Why not just send a rover equipped with a drone and other hordes of technology to prod and probe the Martian surface? Survey the landscape and give us a much better understanding of what the red planet is today and what it was in the past. If the European Space Agency can land a probe on an asteroid and orbit a spacecraft around it then you would have to think that NASA could easily send something to Mars better equipped than all previous missions and confirm if Mars once was an Eden much like Earth and not only that but discover what happened there in the very distant past and if it has significant relations to us humans here on Earth. It has been detected that there is weapons signature on Mars, this means that something happened there in ancient times that was not natural. To create weapons signature you must first detonate a nuclear bomb, meaning there must have been intelligent life on planet Mars one way or another. Dr. John Brandenburg is a well known and respected plasma physicist who has worked on plasma technologies, nuclear fusion and advanced space propulsion for NASA and invented the microwave electro-thermal plasma thruster using water propellant for space propulsion. Brandenburg was the deputy manager of the NASA clementine mission to the moon that discovered water on the moon's poles in 1994. According to Brandenburg the super abundance of XE129 in the Mars atmosphere is a nuclear weapon signature indicating that there was a nuclear blast on Mars that completely changed the planet's surface. This is weapons signature. If you match this xenon to the output of a hydrogen bomb you get a very good match. There is only one place in the solar system where in a planetary atmosphere you find anything like the xenon signature of Mars and that is the part of the US of the atmosphere of the Earth that has changed dramatically since 1945 when we discovered nuclear weapons. This data which was published shortly after the landing on Mars shows that for xenon 129 here is Earth before 1945 or here actually here it is now and here it is from air samples before 1945 and you can see 10% roughly 10% of the xenon 129 wasn't there before. Most of the other xenon isotopes haven't changed at all but 129 has changed considerably and that's because of the open air nuclear tests and also production of plutonium. In fact if I was really interested I could probably try and parse out how much plutonium this intelligent species on this planet has made. It is more than you imagine in your worst nightmares. Imagine finding out that your cousins have been stockpiling gun ammunition at their farm and you go out to visit them and you find that the barn is full under all the cushions on the couch there is ammunition and in all of the desk drawers there is ammunition and in the sock drawers too under the socks there is more and more ammunition. The human race has been arming itself to the teeth for a big nuclear war since 1945 and the way we know that is because of this xenon 129 because of the testing of nuclear weapons and the production of plutonium this requires fast neutrons. Fast neutrons don't react as well as the slow neutrons. Now if we take the weapons xenon and mix it with like 3070 30% or normal earth and then 70% weapons xenon we get Mars and this indicates that on Mars nuclear reactions occurred which dwarf anything that happened on earth most of the Martian atmosphere is a residue from a enormous nuclear explosion. Brandenburg says that there are things out there that have been deliberately kept from us whether this information has been concealed because the human race is not ready for this information or that it's just too mind-altering and would change too many things and because disclosure would threaten various corporate interests of an unknown and socially cataclysmic scale. As we create history and move beyond more people will become open and aware to the idea that we're not alone that we've never been alone and that there is plenty of evidence to prove these claims. John Grunsfield a five-time space flow astronaut and associate administrator of NASA's science mission directive states the following the more we observe Mars the more information we're getting that it really is a fascinating planet from the curiosity rover we know that Mars once was like a planet very much like earth with long salty seas with fresh water lakes probably with snow capped peaks and clouds and a water cycle just like we're studying here on earth something has happened to Mars it's lost its water it was given at a NASA press conference where representatives told the world that there is water on Mars that it's not the dry arid planet we imagined it to be and that it was once very much earth-like and it still holds the possibility of harboring life after all water means life in that conference they admit to a drastic atmospheric shift that occurred on Mars which took it from its earth-like composition to the one we see today American rocket engineers are being urged to push their next Mars mission to the limits of technological performance space scientists have told NASA they want the agency to dream big to ensure their new robot rover scheduled for launch in 2020 visits a maximum number of sites to increase chances of uncovering signs of ancient life on Mars rock samples hopefully bearing fossils would then be left in caches on the Martian surface to be collected several years later and return to earth in a complex series of robot sample return missions costing more than 10 billion dollars isn't that shocking how long this would take is unclear however in a major space policy speech at Kennedy Space Center on April 15, 2010 then US President Barack Obama predicted a crew Mars mission to orbit the planet by the mid 2030s followed by landing for this investment the scientific community says it wants to have the strongest possible chance of finding signs of ancient Martian life hence its call for the next robot rover to the red planet to visit the largest possible number of places where it can collect rocks hopefully rich in fossils however it acknowledges this mission will push the robot vehicle to the very limits of its performance it would be stunning if they also would send a drone to buzz about Mars and give us unprecedented views and also a better understanding of structures that may still exist on the fourth rock from the sun so it may get you a bit excited to learn that by 2021 such a craft will be sent the Mars helicopter a small autonomous rotor craft will travel with the agency's Mars 2020 rover mission currently scheduled to launch in July 2020 to demonstrate the viability and potential of heavier than air vehicles on the red planet the idea of a helicopter flying the skies of another planet is thrilling the Mars helicopter holds much promise for our future science discovery and exploration missions to Mars it's fitting that the United States of America is the first nation in history to fly the first heavier than aircraft on another world this exciting and visionary achievement will inspire young people all over the United States to become scientists and engineers paving the way for even greater discoveries in the future the more young minds that take up positions at NASA with an open mind and a curiosity to discover the cover-ups that may be lurking in the shadowy depths of the NASA archives then this will give us a better chance of feeling more in touch with what is going on and to not feel like we are blatantly being misdirected with answers all the time the helicopter also contains built-in capabilities needed for operations at Mars including solar cells to charge its lithium ion batteries and a heating mechanism to keep it warm through the cold martian nights but before the helicopter can fly at Mars it has to get there it will do so attached to the belly pan of the Mars 2020 rover the altitude record for a helicopter flying here on earth is about 40 000 feet the atmosphere of Mars is only one percent that of earth so when the helicopter is on the martian surface it's already at the earth equivalent of 100 000 feet crazy right also in space news astronomers have recently made a bizarre discovery on the surface of Saturn's moon dione dione is covered in mysterious stripes like no other moon in the solar system the bright long stripes are like nothing astronomers have ever seen and the strangest part is no one knows for sure how they got there or what created them according to astronomers the stripes orientation parallel to the equator and linearity are unlike anything else we've seen in the solar system they're just really bizarre and really exciting when you see something really strange and you're just trying to figure out what the heck it could possibly be the bizarre stripes linear verge on the surface of dione are generally long narrow and brighter than the surrounding terrain the stripes are parallel seem to overlap other features and are not affected by topography suggesting that they are among the younger surface parts of dione their orientation parallel to the equator and linearity are different from anything else we have seen in the solar system suggesting they are something else other than a natural formation almost reminiscent of the nasca lines which as you know is theorized to be an ancient spaceport of sorts it is considered one of the most baffling mysteries in archaeology and his book chariots of the gods von danakin calls them runways for