text:
AEye's Next-Gen AI-Based Perception May Become the 'Eyes' of Self-Driving Cars
【Summary】AEye is relatively new Silicon Valley startup. Founded in 2013, the company is working on lidar and computer vision systems for self-driving cars. AEye's computer vision technology is unique, as it uses computer vision and artificial intelligence to mimic how a human eye focuses on objects.
Eric Walz Jun 13, 2018 11:03 AM PT
AEye is relatively new Silicon Valley startup. Founded in 2013 and headquartered in Pleasanton, California, the company is working on lidar and computer vision systems for self-driving cars. AEye's computer vision technology is unique, as it uses computer vision and artificial intelligence to mimic how a human eye focuses on objects.
The self-driving cars being developed today rely primarily on three technologies, which are cameras, lidar and radar to "see" in their surrounding environment. AEye has developed a way to combine two of these separate technologies–cameras and lidar–together at the hardware level, adding an embedded artificial intelligence layer, which the company says act like a human visual cortex, thereby becoming the eyes of an autonomous vehicle.
AEye's vision technology has its roots in military applications. The company's CEO, Luis Dussan, came up with the idea for the company using his background in computer vision and his prior work on deep-space probes at NASA as well as a stint at Lockheed Martin, where he developed a multi camera targeting pod used in fighter jets.
It is in these military applications where the idea to port the technology to automotive systems was born. In a military fighter jet, the pilot needs to see the object before the object sees it. The same principle is true for a self-driving car. AEye believes that the quicker a self-driving car's vision system can detect and classify an object, such as a pedestrian, the quicker the vehicle can react to it.
I spoke with Jordan Greene, Lead Strategist at AEye, and he described some of the key features of the company's lidar technology, including why many of the current systems under development may not be not good enough for mass-produced autonomous vehicles.
AEye's system uses a camera system capable of capturing up to 500 frames per second. Each camera image can be overlaid with an additional lidar scan of the same view on each frame, for a detailed look at the environment. The lidar allows a 3D point cloud to be placed on top of the image. When combined with sophisticated computer vision algorithms and artificial intelligence, the technology is ideal for use in self-driving cars. The company says this method is far superior than anything currently being developed.
Lidar & Self-Driving Cars
iDAR point cloud on the left and camera image on the right: iDAR's Dynamic Vixels uniquely enable a vehicle to identify the color of a stop light, find road markings, and read signage - challenges unachievable for LiDAR-only solutions.
Lidar, short for "light detection and ranging", and is considered an important part of any autonomous driving system. Lidar is used to create a 3D view of a vehicle's surroundings by bouncing a laser beam off objects. Differences in return times and wavelengths can be used to make digital 3D representations of the road ahead and a vehicle's surroundings.
A spinning lidar unit mounted on the roof of a autonomous test vehicle has become a common sight in Silicon Valley, as many companies are using roof-mounted lidar systems to test self-driving cars. Although lidar is a robust technology, relying on lidar data alone is not enough for a self-driving car to safely navigate. Other primary technologies which include cameras, radar, as well as the software itself are used to supplement the lidar.
AEye core mission is to improve the perception of lidar—by combining it with camera data and AI. Think of it as superhuman vision, which identifies objects and instantly tracks them, using an additional layer of AI to predict their intended behavior.
Robotic Perception
Greene explained that much of the work being done for autonomous driving is using all of these disparate technologies (camera, lidar and radar) to gather data, then stitching it all together, which is known as sensor fusion. However, AEye's technology is different from how other companies are using lidar and computer vision together. It does not rely solely on the raw data collected by each individual component.
The company developed a way of using camera and lidar in tandem, at the hardware level, as opposed to using seperate sensors method to collect the data.
Greene stresses that the cameras and lidar suite are the most important components to successfully navigate a self-driving car. AEye's says its technology allows self-driving cars to "see" intelligently.
I asked Greene to elaborate. He said that "Not all objects are equal and there is greater value place on certain objects over others." This makes sense, as a garbage can on the side of the road is a lesser priority that a person standing there waiting to cross the street. Although lidar is great for a 3D view of a self-driving car's surroundings, it has its limitations.
An AEye test car
Greene told me that lidar is great at gathering 3D data, but its expensive and requires multiple lidars to acheive 360 degree coverage around a vehicle. Whereas digital cameras are great at taking high-resolution 2D images, but they don't capture 3D data. There are tradeoffs Greene said. "Radar has super-low resolution but its great through obscurance." By obscurance he means rain, fog or snow, all weather a self-driving car might contend with.
"All of them have their place, but when you add them together the sum of their parts is greater than each of them alone. We have a lot of novel hardware that's all our our IP, but its really about the way we piece it all together." Greene said. He added, that by combining both the camera and lidar, AEye's technology is much better—also much faster.
AEye uses robust image processing, with Greene calls "embedded artificial intelligence" (AI), by eliminating the limitations of using a seperate camera and lidar and combining the strengths of each, the company says it has built a superior sensing technology.
Greene described AEye's technology as essentially the robotics perception to enable autonomous transportation. "We built this company around robotic perception, not overall system architecture, and the hardware behind it to enable autonomous transportation." he said.
Mimicking a Human Eye
An important part of AEye's technology is having robust capabilities at the hardware level and not rely on sensor fusion and a separate dedicated processor to stitch it all together afterwards.
"Sensor fusion assumes that you have multiple technologies working separately then you bring it to a hub (central processor such as NVIDIA's DRIVE PX2) to do post processing, we don't do that." Greene said.
"In the system itself, we use something very unique." He described how AEye is working to eliminate this extra post-processing by intelligently processing the data as its being collected.
Traditional lidar uses a raster based scan pattern, for example, from the top left to bottom right of an image or a spinning system like Velodyne's HD-64E, which provides 360 degree coverage.
Greene said that these designs have limitations "These lidars are not smart, they cannot tell the difference from a person, car, or a wall behind them." Instead they are utilized to collect as much data as possible with the intent that the extra data can be filtered out in post-processing.
"We actually use the novel datasets of the the lidar and camera to actually cue how intelligently we interrogate a scene. Why that is very interesting is that it is very similar to how the visual cortex in the human eye works." Greene said.
For example, a human eye does not process all of the information a person sees, you only process what you focus on, such as a car speeding towards you when you're crossing the street.
However, there are benefits and weaknesses to how the human eye and visual cortex works because it can be fooled, just like an human sees an "optical illusion". A self driving car can be fooled as well, which are known as edge cases from a software point of view.
This was the case in the recent Uber fatally in Arizona involving a pedestrian. In that incident, Uber's software failed to identify the pedestrian in the road and react before the impending collision. AEye's technology is designed to prevent incidents like this from happening by identifying objects faster.
Camera & Lidar Data Captured Using a Single Aperture
Green told me that the reason why cameras and lidar are the two most important sensors on a self-driving car is because camera data can be collected extremely quickly.
More importantly, he explained, that AEye's camera and lidar share the same aperture to collect data. Each pixel of the camera image corresponds with a voxel, or a point in 3D space captured by the lidar.
Instead of sending this raw data by itself to a processor, AEye sends this raw data to the lidar without it having to be pre-processed first. The lidar data is rendered over each camera frame as a 3D point cloud, which is overlaid onto the 2D color camera image.
"There is an ongoing argument about whether camera-based vision systems or LiDAR-based sensor systems are better," said Luis Dussan, Founder and CEO of AEye last year. "Our answer is that both are required – they complement each other and provide a more complete sensor array for artificial perception systems. We know from experience that when you fuse a camera and LiDAR mechanically at the sensor, the integration delivers data faster, more efficiently and more accurately than trying to register and align pixels and voxels in post-processing. The difference is significantly better performance."
'Dynamic Vixels'
Greene explained that every individual red, green and blue (RGB) pixel composing a digital image has corresponding x,y coordinate in space, which becomes a specific location in 3D point cloud captured using the lidar. AEye refers to these coordinates as a "Dynamic Vixels". "It's a fusion of a pixel and a voxel." Greene said.
"We call it a Dynamic Vixel because there is more to it than just the pixel and voxel itself." Greene added. Where you have multiple voxels you get a 3D lidar point cloud overlaid onto a 2D camera image, all from a single piece of hardware. "We have color data (captured by the camera) corresponding with a point in space instantaneously."
In digital camera, the images are made up of tiny individual pixels, which are 2D, arranged in a grid or array. If the picture was three-dimensional, voxels can be thought of as pixels stacked up on top of each other to render a 3D image.
One of the most important benefits for AEye, as Greene explained, is that every other computer vision algorithm developed for 2D images can now be utilized for 3D.
"From here, we can overlay color data on top of the points and data that we deliver that to customers. We very uniquely can extract information like velocity, acceleration and decelerations in order to predict the path of objects."
