dd

How to Add Native Keyword Aliases to Babel

Those of you who follow this blog know that not every blog post is an endorsement of a technique but simply a tutorial how to accomplish something. Sometimes the technique described is probably not something you should do. This is one of those blog posts. The Babel parser is an essential tool in the web […]

The post How to Add Native Keyword Aliases to Babel appeared first on David Walsh Blog.




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Why You Need Professional Wedding Photographer?

There’s a school of thought which says anyone with a camera could take pictures. Why should pay high to hire someone to do it for you, if you can simply whip out your phone or fancy digital camera and snap away? With regards to wedding photography there’s even of school of thought who says you simply get all your visitors or guests to take pictures and upload them to a website for everyone to see. That is great, if you have some visitors who are skilled with a camera and are ready to spend your memorable event looking through the lens, rather than enjoying the occasion. In general, you’re much better off putting yourselves in the hands of an expert wedding photographer, in order that you end up with an amazing set of pictures which do justice your very special day. Here are the reasons why you need to get ... Read more

The post Why You Need Professional Wedding Photographer? appeared first on Digital Photography Tutorials.




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TrailBuddy: Using AI to Create a Predictive Trail Conditions App

Viget is full of outdoor enthusiasts and, of course, technologists. For this year's Pointless Weekend, we brought these passions together to build TrailBuddy. This app aims to solve that eternal question: Is my favorite trail dry so I can go hike/run/ride?

While getting muddy might rekindle fond childhood memories for some, exposing your gear to the elements isn’t great – it’s bad for your equipment and can cause long-term, and potentially expensive, damage to the trail.

There are some trail apps out there but we wanted one that would focus on current conditions. Currently, our favorites trail apps, like mtbproject.com, trailrunproject.com, and hikingproject.com -- all owned by REI, rely on user-reported conditions. While this can be effective, the reports are frequently unreliable, as condition reports can become outdated in just a few days.

Our goal was to solve this problem by building an app that brought together location, soil type, and weather history data to create on-demand condition predictions for any trail in the US.

We built an initial version of TrailBuddy by tapping into several readily-available APIs, then running the combined data through a machine learning algorithm. (Oh, and also by bringing together a bunch of smart and motivated people and combining them with pizza and some of the magic that is our Pointless Weekends. We'll share the other Pointless Project, Scurry, with you soon.)

The quest for data.

We knew from the start this app would require data from a number of sources. As previously mentioned, we used REI’s APIs (i.e. https://www.hikingproject.com/data) as the source for basic trail information. We used the trails’ latitude and longitude coordinates as well as its elevation to query weather and soil type. We also found data points such as a trail’s total distance to be relevant to our app users and decided to include that on the front-end, too. Since we wanted to go beyond relying solely on user-reported metrics, which is how REI’s current MTB project works, we came up with a list of factors that could affect the trail for that day.

First on that list was weather.

We not only considered the impacts of the current forecast, but we also looked at the previous day’s forecast. For example, it’s safe to assume that if it’s currently raining or had been raining over the last several days, it would likely lead to muddy and unfavorable conditions for that trail. We utilized the DarkSky API (https://darksky.net/dev) to get the weather forecasts for that day, as well as the records for previous days. This included expected information, like temperature and precipitation chance. It also included some interesting data points that we realized may be factors, like precipitation intensity, cloud cover, and UV index. 

But weather alone can’t predict how muddy or dry a trail will be. To determine that for sure, we also wanted to use soil data to help predict how well a trail’s unique soil composition recovers after precipitation. Similar amounts of rain on trails of very different soil types could lead to vastly different trail conditions. A more clay-based soil would hold water much longer, and therefore be much more unfavorable, than loamy soil. Finding a reliable source for soil type and soil drainage proved incredibly difficult. After many hours, we finally found a source through the USDA that we could use. As a side note—the USDA keeps track of lots of data points on soil information that’s actually pretty interesting! We can’t say we’re soil experts but, we felt like we got pretty close.

We used Whimsical to build our initial wireframes.

Putting our design hats on.

From the very first pitch for this app, TrailBuddy’s main differentiator to peer trail resources is its ability to surface real-time information, reliably, and simply. For as complicated as the technology needed to collect and interpret information, the front-end app design needed to be clean and unencumbered.

