What to know while battling palmer amaranth, waterhemp, more

first_imgShare Facebook Twitter Google + LinkedIn Pinterest At the recent Seed Consultants field day, agronomist Bill McDonald talked with Ohio Ag Net’s Dale Minyo about identifying the different types of weeds in Ohio fields. Differences between waterhemp and palmer amaranth is explained as is the need to take care of the weeds quickly and completely.last_img

Energy and Building Programs Brace for Trump Budget

first_imgA long list of federal programs that promote advanced building techniques, renewable energy, and energy efficiency would see less money under President Trump’s budget proposal, but important details on how the budget would affect a number of popular projects are still unknown.In general terms, the budget proposal seeks to increase defense spending by $54 billion in the 2018 budget year, which begins on October 1. To balance those spending hikes, a number of other programs would see deep budget cuts, including the Department of Energy and the Environmental Protection Agency. Published reports peg DOE cutbacks at $3 billion, a 25% reduction in the $12 billion in discretionary spending that the department now has.Among the programs that DOE now pays for are the Office of Energy Efficiency and Renewable Energy (EERE) and its Building Technologies Office; the SunShot Initiative, which seeks to lower the cost of solar energy; and two design competitions for college and university students. The federal Weatherization Assistance Program, a 40-year-old program that helps low-income families make energy-related improvements to their homes, also would be phased out. It’s still early in the processTrump has left little doubt about the direction his administration will take on renewable energy, energy efficiency standards, and climate policy.He is siding with conservative groups like the Heritage Foundation, which has lambasted the climate and energy policies of the Obama administration and argues that government shouldn’t subsidize emerging technologies. “Does America really need a Department of Energy?” Heritage Foundation economic analyst Nicholas Loris opined in an article published last year. EPA chief Scott Pruitt doesn’t think that carbon dioxide’s impact on climate has been proved. The president has signed an executive order aimed at rolling back the Obama Clean Power Plan, and he has repeatedly promised to revive the coal industry.But budget specifics are a long way from being nailed down, and no one realistically expects the proposal to make it through Congress unscathed. Also, while the president and Congress control the budget, electric utilities across the country see a cleaner, less centralized power future for the U.S., no matter what happens to the Clean Power Plan. A survey conducted by Utility Dive among 600 utility professionals earlier this year found that most believe solar and wind will play a bigger role in the utility power mix in the next decade. Eighty-two percent of those polled said that utility-scale solar would increase moderately or significantly, while 83% said that distributed generation would increase moderately or significantly. In contrast, only 2% thought that the use of coal would increase moderately, only 2% thought it would increase significantly, and 18% thought it would stay about the same. Fifty-two percent thought that coal use would decrease significantly.Even staunch Republicans are pressing ahead with clean-energy plans, regardless of what the Trump administration is doing. In Carmel, Indiana, for example, Republican Mayor Jim Brainard is pushing hybrid and biofuel vehicles, LED streetlights, bike paths, and tree plantings to absorb carbon dioxide and create shade, The Washington Post reports.“For a long time, taking care of our environment was a nonpartisan issue,” Brainard told the newspaper. “I have yet to meet a Republican or Democrat who wants to drink dirty water or breathe dirty air.” Green Building in the Trump EraIs Weatherization Cost-Effective?Should the DOE Increase Furnace Efficiency Standards?A Web-Based Information Resource from the DOENew Rules for Ceiling FansAccounting for Renewable Electricity SavingsNew Energy-Saving Standards from Barack Obama Paving the Way for an Efficient Light Bulb in Every Socket Solar Decathlon: The Search for the Best Carbon Neutral HouseMinnesota Students Win ‘Race to Zero’ Title A huge cut at EEREEERE programs of interest to builders look especially threatened, according to a report on Greenwire. In a story published earlier this month, the website quoted sources that predicted a reduction of 30% to 70% for an agency with a hand in many energy-efficiency efforts. The office is responsible for the SunShot program, for example, and provides about 80% of the budget for the National Renewable Energy Laboratory, a major research institution for clean energy.For an idea of what EERE does in the residential building arena, visit an interactive map that the agency posts at its website. Grants range from the tiny $25,000 commitment to help train real estate appraisers on the value of green building attributes to more sizable investments in improving indoor air quality for high-performance homes. Grants go to such programs and agencies as the Building America Program, the Institute for Market Transformation, the University of Central Florida, the Southface Energy Institute, and many others.Another concern is the potential impact on building codes, according to the report. Daniel Bresette, the director of government relations at the Alliance to Save Energy, told Greenwire that one victim could be DOE’s building code program, which works closely with the International Code Council to develop energy and building codes. DOE provides crucial technical assistance that ultimately helps homeowners save money, turning relatively small government investments into big energy savings for consumers.“Without funding, DOE’s ability to do all the great work it’s done historically goes away,” Bresette told Greenwire.Research that helps develop efficiency standards for appliances and lighting also could become more difficult as money becomes harder to find.The National Rural Electric Cooperative Association said that DOE funding has helped it train more than 4,500 co-op board members in designing and financing solar programs, Greenwire said in an earlier article. Co-ops are on track to install 480 megawatts of solar this year, more than double the total of 2015. That, too, could be a thing of the past.center_img Separately, deep cuts in the budget for the EPA’s budget would wipe out federal support for the Energy Star program, which promotes energy efficiency for a variety of products, including appliances, light bulbs, doors, and windows.The general outline of the spending plan is on the table, but unanswered questions — do the programs designed to advance energy-efficient building live, die, or exist in some diminished form? — remain. As a manager in one building program said, “We don’t know yet.”However, officials said that both the Solar Decathlon and the Race to Zero competitions for college students are moving ahead as planned this year. Beyond that, the future of both programs isn’t known. RELATED ARTICLES last_img read more