alien spacecraft that might have visited earth thousands of years ago whereas many researchers and theorists say they were made by a civilization much advanced in agriculture and technology a culture possibly capable of flight miles upon miles of lines patterns and shapes in the form of birds an owl man a monkey a spider and many other creatures etched on the ground consisting of thousands of lines running as far as 65 kilometers and some larger than two soccer fields the lines only make sense once they are viewed from up above because it is only from high up in the air that these creatures drawn on the ground take their shape for what their artists truly intended them to look thousands of years ago we can call them artists because the huge drawings are of such precision that every detail has been kept in mind and the best part is that they have been preserved by nature for thousands of years because this is one of the driest and most arid regions on earth a hot desert southwest of peru between the andes mountains and the peruvian coast but who created these geoglyphs and why after decades of research the whole thing including the idea behind it or the people who made them is still steeped in mystery the lines are scattered over 500 square kilometers on an arid plateau which is around the city of nazca about 50 of these huge drawings are concentrated in a specific area of the desert which is called a pompa some of the lines are as narrow as six inches while some of them are as wide as hundreds of meters including 300 geometric figures and many more are still being discovered all the time according to legend the nazkans lived here around 300 years bc they left no written record of their civilization only some pottery was found etched with figures drawn on them some of them half human and half animal and fanged but one thing researchers do know about them and this is they were farmers archaeologists found a subterranean irrigation system and the nazkans made use of it in fact some archaeologists and anthropologists think that these lines were drawn with the help of large sticks and the figures drawn were so large because the people could please their gods that lived on the plateaus and took care of their crops some also say that they were capable of flying in hot air balloons so the drawings were helpful also in determining the constellation in the sky if the nazkans used sticks to draw these lines according to some experts it would have taken 1000 men to finish the job in 100 years or so drawing the lines would not have been difficult as under the surface of the region soft yellow soil outlines whatever is etched on the ground also the airless climate has preserved this largest and mysterious scratch pad on earth for thousands of years if these huge drawings were agricultural clocks drawn to help farmers know what time of year was best so and to pay homage to the mountain gods the question then arises as to who these mountain gods were or were these lines actually landing strips for UFOs during earth's ancient past the truth is probably out there waiting for someone to read between the lines there is no doubt in our mind that these ancient people were signaling for the return of people who left who made their lives better while they were around and upon their departure things started getting worse what do you guys think let us know in the comments section what you are thinking as always thank you for watching


It's my honor today to have this opportunity to present some of our research in my group. So the title of this talk today is 3D computer vision. We're going to talk about some recent advances in biomedicine. So let me give you some background in the beginning. As we know, we have five senses. We gather information from the environment through what we see, what we smell, we hear, touch, and taste. It turns out that about 80% of the information we gather from the environment comes from what we see, or in other words, through our vision system. The original motivation of computer vision is to try to use computers, including the hardware, software to mimic the brains to capture, process, and understand the information, especially the visual information. So computer vision started in 2D because what we see with one eye is basically 2D images. This is also true for most imaging devices. Now we give you 2D images. Of course, 2D images give you a lot of information which are important in many applications. For example, in identification of a person through a face or her fingerprints or face images, we have 2D images for these purposes. So as we mentioned earlier, we use 2D images for lots of applications, including the identification of a person with face or her fingerprint or the face images. But the lack of depth information in 2D images makes it impossible for many applications like 3D quantification, 3D visualization, or 3D animations. So in order to have the depth information, we can use two eyes, or at least two eyes, or with two lens in the camera system to capture the depth information or to perceive the third dimension of the object. So with multiple views, it's possible to reconstruct 3D models from different views of the objects. Then we have two problems in our group to explore. One is to reconstruct 3D models from multiple images, multiple 2D images. Another question we are interested is to analyze the 3D models which we reconstruct from 2D data set. So in our group, we do have a number of projects going on. So we have the name of our lab is Biomedical Model and Visualization Lab. We basically take 2D images as input. The images can be 2D raw data, can be 3D volume images reconstructed from 2D images. Then what we do is to generate 3D models that can be used for quantification, simulation, and so on and so forth. Related to our work are three subjects or three courses we are teaching in our department, Computer Science Department. One is image processing. Second one is scientific computing. The third is computer graphics. So if you're interested in those topics, you're free to talk to me. Now I'm going to start with the first question we mentioned earlier. How do we reconstruct 3D models from 2D images? We do have two sub-problems here, depending on what we want to do in the problem domain. We can do surface reconstruction. We can do volumetric reconstruction. So with surface reconstruction, we only see the surface detail, either the geometry or the texture on the surface. We cannot see the internal structures through the surface reconstruction techniques. Another way is volumetric reconstruction. We can see through the surface, see the internal structures of the object. So this, of course, depends on what we really want in different applications. And also this depends on what imaging grids we are using for those different problems. So basically imaging grids can be reflective for surface reconstruction, can be transmissive for internal structures or volumetric reconstruction. With reflective grids, we can see the surface details or surface shape, which are reflected on the surface of the object. For example, in this picture, we have about 45 images of the flower from different views. We can reconstruct the 3D surface shape of the flower from those different views. So this is only surface reconstruction because the visible light is reflected on the surface of the flower before they reach our eyes. So they cannot really penetrate into the surface of this object. So that's the reflective grids based approach for surface reconstruction. Now in order to see the internal structures, we do have to consider something different, which is transmissive grids. So transmissive grids, for example, the X-ray or gamma rays can penetrate into the skin of our body to see the internal structures, like the bone structures or some other tissues. So using the transmissive grids, we can see through the surface and then see some internal structures with different intensities or with different shapes or geometries. So this is another problem based on the transmissive grids for 3D volumetric reconstruction. So I will start from the first problem, how we can compute the surface geometry by using model views of the 2D images. I will start with this simple example. So basically what we do is to detect the feature points in two of those images because we have to have at least two images to do the 3D reconstruction. So first we detect the feature points in both images. Then we find out the correspondence between the feature points in those images. So then we can do the back projection because each point in one view gives you one ray starting from the camera to the space. This one gives you another view or another ray. Those two rays will meet somewhere in the 3D space and that intersecting point is your 3D coordinates of this feature point. So if you have a sufficient number of corresponding points in those two images, you can come up with point clouds or a number of points in 3D space. Then we can reconstruct 3D surface model from those point clouds. So that's the basic idea of the multi-view reconstructing from multiple images. So we have been using this idea or this technique for scanning electron microscope reconstruction. This is a very simple picture. Here's the structure of SEM. So we have the electron stores that goes from top to bottom. We have the stage that will hold the biologic sample. The stage can rotate by some degrees from one to the next views. We have two types of detectors, one is BSE. BSE means back-scattered electron detectors. Now there is an SU detector, which is a secondary electron detector. Normally, we use an SU detector for 3D geometry because this one is better suited for geometry information on the surface or on the biologic samples.  