We thought about how users would naturally look for information when setting out to find a trail and what factors they’d think about when doing so. We posed questions like:

  • How easy or difficult of a trail are they looking for?
  • How long is this trail?
  • What does the trail look like?
  • How far away is the trail in relation to my location?
  • For what activity am I needing a trail for?
  • Is this a trail I’d want to come back to in the future?

By putting ourselves in our users’ shoes we quickly identified key features TrailBuddy needed to have to be relevant and useful. First, we needed filtering, so users could filter between difficulty and distance to narrow down their results to fit the activity level. Next, we needed a way to look up trails by activity type—mountain biking, hiking, and running are all types of activities REI’s MTB API tracks already so those made sense as a starting point. And lastly, we needed a way for the app to find trails based on your location; or at the very least the ability to find a trail within a certain distance of your current location.

We used Figma to design, prototype, and gather feedback on TrailBuddy.

Using machine learning to predict trail conditions.

As stated earlier, none of us are actual soil or data scientists. So, in order to achieve the real-time conditions reporting TrailBuddy promised, we’d decided to leverage machine learning to make predictions for us. Digging into the utility of machine learning was a first for all of us on this team. Luckily, there was an excellent tutorial that laid out the basics of building an ML model in Python. Provided a CSV file with inputs in the left columns, and the desired output on the right, the script we generated was able to test out multiple different model strategies, and output the effectiveness of each in predicting results, shown below.

We assembled all of the historical weather and soil data we could find for a given latitude/longitude coordinate, compiled a 1000 * 100 sized CSV, ran it through the Python evaluator, and found that the CART and SVM models consistently outranked the others in terms of predicting trail status. In other words, we found a working model for which to run our data through and get (hopefully) reliable predictions from. The next step was to figure out which data fields were actually critical in predicting the trail status. The more we could refine our data set, the faster and smarter our predictive model could become.

We pulled in some Ruby code to take the original (and quite massive) CSV, and output smaller versions to test with. Now again, we’re no data scientists here but, we were able to cull out a good majority of the data and still get a model that performed at 95% accuracy.

With our trained model in hand, we could serialize that to into a model.pkl file (pkl stands for “pickle”, as in we’ve “pickled” the model), move that file into our Rails app along with it a python script to deserialize it, pass in a dynamic set of data, and generate real-time predictions. At the end of the day, our model has a propensity to predict fantastic trail conditions (about 99% of the time in fact…). Just one of those optimistic machine learning models we guess.

Where we go from here.

It was clear that after two days, our team still wanted to do more. As a first refinement, we’d love to work more with our data set and ML model. Something that was quite surprising during the weekend was that we found we could remove all but two days worth of weather data, and all of the soil data we worked so hard to dig up, and still hit 95% accuracy. Which … doesn’t make a ton of sense. Perhaps the data we chose to predict trail conditions just isn’t a great empirical predictor of trail status. While these are questions too big to solve in just a single weekend, we'd love to spend more time digging into this in a future iteration.



  • News & Culture



dd

METAL INJECTION LIVECAST #535 - Liddle' Ditch

We kick things off with Rob talking about his favorite albums of the year. We then discuss the shocking news...

The post METAL INJECTION LIVECAST #535 - Liddle' Ditch appeared first on Metal Injection.



  • Metal Injection Livecast

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12 Best GoDaddy Alternatives for Domain & Web Hosting (2020)

Are you looking for the best GoDaddy alternative for domain registration and web hosting? Without a doubt, Godaddy is one of the most popular names when it comes to registering domain names and hosting your business online. Over the last 22 years, GoDaddy has managed to establish a stronghold in the market. In this article, […]

The post 12 Best GoDaddy Alternatives for Domain & Web Hosting (2020) appeared first on IsItWP - Free WordPress Theme Detector.




dd

TrailBuddy: Using AI to Create a Predictive Trail Conditions App

Viget is full of outdoor enthusiasts and, of course, technologists. For this year's Pointless Weekend, we brought these passions together to build TrailBuddy. This app aims to solve that eternal question: Is my favorite trail dry so I can go hike/run/ride?

