text:
music / R&B
GEN F: TeeFLii
Meet TeeFLii, an R&B stud for a relationship-weary generation.
By Naomi Zeichner
Photographer Joyce Kim
From the magazine: ISSUE 88, October/November 2013
On the August morning after TeeFLii's deal with Epic Records is committed to ink, R&B's most compulsively raunchy singer is running late. After a long night out in New York hosted by label CEO L.A. Reid, he and a group of friends are still packing for their return home to Los Angeles. When TeeFLii (pronounced "tee-fly") finally arrives to our interview, in sweats and sunglasses big enough to cover his sharp cheekbones, he is friendly but quiet, tightly coiled like a featherweight boxer storing up his energy for a scrappy defense, should the need arise.
Epic has signed TeeFLii to re-release and promote "This Dick," a 2012 mixtape cut that's become a regional hit with the support of DJ Carisma, LA station Power 106's only female host. Shortened to "This D" for radio, its lyrics are bluntly filthy, delivered in a punchy style that verges on rapping; TeeFLii sings like he's not concerned with proving the strength of his own voice, an inclination that complements producer DJ Mustard's gleefully simple cowbell clinks.
Born Christian Jones and now 26, TeeFLii first learned music as a kid in church, drawn to keyboards after seeing how much his grandfather, a pastor, paid his organ players. TeeFLii remembers his childhood as both "spoiled" (he claims to have played drums for Barbra Streisand at a Democratic Party event) and wild. "I was bad—fighting, stealing," he says. "I'd go take your bike, bring it to my backyard, spray-paint it, try to ride it around." As a teenager, he got into dance, performing under the name Baby Tight Eyez and starring in a few Chris Brown videos as well as Rize, David LaChapelle's 2005 documentary about LA's krump scene. After the movie's promotional blitz ended, when TeeFLii was 19, he crashed at friends' houses. "I was going through things with my baby's mother," he recalls. "My daughter was took from me." He started writing songs for other people to "get the vent off." Eventually, seeing a better chance at stability, he pushed himself out front.
Over the past year, TeeFLii has released three tapes, a pair named AnnieRUO'TAY and AnnieRUO'TAY 2 (a baby-talk play on Michael Jackson's Annie are you okay?, from "Smooth Criminal") and Fireworks, a collaboration with DJ Mustard. Some songs, like "Have a Drink," a breezy club spiritual where TeeFLii harmonizes with himself over climbing organs, evidence his love for old-fashioned musicality. But most are memorable for their extravagant nastiness. "Fuck'N in the Function" celebrates sneaking off at a party; "Fuckin on the Floor" is about just that. "I don't wanna be the cute singer that sings high and sounds like the girls," he says. "I wanna be that dude that wasn't afraid to be himself."
TeeFLii is an R&B stud for the relationship-weary, promising good sex on the couch, not candlelit romance. His songs aren't in good taste but they're not intentionally degrading, since ultimately, he says, his goal is to impress women: "I don't make music for dudes. Girls are gonna be the ones that buy my albums." Meanwhile, he's repaired his relationship with his daughter and her mother along with his own mother, a recovering addict who's still active in the church. He says his mom is okay with all the "pussy pussy" singing her son does because it's helped him make "a connection with the people." Those people are TeeFLii's peers—young, with wide-open futures. "Tomorrow's not promised for nobody," he says, raising his voice like he's on the pulpit. "So if I can just go the club and get my party on for a minute? Hell yeah, I'm gonna do it. Come on."
DJ Mustard Teeflii Issue 88 Naomi Zeichner
DJ Mustard, Issue 88, Naomi Zeichner, R&B, Teeflii
music / Features


A decade ago, the pharmaceutical industry experienced a shift in its operating model as a result of paradigm-shattering market dynamics and new regulations. The shockwaves continue to impact many large organizations, as specialty biotech companies enter the market, driving rapid innovation, globalization, and increased competition. Now, the medical devices and diagnostics industry (MDDI) is the next in line for dramatic change.
ness models without much need for differentiation.


The Ford 15 tract is the newest addition to the Teraprom portfolio of properties. It is less than 1 mile away from the Layne Farm and Poverty Point. Set in a beautiful creek valley with spectacular views, it features dense brushy areas near the gravel road it borders as well as mature hardwood timber. It is located in a secluded part of Schuyler County, where there is abundant native wildlife including deer and turkeys. With hillsides, creekbottoms and dense thickets, this is a top notch hunting property in the Golden Triangle!
Below is an aerial photo of the property.


Calculating the likely energy performance of commercial scale solar water heating systems requires computer simulation modelling of the system. The solar simulation tool SolSimNZ tool is aimed at helping solar suppliers, designers and architects better understand how their proposed solar water heating system will work and will help system designers size components and choose the appropriate collectors. SolSimNZ allows the most common solar water heating systems to be modelled for 16 areas in New Zealand. The instructions for operating the simulation tool are available here. (You should note that it is a 10mB file so loading via dial-up will be slow. As the file to be downloaded is a .msi file you will be asked if you want to run it. If you click yes then it will automatically load the files to the desktop of your PC).
Information Sheet 8 - SWH Building Consenting Guide - A Guide to Applying for a Building Consent for a Solar Water Heating System Installation (Please note - Solar Association name change from SIA previously. Document also under review).
The Master Plumbers and Mechanical Services Association Australia (MPSAA) and Sustainability Victoria jointly produced the 'Large Scale Solar Thermal Systems Design Handbook' which provides guidance in designing best practice large scale solar hot water systems used in commercial and small industrial applications with the 60-120o Celsius range such as breweries, abbatoirs and hospitals. The Design Guide addresses common design issues including flow rates, hydraulic configuration, control system and collector arrangements.
Hot Pipe Material, Article from Oct/Nov 2005 issue of NZ Plumbers Journal, published by the Master Plumbers Gasfitters & Drainlayers NZ Inc.
Solar water heating articles and research reports published by BRANZ (www.branz.co.nz) are available by clicking here. This is the link to the BRANZ publications shop. Enter 'solar' in the search field and select 'All publication series' from the drop down menu. It will search all BRANZ publication sources. (Some articles and reports have to be purchased while older articles become free to download after a certain period).


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Richard White on America's Gilded Age ... and How it's Echoed Today
Michael Krasny
Nov 8, 2017 at 10:00 AM
Historian Richard White (Photo: Jesse White)
The Gilded Age was an era of incredible wealth and innovation, but also crushing poverty, government corruption and broken promises to former slaves and Native Americans. Richard White's new book, "The Republic for Which It Stands: The United States During Reconstruction and the Gilded Age," dissects this era of contradictions and what it means for present-day inequality and populism.
Richard White, professor of American history, Stanford University; author, "The Republic for Which It Stands: The United States During Reconstruction and the Gilded Age, 1865-1896"

redpajama_set_name: RedPajamaCommonCrawl


text:
package org.apache.hadoop.hive.ql.exec;

import java.io.Serializable;

import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.fs.PathFilter;
import org.apache.hadoop.hive.common.FileUtils;
import org.apache.hadoop.hive.ql.plan.CopyWork;
import org.apache.hadoop.hive.ql.plan.api.StageType;
import org.apache.hadoop.util.StringUtils;

/**
 * CopyTask implementation.
 **/
public class CopyTask extends Task<CopyWork> implements Serializable {
  private static final long serialVersionUID = 1L;

  public CopyTask() {
    super();
  }

  @Override
  public int execute() {
    Path[] from = work.getFromPaths(), to = work.getToPaths();
    for (int i = 0; i < from.length; ++i) {
      int result = copyOnePath(from[i], to[i]);
      if (result != 0) return result;
    }
    return 0;
  }

  protected int copyOnePath(Path fromPath, Path toPath) {
    FileSystem dstFs = null;
    try {
      Utilities.FILE_OP_LOGGER.trace("Copying data from {} to {} ", fromPath, toPath);
      console.printInfo("Copying data from " + fromPath.toString(), " to "
          + toPath.toString());

      FileSystem srcFs = fromPath.getFileSystem(conf);
      dstFs = toPath.getFileSystem(conf);

      FileStatus[] srcs = srcFs.globStatus(fromPath, new EximPathFilter());

      // TODO: this is very brittle given that Hive supports nested directories in the tables.
      //       The caller should pass a flag explicitly telling us if the directories in the
      //       input are data, or parent of data. For now, retain this for backward compat.
      if (srcs != null && srcs.length == 1 && srcs[0].isDirectory()
          /*&& srcs[0].getPath().getName().equals(EximUtil.DATA_PATH_NAME) -  still broken for partitions*/) {
        Utilities.FILE_OP_LOGGER.debug(
            "Recursing into a single child directory {}", srcs[0].getPath().getName());
        srcs = srcFs.listStatus(srcs[0].getPath(), FileUtils.HIDDEN_FILES_PATH_FILTER);
      }

      if (srcs == null || srcs.length == 0) {
        if (work.isErrorOnSrcEmpty()) {
          console.printError("No files matching path: " + fromPath.toString());
          return 3;
        } else {
          return 0;
        }
      }

      if (!FileUtils.mkdir(dstFs, toPath, conf)) {
        console.printError("Cannot make target directory: " + toPath.toString());
        return 2;
      }

      for (FileStatus oneSrc : srcs) {
        String oneSrcPathStr = oneSrc.getPath().toString();
        console.printInfo("Copying file: " + oneSrcPathStr);
        Utilities.FILE_OP_LOGGER.debug("Copying file {} to {}", oneSrcPathStr, toPath);
        if (!FileUtils.copy(srcFs, oneSrc.getPath(), dstFs, toPath,
            false, // delete source
            true, // overwrite destination
            conf)) {
          console.printError("Failed to copy: '" + oneSrcPathStr
              + "to: '" + toPath.toString() + "'");
          return 1;
        }
      }
      return 0;

    } catch (Exception e) {
      console.printError("Failed with exception " + e.getMessage(), "\n"
          + StringUtils.stringifyException(e));
      return (1);
    }
  }

  private static final class EximPathFilter implements PathFilter {
    @Override
    public boolean accept(Path p) {
      String name = p.getName();
      return name.equals("_metadata") ? true : !name.startsWith("_") && !name.startsWith(".");
    }
  }

  @Override
  public StageType getType() {
    return StageType.COPY;
  }

  @Override
  public String getName() {
    return "COPY";
  }
}

redpajama_set_name: RedPajamaGithub


Can, can, Can-Tainer. Have you been to Key West? Seen all the tourists on their fat-tired cruisers? No, not their sneakers or their asses, but their bicycles. Whoops! How the heck do you bike around and look at all those pretty little buildings without something to hold your coffee? I mean, you have to have your java with you! Actually, I have sincerely felt traveling with coffee is a problem when I bike to the office in the morning as my favorite coffee shop is along the way. Without fail, I buy a coffee, put it into my basket and spill some on my bag during the rest of my trip. Now my coffee cup goes strait into my Can-Tainer. Ride your fat-tired bike and do the can-can while your coffee sits securely in the Can-Tainer. I love the name - I can't stop saying it...Can-Tainer, Can-Tainer, Can-Tainer.