The lidar can then be programmed to look for things in the image using a computer vision algorithm. For example, applying an edge detection algorithm cuts down on processing requirements, by filtering out unimportant data.
"In many cases, up to 90% of all of the data in the scene can be found by the edges and the contrast." using an edge detection algorithm." Green said. Then lidar is used to "interrogate these edges in 3D" to make sense of them and figuring out what they are.
Green explained that most objects of interest in the field of view of the camera can usually be identified by just 16 points. "We can identify a target, calculate velocity and track it." Green said. "Without using the initial 16 lidar points we used to identify them." This further reduces processing requirements.
By designing their system this way, AEye technology extracts only the minimal amount of information required to identify objects, thereby freeing up processing requirements.
"Using just 5 percent of the pixels in the image you can get 90 to 95 percent of the content", Greene said. "This way you're reducing you bandwidth, increasing the speed, (since its from a camera that faster that lidar), and getting a majority of the content." All of this is possible by using a suitable edge detection algorithm.
A typical autonomous driving setup used by other companies might detect and classify multiple vehicles, pedestrians or bicyclists in a scene, even in cases where only a single vehicle's trajectory is needed to make a driving decision. By eliminating all of the other non-essential data, Greene says a more robust system can be designed. These systems can identify hazards much quicker.
"By intelligently looking at a scene, we can address threats much quicker." Green said. For example, a kid jumping out in the road or something falling off the back of a truck requires and instant response. "We can calculate and figure out within microseconds what it is." Greene said. He also explained that there is no intelligence built into lidar or radar itself, AEye's technology is supplying this artificial intelligence (AI) layer.
Limitations of Lidar: Density vs Latency
According to AEye, a shortcoming of traditional LiDAR is that most systems oversample less important information like the sky, road and trees, or undersample critical information such as a fast-approaching vehicle.
Greene explained that one of the limitations of a spinning lidar is that its actually a stacked system, each laser is stacked top of each other—up to 64 of them in some applications. One of the problems that may occur are "SNR (signal to noise ratio) gaps" between each layer, which can be described as areas of decreased resolution. These gaps grow larger at longer ranges at 200 meters or more.
At 200 meters away, another vehicle can be inside one of these gaps and unidentifiable for 10 or more milliseconds. Not an ideal situation for safety critical applications where milliseconds matter. These systems are also more costly.
"The more lasers you stack, the slower your system is and the more expensive your system is. Unless you doing this intelligently, if you cut down costs of if you cut down the resolution you increase speed but lose resolution." Greene said. Likewise, if you increase resolution you increase cost. So there is a trade off for companies working on this lidar technology for mass production in the automotive industry.
These lidar systems require significant processing power and time to identify critical objects like pedestrians, cyclists, other car, or animals.
Harmful to Look At: 1550 vs 905 Nanometer Lidar Systems
Many lidars designed for use in self-driving cars are made using 905 nanometer silicon lasers to save costs. These lasers are inexpensive to produce, However, one problem with 905 nanometer wavelength laser is that they are harmful to the human eye.
"From an eye safety standpoint, it's mind boggling that they would put these on the streets." Greene said.
AEye is being more cautious to its approach—their proprietary system is designed using 1550 nanometer lidar. These 1550 nm wavelengths are longer, so they will not damage a person eyes. They also offer much higher resolution to see through fog, rain, or snow—weather that a self-driving car may encounter. "Eye safety is not even a concern." Greene said.
These 905 nanometer lasers we never really regulated by a government body, as they were never really expected to be deployed in non-military applications, such as autonomous cars. In the future, government regulations may limit their widespread use in the automotive industry.
Surprisingly, Greene told me that these lasers are often associated with medical devices, therefore, they are overseen by the Food and Drug Administration, not the The National Highway Traffic Safety Administration (NHTSA).
"905 nanometer lasers will become obsolete in the automotive industry, its just a matter of time." Greene said.
AEye's iDAR
AEYe's iDAR is a result of the company's most recent engineering efforts. According to AEye, iDAR is a groundbreaking form of artificial perception using sensor fusion that uses computer vision algorithms to focus on specific areas of a camera image. This decreases the hardware demands of analyzing pixel and lidar data from an entire scene.
Traditional LiDAR can be used to generate a 3D point cloud as it bounces light off objects. The point cloud can be used identify an object. In addition, the 'height' of the 'voxel stack' can also be analyzed to represent distance from an object or depth. AEye's iDAR technology can determine the velocity of an object as well.
A visualization of a voxel, Image credit: Intel Corp.
AEye's iDAR system is inexpensive to produce, as each unit houses a single camera and lidar. A self-driving car may use up to five of these for complete 360 degree coverage.
Once the iDAR generates the lidar point cloud, AEye uses artificial intelligence to assess the surroundings to track targets and flag objects of interest, such as another vehicle or pedestrian. AEye's iDAR can target and identify objects within a scene 10 to 20 times more effectively than LiDAR alone, according to the company.
This real-time integration of all the data captured in pixels and voxels is combined into a data type that can be dynamically controlled and optimized by artificial perception systems at the point of data acquisition.
Dynamic Vixels create content that inherits both the ability to evaluate a scene using the entire existing library of 2D computer vision algorithms as well as capture 3D and 4D data concerning not only location and intensity but also deeper insights, such as the velocity of objects.
iDAR uses embedded AI within a distributed architecture, and employs Dynamic Vixels to assess general surroundings to maintain situational awareness, while simultaneously tracking targets and objects of interest.
iDAR's True color LiDAR (TCL) instantaneously overlays 2D real-world color on 3D data, adding computer vision intelligence to 3D point clouds. By enabling absolute color and distance segmentation, and co-location with no registration processing,TCL enables the quick, accurate interpretation of signage, emergency warning lights, brake versus reverse lights, and other scenarios that have historically been difficult for legacy LiDAR-based systems to navigate.
Dynamic Vixels enable iDAR to act reflexively to deliver more accurate, longer range and more intelligent information faster.
"One nice consequence that comes out of the architecture is we give our customers the ability to add the equivalent of "human reflexes" to their sensor stack," said Dussan.
Dynamic Vixels can also be encrypted for security in tomorrow's connected cars. This patented technology enables each sensor pulse to deal appropriately with challenging issues such as interference, spoofing, and jamming. Issues that will become increasingly important as millions of connected cars are deployed worldwide.
The iDAR system enables the autonomous vehicle to more intelligently assess and respond to situational changes within a frame. For example, iDAR can identify objects with minimal structure, such as a bicycle, and differentiate objects of the same color such as a black tire and the road surface.
In addition, Dynamic Vixels can leverage the unique capabilities of lidar to detect changing weather and automatically increase power output during fog, rain, or snow.
iDAR's enhanced sensory perception allows autonomous vehicles to determine contextual changes, such as a person's facial direction, which can be used to calculate the probability of the person stepping out onto the street, enabling the car to prepare for the likelihood of an emergency braking maneuver.
"There are three best practices we have adopted at AEye," said Blair LaCorte, Chief of Staff at AEye. "First, never miss anything; second, not all objects are equal; and third, speed matters."
Dynamic Vixels enables iDAR to acquire a target faster, assess a target more accurately and completely, and track a target more efficiently – at ranges of greater than 230 meters with 10% reflectivity."
The iDAR perception system includes inventions covered by foundational patents, including 71 intellectual property claims on the definition, data structure and evaluation methods of Dynamic Vixels.
These patented inventions contribute to significant performance benefits, including a 16x greater coverage, 10x faster frame rate, and 7-10x more relevant information that boosts object classification accuracy while using 8-10x less power, according to AEye.
AEye's first iDAR-based product, the AE100 artificial perception system, will be available this summer to OEMs and Tier 1s launching autonomous vehicle initiatives.
To date, AEye has raised $25 million from world major investors, including Kleiner Perkins, Airbus Ventures, Intel Capital, and others. Earlier this year, AEye also launched the iDAR Development Partner Program for universities and automotive OEMs.
The company is working with two undisclosed automakers to implement its technology in mass-production vehicles for use in ADAS (advanced driver assist systems) and vision systems designed for autonomous driving.
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Saturday August 10th 1901 I hurried through my baking and brought Mother out to dinner. In the afternoon we went to the West farm. Ida went to town. I brought her home from Woolfs. Will made two trips to the valley for fence parts. Weather cooler. Sunday August 11th 1901 another warm day. I did the work in the morning and read and rested in the afternoon. Ida gone all day It looked like rain but it did not come. Monday August 12th 1901 We had a big washing in the forenoon. Will is having fence built to divide the farm in two fields. Him and I went down town in the afternoon to get some things Did evening work after coming home. Tuesday August 13th 1901 I helped with ironing and did baking in the forenoon I went down town in the afternoon Was out to Aunt Ems but did not find her at home. Came back over home to stay till after supper as the Niphers were to be there. Wednesday August 14th 1901 I went down early with butter for Fannie. I brought Mary Woolf out with me for the day and took her back at 5 oclock Fred & Dorothy were out in the evening.


