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Amancara, a leading producer of interior furnishings, has been making its own cotton, linen, silk and cashmere since 1952. Amancara only uses the highest quality raw materials in the world and transforms them into fabrics in our Italian workshops. Sophisticated techniques, combined with experience gained from a long tradition, guarantee the best products available.
The art of embroidery has a longstanding tradition in Tuscany. Our skilled artisans use the finest materials and with meticulous attention to detail ensure the highest standards. Behind the brand Amancara is a family owned business that has been investing in quality and excellence for generations.


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Q: Ubuntu network interfaces file has only lo and not getting updated even after adding eth0.How to overcome this in VMware 16 pro? Output of ifconfig -a
Contents of /etc/network/interfaces
I was trying to assign a static ip for my Virtual machine but couldn't succeed in pinging it from my windows machine. I also tried all the solutions available.
I am using Ubuntu 20.04
These are the contents of interfaces file:
interfaces(5) file used by ifup(8) and ifdown(8)

auto lo eth0

iface lo inet loopback

iface eth0 inet static
address 192.168.11.10
network 192.168.11.0
netmask 255.255.255.0
gateway 192.168.11.1

These are the output of ifconfig -a :
shakthivel@ubuntu:/etc/network$ ifconfig -a

ens33: flags=4098<BROADCAST,MULTICAST>  mtu 1500
        ether 00:0c:29:5c:14:13  txqueuelen 1000  (Ethernet)
        RX packets 0  bytes 0 (0.0 B)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 0  bytes 0 (0.0 B)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

ens36: flags=4098<BROADCAST,MULTICAST>  mtu 1500
        ether 00:0c:29:5c:14:1d  txqueuelen 1000  (Ethernet)
        RX packets 0  bytes 0 (0.0 B)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 0  bytes 0 (0.0 B)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

lo: flags=73<UP,LOOPBACK,RUNNING>  mtu 65536
        inet 127.0.0.1  netmask 255.0.0.0
        inet6 ::1  prefixlen 128  scopeid 0x10<host>
        loop  txqueuelen 1000  (Local Loopback)
        RX packets 7490  bytes 534296 (534.2 KB)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 7490  bytes 534296 (534.2 KB)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

In VMware Virtual network Editor
Subnet IP : 192.168.11.0

Type:NAT

DHCP:Disabled

Any help is appreciated

redpajama_set_name: RedPajamaStackExchange


DrippingCats1 is a unique transaction for a cat household in or near Dripping Springs, Texas, where it should be 76 degrees and slightly drippy today. These cat people will improve their Vesper cat tree with two columns ( 17.5 and 31.5 inch ) Price includes shipping to domain in question. There will be a $3.95 bonus for completing this purchase by end of day 6/15/18. Transaction evaporates on 6/30/18.


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Bulldogs hold steady in latest NAIA Top 25 Poll
Defense shines in 62-49 win over Dakota Wesleyan
#7 Bulldogs travel to Dakota Wesleyan on Saturday
#7 Concordia picks up 89-87 overtime win over #2 Briar Cliff
Tuesday, Dec. 6, 2011
#7 Concordia to clash with #2 Briar Cliff on Wednesday evening
Monday, Dec. 5, 2011
Bulldogs move up in latest NAIA top 25
Saturday, Dec. 3, 2011
#13 Morningside College holds on for 69-66 win over #9 Concordia
#9 Bulldogs set for Saturday matchup with #13 Mustangs
Bulldogs defeat Midland University to remain perfect on the year
Concordia moves into top 10 in latest NAIA top 25 poll
Bulldogs keep win streak intact while living up island life
There were two wins, but Concordia's trip to Hawaii was so much more than just basketball. The Bulldogs had a "blast" while relaxing on the beach, surfing and visiting Pearl Harbor, among other activities.
Thursday, Jul. 7, 2016
Hero of 2009 national tourney returns to spotlight
Whitney (Stichka) Daberkow didn't necessarily want the spotlight, but she thrived underneath it anyway. The humble Bulldog is set to join the Concordia Athletic Hall of Fame in October.
Tuesday, Jun. 28, 2016
Janovich talks ACL rehab, NFL brother
Mary Janovich recently resumed basketball activities as she continues her recovery from an ACL tear suffered in January. She talks about her injury rehab and her brother Andy in this Q&A.
Tuesday, Apr. 26, 2016
Kohlwey accepts position at Lutheran High School in Colorado
Marty Kohlwey, a 13-year veteran of Concordia men's and women's basketball coaching staffs, has accepted a new role at Lutheran High School in Parker, Colo.
Thursday, Mar. 24, 2016
Bulldogs reinvent themselves on way to fifth-straight national tournament
Following the lead of freshman Quinn Wragge, the Concordia women's basketball program made its fifth-straight national tournament appearance in 2015-16.
Thursday, Mar. 3, 2016
Wragge embraces starring role in rookie season
Quinn Wragge is only a freshman, but she enters the national tournament leading 16th-ranked Concordia in scoring, rebounding, steals, blocks and field goal percentage. She's not your ordinary freshman.
Wednesday, Mar. 2, 2016
Concordia draws Goshen College at national tournament
The Concordia women's basketball program learned on Wednesday that it would make its 15th all-time appearance at the national tournament. The Bulldogs will play in the first round on March 9.
Thursday, Jan. 7, 2016
Tenth-ranked Bulldogs reload with balanced approach
After losing several key pieces from the 2014-15 team that made a run to the national title game, the Bulldogs have reloaded with a balanced approach in 2015-16.
Wednesday, Sep. 30, 2015
Krueger starred during women's basketball's rise to elite status
It was only a matter of time before Sarah Harrison Krueger found her way into the Concordia Athletics Hall of Fame.
Monday, Aug. 10, 2015
2014-15 Bulldog women's basketball delivered memories built to last
Since 1992, 14 Concordia women's basketball teams have appeared at the national tournament with four advancing all the way to the national semifinals. But in 2015, the Bulldogs reached new heights by motoring to the national title game for the first time in program history.

redpajama_set_name: RedPajamaCommonCrawl


text:
Ribometrix Announces $7.8 Million in New Funding to Advance RNA-Targeting Drug Discovery Platform
Ribometrix
DURHAM, NC, September 27, 2019 -- Ribometrix, a biotechnology company developing small molecule therapeutics that directly target RNA to treat human diseases, today announced $7.8 million in additional funding from existing investors, the Dementia Discovery Fund and Illumina Ventures. Ribometrix also announced the appointment of Mike Clayman, M.D., as chairman of the board of directors. The new funding will support the continued development of the Ribometrix discovery platform, which uses sophisticated structural analysis and proprietary assays to identify ligands that bind to highly structured pockets in therapeutically compelling RNAs to modulate protein expression. Ribometrix's approach is designed to enable the discovery of selective, orally bioavailable, RNA-binding small molecules with broad tissue distribution and bioavailability.


"Now is the time for healing." That's how the South Carolina Republican Party chairman described the aftermath of his party's decision to censure their governor, Mark Sanford, who left the state on a secret rendezvous with his Argentine lover two weeks ago. The vote has no bearing on Sanford's ability to remain in office. 22 members of the party's executive committee voted to reprimand Sanford; 10 called for his resignation; nine voted to support him.


text:
Plant a Tree for Dianne
Dianne Mancini
October 16, 1947 ~ January 6, 2022 (age 74)
Dianne Hodges Mancini, 74, of Fayetteville, NC, passed away on January 6, 2022 when her weary heart stopped.
Dianne was born on October 16, 1947 to Harold and Marion Mancini in Fayetteville, NC. Four years later the family welcomed her only sister Dara into the family. She and her sister would spend countless childhood days in the country with aunts, uncles, and cousins horse playing and eating well.
Dianne graduated from E.E. Smith High School in 1965 andwent on to graduate in Education at North Carolina A&T StateUniversity. After graduating with a B.S. in Education, she started teaching for Cumberland County School District,kickstarting a career that would span over three decades. She developed an affinity for working with special needs students and devoted most of her career to the specialty. Her career was personally rewarding, and she was most proud of her students. She stayed in touch with many of her students over the years.
Dianne would go on to further her education by taking graduate coursework at Fayetteville State University (FSU). During this time, she joined Delta Sigma Theta's Graduate Chapter and enjoyed participating in their contributions to the community.
In 1999, Dianne retired from teaching full-time and spent her free time reading, traveling, gaming, and bird-watching – her favorite hobby. She also continued to work with students as a substitute teacher, until health problems prevented her from doing so.
Dianne was preceded in death by her parents Marion Hodges and Harold Mancini. She is survived by her sister Dara Belson [Martin], her niece DaMara Belson, her nephew Devin Belson, and countless cousins.
Dianne's family is most appreciative of the kind words, thoughts, prayers, and calls that have been received to date and welcome family, friends, and colleagues to leave a memory of Dianne on the Tribute Wall. Memorial services will be private. Contributions in her memory may be made to your local Humane Society.
"That it will never come again is what makes life so sweet"
To send flowers to the family or plant a tree in memory of Dianne Mancini, please visit our floral store.
You can still show your support by sending flowers directly to the family, or by planting a memorial tree in the memory of Dianne Mancini