While getting muddy might rekindle fond childhood memories for some, exposing your gear to the elements isn’t great – it’s bad for your equipment and can cause long-term, and potentially expensive, damage to the trail.

There are some trail apps out there but we wanted one that would focus on current conditions. Currently, our favorites trail apps, like mtbproject.com, trailrunproject.com, and hikingproject.com -- all owned by REI, rely on user-reported conditions. While this can be effective, the reports are frequently unreliable, as condition reports can become outdated in just a few days.

Our goal was to solve this problem by building an app that brought together location, soil type, and weather history data to create on-demand condition predictions for any trail in the US.

We built an initial version of TrailBuddy by tapping into several readily-available APIs, then running the combined data through a machine learning algorithm. (Oh, and also by bringing together a bunch of smart and motivated people and combining them with pizza and some of the magic that is our Pointless Weekends. We'll share the other Pointless Project, Scurry, with you soon.)

The quest for data.

We knew from the start this app would require data from a number of sources. As previously mentioned, we used REI’s APIs (i.e. https://www.hikingproject.com/data) as the source for basic trail information. We used the trails’ latitude and longitude coordinates as well as its elevation to query weather and soil type. We also found data points such as a trail’s total distance to be relevant to our app users and decided to include that on the front-end, too. Since we wanted to go beyond relying solely on user-reported metrics, which is how REI’s current MTB project works, we came up with a list of factors that could affect the trail for that day.

First on that list was weather.

We not only considered the impacts of the current forecast, but we also looked at the previous day’s forecast. For example, it’s safe to assume that if it’s currently raining or had been raining over the last several days, it would likely lead to muddy and unfavorable conditions for that trail. We utilized the DarkSky API (https://darksky.net/dev) to get the weather forecasts for that day, as well as the records for previous days. This included expected information, like temperature and precipitation chance. It also included some interesting data points that we realized may be factors, like precipitation intensity, cloud cover, and UV index. 

But weather alone can’t predict how muddy or dry a trail will be. To determine that for sure, we also wanted to use soil data to help predict how well a trail’s unique soil composition recovers after precipitation. Similar amounts of rain on trails of very different soil types could lead to vastly different trail conditions. A more clay-based soil would hold water much longer, and therefore be much more unfavorable, than loamy soil. Finding a reliable source for soil type and soil drainage proved incredibly difficult. After many hours, we finally found a source through the USDA that we could use. As a side note—the USDA keeps track of lots of data points on soil information that’s actually pretty interesting! We can’t say we’re soil experts but, we felt like we got pretty close.

We used Whimsical to build our initial wireframes.

Putting our design hats on.

From the very first pitch for this app, TrailBuddy’s main differentiator to peer trail resources is its ability to surface real-time information, reliably, and simply. For as complicated as the technology needed to collect and interpret information, the front-end app design needed to be clean and unencumbered.

We thought about how users would naturally look for information when setting out to find a trail and what factors they’d think about when doing so. We posed questions like:

  • How easy or difficult of a trail are they looking for?
  • How long is this trail?
  • What does the trail look like?
  • How far away is the trail in relation to my location?
  • For what activity am I needing a trail for?
  • Is this a trail I’d want to come back to in the future?

By putting ourselves in our users’ shoes we quickly identified key features TrailBuddy needed to have to be relevant and useful. First, we needed filtering, so users could filter between difficulty and distance to narrow down their results to fit the activity level. Next, we needed a way to look up trails by activity type—mountain biking, hiking, and running are all types of activities REI’s MTB API tracks already so those made sense as a starting point. And lastly, we needed a way for the app to find trails based on your location; or at the very least the ability to find a trail within a certain distance of your current location.

We used Figma to design, prototype, and gather feedback on TrailBuddy.

Using machine learning to predict trail conditions.

As stated earlier, none of us are actual soil or data scientists. So, in order to achieve the real-time conditions reporting TrailBuddy promised, we’d decided to leverage machine learning to make predictions for us. Digging into the utility of machine learning was a first for all of us on this team. Luckily, there was an excellent tutorial that laid out the basics of building an ML model in Python. Provided a CSV file with inputs in the left columns, and the desired output on the right, the script we generated was able to test out multiple different model strategies, and output the effectiveness of each in predicting results, shown below.