text:
Rear-Admiral Daniel Lionel Hanington, DSC, CD   (July 10, 1921 – January 6, 1999) was a Canadian Rear Admiral.  In 1942, while serving on HMCS Wetaskiwin, he was awarded the Distinguished Service Cross.  His medal citation was "For meritorious services before the enemy."

What follows is his obituary as published in the National Post.

"Rear Admiral Daniel Lionel Hanington, who survived a torpedo attack by a German U-boat during the Battle of the Atlantic, and received the Distinguished Service Cross, has died aged 77.

"Like many young men of his generation, Mr. Hanington entered the armed forces immediately after completing secondary studies at Rothesay Collegiate School, in New Brunswick.

"In June, 1940, he finished school; by the fall, Midshipman Hanington was at sea in HMS Rajputana, an old ocean liner dragooned into convoy escort duty; and in the early morning of Sunday, April 13, 1941, Rajputana was torpedoed and sunk.

"'All of a sudden, there was a phenomenal crash on the port side. The ship, and everything in her, stopped dead, and she listed about 10 degrees,' Mr. Hanington wrote in his unpublished memoirs. After sighting the U-boat's periscope, and shooting at it unsuccessfully, the submarine fired a second torpedo. Standing on top of a magazine with 20 tons of ammunition, Mr. Hanington was scared.  'If they ever salvage the Raj, I guarantee that they will find my handprints on the rail around that magazine hatch.'