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A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic
Neuroscientist By Lisa Mosconi, Ph.D.
Neuroscientist
Lisa Mosconi is a neuroscientist and author currently living in New York City. She received a dual Ph.D. degree in neuroscience and nuclear medicine from the University of Florence, Italy.
A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic
A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic A Neuroscientist On Gender Disparity In Brain Health & The Alzheimer's Epidemic
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All over the world, women's equality, which has come so far since the days of American suffragettes and Women's Lib, is being re-evaluated in real time. Between #MeToo on the one hand and "lean in" on the other, between the increasing demand on women to contribute equally to the workforce and to the household despite the persistent gap in wages, questions come up every day about how equal, or how different, women are. At the same time, there are headline conversations about what it even means to be female to begin with.
But there are deeper shades of nuance in these movements, ones that speak instead to how women are more subtly undermined—not assaulted, but neglected, dismissed, and at times sabotaged. For all the discussions about the many ways women are treated differently from men, one topic that remains woefully neglected is the one that is closest to my heart: the notion of gender disparity around health and wellness.
For too long, women have been overlooked.
It doesn't take a scientist to point out that there is something askew with gender disparity. But it does take a scientist to denounce the way that women are also overlooked medically, where our needs too often go unrecognized, misattributed, or unaddressed. This is in large part due to the fact that the field of medicine has been historically male-dominated, which led to the fundamental model for most medical research being not a person but a man. For a number of reasons, medical interventions have been largely tested with, dosed for, and modeled based on their effects on men.
This is not the source of a conspiracy theory but rather an acknowledgment of the compound effects of assumptions made over centuries, which have led to our teaching and practicing "bikini medicine." For those of you not familiar with the term: Historically, medical professionals believed that the only thing that set women apart from men were those body parts that lie beneath the small triangles of a bikini—namely, our reproductive organs. Setting these "parts" aside, as if one could, meant that most doctors would diagnose and treat both sexes in the exact same way. This biased approach remains just as prevalent and deeply destructive in the hard sciences as it is in many other aspects of culture at large.
Given the worldview derived from that model, the very notion of women's health is problematic. If you ask doctors to look at a female patient through the lens of "women's health," they will likely run a mammogram or collect cells from the cervix to examine them for cancer. Doing blood tests for estrogen and other hormones is just as common a practice. In other words, women's health is confined to the health of our reproductive organs. Let's be clear that all these procedures have indeed changed and bettered the lives of millions of women around the world. However, these same lines of research, inquiry, and intervention are a direct consequence of a reductive understanding of what a woman is.
Brain health is women's health.
Women's brain health is one of the most underrepresented and unspoken concerns, one that is constantly glossed over as a result of the male-based medical paradigm. Somehow, in the landscape of things that we're told a woman should be concerned with, her brain has seldom been one of them. Further, very few doctors have the knowledge or framework to address the many ways that brain health plays out differently in women than in men.
Of all the challenges to brain aging, nothing compares to the unprecedented scale of Alzheimer's disease, which has become the most common form of dementia, currently affecting 5.7 million people in the United States alone. With rates increasing at their current clip, the disease will almost triple by 2050. On a global scale, Alzheimer's patients will number somewhere between the populations of Russia and Mexico! Bottom line: We are facing nothing less than an Alzheimer's epidemic.
Today, Alzheimer's is as real a threat to women's health as breast cancer is.
Women in their 60s are about twice as likely to develop Alzheimer's over the rest of their lives as they are to develop breast cancer. And yet breast cancer is clearly identified as a women's health issue, while Alzheimer's is not.
One of the most startling facts about the disease is that a 45-year-old woman has a one in five chance of developing Alzheimer's during her remaining life, while a man of the same age has only a one in 10 chance. This is no dismissal of the suffering that men with Alzheimer's will experience. But we need to confront the reality that, at the end of the day, many more women end their lives suffering from the disease.
And this is only the first blow in a one-two punch.
The second blow is that, when it comes to providing caregiving throughout this continuing crisis, it is women once more who will bear the bulk of the burden. As it is mostly women who will find themselves, inadvertently or not, drafted into full-time caregiving roles. Currently, there are 10 million American women providing unpaid health care and assistance to loved ones with dementia, all simultaneously shouldering the steep emotional and financial tolls that accompany that crushing task.
It is time to come to terms with these numbers—not only to confront the large-scale epidemic but also to finally acknowledge, investigate, and react to the very targeted crisis ahead in women's health. In recent years, scientists like me have grown more and more eager to uncover what it is about women's brains that makes us susceptible to Alzheimer's as well as to a host of other medical conditions that affect the brain. Why is this happening? Can we stop it from happening? Our investigations have raised an entire range of thought-provoking existential and scientific questions, not the least of which is: How is it possible we haven't figured this out yet?
The bottom line, and where it leads us.
In medicine, the simple fact is that we don't do as good a job of taking care of women as we do men. A woman often ends up having to prove she is as sick as a man, or has to mirror male symptoms, to receive the same level of care.
As this is present in all aspects of our health care, it is no surprise then that it is equally true when it comes to the health of our brains. Women are falling prey to Alzheimer's but also to depression, migraines, and a number of other conditions that affect the brain. Yet modern medicine is largely unprepared to help them.
Fortunately, scientists have come to the rescue. In recent years, an incredible amount of work has been done both to denounce and to investigate the gender disparity in brain health. My mission is to take this work past the rigors and paywalls of peer-reviewed research and to give a wider voice to "the forgotten gender." Since university, my work has focused on developing tools and strategies to optimize cognitive health, while at the same time warding off Alzheimer's, particularly in women.
Witnessing my grandmother's bitter downward spiral into dementia propelled me to devote my entire career to researching any and all possibilities of detecting the disease ahead of time. When both of my grandmother's younger sisters developed Alzheimer's, too, while their brother did not, my determination grew stronger still. I now find myself keeping a close watch on my mom for any warning signs, though I feel reassured as she carefully attends to a healthy diet and practices her yoga headstands at age 76.
As a middle-aged woman, I am concerned about my own risk. As a mother, I want to make sure my daughter has answers, options, and solutions.
As a scientist, it is my intention to help make preventive medical care to maintain cognitive function an integral part of every woman's medical requirements, as commonplace as regular mammograms, Pap tests, and colonoscopies. Together, let us literally turn the page toward a tomorrow in which there is a dedicated equality of assessment and treatment in health care, our brains included, providing true hope for all.
As women, we experience gaps in income, power, and representation, but we also face a gap in knowledge about our health, collectively and individually. It's time to rectify this and to address our unique symptoms and concerns as related to our brains and to our bodies as a whole. We all want our cognitive life span to match our life span—we can't wait until signs of cognitive decline appear. We must be proactive now.
How women can use their diet to protect their brain health
I've found there are seven steps to a well-nourished, active, and resilient female brain. These steps are designed to maximize intake of brain-healthy nutrients with a specific focus on keeping our brains young, balancing our hormones, and improving energy and mood, while also reducing the symptoms of menopause, protecting the heart, and supporting the immune system.
Step 1: Manage your carbs.
Not all carbs are created equal. These are my three golden rules to eating carbs:
Vegetables and fruits are carbs. Vegetables should make up half of your plate at any given meal.
Whole grains are in; refined grains (white flour, white pasta, and white bread) are out.
Legumes and starches like sweet potatoes are also excellent sources of good carbs.
Step 2: Meet Ms. Phytoestrogen.
There are two major types of phytoestrogens: isoflavones, found mostly in soy; and lignans, abundant in seeds, whole grains, and legumes, and in many fruits and vegetables. This makes a plant-based diet an excellent natural estrogen replacement therapy!
If you are interested in eating more soy to boost your estrogens, make sure you eat fermented organic soy, and in small amounts at that. If we're talking about using isoflavones to relieve menopausal symptoms, we're looking to consume approximately 40 to 50 milligrams of isoflavones per day. This is equivalent to two servings of traditional soy foods, like a scoop of tofu served with a bowl of miso soup or a cup of edamame.
Step 3: Protect your brain with antioxidants.
Of all the organs in the body, the brain is the one that suffers most from oxidative stress. Enter antioxidants, vitamin warriors that are at the ready to fight off free radicals on our behalf.
Fruits such as berries, oranges, grapefruits, and apples (all which have a low-glycemic index) are excellent sources of these nutrients. While blueberries receive most of the attention, blackberries and gooseberries pull an even stronger antioxidant punch. Leafy green and cruciferous vegetables (spinach, kale, broccoli, cabbage), as well as onions, carrots, tomatoes, and squash, are also full of these powerhouses. Artichokes top the charts, possessing more antioxidant density than any other fruit or vegetable. Extra-virgin olive oil and flaxseed oil are loaded with vitamin E while also acting as excellent anti-inflammatories. Plant foods to the rescue!
Step 4: Choose the right fats.
Both the type and source (rather than the amount) of fats are key when assessing health risks in women. Does the fat you're eating come primarily from fruits, vegetables, and seeds, or from animal sources? If from animal sources, is it from fatty fish like salmon, or is it from fried bacon? Does it come from fresh, homemade food or from a box?
Step 5: Feed your microbes.
You may have heard by now that a happy tummy equals a happy brain. That's because our gut is home to the trillions of bacteria and other microorganisms that make up our microbiome. The vast majority of gut bacteria are beneficial to our health—they help us digest food, power up our metabolism, and even produce some essential vitamins.
Sticking to a varied diet, limiting processed foods, avoiding prolonged restricted diets, and consuming adequate fiber, all keep our gut going strong.
Step 6: Go organic as much as you can.
Your body's estrogen is not only influenced by the foods you eat— it is also being altered by your environment: what you breathe, absorb, and consume on a regular basis. While natural estrogens can be safe and helpful, there are innumerable man-made chemicals that masquerade as estrogen but in reality are its evil twin. These are known as xenoestrogens or endocrine disrupting compounds (EDCs).
Overall, we are constantly exposed to thousands of substances that can seriously mess with our hormones. Paying attention to our food choices is a significant first step to avoiding contamination and thereby protecting our bodies, our brains, and our hormones. Organic crops are generally grown without synthetic pesticides, artificial fertilizers, irradiation (a form of radiation used to kill bacteria), or biotechnology.
Step 7: Eat less.
Reducing our caloric intake can boost cognitive capacity, reduce cellular aging, and promote longevity. The strategy behind this practice is based on nearly a century of scientific data showing that stressing our bodies and brains via calorie restriction pushes our cells to grow stronger and more resilient.
Just as muscles get stronger the greater the resistance, so do your brain cells strengthen as they resist hunger. Caloric restriction also boosts the brain's antioxidant defense system, which is particularly important for the female brain.
Reprinted from The XX Brain: The Groundbreaking Science Empowering Women to Maximize Cognitive Health and Prevent Alzheimer's Disease by arrangement with Avery, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. Copyright © 2020, Lisa Mosconi, Ph.D.
Lisa Mosconi, Ph.D. Neuroscientist
Dr. Lisa Mosconi is a neuroscientist and author currently living in New York City. She received a dual Ph.D. degree in neuroscience and nuclear medicine from the University of Florence,...
How To Control Anxiety
With Ellen Vora, M.D.
https://www.mindbodygreen.com/articles/brain-health-is-womens-health