For example, this one here, we have two views from different angles. Normally, the angle between two views is from 7 to 10 degrees. We cannot have a two large gap between views or two small gaps. Otherwise, we have some accuracy issue with the 3D reconstruction. So we have a couple other examples. This one is the ash particle in the air. It's a very tiny size, but we're going to see some 3D models for these examples later. So the problem here is to take a number of 2D images as input. Again, those 2D images are taken from different views. The angle is between 7 to 10 degrees, between each two of those. Then our goal is to reconstruct 3D models from those different views of the 2D images. Here's one example. This is 3D models, which is computed from two views, only two views of the 2D pictures, from two views. Those two images are input. Then we can simply compute the 3D models or 3D details of the object from those two views. This is what we call the multi-view reconstruction for the surface. Again, this is only for surface reconstructing. The approach we are taking is called sparse dense approach because of the time I won't give you too much detail about the algorithm, but I will quickly go over the steps we have for this approach. The first step for this approach is to detect some sparse feature points in those two images. We use a SIFT, which is scale invariant feature transform, which is a popular technique for feature detecting in imaging data. So we use SIFT to detect the features in each one of those two images. Then we use a RANSAC to build up the correspondence between the feature points in those two images. Next, we estimate the matrix that will transform one image to the other so that those two images will be rectified. Because when you take the images, the images may be rotated absolutely. So in order to have a better accuracy, we have to rectify those two images so that they have the same orientation in the 3D space. So that's a rectification step for this algorithm. Then we estimate the disparity. Disparity is kind of the distance between two corresponding points in the two images. This disparity will tell you some information about the depth, as we're going to see in the next few slides. So this is a very important step in this algorithm. What we do here is to minimize the difference of the intensities between two corresponding points in those two images. By minimizing the intensity difference, we can find out the best disparity for each pair of the feature points in the images. So in order to have a better robustness to the noise, we have introduced the waiting window in this algorithm. So instead of simply computing the difference between two images, we added the exponential function into this formula. So we have a much more robust algorithm to the noise in the image. Now the next step is to, in addition to the weight we added into the formula, we also use a patch match technique to fix the problem. This problem is what we call the staircase problem, because when we take the picture of the 3D object, we simply have some slices from the farthest of the object relative to the camera. So those different slices will give you the staircase artifact. So this artifact can be fixed by using this patch match technique. We're going to see some examples later. Now once you have the disparity, we can first check the consistency, because when you compute a disparity, we do the disparity calculation for the left image by using the right image. In the meantime, we also have to do the disparity estimation by using the left image for the right image. So those two disparity maps have to be consistent to each other. So that's what we do in this step. The consistency checking between those two disparity maps. Once we do this one, then we compute the depth or the height information of the object. That's the third dimension of the object by using the disparity map, where D is what we computed from the previous steps. That's the disparity map. P is a pixel size. That's a physical size for each pixel in the image. The theta is the angle between those two views. You could have a 7 degree, 10 degree, and so on and so forth. So that would be the degree between the two views. So you can see the connection or conversion from D to H, from disparity to the height of the image. So that will give you the third dimension or the depth information of the 3D object. This one gave you a 3D animation of the example we have seen earlier. We have two images as input. This is the SEN image of the ash particle. We can see the 3D models. In the first approach we are taking here, we can see some artifact, the staircase artifact, in the depth information. So you can see those artifacts. Then we switch to the patch mesh technique. We can see much smoother your 3D models on the surface of this ash particle. While using just two views of the object, we can create our very nice 3D models. We have been working with our colleagues in our school, Pratip, trying to use this technique to analyze the surface roughness of the aluminum materials. So this is one of the applications we have tried with the technique we have developed so far. Now the technique we have seen so far is what we call the passive reconstruction technique. It's passive because we simply take whatever images we have. We cannot really deal with something different, something additional information we don't have. This technique heavily depends on the feature detection. Heavily depends on the correspondence between the feature of the two images. If you have an image in a very dark environment, you don't have much information about the features. Or if you have an image in a very bright environment, again, the features will disappear or you don't see much features in the images. So those cases will render this passive technique as a problem. So in order to fix that problem, we have another technique called active reconstruction technique. By active, we mean we can use not just the camera, we also use a projector to assist this process. So camera will take the picture of the surface. You will reflectively on the surface. We also have a projector that will cast some specially designed patterns on the surface. So you can see the patterns we have here is just a number of colored line structures. Ideally, if you have a flat surface, those line structures will still be imperial to each other. But if you have a curved surface, those patterns will become curved. So according to how the curves will be displayed in the image, we can estimate the depth. So that's the idea of this active approach. With this active approach, we can achieve better accuracy. We can also reduce the dependency of the approach to the image quality in the input. So we have been seeing some examples for some applications in dental industry. People have been using active 3D scanning technique for model scanning and also for intraoral scanning. This one is to scan the model of the teeth. This scanner, 3D scanner is like a 3D printer of this size. You can put the 3D models in this equipment. Then there's a visible light source on the top. You can rotate the platform here to take the pictures of the object from different views. Then you can stitch different 3D depth information together to form the final complete 3D models. So that's the model scanning. This one is more challenging because you have a very different environment in the models. You have much worse image quality. You also have to do this in a much higher speed because you have so many frames to process. This is like a video. So we have been working with some of our collaborators to develop some algorithms for intraoral scanning. This video here shows our algorithm to stitch different views together into our 3D models. So you can see one patch in green is just one frame. We take from the intraoral scanner. Those are given as 3D point clouds. So that means we have lots of points in each frame. We are trying to stitch together into our 3D models. So in this process, it's very critical to have the correct correspondence between each one pair of the adjacent frames. In this video or in this example, we have about 500 frames. Each frame contains about 30,000 points. In order to reduce the number of points for speed, accuracy, and so on and so forth, we reduce the number of points in each frame to 3,000 or 3K. Still, we have about 1.5 million points in this final 3D model. So you can see we have very decent accuracy in the final 3D reconstructed model. This slide shows you some analysis for the accuracy. We have the color map. The red color means higher error. Your green color means lower error. We use the model scanner as a ground source for this intra-owner scanner. So we do the comparison. We first register those two models. Then we calculate the distance between the two models. So that's how we do the error analysis for this study. Now you can see we do have average error distance, which is about 8 micron. 