"Forty-two of the crew died; the survivors escaped in lifeboats. 'We turned the boats in the general direction of Iceland, and started rowing. The weather was rapidly deteriorating; it wasn't so bad for us who were rowing, but the injured who had been picked out of the water were rapidly freezing solid and we had to give them much of our warm clothing.' Eventually rescued, Mr. Hanington's good humour and self-depreciating wit - 'he never took himself too seriously,' Brian, his son, said — were clear in the cable he sent home from Reykjavik: 'Sunk, saturated, saved. Love Dan.'

"'Anyway, the whole experience taught me three things—to inhale cigarettes, to enjoy brandy, and that the 13th, on which I was a survivor, is my lucky day. A final note, about nerves—the day before the sinking I weighed 197 pounds, the day after - 168! I had shivered off 29 pounds in about 20 hours. It stayed off for a long while, but I can't really recommend periodic sinking as a way to lose weight,' he wrote.

"He soon returned to sea duty, serving on a series of corvettes and destroyers, slowly being promoted, and developing a celebrated talent for navigation.

"In 1942, serving on HMCS Wetaskiwin as Group Navigator, he was involved in the sinking of U-588. 'I was able to figure out where the U-boat was when we lost contact with him. Partially, because of this, he was sunk. The Canadian Navy, which was 92% made up of ignoramuses like me, hadn't been having a great deal of practical success, so there was high excitement and medals all round. Which is how I came by the Distinguished Service Cross.'

"He said the rest of his career was not as exciting. But it was full of honour and promotions, as he became steadily more expert in navigation, and in the theory and practice of escorting convoys. He served on an aircraft carrier, was hydrographer on HMCS Swansea's Arctic cruise in 1949, did early work with military computers, was part of a team at the Massachusetts Institute of Technology planning the air defence of North America, held commands afloat and ashore, and ended his professional career as Deputy Chief of Staff (Support) for NATO's naval command.

"He later taught at a U.S. Navy school, wrote a manual for the U.S. Navy, worked on Trident, a Canadian naval newspaper in Halifax, and retired to Victoria in 1989, where he was buried Monday among naval officers and admirers from virtually every province, as well as Hawaii and Washington.

"Rear Admiral Hanington was born in London, although both his parents were from New Brunswick. He moved to Trinidad, where his father managed The Guardian newspaper, and was sent to live with relatives in New Brunswick upon his father's death. He married his wife, Margot, during the war, and raised two daughters and two sons.

"In his own words, 'I am not one of your forceful Haningtons, I am one of your stubborn Haningtons. I have brains enough, but have concentrated on my own field, the navy, to the detriment of a broader education. I try to make myself look as good as possible even when I am clearly wrong, so I say that I am kind, and I have tried to be a good father and grandfather. And husband, come to that. I have been fortunate beyond my deserts.'"

References

External links
.  Includes a summary of Hanington's oral interview (Halifax, May 1985), which details his career and provides anecdotes from his service with the Canadian Navy.

1921 births
1999 deaths
Military personnel from London
Canadian admirals
Recipients of the Distinguished Service Cross (United Kingdom)
Royal Canadian Navy personnel of World War II

redpajama_set_name: RedPajamaWikipedia


I first noticed the Nonesuch Gallery when I went into Ontonagon for breakfast at Syl's (it's right across the street). As I passed, I liked the look of the historic building and noticed its sign said the gallery was founded in 2001, the same year I found Inspiration and started RVing – so with that and such a name, I couldn't resist!
I was warmly welcomed by the owner, Edna, but upon entering I soon had a puzzled look on my face. I had just been in my car listening to the Ray Bonneville CD that I bought at the recent Porcupine Mountains Music Festival. But when I walked into her store and she was playing the very same song from the very same CD, I thought the odds of that were pretty phenomenal! We started talking about the festival and agreed that Ray had been our favorite performer. The festival had been one of my favorite things since I'd been in the Porkies, so with that much already in common, soon we were chatting like old friends.
Here's a link to a little video I did at the Festival on YouTube and here are some pictures I posted.
The store is immediately sensed as a labor of love – it just has that kind of vibe to it. There are many examples of local artisans here – one-of-a-kind pieces representative of the wonders of the Upper Peninsula, including antler furniture, woodcarvings, pottery and jewelry.
The quilt at lower right is named "Song for the Earth" and she said it was created in celebration of the 20th anniversary of Earth Day and "designed as a valentine expressing my great love for our planet earth which is our lovely home."
I found my perfect picture frame for one of my Michigan photo souvenirs here, and I really enjoyed looking at all the other craftsmanship, but even with nothing else, it's worth a visit just to see Edna's quilts. Although I've appreciated the beauty of quilts before, I certainly wasn't an educated connoisseur, and I'd never even heard of "quilt art" before. But this work definitely deserves the title Art – I've never seen anything like it and it still blows my mind to think of the amount of work (and love) involved in the creations I saw here.
My favorite was this one called "Living a Dream." Edna explained this quilt was inspired by the dream she and her husband, Boe, shared. They had lived in the city and both had factory jobs, but constantly talked about "living up in the woods." That dream came true when they moved to a tiny log cabin deep in the woods of the U.P. in 1976.
Edna designed and made the quilt in 1992 for the All American Quilt Contest sponsored by Good Housekeeping and Land's End. There were over 4,000 quilts in competition, and Edna's was the winner from the state of Michigan. And it was also chosen to be documented in the All-American Quilt Collection in the Library of Congress!
"This quilt is meant to encourage people to reach for their dreams, no matter how overwhelming they may seem. Step by step, one can accomplish even the biggest goals. This quilt was inspired by my husband and my dream of someday calling a log cabin we built one summer in the woods of the Upper Peninsula, our "home". Through hard work, perserverance, and trying again and again, we were able to accomplish it. We moved here for the third time in 1984 after going broke several times before. But we've been here ever since and though, our goals have changed to new ones, this quilt still remains my true expression of living a lifelong dream."
Since they repeatedly stated their dream as "living up in the woods," she put the house up in the tree (up in the woods) – so perfect! She also depicted the putting down of their roots here, enjoying rivers, lakes and sunsets.
"At that time I loved that pattern… it reminded me of playful water droplets, floating…and I actually used that in my tree, too. Each section of tree is actually a paisley shape layered over each other. Which is probably kind of odd, but it just seemed like that was what I wanted to do…"
I told her this was my idea of the best kind of odd!
"Altho I sell small quilts and duplicate many of them, I have kept my original, one-of-a-kind art quilts for my own collection. They are so much a part of me, they are like a journal of my life."
I can certainly understand not parting with something that valuable for any amount of money!
I was so impressed and inspired by Edna and her stories. Even after Boe died way too young in 2004, she persevered, and with the help of friends, kept the store open. She is now remarried to a wonderful man who shares her vision and love of the U.P. I left her store with my favorite U.P. souvenirs and a feeling of being blessed to have met her.
Now meet Dar Fredrikson, another local artisan who sells some of her work here, including this white birch basket. I really enjoyed visiting with her, too, and got a kick out of her later comment on my blog post about Michigan Dreaming when she called herself the "birch bark queen."
"Fredrikson, who has a master's degree in Art Education and Art Therapy from Wayne State University, has an interest in sustainable living, which has motivated most of her creative processes. She enjoys the self-imposed challenge of finding functional forms for both reusable and found natural materials. Some of the materials she has chosen to investigate and use include cedar shingles, woolen fabrics, birch bark, spruce trunks and roots, sweet grass and local clays."
Here she is modeling the oh-so-fashionably-sensible-for-Yoopers Kromer cap with the ear flaps. Cute, eh?
"Her poetry, prose, and photographs have been published, and pen and ink illustrations copyrighted. Otherwise, many of her watercolor paintings and etchings are in private collections. With her husband Bob and English setter Gandhi, Dar enjoys summer and fall in Ontonagon County and winter near Detroit."
Another great example of the fabulous and fascinating people I met here every day – I experienced the best of the Porkies and the U.P. in every way!
Later, when I was reading "The Porcupine Mountains Companion," I learned the meaning of the name "Nonesuch." They explain, "In the vernacular of the 1800's, 'nonesuch' meant the greatest, most promiment, most eminent." You do the name proud, Edna!