Norman, OK (population: 109,682) has one	astronomy school within its city limits. University of Oklahoma Norman Campus	has a total student population of 25,881. It is the 919th	highest ranked school in the USA and the 7th	highest in the state of Oklahoma (#1 is Oklahoma City University).
Astronomy students from Norman schools who go on to become astronomers, astrophysicistss, lunar and planetary institute directors, national radio astronomy observatory directors, etc. have a good chance at finding employment. For example, there are 1,240 people working as astronomers alone in the US, and their average annual salary is $102,740. Also, Atmospheric and space scientists make on average $85,160	per year and there are about 8,320	of them employed in the US today.
Norman lies in Cleveland county, which is one of the 41 counties in Oklahoma. Overall, the Oklahoma City area has 564,710 total employed workers according to the US Bureau of Labor Statistics, with a 1.5% unemployment rate, $18.31/hr average worker wage, and a $38,090 average annual salary.
After taking into account tuition, living expenses, and financial aid, University of Oklahoma Norman Campus ends up costing a student $11,909/yr.
Norman, OK (population: 109,682) has one	astronomy school within its city limits. University of Oklahoma Norman Campus has a total student population of 25,881. It is the 919th	highest ranked school in the USA and the 7th	highest in the state of Oklahoma (#1 is Oklahoma City University).


Student life during the Great Depression.
This site features poems, articles, and short stories that give a glimpse of student life in New York City during the Great Depression.
Wide margins, large text, and well-placed photographs to break up the text make the site user-friendly and attractive.
A top and a bottom navigation bar make navigation simple. Visitors can browse the content by year, author/artist, subject, and images.
The Magpie is the literary magazine of DeWitt Clinton High School in New York City. This site presents articles, poems, short stories, photographs, and graphics from the magazine that cover the years 1929 to 1942. Students can get a glimpse of what the life of high school students in New York City was like during the Great Depression. These selections from the Magpie are not only representative of student life at that time but also a great example of how powerful literary magazines can be for those who contribute to them as well as to those who read them.


text:
Grace United Methodist Church N2133 Southgate Terrace, Wautoma, Wisconsin 54982 (920-787-4668)
Ministry Team >
Pastor Stephanie
Missions and Outreach
What is Methodism?
Methodism was started by John Wesley, an Anglican Priest who sought to reform and revitalize the church of his day, in doing so he and his brother Charles started the Methodist movement and the movement eventually turned into its own church. (The name "Methodist" comes from an observation by others that they were very methodical in how they lived out their faith).
United Methodist's are the second largest Protestant denomination in the United States, we have more churches than there are U.S. post offices. Methodists have played a significant role in shaping our nation. Several of the first public schools, universities, hospitals, and a host of other community agencies and social ministries were started by Methodists. We are the home to both former President Bush's and former President Clinton's families. Many other state and national leaders have been and are Methodists; it is said that President Lincoln went to consult and pray with one of our Bishop's before making the Emancipation Proclamation.
United Methodists hold to the historic doctrines of the Christian faith and have both evangelical and Anglican roots. We value the intellect and modern science, while at the same time look to the Bible as the authoritative guide for faith and practice. Methodists have a passionate faith with strong convictions but we also recognize that the world is not always black and white. We are willing to ask questions, to wrestle with difficult issues, and to do so with grace and compassion holding to John Wesley's notion that in the essential beliefs of the faith we must have unity, but in the nonessentials we may have a variety of views. We also follow John Wesley's teaching that the living core of the Christian faith is revealed in Scripture, illumined by tradition, vivified in personal experience, and confirmed by reason.
Methodists are known for our emphasis on a growing personal faith, lived out in concrete ways in the world, and for taking the Gospel message in word and deed to where the people are. We have historically valued well-informed and passionate preaching, worship that was lively, and small groups where people could grow in faith. We are also known for organization and that our theology is present in our songs. Methodists have open hearts, and open minds, and open doors, we welcome anyone interested in learning more about the Christian faith and in being a follower of Christ.
N2133 Southgate Terrace
Wautoma, WI


Kensington SD1600P USB-C dock is designed to add greater ports to any USB-C or Thunderbolt 3 equipped laptop or computing device computer, and its pass-through charging feature lets in it to charge your computer when connecting with other gadgets. Offering typical compatibility, this 4K dock works with any computer with a Thunderbolt three or USB-C port. Likewise, it helps MacOS, Chrome, and Windows running systems. Simply plug the USB-C electricity adaptor from your laptop into the dock to present pass-thru power for your device. you can also power the dock and linked devices by using your computer's battery. eliminating the need for an additional strength adapter, this travel-pleasant machine continues your setup exceptional and tidy. The SD1600P mobile Docking Station offers options for HDMI or VGA output to a TV, projector, or outside monitor. Its flexible design makes it smooth to connect with specific AV setups at paintings and saves precious luggage area. Furthermore, the SD1600P comes with an 8-inch USB-C cable that you could hide when now not in use.
The SD1600P is a multi-useful and realistic USB-C hub that measures 5.33 x 3.05 x 0.63 inches and weighs 3.84 oz. As we are able to see from the pictures, the dock functions a light-weight and compact layout so that you can effortlessly take it together with your laptop everywhere you go. The incorporated USB-C cable can be stowed in a custom groove for neat and tangle-free charge. In the meantime, the matte finished black outdoors suggests off low-profile and minimalistic aesthetics that's compliant to any computer and desktop.
The SD1600P USB-C dock comes geared up with an HDMI port that helps 4K video output for more modern displays and HDTVs, and a VGA full HD port also lets in it to work with VGA equipped video display units, TVs and projectors. Frthermore, an Ethernet port is designed for wired internet connection, and two USB 3.0 ports lets in it to attach extra devices together with your PC. An extra USB-C port may be used for high-speed statistics synchronization. Further, when connecting with PC's USB-C power adapter, its USB-C go-thru charging provides electricity to the laptop or the dock and related gadgets the use of the PC's battery.
The SD1600P USB-C dock with pass-through charging is priced at $93.80 USD. If you are really interested in this new USB-C Dock and want to grab it, then jump to the Amazon page for its more details.


This Indiana engagement photography session all began with a few messages and texts, which led to a FaceTime date. Hannah was prepared with a notecard of talking points – it was the start of Shark Week which is their" thing". When it was time to meet face-to-face, Sam remembers just how bright the stars were. The weather was warm, his heart was pounding with nervousness and excitement, and he had no idea what to expect. The moment his eyes caught a glimpse of her in that beautiful, pink summer dress as she walked out her front door, his world stopped. His breath was taken away; he knew she was the one.
Their relationship has been long-distance since the beginning. Visits were limited to weekends, minus a few surprise visits when they needed to see each other. Both will admit that the distance has been a challenge, but the feeling each has when they are together is what makes it all worthwhile. Sam will tell you that his heart still skips a beat every time he sees Hannah. Her beauty is not just on the outside but radiates from within. She makes him laugh, she is his rock, and she brightens every day with her gorgeous smile.
Hannah finds comfort in his arms. Sam drives her so crazy that she can only laugh. Sam supports her in everything she does and that only can make her smile. And, when Sam kisses her she only feels warmth and love. Hannah stated she knew the moment she saw Sam that he was the one God sent for her.
Every session leads to some less-than-traditional portraits, but ultimately shows the energy of the shoot. I love this image of Hannah smiling as she manages herself through the great outdoors in heels while Sam is being chivalrous with the flowers.


I am great with people and other dogs!
My siblings are: Dayton and Beamer.
I will be available for adoption on April 14th. I cannot have any visitors before April 14th. You will need to fill out an online application and receive a phone call back from a staff member that everything is ok with the application prior to April 14th. All family members and any dogs from the household need to be present at the time of the adoption.


The 2019 NCAA tournament selection show is Sunday, March 17. Tonight, the 68-team bracket will be revealed, including all seeds and First Four teams.
Games begin Tuesday, March 19 with the First Four, which continues Wednesday, March 20. Once the field is down to 64 teams, the First Round will start Thursday, March 21.
Along with being on CBS, you can watch the NCAA tournament Selection Show on March Madness Live. The show is available here. The show starts at 6 p.m. ET.
East Regional March Madness Live CBS, TBS March 29/31 Washington, D.C.


Image: The EasyOne Air portable, connected spirometry device (Photo courtesy of ndd Medical Technologies).
A new spirometry solution helps primary care physicians conduct lung function testing at the point of care (POC).
The EasyOne Air is a portable, connected spirometry device that requires no calibration, warm-up time, or maintenance. An intuitive, full-color touch screen display ensures accurate and immediate results through real-time curves and parameters. The device connects via USB to a computer for direct printing and downloading, with an added option of portability through a secure Bluetooth connection, providing seamless integration to the patients' electronic medical record (EMR) via EasyOne Connect, a platform for all EasyOne point-of-care solutions.
EasyOne Air is powered by propriety ultrasound flow technology that anticipates interfering variables, allowing it to function independently of gas composition, pressure, temperature, and humidity, thus ensuring a consistent, standardized test that is based on American Thoracic Society/European Respiratory Society (ATS/ERS) guidelines. Test types include FVC, FVL, SVC, MVV, and Pre/Post Testing. EasyOne Air is a product of ndd Medical Technologies (Zurich, Switzerland), and has been approved by the U.S. Food and Drug Administration (FDA).
Spirometry is the most common pulmonary function test (PFT); it measures lung function, specifically the volume and/or flow of air inhaled and exhaled, which is helpful in assessing breathing patterns that identify conditions such as asthma, pulmonary fibrosis, cystic fibrosis, and chronic obstructive pulmonary disease (COPD). Spirometry generates pneumotachograph charts that plot the volume and flow of air coming in and out of the lungs from one inhalation and one exhalation.