We assembled all of the historical weather and soil data we could find for a given latitude/longitude coordinate, compiled a 1000 * 100 sized CSV, ran it through the Python evaluator, and found that the CART and SVM models consistently outranked the others in terms of predicting trail status. In other words, we found a working model for which to run our data through and get (hopefully) reliable predictions from. The next step was to figure out which data fields were actually critical in predicting the trail status. The more we could refine our data set, the faster and smarter our predictive model could become.

We pulled in some Ruby code to take the original (and quite massive) CSV, and output smaller versions to test with. Now again, we’re no data scientists here but, we were able to cull out a good majority of the data and still get a model that performed at 95% accuracy.

With our trained model in hand, we could serialize that to into a model.pkl file (pkl stands for “pickle”, as in we’ve “pickled” the model), move that file into our Rails app along with it a python script to deserialize it, pass in a dynamic set of data, and generate real-time predictions. At the end of the day, our model has a propensity to predict fantastic trail conditions (about 99% of the time in fact…). Just one of those optimistic machine learning models we guess.

Where we go from here.

It was clear that after two days, our team still wanted to do more. As a first refinement, we’d love to work more with our data set and ML model. Something that was quite surprising during the weekend was that we found we could remove all but two days worth of weather data, and all of the soil data we worked so hard to dig up, and still hit 95% accuracy. Which … doesn’t make a ton of sense. Perhaps the data we chose to predict trail conditions just isn’t a great empirical predictor of trail status. While these are questions too big to solve in just a single weekend, we'd love to spend more time digging into this in a future iteration.



  • News & Culture

dd

An embedding of the Morse boundary in the Martin boundary. (arXiv:2004.14624v2 [math.GR] UPDATED)

We construct a one-to-one continuous map from the Morse boundary of a hierarchically hyperbolic group to its Martin boundary. This construction is based on deviation inequalities generalizing Ancona's work on hyperbolic groups. This provides a possibly new metrizable topology on the Morse boundary of such groups. We also prove that the Morse boundary has measure 0 with respect to the harmonic measure unless the group is hyperbolic.




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On the partitions into distinct parts and odd parts. (arXiv:2005.03619v1 [math.CO])

In this paper, we show that the difference between the number of parts in the odd partitions of $n$ and the number of parts in the distinct partitions of $n$ satisfies Euler's recurrence relation for the partition function $p(n)$ when $n$ is odd. A decomposition of this difference in terms of the total number of parts in all the partitions of $n$ is also derived. In this context, we conjecture that for $k>0$, the series

$$

(q^2;q^2)_infty sum_{n=k}^infty frac{q^{{kchoose 2}+(k+1)n}}{(q;q)_n}

egin{bmatrix}

n-1\k-1

end{bmatrix}

$$ has non-negative coefficients.




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Measuring Social Bias in Knowledge Graph Embeddings. (arXiv:1912.02761v2 [cs.CL] UPDATED)

It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has been no similar work done in the field of graph embeddings. We present the first study on social bias in knowledge graph embeddings, and propose a new metric suitable for measuring such bias. We conduct experiments on Wikidata and Freebase, and show that, as with word embeddings, harmful social biases related to professions are encoded in the embeddings with respect to gender, religion, ethnicity and nationality. For example, graph embeddings encode the information that men are more likely to be bankers, and women more likely to be homekeepers. As graph embeddings become increasingly utilized, we suggest that it is important the existence of such biases are understood and steps taken to mitigate their impact.




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Anonymized GCN: A Novel Robust Graph Embedding Method via Hiding Node Position in Noise. (arXiv:2005.03482v1 [cs.LG])

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




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Brain-like approaches to unsupervised learning of hidden representations -- a comparative study. (arXiv:2005.03476v1 [cs.NE])

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The saliency and separability of the hidden representations when trained on MNIST dataset is studied using an external classifier, and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.




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Football High: Small Hits Add Up

Research is showing that the accumulation of sub-concussive hits in sports like football can be just as damaging as one or two major concussions.