Schreiben Sie den ersten Kommentar zu "Body Not Recovered".
On June 17, 1966, the author's high school classmate, M. J. Savoy, was killed in a military plane crash into the South China Sea off the coast of Vietnam. The search for M. J. and his crewmates was unsuccessful, and each has since been listed as Killed in Action/Body Not Recovered. But what if M. J. did not really die in that crash? What if it were staged for some reason? Body Not Recovered is inspired by and dedicated to M. J. Savoy.
When 1964 University City graduate and teenage loner, JR Spears, enlists to fight in Vietnam for the noblest of reasons, he begins a journey that takes him from committed warrior to reluctant soldier to underground antiwar leader. Confronted by a conflict they increasingly find abhorrent and unjust, Spears and a small cadre of comrades conspire, with both commitment and foreboding, to stage a helicopter crash, fake their deaths, and surreptitiously return to the states to join the protest movement.
These deserters are not the only ones who risk their lives and personal freedom or whose families are ravaged by the war. When Maggie Blessing's brother is killed in action, she runs away from home to join the antiwar movement, leaving her mourning parents. John Muccelli, Spears's high school classmate, fulfills his lifelong dream of becoming an FBI agent, only to be conflicted by the illegal tactics he is ordered to use to hunt down protest instigators. Bernice Williams, Maggie's mentor and lover, turns from influential antiwar leader to zealous bomber to victim of her own fanaticism. The war, the protests, these characters, and the soul of a nation converge in the supercharged 1960s and 1970s environment of the Bay Area.
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If You're Vegan, How Do You Get Enough Protein?
If you're worried about getting enough protein on a vegan diet, DON'T BE!. The truth is, most people get to much protein in their diet and vegans can easily get more than enough. Lentils, seeds, beans, tofu, quinoa, soya, nuts.. are an excellent source of protein. There are no known cases of protein deficiency in the US unless the person is malnourished overall..


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text:
The Dream Things True Duology
Epic High Fantasy
Long ago, the Gods lived among their creations. Until one of their brothers went mad with power, unleashing war and destruction upon the land. In order to banish him, the Gods removed themselves from creation, forming Heaven and Hell. But the devil didn't stay long in his domain, and humanity has become trapped in the struggle between eternal powers.
The time has come for the mortal realm to end Lucifer's war once and for all, lest they become his pawns, trapped in the middle of his war for revenge and dominance. As a descendant of the divine beings who once helped lock Lucifer in his prison, the task falls to Kaylie.
Unfortunately, Lucifer has lured her into his hellish realm and robbed her of her memories using water from the river Lethe. Can she regain her memories in time to thwart his plans? Or is she doomed to spend eternity in the realm of torment?
Dream Things True includes elements inspired by Dante's Inferno, ancient Greek and ancient Canaanite mythology, and will appeal to fans of Good Omens and Lord of the Rings.
Kindle / Kindle Unlimited and Paperback! Kindle / Kindle Unlimited and Paperback!
-Rich history and world-building
-Epic journey and heart-stopping action sequences
-Complex three-dimensional character relationships
-Strong female characters
-LGBTQ+ characters and relationships
Read more in my project report.
Keep up to date using the Project DDL tag!