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"Disgusting": Erdogan Vows Action Against Charlie Hebdo Cartoon On Him
HomeWorld"Disgusting": Erdogan Vows Action Against Charlie Hebdo Cartoon On Him
This Article is From Oct 29, 2020
Erdogan's office vowed to take "legal and diplomatic action" while Turkey's NTV television said Ankara had also summoned the second-most senior diplomat at the French embassy to express its "strong condemnation".
WorldAgence France-PresseUpdated: October 29, 2020 7:31 am IST
Erdogan's office vowed to take "legal and diplomatic action".
Istanbul:
Turkish President Recep Tayyip Erdogan vented his outrage Wednesday at a "disgusting" cartoon in the French satirical weekly Charlie Hebdo depicting him looking up a woman's skirt while drinking beer in his underpants.
Under normal circumstances, France's ambassador would have been summoned, but he has been recalled to Paris for consultations in a further sign of the deteriorating diplomatic relations between the two NATO allies.
The front-cover Charlie Hebdo cartoon came out just days after Erdogan called for a boycott of French products and questioned President Emmanuel Macron's sanity for promoting a drive against Islamic extremism.
Macron's defence of the media's right to mock religion -- as exemplified by Charlie Hebdo's blasphemous cartoons of the Prophet Mohammed -- has stirred angry protests across Turkey and swathes of the Muslim world.
Iran's supreme leader Ayatollah Ali Khamenei on Wednesday became the latest Islamic figure to criticise the French president, saying his defence of cartoons of the prophet was a "stupid act" and an "insult" to those who voted for him.
"Ask (Macron) why he supports insulting God's messenger in the name of freedom of expression. Does freedom of expression mean insulting, especially a sacred personage?" Khamenei said in a message to "French youth" on his official website.
Erdogan said he had not personally seen the Charlie Hebdo caricature because he did not want to "give credit to such immoral publications."
"I don't need to say anything to those scoundrels who insult my beloved prophet on such a scale," Erdogan said in a speech to his party's lawmakers.
"I am sad and frustrated not because of this disgusting attack on me personally, but because of the impertinence taking aim at our prophet we love more than ourselves."
Turkey is a mostly Muslim but officially secular country that has taken a more conservative and nationalist course under Erdogan's rule.
'Vicious and ugly'
Macron's defence of Charlie Hebdo's right to publish drawings of the prophet, which is forbidden under Islam, came after the brutal murder on October 16 of a school teacher who had shown cartoons to pupils during a class discussion about freedom of speech.
The magazine was also targeted by jihadists in a 2015 massacre that killed 12 people, including some of its most famous cartoonists.
Turkish officials accuse Macron of unfairly targeting Muslims and cultivating a culture that encourages Charlie Hebdo to use its right to offend.
Over the last week, protests and rallies have taken place in many Muslim-majority countries to denounce Macron.
Tens of thousands marched Tuesday through the Bangladeshi capital Dhaka.
In Syria, protesters burned pictures of Macron and French flags, while others rallied across the Indian city of Mumbai and parts of the Gaza Strip on Wednesday.
"If the statesmen of Europe want peace and stability in their countries, they must honour the dignity of Muslims, respecting their values," protester Ozgur Bursali said at a rally outside the French embassy in the Turkish capital Ankara.
Pakistan's Prime Minister Imran Khan on Wednesday wrote to the leaders of Muslim countries calling on them to act together against Islamophobia, while a leading Kuwaiti supermarket chain said that most of its stores had stripped their shelves of French products.
EU sanctions?
But Macron has been staunchly defended by fellow European leaders and he drew support from India on Wednesday under Hindu nationalist Prime Minister Narendra Modi.
"We strongly deplore the personal attacks in unacceptable language on President Emmanuel Macron in violation of the most basic standards of international discourse," said a statement from the Indian foreign ministry.
French government spokesman Gabriel Attal said his country "will never renounce its principles and values" in regard to media freedom and fight against Islamic extremism.
France on Wednesday officially shut down a group called Barakacity that it blames for inciting hate and justifying terrorist acts.
Erdogan's policies have put Turkey at growing odds with the European Union and Macron has become one of Turkish leader's most vocal critics.
The two statesmen have sparred over the eastern Mediterranean as well as Turkey's policies across the Middle East and -- most recently -- in the war between Azerbaijani and Armenian separatist forces in Nagorno-Karabakh.
France's European Affairs Minister Clement Beaune said that Paris would "push for strong European responses, which include sanctions" over Erdogan's series of "provocations."
Recep Tayyip ErdoganCharlie Hebdo cartoons
UK Queen's Windsor Castle Gets No-Fly Zone Protection


think. Nobody would bother me there and usually I felt better about my life once I had time to just think. Since I moved to Kutztown for schooling, there has not been any quiet time for me to figure out what it is I want. At Kutztown, I see people walking by and or in my classes and they just seem as if they have life all figured out. They know what classes they need to complete, they know what they want to do with their life and knew it since they were very young. I on the other hand walk by and think, "Boy I would like to be like them" and have life all figured out. I just need some time alone to think and college is not allowing me this time. Since I move up here to Kutztown, I have been on the run. Without having time for silence because I am constantly reading or being bombarded by friends, I am feeling more alone and confused about life.


I know I get a little bit excited every time I share a recipe with you. And always end up saying it's probably one of the best things I made. But this dish truly is one of the best things I put together with my two little hands!
What makes this so special then? First it's the combination of textures and flavors of the noodles. There's crunchy and earthy beetroot and carrot noodles and then there's chewy and curly kelp noodles. Together they work beautifully. But proof is in the dressing! Oh, the creamy, garlicky dressing that brings the best out of those noodles! So good, half of it might have ended up straight in my belly. So good I might have dipped carrot sticks and eaten the first batch straight up, just like that. Who knew tahini and miso are a perfect match? I recently discovered and am in awe. Or in love. Both! Two of my always favorite ingredients bring the best out of each other. Fancy my luck.
The tempeh, you want it in your belly! Perfectly moist, yet firm, coated in a beautiful sweet salty glaze that is known to cause addiction. I might have eaten half the tempeh right from the pot. Might just be incredibly delicious. And with the noodles, brace yourself for a feast!
Mix the marinade ingredients for the tempeh in a pot over medium low heat and add the cubed tempeh. Toss to coat.
Bring to a boil, cover and simmer until the tempeh absorbs all the liquid. Toss every 2-3 minutes to coat all the tiny precious tempeh cubes.
While the tempeh is cooking, add all the ingredients for the dressing in a small bowl and mix until they blend together into a nice, creamy, rich sauce. Set aside.
Don't forget to toss the tempeh!
Prepare the noodles. Wash, spiralise, slice and dice. Add everything to a bowl.
When the tempeh is cooked, add the dressing to the noodles and toss to coat them well. Using your hands works best.
Sprinkle sesame seeds and crumbled nori over the noodles and serve with hot tempeh.
Note: This is also beautiful when served with a bit of kimchi. I use this homemade vegan recipe.
12 Responses to "Raw seaweed noodles with maple orange tempeh"
That tempeh looks delicious. I always forget about tempeh and almost never eat it. Do you make your own or have a favourite brand?? Tahini and miso on the other hand, I combine all the time – soooo yummy!! This whole dish looks so healthy and beautiful!
This looks so amazing! I love how colourful it is. Eat a rainbow, always!
This is exactly the kind of food I love to cook and eat. And orange/maple tempeh? I'm in!
Where do you get your kelp noodles from? I really enjoyed them in your noodle bowl you made me. They give a great textural contrast to the carrot and zucchini.


Despite assurances to the contrary from J.J. Abrams himself and Paramount acknowledging talks about a settlement, it now looks like the whole Star Trek: Axanar lawsuit saga is far from over.
According to a report over at The Hollywood Reporter, Paramount and CBS told a California federal judge on Wednesday that the legal action against the fan movie remained pending – despite the fact that Star Trek Beyond director Justin Lin had seemingly managed to get Paramount to drop the lawsuit against the crowdfunded Star Trek movie.
And now, despite all of that, the fight over Axanar is far from ended as all parties involved continue to throw court filings at each other. Le sigh.
If you guys will recall, Paramount and CBS filed a copyright infringement lawsuit at the end of 2015 over Axanar, a project that was hyped as a "would-be studio-quality film." The movie was to be set before Capt. James T. Kirk's and the USS Enterprise's five-year mission, at a time when the studios had previously tolerated and even encouraged fan films over the years.
Since the judge had rejected a motion to dismiss, Axanar Productions had a deadline to file an answer to the claims, and they did so on May 23, also filing a counterclaim asking for declaratory relief that the film was non-infringing. They mentioned J.J. Abrams' comments and a tweeted statement from Paramount and CBS that confirmed talks about settling the case, and that they were working on fan film guidelines.
But instead of asking for an extension, Paramount and CBS have filed their own answer to the counterclaim by fully admitting to those public statements and saying said statements speak for themselves, but they're still acting as though the lawsuit is moving forward. Le sigh, part deux.
This is a huge bummer, especially since fans were assured this lawsuit would go away. What do you guys make of this?


Redeemer decided early on that we don't want to just be a church that has small groups, we want to be a small group church. Christian community has been a strong value at Redeemer since the beginning and we've seen it grow and change in ways we couldn't have imagined or even dreamed of in those first days. The theme and emphasis on community does not refer to our Sunday worship, though this is one place community takes place. More importantly, we see community happening beyond our Sunday worship bleeding into where we live, where we work, how we use our time and how we engage the Kingdom around us.
Our community group ministry is a driving force here in that it is not just a bible study that takes place for an hour each week. Our community groups are centered on engaging in intentional Christian community where we live and how we seek to be a Kingdom presence there. In connecting people to Jesus within community groups, we believe the people of Redeemer will be growing more and more in the gospel and their identities as sons and daughters of the King. We trust this Spirit-changing work will ignite a movement of the gospel that changes Indianapolis, spiritually, socially and culturally by connecting people to Jesus.
To join a community group, please contact Amanda Edgell, our Parish and Community Group Coordinator, through the form on the right. Amanda will help place you in a group in your area.
We are also always looking for new community group leaders. Please reach out to Amanda if you are interested in leading or hosting a group.


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Zeiss DTI 3/35 Thermal Monocular **REVIEW**
Full Review of the Zeiss DTI 3/35 Thermal Monocular. See a full rundown of specs, functionality, optic walk-around, and thermal video through the monocular. Thank you to @Outdoor Legacy for providing the optics for review and we appreciate all of you that watching and listen to @The Late Night Vision Show Podcast! __________________________________________________ **** Find Night Vision/Thermal Scopes **** Find the Zeiss DTI 3/35 Here: https://outdoorlegacygear.com/products/zeiss-dti-3-35-2-5x-thermal-monocular?variant=39251474350154 Find All Night Vision/Thermal Scopes and Optics Here: http://bit.ly/outdoorlegacygear Call Jason at Outdoor Legacy Gear: (877) 350-1818 __________________________________________________ **** "The Late Night Vision Show" Podcast **** "The First and Only Podcast Dedicated to Night Vision/Thermal Hog and Predator Hunters!" Available on YouTube, iTunes, Google Play, Spotify, and Everywhere Else!... Subscribe To "The Late Night Vision Show" Podcast on YouTube: http://bit.ly/tlnvs-youtube Find All "The Late Night Vision Show" Episodes and Much More: http://bit.ly/tlnvs-website __________________________________________________ **** HansETX Social Media/Contact Info **** INSTAGRAM: http://bit.ly/ig-hansetx YOUTUBE: http://bit.ly/HansETX-YouTube HansETX WEBSITE: http://bit.ly/hansetx-blog HansETX EMAIL: hans@hansetx.com __________________________________________________ **** HansETX Merch **** HansETX Hat Here: http://bit.ly/HansETX-Merch __________________________________________________ **** Zeiss DTI 3/35 **** * 384 x 288 Resolution * 2.5x – 10x Magnification * Field of View: 57 feet and 100 yards * 50 HZ Refresh Rate * Rechargeable Internal Battery (7 hours) * Internal Video Recorder/No Audio * Wifi capable (StreamVision App) * 17 Microns * 1350 yard detection range * Weight: 0.92 lbs. * IP66 Weather Rating * Operating Temperatures: 14 to 104 degrees F * 4 Color Palettes * Price $2,999.99 __________________________________________________ **** Friends of HansETX **** * Outdoor Legacy Gear (Thermal/Night Vision Scopes and Optics) * The Late Night Vision Show YouTube Channel * ETXThermalHunter YouTube Channel * TxBaconTech YouTube Channel * ETXHooligan YouTube Channel __________________________________________________ **** Why Hunt Hogs? **** My journey into wild/feral hog hunting started like many others living in rural communities. I didn't start hog hunting for fun or sport... I did it because I had to. My father-in-law started me on this path several years ago when he asked me to help out with a problem. You see, hogs cause millions of dollars of damage and destruction each year in rural communities and our situation was no different. Hogs were destroying our land, our family's land, and the land our family tree farming business. The vast majority of hunting videos that you see on this channel are hogs, boars and sows, taken from family land... this problem is personal for me. We have had hogs come as close as 20 feet from my front door, and having 2 small children at home and knowing how aggressive and dangerous hogs can be, getting rid of the problem was a safety concern. This is why when people ask how I got into hog hunting... my answer is always "I didn't get into it because I wanted to, I started doing it because I had to!". It's a personal fight and struggle... __________________________________________________ **** Why Hunt Coyotes and Predators? **** Coyotes and Predators, similar to hogs, cause financial stress for many families in rural communities. Coyotes kill livestock, plain and simple. Ranchers have battled coyotes killing cattle since the early days of ranching and it costs them dearly. Cattle ranching is a business, the cows aren't pets, every time they lose a calf to a coyote those families lose a paycheck. Cattle ranchers hate coyotes just as much as Farmers hate hogs! I hunt coyotes to protect the livelihood of my friends and neighbors that depend on their cattle business to feed their family... __________________________________________________ **** Comin' Up Next **** Next week it's back to more Thermal Hog Hunting right here in East Texas with the Pulsar Thermion 2 XP50 and coming soon is my full review of the N-Vision Halo X50. Thank y'all for watching! -Hans