8 micron is a very high accuracy for dental applications. The positive average distance is about 30 micron. The negative average distance is about 25 micron. So all those numbers are good enough for most of the dental applications. So with the 3D models reconstructed from the active 3D scanning technique, we can do lots of different applications in dental or in dentistry. For example, you can do implant design based on the 3D model. Of course, in order to do this implant design, we have to also incorporate the CT of the patient to design the implant. So this is the first one. We can also do the dental restoration based on what should be covered from the patient. We can also do the TIS alignment for the patient based on the 3D models you have. So you can do different applications based on the 3D models. But 3D model is not sufficient. As I said, if you want to do implant design, we cannot just rely on the 3D models. We have to consider the CT internal structures. So that's what I want to talk about in the next few slides. We have surface reconstruction. We also have volumetric reconstruction, which is critical in many areas. So the internal structure determination has been on the market for many years because CT, MRI, lots of medical imaging devices are based on this concept. The basic idea for CT is to take the projecting data from different views and try to use some mathematics, techniques to reconstruct the 3D volumes from those 2D projecting data. So the theory has been very mature in the past decades. But we have been using similar techniques for a number of different areas. So for example, we have been thinking about the medical imaging for tissue or organ scale. We have been working with our collaborators for right microscope imaging at the micrometer scale. We also work with EN, which means in extra microscope imaging or tomography. At the nanometer scale, we have been using cryoEN. CryoEN means a very low temperature for the in-exomex scope imaging technique for the sub-nanometer scales. Now if you go further, you can go to the crystal structures using X-ray, X-ray crystal structures that can give you atomic resolution. So because of the time, I only gave you a very brief introduction to this cryoEN. How we can use imaging or 3D reconstruction for cryoEN volumetric reconstruction. So again, the concept is very similar to the medical city. We use model views to reconstruct 3D volumes. But in the cryoEN community, the signal to noise ratio is very, very low compared to the medical imaging devices. So if you look at those particles in the background, this has a very low quantity compared to the brain data or the heart data we take from the CT machines. So in order to achieve the sub-nanometer or even the atomic resolution, we have to consider some kind of averaging technique. Because averaging can reduce the noise. If you have lots of noise, the only way you can do is to use a huge amount of data to reduce the noise by averaging those data together. So we are using this technique by averaging thousands or 10,000 particles together to reduce the noise or to increase the signal to noise ratio. So that's why we call it a single particle technique. This has been a very powerful technique for your molecular structure determination. This pipeline shows you how we start from the very beginning. Of course, we don't do the experiments in our group. We only do the calculation or the software part of this pipeline. So starting from the biochemistry preparation, cryoEM sample preparation, doing the 2D imaging, data connection, 3D image processing to increase the quality of the image, then 3D reconstruction to some structure analysis. So our work is basically the data connection here. How do we detect the particles in the imaging data? How do we do the 3D reconstruction? How do we do the structure analysis in this process? This technique has been very powerful in recent years. So it's not impossible now to achieve almost atomic resolution. You can achieve almost two angstrom or three angstroms or even beyond that one by using this cryoEM technique. And this is why the Nobel Prize in cap three was awarded to three scientists last year for developing cryoEM for the high resolution structure determination of bio molecules in solutions. So that's a very important milestone for 3D structure determination in biology or biochemistry. So this will be the first question we have raised earlier. How do we do 3D reconstruction from 2D imaging data? Now the next question is how do we analyze the 3D models or 3D imaging volumes? So OK, we have two types of techniques. One is what we call the knowledge-based. Now there is data-driven. Data-driven is probably something you have heard about. This is like margin learning or deep learning. You simply use a huge amount of data to train the system. Then you can use the train system to do the work for you. So that's data-driven. The knowledge-driven is more like a traditional method. You have some understanding of the system, the problem. Then you design some algorithm to do the work for you. So both are powerful in terms of different problems or different understanding of the problems. So in the following slides, I want to give you a quick summary of those two problems. How we can use your knowledge-based method and data-driven method to do 3D analysis, especially for 3D segmentation of the models or 3D segmentation of the imaging volumes. So the first one is the segmentation is a very hard problem, traditionally. Given the image, can you quickly, accurately find out the features or the region of the features or the boundary of the features? This is a very hard problem before we actually started to use deep learning. So we have been using level-set method. This one has been considered as the best approach for segmentation before deep learning has been used in the community. So we use level-set method to detect the heart. We have a left ventricle, right ventricle in this heart of the mouse. We can segment two regions from one to your left and right ventricles in those different slices of the 3D volumes. So then you can construct the 3D models from those segmented contours on each slice. Then this is a 3D model of different time steps. You can see how the shape or size of the heart is changing over time. Again, this is possible because of the segmentation of the 3D volumes. I don't think I can play this video because it's not available on this computer. So let me. So in this video, I was trying to show the segmentation of some of the important structures, like the blood vessels, the spine structures, or the kidney and so on and so forth. So this has been based on the level-set method. You can segment some important features from the imaging volumes. But this is still the knowledge-based method. When you segment, for example, the heart of the mouse, you assume that the feature has higher intensity, higher intensity values than the background or some other features. So that's the knowledge you are using for this segmentation. You have certain understanding of the problem you have. In this case, it's simply the intensities. Now, in some other cases, you may use, for example, the symmetry. Number of cryo-yam I mentioned earlier, we can use cryo-yam to reconstruct very high resolution of the viruses or some other molecular structures. This is the 3D reconstructed structure map for Roystorf virus. We have a number of proteins on the capsid of this virus. The first stage is to segment the capsid containing a number of proteins in this 3D map. So that's the first stage to segment. In this stage, we are using the radiance information because we know the capsid is somewhere from the center of the virus. So that's the information we are using, or that's the knowledge we are using. Still the knowledge-based method. Then we detect the symmetry of this virus because symmetry is another information or another knowledge we are using for structure analysis. Once you detect the symmetry, we can segment the individual trimer. Trimer is a structure with three proteins together. So trimer segmentation for this whole map of the virus. Then we can segment individual trimmers. We can average those trimmers together to form this 3D map with less noise in the density map. Then we can further segment individual protein. We call this as monomer protein. We are using the symmetry information. So you can see this whole process is based on some knowledge, some understanding of the map. Without the knowledge, it's very hard to accurately segment the features. So we call this as knowledge-based method. So in order to do this, you have to have a deep understanding of the problems you have. So that's why when you consider knowledge-based method, you have to do lots of studies for the problems. Otherwise, your algorithm may not be working properly for those problems. So I have another video here, but I don't think it's wrong. So now I'll go into the next type of algorithm. Knowledge-based method depends on how you understand the problems. If you don't have a good understanding of the problem, but fortunately, you have a huge amount of data available, then you can switch to the data-driven approach. For data-driven approach, you normally have to construct a neural network. For example, this one here gives you a number of layers from the input to the output. So this one can give you some structure. You design a structure, but the structure are normally regular. You can use some structures that other people are using. Then use your own data to train the network. So this process purely depends on how much data you have and how much understanding you have on