Every artist has a story to tell, for one of the biggest and most important collections of artists in Hip-Hop history, moviemakers are bringing in some of today's stars. Dave East is looking to take on the role of Method Man in the forthcoming Wu-Tang: An American Saga film.
Offset has had a lot to say in his press run, however, his take on the younger generation may rub a few Hip-Hop heads the wrong way. Offset looks at the current success of Hip-Hop and cites the work that is being done and the popularity of the genre as reasons to show the current popular acts love.


One of Bournemouth's top hotels has turned to Smart Fire UK to reinvigorate its empty and redundant fireplaces. Done as part of an on going development of the four star hotel, The Miramar wanted to give their old and disused fireplaces a new lease of life.
After seeing Smart Fire's range of bio ethanol fuelled fires displayed at a trade show, hotel bosses knew they had found the perfect solution to their needs. The Edwardian hotel had the fireplaces installed last year, placing the bio ethanol burners inside the original fireplaces.
As the existing fireplaces had their chimneys and flues blocked up, it was thought impossible to re-fashion them back into working fireplaces. However, because the EcoSmart fires are clean burning and produce no fumes, they were seen as the perfect alternative. On top of that, the new fires have provided the Hotel Miramar with a cost effective and green source of heating.
"We're really happy that Hotel Miramar are pleased with their fires. Often people think that once a chimney has been blocked up then that's the end of a fireplace, however bio-ethanol can change all that.
We are really pleased with the final result and our guests think the fires are absolutely fantastic. For many years the fireplaces in the hotel have been purely decorative because our chimneys are no longer functional. So when we saw Smart Fire UK at the Independent Hotel Show and discovered their burners needed no fume extraction, we knew we had to have them. The fireplaces now look stunning and in the winter give the hotel and incredibly cosy feel. In fact they give off a surprisingly effective amount of heat. For example, this winter we were able to turn the central heating off in the main foyer and just rely on the heat from the fires alone. They're easy to use, safe, and clean and we are extremely happy with the final result.


Psychotherapy is often sought out to help navigate difficult moments in life, and can also be tremendously helpful to cope with the pain and joy that come with simply being human. My intent is to create a collaborative therapeutic relationship so we can embark in an exploration of ways that unconscious conflicts can manifest via painful life choices and problematic patterns, to seek the root of issues in order to attain lasting and meaningful change. Grounded in psychodynamic theories of mind, I recognize the value that creativity and embodiment bring to deeper understanding of human beings. I cultivate curiosity about the fullness of your humanity, integrating your strengths as we explore the challenges.
"For one human being to love another: that is perhaps the most difficult of all our tasks… the work for which all other work is but preparation.
Elizabeth's two decades of clinical experience has taken her to various settings prior to establishing her private practice, such as: The Children's Assessment Center, Harris County Hospital District, the Council on Recovery, the University of Pittsburgh's Medical Center's Pediatric OCD IOP program, as well as the Menninger Clinic, where she spent several years working in the Compass unit for young adults.
Her clinical specialities include trauma, personality disorders, emotions manifested in the body and family dynamics. Elizabeth has specialized post-graduate training in group psychotherapy, DBT, Exposure-Response Prevention therapy for OCD, ACT Therapy and play therapy. At the end of April 2019 she will be trained in EMDR and be adding this service to her practice.
Elizabeth enjoys training future clinicians as a clinical supervisor and at the University of Houston's Graduate School of Social Work, where she has served as adjunct faculty and provides continuing education workshops. She is a certified yoga instructor and founded THRIVE, a trauma-informed Yoga program. Yoga can be integrated into psychotherapy sessions, if desired.
Her commitment is to help people inhabit their bodies and lives in a meaningful manner.
Ofrezco servicios en Espanol. Si eres inmigrante no te pedire papeles de residencia en ningun momento.
Please fill the form for inquiries about psychotherapy, consultations, or trauma-informed yoga.


The world, cold, will continue as if nothing had happened.
The machine cannot be negotiated with.
But what was created was not a machine.
could be pushed back, contended with.
And turn away from what had been done.
Though I have stopped, the evening has not.
Moves lower and lower and lower.
Depriving the cold day of the little heat it sought.
Everything seems closer in the dim.
They approach closer and closer and closer.
I allow my tired brain its whim.
Though I feel I've gone into the woods.
I'm seeing spots, my vision confused.
But I'm waiting for nothing.
I should continue but I'm locked in the sunset.
I must leave, while I'm still awake.
Or give in, and let sun and sight leave me behind.
Each person was given a pair of lines from that poem, and they had to write their own poem in between the lines. I got lines 8 and 9: "The darkest evening of the year." and "He gives his harness bells a shake." You can see those are my first and last lines.
There weren't any other constraints except that it had to be fairly short.
What we as a class are working to do now is compile everyone's piece into a little book. It'll be kinda cool, because the last line of one person's poem becomes the first line of the next one, and so on until the entire Frost poem has been covered. What this means is that the table of contents will read exactly like Robert Frost's piece!


text:
Bill Pay Email Login
Business TV
About Fiber
Taxes and Tariffs
Protecting Our Customers' Privacy Online
Broadband Internet Service Privacy Policy
Twin Lakes TV Privacy Policy
Broadband Internet Service Network Management Policy
Twin Lakes Telephone Cooperative Corporation ("Twin Lakes" or "Company") is committed to protecting its Broadband Internet service customers' privacy. Users should review this privacy policy to better understand Twin Lakes' views and practices, and how they pertain to them as customers of Twin Lakes' web service.
What types of information does Twin Lakes collect?
Twin Lakes may collect two types of information: personal information and non-personal information. "Personal information" refers to information that is particular to you, such as, your name, address, phone number, fax number, and e-mail address. "Non-personal information" refers to information that does not by itself identify a specific individual. This information may include the Twin Lakes site's Uniform Resource Locator ("URL") that you just came from, what browser you are using and your Internet Protocol ("IP") address.
How do we collect information from you?
Twin Lakes does not collect personal information on this site without your knowledge and active participation. In general, you can visit our site without divulging any personal information; however, there are areas of this site that might require personal information in order to contact Twin Lakes directly, specifically, when registering e-mails, obtaining remote access, and contacting online technical support.
Information is collected on this site in the following ways:
Browsing our site
Twin Lakes may collect IP addresses for the purposes of system administration, to gather broad demographic information, and to monitor the level of activity on our site. Twin Lakes may collect information from visitors to our site regarding the referring URL, your IP address, which browser you used to come to our site, and the pages of our site that you viewed during your visit and any search terms entered on our site.
"Cookies" are small pieces of information that are stored by your browser on your computer hard drive. Our cookies do not contain any personally identifying information. Your web browser may allow you to be notified when you are receiving a cookie, giving you the choice to accept it or not. While you can still navigate through our site if you do not accept cookies, you may not be able to register for e-mails or obtain remote access from Twin Lakes.
As noted above, on this Twin Lakes site, you are given the opportunity to send e-mail to us. Twin Lakes retains the information in any e-mail that you send to us, such as your name, e-mail address, address, telephone number, etc. Twin Lakes may periodically send you e-mails or other company information. Twin Lakes will not send you e-mails if you have previously requested us not to do so and/or have not registered to receive them.
How is your personal information used?
Twin Lakes uses your information in connection with delivering its products and services to you. More importantly, Twin Lakes wants to deliver a compelling online experience that will keep customers coming back to visit the service again and again. Twin Lakes uses the information supplied to it as a means to personalize portions of the service. Some of these personalized services are made available directly by Twin Lakes, while others may be offered by its content partners. In addition, Twin Lakes analyzes and evaluates customer preferences, comments, and criticisms of its offerings in order to make improvements to the present and future versions of the service.
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\section{Introduction}
Achieving efficient and robust visual control towards real-world scenarios remains a long-standing challenge in deep reinforcement learning (RL). The critical problems can be summarized as (i) the partial observability of environments, (ii) learning effective representations of visual observations, and (iii) sparse or even missing reward feedback \citep{mnih2015human, lillicrapHPHETS15, kalashnikov2018scalable, badia2020never}. To address the problem of partial observability, a simple approach is to construct the state by stacking consecutive frames \citep{mnih2015human} or use the recurrent neural network to capture the environment dynamics \citep{hausknecht2015deep}. However, most existing RL algorithms only consider observations from a single view of state space, which are limited by insufficient information. Multiple images captured from different viewpoints or times can provide more reference knowledge, making the task much easier. For instance, an autonomous vehicle will use multiple sensors to sense road conditions and take safe actions. Therefore, \citet{li2019multi} propose the concept of multi-view RL and extend the partially observable Markov decision process (POMDP) to support multiple observation models. As a result, the agent can obtain more environment information and learn robust policies with higher generalization ability across domains.

However, it is always challenging to learn comprehensive low-dimensional representations from high-dimensional observations such as raw pixels. To address the problem, \citet{hafner2019learning} use an auto-encoder to learn the environment dynamics from raw pixels and select actions via fast online planning in the latent space. \citet{yarats2021improving} insert a deterministic auto-encoder to model-free RL algorithms and jointly trains the policy and latent state space, which is shown to be successful on tasks with noisy observations. \citep{stooke2021decoupling, schwarzer2021dataefficient} use contrastive learning to extract high-level features from raw pixels and subsequently perform off-policy control using the extracted features. In contrast, \citet{laskin2020reinforcement} propose to perform data augmentations (e.g., random translate and random amplitude scale) for RL on inputs, enabling simple RL algorithms to outperform complex methods.