Classic & modern: Advent wreath with red poinsettia miniatures. This delicate Advent wreath made of straw rings and planted poinsettia miniatures presents itself in a modern yet classic way. It can be made fairly easily with a bit of crafting skill. You will need two bigger and three smaller straw wreaths, each with the same diameters, some parcel string, cardboard, plastic foil, soil, six red poinsettia miniatures, stay wire, red wooden stars, spruce cones, moss, four red taper candles and hot glue.


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You are here: Home » Articles » Kern-Castaic Water Transfer EIR Upheld
Kern-Castaic Water Transfer EIR Upheld
California Environmental Quality Act, Legal Digest, Los Angeles County, Paul Shigley, Vol. 24 No. 18 Dec 2009 - 2
The environmental impact report for a water transfer from a Kern County irrigation district to an urban water supplier in the Santa Clarita Valley has been upheld by the Second District Court of Appeal.
The unanimous three-judge panel overturned a trial court judge,who had struck down the EIR because it did not adequately address a State Water Project framework and ongoing environmental review. The Second District ruled that the EIR did address the State Water Project issues and said the water transfer is a separate matter anyway.
The ruling is a victory for water providers and developers in Los Angeles County's Santa Clarita Valley, and a setback for environmentalists and slow-growth activists. The rapidly growing Santa Clarita Valley has been at the center of numerous legal and political battles over water plans and water supply assessments. A transfer of 41,000 acre-feet of State Water Project (SWP) water to the Castaic Lake Water Agency has been a major point of contention. In 1999, the agency signed a contract under which the Wheeler Ridge-Maricopa Water Storage District (which receives SWP deliveries via the Kern County Water Agency) would permanently transfer the rights to 41,000 acre-feet of SWP water to Castaic. That transfer was based on the "Monterey Agreement," a 1995 plan that modified how the State Water Project allocated water during dry years and which authorized the transfer of 130,000 acre-feet of water from agricultural use to urban suppliers.
In 2000, an appellate court struck down the Monterey Agreement EIR because a Monterey County water agency � and not the Department of Water Resources (DWR) � served as the "lead agency" that certified the EIR (Planning & Conservation League v. Department of Water Resources, (2000) 83 Cal.App.4th 892). The Second District followed up by invalidating the EIR for the Kern-Castaic water transfer because the environmental document tiered off the Monterey Agreement EIR. In Friends of the Santa Clara River v. Castaic Lake Water Agency, (2002) Cal.App.4th 1373, the court ruled that an EIR that tiers off a program EIR is not valid if the program EIR is no longer in place (see CP&DR Legal Digest, March 2002). Importantly, though, the court rejected Friends' request for an injunction halting the water transfer. Castaic has continued to have access to the SWP water since 1999.
In late 2004, Castaic certified a second EIR and approved the water transfer again. The new EIR did not tier off any other document and analyzed the impacts of the transfer under three different scenarios: (1) a transfer based on 2003 amendments to the Monterey Agreement (known as Monterey Plus), (2) a transfer without Monterey Plus or a reallocation of water in dry years, (3) a transfer without Monterey Plus but with other SWP permanent cutbacks. The EIR also included five alternatives to the transfer.
Although the EIR had to go back to Los Angeles County Superior Court because of the 2002 Friends ruling, Friends dismissed its action because the group lacked money to continue litigating. However, the Planning and Conservation League (PCL) and the California Water Impact Network (CWIN) filed a new suit over the second EIR. Superior Court Judge James Chalfant rejected most of PCL and CWIN's arguments. Still, he concluded the EIR was flawed because it did not adequately explain the relevance of the pending Monterey Agreement EIR to the scenarios in the Castaic EIR. Parties on both sides appealed, and the Second District ruled entirely for Castaic.
The appellate panel overturned Chalfant on the issue of the scenarios for two reasons. First, the court ruled, PCL and CWIN never specifically objected to the discussion of the scenarios during the administrative process and, therefore, should not be able to raise the argument in court. Second, the court determined, "[T]he 2004 EIR adequately explains why the three scenarios discussed in connection with the transfers are possible outcomes of DWR's pending Monterey Agreement EIR."
"[T]he 2004 EIR describes the relationship between the pre- and post-Monterey Agreement contractual requirements and the three water supply scenarios in considerable detail," Justice Nora Manella wrote for the appellate panel. "Although we agree with the trial court that the 2004 EIR's discussion could have been clearer, �absolute perfection' is not required of an EIR."
The primary contention of PCL and CWIN was that the Department of Water Resources should have prepared the EIR for the Kern-Castaic water transfer because the Monterey Agreement enabled the transfer. Castaic could serve as the lead agency only after a new Monterey Agreement EIR is complete, they argued. Both the trial court and the appellate court disagreed.
"[N]othing before us suggests that the Monterey Agreement, viewed as a CEQA project, included the Kern-Castaic transfer when the original Monterey Agreement was prepared and certified in 1995," Manella wrote. "As the Kern-Castaic transfer was no more than �a gleam in a planner's eye' at the time of the Monterey Agreement, the transfer fell outside the original Monterey Agreement EIR, and was properly considered in a separate EIR.
"We also conclude that the decertification of the 1995 Monterey Agreement EIR and its aftermath have not brought the transfer within the compass of the new Monterey Agreement," Manella continued.
The water transfer opponents pointed to the 2000 decision in Planning & Conservation League, in which the court ruled that DWR's statewide perspective made the agency the logical choice to complete the Monterey Agreement EIR. But the Monterey Agreement, the Second District noted, involves the entire State Water Project. "In contrast, Castaic's preeminent role regarding the water transfer renders it the logical choice for lead agency, in view of the transfer's confined scope" Manella wrote.
The opponents further argued that the EIR represented the water transfer as fait accompli and that a "no project" alternative that involved both no Kern-Castaic transfer and the state's abandonment of the Monterey Agreement should have been studied. The court rejected both arguments and accepted Castaic's arguments that the water transfer could go forward with or without the Monterey Agreement, and that the transfer and the Monterey Agreement "constitute different projects under CEQA, and only the transfer is subject to Castaic's approval."
Planning and Conservation League v. Castaic Lake Water Agency, No. B200673, 2009 DJDAR 17603. Filed December 17, 2009.
For PCL: Roger Moore, Rossmann & Moore, (415) 861-1401.
For Castaic: William Hancock, Eisenberg & Hancock, (415) 984-0650.
For Kern County Water Agency: Amelia Minaberrigarai, Kronick, Moskovitz, Tiedemann & Girard, (661) 634-1400.
For Wheeler Ridge-Maricopa Water Storage District: Steven Torigiani, Young Wooldridge, (661) 327-9661.
For the Department of Water Resources: Deborah Wordham, attorney general's office, (916) 323-3549.


Age is just a number. And for the AARP® Credit Card from Chase, the magic number was 50 – the age of the youngest baby boomers. With a fresher look and more rewarding cash back rewards, this comprehensive campaign reinvigorated and repositioned the brand to better capture this active and growing demographic.
All print and digital materials were redesigned to reflect the new look and strategy. A microsite was also created to showcase the new card.
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An integrated communication strategy, including print, direct mail, display ads and more, helped grow the portfolio and deepen relationships.


Table of Contents Next: Wright, Carleton H.
Wotje (170.244E 9.450N) is a large (30 miles or 50 km long and 12 miles or 20 km wide) atoll in the Marshall Islands. Wotje's capacious lagoon had room for 2000 ships, and could be reached by four passes (the deepest 15 fathoms). It also had enough land for an airfield, making it an excellent candidate for a naval base. The Japanese had constructed perhaps their strongest base in the Marshalls here by 1941, with 1050-meter (1150-yard) and 1500-meter (1640-yard) runways at Otdia airbase, but no aircraft were yet based there when war broke out in the Pacific. There was also a seaplane base, numerous antiaircraft and coastal batteries, and a system of roads and trenches,The atoll was garrisoned by 53 Guard Force, which arrived just before war broke out.
The atoll was bombarded by a cruiser force under Spruance on 1 February 1942 and by an Allied battleship force on 29 January 1944, during the Marshalls campaign. Thereafter the atoll was bypassed, and though it was repeatedly bombarded by air and sea, it remained in Japanese hands throughout the war. Two night evacuations of the native population were carried out by the Allies, in September 1944 and March 1945, which brought out over 1100 civilians.


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\section{Introduction}

Deformable linear objects (DLOs) refer to deformable objects in one dimension, such as ropes, elastic rods, wires, cables, etc. The demand for manipulating DLOs is reflected in many applications, and a significant amount of research efforts have been devoted to the robotic solutions to these applications. For example, wires are manipulated to assemble devices in 3C manufacturing \cite{8460694}; belts are manipulated in assemblies of belt drive units \cite{jin2021trajectory}; and in surgery, sutures are manipulated to hold tissue together \cite{cao2020sewing}.

The manipulation tasks of DLOs can be divided into two categories \cite{rita2021reform}. In the first category, the goals are not about the exact shapes of DLOs; rather, they concern high-level conditions such as insertion \cite{weifu2015anonline}, tangling or untangling knots \cite{wakamatsu2006knotting}, obstacle-avoidance \cite{mcconachie2020learning}, flex and flip \cite{jiang2019dynamic},  etc. The second category is about manipulating DLOs to desired shapes, where one key challenge is to obtain the unknown deformation models, i.e., how the robot motion affects the DLO shapes. This paper focuses on the shape control tasks.