the data, because you have to label the data before you can train the system. So in this example here, we have trained this neural network to detect where each individual T is in the image. We can detect the upper T's, lower T's, and also we can locate each one of the T's in the image. Then we can segment the boundary, the accurate boundaries of each T's. Then this is what we call the instant segmentation of the object by using this what we call the CNN-based method. CNN is a convolutional neural network, which is a very popular network used for image processing. We use similar neural network to segment the tubes in the city of the head. Under this lower T's, we have this tube. We call this a mandibular canal extraction. So we try to extract those tubes from the city data. What we do here is we have to convert the 3D problem. This is the 3D data. We can convert the 3D problem into a 2D problem by cutting through the 3D data into a number of 2D slices. But this cutting direction is based on the arc of the T's. We cut through the perpendicular direction of the arc. Then we generate a number of 2D images. We have about 1.8 images for one host data set. Then we do the segmentation or feature detection individually on each image. Once you have the results from each image or slice, you can reconstruct 3D tube from all those detection of individual slices. This one is another area. We use the data-driven approach to segment the intervertebrate disks. You can see we have two models here. One is the current model, not the labeled model. So those are the manually segmented models by expert. We also have the predicted models in mesh like this one, or this white color. That gives you the predicted results. So those are the results predicted by our own trained network. So in most cases, they match very well to each other. You can even see the details. If you zoom in those three disks here, you can see some more details between the two models. Or you can see the correspondence between the two results in number of slices in 2D. So you can see the red color is our prediction. The yellow color is our labeled data. The match very well. We also remember the CNN, the neural network, or deep neural network, has been used extensively for imaging data, because images are regular. You have X, Y, or XYZ pixels or voxels regularly defined in three axes. That's very suitable for neural network, especially for CNN network. What if you have a 3D model? Because 3D model is given by a number of triangles. We call 3D models in triangle formator as unstructured model. Unstructured means we don't have a regular mesh. We don't have a regular grid. It's ops 3 in the 3D model or in the 3D space. Now in this case, it's harder. It's more challenging. But what we do here is to convert the 3D model segmentation into a 2D image segmentation problem. So we can simply use a graphics technique to take some virtual pictures of the 3D model from different views. We can place a virtual camera somewhere surrounding the 3D model. Then we take pictures. And those pictures will give you 2D images. Then we run the CNN technique we mentioned earlier for 2D image segmentation. Then we can reconstruct 3D models by going back to the 3D space. So that's what we do in this scenario. So first work we do is to segment the T's from the rest of the model. So that's the first one. Then we go further to segment individual T's by using different corners. So this is again using the idea we mentioned earlier. So we have converted 3D models into a number of 2D images. Then we do the 2D image segmentation, then going back to 3D models to stitch together the results. All right, so that's basically what we have for today's talk. Here's a quick summary for this talk. We have discussed two problems. The first one is 3D reconstruction. We have two techniques or two different scenarios. One is for surface reconstruction. We can simply have a 3D surface geometry by using reflective rays. Because rays will be reflected on the surface, so you cannot see through the surface to see the internal structures. We have active method. This is actually the method that most 3D scanners are using on the market. We also have a passive method. Passive method gives you less accuracy, but this is more accessible. Because you can simply take pictures of the object than do the 3D reconstruction. You don't have to have a 3D scanner equipment. So that's more accessible, but gives you less accuracy. We call this as a multi-view technique. Then we also talk about the volumetric or volume reconstruction based on the CT concept or CT theory. We have a computed tomography used by medical city or in actual Mexico or in actual tomography. We also give you a quick summary of the single particle method, which is also called cryoEM technique. So that's the first problem we have. Then the second problem is 3D analysis. In particular, we talk about 3D segmentation of the models or 3D segmentation of the volumes. We have a knowledge-based method. We have a data-driven method. But how do you choose between the two? As I said, if you have a deep understanding of the problem, but you don't have much data to use, then try the knowledge-based method. If you have lots of data to use and you have the labeled for the data, but you don't have much knowledge about the problem, then try the data-driven method. If you have both data and knowledge, then try learning from the knowledge. Because if you combine knowledge with data, you should have a better model for the data analysis. Now what if you don't have a data, you don't have knowledge? We always have the robots. So I believe if AI is today, then robotics should be tomorrow. But tomorrow is built upon today because AI is a brain of robots. So we have to first make AI work well before we can really have working robots. So that's all for this talk. Thank you very much. Yes? When you're reconstructing a surface, I'm saying two images having a different angle. When you're reconstructing a surface using two images taking different angles, is all the information that you need in the images themselves or do you also need to know what those relative angles were? If you know the angle, it's going to be easier for the carbon. But the reconstructing itself doesn't require the angle information. It's all about the two images. Because once you know the features and the correspondence between the features, you can estimate the angle. You can estimate the transformation from one to the other. So that's actually given. But you have to make sure the correspondence is accurate. Otherwise, the transformation may give you some problems. But yes, you don't have to use any other information. There's one. So when you showed that you reconstruct the teeth, it was in some green area that gradually getting the different part of the entire teeth that we had. So how you track the, I mean, every time you take some picture and then you add up together, right? Yes. So how it knows that, where it is looking for, or I mean, is there any correspondence between the two frames? Yes. OK. So this is depending on the features you have on each frame. If you have just a two-flat patch, it's impossible to match those two accurately. So you are using some correction information. So that means each frame must give you some curvature. And you use the correction information to match those two together. So otherwise, you really cannot have accurate registration between two frames if you don't have that information, the features. The user could change even in any direction that it wants. I mean, the camera. No, you have to make sure two frames overlap for about at least 70%. If you only have, let's say, 30% overlap between two frames, I mean, the accuracy may be very low. You may have some problems with that. So that's why when you take the videos by using the intra-order scanner, you have to make sure you move slowly. If you move too fast, then the overlap will be very minimal. Then you have some problems with the stitching process. Just for grins, have you guys tried using backscatter electron imaging? Why get to that to get subsurface information? I don't think we have tried that. But since those images coming from the EM lab in the bioengineering department, we are working with a professor there. So what we have been using so far is only the SE detector. Yeah. Thanks. OK. All right. OK. I have one question, actually, is I, personally, I have a dental implant. So when I saw your examples, oh, OK. So I wonder if any of those algorithms have generated any real-world application or commercially available products? This project is a collaboration with a group in China. So they are doing something in that direction. But for our case, we only focus on the algorithm, like the stitching of two frames or different frames. Those are our focus. So regarding the segmentation of the teeth, how people get the ground truth information? By drawing on the 3D model. So we ask the dentist to draw the curves by using some software. So we use that as a labeling or as the ground truth for the training. I see, I see. So for labeling, I think that's probably the common case. You always ask experts to provide the labeling data for you, because the accuracy of the network depends on how accurate the label data is. If you have some problem with labeling, then you may have some problems with the network too. So we ask the dentist to draw this for us. Excellent. Thank you very much.