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CANCELED CONCERTS: Phish and Dave Matthews at the Gorge, the Festival at Sandpoint, Browne's Addition summer concerts

This is normally the time of year when we're up to our eyeballs in concert announcements, but in these topsy-turvy times, we're instead having to write about all the concerts being canceled due to COVID-19. It's a real bummer.…




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When facing impossible odds, look to the teamwork of space explorers for inspiration



  • Arts & Culture

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Best of Broadway announces its 2020-21 season for Spokane, featuring Cats, fiddlers and, finally, Hamilton

We've known that Hamilton was going to be part of the 2020-21 STCU Best of Broadway season for a while, but now we finally know the exact dates, as well as the rest of the featured shows for the season. Granted, the whole world has changed since WestCoast Entertainment announced Hamilton was coming to town back when they announced their 2019-20 season — a season that's been roiled, along with the rest of our lives, by the coronavirus pandemic.…



  • Arts & Culture

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Acid addition salts of risperidone and pharmaceutical compositions thereof

The present invention relates to a novel acid addition salt of risperidone, wherein acid counterion is selected from the group consisting of pamoic acid, caproic acid, cypionic acid, decanoic acid, camphor sulfonic acid, enanthic acid, palmitic acid, fusidic acid, gluceptic acid, gluconic acid, lactobionic acid, lauric acid, levulinic acid and valeric acid, a process for the preparation and pharmaceutical composition comprising the same. Further, the invention relates to the use of said pharmaceutical composition comprising the acid addition salt of risperidone in the treatment of patient suffering from psychotic disorders.




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Formulations comprising isosorbide-modified unsaturated polyester resins and low profile additives which produce low shrinkage matrices

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.




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Additive combination for sealants applications

The present invention pertains to an additive combination comprising at least two sterically hindered amines, at least one further stabilizer, a dispersing agent and a plasticizer. The present invention also pertains to a composition comprising an organic material susceptible to degradation by light, oxygen and/or heat, and the additive combination and to the use and the process for stabilizing organic material against degradation by light, oxygen and/or heat by the additive combination.




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Preparation of addition polymerization catalysts

Metal complexes useful as components of addition polymerization catalysts are prepared by oxidizing Group 4 or Lanthanide metal containing complexes using an organic halide oxidizing agent in a unique one electron oxidation.




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Matting and/or frosting additive for polymers or polymer blends

The invention is directed to a matting and/or frosting additive concentrate for polymers or polymer blends, said additive comprising to 75% by weight of hollow glass microspheres and 20 to 95% by weight of a liquid or waxy carrier material and optionally up to 75% by weight of additives.




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Additive process for production of dimensionally stable three dimensional objects

Generally, compositions and methods of producing dimensionally stable three dimensional objects using an additive build up process. Specifically, materials combinable in an additive build up process using a materials printer for the production of stable three dimensional molds useful in the production of molded or formed parts.




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Method and apparatus for output of high-bandwidth debug data/traces in ICS and SoCs using embedded high speed debug

Methods and apparatus for output of high-bandwidth debug data/traces in electronic devices using embedded high-speed debug port(s). Debug data is received from multiple blocks and buffered in a buffer. The buffer's output is operatively coupled to one or more high-speed serial I/O interfaces via muxing logic during debug test operations. The buffered data is encoded as serialized data and sent over the one or more high-speed serial I/O interfaces to a logic device that receives serialized data and de-serializes it to generate parallel debug data that is provided to a debugger. The buffer may be configured as a bandwidth-adapting buffer that facilitates transfer of debug data that is received at a variable combined data rate outbound via the one or more high-speed serial I/O interfaces at a data rate corresponding to the bandwidth of the serial I/O interfaces.