Despite their good performance, using visual representation learning produces significant computational complexity, and imperfect representation learning may incur severe performance loss. Moreover, they cannot handle environments with sparse reward space, and the learned policies are sensitive to visual distractions \citep{schmidhuber1991possibility, oudeyer2008can, oudeyer2007intrinsic}. To address the problem, recent approaches propose to leverage intrinsic rewards to improve the sample-efficiency and generalization ability of RL agents \citep{pathak2017curiosity, burda2018exploration, stadie2015incentivizing}. \citet{burda2018large} demonstrate that RL agents can achieve surprising performance in various visual tasks using only intrinsic rewards. \cite{seo2021state} design a state entropy-based intrinsic reward module entitled RE3, which requires no representation learning and can be combined with arbitrary RL algorithms. Simulation results demonstrate that RE3 can significantly promote the sample-efficiency of RL agents both in continuous and discrete control tasks. Meanwhile, \citep{raileanu2020ride} utilize the difference between two consecutive states as the intrinsic reward, encouraging the agent to take actions that result in large state changes. This allows the agent to realize aggressive exploration and effectively adapt to the procedurally-generated environments.

\begin{table}[t]
	\caption{Comparing MEM with related work. RAD \citep{laskin2020reinforcement}, DrAC \cite{raileanu2020automatic}, CURL \citep{srinivas2020curl}, RE3 \citep{seo2021state}, DRIBO \citep{fan2022dribo}. \textbf{Repr.}: the method involves special visual representation learning techniques. \textbf{Multi-view}: the method considers multi-view observations. \textbf{Exploration}: the method can encourage exploration. \textbf{Visual}: the method works well in visual RL.}
	\label{tb:methods comparison}
	\begin{center}
		\begin{tabular}{llllll}
			\hline
			\textbf{Method} & \textbf{Key insight}                                                                       & \textbf{Repr.} & \textbf{Multi-view} & \textbf{Exploration} & \textbf{Visual} \\ \hline
			RAD    & Data augmentation                                                                  & \cmark          & \cmark      & \xmark       & \cmark  \\
			DrAC & Data augmentation                                                                  & \xmark          & \cmark      & \xmark       & \cmark  \\
			CURL   & Contrastive learning                                                                & \cmark          & \xmark      & \xmark       & \cmark  \\
			RE3    & Intrinsic reward                                                                    & \xmark          & \xmark      & \cmark       & \cmark  \\
			DRIBO  & \begin{tabular}[c]{@{}l@{}}Multi-view \\ information bottleneck\end{tabular}                                                                                & \cmark          & \cmark      & \xmark       & \cmark  \\ \hline
			MEM (ours)    & \begin{tabular}[c]{@{}l@{}}Multi-view encoding \\ and intrinsic reward\end{tabular} & \cmark          & \cmark      & \cmark       & \cmark  \\ \hline
		\end{tabular}
	\end{center}
\end{table}

In this paper, we deeply consider the three critical challenges above and propose a novel framework entitled \textbf{M}ulti-view \textbf{E}xploration \textbf{M}aximization (MEM) that exploits multi-view environment information to facilitate visual control tasks. Our contributions can be summarized as follows:
\begin{itemize}
	\item Firstly, we introduce a novel multi-view representation learning method to learn high-quality features of multi-view observations to achieve efficient visual control. In particular, our multi-view encoder has simple architecture and is compatible with any number of viewpoints.
	\item Secondly, we combine multi-view representation learning and intrinsic reward-driven exploration, which computes intrinsic rewards using the learned multi-view features and significantly improves the sample-efficiency and generalization ability of RL algorithms. To the best of our knowledge, this is the first work that introduces the concept of multi-view exploration maximization. We also provide a detailed comparison between MEM and other representative methods in Table~\ref{tb:methods comparison}.
	\item Finally, we test MEM using multiple complex tasks of DeepMind Control Suite and Procgen games. In particular, we use two cameras to generate observations in the tasks of DeepMind Control Suite to simulate more realistic multi-view scenarios. Extensive simulation results demonstrate that MEM can achieve superior performance and outperform the benchmarking methods with higher efficiency and generalization ability.
\end{itemize}

\section{Related Work}
\subsection{Multi-View Representation Learning}
Multi-view representation learning aims to learn features of multi-view data to facilitate developing prediction models \citep{zhao2017multi}. Most existing multi-view representation learning methods can be categorized into three paradigms, namely the joint representation, alignment representation, and shared and specific representation, respectively \citep{jia2020semi}. Joint representation integrates multiple views via concatenation \citep{tao2017scalable,srivastava2012multimodal,nie2017auto} while alignment representation maximizes the agreement among representations learned from different views \citep{chen2012unified, frome2013devise, jing2017semi}. However, the former two methods suffer from the problem of information redundancy and the loss of complementary information, respectively. Shared and specific representation overcomes their shortcomings by disentangling the shared and specific information of different views and only aligning the shared part. In this paper, we follow the third paradigm and employ a simple encoder to learn comprehensive features from multi-view observations.

\subsection{Representation Learning in RL}
A common and effective workflow for visual control tasks is to learn representations from raw observations before training RL agents using the learned representations. \citet{lee2020stochastic} integrate stochastic model learning and RL into a single method, achieving higher sample-efficiency and good asymptotic performance of model-free RL. \citep{hafner2019dream, hafner2020mastering} leverage variational inference to learn world models and solve long-horizon tasks by latent imagination. \citet{mazoure2020deep} maximize concordance between representations using an auxiliary contrastive objective to increase predictive properties of the representations conditioned on actions. \citet{schwarzer2021dataefficient} train the agent to predict future states generated by a target encoder and learn temporally predictive and consistent representations of observations from different views, which significantly promotes the data efficiency of DRL agents. \citet{yarats2020image} demonstrate that data augmentation effectively promotes performance in visual control tasks and proposes an optimality invariant image transformation method for action-value function. In this paper, we consider multi-view observation space and use multi-view representation learning to extract low-dimensional features from raw observations. A closely related work to us is DRIBO proposed by \citep{fan2022dribo}. DRIBO introduces a multi-view information bottleneck to capture task-relevant information from multi-view observations and produces robust policies to visual distractions that can be generalized to unseen environments \cite{federici2019learning}. However, DRIBO overemphasizes the shared information of different viewpoints, resulting in the leak of complementary information. Moreover, the experiments performed in \citep{fan2022dribo} only use a single camera and apply background replacement to generate multi-view observations, which may provide insufficient multi-view information.

\subsection{Intrinsic Reward-Driven Exploration}
Intrinsic rewards have been widely combined with standard RL algorithms to improve the exploration and generalization ability of RL agents \citep{ostrovski2017count,badia2020never,yu2020intrinsic, yuan2022rewarding}. \citet{strehl2008analysis} proposes to use state visitation counts as exploration bonuses in tabular settings to encourage the agent to revisit infrequently-seen states. \citet{bellemare2016unifying} further extend such methods to high-dimensional state space. \citet{kim2019emi} define the exploration bonus by applying a Jensen-Shannon divergence-based lower bound on mutual information across subsequent frames. \citet{badia2020never} combine episodic state novelty and life-long state novelty as exploration bonuses, which prevents the intrinsic rewards from decreasing with visits and provides sustainable exploration incentives. \citet{yuan2022renyi} propose to maximize the R\'enyi entropy of state visitation distribution and transform it into particle-based intrinsic rewards. In this paper, we compute intrinsic rewards using multi-view features to realize multi-view exploration maximization, which enables the agent to explore the environment from multiple perspectives and obtain more comprehensive information.

\section{Preliminaries}
\subsection{Multi-View Reinforcement Learning}
In this paper, we study the visual control problem considering a multi-view MDP \citep{li2019multi}, which can be defined as a tuple $\mathcal{M}=\langle\mathcal{S},\mathcal{A},P,\mathcal{O}_{1},P^{1}_{\rm obs},\dots,\mathcal{O}_{N},P^{N}_{\rm obs},\check{r},\gamma\rangle$. Here, $\mathcal{S}$ is the state space, $\mathcal{A}$ is the action space, $P:\mathcal{S}\times\mathcal{A}\times\mathcal{S}\rightarrow[0,1])$ is the state-transition model, $\mathcal{O}_{i}$ is the observation space of the $i$-th viewpoint, $P^{i}_{\rm obs}:\mathcal{S}\times\mathcal{O}_{i}\rightarrow[0,1]$ is the observation model of the $i$-th viewpoint, $\check{r}:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}$ is the reward function, and $\gamma\in[0,1)$ is a discount factor. In particular, we use $\check{r}$ to distinguish the extrinsic reward from the intrinsic reward $\hat{r}$ in the sequel. At each time step $t$, we construct the state $\bm{s}_t$ by encoding consecutive multi-view observations $\{\bm{o}_{t}^{i},\bm{o}_{t-1}^{i},\bm{o}_{t-2}^{i},\dots\}_{i=1}^{N}$, where $\bm{o}_{t}^{i}\sim P^{i}_{\rm obs}(\cdot|\bm{s}_{t})$. Denoting by $\pi(\bm{a}_t|\bm{s}_t)$ the policy of the agent, the objective of RL is to learn a policy that maximizes the expected discounted return $R_{t}=\mathbb{E}_{\pi}\left[\sum_{k=0}^{\infty}\gamma^{k}\check{r}_{t+k+1}\right]$ \citep{sutton2018reinforcement}.

\subsection{Fast Entropy Estimation}
In the following sections, we compute the multi-view intrinsic rewards based on Shannon entropy of state visitation distribution \citep{shannon1948mathematical}. Since it is non-trivial to evaluate the entropy when handling
complex environments with high-dimensional observations, a convenient estimator is introduced to realize efficient entropy estimation. Let $X_1,\dots,X_n$ denote the independent and identically distributed samples drawn from a distribution with density $p$, and the support of $p$ is a set $\mathcal{X}\subseteq\mathbb{R}^{q}$. The entropy of $p$ can be estimated using a particle-based estimator \citep{leonenko2008class}:
\begin{equation}\label{eq:estimation}
	\begin{aligned}
		\hat{H}^{k}_{n}(p)&=\frac{1}{n}\sum_{i=1}^{n}\log \frac{n\cdot\Vert X_{i}-\tilde{X}_{i}\Vert_{2}^{q}\cdot\hat{\pi}^{\frac{q}{2}}}{k\cdot\Gamma(\frac{q}{2}+1)}+\log k-\Psi(k)\propto \frac{1}{n}\sum_{i=1}^{n}\log \Vert X_{i}-\tilde{X}_{i}\Vert_{2},
	\end{aligned}
\end{equation}
where $\tilde{X}_{i}$ is the $k$-nearest neighbor of $X_{i}$ among $\{X_i\}_{i=1}^{n}$, $\Gamma(\cdot)$ is the Gamma function, $\hat{\pi}\approx 3.14159$, and $\Psi(\cdot)$ is the Digamma function.

\begin{figure*}[t]
	\centering
	\includegraphics[width=1.\linewidth]{figures/mem.pdf}
	\caption{An overview of the MEM. Here, \textbf{avg.} represents the average operation, \textbf{concat.} represents the concatenate operation, and \textbf{dist.} represents the Euclidean distance.}
	\label{fig:mem}
\end{figure*}

\section{MEM}
In this section, we propose the MEM framework that performs visual control based on multi-view observations. As illustrated in Figure~\ref{fig:mem}, MEM is composed of two key components, namely the multi-view encoder and the multi-view intrinsic reward module, respectively. At each time step, the multi-view encoder transforms the multi-view observations into shared and specific information, which is used to make an action. Meanwhile, the shared and specific information is sent to the multi-view intrinsic reward module to compute intrinsic rewards. Finally, the policy will be updated using the augmented rewards.