Different from rigid objects, it is challenging to obtain the exact models of DLOs, because they are hard to calculate theoretically, and may vary significantly among DLOs. Some analytical physics-based modeling methods can be used to model DLOs, such as mass-spring systems, position-based dynamics, and finite element methods \cite{yin2021modeling}. However, all are approximate models, and require accurate parameters of DLOs which are difficult to acquire. Data-driven approaches have been applied to learn the deformation models, without studying the complex physical dynamics. A common method is to first learn a forward kinematics model offline, and then use model predictive control in manipulation \cite{yan2020learning, wenbo2021deformable,yan_self-supervised_2020,yang2021learning}. Although this allows for learning accurate forward kinematics of an offline-trained DLO, problems may arise when manipulating a different untrained DLO since there is no online update. Reinforcement learning methods have also been studied \cite{lin2020softgym,rita2021learning}, but they are less data-efficient, and the transfer from trained scenarios to untrained scenarios is challenging. Besides these offline approaches, some studies have used purely online methods to estimate the local linear deformation model of manipulated DLOs, which can be applied to any new DLO \cite{navarro2016Automatic,zhu_dual-arm_2018, jin2019robust, lagneau_automatic_2020}. However, these online estimated models are less accurate because only a small amount of local data can be utilized.

\begin{figure}[tb]
    \centering
    \includegraphics[width=8.5cm]{figs/overview.pdf}
    \vspace{-2mm}
    \caption{Overview of the proposed scheme for DLO shape control. The shape of the DLO is represented by multiple features along the DLO. Some of the features are chosen as target points, and the task is defined as moving the target points to their desired positions. In the offline phase, an initial estimation of the deformation model is learned. Then, in the online phase, the shape control task is executed, and the model is further updated to compensate for offline modeling errors.}
    \label{fig:overview}
    \vspace{-7mm}
\end{figure}

In this paper, we propose a scheme for the shape control of DLOs, where the unknown deformation model is estimated with both offline and online learning, shown in Fig. \ref{fig:overview}. It allows more accurate modeling through offline learning and further updating for specific DLOs via online learning during manipulation. Specifically, we use a radial-basis-function neural network (RBFN) to model the mapping from the current state to the current local linear deformation model. In the offline phase, the RBFN is trained on the collected data. The offline model then directly migrates to the online phase as an initial estimation. In the online manipulation phase, an adaptive controller is proposed to control the shape, in which the RBFN is further updated to adapt to the manipulated DLO concurrently. Thus, the offline learning and online learning complement each other. In addition, we apply proper state representations and domain randomization methods to improve the model's generalization ability. The simple structure of RBFN and the linear format of the deformation model enable data-efficient offline training and fast online adaptation. Compared to the nonlinear offline methods, our method is more data-efficient and can adapt to untrained DLOs; compared to the purely online methods, our method is more stable and can handle more complex tasks. The stability of the closed-loop system and the convergence of task errors are analyzed using the Lyapunov method. Simulation and real-world experiment results are presented to demonstrate the better performance of the proposed scheme compared to the previous data-driven methods. The code will be available at \url{https://github.com/Mingrui-Yu/shape_control_DLO}.

\section{Related Work}

In this section, existing approaches for the shape control of DLOs will be discussed.

Analytical physics-based modeling of DLOs has been researched over the past several decades \cite{yin2021modeling}. Some works about shape control were based on physics-based models. In \cite{duenser2018interactive}, finite element model (FEM) simulation of DLOs was used for open-loop shape control. An approach using reduced FEM to closed-loop shape control of DLOs was proposed in \cite{koessler2021anefficient}. These methods highly rely on the accuracy of the analytical model, requiring the accurate DLO parameters which are hard to obtain in reality.

Data-driven approaches have been applied to the shape control of DLOs recently, dispensing with analytical modeling. In \cite{rambow2012autonomous,nair2017combining,tang2018aframework}, the shaping of DLOs was addressed by learning from human demonstrations. Robots could reproduce human actions for specific tasks.
Reinforcement learning (RL) has also been applied to learn policies for DLO shape control in an end-to-end manner.
A simulated benchmark of RL algorithms for deformable object manipulation was presented in \cite{lin2020softgym}, in which the Soft Actor Critic (SAC) algorithm performs best in rope straightening tasks. RL policies for shape control of elastoplastic DLOs were learned in \cite{rita2021learning}.
Like other RL applications, these methods suffer from high training expenses and challenging transfer from simulation to real-world scenarios.

Different from the end-to-end methods, many works first learn forward kinematics models of DLOs offline, and then use model predictive control (MPC) to control the shape. The forward kinematics model predicts the shape at the next time step based on the current shape and input action. In \cite{yan2020learning, wenbo2021deformable}, an encoder from the image space to the latent space, and a forward kinematics model in the latent space, were jointly trained. A more robust and data-efficient approach is to estimate the DLO state first and then learn the forward kinematics in the physical state space. A bi-directional LSTM network whose structure is similar to chain-like DLOs was applied in \cite{yan_self-supervised_2020}. An interaction network was integrated with a bi-directional LSTM network in \cite{yang2021learning} to better learn the local interactions between segments of DLOs. The problem with these offline methods is that the generalization to different untrained DLOs cannot be guaranteed.

To control the shape of unknown objects, a series of methods tackle the shape control problem based on purely online estimation of the local linear deformation models of DLOs, in which a small change of the DLO is linearly related to a small displacement of the robot by a locally effective estimated Jacobian matrix. The control input can be directly calculated using the inverse of the Jacobian matrix.
In \cite{zhu_dual-arm_2018, jin2019robust, lagneau_automatic_2020}, the local Jacobian matrix was obtained using the (weighted) least square estimation on only the data in the current sliding window. However, the accuracy of the online estimated models is limited and cannot be improved with more data. Thus, these methods mostly handle tasks with local and small deformation.

\section{Methodology}

This paper considers the quasi-static shape control of elastic DLOs. `Quasi-static' refers to the motion being slow, in which the shapes of DLOs are assumed to be determined by only their potential energies and no inertial effects \cite{navarro2016Automatic}. As illustrated in Fig. \ref{fig:overview}, the robot end-effectors grasp the ends of the DLO and manipulate it to the desired shape. The overall shape of the DLO is represented by the positions of multiple features uniformly distributed along the DLO. The target points are chosen from the features, and the task is defined as moving the target points on the DLO to their corresponding desired positions. The specific choice of the target points depends on the task needs.

Some frequently-used notations are listed as follows. The vertical concatenation of column vector $\bm a$ and $\bm b$ is denoted as $[\bm a; \bm b]$. The position vector of the end-effectors is represented as $\bm r \in \Re^{n}$. The position of the $i^{\rm th}$ feature is represented as $\bm x_i \in \Re^l$. The overall shape vector of the DLO is represented as $\bm{x} = [\bm{x}_1; \cdots; \bm{x}_m] \in \Re^{lm}$ , where $m$ is the number of the features. The dimension $n$ and $l$ are adjustable according to the requirements of the task.

\subsection{Local Linear Deformation Model}
One key problem of the shape control of DLOs is to study the mapping from the motion of the end-effectors to the motion of the DLO features. The velocity vector of the DLO features can be locally linearly related to the velocity vector of the end-effectors using a Jacobian matrix \cite{zhu_dual-arm_2018,lagneau_automatic_2020, jin2019robust, navarro2016Automatic}. Different from the previous works, we estimate the Jacobian matrix by learning the mapping from the current state $(\bm x, \bm r)$ to the current Jacobian matrix $\bm J$, i.e., $\bm J$ can be obtained as a function of $\bm x$ and $\bm r$:
\begin{equation} \label{Jacob1}
    \dot{\bm x} = \bm J(\bm x, \bm r) \dot{\bm r}
\end{equation}

\textit{Proposition 1:} With the quasi-static assumption, the velocity vector of the features on the elastic DLO can be related to the velocity vector of the end-effectors as (\ref{Jacob1}).

\textit{Proof:} Denote the potential energy of the elastic DLO as $E$, which is assumed to be fully determined by $\bm x$ and $\bm r$.
In the quasi-static assumption, internal equilibrium holds at all states during the manipulation, where the DLO's internal shape $\bm x$ locally minimizes the potential energy $E$ \cite{bretl2014quasi}. That is, $\partial E / \partial \bm x \hspace{-0.5mm}=\hspace{-0.5mm} \bm 0$ at any state.
Consider the DLO is moved from  state $(\bar{\bm x}, \bar{\bm r})$ to  state $(\bar{\bm x} + \Delta \bm x, \bar{\bm r} + \Delta \bm r)$ where $\Delta \bm x$ and $\Delta \bm r$ are small displacements of the features and the end-effectors. Denote $\partial E / \partial \bm x$ as $\bm g(\bm x, \bm r)$, $\partial^2 E / (\partial\bm x \partial\bm x)$ as $\bm A(\bm x, \bm r)$, and $\partial^2 E / (\partial\bm x \partial\bm r)$ as $\bm B(\bm x, \bm r)$. Using Taylor expansion and neglecting higher order terms, we have
\begin{equation}
    \bm g(\bar{\bm x} \hspace{-0.3mm}+\hspace{-0.3mm} \Delta \bm x, \bar{\bm r} \hspace{-0.3mm}+\hspace{-0.3mm} \Delta \bm r) \approx \bm g(\bar{\bm x}, \bar{\bm r}) + \bm A(\bar{\bm x}, \bar{\bm r}) \Delta {\bm x} + \bm B(\bar{\bm x}, \bar{\bm r}) \Delta {\bm r}
\end{equation}
where $\bm g(\bar{\bm x} \hspace{-0.3mm}+\hspace{-0.3mm} \Delta \bm x, \bar{\bm r} \hspace{-0.3mm}+\hspace{-0.3mm} \Delta \bm r) = \bm g(\bar{\bm x}, \bar{\bm r}) = \bm 0$. Note that $\bm A$ and $\bm B$ physically represent the unknown stiffness matrices. Assuming the DLO has a positive and full-rank stiffness matrix around the equilibrium point, matrix $\bm A$ is invertible \cite{navarro2016Automatic}. Then, it can be obtained that
\begin{equation}
    \Delta {\bm x} \approx -  \left(\bm A(\bar{\bm x}, \bar{\bm r})\right)^{-1} \bm B(\bar{\bm x}, \bar{\bm r}) \Delta {\bm r}
\end{equation}
In slow manipulations, $\dot{\bm x} \approx \Delta \bm x / \Delta t$ and $\dot{\bm r} \approx \Delta \bm r / \Delta t$ with small $\Delta t$. Then, denoting $-(\bm A(\bm x, \bm r))^{-1} \bm B(\bm x, \bm r)$ as $\bm J(\bm x, \bm r)$, we derive (\ref{Jacob1}) and prove the proposition.