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Phase-to-amplitude converter for direct digital synthesizer (DDS) with reduced AND and reconstructed ADD logic arrays

A sine wave generator for a Direct Digital Synthesizer (DDS) converts a digital phase input into a digital sine wave output. Sine values and slopes are stored in read-only memory (ROM) for coarse upper phase bits in a first quadrant. A quadrant folder and phase splitter reflects and inverts values from the first quadrant to generate amplitudes for all four quadrants. Each sine value and slope is stored for a range of lower phase bits. A Delta bit separates upper and lower phase bits. Delta conditionally inverts the lower phase bits, the sine value, and the final polarity. A reduced AND logic array multiplies the slope by the conditionally inverted lower phase bits. A reconstructed ADD logic array then adds the conditionally inverted sine value. The conditionally inverted polarity is added to generate the final sine value. Sine generation logic is streamlined with conditional inversion based on the Delta bit.




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Providing indirect data addressing in an input/output processing system where the indirect data address list is non-contiguous

A method includes configuring a processing circuit to perform: receiving a control word for an I/O operation, forwarding a transport command control block (TCCB) from the channel subsystem to a control unit, gathering data associated with the I/O operation, and transmitting the gathered data to the control unit in the I/O processing system. Gathering the data includes accessing entries of a list of storage addresses that collectively specifying the data. Based on an entry of the list comprising a not-set first flag and a corresponding first storage address, gathering data from a corresponding storage location, and based on an entry of the list comprising a set first flag and a corresponding second storage address, obtaining a next entry of the list from a second storage location.




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1,4-fullerene addends in photovoltaic cells

1,4 fullerene deriatives useful for solar cells are provided, where their structures allow for straightforward functionalizations to tune their properties in terms of solubility and LUMO energy levels.




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Additives for inhibiting gas hydrate formation

The invention relates to the use of polymers, containing between 1 and 100 mol % of structural units of the formula (1), wherein R1 means hydrogen or C1-C6 alkyl, A means C2-C4 alkylene groups, and B means C2-C4 alkylene groups, with the stipulation that A is different from B, and x and y mean an integer from 1 to 100 independent of each other, in amounts of 0.01 to 2 wt % relative to the water phase, as gas hydrate inhibitors.




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Additives for inhibition of gas hydrate formation

The invention provides for the use of copolymers comprising 1 to 99 mol % of structural units of the formula (1) in which R1 is hydrogen or C1-C6-alkyl, A is C2-C4-alkylene groups and B is C2-C4-alkylene groups, with the proviso that A is different than B, and x, y are each independently an integer of 1-100, and 1 to 99 mol % of structural units of the formula (3) in which R6 is hydrogen or C1-C6-alkyl, D is C2-C4-alkylene groups and z is an integer of 1-50, in amounts of 0.01 to 2% by weight, based on the water phase, as gas hydrate inhibitors.




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Polymers as additives for the separation of oil and water phases in emulsions and dispersions

Oil-water dispersions and emulsions derived from petroleum industry operations are demulsified and clarified using anionic polymers. Formation of such oil-water dispersion and emulsions is inhibited and mitigated using the anionic polymers. The anionic polymers comprise: A) 2-80% by weight of at least one C3-C8 α,β-ethylenically unsaturated carboxylic acid monomer; B) 15-80% by weight of at least one nonionic, copolymerizable α,β-ethylenically unsaturated monomer; C) 1-50% by weight of one or more of the following monomers: C1) at least one nonionic vinyl surfactant ester; or C2) at least one nonionic, copolymerizable α,β-ethylenically unsaturated monomer having longer polymer chains than monomer B), or C3) at least one nonionic urethane monomer; and, optionally, D) 0-5% by weight of at least one crosslinker.




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System for accessing a register file using an address retrieved from the register file

A data processing system and method are disclosed. The system comprises an instruction-fetch stage where an instruction is fetched and a specific instruction is input into decode stage; a decode stage where said specific instruction indicates that contents of a register in a register file are used as an index, and then, the register file pointed to by said index is accessed based on said index; an execution stage where an access result of said decode stage is received, and computations are implemented according to the access result of the decode stage.




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System and method for Controlling restarting of instruction fetching using speculative address computations

A system and method for controlling restarting of instruction fetching using speculative address computations in a processor are provided. The system includes a predicted target queue to hold branch prediction logic (BPL) generated target address values. The system also includes target selection logic including a recycle queue. The target selection logic selects a saved branch target value between a previously speculatively calculated branch target value from the recycle queue and an address value from the predicted target queue. The system further includes a compare block to identify a wrong target in response to a mismatch between the saved branch target value and a current calculated branch target, where instruction fetching is restarted in response to the wrong target.