Wee Jay Can’t Go To World Cup

first_imgThe family of an 11-year-old Scottish boy with Down’s Syndrome, seen on a video wildly celebrating Greece’s last-second victory over the Ivory Coast, said he won’t be able to accept an invitation from the Hellenic Football Federation (EPO) to go to Brazil to watch the Greeks play Costa Rica in the quarter-finals.Jay Beatty, known as Wee Jay, is a fan of Greek striker Giorgos Samaras, who scored the winning kick on a penalty and who carried the boy across the field in Scotland this year after the Celtic team the Greek star plays for won the championship there.A Facebook campaign went viral fast to get the boy invited to the game and the Greek soccer organization responded almost immediately with an invitation for him to see the game on June 29.“We were unable to take up the offer and are so gutted, but truly grateful that people would do this for Jay. We cannot believe that this has happened and are so humbled and would like to wish Greece all the best in the World Cup,” his father Martin wrote on Facebook.“Thanks Sammy I hope you win the World Cup. I am sorry I won’t be there, but I still love you very much and I hope you win. Come on Sammy!”, Jay said to Samaras in a video posted by his father on Facebook,In an interview Samaras gave on June 26, he revealed the invitation. “It was thrilling, moving. This kid gives me so much strength it is unbelievable,” confessed Samaras, who picked up Jay during Celtic’s title celebrations in May in one of that day’s most striking images.Before it was found the boy couldn’t come, Samaras said, “In talks I’ve had this morning with (EPO President Giorgos) Sarris we said if we can arrange for his tickets so that he can come to see the match with Costa Rica. We will speak with the child’s father and we’ll see, whether we get his OK.”He said that, “We’ve got a Greece shirt that I will sign now and all the lads will sign later, during lunch. It’s just a small present we can offer to him. We have set a date to meet in Belfast.”Beatty, a fervent Celtics fan, has managed to meet all the players of his favorite team and its management. He is also an avid fan of Samaras, a former Celtic star who picked up the young boy out of the crowd when Celtic lifted the Scottish Premiership trophy in May.Jay’s father recently posted the video showing his son dressed in blue and celebrating Greece’s victory over Ivory Coast. “It’s not often you see Jay in Blue but he had to get a Greece T-shirt,” he comments below the photograph posted on Facebook.The O JAY PAEI Mundial Facebook page garnered more than 50,600 “likes” when it was first posted as the scene of the boy exclaiming joy moved viewers.TweetPinShare0 Shareslast_img read more

Shahid Kapoors wedding preparations on Supriya

first_imgActress Supriya Pathak says the wedding preparations of her son Shahid Kapoor are going on in full swing.“Lots of things happening… it is like a normal wedding house I suppose,” Pathak told reporters.The 34-year-old actor is all set to tie the knot with his Delhi-based fiancee Mira Rajput, 21, in Gurgaon on July 7. The Haider actor will host a reception in Mumbai for his friends and colleagues from Bollywood.Pathak, Shahid’s stepmother, was speaking at the trailer launch of her upcoming film All is Well. Also Read – A fresh blend of fameThe film, directed by Oh My God fame director Umesh Shukla, stars Abhishek Bachchan, Rishi Kapoor, Asin and Pathak in pivotal roles.“I had a great time doing this film. I hope everyone will enjoy watching it as well,” Pathak said.All Is well is about a road trip undertaken by Rishi Kapoor and Abhishek Bachchan, who are later joined by their mother and then Asin, who plays an important role in the film. Mira Rajput studied in lady Shri Ram College.last_img read more