Note that (\ref{Jacob1}) can be rewritten as
\begin{equation} \label{Jacob2}
    \dot{\bm x} = \left[ \begin{array}{c}
      \dot{\bm{x}}_1   \\ \vdots \\ \dot{\bm{x}}_m \end{array} \right]
    = \left[ \begin{array}{c}
      \bm{J}_1(\bm x, \bm r)   \\ \vdots \\ \bm{J}_m(\bm x, \bm r) \end{array} \right]
      \dot{\bm r}
\end{equation}
where $\bm{J}_k(\bm x, \bm r)$ is the $((k-1)l+1)^{\rm th}$ to $(kl)^{\rm th}$ rows of $J(\bm x, \bm r)$. Thus, it can be obtained that
\begin{equation} \label{Jacob3}
    \dot{\bm x}_k = \bm J_k(\bm x, \bm r) \dot{\bm r}, \quad k =  1,\cdots , m
\end{equation}
which indicates that different features correspond to different Jacobian matrices. This formulation makes it convenient when choosing any subset of features as the target points in the manipulation task.

However, it is difficult to theoretically calculate the Jacobian matrix. We estimate the Jacobian matrix in a data-driven way, combining both offline learning and online learning.

\subsection{Offline Learning}

Prior to the shape control tasks, a data-driven learning method is employed to obtain the initial estimation of the model, based on offline collected data.

We apply a neural network (NN) to approximate the Jacobian matrix, in which the input is the current state and the output is the Jacobian. Two properties of the Jacobian can be noticed intuitively: (1)translation-invariance: translation of the whole DLO without changes of the shape will not alter the Jacobian matrix; (2)approximate scale-invariance: DLOs with different lengths but similar overall shapes and the same number of features may have similar Jacobian matrices. Thus, to improve the NN's generalization ability, we modify the representation of the input state from $[\bm x; \bm r]$ to
\begin{equation} \label{equation:DLO_state_representation}
    \bm \phi \stackrel{\triangle}{=} \left[ \bar{\bm{x}}_1; \cdots; \bar{\bm{x}}_m; \hspace{1mm} \bar{\bm p}; \bm q_0; \bm q_1 \right]
\end{equation}
where
\begin{equation}
    \bar{\bm{x}}_k = \left[ \frac{\bm x_k - \bm p_0}{\| \bm x_k - \bm p_0 \|}; \frac{\bm x_k - \bm p_1}{\| \bm x_k - \bm p_1 \|}  \right], \quad \bar{\bm p} = \frac{\bm p_1 - \bm p_0}{\| \bm p_1 - \bm p_0 \|}
\end{equation}
$(k = 1, \cdots, m)$, where $\bm p_0$, $\bm p_1$ are the positions of the left and right grasped ends of the DLO, and $\bm q_0$, $\bm q_1$ are the orientations of the left and right grasped ends. As illustrated in Fig. \ref{fig:state_representation}, this relative representation only determines the overall shape, ignoring the scale and overall translation. Therefore, it is much more data-efficient than the absolute representation $[\bm x; \bm r]$ which requires a larger network and training dataset to guarantee the generalization to different DLOs. Note that this representation avoids using relative positions between adjacent features, which may suffer from accumulated errors when perception errors exist.

\begin{figure}[tb]
    \centering
    \includegraphics[width=8.5cm]{figs/state_representation.pdf}
    \vspace{-3mm}
    \caption{An illustration of the proposed DLO state representation in (\ref{equation:DLO_state_representation}). The position of the $k^{\rm th}$ feature $\bm x_k$ can be determined by $\bar{\bm x}_k = \left[ (\bm x_k - \bm p_0)/\| \bm x_k - \bm p_0 \|; (\bm x_k - \bm p_1)/\| \bm x_k - \bm p_1 \|  \right]$, if the positions of the left and right ends ($\bm p_0$ and $\bm p_1$) are given. In (\ref{equation:DLO_state_representation}), the positions of the ends are described by $\bar{\bm p} = (\bm p_1 - \bm p_0)/\| \bm p_1 - \bm p_0 \|$, so the overall translation and scale are ignored in the representation.}
    \label{fig:state_representation}
    \vspace{-5mm}
\end{figure}

Then, (\ref{Jacob3}) can be rewritten as
\begin{equation} \label{Jacob4}
    \dot{\bm x}_k = \bm J_k(\bm \phi) \dot{\bm r}, \quad k =  1,\cdots , m
\end{equation}

We apply a radial-basis-function neural network (RBFN) to represent the actual Jacobian matrix as a function of $\bm \phi$:
\begin{equation} \label{vecJ=W*theta}
    {\rm vec} \left(\bm J_k(\bm \phi)\right) = \bm W_k \bm{\theta}(\bm \phi), \quad k =  1,\cdots , m
\end{equation}
where ${\rm vec}(\cdot)$ refers to the column vectorization operator, and $\bm W_k$ is the matrix of unknown actual weights of the RBFN for the $k^{\rm th}$ feature. The $\bm \theta (\bm \phi)$ represents the vector of activation functions, and $\bm{\theta}(\bm{\bm \phi})=[\theta_1(\bm{\bm \phi}), \theta_2(\bm{\bm \phi}), \cdots, \theta_q(\bm{\bm \phi})]^T \in \Re^q $. We use gaussian radial function as the activation function:
\begin{equation} \label{singleNeuron}
\theta_i(\bm{\phi})={\rm e}^{\frac{-||\bm{\phi} -\bm \mu_i||^2}{\sigma_i^2}}, \quad i =  1,\cdots , q
\end{equation}
where the parameters $\bm \mu_i$ and $\sigma_i$ are trainable in the offline phase but fixed in the online phase.

Equation (\ref{vecJ=W*theta}) can be decomposed as
\begin{equation} \label{equation:J=Wtheta}
    \bm{J}_{ki}(\bm{\phi}) = \bm{W}_{ki} \bm{\theta}(\bm{\phi}), \quad i =  1,\cdots , n
\end{equation}
where $\bm{J}_{ki}$ is the $i^{\rm th}$ column of $\bm J_k$, and $\bm{W}_{ki}$ is the ${((i-1) l+1)}^{\rm th}$ to ${(i l)}^{\rm th}$ rows of $\bm{W}_k$. Subscribing (\ref{equation:J=Wtheta}) into (\ref{Jacob4}) yields
\begin{equation}
    \dot{\bm{x}}_k = \bm{J}_k(\bm{\phi})\dot{\bm{r}} = \sum_{i=1}^{n}\bm{J}_{ki}(\bm{\phi})\dot{r}_i = \sum_{i=1}^{n}\bm{W}_{ki}\bm{\theta}(\bm{\phi})\dot{r}_i
\end{equation}
where $\dot{r}_i$ is the $i^{\rm th}$ element of $\dot{\bm r}$.

The estimated Jacobian matrix is represented as
\begin{equation} \label{estimatedJ}
    {\rm{vec}} (\hat{\bm{J}}_k(\bm{\phi})) = \hat{\bm{W}}_k\bm{\theta}(\bm{\phi})
\end{equation}
where $\hat{\bm{W}}$ is the matrix of estimated weights. The approximation error for the $k^{\rm th}$ feature $\bm{e}_k$ is specified as
\begin{equation} \label{ew}
\begin{aligned}
    \bm{e}_k &= \dot{\bm{x}}_k - \hat{\bm{J}}_k(\bm{\phi})\dot{\bm{r}}
    \\
    &= \sum_{i=1}^{n}\bm{W}_{ki}\bm{\theta}(\bm{\phi})\dot{r}_i - \sum_{i=1}^{n}\hat{\bm{W}}_{ki}\bm{\theta}(\bm{\phi})\dot{r}_i = \sum_{i=1}^{n}\Delta{\bm{W}}_{ki}\bm{\theta}(\bm{\phi})\dot{r}_i
\end{aligned}
\end{equation}

\begin{figure}[tb]
    \centering
    \includegraphics[width=6.5cm]{figs/RBFN.pdf}
    \vspace{-3mm}
    \caption{The architecture of the RBFN for learning the local linear deformation model. The network takes the state representation in (\ref{equation:DLO_state_representation}) as the input and outputs the estimated Jacobian matrices which relate the velocity vectors of DLO features to the velocity vector of the robot end-effectors.}
    \label{fig:RBFN}
    \vspace{-5mm}
\end{figure}

The architecture of the RBFN is shown in Fig. \ref{fig:RBFN}. Note that the learning or estimation for Jacobian matrices of different features is carried out in parallel. In the offline phase, the ends of the DLO are controlled to move randomly to collect the training dataset, which contains $\bm x_k, \dot{\bm{x}}_k, \bm r, \dot{\bm{r}}, (k=1,\cdots,m)$. Then, the RBFN is trained on the collected data.  Considering the noise and outliers in the data, we use the smooth $L1$ loss \cite{girshick2015fast} of $\bm e_k$ for training.
.

The k-means clustering on a subset of the training data is used to calculate the initial value of $\bm{\mu}_i$ and $\sigma_i, (i = 1, \cdots,q)$. Then, all parameters including $\bm{\mu}_i$, $\sigma_i$ and $\hat{\bm{W}}$ are updated using the {\em Adam} optimizer \cite{kingma2014adam}. We choose RBFN for its simple structure, robustness, and online learning ability \cite{yu2011advantages}. Though less expressive than some more complex network architectures, it performs well enough in this work.

\subsection{Adaptive Control through Online Learning}
Considering the differences between the manipulated DLO in the online phase and the trained DLOs in the offline phase, online learning during manipulation is required. We propose an adaptive control scheme, in which the offline estimated model is treated as an initial approximation and then further updated during the shape control tasks.

The control objective is to move the target points on the DLO to the desired positions. The target points can be any subset of the features, whose indexes form set $\mathcal{C}$. Then, the target shape vector $\bm x^c$, target Jacobian matrix $\bm J^c(\bm{\phi})$, and target weights $\bm W^c$ are denoted as
\begin{equation} \label{equation:all_c}
    \bm{x}^c \stackrel{\triangle}{=} \left[\hspace{-1mm} \begin{array}{c}
      \vdots   \\ \bm x_k \\ \vdots \end{array} \hspace{-1mm}\right] \hspace{-1mm},
    \bm{J}^c(\bm{\phi}) \stackrel{\triangle}{=}  \left[\hspace{-1mm} \begin{array}{c}
      \vdots   \\ \bm J_k(\bm{\phi}) \\ \vdots \end{array} \hspace{-1mm}\right]
      \hspace{-1mm},
    \bm{W}^c \stackrel{\triangle}{=}  \left[\hspace{-1mm} \begin{array}{c}
      \vdots   \\ \bm W_k \\ \vdots \end{array} \hspace{-1mm}\right]
  \hspace{-1mm},
  k \in \mathcal{C}
\end{equation}

The velocities of the robot end-effectors $\dot{\bm r}$ are controlled, and the control input is specified as
\begin{equation} \label{control_law}
\dot{\bm r} = - \alpha \left(\hat{\bm J}^c(\bm{\phi})\right)^{\dagger} \Delta\bm x^c
\end{equation}
where $\left(\hat{\bm J}^c(\bm{\phi})\right)^{\dagger}$ is the Moore-Penrose pseudo-inverse of the estimated Jacobian matrix. In addition, $\Delta \bm x^c = \bm x^c - \bm x^c_{desired}$ where $\bm x^c_{desired}$ is the desired position vector of the target points, and $\alpha \in \Re$ is a positive control gain. In actual implementations, $\dot{\bm r}$ is bounded to avoid too fast motion.