\subsection{Multi-View Encoding}
Figure~\ref{fig:multi-view encoder} illustrates the architecture of our multi-view encoder, which has an encoding network and discriminator. Let $F_{\rm \bm{\theta}}$ denote the encoding network with parameters $\bm{\theta}$ that has two branches to extract the inter-view shared and intra-view specific information of the observation, respectively. For the observation $\bm{o}^{i}$ from the $i$-th viewpoint, we have
\begin{equation}
	(\bm{x}^{i},\bm{y}^{i})=F_{\rm \bm{\theta}}(\bm{o}^{i}),
\end{equation}
where $\bm{x}^{i}$ is the inter-view shared information and $\bm{y}^{i}$ is the intra-view specific information. To separate the shared and specific information, we propose the following mixed constraints:
\begin{equation}\label{eq:diff}
	L_{\rm diff}=\max \biggl\{\frac{<\bm{x}^{i},\bm{y}^{i}>}{\Vert\bm{x}^{i}\Vert_{2}\cdot\Vert\bm{y}^{i}\Vert_{2}},0\biggr\}+\Vert\bm{x}^{i}\Vert_{1}+\Vert\bm{y}^{i}\Vert_{1}.
\end{equation}
The first term of Eq.~(\ref{eq:diff}) is the cosine similarity of $\bm{x}^{i}$ and $\bm{y}^{i}$ to disentangle the two kinds of information. In particular, \citet{liu2019utility} found that sparse representations can contribute to control tasks by providing locality and avoiding catastrophic interference. Therefore, the $\ell_1$-norm of $\bm{x}^{i}$ and $\bm{y}^{i}$ are leveraged to increase the sparsity of the learned features to improve the generalization ability.

\begin{figure*}[t]
	\centering
	\includegraphics[width=0.95\linewidth]{figures/encoder.pdf}
	\caption{The architecture of the multi-view encoder.}
	\label{fig:multi-view encoder}
\end{figure*}

To guarantee the discriminability of the specific information from multiple viewpoints, we follow the insight of the Siamese network that maximizes the distance between samples from different classes \citep{chopra2005learning}. For a mini-batch, we define the contrastive loss as follows \citep{jia2020semi}:
\begin{equation}
	L_{\rm con}=\frac{1}{2M}\sum_{j=1}^{M}\left[\Vert\bm{y}_{j}-\mu_{\rm same}\Vert_{2}^{2}+{\rm max}^{2}({\rm Margin}-\Vert\bm{y}_{j}-\mu_{\rm diff}\Vert_{2}, 0)\right],
\end{equation}
where $M$ is the batch size, $\mu_{\rm same}$ is the average of samples in this mini-batch from the same viewpoint with $\bm{y}_{i}$, and $\mu_{\rm diff}$ is the average of samples in this mini-batch from the different viewpoint with $\bm{y}_{i}$.

To extract the shared information, we follow \citep{jia2020semi} who design the similarity constraint using a generative adversarial pattern \cite{goodfellow2014generative}. More specifically, the encoding network is regarded as the generator, and the shared information is considered as the generated results. Meanwhile, a classification network is leveraged to serve as the discriminator. Therefore, the discriminator aims to judge the viewpoint of each shared information while the generator aims to fool the generator. Denoting by $D_{\bm \phi}$ the discriminator represented by a neural network with parameters $\bm{\phi}$, the loss function is
\begin{equation}\label{eq:adv}
	L_{\rm adv}=\min_{\bm \theta}\max_{\bm \phi}\sum_{j=1}^{M}\sum_{i=1}^{N}l^{i}_{j}\cdot\log P(\bm{x}^{i}_{j}),
\end{equation}
where $P(\bm{x}^{i}_{j})=D_{\bm \psi}(\bm{x}^{i}_{j})$ is the probability that $\bm{x}^{i}_{j}$ is generated from the $i$-th viewpoint. Then the generator and discriminator will be trained until the discriminator cannot distinguish the differences between shared information of different viewpoints. Finally, the total loss of the multi-view encoder is
\begin{eqnarray}\label{eq:total loss}
	L_{\rm total}=\lambda_{1}\cdot L_{\rm diff}+\lambda_{2}\cdot L_{\rm con}+\lambda_{3}\cdot L_{\rm adv},
\end{eqnarray}
where $\lambda_{1},\lambda_{2},\lambda_{3}$ are the weighting coefficients.

Equipped with the shared and specific information, we define the state of timestep $t$ as
\begin{equation}
	\bm{s}_{t}=\mathrm{Concatenate}(\bm{y}^{1}_{t},\dots,\bm{y}^{N}_{t},\bar{\bm{x}}_{t}),
\end{equation}
where $\bar{\bm{x}}_{t}=\frac{1}{N}\sum_{i=1}^{N}\bm{x}^{i}_{t}$ is the average of the shared inforamtion of $N$ viewpoints. Then the learned $\bm{s}$ is sent to the agent to make actions.

\subsection{Multi-View Intrinsic Reward}
Next, we transform the learned features into intrinsic rewards to encourage exploration and promote sample-efficiency of the RL agent. Inspired by the work of \citet{seo2021state} and \citet{yuan2022renyi}, we propose to maximize the following entropy:
\begin{equation}
	H(d)=-\mathbb{E}_{\bm{o}^{i}\sim d(\bm{o}^{i})}[\log d(\bm{o}^{i})],
\end{equation}
where $d(\bm{o}^{i})$ is the observation visitation distribution of the $i$-th viewpoint induced by policy $\pi$. Given multi-view observations of $T$ steps $\{\bm{o}^{1}_{0},\bm{o}^{1}_{1},\dots,\bm{o}^{1}_{T},\bm{o}^{N}_{0},\bm{o}^{N}_{1},\dots,\bm{o}^{N}_{T}\}$, using Eq.~(\ref{eq:estimation}), we define the multi-view intrinsic reward of the time step $t$ as
\begin{equation}\label{eq:irs}
	\hat{r}_{t}=\frac{1}{N+1}\left[\left(\sum_{i=1}^{N}\log(\Vert\bm{y}^{i}_{t}-\tilde{\bm{y}}^{i}_{t}\Vert_{2}+1)\right)+\log(\Vert\bar{\bm{x}}_{t}-\bar{\bm{x}}^{k}_{t}\Vert_{2}+1)\right],
\end{equation}
where $\tilde{\bm{y}}^{i}_{t}$ is the $k$-NN of $\bm{y}^{i}_{t}$ among $\{\bm{y}^{i}_{t}\}_{t=0}^{T}$ and $\bar{\bm{x}}^{k}_{t}$ is the $k$-nearest neighbor of $\bar{\bm{x}}_{t}$.

We highlight the advantages of the proposed intrinsic reward. Firstly, $\hat{r}_{t}$ measures the distance between observations in the representation space. It encourages the agent to visit as many distinct parts of the environment as possible. Similar to RIDE of \citep{raileanu2020ride}, $\hat{r}_{t}$ can also lead the agent to take actions that result in large state changes, which can facilitate solving procedurally-generated environments. Moreover, $\hat{r}_{t}$ evaluates the visitation entropy of multiple observation spaces and reflects the global exploration extent more comprehensively. Finally, the generation of $\hat{r}_{t}$ requires no memory model or database, which will not vanish as the training goes on and can provide sustainable exploration incentives.

\subsection{Training Objective}
Equipped with the intrinsic reward, the total reward of each transition $(\bm{s}_{t},\bm{a}_{t},\bm{s}_{t+1})$ is computed as
\begin{equation}\label{eq:total reward}
	r_{t}^{\rm total}=\check{r}(\bm{s}_{t},\bm{a}_{t})+\beta_{t}\cdot\hat{r}(\bm{s}_{t}),
\end{equation}
where $\beta_{t}=\beta_{0}(1-\kappa)^{t}\geq 0$ is a weighting coefficient that controls the exploration preference, and $\kappa$ is a decay rate. In particular, this intrinsic reward can be leveraged to perform unsupervised pre-training without extrinsic rewards $\check{r}$. Then the pre-trained policy can be employed in the downstream task adaptation with extrinsic rewards. Letting $\pi_{\bm{\varphi}}$ denote the policy represented by a neural network with parameters $\bm{\varphi}$, the training objective of MEM is to maximize the expected discounted return $\mathbb{E}_{\pi_{\bm{\varphi}}}\left[\sum_{k=0}^{T}\gamma^{k}r_{t+k+1}^{\rm total}\right]$. Finally, the detailed workflows of MEM with off-policy RL and on-policy RL are summarized in Algorithm~\ref{algo:mem off-policy} and Appendix~\ref{appendix:mem on-policy}, respectively.

\begin{algorithm}
	\caption{MEM with Off-policy RL}
	\label{algo:mem off-policy}
	\SetAlgoLined
	Initialize encoding network $F_{\bm\theta}$ and discriminator $D_{\bm\phi}$\;
	Initialize policy network $\pi_{\bm{\varphi}}$, maximum environment steps $t_{\rm max}$, coefficient $\beta_0$, decay rate $\kappa$, and replay buffer $\mathcal{B}\leftarrow\emptyset$\;
	\For{${\rm step}$ $t=1,\dots,t_{\rm max}$}{
		Get multi-view observation $\{\bm{o}_{t}^{1},\dots,\bm{o}_{t}^{N}\}$\;
		\For{$i=1,\dots,N$}{
			$\bm{x}_{t}^{i},\bm{y}_{t}^{i}=F_{\bm\theta}(\bm{o}_{t}^{i})$\;
		}
		Get state $\bm{s}_{t}=\mathrm{Concatenate}(\bm{y}^{1}_{t},\dots,\bm{y}^{N}_{t},\bar{\bm{x}}_{t})$\;
		Sample an action $\bm{a}_{t}\sim\pi_{\bm{\varphi}}(\cdot|\bm{s}_{t})$\;
		$\mathcal{B}\leftarrow\mathcal{B}\cup\{\bm{o}_{t}^{1:N},\bm{a}_{t},\check{r}_{t},\bm{o}_{t+1}^{1:N}\}$\;
		Sample a random mini-batch $\{\bm{o}_{j}^{1:N},\bm{a}_{j},\check{r}_{j},\bm{o}_{j+1}^{1:N}\}_{j=1}^{B}\sim\mathcal{B}$\;
		Get representations $\{\bm{x}_{j}^{1:N},\bm{y}_{j}^{1:N}\}_{j=1}^{B}$ \;
		\For{$j=1,\dots,B$}{
			Compute the intrinsic reward $\hat{r}_{j}$ using Eq.~(\ref{eq:irs})\;
			Update $\beta_{t}=\beta_{0}(1-\kappa)^{t}$\;
			Let $r^{\rm total}_{j}=\check{r}_{j}+\beta_{t}\cdot\hat{r}_{j}$\;
		}
		Update the policy network with $\{\bm{o}_{j}^{1:N},\bm{a}_{j},r^{\rm total}_{j},\bm{o}_{j+1}^{1:N}\}_{j=1}^{B}$ using any off-policy RL algorithms\;
		Update $\bm{\theta},\bm{\phi}$ to minimize $L_{\rm total}$ in Eq.~(\ref{eq:total loss}).
	}
\end{algorithm}