SBI Card launches Simply Click

first_imgOur Correspondent SBI Card, one of India’s leading credit card issuers, announced a strategic partnership with seven of India’s biggest e-commerce players for its newest offering, the Simply Click SBI Card. The collaboration is with the leading players in the e-commerce industry – Amazon India, BookMyShow, Cleartrip, FabFurnish, Food Panda, LensKart and Ola Cabs – all front runners in their respective categories.  Also Read – Punjab & Sind Bank cuts MCLR by up to 20 basis pointsThis latest offering from SBI Card is a tailor made credit card for the generation that is always online, and comes with many features to ensure the best and most productive online shopping experience for the consumers. The Simply Click SBI Card is the country’s 1st ever credit card that focuses on online shopping across diverse categories. “We wanted to launch a card with special benefit meant for online purchases. In this card, whatever the spending be, we will offer a five times more reward than in case of normal cards,” Vijay Jasuja, CEO SBI Card said after the launch of the new product.“And for the seven online partners, spending on those platform will attract ten times more reward. So the consumers will get 15 times more reward (points),” he added.With this new product, SBI Card is poised to leverage the popularity of online shopping and e-commerce in India.last_img read more

Tabbedout A Mobile Payment App for Restaurants and Bars

first_imgDecember 15, 2011 Tech-savvy: Chris Dilla of Bocktown Beer and Grill has embraced Tabbedout.Photos© David JohnsonBocktown Beer and Grill’s patrons have grown accustomed to change. The Pittsburgh full-service restaurant’s signature list of American craft beers rotates on a daily basis, exposing customers to a procession of new brews and guaranteeing Bocktown–and its clientele–doesn’t fall into a rut.”We call ourselves ‘The place where beer meets grill,'” says Chris Dilla, Bocktown’s founder and owner. “Other places don’t seem to have an interest in serving good beer, but we pull it all together. Our beers change every day, so our customers have to be the kind of people who want to explore something new.”Bocktown’s progressive attitude extends beyond its menu. Dilla is the epitome of the tech-savvy entrepreneur, rolling out a mobile-optimized version of the Bocktown website, actively leveraging Facebook, Twitter and foursquare, and even adding a scannable QR bar code to Bocktown’s take-home beer growler label. Now Bocktown is introducing Tabbedout, a mobile payment app that lets consumers open a bar tab, view their bill in real time and pay at their discretion, all via iPhone or Android smartphone.”We can get very busy–we get a lot of corporate guys in here for lunch, and they’re always in a hurry,” Dilla says. “Now when it’s time to go, you can pay up and get out of here whenever you like. You can even pay your tab before you get your food.”Here’s how it works: After downloading the free Tabbedout app, consumers enter their credit card information (stored on the device and secured with 256-bit AES encryption), select “nearby locations” to identify Tabbedout merchant partners and tap the “open a tab” option on arrival. Tabbedout then generates a unique five-digit code that patrons show to their server. The server will see a button matching the code within the bar’s point-of-sale system, connecting the bill directly to the guest’s smartphone. Patrons order as usual, and close out the check via Tabbedout when they’re ready. There’s no more handing off a credit card to waitstaff, or forgetting the card behind the bar.350 merchants in 100 cities are using tabbedout.Austin, Texas-based Tabbedout is the brainchild of co-founder and CEO Rick Orr, who previously co-founded WholeSecurity, a security software firm acquired by Symantec in 2005. The idea behind the app first came to Orr eight years ago while he waited 55 excruciating minutes to close out a restaurant bill. But it wasn’t until smartphones entered the mainstream that he translated his irritation into innovation. “Like today’s physical wallet, your phone is always on your person,” Orr says.But Tabbedout doesn’t only benefit harried patrons–according to Orr, the solution reduces friction for merchants and their staffers as well, because it eliminates time-consuming payment-processing chores and frees them up to focus on other tasks. “It takes four times longer to close a tab than it does to pour a drink,” Orr says. “We help you serve more drinks and clear more tables during peak hours.”Orr designed Tabbedout to integrate seamlessly and painlessly with existing POS technologies, requiring no additional hardware or new financial accounts. (The installation process requires about 20 minutes in all.) Because the app submits all payment information to the POS upfront, merchants are protected from dine-and-dash schemes, dead phone batteries and other potential wrinkles, and they can manually close a tab within the POS at any time. Tabbedout guarantees servers and bartenders a default tip amount (determined by the venue management) and offers patrons social media sharing, e-mail receipts and even a click-to-call option for local taxi companies in the event happy hour extends into the wee small hours.As of fall 2011, about 350 merchants in 100 U.S. cities had added Tabbedout to their menu. Costs vary from business to business, based on venue size, location and contract terms. Orr plans to expand the service in the months ahead, adding geo-targeted offers, loyalty programs and other mobile marketing efforts. “Tabbedout allows merchants to interact with their consumers in a more meaningful way,” he says. “Mobile payments are just a start.”Dilla raises her glass to Tabbedout. “The servers love it,” she says. “One of my servers said to me, ‘It’s great–now I don’t have to deal with paperwork, and I can spend more time engaging with the customers.’ There’s a lot of attractiveness to this method. And everybody’s already on their phones anyway. It’s just the way the world’s going.” Growing a business sometimes requires thinking outside the box. This story appears in the December 2011 issue of . Subscribe » Free Webinar | Sept. 9: The Entrepreneur’s Playbook for Going Global 4 min read Register Now »last_img read more