redpajama_set_name: RedPajamaCommonCrawl


text:
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text:
A Framework for Designing Higher Education e-Learning Environments
Shirley Reushle, University of Southern Queensland, Australia
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, October 2006 in Honolulu, Hawaii, USA ISBN 978-1-880094-60-0 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
This paper describes a framework for the design of higher education learning and teaching (e-learning) environments. The framework has emerged from the author's doctoral research. The author, located at a regional, Australian university, collaborated with teachers from a polytechnic in Singapore to build the professional capacity of the group in the area of e-learning. The exploration of themes evident in the literature assisted in the creation of the design framework. Findings from the study which led to the design of the framework are briefly discussed and the principles and practices, which define that framework, are outlined. Implications for educators and educational institutions are discussed. It is an unrealistic expectation that a proposed framework is relevant to users in all educational environments, but it is anticipated that the framework could be applied to a wide variety of e-learning settings.
Reushle, S. (2006). A Framework for Designing Higher Education e-Learning Environments. In T. Reeves & S. Yamashita (Eds.), Proceedings of E-Learn 2006--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1405-1412). Honolulu, Hawaii, USA: Association for the Advancement of Computing in Education (AACE). Retrieved January 27, 2023 from https://www.learntechlib.org/primary/p/23907/.
e-learning environment
e-learning environments
View References & Citations Map
Anderson, T., & Kanuka, H. (2003). E-Research: Methods, strategies, and issues. Boston: Pearson Education.
Garrison, D. (1997). Computer conferencing: The post-industrial age of distance education. Open Learning, 12 (2), 3-11.
Berge, Z. (2000). New roles for learners and teachers in online higher education. Baltimore: University of Maryland System, UMBC. Retrieved March 29, 2006, from http://www.globaled.com/articles/BergeZane2000.pdfDick.B.(1993).Youwanttodoanactionresearchthesis?Howtoconductandreportactionresearch.RetrievedNovember11,2005,fromhttp://www.scu.edu.au/schools/gcm/ar/art/arthesis.html
Herrington, J., Oliver, R., Herrington, T., & Sparrow, H. (2000). Towards a new tradition of online instruction: Using situated learning theory to design web-based units. Paper presented at the ASCILITE00 17th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education. Retrieved June 15, 2005, from http://www.ascilite.org.au/conferences/coffs00/papers/jan_herrington.pdf Hung, D. (2001). Theories of learning and computer-mediated instructional technologies. Education Media International, 281-287.
Kanuka, H., & Anderson, T. (1998). Online social interaction, discord, and knowledge construction. Journal of Distance Education, 13(1), 57-74.
Kassop, M. (2003). Ten ways online education matches, or surpasses, face-to-face learning. The Technology Source, May/June. Retrieved January 16, 2006, from http://ts.mivu.org/default.asp?show=article & Id=1059
Kimball L. (1998). Managing distance learning—new challenges for faculty. In R. Hazemi, S. Hailes, & S. Wilber (Eds.).The digital university: Reinventing the academy. New York: Springer.
King, K.P. (2003). Keeping pace with technology: Educational technology that transforms. New Jersey: Hampton Press, Inc. 2006, from http://www.ascusc.org/jcmc/vol7/issue4/lapadat.html
McDonald, J., & Reushle, S.E. (2000, June). Online interaction: An exploratory quantitative perspective. Proceedings of the CRIDALA 2000 conference. Hong Kong: Open University of Hong Kong.
McKendree, J., & Mayes, J.T. (1997). "The vicarious learner": Investigating the benefits of observing peer dialogues. CAL'97. Retrieved January 6, 2006, from http://www.media.uwe.ac.uk/masoud/cal97/papers/mckendre.htm
Mayes, T. (2002). The technology of learning in a social world. In R. Harrison, F. Reeve, A. Hanson, & J. Clarke, (Eds.), Supporting lifelong learning: Vol. 1, Perspectives on learning. Routledge: London.
Merriam, S.B., & Caffarella, R.S. (1999). Learning in adulthood: A comprehensive guide (2nd ed.). San Francisco: Jossey-Bass.
Mezirow, J. (1997). Transformative learning: Theory to practice. In P. Cranton (Ed.), Transformative learning in action: Insights from practice. New directions for adult and continuing education, No. 74. (pp. 5-12). San Francisco:
Russell, T. (1999). No significant difference phenomenon, Retrieved October 20, 2001, from http://cuda.teleeducation.nb.ca/nosignificantdifference/ (no longer available)
Salmon, G. (2002). Approaches to researching teaching and learning online. In C. Steeples& C. Jones (Eds.), Networked learning: Perspectives and issues (pp. 195-212), London: Springer.
Sandlin, J.A. (2000, August). The politics of consumer education materials used in adult literacy classrooms. Adult Education Quarterly, 50 (4), 289-307.
Steeples, C., Jones, C., & Goodyear, P. (2002). Beyond e-learning: A future for networked learning. In C. Steeples& C. Jones (Eds.). Networked learning: Perspectives and issues (pp. 323-342). London: Springer.
Wenger, E., McDermott, R., & Snyder, W. (2002). Cultivating communities of practice. Boston, MA: Harvard Business School Press.
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On October 9, 1999 I married the love of my life. The following day we were whisked off to beautiful Hawaii for an amazing honeymoon trip we still talk about now almost 15 years ago.
2 days into our trip, I was videotaping the sunset and noticed on the date stamp on the old camcorder screen that it was October 12th – OMG I forgot it was my birthday!
But it's easy to see how this happened. I had gotten married, spent almost a whole day on a plane to fly half-way around the world. I landed in a tropical paradise and I guess part of my brain just checked out. This was 1999. Before Facebook. Before Twitter. Before iPhones with reliable cell service.
Linkedin launched in June, 2003. Facebook launched in 2004. YouTube launched in 2005. Twitter in 2006. And now we also have Pinterest. And Instagram. And Snapchat. All of these mechanisms are pushing content across a world that now also sends millions of texts per second.
According to the National Center for Biotechnology Information, at the U.S. National Library of Medicine, the average attention span of a human being has dropped from 12 seconds in 2000 to 8 seconds in 2013. This is one second less than the attention span of a goldfish. That's right, goldfish have an attention span of 9 seconds – 1 second more than you and I.
This was one of the key stats that really seemed to resonate with the audience I spoke to this morning at #MPOMMA #IWNY.
I was thrilled to kick off the day as the opening keynote. And although I was competing with the mayor of NY, Bill de Blasio on another #IWNY stage, the audience filled the Media Post Theater at OMMA Native #MPOMMA.
My job was to set the stage for the discussion on Native Advertising.
I asked "Why are we talking about Native Advertising?" And the answer is because digital, social and mobile access has changed the world. Marketing has become highly ineffective because consumers can now tune us out.
What do they tune in to? Stories. Stories that connect on a human and emotional basis.
I also provided an overview of the journey we've taken and some of the native advertising we've tested.
Thanks Tony, I don't think it is good or bad. It just is an outcome of our desire for progress in all aspects of our lives. I am optimistic at the minds ability to deal with everything and think we will continue to find ways to thrive in spite of some of these consequences of digital life.
Interesting article Michael! I'm very curious what's going to happen in the future… With content marketing but also the effects on the human being in general.
At least I see some serious concentration issues coming at us, or actually they are already here!
I expect that people are gonna act against this over-stimulation in one way or another.
Buying hard copy books again?
I saw a study this week about a huge decline in the number of people who reported that they actually read a real book in the past month. Crazy!
Huge fan of detaching from social for extended periods Michael….so I can out do the goldfish lol….nice post!
Thanks Ryan, I agree. Had a semi digital detox this weekend. back at it today feeling refreshed and ready to focus!
Thanks Mayank. I think you're right.
Michael – Great stuff! I fall victim to the Goldfish Syndrome myself and purposely unplug so I can get important work completed.
For content marketers in general, I would love to see "us" create more valuable content; not just produce more. The format is obviously important (visual over text-heavy) but I believe it all starts with knowing your audience and what they care about to elicit an emotional connection or response. Again, great post!
I agree Heather, I try to do that too!
And yes we need to create quality stuff that people actually want. Listening and watcing and learning what your audience wants is definitely the key!
I was never able to keep up with social media. I guess this is a good thing, so I can still retain a longer attention span than a goldfish's.
If this is true, then no one read beyond the third paragraph or remembered what the first part of the article said by the time they finished reading the end. I would like to read the study to see the methods and results. No doubt we PREFER as much stimulation of our senses as we can. But saying that is decreased attention span is a stretch. Besides, how did all of those politicians read the affordable care act?
Nobody reads everything. Mostly scrolling down to second paragraph. If you have images that grab attention, they are likely to go down to see the stuff.