\section{Experiments}
In this section, we designed the experiments to answer the following questions:
\begin{itemize}
	\item Does the learned multi-view information contribute to achieving higher performance in visual control tasks? (See Figures~\ref{fig:dmc result 1} \& \ref{fig:procgen result 1})
	\item Can MEM outperform other schemes that involve multi-view representation learning and other representation learning techniques, such as contrastive learning? (See Figure~\ref{fig:dmc result 2})
	\item How does MEM compare to other intrinsic reward-driven methods? (See Figures~\ref{fig:dmc result 1} \& \ref{fig:dmc result 3} \& \ref{fig:procgen result 1})
	\item Can MEM achieve remarkable performance in the sparse-reward setting? (See Figure~\ref{fig:dmc result 3})
	\item How about the generalization ability of MEM in procedurally-generated environments? (See Figure~\ref{fig:procgen result 1})
\end{itemize}

\begin{figure*}[h]
	\centering
	\includegraphics[width=1.\linewidth]{figures/dmc_result_1.pdf}
	\caption{Performance on six complex control tasks from DeepMind Control Suite over five random seeds. MEM significantly promotes the sample efficiency of DrQ-v2. The solid line and shaded regions represent the mean and standard deviation, respectively.}
	\label{fig:dmc result 1}
\end{figure*}

\begin{figure*}[h]
	\centering
	\includegraphics[width=1.\linewidth]{figures/dmc_result_2.pdf}
	\caption{Performance on six complex control tasks from DeepMind Control Suite over five random seeds. MEM outperforms the benchmarking schemes in all the tasks. The solid line and shaded regions represent the mean and standard deviation, respectively.}
	\label{fig:dmc result 2}
\end{figure*}

\begin{figure*}[h]
	\centering
	\includegraphics[width=0.95\linewidth]{figures/dmc_result_3.pdf}
	\caption{Performance on six tasks with sparse rewards over five random seeds. MEM can still achieve remarkable performance by providing high-quality intrinsic rewards. The solid line and shaded regions represent the mean and standard deviation, respectively.}
	\label{fig:dmc result 3}
\end{figure*}

\subsection{DeepMind Control Suite}
\subsubsection{Setup}
We first tested MEM on six complex visual control tasks of DeepMind Control Suite, namely the \textit{Cheetah Run}, \textit{Finger Turn Hard}, \textit{Hopper Hop}, \textit{Quadruped Run}, \textit{Reacher Hard}, and \textit{Walker Run}, respectively \citep{tassa2018deepmind}. To evaluate the sample-efficiency of MEM, two representative model-free RL algorithms Soft Actor-Critic (SAC) \citep{haarnoja2018soft} and Data Regularized Q-v2 (DrQ-v2) \citep{yarats2021mastering}, were selected to serve as the baselines. For comparison with schemes that involve multi-view representation learning, we selected DRIBO of \citep{fan2022dribo} as the benchmarking method, which introduces a multi-view information bottleneck to maximize the mutual information between sequences of observations and sequences of representations. For comparison with schemes that involve contrastive representation learning, we selected CURL of \citep{srinivas2020curl}, which maximizes the similarity between different augmentations of the same observation. For comparison with other exploration methods, we selected RE3 of \citep{seo2021state} that maximizes the Shannon entropy of state visitation distribution using a random encoder and considered the combination of RE3 and DrQ-v2. The following results were obtained by setting $k=3, \beta_{0}=0.05$ and $\kappa=0.00001$, and more details are provided in Appendix~\ref{appendix:dmc experiments}.

\subsubsection{Results}
Figure~\ref{fig:dmc result 1} illustrates the comparison of the average episode return of six complex control tasks. It is obvious that MEM significantly improved the sample-efficiency of DrQ-v2 on various tasks. In \textit{Figer Turn Hard} task, DrQ-V2+MEM achieved an average episode return of 400.0, producing a big performance gain when compared to the vanilla DrQ-v2 agent. The multi-view observations allow the agent to observe the robot posture from multiple viewpoints, providing more straightforward feedback on the taken actions. As a result, the agent can adapt to the environment faster and achieve a higher convergence rate, especially for the \textit{Hopper Hop} task and \textit{Reacher Hard} task. In contrast, the SAC agent achieved low performance in most tasks due to its performance limitation, and MEM only slightly promoted its sample-efficiency. Figure~\ref{fig:dmc result 1} proves that the multi-view observations indeed provide more comprehensive information about the environment, which helps the agent to fully comprehend the environment and make better decisions.

Next, we compared MEM with other methods that consider representation learning and intrinsic reward-driven exploration. As shown in Figure~\ref{fig:dmc result 2}, DrQ-v2+MEM achieved the highest policy performance on all six tasks. Meanwhile, DrQ-v2+RE3 also performed impressive performance on various tasks, which also significantly promoted the sample-efficiency of the DrQ-v2 agent. In particular, DrQ-v2+MEM achieved an average episode return of 220.0 in \textit{Hopper Hop} task, while DRIBO and CURL failed to solve the task. We also observed that CURL and DRIBO had lower convergence rates on various tasks, which may be caused by complex representation learning. Even if the design of representation learning is more sophisticated, the information extracted from single-view observations is still limited. In contrast, multi-view naturally contains more abundant information, and appropriate representation learning techniques will produce more helpful guidance for the agent.

Furthermore, we tested MEM on six locomotion tasks with sparse rewards to evaluate its adaptability to real-world scenarios, namely \textit{Acrobot Swingup}, \textit{Cartpole Swingup Sparse}, \textit{Cartpole Balance Sparse}, \textit{Ball in Cup Catch}, \textit{Pendulum Swingup}, and \textit{Reach Duplo}, respectively. Figure~\ref{fig:dmc result 3} illustrates the performance comparison between MEM, RE3 and, vanilla DrQ-v2 agents. By providing high-quality intrinsic rewards, MEM still achieved superior sample-efficiency in sparse-reward setting, especially in the \textit{Acrobot Swingup} task. The combination of DrQ-v2 and RE3 also obtained remarkable performance on various tasks. It achieved the fastest convergence rate in the \textit{Cartpole Swingup Sparse} task, which demonstrates the high effectiveness and efficiency of the intrinsic reward-driven exploration.

\subsection{Procgen Games}
\subsubsection{Setup}
Next, we tested MEM on nine Procgen games with graphic observations and discrete action space \citep{cobbe2020leveraging}. Since Procgen games have procedurally-generated environments, it provides a direct measure to evaluate the generalization ability of an RL agent. We selected Proximal Policy Optimization (PPO) as the baseline \citep{schulman2017proximal}, and three approaches were selected to serve as the benchmarking methods, namely RIDE, RE3, and UCB-DrAC, respectively \citep{raileanu2020automatic, seo2021state, raileanu2020ride}. RIDE uses the difference between consecutive states as intrinsic rewards, motivating the agent to take actions that result in significant state changes. In contrast, UCB-DrAC tackles visual control tasks via data augmentation and uses upper confidence bound algorithm to automatically select an effective transformation for a given task. The following results were obtained by setting $k=5, \beta_{0}=0.1$ and $\kappa=0.00001$, and more details are provided in Appendix~\ref{appendix:procgen experiments}.
\begin{table}[h]
	\caption{Performance on nine Procgen games for training 25M environment steps. The mean and standard deviation are computed over ten random seeds.}
	\label{tb:procgen performance comparision}
	\begin{center}
		\begin{tabular}{l|llll|l}
			\hline
			Game      & PPO          & UCB-DrAC     & RE3          & RIDE         & MEM          \\ \hline
			BigFish   & 4.0$\pm$1.2  & 9.7$\pm$1.0  & 5.5$\pm$1.6  & 8.3$\pm$2.8  & $\mathbf{18.5\pm5.2}$ \\
			StarPilot & 24.7$\pm$3.4 & 30.2$\pm$2.8 & 31.6$\pm$4.2 & $\mathbf{32.5\pm8.1}$ & 31.2$\pm$5.7 \\
			FruitBot  & 26.7$\pm$0.8 & 28.3$\pm$0.9 & 28.4$\pm$2.5 & 29.2$\pm$2.3 & $\mathbf{32.2\pm1.8}$ \\
			BossFight & 7.7$\pm$1.0  & 8.3$\pm$0.8  & 9.4$\pm$2.2  & 8.6$\pm$1.7  & $\mathbf{10.1\pm2.3}$ \\
			Ninja     & 5.9$\pm$0.7  & $\mathbf{6.9\pm0.6}$  & 5.0$\pm$1.4  & 6.0$\pm$1.9  & 5.2$\pm$1.4  \\
			CoinRun   & 8.5$\pm$0.5  & 8.5$\pm$0.6  & 9.0$\pm$1.2  & 9.0$\pm$1.6  & $\mathbf{9.5\pm1.3}$  \\
			Jumper    & 5.8$\pm$0.5  & 6.4$\pm$0.6  & 6.0$\pm$1.3  & 6.5$\pm$1.3  & $\mathbf{7.0\pm0.9}$  \\
			DodgeBall & $\mathbf{11.7\pm0.3}$ & 4.7$\pm$0.7  & 3.6$\pm$0.8  & 3.0$\pm$0.7  & 4.0$\pm$0.5  \\
			Miner     & 8.5$\pm$0.5  & 9.7$\pm$0.7  & 4.8$\pm$1.3  & 5.4$\pm$0.7  & $\mathbf{9.6\pm0.6}$  \\ \hline
		\end{tabular}
	\end{center}
\end{table}

\begin{figure*}[h]
	\centering
	\includegraphics[width=0.95\linewidth]{figures/procgen_result_1.pdf}
	\caption{Smoothed training curves of six Procgen games over ten random seeds. MEM outperforms the benchmarking schemes by combining the advantages of multi-view observations and intrinsic reward-driven exploration. The solid line and shaded regions represent the mean and standard deviation, respectively.}
	\label{fig:procgen result 1}
\end{figure*}