The Roaring 20s are live at the Brainerd Airport this week as part of the American Barnstormers Tour. Our Anthony Scott got a first-hand look of what life was like in the 20s. Old cars, barbershop quartets, and best of all, 1920s biplanes were all present at the American Barnstormers Tour in Brainerd. We want people to walk through the ramp and go, I'm back in the 20s, you know, and our barbershop quartet helps us do that and the food that we're selling, you know, for a nickel popcorn? Why? Because you could buy it for a nickel in 1929. The American Barnstormers Tour brings their 12 biplanes to five different states for people to enjoy and admire the history. I think as time goes by, people kind of gravitate more towards the old things like going to old car show, tractor show, certainly something like this, see what old airplanes were like 80 years ago. And community members could even take a ride in one of the vintage aircrafts. And so for my third time ever riding in an airplane, it was a trip that I would never forget as I was able to take a ride in one of these vintage 1920s airplanes. The view from above was breathtaking, flying over some of Minnesota's majestic lakes. It was quite a view, even for people that fly every day. My first time flying in Minnesota, and you know what? You guys have a lot of lakes here. It's flying in from South Dakotas, lakes everywhere. Beautiful flight in today. I mean, it was one of those memorable things. All of the biplanes at the tour were travel-air biplanes, the most popular company back in the 20s. The Barnstormers Tour took an eight-year break until finally returning to Brainerd. Well, we had such a great time last time, and we had just a wonderful turnout. The community really embraced it, and we thought, well, you know, this is the first time in six years that they've toured, and we want to do it again. The Barnstormers fly their own biplanes from city to city on their tour, a reminder that these vehicles belong in the air instead of a museum. We want for people not to forget our routes. We love these airplanes, and having these airplanes in a hangar is not a good thing. They need to be out or a museum. They need to be out flying, and they need to be in a position where people can have access to them. Reporting from Brainerd, Anthony Scott, Lakeland News. The Barnstormers Tour will be in Brainerd for one last day tomorrow from nine in the morning until six in the evening, and they'll make their final stop on the tour in Eau Claire, Wisconsin.


What traits should an EFL teacher have in teaching Wanda One Business English? As I learned from the 50-hour C-T-B-E course, teaching Business English always involves Wanda One mode. Imagine there is a student who is a high-profile business person in China. He needs to learn English for his own business work and he prefers Wanda One lessons. Then, what traits should an EFL teacher have in order to get this teaching job? In this essay, I would like to use this case to discuss some attributes that a businessman would seek in an EFL teacher and why he would demand those traits. English Language Plus Business Knowledge First, the EFL teacher should have a broad knowledge of English language and some basic knowledge of the businessman's first language. If the teacher is a native speaker, he or she will have a high priority in winning the businessman's trust and getting this teaching job. This is because native speakers are likely to be good models of speaking, more accurate in language use and know the grammar instinctively. Moreover, the teacher is expected to know some of the student's native language and prepares the English courses based on his learning needs. In this case, if he or she knows the businessman's native language, Chinese, they may have better communications and the businessman can fully address his learning needs. Good Teaching Background and Practice Second, the EFL teacher should have good teaching background and practice. He or she is expected to have a recognized teaching qualification. This is a proof of professional training, like a driver license of a driver. The teacher is also expected to have a degree level of education. The majoring education and language subjects is preferable. This proves that he or she is well-educated and equipped with this language expertise. The businessman can rely on his or her teaching. After that, he or she is expected to have more than three years English teaching experience. Experienced teachers are likely to know more about how to teach and guarantee the businessman's learning effect. Last but not least, the teacher should be highly responsible for teaching and like teaching English. This is important because it determines whether he or she can keep good quality of teaching. Knowledge about the business industry Third, the EFL teacher should know something about business industry, like learning and have some skills in communication and IT. Having some working experience in business industry is preferable, or at least, he or she should be familiar with some business culture in English-speaking countries. This is because he or she can help the Chinese businessman cope with some business terms in English and provide useful cross-cultural tips. In addition, the teacher should like to learn and have good communication skills. I believe that a good EFL teacher is also a good learner. He or she should always learn how to become a better teacher. As for communication skills, it may be self-evident that teaching and learning is a process of communication. Therefore, good communication can guarantee good learning results. Furthermore, the teacher should have proficiency in Microsoft's programs and computer usage. In this case, the businessman may prefer to use a computer to learn, so he may need a tutor who is also a good editor in order to help him with English learning. Honesty, patience and easygoingness are key. Fourth, an EFL teacher's good personal characters may be also taken into the learner's consideration. For example, the teacher is expected to be honest, patient and easygoing. When the businessman has problems, he or she could encourage him to overcome them and be always supportive throughout his study. The teacher is also expected to be punctual for the businessman's one-to-one tutorials and can come to his company to provide them. It may be sometimes difficult for the businessman to rearrange his timetable or meet the teacher at another venue. Manager class is well. Finally, there may be some other traits that a business English learner would seek from an EFL teacher, such as schedule, age, and course price. In terms of scheduling, the teacher is expected to have a flexible timetable in order to adapt to the businessman's learning schedule. Since he is a high-profile business person and always busy with his work, flexible timing is more suitable for him to learn English. In addition, the teacher should be above 24 years old, but not too old. A younger tutor is likely to be inexperienced in teaching and lack of business knowledge. However, if the tutor is older than 50 years old, he or she may not have enough energy to keep his teaching up to date. As for course price, the teacher is expected to have a proper price for his or her teaching services. This should be written in a tutoring contract for legal protection. Charging too little or too much is non-professional, which seems that he or she may not know the entire language teaching market. Are you ready to teach English abroad? Speak with an ITTT advisor today to put together your personal plan for teaching English abroad. Send us an email or call us toll-free at 1-800-490-0531 to speak with an ITTT advisor today.