The online updating law of the $j^{\rm th}$ row of $\hat{\bm W}_{ki}$ of the RBFN is specified as
\begin{equation} \label{control_update}
\dot{\hat{\bm W}}_{kij}^T = \hspace{0.1cm} \dot{r}_i\bm\theta(\bm{\phi}) (\eta_1 \Delta x_{kj} + \eta_2 e_{kj}), \quad j=1 ,\cdots, l
\end{equation}
where $\Delta x_{kj}$ is the $j^{\rm th}$ element of the task error $\Delta \bm{x}_k$, and $e_{kj}$ is the $j^{\rm th}$ element of the approximation error $\bm e_k$. The $\eta_1$ and $\eta_2$ are positive scalars. Such updating is done for all $k \in \mathcal{C}$ and $i = 1, \cdots, n$.

The proposed control scheme by (\ref{control_law}) and (\ref{control_update}) allows controlling the target points on the DLO to the desired positions while updating the RBFN concurrently to compensate for any offline modeling errors.

The stability of the system is analyzed as follows. Below ${\bm J}^c(\bm{\phi}), \bm J_k(\bm{\phi}), {\bm \theta}(\bm{\phi})$ are shortened to ${\bm J}^c, {\bm J}_k, \bm \theta$ for simplicity. Premultiplying both sides of (\ref{control_law}) by $\hat{\bm J}^c$, we have
\begin{equation} \label{control_loop_equation_2}
\hat{\bm J}^c\dot{\bm r} = - \alpha \hat{\bm J}^c (\hat{\bm J}^c)^{\dagger}\Delta\bm x^c
\end{equation}
Note that from (\ref{ew}) and (\ref{equation:all_c}), it can be obtained that
\begin{equation} \label{control_loop_equation_3}
    \hat{\bm J}^c \dot{\bm{r}}
    = \hat{\bm J}^c \dot{\bm{r}} - {\bm J}^c \dot{\bm{r}} +{\bm J}^c \dot{\bm{r}}
    = - \bm e^c + \dot{\bm x}^c
\end{equation}

where $\bm e^c = [\cdots; \bm e_k; \cdots], k \in \mathcal{C}$. Since the desired positions are fixed, substituting (\ref{control_loop_equation_3}) into (\ref{control_loop_equation_2}) yields
\begin{equation} \label{control_loop_equation_4}
\bm e^c = \Delta\dot{\bm x}^c + \alpha \hat{\bm J}^c (\hat{\bm J}^c)^{\dagger} \Delta\bm x^c
\end{equation}
A Lyapunov-like candidate is given as
\begin{equation} \label{equation:V}
    V = \frac{1}{2} (\Delta\bm x^c)^T \Delta\bm x^c + \frac{1}{2\eta_1} \sum_{k\in \mathcal{C}}\sum\limits_{i=1}^n\sum\limits_{j=1}^l \Delta\bm W_{kij} \Delta\bm W_{kij}^T
\end{equation}
Differentiating (\ref{equation:V}) with respect to time and substituting (\ref{control_loop_equation_4}) (\ref{control_update}) and (\ref{ew}) into it, we can obtain that
\begin{equation} \label{equation:dotV}
\begin{aligned}
    & \dot V
    = (\Delta \bm x^c)^T \Delta \dot{\bm x}^c - \sum_{k\in \mathcal{C}}\sum\limits_{i=1}^n\sum\limits_{j=1}^l \frac{1}{\eta_1} \Delta\bm W_{kij}  \dot{\hat{\bm W}}_{kij}^T \\
    &= - \alpha (\Delta \bm x^c)^T \hat{\bm J}^c (\hat{\bm J}^c)^{\dagger} \Delta\bm x^c + (\Delta \bm x^c)^T \bm e^c \\
    &\quad - \sum_{k\in \mathcal{C}}\sum\limits_{i=1}^n\sum\limits_{j=1}^l \frac{1}{\eta_1} \Delta\bm W_{kij} \left[\dot{r}_i\bm\theta (\eta_1 \Delta x_{kj} + \eta_2 e_{kj})\right] \\
    &= - \alpha (\Delta \bm x^c)^T \hat{\bm J}^c (\hat{\bm J}^c)^{\dagger} \Delta\bm x^c + (\Delta \bm x^c)^T \bm e^c \\
    & \quad - (\bm e^c)^T \Delta \bm x^c - \frac{\eta_2}{\eta_1} (\bm e^c)^T \bm e^c \\
    &= - \alpha (\Delta \bm x^c)^T \hat{\bm J}^c (\hat{\bm J}^c)^{\dagger} \Delta\bm x^c - \frac{\eta_2}{\eta_1} (\bm e^c)^T \bm e^c
    \leq 0
\end{aligned}
\end{equation}
As $V \hspace{-0.5mm} > \hspace{-0.5mm} 0$ and $\dot V \hspace{-0.5mm} \leq \hspace{-0.5mm} 0$, the closed-loop system is stable. The boundedness of $V$ ensures the boundedness of $\Delta \bm x^c$ from (\ref{equation:V}). If $l  \hspace{-0.5mm} \times \hspace{-0.5mm} |\mathcal{C}| \hspace{-0.5mm} \leq \hspace{-0.5mm} n$ and $\hat{\bm J}^c$ holds full row rank, $\hat{\bm J}^c (\hat{\bm J}^c)^{\dagger}$ is an identity matrix. Then, it can be proved that $\Delta \bm x^c \hspace{-0.5mm} \rightarrow \hspace{-0.5mm} \bm 0$ as $t \hspace{-0.5mm} \rightarrow \hspace{-0.5mm} \infty$, following \cite{arimoto1996control}. Otherwise, $\hat{\bm J}^c (\hat{\bm J}^c)^{\dagger}$ is only positive semi-definite, resulting in an underactuation system. However, from (\ref{ew}), (\ref{control_law}) and (\ref{equation:dotV}) it can be proved that $\dot V \hspace{-0.5mm} = \hspace{-0.5mm} 0$ if and only if $\dot{\bm r} \hspace{-0.5mm} = \hspace{-0.5mm} \bm 0$, which only happens when there are huge conflicts between the desired moving directions of different target points so that the robot movement in any direction cannot reduce the $\Delta \bm x^c$ at this "local minimum point". Actually, this situation happens rarely in experiments owing to the coupling between the target points, so in most cases $\dot{V} \hspace{-0.5mm} < \hspace{-0.5mm} 0$ always holds and finally $\Delta \bm x^c \hspace{-0.5mm} \rightarrow \hspace{-0.5mm} \bm 0$.

\section{Results}

\begin{figure} [tb]
  \centering
  \subfigure[2D]{

    \includegraphics[width=3.6cm]{figs/exp/2D_sim.jpg}
  }
  \subfigure[3D]{

    \includegraphics[width=3.6cm]{figs/exp/3D_sim.jpg}
  }
  \vspace{-2mm}
  \caption{The simulation environment for the DLO shape control tasks. The blue points represent the features along the DLO. The green points represent the desired positions of the features.}
  \label{fig:simulation_environtment}
    \vspace{-5mm}
\end{figure}

We carry out both simulation and real-world experiments to validate the proposed method. The simulation of DLOs is based on Obi \cite{obi}, a unified particle physics engine for deformable objects in Unity3D \cite{unity}, as shown in Fig. \ref{fig:simulation_environtment}. In the simulation, the two ends of the DLO are grasped by two grippers, which can translate and rotate. Both 2D and 3D tasks are tested. In the 2D tasks, the environment dimension $l$ is $2$ and the control input dimension $n$ is $6$; in the 3D tasks $l\hspace{-1mm}=\hspace{-1mm}3, n\hspace{-1mm}=\hspace{-1mm}12$. In the real-world experiments, the DLOs are placed on a table. One end of the DLO is grasped by a UR5 arm, and the other is fixed. Thus, $l\hspace{-1mm}=\hspace{-1mm}2$ and $n\hspace{-1mm}=\hspace{-1mm}3$. The shape of the DLO is represented by 8 features ($m\hspace{-1mm}=\hspace{-1mm}8$), and the positions of the features are obtained by measuring the markers on the DLO with a calibrated RGB camera in the experiments. Both the data collection frequency and control frequency are 10 Hz.

We choose three representative classes of methods for comparison. The first class is learning forward kinematics models of DLOs offline and using MPC for shape control (FKM+MPC). According to \cite{yan_self-supervised_2020}, we choose bi-directional LSTM (biLSTM) for modeling and Model Predictive Path Integral Control (MPPI) for control. The second class is estimating the Jacobian matrix online using weighted least square estimation (WLS). We specifically use the method in \cite{lagneau_automatic_2020}. The third is based on reinforcement learning. We train an agent using Soft Actor Critic (SAC) \cite{haarnoja2018soft}.

\subsection{Offline Learning of the Deformation Model}

The offline data of DLOs are collected in simulation, by randomly moving the ends of the DLOs. A RBFN with 256 neurons in the middle layer ($q=256$) is first trained offline to learn the initial deformation model.

First, we test the offline modeling accuracy on a certain DLO and its relationship to the amount of training data, in which we compare our local linear Jacobian model with nonlinear forward kinematics models based on multi-layer perceptrons (MLP) or biLSTM. Training data and 10k test data are collected on the same DLO. For testing, we use trained models to predict the shape of the DLO after 10 steps. The prediction of the shape at the next step using our method is calculated as $\hat{\bm x}_{[t+1]} = \bm x_{[t]} + \Delta t \hspace{1mm} \hat{\bm J}(\bm \phi_{[t]}) \dot{\bm r}_{[t]}$, where the subscript $[t]$ represents the variables at step $t$ and $\Delta t$ is the step interval. Shown in Fig. \ref{fig:exp_offline}, the results indicate that our Jacobian model can achieve higher prediction accuracy with less training data. The biLSTM-based model incorporates the physics priors of chain-like DLOs \cite{yan_self-supervised_2020}, so it performs better than MLP when the training set is small. Our Jacobian model implies strong local linear prior, which is theoretically and practically reasonable. Hence, the learning efficiency is highly improved.