\subsubsection{Results}
Table~\ref{tb:procgen performance comparision} illustrates the testing performance comparison of MEM and benchmarking methods over ten random seeds. The combination of PPO and MEM achieves a higher average episode return as compared to the vanilla PPO agent. Meanwhile, MEM outperformed the UCB-DrAC in seven of nine games by combining the advantages of multi-view observations and intrinsic reward-driven exploration. UCB-DrAC beat the vanilla PPO agent in seven of nine games and achieved the highest performance in the \textit{Ninja} game. In addition, RE3 and RIDE outperformed the vanilla PPO agent in six games and seven games, and RIDE achieved the highest performance in the \textit{StarPilot} game. However, the vanilla PPO agent won the \textit{DodgeBall} game while the other methods failed to learn. Figure~\ref{fig:procgen result 1} illustrates the comparison of training curves of six selected games. It is obvious that intrinsic reward-driven approaches realized higher sample-efficiency as compared to the vanilla PPO agent, especially in the \textit{BossFight} and \textit{FruitBot} games. We also observed that MEM produced more oscillating learning curves and converged more slowly in most games. This is mainly because modeling from multi-view observations requires more meticulous adjustment, and our multi-view intrinsic rewards forced the agent to explore aggressively. For a procedurally-generated environment like Procgen games, the agent must quickly adapt to the dynamically changing scenes. To that end, multi-view observations can provide more reference information, while intrinsic rewards allow the agent to thoroughly comprehend the environment. Therefore, MEM can achieve higher generalization ability and facilitate solving real-world problems.

\section{Conclusion}
In this paper, we investigated the visual control problem and proposed a novel method entitled MEM. MEM is the first approach that combines multi-view representation learning and intrinsic reward-driven exploration in RL. MEM first extracts high-quality features from multi-view observations before performing RL on the learned features, which allows the agent to fully comprehend the environment. Moreover, MEM transforms the multi-view features into intrinsic rewards to improve the sample-efficiency and generalization ability of the RL agent, facilitating solving real-world problems with sparse-reward and complex observation space. Finally, we evaluated MEM on various tasks from DeepMind Control Suite and Procgen games. Extensive simulation results demonstrated that MEM could outperform the benchmarking schemes with simple architecture and higher efficiency. This work is expected to stimulate more subsequent research on multi-view reinforcement learning and intrinsic reward-driven exploration.

\clearpage

redpajama_set_name: RedPajamaArXiv


text:
Seven & i Holdings Co Ltd (3382) - Financial and Strategic SWOT Analysis Review
Market: Retailing
Japan, 37 pages report, published by GlobalData
Seven & i Holdings Co Ltd (3382) - Financial and Strategic SWOT Analysis Review provides you an in-depth strategic SWOT analysis of the company's businesses and operations. The profile has been compiled by GlobalData to bring to you a clear and an unbiased view of the company's key strengths and weaknesses and the potential opportunities and threats. The profile helps you formulate strategies that augment your business by enabling you to understand your partners, customers and competitors better
Seven & i Holdings Co Ltd, Performance Chart (2016 - 2020)
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Seven & i Holdings Co Ltd, Key Facts
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Seven & i Holdings Co Ltd, History
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Seven & i Holdings Co Ltd, Key Competitors
Seven & i Holdings Co Ltd, Ratios based on current share price
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Section 1 - About the Company
Seven & i Holdings Co Ltd - Key Facts
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If you still haven't decided whether or not to attend the ASLA 2013 Annual Meeting & EXPO, you might benefit from a few minutes clicking around ASLA's The Landscape Architect's Guide to Boston. It will hopefully tip the balance in favor of attendance. This online guide highlights the diversity of the landscapes one can experience in and around Boston. And there is surely something for everyone.
image: Ayers Saint Gross Inc.
When the US Secretary of the Interior first introduced the Standards and Guidelines for Architectural and Engineering Documentation: HABS/HAER in 1983, Ronald Reagan was in the White House and most of us did not yet know how to type—let alone know how to work on a PC. This document was formulated in a pre-digital age and is, not-surprisingly, pre-digital in orientation; specifying such parameters for the documentation of historic structures as the use of black and white photography, the requisite submission of film negatives and consistency of hand-lettering. Today, some of the specific requirements seem almost quaint: "Level I measured drawings will be lettered mechanically (i.e., Leroy or similar) or in a hand printed equivalent style." Incidentally, these standards served as a prototype for the Historic American Landscape Survey (HALS) when it was initiated in 2000.
In the decades since 1983, we have witnessed a revolution in Information Technology. It has resulted in fundamental changes to the way that disciplines such as landscape architecture and history are practiced. In the 1990s, Computer-Aided Design transformed the workflow of landscape architectural practice from design and documentation through construction. A second wave of transformation has arrived with Building Information Management (BIM) / Site Information Management (SIM) applications and is beginning to transform the roles of designer and contractor in project delivery. In the study of history today, the use of Geographic Information Systems (GIS) for research and analysis is not uncommon. Other new technologies and software applications are now emerging with the potential to transform a wide array of disciplines from ecology to historic preservation. What follows is a discussion of one of these tools in particular—the digital "Point Cloud Survey"—and a review of its use in the context of a preservation and adaptive reuse project in Saudi Arabia.
The Historic American Landscapes Survey (HALS) was created in 2000 to document our country's dynamic landscapes. Each year the HALS office at the National Park Service issues a challenge, encouraging landscape architects and preservation professionals to document historic landscapes related to a new theme.
Individuals and groups from every state are encouraged to complete at least one HALS short format history for a cultural landscape related to this theme, whether vernacular or designed, in order to increase awareness of the role of women in shaping the American landscape. The top three submissions will receive awards and be announced at the 2013 ASLA Annual Meeting and Expo in Boston during the HALS Meeting.
If you have not already begun a submission, there is still time to start. Short format histories should be submitted to HALS at the National Park Service no later than July 31, 2013 (c/o Paul Dolinsky, Chief of HALS, 202-354-2116). All HALS documentation is permanently housed and publicly accessible at the Library of Congress.
The ASLA Annual Meeting & Expo will be here before we know it in Phoenix, Arizona from September 29 – October 1.
Here is a list of Historic Landscape Preservation related meetings, field sessions, and education sessions.


text:
Yudikha () is a rural locality (a village) in Klyazminskoye Rural Settlement, Kovrovsky District, Vladimir Oblast, Russia. The population was 67 as of 2010.

Geography
Yudikha is located on the Klyazma River, 36 km northeast of Kovrov (the district's administrative centre) by road. Doronikha is the nearest rural locality.

References

Rural localities in Kovrovsky District

redpajama_set_name: RedPajamaWikipedia


text:
Apochinomma bilineatum är en spindelart som beskrevs av Cândido Firmino de Mello-Leitão 1939.

Apochinomma bilineatum ingår i släktet Apochinomma och familjen flinkspindlar. Inga underarter finns listade i Catalogue of Life.

Källor

Flinkspindlar
bilineatum


North Auckland and West Auckland - it's Time 2 Go!
Be at bliss in the cool, calm and collected North Auckland, or prepared to get rough in the wild West. Better yet, enjoy the best of both regions.
Plan your tailor-made holiday in New Zealand with expert local kiwi advice! It's Time 2 Go!
A number of popular activities attract visitors to the Kowhai Coast. Take a trip on horseback along the beautiful beaches and through surrounding forests and farmland. Snorkel or dive off the coast or follow the wine trail. See sheep dog displays, try your hand at shearing and explore the old copper mines on Kawau Island, or kayak down the Puhoi River and enjoy a picnic under the trees.
From its award-winning pinot noir and chardonnay to its wild surf beaches, huge kauri trees, grand waterfalls, and thermal springs, it's all here in Waitakere, on Auckland's west coast. Hike the Waitakere Ranges, mountain bike or 4WD through the Woodhill Forest, ride a horse on Muriwai's glittering black iron-rich sands, or simply relax in hot thermal springs. Two hundred kilometres of walking and hiking tracks ensure you won't be bored.
The Hibiscus Coast, a narrow coastal strip incorporating the beautiful protruding peninsula of Whangaparaoa, boasts more than 208 hectares of reserves and golf courses. There's access to 17 safe and sandy beaches plus a multitude of sandy coves, all within easy access of local shops, restaurants, wine bars and cafes.
For more ideas on the best things to see and do, our favourite places to stay, and the top spots to wine and dine tantalise your taste buds with the best of New Zealand cuisine and delicious kai in the North + West Auckland region check out our website.


On Easter Monday, 24 April 1916, the tricolour flag was hoisted over the General Post Office. Shortly after noon Padraig Pearse, standing beneath the high portico, read the Proclamation publicly proclaiming Ireland a republic and a sovereign independent state. John O'Connor recounts the birth of this historic document which was to become one of the cornerstones of the new state. Why was it necessary? Who wrote it? Who secretly printed it and where? How was it distributed? How many exist? How would you know an authentic print?
To see other books by John O'Connor, click here.


No matter how comfortable the couch, couch surfing is never an ideal situation.
At the beginning of the semester I was bouncing between friends' couches. I would stay on one friend's couch for a few days before moving onto the next. Frankly, I hated it. I always felt like I was imposing on their space.
Not having a place to call my own made me realize how grateful I was for what I had been taking for granted. Although my friends were welcoming me with open arms, it never felt like home. I was beginning to miss warm, home-cooked meals and my cat.
Another setback to couch surfing was having to schedule my day around when my friends would be home. Personally, I never felt comfortable being at my friends' houses without them.
During the time period that I couch surfed, I wasn't getting homework done because I was always hanging out with whoever I was staying with or bending my schedule to fit theirs.
Not knowing where you will sleep the following night is one of the most terrifying experiences. I never thought it would be as scary as it was, but I was surprised. It was unnerving. The last thing I wanted to do was sleep in my car.
Luckily, it never came to that for me. But for many people it does.
Not everybody has family or friends they can stay with. Some people may not even have family in the area. Where does that leave them to sleep when they have no other place to go? If they have a car, maybe they can sleep there, but often the only choice is homeless shelters.
I had friends joke that I was homeless. Often it offended me. I didn't like the thought of being homeless; it wasn't a reality I wanted to accept. I felt like I was less than everybody else, and that made me feel as if I was losing pride.
Thankfully, couch surfing didn't last a long time before I found roommates and a place I could call home. But even then, the place I landed didn't feel like home. It wasn't home.
I was lucky. Not all people who couch surf find somewhere more permanent as quickly as I did. Couch surfing made me realize very quickly all that I had to be thankful for when I lived with my parents.
It's that time of season to be thankful. Take a minute and show gratitude for having a place to call home, because other people may not be as fortunate as a lot of us are.
Miss/Mrs. Murphy, I loved your column on couch surfing I was one of those people. God bless you! Happy Holidays to you and loved ones.