You know what you say to that? The gumbo. Push-ya! Josh! That's as legend as it is. Monsayton. If not more legend. I'd say it's probably more quotable. It's more quotable and quirky and weird. You know, it's so good.  Today we are doing... What? You said it and I saw Farley saying it.  He's my favorite SNL character. Person ever? He's effortlessly funny. You know people that you just meet that they don't have to try? They're just hilarious. Did you ever get to see because you weren't alive when he was around? Did you ever see any reruns of John Belushi on set? Oh yeah, I love John Belushi as well. Cut from the same cloth. It's like Jonathan Winters and then Robin Williams. John Belushi. Will Farrell is really close with John. Yeah, Will Farrell is a funny man. He's one of the funniest. So funny, but Chris Farley, I think he's like... There's people you meet in your life that are just... They don't have to try. You just laugh at everything they do. Even if they're not trying to be funny. Chris Farley is one of those people. Sorry, we're going off on Chris Farley, but... My favorite one when he did his interviews with people, his bad interviews on SNL, was the one with Paul McCartney. So Paul, when you were younger, you were in the band of Beatles? That was awesome. That was awesome. Anyway, this is not about Chris Farley. Anyway, what are we doing today? It's your favorite SNL. Of all time. This is a Malayalam. It's from Thakerum Bridge. Ah, cool. We've seen some of these before. We've seen a couple by them. Yeah. But it's called One. The song by the band Thakerum Bridge, the same band which gave you the fish rock. Oh, we remember. The song highlights Kerala's authentic natural beauty. It's wildlife, people from different tribal and religious communities. The one signifies we, nature, wildlife, people. All our one should strive for unity and harmony. I enjoyed the song. Excellent. Love it. I think we've seen two or three things. At least, yeah, at least two. And they're love, they're fantastic. Awesome. I'm excited. Here we go. This old monk is so much better than just standard old monk. It'll be interesting when I get the next one. Yeah, because it's so good. If they came to Los Angeles, I would see them. While we were watching it, I was thinking I so would love to see them live. I hope they're in LA. Yeah, I'm sure. I don't know right now, obviously, even though we're doing a lot better here, I don't know if they'll come here anytime soon. Not because I would love to be able to go see them, because everything we've seen in front of them is really great. I love it when rock bands incorporate not just, obviously, guitar, drums, and even though obviously all that's great, but when they incorporate different stuff, like ska bands incorporate, you know, more horns, and some bands incorporate string instruments. Yeah, that's one of my favorite things about Jethro Tall is the flute being in a rock band. Yeah. It's a really cool idea. Great message, obviously, of it. I agree of the video, and I just can't get it. I love looking at people's faces. Mm-hmm. I think they're just, they are, they're works of art. They're, a human face speaks so much. Anytime I see Kerala as well, that's probably the number one place I can't wait to go to that we haven't already been to in India is just to see Kerala, because everything we've seen in films, and Assam as well. I think Assam, the jungle-y, that's just my style. Yeah, jungle and mountains. Mountains, waterfalls, all that jazz is my thing, and so the Kerala is right up my alley, and so there's obviously a bunch of the rest of India, but I know when we go to India again, obviously we're going to the south, and Kerala will definitely be multiple stops probably in that trip. Hopefully can spend a good amount of time there, because everything we've ever seen about it is beautiful, and this lived up to what I was anticipating. Great song, great message, great video. Let us know more from them, and any other bands we should react to, because we do love listening to rock bands. My favorite genre of music is classic rock, so send us more, please, down below. Josh!


Hi, thanks for joining our session. Today, we are going to talk lessons learned from the utilization of machine learning pipelines in production environment. First of all, let us introduce ourselves. I'm Kyosuke Hashimoto. I'm a research engineer at Hitachi, and I'm interested in developing AI solutions with open source software, such as Kubeflow and Kedro. And also, I'm interested in public clouds, such as AWS and Microsoft Azure. Hi, I'm Masahiro Ito. I work at Hitachi as a software engineer. I'm responsible for leveraging open source software related to big data and AI. I've been developing data lakes and data pipelines using Apache Hadoop, Spark, HBS, Kafka, and more. Now, I'm developing MLOP solutions for customers who are going to build enterprise systems. These are the outline of our talk. Our presentation is in four parts. First, we will introduce our business and explain the challenges of our machine learning projects. Next, we would like to talk about the development process of executable ML calls in production by Kedro. In part three, we will give some discussion about the lessons learned from the project. Finally, we will give a brief summary of what we have covered. OK, let's move on to the introduction. To begin with, I'm going to talk about our motivations to focus on machine learning. Machine learning technology is applied in various industries, such as IT and energy manufacturing and city development. Our company Hitachi has a lot of products and services in these industries. When we applied ML from experiment to production, we faced many gaps between data science and software engineering. So our challenge was how to move ML from experimenter to production. I'll talk about job specialization in machine learning projects. Most of the machine learning projects consist of three steps. The first step is proposal. In this step, sales persons or consultants suggest how to solve customers' problems by data analysis. The second step is experiment. In this step, data scientists showing ML models and generated logics by Jupyter Notebook in experimental environment. In many cases, they have good mathematical skills, but they are not good at production level software engineering. The third step is production. In this step, software engineers will implement logics from scratch for production environment based on the result of data scientists' experiments. I'd like to talk about an issue and a hypothesis for applying logics into production environment. The issue is that it takes a lot of effort to implement the logics and test scores from scratch in production environment. So we thought we could reduce these efforts by refactoring the Jupyter Notebook logics and converting them into production scores. In this presentation, we will focus on how to transform logics from such refactored notebooks. I'll explain the gaps between experimental and production environment. There are three gaps that make logics re-implementation difficult. The first gap is logic majority. In the experimental environment, data scientists write logics such as data preprocessing, model training, and evaluation in the same notebook. But in the production environment, we need to execute model training, evaluation, and inference effectively. The second gap is flexibility in data source. In the experimental environment, data scientists load small load datasets such as CSV files from local storage. But in the production environment, we need to load large datasets from object storage, data warehouses, or our regional databases. The third gap is logic scalability. In the experimental environment, data scientists train ML models on local machines using small datasets. But in the production environment, we need to train a lot of models with large datasets and execute inference in parallel. Let's talk about our challenges towards production environment. There are three challenges to transform some logics in Jupyter notebooks to execute the course in production environment. Firstly, we need to pick up the parameters and outputs from the notebooks to achieve logic majority. However, this is difficult because multiple outputs and parameters developed by other cells are packed in a single cell. Secondly, we need to modify datasets' loading logic since our system specifications have not been determined under the experimental environment. Thirdly, we need to make some logic scalability to handle large datasets. There are several popular open source frameworks to decompose complex logics into simple procedures. We use the framework called KEDL for these logic transformations for production environment. KEDL is a specially popular framework among data scientists since it enables integrating popular tools such as Jupyter Notebook. From the comparison below, we consider KEDL is suitable for overcoming the three challenges. So we try KEDL in our machine learning project to modify logics for production environment. Here is overview of validation scenario and its target system. The goal of this ML system is to classify the condition of machines for maintenance recommendation. In this scenario, engineers modify notebook calls generated by data scientists to production calls. The input data for ML model is sensing data of machines. On the output of ML model is machines condition. The target production ML system consists of training, evaluation, and inference part. First, the training system trains ML models from sensing data. Then, the evaluation system evaluates the trained models. After that, the best model is deployed into the inference system. And the inference system predicts the machine condition from new sensing data. The projected