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Verification module apparatus for debugging software and timing of an embedded processor design that exceeds the capacity of a single FPGA

A plurality of Field Programmable Gate Arrays (FPGA), high performance transceivers, and memory devices provide a verification module for timing and state debugging of electronic circuit designs. Signal value compression circuits and gigabit transceivers embedded in each FPGA increase the fanout of each FPGA. Ethernet communication ports enable remote software debugging of processor instructions.




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Method of reducing soil redeposition on a hard surface using phosphinosuccinic acid adducts

Methods employing detergent compositions effective for reducing soil redeposition and accumulation on hard surfaces are disclosed. The detergent compositions employ phosphinosuccinic acid adducts in combination with an alkalinity source and gluconic acid or salts thereof, copolymers of acrylic acid and maleic acids or salts thereof, sodium hypochlorite, sodium dichloroisocyanurate or combinations thereof.




dd

Additive manufacturing method for SRF components of various geometries

An additive manufacturing method for forming nearly monolithic SRF niobium cavities and end group components of arbitrary shape with features such as optimized wall thickness and integral stiffeners, greatly reducing the cost and technical variability of conventional cavity construction. The additive manufacturing method for forming an SRF cavity, includes atomizing niobium to form a niobium powder, feeding the niobium powder into an electron beam melter under a vacuum, melting the niobium powder under a vacuum in the electron beam melter to form an SRF cavity; and polishing the inside surface of the SRF cavity.




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Addition-fragmentation agents

Addition-fragmentation agents of the formula are disclosed having the following functional groups: 1) a labile addition-fragmentation group that can cleave and reform to relieve strain, 2) a free-radically polymerizable group, and 3) a surface-modifying functional group that associates with the surface of a substrate.




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Synthesis of and curing additives for phthalonitriles

A composition having a mixture of the below compounds having a mole ratio of at least 1:20. Ar1 and Ar2 are independently selected aromatic groups. A composition comprising phthalonitrile compounds that comprise at least 5 mol % of the first compound below. A method of: providing a solution of a dichloroaromatic compound having an electron-withdrawing group bound to each aromatic ring containing one of the chloride groups; a dihydroxyaromatic compound or anion thereof; an organic transition metal complex or a transition metal salt; an alkaline hydroxide base; and a solvent; and heating the solution to a temperature at which the dichloroaromatic compound and the dihydroxyaromatic compound react to form a dimetallic salt of an aromatic ether oligomer. The molar ratio of the dihydroxyaromatic compound to the dichloroaromatic compound is greater than 2:1. Water formed during the heating is concurrently distilled from the solution.




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Virtualizing embedded devices

A device virtualization system is described herein that separates common computer processing tasks of an embedded device from the application-specific peripherals of the embedded device to allow at least some functions of the device to be offloaded to servers or other computing devices. The device virtualization system greatly simplifies management, configuration, and control of virtual embedded devices. Because much of the software of the device runs on the virtualization server, information technology (IT) personnel can be assigned to manage the virtualization server and to apply any updates and diagnose any problems that occur. Moreover, a single server or group of servers can be used to provide virtualization services for a wide variety of virtualized embedded devices for a wide variety of users. Thus, the system enhances the usefulness of embedded devices and allows a longer lifetime and wider variety of uses than embedded devices today.




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Apparatus and method for controlling peripheral in wireless communication system using an IP address

An apparatus and method control a connection between peripheral devices in a control server of a wireless communication system providing an IP-based communication service. The method for controlling the connection between peripheral devices includes registering at least one device in a group list classified by user identification information. The method also includes, if a control node including a user identifier of the group list requests a control of any one device included in the group list, checking an IP address of the device that is requested to be controlled by the control node. The method further includes sending a control command of the control node using the IP address.




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Continuous production process of tertiary alcohols by radical addition of secondary alcohols to alkenes

A process is disclosed for producing tertiary alcohols by radical addition of secondary alcohols to alkenes. The reaction is carried out as a continuous process in the presence of an organic peroxide as a radical initiator and during a mean contact time of the reaction mixture of up to a maximum of 1 hour.