Sherin Thomas explains how to build a pipeline in PyTorch for deep

first_imgA typical deep learning workflow starts with ideation and research around a problem statement, where the architectural design and model decisions come into play. Following this, the theoretical model is experimented using prototypes. This includes trying out different models or techniques, such as skip connection, or making decisions on what not to try out. PyTorch was started as a research framework by a Facebook intern, and now it has grown to be used as a research or prototype framework and to write an efficient model with serving modules. The PyTorch deep learning workflow is fairly equivalent to the workflow implemented by almost everyone in the industry, even for highly sophisticated implementations, with slight variations. In this article, we explain the core of ideation and planning, design and experimentation of the PyTorch deep learning workflow. This article is an excerpt from the book PyTorch Deep Learning Hands-On by Sherin Thomas and Sudhanshi Passi. This book attempts to provide an entirely practical introduction to PyTorch. This PyTorch publication has numerous examples and dynamic AI applications and demonstrates the simplicity and efficiency of the PyTorch approach to machine intelligence and deep learning. Ideation and planning Usually, in an organization, the product team comes up with a problem statement for the engineering team, to know whether they can solve it or not. This is the start of the ideation phase. However, in academia, this could be the decision phase where candidates have to find a problem for their thesis. In the ideation phase, engineers brainstorm and find the theoretical implementations that could potentially solve the problem. In addition to converting the problem statement to a theoretical solution, the ideation phase is where we decide what the data types are and what dataset we should use to build the proof of concept (POC) of the minimum viable product (MVP). Also, this is the stage where the team decides which framework to go with by analyzing the behavior of the problem statement, available implementations, available pretrained models, and so on. This stage is very common in the industry, and I have come across numerous examples where a well-planned ideation phase helped the team to roll out a reliable product on time, while a non-planned ideation phase destroyed the whole product creation. Design and experimentation The crucial part of design and experimentation lies in the dataset and the preprocessing of the dataset. For any data science project, the major timeshare is spent on data cleaning and preprocessing. Deep learning is no exception from this. Data preprocessing is one of the vital parts of building a deep learning pipeline. Usually, for a neural network to process, real-world datasets are not cleaned or formatted. Conversion to floats or integers, normalization and so on, is required before further processing. Building a data processing pipeline is also a non-trivial task, which consists of writing a lot of boilerplate code. For making it much easier, dataset builders and DataLoader pipeline packages are built into the core of PyTorch. The dataset and DataLoader classes Different types of deep learning problems require different types of datasets, and each of them might require different types of preprocessing depending on the neural network architecture we use. This is one of the core problems in deep learning pipeline building. Although the community has made the datasets for different tasks available for free, writing a preprocessing script is almost always painful. PyTorch solves this problem by giving abstract classes to write custom datasets and data loaders. The example given here is a simple dataset class to load the fizzbuzz dataset, but extending this to handle any type of dataset is fairly straightforward. PyTorch’s official documentation uses a similar approach to preprocess an image dataset before passing that to a complex convolutional neural network (CNN) architecture. A dataset class in PyTorch is a high-level abstraction that handles almost everything required by the data loaders. The custom dataset class defined by the user needs to override the __len__ and __getitem__ functions of the parent class, where __len__ is being used by the data loaders to determine the length of the dataset and __getitem__ is being used by the data loaders to get the item. The __getitem__ function expects the user to pass the index as an argument and get the item that resides on that index: from dataclasses import dataclassfrom