machine conditions are used for maintenance recommendation and other applications. Next, we are going to explain how we develop executable machine learning codes in production by KEDL. By transforming logics into system-executable codes by KEDL for production environment, we try to solve three challenges below. First challenge is logic modularity. In order to pick up outputs and parameters from single cells in Jupyter notebook, we decompose complex logics into a set of functions and data processing flows by KEDL. This time, we call this procedure pipelining. Second challenge is flexibility in data source. In order to modify logics to load data set corresponding to the system specification change, we replace the composed logics, which in this time we call them nodes, and the type of data set changes for modifying data loading logics. Third challenge is logic scalability. In order to modify some logics to finish in reasonable time for larger data set, we execute independent nodes in parallel by using KEDL's function. This is the process overview of conversion from experimental logics into executable codes for production environment. We transformed cells into nodes and connect nodes to develop pipelines in following steps. Firstly, in logic modularity, we transformed pipelines in KEDL style. Secondly, in flexibility in data source, we applied data catalog for data and model loading function. And thirdly, in logic scalability, remove loops inside nodes. By these solutions, we could easily transform logics for production environment by KEDROL. First solution, transforming pipelines in KEDROL style consists of following four steps. First one is generating project template by KEDROL and second one is extracting definition of nodes from Jupyter Notebook. And third one is adding nodes not in the Jupyter Notebook and then we can connect nodes to develop pipeline. Through these four steps, we transformed cells into nodes in KEDROL style. From next slide, we are going to explain these steps in detail. First step is to generate project template when we started to use KEDROL by typing comments KEDROL in it. Following directories and files are generated automatically. We use some of those files afterwards. Firstly, we define nodes in nodes.py and describe definition of pipelines in pipeline.py which is located in the right side of these slides. And also we use KEDROL.yaml which is on the left side of this slide to use solve challenge number two that is flexibility in data source. Next, we extract logics from Jupyter Notebook and transform them as a nodes of pipeline. For example, we have training logics in Jupyter Notebook here and then we divide as a node in a part of pipeline.py and also a part of node.py. Next, we added node not in Jupyter Notebook. Before connecting nodes, we found a node for inference pipeline is missing in production system which is post process. In Jupyter Notebook, training evaluation and the inference are mixed without any categorization in single notebook. However, when we develop a pipelines, training evaluation inferences are separated and we could find missing part easily. Post process is defined as follows in non.py and pipeline.py. We input inference result as arguments of function and output results as we could input them to other applications for maintenance recommendation. Next, we connect nodes to develop pipelines. Since input and output of logics in notebooks are not clear, we had to clarify them to develop pipelines. Kedro offered simple and easy to understand way to define pipeline and node as simple playing Python codes. In notebooks, inputs and outputs of each logics are not clear and then dependencies among cells is not clear. Also recovery policy is not clear when something go wrong with exacting notebooks. On the other hand, by using pipeline in Kedro, since flow is modularized by nodes, we could make inputs and outputs, dependencies clear. Also, we could design recovery policy for every node. For example, we could connect nodes in pipeline.py, which is shown in left codes and develop training pipeline, like right figure. Next, we are going to explain how we solve challenge number two, which is flexibility in data source. We apply data catalog function or data and model loading function. Kedro supports declarative definitions for specifying data source and be the writer in separate configuration paths, which is called data catalog. By using data catalog, we can check existence of data and apply embedded wrappers for read and write data, which enables us to switch data source by replacing single configuration file, which is Kedro.yaml without editing the definition of nodes. Developers can decouple location of data sources, type of data source from implementation of the 12 logics. How to apply data catalog in data and model loading function is consisted of these two steps. First step is analyzing logics in Jupyter Notebook. And second one is decoupling data loading and converging codes. First step is to analyze logics in data loading logics, implementing in Jupyter Notebook. This is an example of data loading logic. As you can see in this example, data loading logics number one, number two, and conversion logic number three are mixed in one loop. When data source is changed in production environment, analyzing and changing logics are difficult. So we should decouple these two different types of logics into another files. And this is how we decouple data loading and converging code into different configurations and codes. Firstly, we apply data catalog and describe data loading function in kettle.yaml. And secondly, we really implement rolling and converging codes in node.py and pipeline.py. Third challenge to solve logic scalability, we remove loop inside nodes extracted from Jupyter Notebook. There are some logics which have loop insights such as training logic and devaluation logic for testing many algorithm or data sets. In experimental environment, these loop structures may be useful, but in production environment, this causes the delay to scale out pipelines. In order to finish completion in reasonable time, we should remove loops inside nodes, explicitly expressing loop structures in the pipeline flow and execute nodes in parallel by single command option. We removed loops of each nodes as follows. Please look at the left figure. As you can see, before refactoring, training and devaluation has loop at the number of locations, which indicates that increasing the number of locations accompanies delay of wall processing time. On the other hand, by removing loops of each nodes which is indicated in the right figure, we could distribute it processing at the increase of locations. Now, we discussed the effect of utilizing Kedro in our scenario. These are good points of Kedro, which we learned in our validation scenario. Firstly, we could easily develop structured pipelines by simple definition of functions and pipelines in Python without learning any special framework to overcome challenge number one. Secondly, we could easily modify pipelines by separating codes which were in different environment and scenarios and modifying configuration paths to overcome challenge number two. Thirdly, we could scale out logics and run them in parallel with simple command option, which is related in challenge number three. This is a point to consider we learned in our scenario. We believe transforming logics by Kedro reduces much effort than refactoring logics from scratch, but transforming logics still takes decent costs. As a future, we should evaluate those quantitative costs. This is our idea of reducing costs to transforming logics from Jupyter Notebook. We think developing tool for data scientists to tag cells and generate pipelines and functions should be the possible solution. For instance, Kail allows us to transform logics in notebooks into machine learning pipeline automatically by adding tags and off dependencies. Kail stands for a Kubeflow automated pipelines engine and it has annotation feature for tagging cells in Jupyter Notebook and transforming cells for nodes of Kubeflow pipelines. We expect those integration is applied to Kedro as well. Finally, we wrap up our presentation. This time, we try to apply Kedro to accelerate our machine learning project from experiment to production. We have found that Kedro enables more efficient implementation of production machine learning systems than re-implementing them from scratch. Kedro enables manual logic conversion from Jupyter Notebooks to production level structured machine learning pipelines which are easy to develop, modify and scale out. Future works are as follows. Firstly, we need to evaluate the effect of saving costs for transforming logics from experiment to production. Also, we should consider more automated solutions such as Kail for those transformation. This is the end of our presentation. Thanks for joining us. We are happy if you are interested in our presentation. Thank you very much.


We're here today at Krieger Farms announcing open access to our blueberries right into China. This is going to be a great benefit to farmers right here in British Columbia. Opening more and more markets for us is obviously helping us to market our product, to keep the prices up. As the blueberries are coming on more and more, we need more places to ship them. It's a great thing for blueberry producers because it allows us to diversify our markets. It allows us to employ more labor, keep labor force more active. It allows a whole host of things, opportunity for higher revenues on some products. And hopefully over the next few years, as we wrap up, we'll get about $65 million more in export value out of our blueberries into the Chinese market.


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