This is interesting – Mumia's plight was one of the early incidents that raised my hackles – my brother gave me a VCD for my 17th birthday, I think, that was all about Mumia – and I was hooked. From that conciousness-raising, I've gone onto other things, but that was the start, really.
"Should the Commonwealth of Pennsylvania not have conducted a new sentencing hearing … the Commonwealth shall sentence petitioner to life imprisonment," the judge said in his 272-page ruling.
, which I still feel is better. And this is apparently continuing a trend away from the death sentence since 11.9.2001 – I can't remember where I read it (please send the link if you have it), but several states have commuted sentences, and looked at their legislation with regards to the death penalty.
Previous PostPrevious Andy Adventure Book Series!


text:
Council Committee Approves Mandatory Housing Affordability Program, Addresses Displacement Risks
The Council's Planning, Land Use & Zoning committee unanimously (5-0) approved framework legislation today that would require that residential developers provide for affordable housing by either including it in their development or paying into a fund used to support the development of affordable housing in Seattle. The requirement will apply after Council adopts future zoning changes, scheduled for Council consideration at the end of 2016/early 2017.
"This program is one of the most ambitious efforts Seattle has ever undertaken to address our growing need for affordable housing in our city," said Councilmember Rob Johnson (District 4, Northeast Seattle), the legislation's sponsor. "This bill sets the stage to create affordable housing that will stay affordable for 75 years to serve current residents and future generations of Seattleites."
Under the program, if residential developers create rental housing, a select number of units must be made affordable to households earning up to 60% of Area Median Income (AMI), or $43,380 annually for a 2-person household. If developers to create housing for homeownership, a select number of units must be made affordable to households earning up to 80% of AMI, or $55,450 annually for a 2-person household. The number of required affordable units would be established by future legislation, as would the required amount of funds to be alternatively paid into an affordable housing development fund.
The Committee also adopted a series of amendments, including prioritizing that projects using the affordable housing development fund are developed near the projects that paid into the fund; that housing units be kept affordable for a term of 75 years; that when an affordable rental unit is converted to an ownership unit, the existing tenant has a right of first offer; and that accountability requirements be imposed to ensure that projects remain affordable. Additionally, Councilmembers took steps to address the risk of displacing low-income populations from Seattle.
Councilmember Lisa Herbold (District 1, West Seattle & South Park) said, "It doesn't make sense to lose more than we gain, which is why we will require a displacement risk analysis to approximate the number of existing affordable housing units demolished as a result of future upzones. The mandatory affordability requirement will be adjusted higher over the entire area being up-zoned commensurate with the number of units likely to be demolished. While this doesn't require any mitigation of increased rents from other market forces, it is a modest but necessary amendment."
"Our amendment creates consistency between our anti-displacement efforts and commits to exploring how we can use the Mandatory Affordable Housing framework to help ensure that the communities in our city most threatened by displacement can stay and thrive in place," said Councilmember Mike O'Brien (District 6, Northwest Seattle). "The framework legislation itself is an anti-displacement tool, by helping ensure more long term affordable housing exists in our city, and today's vote strengthens the policy as we strive to be a city where all people can benefit from our shared prosperity."
The Mandatory Affordable Housing – Residential (MHA-R) legislation is a key component of the Housing Affordability and Livability Agenda (HALA), which is a multi-faceted policy agenda intended to create 20,000 affordable units over the next 10 years.
The Full Council is scheduled to consider the Mandatory Housing Affordability legislation at its 2 p.m. meeting on August 15, 2016.
Posted: August 2nd, 2016 under Herbold, Johnson, News Releases, Obrien.
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Time August 2, 2016 at 9:29 pm
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Time August 3, 2016 at 6:18 am
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text:
"""Implementation of the Video Panoptic Quality metric.

Video Panoptic Quality (VPQ) is an instance-based metric for evaluating the task
of video panoptic segmentation.
Please see the paper for details:
Dahun Kim, Sanghyun Woo, Joon-Young Lee, and In So Kweon.
"Video panoptic segmentation." In CVPR, 2020.

Note that our TensorFlow re-implementation of Video Panoptic Quality may give
a bit higher VPQ score than the official numpy implementation
(https://github.com/mcahny/vps).
This re-imeplementation is simply used as an estimate of the prediction
quality during TensorFlow training. The users should convert their video
panoptic prediction to COCO json format and run official numpy implementation,
in order to have a fair comparison with others. See Q4 in our FAQ (g3doc/faq.md)
for more details.
"""

from typing import List, Tuple

import numpy as np
import tensorflow as tf
from deeplab2.evaluation import panoptic_quality

class VideoPanopticQuality(panoptic_quality.PanopticQuality):
  """Metric class for Video Panoptic Quality.

  Dahun Kim, Sanghyun Woo, Joon-Young Lee, and In So Kweon.
  "Video panoptic segmentation." In CVPR, 2020.

  Video Panoptic Quality can be modeled as Image Panoptic Quality with the
  sequences of predictions and the ground-truth labels horizontally concatenated
  as two images, separately. Therefore, this class inherits the image panoptic
  quality class and changes the implementation to concatenated comparisons.

  Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen.
  "ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic
  Segmentation." In CVPR, 2021.

  Stand-alone usage:
  vpq_obj = video_panoptic_quality.VideoPanopticQuality(
    num_classes, max_instances_per_category, ignored_label)
  vpq_obj.update_state(y_true_1, y_pred_1)
  vpq_obj.update_state(y_true_2, y_pred_2)
  ...
  result = vpq_obj.result().numpy()
  """

  # pylint: disable=useless-super-delegation
  def __init__(self,
               num_classes: int,
               ignored_label: int,
               max_instances_per_category: int,
               offset: int,
               name: str = 'video_panoptic_quality',
               **kwargs):
    """Initialization of the VideoPanopticQuality metric.