torch.utils.data import Dataset, [email protected](eq=False)class FizBuzDataset(Dataset):    input_size: int    start: int = 0    end: int = 1000    def encoder(self,num):        ret = [int(i) for i in ‘{0:b}’.format(num)]        return[0] * (self.input_size – len(ret)) + ret    def __getitem__(self, idx):        x = self.encoder(idx)        if idx % 15 == 0:            y = [1,0,0,0]        elif idx % 5 ==0:            y = [0,1,0,0]        elif idx % 3 == 0:            y = [0,0,1,0]        else:            y = [0,0,0,1]        return x,y           def __len__(self):        return self.end – self.start The implementation of a custom dataset uses brand new dataclasses from Python 3.7. dataclasses help to eliminate boilerplate code for Python magic functions, such as __init__, using dynamic code generation. This needs the code to be type-hinted and that’s what the first three lines inside the class are for. You can read more about dataclasses in the official documentation of Python (https://docs.python.org/3/library/dataclasses.html). The __len__ function returns the difference between the end and start values passed to the class. In the fizzbuzz dataset, the data is generated by the program. The implementation of data generation is inside the __getitem__ function, where the class instance generates the data based on the index passed by DataLoader. PyTorch made the class abstraction as generic as possible such that the user can define what the data loader should return for each id. In this particular case, the class instance returns input and output for each index, where, input, x is the binary-encoder version of the index itself and output is the one-hot encoded output with four states. The four states represent whether the next number is a multiple of three (fizz), or a multiple of five (buzz), or a multiple of both three and five (fizzbuzz), or not a multiple of either three or five. Note: For Python newbies, the way the dataset works can be understood by looking first for the loop that loops over the integers, starting from zero to the length of the dataset (the length is returned by the __len__ function when len(object) is called). The following snippet shows the simple loop: dataset = FizBuzDataset()for i in range(len(dataset)):    x, y = dataset[i]dataloader = DataLoader(dataset, batch_size=10, shuffle=True,                     num_workers=4)for batch in dataloader:    print(batch) The DataLoader class accepts a dataset class that is inherited from torch.utils.data.Dataset. DataLoader accepts dataset and does non-trivial operations such as mini-batching, multithreading, shuffling, and so on, to fetch the data from the dataset. It accepts a dataset instance from the user and uses the sampler strategy to sample data as mini-batches. The num_worker argument decides how many parallel threads should be operating to fetch the data. This helps to avoid a CPU bottleneck so that the CPU can catch up with the GPU’s parallel operations. Data loaders allow users to specify whether to use pinned CUDA memory or not, which copies the data tensors to CUDA’s pinned memory before returning it to the user. Using pinned memory is the key to fast data transfers between devices, since the data is loaded into the pinned memory by the data loader itself, which is done by multiple cores of the CPU anyway. Most often, especially while prototyping, custom datasets might not be available for developers and in such cases, they have to rely on existing open datasets. The good thing about working on open datasets is that most of them are free from licensing burdens, and thousands of people have already tried preprocessing them, so the community will help out. PyTorch came up with utility packages for all three types of datasets with pretrained models, preprocessed datasets, and utility functions to work with these datasets. This article is about how to build a basic pipeline for deep learning development. The system we defined here is a very common/general approach that is followed by different sorts of companies, with slight changes. The benefit of starting with a generic workflow like this is that you can build a really complex workflow as your team/project grows on top of it. Build deep learning workflows and take deep learning models from prototyping to production with PyTorch Deep Learning Hands-On written by Sherin Thomas and Sudhanshu Passi. Read Next F8 PyTorch announcements: PyTorch 1.1 releases with new AI tools, open sourcing BoTorch and Ax, and more Facebook AI open-sources PyTorch-BigGraph for faster embeddings in large graphs Top 10 deep learning frameworkslast_img read more