Second, we further compare the performance of our methods using the absolute state input $[\bm x; \bm r]$ and relative state input $\phi$ as (\ref{equation:DLO_state_representation}) in the RBFN. We collect data of 10 different DLOs in the simulation. For testing, we perform cross validation, i.e., for each round the model is trained on $9\hspace{-0.1mm}\times\hspace{-0.1mm}3$k data of 9 DLOs and tested on 10k data of the remaining one. We also test the performance on the test set with constant position translation. The average results of 10 rounds shown in Table \ref{tab:exp_abs_state_rel_state} reveal that the relative state input can achieve higher prediction accuracy on new DLOs with different lengths. In addition, when position translation is added, the relative state input is not affected, while the performance of the absolute state input significantly decreases.

\begin{figure} [tb]
  \centering
  \subfigure[2D]{

    \includegraphics[width=4.1cm]{figs/exp/offline_2D.pdf}
  }
  \hspace{-0.4cm}
  \subfigure[3D]{

    \includegraphics[width=4.1cm]{figs/exp/offline_3D.pdf}
  }
  \vspace{-3mm}
  \caption{The relationship between the offline modeling accuracy and the amount of training data. Training data samples and 10k test samples are collected on the same DLO. The error is the average Euclidean distance between the prediction and ground truth of the shape after 10 steps.}
  \label{fig:exp_offline}
    \vspace{-2mm}
\end{figure}

\begin{table}[tb]
\centering
\caption{Comparison of the absolute state input and relative state input in our method.}
\vspace{-2mm}
\label{tab:exp_abs_state_rel_state}
\begin{threeparttable}[b]
\begin{tabular}{@{}c|cc|cc@{}}
\toprule
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Feature velocity\\ prediction\\ relative error\tnote{a}\end{tabular}} & \multicolumn{2}{c|}{2D}                                                                 & \multicolumn{2}{c}{3D}                                                                  \\
                                                                                & New DLO\tnote{b}$\hspace{-3mm}$ & \begin{tabular}[c]{@{}c@{}}New DLO\\ +translation\tnote{c}\end{tabular} & New DLO$\hspace{-3mm}$ & \begin{tabular}[c]{@{}c@{}}New DLO\\ +translation\end{tabular} \\ \midrule
Absolute state input                                                                                & 30.0\%                 & 54.5\%                                                         & 41.1\%                 & 81.4\%                                                         \\
Relative state input                                                                                & \textbf{25.9\%}        & \textbf{25.9\%}                                                & \textbf{36.2\%}        & \textbf{36.2\%}                                                \\ \bottomrule
\end{tabular}
\begin{tablenotes}
     \item[a] The relative error is calculated as the average of $\|\dot{\bm x} - \hat{\bm J} \dot{\bm r}\| / \|\dot{\bm x}\|$.
    \item[b] A new DLO whose length may be different from the trained DLOs'.
    \item[c] A 0.2m position translation offset is added in each dimension.
\end{tablenotes}
\end{threeparttable}
\vspace{-5mm}
\end{table}

\begin{table*}[tb]
\centering
\caption{Performance of the methods in 2D and 3D DLO shape control tasks in simulation.}
\vspace{-2mm}
\label{tab:sim_control_results}
\begin{tabular}{@{}c|cccc|cccc@{}}
\toprule
\multirow{2}{*}{Methods} & \multicolumn{4}{c|}{2D tasks}                                                                                & \multicolumn{4}{c}{3D tasks}                                                                                \\
                         & \begin{tabular}[c]{@{}c@{}}Offline\\ training samples\end{tabular} & \begin{tabular}[c]{@{}c@{}}Success\\ rate\end{tabular} & \begin{tabular}[c]{@{}c@{}}Average\\ task error (cm)\end{tabular} & \begin{tabular}[c]{@{}c@{}}Average \\ task time (s)\end{tabular} & \begin{tabular}[c]{@{}c@{}}Offline\\ training samples\end{tabular} & \begin{tabular}[c]{@{}c@{}}Success\\ rate\end{tabular} & \begin{tabular}[c]{@{}c@{}}Average\\ task error (cm)\end{tabular} & \begin{tabular}[c]{@{}c@{}}Average \\ task time (s)\end{tabular} \\ \midrule
FKM+MPC                  & 180k                                                               & 82/100                                                 & 1.662                                                             & 10.488                                                            & 180k                                                               & 52/100                                                 & 3.298                                                             & 14.275                                                           \\
WLS                      & -                                                                  & 85/100                                                 & 0.992                                                             & 14.351                                                           & -                                                                  & 55/100                                                 & 1.940                                                             & 20.214                                                           \\
SAC                      & 1000k                                                              & 42/100                                                 & 3.185                                                             & 6.486                                                            & 1000k                                                              & 10/100                                                  & 3.412                                                             & 8.070                                                            \\
Ours(w/o online)         & \textbf{30k}                                                       & 94/100                                                 & 0.461                                                             & 8.568                                                            & \textbf{30k}                                                       & 69/100                                                 & 1.446                                                             & 9.521                                                            \\
Ours                     & \textbf{30k}                                                       & \textbf{97/100}                                        & \textbf{0.457}                                                    & 8.512                                                            & \textbf{30k}                                                       & \textbf{92/100}                                        & \textbf{1.254}                                                    & 9.529                                                            \\ \bottomrule
\end{tabular}
\vspace{-2mm}
\end{table*}

\begin{figure*} [tb]
  \centering
  \subfigure[Case 1]{

    \includegraphics[width=2.8cm]{figs/exp/real_desired_0.jpg}
  }
  \hspace{-0.4cm}
  \subfigure[Case 2]{

    \includegraphics[width=2.8cm]{figs/exp/real_desired_1.jpg}
  }
  \hspace{-0.4cm}
  \subfigure[Case 3]{

    \includegraphics[width=2.8cm]{figs/exp/real_desired_2.jpg}
  }
  \hspace{-0.4cm}
  \subfigure[Case 6]{

    \includegraphics[width=2.8cm]{figs/exp/real_desired_5.jpg}
  }
  \hspace{-0.4cm}
  \subfigure[Case 7]{

    \includegraphics[width=2.8cm]{figs/exp/real_desired_6.jpg}
  }
  \hspace{-0.4cm}
  \subfigure[Case 8]{

    \includegraphics[width=2.8cm]{figs/exp/real_desired_7.jpg}
  }
  \vspace{-2mm}
  \caption{Some of the shape control tasks accomplished using our method in real-world experiments. The left end of the DLO is grasped by a UR5 arm and the right end is fixed. The green+black circles represent the desired positions of the DLO features. In all cases, the DLO starts from a straight line.}
  \label{fig:real_desired_shapes}
  \vspace{-4mm}
\end{figure*}

\begin{table}[tb]
\centering
\caption{Performance in real-world 2D shape control tasks.}
\vspace{-2mm}
\label{tab:real_control_results}
\begin{tabular}{@{}c|ccc@{}}
\toprule
Methods          & \begin{tabular}[c]{@{}c@{}}Success\\ rate\end{tabular} & \begin{tabular}[c]{@{}c@{}}Average\\ task error (cm)\end{tabular} & \begin{tabular}[c]{@{}c@{}}Average\\ task time (s)\end{tabular} \\ \midrule
FKM+MPC          & \textbf{10/10}                                         & 1.394                                                             & 7.670                                                           \\
WLS              & 9/10                                                   & 1.164                                                             & 19.089                                                          \\
SAC              & 1/10                                                   & 4.955                                                             & 12.300                                                          \\
Ours(w/o online) & \textbf{10/10}                                         & 1.153                                                             & 10.090                                                          \\
Ours             & \textbf{10/10}                                         & \textbf{0.620}                                                    & 8.220                                                           \\ \bottomrule
\end{tabular}
\vspace{-2mm}
\end{table}

\begin{figure} [tb]
  \centering
  \subfigure[Case 3 (first DLO)]{

    \includegraphics[width=4.2cm]{figs/exp/real_control_2.pdf}
  }
  \hspace{-0.5cm}
  \subfigure[Case 8 (second DLO)]{

    \includegraphics[width=4.2cm]{figs/exp/real_control_7.pdf}
  }
  \caption{The control processes in real-world experiments. The $\| \Delta \bm x \|$ refers to the Euclidean distance between the current shape and the desired shape.}
  \label{fig:exp_real_control_plot}
  \vspace{-5mm}
\end{figure}

\subsection{Shape Control with Online Learning}

We evaluate the proposed method in DLO shape control tasks and compare it with other methods. All $m$ features are set as target points for shape control. During the tasks, the offline-trained models are used, and in our method the model will be further updated concurrently. In addition, domain randomization is applied during offline training to improve the models' generalization abilities. All the offline methods are trained on data collected on 10 DLOs with different lengths or diameters in simulation, while our method uses much less data than other offline methods.

Several criteria are defined to evaluate the performance: (1)final task error: the Euclidean distance between the desired shape and final shape within 30s; (2)success rate: if the final task error is less than 5cm, this case is regarded successful; (3)average task error: the average of the final task errors over all successful cases; (4)average task time: the average time used to achieve success over all successful cases. Note that the task time is for reference only, since it depends on the control gain in servo methods or the sample range of control input in MPC or RL.

\subsubsection{Simulation}

First, we test their performance in both 2D and 3D DLO shape control tasks in simulation. The manipulated DLO is an untrained DLO, and 100 cases with different feasible desired shapes are tested. We also do the ablation study to validate the effect of the online learning in our method. The parameters are set as $\alpha=0.3, \eta_1=10^{-3}, \eta_2=50$. As shown in Table \ref{tab:sim_control_results}, our method significantly outperforms the compared methods on both success rate and average task error, even using much less offline training data. The task time of the online method WLS is the highest because it needs to initialize the Jacobian by moving the DLO ends in each DoF every time it starts. The average task error of FKM+MPC is higher because it has no further updating for the untrained manipulated DLO. The poor performance of SAC may be due to insufficient training. The contrast is starker in more challenging 3D tasks, where the success rates of compared methods are very low ($\leq$55\%), including our method without online learning (69\%), while our method with online learning achieves a 92\% success rate and the lowest average task error.

\subsubsection{Real-world experiments}

We also evaluate these methods in real-world 2D tasks. The same offline models as those in the simulation are used, which means no real-world data are collected for offline training. We separately carry out 5 tests with different feasible desired shapes on two DLOs: an electric wire with a length of 0.45m and diameter of 8mm, and an HDMI cable with a length of 0.6m and diameter of 5mm, as shown in Fig. \ref{fig:real_desired_shapes}. The parameters are set as $\alpha=0.3, \eta_1=10^{-3}, \eta_2=200$. The results are shown in Table \ref{tab:real_control_results}. Fig. \ref{fig:exp_real_control_plot} visualizes the control processes of two cases. Since the control input dimension is only $3$, all methods perform well in these relatively simple tasks except SAC. It is shown that the processes of WLS are slow and unsmooth, and those of FKM+MPC are fast but less precise. Our method completes all 10 tasks and achieves the lowest average task error, where the online learning enables faster and more precise control.

\section{Conclusion}

This paper considers the shape control of DLOs with unknown deformation models. We formulate the deformation model in a local linear format, which is estimated in both offline and online phases. First, the offline learning well initiates the estimation of the model. Then, the adaptive control scheme with online learning further updates the model and achieves the shape control tasks in the presence of an inaccurate offline model. The experiments demonstrate that the offline learning of the local linear deformation model is accurate and data-efficient. By combining the offline and online learning, our method outperforms the compared methods, and adapts well to untrained DLOs. Future work will focus on validating the method in 3D real-world tasks with a high-precision 3D camera.

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redpajama_set_name: RedPajamaArXiv


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