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Using extended asynchronous data mover indirect data address words

An abstraction for storage class memory is provided that hides the details of the implementation of storage class memory from a program, and provides a standard channel programming interface for performing certain actions, such as controlling movement of data between main storage and storage class memory or managing storage class memory.




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Using extended asynchronous data mover indirect data address words

An abstraction for storage class memory is provided that hides the details of the implementation of storage class memory from a program, and provides a standard channel programming interface for performing certain actions, such as controlling movement of data between main storage and storage class memory or managing storage class memory.




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Feed additive for animals of P-thymol, salt derivative or ester derivative thereof

A feed additive includes at least one of p-thymol, a salt derivative and an ester derivative thereof for animals.




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Silanes with embedded hydrophilicity, dispersible particles derived therefrom and related methods

The invention provides a silane compound that includes a hydrophobic group and a silane ester group linked by a hydrophilic group for use as a surface treatment to an inorganic material, such as a pigment, the silane including a hydrophobic group and a silane ester group linked by a hydrophilic group. The invention includes a coated particle including an inorganic material coated with the silane compound(s) and methods of improving the wettability and/or dispersibility of an inorganic material such as a pigment, wherein the method comprises depositing the silane compounds on the surface of a pigment.




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Construction of a multivalent scFv through alkyne-azide 1,3-dipolar cycloaddition

The present invention provides for a practical, universal and efficient method to ligate two large macromolecules (e.g., proteins) using the alkyne-azide 1,3-dipolar cycloaddition reaction to produce a conjugated macromolecule, such as a multivalent scFv. The present invention also provides for conjugate macromolecules comprising a plurality of macromolecule components cross-linked through at least one linking group comprising at least one 1,2,3-triazole moiety, wherein at least 50 percent of the macromolecule components in the conjugate macromolecule has only one site available for cross-linking.




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Blends containing photoactive additive

Polymeric blends having improved flame retardance properties and good ductility at low temperatures are disclosed. The blend is formed from (A) a photoactive additive containing a photoactive group derived from a monofunctional benzophenone; and (B) a polymer resin which is different from the photoactive additive. The additive can be a compound, oligomer, or polymer. When exposed to ultraviolet light, crosslinking will occur between the photoactive additive and the polymer resin, enhancing the chemical resistance and flame retardance while maintaining ductility.




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Cascaded eddy simulation

A fluid flow is simulated by causing a computer to perform operations on data stored in the memory to compute at least one eddy of a fluid flow at a first scale and perform operations to compute at least one eddy of the fluid flow at both the first scale and a second scale. The second scale is a finer scale than the first scale, and the computation of the at least one eddy of the fluid flow at the second scale is constrained by results of the computation of the at least one eddy of the fluid flow at the first scale.




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Liquefier assembly for use in extrusion-based additive manufacturing systems

A liquefier assembly for use in an extrusion-based additive manufacturing system, the liquefier assembly comprising a downstream portion having a first average inner cross-sectional area, and an upstream having a second average inner cross-sectional area that is less than the first inner cross-sectional area, the upstream portion defining a shoulder configured to restrict movement of a melt meniscus of a consumable material.




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Distributed management with embedded agents in enterprise apps

Distributed mobile device management including a plurality of management agents is disclosed. Management-related information may be retrieved from a storage location accessible to a plurality of management agents. The management-related information may have been provided to the storage location from a management agent associated with a managed application. And at least one operation may be performed based at least in part on the management-related information.




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System and method for using a multicast group to support a flooding mechanism in a middleware machine environment

A system and method can support a flooding mechanism using a multicast group in a middleware machine environment. The middleware machine environment can comprise a gateway instance that includes an external port for receiving one or more data packets from an external network. The middleware machine environment also comprises one or more host servers, each of which is associated with one or more virtual machines that can process the data packets. Furthermore, said host servers can provide virtual interfaces that belong to a virtual hub associated with the gateway instance. At least one said packet is a flooded packet that is specified with an unknown destination address when it is received at the external port. The gateway instance operates to send the flooded packet to the multicast group that operates to forward the flooded packet to one or more said host servers in the multicast group.