had been culled in the operation on Wednesday. They include 464 chicken, 326 and 173 domestic birds, 20 pets and six turkeys found within the one kilometre radius of the bird flu epicentre.

The culled birds were buried deep in trenches dug up for the purpose as per the bird flu management protocols and later the areas disinfected as a precautionary measure.

In total, about 6,000 birds had been identified from the infected area for culling. Around 4,100 birds were culled on day one of the operation on Tuesday. The department may take another day or two to survey any leftover birds and cull them accordingly. The process of sanitising the infected area has begun.

The sale of chicken has been banned in a 10 km radius in the city until further notice.

Meanwhile, workers at the Sri Ganapathi Sachchidananda Ashram on Wednesday sprayed disinfectants at the Shukha Vana or the bird park which houses parrots from the South American continent. The park has been closed for tourists since many days following the COVID-19 scare. The park had been sanitised as a precautionary measure in view of bird flu outbreak here.

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Avian influenza: More birds culled on day two - The Hindu

The self-styled godman fled India after being accused of rape, kidnapping and torture. (Photo: nithyanandapedia.org)

As coronavirus panic grips the entire world, one man seems to be having the last laugh.

With more than 1,60,000 people being infected by Covid-19 across the world, countries are stressing on social distancing (steps taken to reduce the probability of contact between infected persons and others) as the only effective method of containing the viral pandemic.

One man claims to have already pioneered the technique. He is self-styled godman Nithyananda Paramasivam.

"Some Indian people had laughed and mocked me when I isolated myself by creating the new country as Kailasaa. Now, the whole world is talking about social distancing. Lord Paramasivam has saved us. Power of God," he said.

Nithyananda has been practising self-isolation for quite some time now. Not because of coronavirus but because he is a fugitive.

The self-styled godman fled India after being accused of rape, kidnapping and torture. He also has an Interpol Blue Corner Notice issued against him.

Taking things to the next level, he bought an island in Ecuador, set up his own nation and named it Kailasaa and became the prime minister of the island nation.

Earlier, video ads doing the rounds on social media claimed that Nithyananda can cure the coronavirus outbreak that has killed thousands around the world.

Nithyananda, whose main ashram is in Bidadi in Karnataka, and two of his disciples were booked on charges of kidnapping and keeping children in wrongful confinement at an ashram in Ahmedabad. The two disciples have been arrested.

The controversial godman had fled the country last year and the Interpol has issued a Blue Corner Notice to locate him.

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Was mocked for self-isolation, now world talking of social distancing: Kailasaa 'PM' Nithyananda on Covid-19 - India Today

Press coverage about Commerce Resources (CVE:CCE) has been trending very positive recently, InfoTrie Sentiment reports. The research group ranks the sentiment of news coverage by analyzing more than 6,000 blog and news sources in real time. The firm ranks coverage of companies on a scale of -5 to 5, with scores nearest to five being the most favorable. Commerce Resources earned a coverage optimism score of 4.00 on their scale. InfoTrie also gave headlines about the company an news buzz score of 0 out of 10, indicating that recent news coverage is extremely unlikely to have an impact on the stocks share price in the next few days.

These are some of the news stories that may have effected Commerce Resources score:

Shares of Commerce Resources stock opened at C$0.13 on Friday. The stock has a market capitalization of $5.39 million and a price-to-earnings ratio of -3.61. Commerce Resources has a 1-year low of C$0.04 and a 1-year high of C$0.41. The firms 50-day moving average price is C$0.26 and its two-hundred day moving average price is C$0.26.

Commerce Resources Corp., an exploration and development company, acquires, explores, develops, and evaluates mineral resource properties in Canada. The company focuses on the development of its Ashram Rare Earth project at the Eldor property in Quebec, and its Upper Fir tantalum and niobium deposit at the Blue River project in British Columbia.

Further Reading: What is the S&P 500 Index?

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Commerce Resources (CVE:CCE) Receiving Very Favorable Media Coverage, Analysis Finds - Redmond Register