It has been confirmed by the Premier League that they intend to use VAR from next season, provided the decision is ratified by the International Football Association Board and Fifa.
The story will come as great news for Reds fans, who have not seen any referee in the Premier League award a penalty for us at Anfield since Mo Salah missed in the 3-0 win over Huddersfield on October 28 2017.
That the Reds have not been awarded a spot-kick for over a year is ridiculous – in that time, Spurs have got more penalties at Anfield than we have.
With VAR, though, that could all be about to change. No more will officials be able to miss obvious decisions like Salah being fouled against Brighton on the last day of last season or against Newcastle back in March.
VAR would have denied the Reds the opportunity to counter-attack against Fulham while the visitor's goal ruled out for offside was checked, so it's not all good news, but it will certainly benefit us more than hinder us overall.
Penaltypool might be making a return!


text:
A historical sketch; life and time of Jonathan Hutchinson (1828-1913), the first sarcoidologist
O.P. Sharma, H. Shigemitsu
Inhibitors of tumor necrosis factor (TNF) in sarcoidosis:Who,What, and How to use them
R.P. Baughman, E.F. Lower, M. Drent
Original Articles: Basic Research
Differentiation of sarcoidosis from tuberculosis using real-time PCR assay for the detection and quantification of Mycobacterium tuberculosis
Y. Zhou, H.P. Li, Q.H. Li, et al.
M. avium binding to HLA-DR expressed alleles in silico: a model of phenotypic susceptibility to sarcoidosis
C. Saltini, M. Pallante, E. Puxeddu, et al.
Bronchiolitis obliterans syndrome and survival following lung transplantation for patients with sarcoidosis
K.M. Wille, A. Gaggar, A.S. Hajari, et al.
HLA and environmental interactions in sarcoidosis
M.D. Rossman, B. Thompson, M. Frederick, et al.
Effects of oxygen on exercise-induced increase of pulmonary arterial pressure in idiopathic pulmonary fibrosis
S. Pouwels-Fry, S. Pouwels, C. Fournier, et al.
Case Series
Improvement of cardiac sympathetic nerve function in sarcoidosis
N.M. Smulders, A. Bast, M.J.P.G. van Kroonenburgh, et al.
18F-FDG PET in sarcoidosis: an observational study in 12 patients with infliximab
R.G.M. Keijsers, J.F. Verzijlbergen, D.M. van Diepen, et al.
Page 1403-150


This kind of salad is one of my all time favourites! They are so easy to throw together and you can easily mix and match the ingredients with what you have on hand.
This is my version of a Mexican inspired rice salad. I just love the punch of flavour you get from the lemon and lime juice combined with the chipotle spice and the crunch from the capsicum make it a party in your mouth!
The chunky guacamole I created to go on the top is another favourite, the red onion brings the perfect amount of heat while the creamy avocado adds a wonderful texture. The tomatoes also bring a refreshing acidity to the mix.
Start by cooking the brown rice in 2 cups of vegetable stock and 1 cup of water. Bring everything to a boil, then turn down to simmer for 25 mins, then set aside for an extra 5 mins for the rice to absorb any leftover water.
While the rice is cooking chop the mushrooms, capsicums and coriander and place them in a large bowl along with the corn and black beans. Then add the dressing ingredients straight into the bowl and mix everything well.
Also make the chunky guacamole while the rice is cooking. Simply mash the avocados in a bowl and add in the red onion, cherry tomatoes and lime juice and stir to combine.
When the rice is finished drain any extra liquid then mix it in to the salad and stir to ensure the dressing coats everything.
To serve simply add desired amount of the rice salad to a bowl and top with the chunky guacamole! Enjoy.


text:
Working in the age of technological socialism [Opinion]
The world's facing many grand challenges, with mega-trends emerging that have the potential to disrupt life as we know it. The granddaddy of all challenges facing mankind has to be the way technology is and will continue to transform our lives.
While many tech advancements will fundamentally improve our lives, certain underlying technologies are going to turn contemporary life on its head. It's a scenario predicted by author, inventor and futurist Ray Kurzweil, who believes that artificial intelligence (AI) will become so advanced that technology will surpass mankind's collective intelligence and our ability to comprehend or, potentially, even control it.
We will, quite literally, experience the rise of the machines. In fact, we already are.
Kurzweil, now the director of engineering at Google, estimates that at our current rate of progress, machines will reach human levels of intelligence by 2029, accelerating our journey toward the Singularity.
The prospect and implications of this have many people spooked. Some still question whether these predictions will ever come to fruition, while others are fearful of what this means for mankind. But more than the far-fetched threat of a robot uprising, the major issue we need to grapple with is the impact that AI will have on jobs and our ability to earn a living.
Technologies underpinned by quantum computing and AI, such as machine learning, nanotechnology, robotics, and the Internet of Things, are already transforming almost every sphere of life, from energy, transportation and communication, to agriculture, education, modern medicine and commerce.
We will, quite literally, experience the rise of the machines
Quantum computing utilises the principles inherent in nature to process information, except it's literally billions of times faster and millions of times smaller and less resource intensive. AI, on the other hand, will replace all our left brain activities, as it becomes leaner, faster and more efficient than our analytic minds.
AI's impact on an evolutionary scale will be comparable to effect electricity had on society. AI will add intelligence to everything, like electricity brought power to our world and sparked the second industrial revolution. Through AI, everything around us will become intelligent and think for us, to the point where ambient intelligence will pervade our lives, ushering us into the fourth industrial revolution.
While quantum computing will exponentially increase processing power, unlocking untold computing potential, and AI will improve our lives by making the human-technology interface seamless and fluid, the white robotic elephant in the room for most people is how their jobs will be impacted.
Let's just state this upfront and get it out of the way: Billions of jobs will be lost, but before you completely freak out, know that this will only be temporary.
Consider the first industrial revolution. At the turn of the 19th century steam-powered machines replaced human labour in industry. It's reported that 80% of the population were farming at the time and when the revolution began, the farmers revolted. They demolished tractors and factories in protest against job losses. But, you know what? We adapted and evolved.
Today, a mere 2% of the global population farms. The majority of us have learnt new skills that enable us to apply ourselves in different ways.
I'd also like to propose that a loss of jobs isn't necessarily a bad thing. Our concept of work is an archaic social construct — a relic of the first industrial revolution that's been ingrained in us through generations. This idea of clocking in and out from nine to five is ridiculous in the modern context.
Ultimately, jobs have become a crappy substitute for purpose. We're stuck in the rat race and few of us ever get the privilege to dig deeper to find out what it means to be human by following our curiosity. It's little wonder then that the anti-depressant pharmaceutical market has become a multi-billion dollar industry. There's no doubt that the concepts of careers, jobs, income, self-worth, and purpose are in desperate need of a paradigm shift.
The next industrial revolution can usher in a period of technological utopianism
Thankfully, more than just pose problems, our current predicament offers solutions to this issue, but only if we tackle it with the correct approach. By thinking strategically and equipping ourselves with the mindset and framework needed to adapt, the next industrial revolution can usher in a period of technological utopianism — one where technology helps to fulfill mankind's new ideals.
By embracing technology-mediated change, rather than resisting or ignoring it, society can move towards an era of technological socialism or conscious capitalism, where many aspects of our lives become freely available and where everything operates for the benefit and well-being of all mankind.
It's the utopian vision that everyone at the Singularity University are working towards. I recently spent a week there, immersed in the institution's exponential thinking around how the world should adapt its mindset to help humanity thrive.
And as the future continues to unfold as predicted, we'll be faced with one of two choices: Either we fall victim to this rapid change, or we become the architects of it. To do so we need to become flexible and agile in our approach to change management, embracing this fast-paced evolution, instead of resisting it.
Offering organisations a framework to contextualise and manage the disruption associated with exponential technology-fuelled growth, Peter Diamandis, co-founder and executive chairman of Singularity University, offers a guide to navigating the digital economy.
Diamandis states that the exponential growth cycle of disruptive digital technologies follows six key steps, which he terms the 6Ds. Every organisation that plans to innovate and disrupt in the new digital economy should embrace this roadmap for rapid growth and development through technology-led innovation.
According to Diamandis, when an industry becomes Digitised, it starts behaving like information technology, experiencing the same degree of exponential growth witnessed in computing.
However, the initial stages of digitisation can be Deceptive, as growth is initially slow and few believe that this new technology will materially impact the sector. That growth rate accelerates, though, and this new exponential technology Disrupts the industry by creating entirely new markets. This leads to Demonetisation as money leaves the industry because technology makes everything cheaper, often to the point of being free.
What follows is Dematerialisation, where physical products are removed from the industry, and, finally, Democratisation happens because everyone gains access to a truly digitised product or service.
It's a pattern already emerging in a number of industries, like transport and communication. And all future trends that will impact business already fall somewhere within this framework.
Take Google Glass as a prime example. It's currently in the deceptive stage. Many have written off the concept, but it's going to make a massive resurgence, with the potential to disrupt the trillion dollar screen industry. The same happened with digital cameras, which ultimately led to the death of Kodak.
To ensure your organisation doesn't follow Kodak into at annuls of business history, and the footnotes of business school literature about what not to do, then use the 6Ds to leverage the potential of exponential technology, rather than fall victim to its awesome power.


We joked about the "driveway" to our rental house in Viscos, France. It's actually the D149, which climbs 751 feet in only 1.5 miles, averaging 9%. Let's just say that the climb up to Viscos was a very tough way to finish the day . . . especially in the hot afternoon sun!
Here's a video of the descent starting outside our rental house and ending at the main road, the D921, on Friday, July 27, 2018 on our way to the watch the Tour de France (hence the crowd and Gendarme at the bottom).