    Args:
      num_classes: Number of classes in the dataset as an integer.
      ignored_label: The class id to be ignored in evaluation as an integer or
        integer tensor.
      max_instances_per_category: The maximum number of instances for each class
        as an integer or integer tensor.
      offset: The maximum number of unique labels as an integer or integer
        tensor.
      name: An optional variable_scope name. (default: 'video_panoptic_quality')
      **kwargs: The keyword arguments that are passed on to `fn`.
    """
    super().__init__(num_classes, ignored_label, max_instances_per_category,
                     offset, name, **kwargs)

  def compare_and_accumulate(
      self, gt_panoptic_labels: List[tf.Tensor],
      pred_panoptic_labels: List[tf.Tensor]
  ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    """Compares predicted segmentation with groundtruth, accumulates its metric.

    Args:
      gt_panoptic_labels: A list of tensors for the ground-truth video panoptic
        segmentation labels.
      pred_panoptic_labels: A list of tensors for video panoptic segmentation
        predictions.

    Returns:
      The value of the metrics (iou, tp, fn, fp) over all comparisons, as a
      float scalar.
    """
    gt_panoptic_label = tf.concat(gt_panoptic_labels, axis=1)
    pred_panoptic_label = tf.concat(pred_panoptic_labels, axis=1)
    return super(VideoPanopticQuality,
                 self).compare_and_accumulate(gt_panoptic_label,
                                              pred_panoptic_label)

redpajama_set_name: RedPajamaGithub


It seems to me we'd be better off protecting individual freedom and curtailing the evil deeds of renegade corporations but our society has done exactly the opposite, possibly because we are under the influence of crazy people. Here's an instance where you, the individual, will be eating genetically modified sugar beets whether you like it or not because the USDA has decided to deregulate them.
What can we do about this sort of abuse? I suggest listening to this 11 year-old boy. In 5 1/2 minutes he covers the whole issue, to the roaring applause of his audience.
It's bad if the lunatics are running the asylum but when they are running everything it's a lot worse. Here's an Oregon scofflaw who has the audacity to collect rainwater and snow runoff in "illegal reservoirs" (read "ponds") on his property. Any lunatic would know you're supposed to have a permit. Even though he isn't a lunatic, the violator did have a permit, but the govt. revoked it.
Not everyone is a lunatic. 50 years ago a marine biologist, Rachel Carson, published a book warning of the dangers of chemicals in our environment. We've come a long way since then, but I fear we've been going the wrong way.
I've slammed enough giant corporations for one day. How about a huge energy company that uses a staff-run meditation program to promote innovation. A video and a partial transcript tell the story. Let's hope that the innovation has to do with not destroying the planet for profit.
An Indian Olympic hockey team sounds about as crazy as a Jamaican bobsled team, but it makes more sense when you know they're talking field hockey, not ice. It seems hardly necessary to point out that they meditate to improve performance, but we're doing it here.
The monk at the Buddhist shrine inside Olympic Village offers guidance not only to the 500 Buddhists in the Village but to others as well. The monk is happy to be there and I suppose being a Buddhist he's not giving anybody unfair powers to win.


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text:
Q: Problem with Many To Many RelationShip Spring Boot I'm using Spring boot as the backend of my application but I have a problem here.
This is my Entity relationship diagram:

And these are my classes
Product class
@Entity
@Table(name="productos")
public class Producto implements Serializable {

private static final long serialVersionUID = 1L;

@Id
@GeneratedValue(strategy=GenerationType.IDENTITY)
private int id;

@NotEmpty
@Size(max=45)
@Column(nullable=false,unique=true)
private String name;
@Column(nullable =false)
private String descripcion;

@Column(nullable = false)
private float precio;

@Column(nullable=false,length = 255)
private String urlFoto;

@Column(nullable=false)
private float calorias;

@ManyToMany(fetch = FetchType.EAGER, cascade = CascadeType.PERSIST)
@JoinTable(
        name = "Producto_Ingrediente",
        joinColumns = {@JoinColumn(name = "id_producto")},
        inverseJoinColumns = {@JoinColumn(name = "id_ingrediente")}
)
private List<Ingrediente> ingredientes;
//getters and setters

Ingrediente class
@Entity
@Table(name = "ingredientes")
public class Ingrediente implements Serializable {
private static final long serialVersionUID = 1L;

@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private int id;
@Column(nullable = false)
private String nombre;
@ManyToMany(mappedBy = "ingredientes", cascade = CascadeType.PERSIST, fetch = FetchType.EAGER)
private List<Producto> productos;
//getters and setters

But when i try to recover my products from the API, look at this json (its from CrudRepository)
{
"id": 2,
"name": "pancakes",
"descripcion": "aaaaa",
"precio": 10.0,
"urlFoto": "no hay",
"calorias": 2.0,
"ingredientes": [
    {
        "id": 2,
        "nombre": "miel",
        "productos": [
            {
                "id": 2,
                "name": "pancakes",
                "descripcion": "aaaaa",
                "precio": 10.0,
                "urlFoto": "no hay",
                "calorias": 2.0,
                "ingredientes": [
                    {
                        "id": 2,
                        "nombre": "miel",
                        "productos": [
                            {
                                "id": 2,
                                "name": "pancakes",
                                "descripcion": "aaaaa",
                                "precio": 10.0,
                                "urlFoto": "no hay",
                                "calorias": 2.0,
                                "ingredientes": [
                                    {
                                        "id": 2,
                                        "nombre": "miel",
                                        "productos": [
                                            {
                                                "id": 2,
                                                "name": "pancakes",
                                                "descripcion": "aaaaa",
                                                "precio": 10.0,
                                                "urlFoto": "no hay",
                                                "calorias": 2.0,
                                                "ingredientes": [

and it continues, what can I do?

A: If you are using Jackson, you could try to add @JsonIgnoreProperties("ingredientes") above the private List<Producto> productos;:
@ManyToMany(mappedBy = "ingredientes", cascade = CascadeType.PERSIST, fetch = FetchType.EAGER)
@JsonIgnoreProperties("ingredientes")
private List<Producto> productos;

redpajama_set_name: RedPajamaStackExchange


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text:
rock-climbing
My Wife Went Behind My Back
January 16, 2023 January 16, 2023 / Ask the Rabbi / By Rabbi Daniel and Susan Lapin
I went out of town with one of my children to visit my grandmother yesterday, and my wife decided she wanted to take my other two children to a rock climbing gym. Personally, I find the idea dangerous because I think that it is subjecting children to a serious injury if equipment were to fail...
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Brattleboro, Vt, in home of daughter, Eleanor.
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